Equation for IRC 81

GIS based PAVEMENT MAINTENANCE &

MANAGEMENT SYSTEM (GPMMS)

A Thesis Submitted in Partial Fulfillment of the Requirements for the Award of the Degree of

Master of Technology In

Civil Engineering (Traffic and Transportation Planning)

NIJU.A

Roll No. CEO4M024

Department of Civil Engineering National Institute Of Technology Calicut

Calicut, Kerala 673 601

May 2006

Certificate

This is to certify that the thesis entitled “GIS based PAVEMENT

MAINTENANCE & MANAGEMENT SYSTEM (GPMMS)” is a record of the

bona fide work done by Mr. NIJU.A (Roll No. CE04M024) under my

supervision and guidance. This thesis is submitted to the National

Institute of Technology Calicut in partial fulfillment of the requirements

for the award of the degree of Master of Technology in Civil

Engineering (Traffic & Transportation Planning) during 2004-06.

Sri M.V.L.R. Anjaneyulu Dr. S. Chandrakaran Programme Coordinator Professor and Guide Department of Civil Engineering Department of Civil Engineering N.I.T. Calicut National Institute of Technology Calicut, Kerala-673601

NITC, Calicut

Date :

Dr. V. Mustafa Professor & Head Department of Civil Engineering National Institute of Technology Calicut, Kerala-673601

i

ACKNOWLEDGMENTS

I express my profound sense of gratitude to Dr. S. CHANDRAKARAN,

Professor, Department of Civil Engineering, for his systematic guidance, valuable advice

and constant encouragement throughout this project work.

I express my sincere gratitude to Dr. B.N NAGARAJ, Professor (Retd.),

Department of Civil Engineering, National Institute of Technology, Calicut, for his

valuable suggestions for the improvement of this work.

I am thankful to Dr. V. MUSTAFA, Professor & Head, Department of Civil

Engineering and Dr. N GANESAN, former Head of the Department of Civil

Engineering, National Institute of Technology, Calicut for providing all the facilities in

the department.

I wish to convey my sincere thanks to Mr. JAYASURIAN. M, MCA student,

NITC, for all his supports and backups render to me throughout, then Mr. SIJU, Lab

Assistant, Transportation Engineering laboratory and all members of transportation

family for their wholehearted co-operation.

Finally I would like to extend my deepest gratitude to all my friends who gave

valuable suggestions and encouragement especially Ms. KEERTHI.M.G, MTech,

Traffic and Transportation, which were very helpful to me throughout this project work.

NIJU.A

ii

ABSTRACT

The road networks are capacity constraint and structurally deficient due to

lack of timely maintenance, rehabilitation and upgradation. This has adversely

affected the traffic movement, resulting into higher operating costs and delays.

Maintenance and upgradation of such a large network is a challenging task

because of the logistics and constraints of resources. There is a need to manage

the network more efficiently in a scientific manner; the most important aspect

lacking is the application of information system.

Therefore there is a need to establish a centralized facility where

information on road and road transportation can be utilized for the development

of effective and efficient maintenance and rehabilitation measures and for

planning upgradation strategies.

Aim of this work is to build a GIS based system that provides information

for use in implementing cost-effective reconstruction, rehabilitation, and

preventive maintenance programs and results in pavement design to

accommodate current & forecasted traffic and pavement deteriorations, in a safe,

durable, and a cost-effective manner.

A well-designed geographic information system (GIS) provides a platform

on which all aspects of the PMMS process can be built. The resulting system,

GPMMS, represents a significant enhancement of all aspects of the PMMS

process. A variety of spatially integrated data are important to pavement

management decision making. GIS technology is shown to be the most logical

way of relating these diverse, but relevant, data.

The GIS based pavement management system would eventually lead to

the development of the frame work for GIS based Pavement Maintenance &

Management System (GPMMS). Here I had reviewed the role of GIS (GeoMedia

environment) for pavement management system.

Looking at the PMMS process in its entirety leads to the enumeration of a

set of functions to be embedded in the GIS platform that is required for effective

GPMMS. These functions include thematic mapping, a flexible data base editor,

iii

Linear Referencing System, dynamic segmentation, statistics, charting, network

generation, and integration with external programs.

The most important pavement management tools in GeoMedia are Linear

Referencing System and Dynamic Segmentation. Dynamic segmentation is the

overlay and display of attributes describing a linear referenced road network.

Dynamic segmentation can accommodate multiple attribute tables, describing a

road network, without requiring duplication of network geometry or data. Only a

single, graphic representation of the highway network is required. The locations

of attribute records along the road network are identified using a linear

referencing method.

A comprehensive plug-in software, GeoMedia Pavement Maintenance and

Management Assistant (GPMMA) for GeoMedia has also been developed, which

provides no bounds for PMMS analysis in GeoMedia. Important features in

GPMMA are Deterioration prediction, Economic analysis, BBD overlay design,

Maintenance Prioritization, Overlay Cost Calculator, Maintenance scheduler etc

An exemplar GPMMS analysis was carried out on the whole of Calicut

district. A well scaled georeferred Calicut district road map was developed in

GeoMedia, GIS environment. Almost all the available data, including bridge

inventory details, culvert inventory details, and condition survey details had been

incorporated using dynamic segmentation for the analysis.

Altogether a concise and succinct approach for pavement maintenance

and management have been developed using GeoMedia in hand with GPMMA.

iv

CONTENTS

TITLE PAGE NO

CERTIFICATE

ACKNOWLEDGEMENT i

ABSTRACT ii

CONTENTS iv

LIST OF FIGURES viii

LIST OF TABLES xi

CHAPTER 1 INTRODUCTION 1-6

1.0 GENERAL 1

1.1 PAVEMENT MAINTENANCE & MANAGEMENT SYSTEM 1

1.2 FEATURES OF PMMS 2

1.3 PMMS INPUTS 2

1.4 ANALYTICAL TOOLS AND OUTPUTS 2

1.5 STRUCTURE OF PMMS 3

1.6 NEED FOR THE STUDY 4

1.7 OBJECTIVES OF THE STUDY 4

1.8 PROBLEMS, CHALLENGES AND THREATS 5

1.9 SCOPE OF THE STUDY 5

1.10 ORGANIZATION OF THE DISSERTATION WORK 5

1.11 CONCLUSIONS 6

CHAPTER 2 LITERATURE REVIEW 7-24

2.0 GENERAL 7

2.1 STATE - OF - THE – ART 7

2.2 GLOBAL PMMS SCENARIO 8

2.2.1 United Stases of America 8

2.2.2 Canada 10

2.2.3 Australia 11

2.2.4 United Kingdom 12

2.2.5 France 13

2.2.6 Germany 13

2.2.7 Denmark 14

v

2.2.8 New Zealand 14

2.2.9 Sweden 15

2.2.10 Austria 16

2.2.11 Indian Scenario 16

2.2.12 Studies at universities and Research Institutions 17

2.3 GIS TECHNOLOGY AND BENEFITS 20

2.4 INFORMATION TECHNOLOGY FOR PMMS 21

2.5 CONCLUSION 24

CHAPTER 3 A BRIEF REVIEW OF GeoMedia 25-34

3.0 GENERAL 25

3.1 GEOMEDIA PROFESSIONAL 25

3.1.1 GeoWorkspace 25

3.1.2 Co-ordinate system 26

3.1.3 Warehouse 27

3.1.4 Windows 27

3.1.5 Legend 27

3.1.6 Feature and feature class 29

3.2 FUNCTIONS OF GEOMEDIA 31

3.2.1 Digitization 31

3.2.2 Queries 31

3.3 GEOMEDIA TRANSPORTATION MANAGER 5.2 32

3.3.1 Linear referencing 32

3.3.2 Dynamic segmentation 33

3.3.3 Routing network 33

3.3.4 Routing analysis 33

3.4 CONCLUSIONS 34

CHAPTER 4 PAVEMENT MAINTENANCE & MANAGEMENT ASSISTANT 35-55

4.0 GENERAL 35

4.1 AN OVERVIEW OF GPMMA 35

4.2 BEHIND GPMMA 35

4.2.1 GPMMA & External Component Installation 35

4.2.2 GPMMA Command Installation 37

4.2.3 External Software Installation 38

vi

4.3 GPMMA OVERVIEW & IMPORTANT FEATURES 39

4.3.1 PMMA Deterioration Prediction 40

4.3.1.1 Prediction of Yearly Change in CSA 40

4.3.1.2 Prediction of Yearly Change in Deflection 41

4.3.1.3 Prediction of Yearly Change in Unevenness 41

4.3.1.4 Prediction of Yearly Change in PSR 41

4.3.2 PMMA Economic Analysis 42

4.3.2.1 Construction Cost 43

4.3.2.2 Vehicle Operation Cost 43

4.3.3 PMMA BBD Overlay Design 46

4.3.3.1 Conversion of Curves into Mathematical Forms 47

4.3.4 PMMA Maintenance Prioritisation 50

4.3.4.1 Index Ranking Method 50

4.3.4.2 Percentile Ranking Method 51

4.3.4.3 Weightages Given For Various Parameters 52

4.3.5 PMMA Overlay Cost Calculator 53

4.3.6 PMMA Maintenance Scheduler 53

4.3.7 Hooks to External Softwares 54

4.4 CONCLUSIONS 55

CHAPTER 5 GPMMS INPUTS 56-67

5.0 GENERAL 56

5.1 GPMMS INPUTS 56

5.2 GPMMS DATABASE 56

5.2.1 Inventory Data 57

5.2.2 Construction Data 58

5.2.3 Traffic Data 59

5.2.4 Condition Data 60

5.2.4.1 Physical Distress Data 60

5.2.4.2 Roughness Data 61

5.2.4.3 Structural Capacity Data 62

5.2.4.4 Friction Data 62

5.3 UPDATING GPMMS DATABASE 62

5.3.1 Linear Referencing 62

vii

5.3.2 Dynamic Segmentation 64

5.4 DATA ENTRY 65

5.5 GEOREFERENCED DIGITISED BASE MAP 66

5.6 PMMS ANALYSIS TOOLS (GPMMA) 67

5.7 CONCLUSIONS 67

CHAPTER 6 GPMMS OUTPUTS & RESULTS 68-88

6.0 GENERAL 68

6.1 GPMMS OUTPUTS 68

6.1.1 Thematic Maps 68

6.1.2 Deterioration Prediction 73

6.1.3 Economic Analysis 74

6.1.3.1 Net Present Value Method (NPV) 75

6.1.4 Maintenance Scheduling 79

6.1.5 Maintenance Prioritization 80

6.1.6 BBD Overlay Design 83

6.1.7 Overlay Cost Calculation 84

6.1.8 Other Outputs 85

6.2 CONCLUSIONS 88

CHAPTER 7 CONCLUSIONS, LIMITATIONS & SCOPE OF FUTURE WORK 89-90

7.0 GENERAL 89

7.1 SUMMARY AND CONCLUSIONS 89

7.2 LIMITATIONS AND SCOPE OF FURTHER WORK 90

7.3 CONCLUSIONS 90

REFERENCES 91

viii

LIST OF FIGURES

FIGURE NO.

TITLE PAGE NO.

1.1 Conceptual representation of PMMS 3

2.1 Structure of the PMS implemented in the Oklahoma State 10

2.2 Framework of Canadian Pavement Management System 11

2.3 Principal Components of HAPMS 12

2.4 Structure of VISAGE and GIRR 13

2.5 Three Levels of PMS Along With Three Types of Databases 15

2.6 Inputs – Analysis – Output - Chart 21

3.1 A Legend 28

3.2 Style Keys representing feature classes in Legend 29

3.3 Style Keys representing errors in Legend 29

3.4 Illustrating Linear Referencing 33

4.1 GPMMA & External component installation setup files 36

4.2 GPMMA & External component installation setup-1 36

4.3 GPMMA & External component installation setup-2 37

4.4 GPMMA Command Installation setup-1 37

4.5 GPMM Command Installation setup-2 38

4.6 GPMMA Command Installation setup-3 38

4.7 External Software Installation linking form 38

4.8 PMMA command installed in menubar 39

4.9 PMMA command windows 39

4.10 GUI for PMMA Deterioration predictor 42

4.11 GUI for PMMA Economic analysis 1 45

4.12 GUI for PMMA Economic analysis 2 45

4.13 GUI for PMMA Economic analysis 3 46

4.14 GUI for PMMA BBD overlay design 49

4.15 Access Database for PMMA BBD overlay design 49

4.16 Index Ranking Method process 50

ix

4.17 GUI for Priority Ranking 52

4.18 GUI for PMMA Overlay Cost Calculator 53

4.19 Graph showing best time for Maintenance scheduling 54

4.20 GUI for PMMA Maintenance scheduler 54

4.21 Snap Shots of External Softwares windows 55

5.1 Microsoft Access Database showing Inventory data 58

5.2 Microsoft Excel Database showing Traffic data 59

5.3 Microsoft Access Database showing Physical distress data 60

5.4 Microsoft Access Database showing Roughness data 61

5.5 Linear Referencing Command Toolbar & Work Flow 63

5.6 Linear Referenced Access Warehouse table & road network 63

5.7 Basic Concepts of Dynamic Segmentation 64

5.8 Dynamic Segmentation Command Toolbar & Work Flow 65

5.9 Microsoft InfoPath form Query View and Data Entry View 66

5.10 Georeferenced Raster Images & Digitized Map 67

6.1 Thematic Map showing the lengthwise distribution of MDR 69

6.2 Thematic Map showing the bridge inventory details 70

6.3 Thematic Map showing the bridge attribute details 70

6.4 Thematic Map showing the ODR condition details 71

6.5 Thematic Map showing the SH condition details 71

6.6 Thematic Map showing the Culvert inventory details 72

6.7 Thematic Map showing the Culvert attribute details 72

6.8 Thematic Map showing the IRQP Phase of NH 73

6.9 Deterioration Prediction - output 74

6.10 Economic Analysis – Deterioration Prediction 76

6.11 Economic Analysis – Overlay Cost Calculation 77

6.12 Economic Analysis – NPV & Strategy Selection 78

6.13 Present Serviceability Index Vs Age of Pavement 79

6.14 Maintenance scheduling- output 80

6.15 Maintenance prioritisation of MDR’s 81

6.16 Maintenance prioritisation of SH’s 81

x

6.17 BufferZones & Closest path around Koduvally 82

6.18 BufferZones & Closest path around Koduvally 83

6.19 BBD Overlay design-output 84

6.20 Overlay cost calculation -output 85

6.21 Customised Window for chart output 85

6.22 Bar Chart showing Raveling details along the stretch 86

6.23 Bar Chart showing Crack details along the stretch 86

6.24 Bar Chart showing Potholes details along the stretch 87

6.25 Bar Chart showing Patchwork details along the stretch 87

xi

LIST OF TABLES

TABLE

NO. TITLE PAGE NO.

4.1 Equations for vehicle operating cost 44

4.2 Percentage of vehicles and growth rate 44

4.3 Equations for moisture correction factor 48

4.4 Equations for moisture correction factor 48

5.1 Data collected from 12th Mile to Kattankal of MDR-2 57

1

Chapter 1

INTRODUCTION

1.0 GENERAL

A comprehensive fully integrated Pavement Maintenance & Management

Systems (PMMS) is the key to better reconstruction, restoration and maintenance

decision-making of pavements. It weaves together information on all pavement

inventories, condition and performance databases, and alternative investment options. An

operating PMMS provides the road authorities the ability to better plan and manages

highway, street, and road pavements. The Pavement Maintenance & Management

Systems is a set of tools or methods that can assist decision makers in finding cost

effective strategies for providing, evaluating, and maintaining pavements in a serviceable

condition. It provides the information necessary to make these decisions. The PMMS

consists of two basic components: A comprehensive database, which contains current and

historical information on pavement condition, pavement structure, and traffic. The second

component is a set of tools that allows us to determine existing and future pavement

conditions, predict financial needs, and identify and prioritize pavement projects.

1.1 PAVEMENT MAINTENANCE & MANAGEMENT SYSTEM

According to the World Bank Report, “The developing countries have lost

precious infrastructure worth billions of dollars through the deterioration of roads. The

cost of restoring these roads is going to be three to five times greater than the bill would

have been for timely and effectively maintenance. Vehicle operating cost rapidly

outpaces the cost of road repair as the condition of road passes from good to fair to poor”.

With several thousand vehicles per day moving on the highways, even a small saving in

vehicle operation cost can justify very large investments on pavements.

Pavement Maintenance & Management Systems are useful tools in quantifying

the overall maintenance needs of pavements and presenting the alternative maintenance

strategies under budget constraints. The most important aspect of development of a

PMMS is to collect, manage and analyse the pavement condition data in a considerably

detailed format. Since geographical information systems (GIS), with their spatial analysis

capabilities, match the geographical nature of the road networks, they are considered to

2

be the most appropriate tools to enhance pavement management operations, with features

such as graphical display of pavement condition. The Pavement Maintenance &

Management Systems is comprised of:

?? Storage, analysis and reporting software

?? Collection of stored pavement data

?? Maintenance and treatment costs

?? Data and formulas on pavement deterioration

?? Algorithms that calculate future needs and budget scenarios

1.2 FEATURES OF PMMS A PMMS is any tool or process that helps a road agency to manage pavement in

other words, any tool or process that helps an agency to maintain a network of safe and

serviceable pavements in a cost-effective manner. When most agencies refer to the term

“pavement management system,” they usually mean a computerized system where

pavement condition information is stored, analyzed, and displayed.

1.3 PMMS INPUTS

At the heart of the pavement management system is the database, which is the

storehouse for all pavement-related information collected. This database possesses

several features, including: a large capacity, user friendly access, flexibility for future

expansion, security features, and compatibility with other databases that store related

information (such as bridge, congestion, and traffic crash data). Every piece of

information in the database is referenced to the particular section of pavement (i.e., the

particular intersection or segment of road) which it describes.

The information collected and stored in the database can be divided into five categories:

?? Inventory data,

?? Pavement history,

?? Construction data,

?? Traffic data,

?? Condition data (physical distress, roughness, structural capacity, friction),

1.4 ANALYTICAL TOOLS AND OUTPUTS

While the database is the “heart” of a pavement management system, data

are not useful unless they are presented in a meaningful way. It is the role of analysis

3

procedures to transform the raw collected data into products such as charts, graphs, and

reports that are helpful to decision-makers. A pavement management system can

transform a spreadsheet containing pavement condition data into a map. A map can be

quickly and easily used to examine the health of pavement in ways that are not readily

apparent from columns of numbers. Analytic procedures are grouped into four categories:

?? Simple queries,

?? Pavement condition score calculations,

?? Remaining service life calculations, and

?? Strategy selection procedures.

1.5 STRUCTURE OF PMMS

Pavement Management in its broadest sense, encompasses all the activities

involved in planning, design, construction, maintenance and rehabilitation of the of the

pavement portion of public works program. The integration of both Attribute data and

spatial data is made possible through GIS. The analysis of data is carried out through

PMMA & GeoMedia analysis tools. This can be conceptually represented in fig 1.1

Fig 1.1 Conceptual representation of PMMS

4

1.6 NEED FOR THE STUDY

In India, due to the large scale industrialization and commercial activities, there

has been an unprecedented traffic growth during the last four decades. The high volume

of vehicular traffic and increasingly heavy axle loads witnessed on Indian highways have

brought the existing arterial road network to such a crippling stage that heavy

investments are needed for restoring it to a desired serviceability level. This is a

particular difficult situation, because pavements often are deteriorating faster than they

are being corrected. Effective management of pavements is essential in these challenging

times. Therefore, there is a need to link together explicitly the activities of planning,

design, construction and maintenance of pavements.

The Road User Cost Study in India has established that due to improper

maintenance and poor surface condition of road pavements, there is a considerable

economic loss to the country due to increase in vehicle operation costs. If the road

pavements are maintained to the desired level at an appropriate time, it is possible to save

the losses in road user cost. In view of the budgetary constraints and the need for

judicious spending of available resources, the maintenance planning and budgeting are

required to be done based on scientific methods.

The whole life cycle cost analysis based on the road user cost relationships

enables the decision makers to examine financial and economic implications of various

options for formulating appropriate strategies for cost effective use of resources.

1.7 OBJECTIVES OF THE STUDY

?? Collection of relevant data for analysis.

?? To develop a digitised road map of Kozhikode district in GIS environment.

?? To assess the overall pavement condition based on functional and structural

evaluation data.

?? Design of flexible pavement overlays.

?? To find the rate of progression of structural and functional deterioration.

?? To develop a plug-in software program for GeoMedia using Microsoft Visual

Basic 6.0 environment,

?? To assess the impact of different maintenance strategies on the performance of

pavement during the design period.

5

1.8 PROBLEMS, CHALLENGES AND THREATS

?? Lack of structural information regarding the thickness of the overlays, the

maintenance method used, the type of bitumen used, or the construction quality.

?? Lack of knowledge about the exact age of pavement.

?? Lack of fixed evaluation segments for the condition and other surveys.

?? Increased rate of deterioration. (pavements deteriorate fast)

?? Overloading of vehicles. (no commitment with the legal loading)

?? Rapid traffic growth. (high increase of vehicle ownership )

?? Poor maintenance. (improper materials, wrong implementation, etc)

?? Improper design and implementation.

?? Limited resources (geometry, funds, equipment, materials, etc)

?? Insufficient information for decision-making.

?? Inefficient current traditional management system.

1.9 SCOPE OF THE STUDY

This work is an essential requirement for project planning and budget allocation.

This work will help to reduce the effort needed than in conventional methods, to collect

and analyse the data periodically by reducing the repeated works. The present work

consists of the analysis and design of pavement data of Kozhikode District. The

important aims of PMMS are:

?? An essential requirement for project planning and budget allocation.

?? Flexible pavement deterioration models include Deflection, Unevenness, and

Present Serviceability Rating models representing structural condition and also

functional condition model.

?? The performance and life of the Overlay has been assured on the basis of

acceptable limits for deflection, UI and maintenance cost.

1.10 ORGANIZATION OF THE DISSERTATION WORK

The contents of the study are organized and presented in a chapter wise manner as

follows:

Chapter 1: General introduction, need, objectives, scope of the study,

challenges.

6

Chapter 2: Literature Review, general. Introduction, some definition, its basic

components are explained. Also planning activities are described in detail.

Chapter 3: Discusses the capability of the software, GeoMedia Professional

used for this work.

Chapter 4: In this chapter, About the Plug-in Software PMMA and the

methodology used were described.

Chapter 5: The inputs for the GPMMS, data entry methods and Dynamic

segmentation methods were discussed.

Chapter 6: The inputs for the GPMMS, Data entry methods and Dynamic

segmentation methods were discussed.

Chapter 7: Concludes the thesis by pointing at the limitations of the study and

scope for the further study.

1.11 CONCLUSIONS

This chapter gives a brief review about the basics of Pavement Maintenance &

Management Systems. The objectives, scope, layout of the thesis were also explained.

7

Chapter 2

LITERATURE REVIEW

2.0 GENERAL

The purpose of this chapter is to review the available literature on Pavement

management system, maintenance system and pavement management softwares and to

discuss about the international PMS scenarios. An extensive literature survey was carried

out to keep abreast with the latest development in the Pavement Management Systems.

The work done both at the International level and domestic level is reviewed. Research

work being carried out in various academic research institutions is also considered.

2.1 STATE - OF - THE – ART

Most highway agencies of the developed countries are now engaged in the

development, implementation, and operation of pavement management systems. As early

as 1980, five states in USA viz. Arizona, California, Idaho, Utah, and Washington were

reported to be in various stages of development of systematic procedures for managing

pavement networks on a project-by-project basis. AASHTO has had a significant role in

furthering the development and use of Pavement Management Systems through the years.

Federal Highway Administration (FHWA), the Transportation Research Board (TRB),

and the National Cooperative Highway Research Programme (NCHRP) have contributed

to major Technical studies. Notable among them is the NCHRP- Project 1-35 A, FY 1997

which was the basis for Guide for Pavement Management.

The “Guidelines for Pavement Management Systems,” published by AASHTO in

July 1990, contains information needed for establishing a framework for a pavement

management system. However, this document didn’t address the day-to-day issues

encountered by pavement engineers or the issues associated with new and emerging

technologies. Upon further research, a revised final report has been distributed to the

members of the AASHTO Joint Task Force on Pavements and the AASHTO Highway

Subcommittee on Design and has been approved for publication as AASHTO Guide for

Pavement Management.

8

Universities and Research Institutes have developed research based Pavement

Management System. Notable contributions are from Texas Transportation Research

Institute (USA), Louisiana Transportation Research Centre (UK), University of

Birmingham (UK) and Transport Research Laboratory (TRL, UK). Apart from these

studies there had been a number of studies by Private Consulting Agencies as part of

World Bank funded Highway Development projects in developing countries.

2.2 GLOBAL PMMS SCENARIO

In the early seventies, the phrase “Pavement Management System” began to be

used by researchers to describe the entire range of activities involved in managing and

maintaining pavements. At the same time, initial operational systems were also

developed. Since then, the following factors have provided great impetus for growing

interest in PMS development

?? Increasing budgetary constraints in relation to maintenance needs.

?? Recognition of direct effect of pavement condition on road user costs

?? Awareness of social and environmental values affected by road transport and road

surface characteristics.

?? Advances in the development of pavement technology.

?? Increased capacity in pavement condition monitoring through advanced

measuring equipment.

?? Advanced in computerization and information systems

?? General growth in awareness of management methods.

Presently, highway authorities in developed countries are using systematic and

objective method to determine pavement condition and programming maintenance in

response to observed conditions, as budget permits. In many of the developing countries,

PMS is in various phase of working process with diversified approaches as per the

respective needs and problems of each country.

2.2.1 United Stases of America

The concept of PMS took root in USA during the recent era of austere budgets.

The first PMS model was developed by the Washington State Department of Highways

in the mid seventies. This model consisted of development of performance prediction

9

model and a cost model based on a databank of information collected in Washington over

a period of 6 to 8 years. Since then, various state departments of transportation have

developed their own PMS methodologies suitable to their own needs and requirements.

The Arizona Department of Transportation has reported savings to the tune of $2000

million over a period of five years as a consequence of successful implementation of

PMS program to optimize pavement rehabilitation expenditure. The use of PMS at

California Department of Transportation has resulted in improved communications

between political and technical decision makers as regarding priority programming of

pavement rehabilitation projects.

The Pavement Management System (called PMS-III) developed and implemented

for the Ohio Department of Transportation (ODOT), is a network level system that can

prescribe optimal maintenance and rehabilitation actions and the required budget for each

year for a 6 year planning period. On the basis of present network condition and

deterministic prediction model, PMS-III forecasts future network condition and

rehabilitation needs and as associated budget. The optimal maintenance policies

recommended by this pavement management system are based on maximizing the

preservation of pavement investments for a given annual budget or on minimizing the

cost of maintaining the network condition for a given performance level.

The State of Iowa has developed an Iowa Pavement Management Program

(IPMP) to support both project level and network level PMS conducted by local and

regional governments and the Iowa Department of Transportation. The PMS of

Oklahoma Department of Transportation is characterized by the integration of a

pavement performance - modeling tool with a new pavement network optimization model

for identifying and selecting cost effective projects for maintenance and rehabilitation.

The unique feature of this system is the integration of a pavement performance -

modeling tool and a global optimization model for pavement network analysis. Pavement

performance models can be updated whenever new data is available. All models can be

evaluated and alternative models can be developed using the interactive modeling too.

This system can produce satisfactory results for pavement engineers to perform pavement

maintenance and rehabilitation planning up to 20 years. Flow chart shows the structure of

the Pavement Management System implemented in the Oklahoma State.

10

Fig 2.1 Structure of the PMS implemented in the Oklahoma State

2.2.2 Canada

The Primary method of structural evaluation in Canada are deflection base e.g.

Benkelman beam, Dynaflect and Falling Weight Deflectometer, with the latter becoming

the primary device of choice and the former now seeing only very limited use. The

indices commonly used are Riding Comfort Index (RCI), Structural Adequacy Index

(SAl), Surface Distress Index (SDI), a composite measure Pavement Quality Index (PQI)

and Pavement Condition Index (PCI), Performance prediction models commonly are for

RCI, PQI, or PCI v/s pavements age, and are mainly developed through regression,

Markov and Bayesian techniques.

Database

Sufficiency

Structural History

Master Table

Build Performance Model

Setup Distress Deducted Values

Material Categories Treatments

Performance Indices Grouping Variables Pavement Groups

Performance Models

Analyze Models

Network Optimization

Treatments Assigned to each pavement group

for each year

Choose Best Scenario

Multiple Years Prioritization

Treatments Assigned to each pavement section

for each year.

Generate / Modify Projects

Sections are aggregated in to

Projects

11

Fig 2.2 Framework of Canadian Pavement Management System

2.2.3 Australia

For a population of just over 20 million, Australia covers a very large landmass

with an extensive network of roads, providing one of World’s road lengths per head of

population. The Australian Road Research Board (ARRB) has been actively engaged in

developing and promoting the adoption of PMS by road authorities to improve the

efficiency of decision making and ensuring that maximum value is obtained from the

funds allocated to road improvement. There are over 12 different PMS software packages

available, which can cater for range of roads from National Highways to low volume

rural roads.

12

2.2.4 United Kingdom

The United Kingdom Pavement Management System (UKPMS) has been

designed to assist highway authorities in structural maintenance of pavements, It does so

by improving both the systematic collection of information and the decision making

process required to optimize resources and to generate a works program and the

corresponding budget. UKPMS uses innovative technology to improve treatment

selection and by optimizing the allocation of funds for various rehabilitation schemes.

A new generation pavement management system called HAPMS, for the National

Roads of England has been developed by the Highways Agency, an executive body of the

Department of Environment, Transport and the Regions (DETR) of the Central

Government. The network of roads managed by the Highways Agency comprises only 4

per cent of the total road network of England, but carries 25 per cent of the total traffic.

The applications working on this data are concerned with the presentation to all users of

data in convenient format, including map backgrounds, the preparation and allocation of

budgets and the determination of priorities for investment in major pavement

maintenance. Fig. 2. 3 show the principal components of HAPMS.

Fig 2.3 Principal Components of HAPMS

13

2.2.5 France

The French Directorate of Road in association with LCPC (Central Public Works

Laboratory) and the SETRA (Roads and Motorways Engineering Department) has

developed a road management system, based on two complimentary suits of tools the

VISAGE road database with its own facilities for the graphic representation of data and

the GIRR package with its various data analysis modules. This system was implemented

in 1992 by the state departments on the National Roads Network and extended more

widely in 1996 to regional network also. The VISAGE AND GIRR software package as

shown in Figure 2.4 use a rational approach for the management of pavement

maintenance. It is an approach designed around four main stages; after an inventory on

the nature of the road network, and an assessment of its condition, a maintenance policy

must be defined and then applied through works programming. Finally, network follow -

up is carried out on a regular basis to measure the effects of the policy put in place and,

when applicable, to adopt the appropriate corrective actions.

Fig 2.4 Structure of VISAGE and GIRR

2.2.6 Germany

In Germany, there are 11000 km of motorways and 45000 km of federal roadways

reported. To ensure that this road network is always functional for economy and society,

a system was introduced in 1992, whereby the condition of the federal trunk roads is

rated every three years. A road preservation management program called UMASTER has

been developed with the objective of computations of the cost to the agencies responsible

for financing road construction and preservation. But the road preservation program

14

includes only those types of conditions, which are relevant to medium and long range

planning, and not those which require immediate attending e.g. filling of potholes.

Funding needs are computed based on periodically performed surveys of roadway

conditions and related predictions of the behavior of the future course of development of

the condition of highway.

Methods have also been developed for forecasting the behavior of flexible

pavements taking into account their conditions of use and climate influences,

Quantitative forecasting model for the aspect of road conditions described as rut depth,

water retention and network of cracks are in the process of development. On the basis of

the condition rating and evaluation process ( called ZEB in German), a standard National

Pavement Management System has been developed and applied by road construction

authorities in 8 different states since January 1999. The Federal Ministry of Transport is

now contemplating to extent this pavement management process to all 16 states. Tools

now exist for the data required for a PMS, which can create a graphical representation of

the data on plan and maps so that it is possible to get a quick overview of the road

network condition.

2.2.7 Denmark

The Technical University of Denmark in cooperation with Dynatest Engineering

developed the Dynatest Pavement Maintenance and Rehabilitation Management System

(DPMS) during eighties. This system is capable of predicting future pavement condition

for number of years on project as well as network level. It contains an optimization

procedure to determine that combination of M&R measures that will ensure the optimal

use of the budget. The system is not only based upon objectively measured functional and

structural pavement characteristics, but it also ensures that the knowledge and experience

of the local engineer is incorporated. The system ensures compatibility in all the steps of

data flow, right from the collection of data out on the road to the final consequence

analysis.

2.2.8 New Zealand

It was only in late 1998 that the Government of New Zealand decided to

implement a National PMS. The initial objective was to have a completed preliminary

system is place and integrated with the existing National Road Asset Management

Program (RAMM). This consisted of a basis inventory and pavement condition database

15

along with an algorithm for selecting maintenance treatments. The new PMS has been

build on the existing road management inventory system and the existing funding

framework. The software package dTIMS (Deighton Total Infrastructure Management

System) design total Infrastructure funding framework. The software package dTIMS

along with a hybrid set of predictive models from HDM-II and HDM-4 has been adopted

for the development of PMS would be used by over 70 different road - controlling

authorities (city, district and state level) responsible for a network of more than 100,000

km of sealed and unsealed roads. Consultants engaged in management of the road

networks would also use it.

2.2.9 Sweden

The Swedish PMS has been developed and implemented at several levels i.e,

strategic level, programming level and Project level. The main objective of PMS at

strategic level is to produce objective information as decision support in fund raising, in

allocation of available budgets to the regions. The objective of PMS at programming

level is to serve as a tool to identify candidate projects and the objective of the PMS at

project level is to assist in planning and design of individual projects. Figure 2.5 shows

the three levels of PMS along with three types of databases.

Fig 2.5 Three Levels of PMS Along With Three Types of Databases

16

2.2.10 Austria

Increased road deterioration in Austria combined with the demand for a fair

distribution of the available budgets urged the Australian Federal Road Administration to

take necessary steps for the implementation of a nationwide PMS. A number of extensive

studies by different investigators and improvements in administration ultimately paved

the way for the start of very first application in practice of the Austrian Pavement

Management System VIAPMS-AUSTRIA in late 1999. The Institute for Road

Construction and Maintenance and Vienna University of Technology provided the road

specific data and the practical use of VIAPMS analytical software. This PMS is the only

available tool in Austria that underlined the necessity of allocating budget resources for

pavement maintenance.

2.2.11 Indian Scenario

The absence of coordinated research has impaired the progress of developing an

implementable PMS for India. Nevertheless there had been notable contributions from

different research institutes and organizations like Central Road Research Institute

(CRRI) (Updating Road User Cost Data URUCS 1991, Pavement Performance Study

(PPS-EPS 1993), RITES (HDM Calibration Studies -1994), Bangalore University

(Transition Probability Matrices for Optimal maintenance decisions-1995), & Indian

Institute of Technology Kharagpur (Analytical Pavement Design (999). These studies

laid the basis for the pavement data analysis and development of Pavement deterioration

model for Indian roads. Apart from research institutes some private consultants have also

tried to develop Pavement Management options in connection with some of the externally

aided projects.

The PMS for National Highways funded by Asian Development Bank had been

completed in 1995. Initially it was installed on a pilot basis in the states of Karnataka and

Uttar Pradesh and at the National level in the ministry of Surface Transport. Another

World Bank study was the 4 States PMS towards instituting a network level PMS in the

States of Bihar, Maharashtra, Rajasthan and Uttar Pradesh. The study included a section

of State Highways, Major District Roads and Other District Roads which are directly

managed by States. The ARAN (Automated Road Analyzer) was used for the collection

of pavement data. For the National Highways the PMS adopted is called NETTER-PMS

and for the four states the PMS adopted is dTIMS (Deighton Total Infrastructure

17

Management System). The NETTER VOC model uses the Brazilian relationships from

the World Bank HDM III model. The road deterioration and maintenance models for

dTIMS had been established using HDM-III equations with adaptation for local

conditions.

2.2.12 Studies at universities and Research Institutions

A brief review of the studies reported by various researchers is attempted in the

following pages.

Dr. S S Jam, Dr. A K Gupta & Sanjeev Rastogi (1992) have made an attempt to

analyze the data of nine test sections of overlaid flexible pavements located in the States

of Uttar Pradesh and Himachal Pradesh. The data has been analyzed for pre-monsoon,

post-monsoon and winter season for the years between 1980 to 1990. The performance

and life of the overlays has been assessed on the basis of acceptable limits for deflection,

rut depth, cracks and cracking pattern and maintenance cost. Models are also

incorporated for the choice of type and thickness of materials for overlays on different

sub grade soils economically without sacrificing the safety of road structure. Models

developed in these studies are capable of predicting life of an overlay for given values of

pavement thickness, overlay thickness, traffic intensity and acceptable limits for

deflection, rut depth and cracking. The general model includes wide variation of climate,

terrain, rainfall, temperature and thickness of overlay can be chosen economically.

Prof. (Dr.) S S Jam, Prof. (Dr.) A K Gupta, Prof. (Dr.) S K Khanna and Dayanand

(1992) have studied the performance of twelve test sections of overlaid flexible pavement

located in the states of Himachal Pradesh and Uttar Pradesh. The data has been analyzed

for pre-monsoon and post monsoon seasons for the year 1994 for assessing the needs of

corrective and strengthening measures and the pavement outputs with time has been

presented using data from 1980 to 1993 from studies conducted at University of Roorkee.

The influence parameter considered includes deflection, roughness, rutting, cracking and

pot holes and the availability of resources for the choice of the type and thickness of

materials for overlay and these parameters were recorded for the year 1993 and 1994

whose comparison showed the deterioration of flexible pavements with passage of time.

The investment need for corrective measures were obtained and the investment strategy

has been developed for maintenance and rehabilitation of flexible pavements. The

conditions of the flexible pavements can be asserted by functional and structural

18

evaluations. The maintenance and rehabilitation need comes if any of the influencing

parameters reaches its lowest acceptable limits, the overlays needs to be designed based

on the cumulative number of standard axles rather than total number of commercial

vehicles. Based on the requirements of corrective and strengthening measures, the

investment estimation is done. Hence the study helps in taking decisions for maintenance

and rehabilitation of flexible pavements rationally.

Maj. C R Ramesh, B P Nityananda, Y S Madvesh and Dr. C E G Justo (1994)

conducted a study done jointly by Karnataka PWD and Department of Civil Engineering,

Bangalore University on a stretch of cracked Cement Concrete pavement on Bangalore -

Mysore State Highway to compute the relative performance of bituminous overlay with

and without geo fabric at the interface. Installation of geo fabric at the interface between

cracked CC pavement and bituminous overlay retards the propagation of reflection

cracking and also there is a reduction in formation of new cracks on the overlay surface

and a lower rate of increase in unevenness index. Geo fabric is a geo synthetic material

like non-woven polypropylene fabric. A field study for ten years period using geo textile

as stress-relieving interlayer has been reported to have given excellent results. In terms of

service life treatment is likely to be economical. It is also desirable to vary the thickness

of bituminous overlay with or without geo synthetic and to continue observation until

failure of these overlays, for arriving at equivalency factors and also for comparison of

cost on more realistic terms. Cost analyses of various alternatives are also done.

S. Chakrabarti, Ms Rawat and B Mondal (1995) have done the calibration of

HDM-II and adaptation aspects of HDM Road Deterioration and Maintenance Effect

(RDME) relationship for Indian conditions. The methodology for calibrations using

pavement performance data, notably, the Pavement Performance Study (PPS) has been

described. The deterioration factors have been derived for the pavement types and traffic

loading levels appropriate for the country. The study claims that DDM RDME is robust,

yet flexible enough to predict the deterioration for road in the country with the

deterioration pavements factors very close to default values.

V K Sood and B M Sharma (1996) have reported the status of road network

deficiencies in the present maintenance practices in our country. A pavement

Performance Study was conducted with a view to develop data for total transportation

19

cost model for Indian conditions, to be achieved through development of pavement

performance data to attempt development of layer equivalence and strength coefficients

as feasible. Data was collected on the construction and maintenance inputs of different

pavements based on studies carried out on nine pavement sections for a period of about

10 years. A brief description on various models such as cracking models, cracking

progression models, ravelling models, pot holes models and roughness progression

models have been included. Validations of models have been done based on limited

fieldwork.

Turki I Al-Suleiman and Azm. S Al-Homoud (1996) have reported the work of

evaluating the effects of pavement characteristics on pavement condition of the street

network in Irbid City in Northern Jordan using the concept of Pavement Condition Index

(PCI). It was found that 35.48 percent of the inspected pavement sections in Irbid City

were in poor condition while 6.45 percent of the inspected pavement sections were in

excellent condition. Alligator cracking, rutting, depression and swell distresses were

found to be the most frequent distress types that caused the pavement deterioration.

Pavement age, traffic level and pavement thickness were found to be highly significant

and affect the pavement condition to great extent. Some of the asphalt mix properties

such as air voids, bulk specific gravity and asphalt contents were found to have small

effect on pavement condition. Pavement section of low air voids in the asphalt mix

suffered from distortion and cracking due to the small resistance to compaction under

traffic. Statistical models were developed to describe the relationship between PCI and

pavement characteristics.

Mr. S C Sharma and R K Pandy (l997) made a study on the existing pavements

completed in recent years presenting and extensive indigenous research back up and basic

relationships to develop a total transportation cost model for Indian conditions. Indian

research results have been used to develop this model and therefore, its predictions and

results are considered truly reflective of Indian conditions. The model makes it possible

to apply a rational approach in road maintenance decisions for obtaining best results from

available funds including benefits of periodic maintenance, cost effectiveness of

maintenance strategies etc.

20

Maj. Ramesh, Dr. A Veeraraghavan, R Sridhar, and Chandrasekhar S Pichika

(1999)’ have collected an extensive field data develop the performance models and in the

determinations of the life cycle cost. A computer program is developed to calculate the

cost, first stage strengthening cost, user delay cost and salvage value. The program has

the capability to compute the life cycle cost for any design period and for any number of

sections by varying the threshold Present Serviceability Index Value which is on a scale

of 1 to 10. The budget scenario can also be varied and the effect of budget level on

pavement performance can be studied.

2.3 GIS TECHNOLOGY AND BENEFITS

A GIS is a computerized data base management system for accumulating, storage,

retrieval, analysis and display of spatial (i.e. locationally defined) data. A GIS contains

two broad classifications of information, geocoded spatial data and attribute data.

Geocoded spatial data define objects that have an orientation and relationship in two or

three-dimensional space. Attributes associated with a street segment might include its

width, number of lanes, construction history, and pavement condition and traffic

volumes. An accident record could contain fields for vehicle type, weather conditions,

contributing circumstances and injuries. This attribute data is associated with a topologic

object (point, line or polygon) that has a position somewhere on the surface of the earth.

A well-designed GIS permits the integration of these data. The sophisticated database in a

GIS has the ability to associate and manipulate diverse sets of spatially referenced data

that have been geocoded to a common referencing system. The software can transform

state plane coordinates and mile point data to latitude-longitude data and vice versa.

A GIS can expand the decision making on repair strategies and project scheduling

by incorporating such diverse data as accident histories, economic needs hazardous

materials shipment and vehicle volumes. A GIS can perform geographic queries in a

straightforward, intuitive fashion rather than being limited to textual queries; A GPMMS

can be used to build projects through spatial selection, can compute traffic impacts of

various PMS plans and can incorporate the results of life cycle forecasts into

measurements of future mobility.

The network-level PMS has been integrated with GIS for the selected highway

network. The input data and results obtained under life-cycle cost analysis of the highway

21

network have been used to demonstrate the use of GIS in enhancing the pavement

management system. The commercially available GIS software GeoMedia has been used

for this purpose. A spatial map of the study area has been created, which is comprised of

various GIS themes such as national highways, pavement sections, section nodes and

districts. The input data and the results of the life-cycle cost analysis of highway network

have been imported into GIS as attributes of the pavement sections. GIS has been used to

enhance pavement management information with its typical features, such as graphical

display of highway network and current and future pavement condition of the selected

pavement sections. GIS also provides an excellent spatial query and analysis capability to

select the candidate pavement sections in need of immediate maintenance.

Fig 2.6 Inputs – Analysis – Output - Chart

2.4 INFORMATION TECHNOLOGY FOR PMMS

Pavement Performance Forecasting, Optimization & Simulation

Historical Pavement distress condition

Where, when, what to treat

Pavement Performance Forecasting,

Optimization and Simulation models

INPUT

4. Future pavement performance visualization and spatial analysis

Optimization models

Simulation models

Deterioration models

GIS mapping, visualization and spatial analysis

Historical traffic condition

Given funding

Forecasting time frame (5 or 10 year later) Treatment Determination

2. Funding balance among congressional districts

OUTPUT 1. Avg. statewide composite

future performance index

3. Workload balance among working districts

22

For performing GIS based Pavement Maintenance & Management Analysis, there

are so many softwares used world wide. Among them the leading PMS software’s in

practice are:

RoadSoft

RoadSoft, available from the Local Technical Assistance Program (LTAP) at

Michigan Technological University, is a GIS-based roadway management system. The

software package was developed for local road agencies within Michigan and engineers

and managers to analyze roadway information within their jurisdictions. The software

uses the Michigan Accident Location Index (MALI) as a physical reference base.

RoadSoft has a road surface inventory module for rating pavement condition

using the PASER (Pavement Surface Evaluation and Rating) system. PASER is a simple

method of rating asphalt, concrete, and gravel roads developed by the University of

Wisconsin’s Transportation Information Center. Manuals filled with pictures detail a one-

to-10 evaluation system in which "10" means excellent while "one" indicates a failed

road. This system is used to obtain consistent ratings based on the types of wear evident

on the roadway surface, such as cracks and deformations. Based on the types of defects,

general characteristics of the roadway, and age of the pavement, PASER makes

recommendations for the types of fixes that would be appropriate for the road. There are

rating manuals available for concrete, asphalt, and gravel roads.

MicroPAVER

MicroPAVER is a pavement management system developed by the U.S. Army

Construction Engineering Research Laboratory and distributed by the Technical

Assistance Center at the University of Illinois at Urbana-Champaign. The software

contains a full-featured PMS, including manuals for evaluating pavement conditions.

MicroPAVER is a decision-support tool, allowing agencies to systematically determine

maintenance and repair needs and priorities. The system also enables users to compare

budget scenarios and their effect on pavement networks, and data can be linked to a GIS.

Inspection data from the road network is input into the system’s database. By

taking samples of a section of the entire network, MicroPAVER can calculate the

Pavement Condition Index (PCI). Information from the PCI is used to accurately predict

the overall health of the pavement network. Using pavement life cycle models in the

23

software, this system can quickly determine which pavements need attention the most,

and also calculate the critical condition pavements. (Critical condition pavements are

those that are close to the point where they deteriorate rapidly.)

HDM - 4

The internationally recognised Highway Development and Management System

(HDM-4) have been used to develop this PMS. Since the size of the highway network is

not very large and the analysis period is of medium duration, the ‘programme analysis’

application of HDM-4 has been used for the network-level pavement management

analysis. The results obtained through the network-level pavement management analysis

have been presented through various applications of GIS.4 This helps in easy

identification of the candidate pavement sections, due for maintenance during the

analysis period, and the associated details of timing, type and cost of maintenance

activities can also be readily determined.

GeoPave

GeoPave can extract and display information that a Pavement Manager requires.

It has Dynamic GIS link to your MTC-PMS system. Easy to use menu selections. Also it

has Plug-in extension to ArcView / ArcMap.

The main features of GeoPave are, it can maintain a good Pavement History, it

can analyse Maintenance & Rehabilitation Workplans. It can also directly account for

Funding Scenarios.

Stantec PMS

Stantec developed a Pavement Management Application (PMA) within their

Infrastructure Management System (IMA) software. The Infrastructure Management

Application is a tool for the management and graphical display of asset information,

structural condition, and other available data for municipal utilities and right-of-way

assets, either individually or as a group. IMA is a network planning tool for municipal

assets.

Physical characteristics, structural condition assessment, and rehabilitation

program development for each asset can be objectively compared with the entire network

for planning and rehabilitation and maintenance programs. Information can be analyzed

24

graphically, or through the use of a GIS. A rehabilitation module analyzes road condition

to prioritize rehabilitation activities and budget programming. The system can also

recommend maintenance activities, such as crack sealing and pothole patching.

Hansen PMS

Hansen develops asset management software for both the public and private

sector. Hansen developed a pavement management system to complement the

infrastructure management software developed to assist agencies maintain assets under

their control.

The pavement management system is completely customizable by the user.

Working with consultants from the company, the management system is tailored to meet

the needs of the user. Hansen works with the agency to determine which inputs are

needed to perform analysis, fits the deterioration curves to inputs, and calculates outputs.

CarteGraph PavementView

PavementView pavement management software, developed by CarteGraph

Systems, is part of their more general infrastructure management products. Based on

concepts introduced by the Federal Highway Administration and the U.S. Army Corps of

Engineers, this system integrates data collection, inspection records, asset history, along

with performance modeling to accurately assess current and future pavement condition.

PavementView is able to graphically display pavement performance using maps,

graphs, or charts. This system allows users to develop queries and reports using all

database fields. Users can generate standard or custom reports to assess inventory

condition and help manage scheduled and completed

2.5 CONCLUSION

A brief review of the available literature on Pavement management system,

Pavement maintenance system and pavement management softwares were done. The

international PMS scenario was also discussed.

25

Chapter 3

A BRIEF REVIEW OF GeoMedia

3.0 GENERAL

This chapter gives a brief description about various functions and tools available

in GeoMedia 5.2 as well as GeoMedia Transportation Manager 5.2.

3.1 GEOMEDIA PROFESSIONAL

GeoMedia Professional is a fully functional desktop GIS solution. Based on

Jupiter technology from Intergraph Corporation, this product is an enterprise GIS for the

Windows 2000, Windows NT and Windows XP operating systems. Using GeoMedia

Professional we can make live connections to geospatial data in multiple data warehouses

simultaneously; analyse data relationships; turn information into precise, finished maps

for distribution and presentation; and put geospatial data into the hands of users.

GeoMedia’s extensive object model is accessible for customization through industry-

standard programming languages such as Microsoft Visual Basic, Microsoft Visual C++,

PowerBuilder, and Delphi.

Some of the important terminologies associated with GeoMedia are given below.

3.1.1 GeoWorkspace

A GeoWorkspace is the container for all our work. Within its confines are the

warehouse connections to our data, map windows, data windows, toolbars, coordinate-

system information, and queries we have built. The first thing we have to do is to open an

existing GeoWorkspace or create a new one. Once we are in a GeoWorkspace, we can

change its coordinate system, establish warehouse connections, run queries, display data,

and perform spatial analyses. The settings and connections we define in a GeoWorkspace

are saved in a .gws file, although the actual data remains stored in the warehouse. Every

GeoWorkspace is built on a template, and we can create our own templates or use an

existing one. The software is delivered with a default GeoWorkspace template,

normal.gwt, which contains an empty map window, an empty legend, and a predefined

26

coordinate system. Opening an existing GeoWorkspace may take a long time. The

amount of time varies with the number of feature classes being loaded into displays, the

amount of data per feature class, and the processing time of any queries. To improve

performance, we can delay the loading of data by selecting the ‘Do not load data when

opening GeoWorkspace’ check box on the General tab of the Options dialog box.

3.1.2 Co-Ordinate System

A coordinate system provides the mathematical basis for relating the features in

our study area to their real-world positions. The software supports the following types of

coordinate systems:

?? ?A geographic coordinate system (the default) references a spheroid, expressing

coordinates as longitude, latitude, where longitude is the angular distance from a

prime meridian, and latitude is the angular distance from the equator.

?? ?A projected coordinate system references a projection plane that has a well-

known relationship to a spheroid, expressing coordinates as X,Y, where X

normally points east on the plane of the map, and Y points north at the point

chosen for the origin of the map. The X coordinate called easting, and the Y

coordinate is called northing.

?? A geocentric coordinate system references an earth-centred Cartesian system,

expressing coordinates as defining the position of a specific point with respect to

the centre of the earth. These coordinates are Cartesian (X, Y, Z) where the X

axis of the geocentric system passes through the intersection of the prime

meridian and the equator, the Y axis passes through the intersection of the

equator with 90 degrees East, and the Z axis corresponds with the earth’s polar

axis. The X and Y-axes are positive pointing outwards, while the Z axis is

positive towards the North Pole.

Each feature class stored in a warehouse can have its own unique coordinate

system. If we change the co-ordinate system after displaying data, the data is transformed

to the new co-ordinate system, and the display is updated. Changing the co-ordinate

system in the GeoWorkspace does not affect the data in the warehouse, only data in the

map window. Finally, co-ordinate systems are heavily data dependant; therefore, one

27

should not define them arbitrarily. To be displayed accurately in a GeoWorkspace, all

data must specify a co-ordinate system. To accommodate data with no specified co-

ordinate system, we must first define a co-ordinate system file (.csf) outside of the

software.

3.1.3 Warehouse

Warehouse is considered as the source of both graphic and non-graphic

information. We can display feature geometries and attribute data in a GeoWorkspace

through connections to warehouses where the data are stored. Each warehouse connection

uses a data server to convert the data into a format that the software can display. All

warehouse types are read-only, except for Access, Oracle, and SQL Server. This protects

the integrity of our source data. So, if we want only to display data in the software from

one or more warehouses, we can simply create one or more warehouse connections and

then use map window and data window to display the data.

3.1.4 Windows

The GeoMedia GeoWorkspace can contain one or more windows—map window,

data window, and a layout window. These windows provide us with different ways of

visualizing our data. The map window shows graphic display or features. The data

window shows the same features in attribute form, that is, non-graphic data associated

with the geographic objects. Thus, if a feature is displayed in multiple map and data

window, it highlights in all when selected. The window allows you to design and to plot a

map layout. Map graphics in the layout window can be optionally linked to reflect

changes made in the map window, or they can be a static snapshot reflecting the

characteristics of the map window at the time of placement. Each map window contains

the following marginalia items: a legend, a north arrow, and a scale bar.

3.1.5 Legend The legend contains the following parts:

?? ?A title bar, which we can turn on or off. The title bar must be turned on before

we dismiss the legend.

28

?? ?Legend entries, which we use to control the display of the objects in the active

map window. Legend entries can have titles, subtitles, and headings.

The legend contains a separate entry for each map object. When a feature class or

query has multiple geometry or text attributes, a separate entry is added to the legend

for each of these attributes. An example of a legend is shown in Fig 3.1.

Fig 3.1 A Legend

Each entry contains a title and a style key. If statistics for a legend are turned on,

the entry displays the count of map objects in parentheses next to the title. Style keys for

feature classes and queries are dynamic and represent the geometry type of the feature

class (point, line, area, or compound). Style keys for thematic displays, images, and text

are static and represent the object type. The various feature classes and their style keys

appearing in the legend are shown in Fig 3.2 and Fig 3.3. Style keys include the

following:

29

Fig 3.2 Style Keys representing feature classes in Legend

Style keys can also indicate the state of the following legend entries:

Fig 3.3 Style Keys representing errors in Legend

We can add the following types of map objects as entries to the legend:

?? ?Feature classes

?? ?Queries

?? ?Thematic displays

?? ?Raster images

3.1.6 Feature and Feature Class

A feature is represented in a map window by geometry and is further defined by

non-graphic attributes in the database. The values of these non-graphic attributes can be

viewed as cells in the data window view on the non-spatial data of the feature. In a

read/write warehouse, we can create a new feature class, delete a feature class, and edit a

30

feature class definition. We can edit a feature class in the following ways: by adding

attributes, by removing attributes or ?by changing attributes

In a read/write warehouse, we can also manage feature data in the following

ways: by changing attribute values??by adding or deleting features.

Geometry refers to the graphic representation of a feature in the map window. The

following geometry types represent features:

?? A point feature is represented by one or more points on a map that

represent the location of a feature. A point can also represent

features that cannot be mapped at the defined map scale. Elevation

control points, buildings, and manholes are all examples of point

features.

A linear feature is represented by one or more lines and/or arcs.

Rivers, railroad tracks, utility lines, and roads are examples of

linear features.

An area feature is represented by closed boundaries. Counties, land

parcels and water bodies are examples of area features.

A compound feature may have point, linear, and/or area geometry

within the feature class or even within a single feature.

A text feature is represented by text that appears at a point location

on a map. You can place text in an existing text feature class or

create a new one to contain it. Text can have an orientation, that is,

it can be rotated.

An image feature is a raster image.

A feature class is the classification in which each instance of feature is assigned.

The software allows creating feature classes in three ways: from scratch, by copying

some of the information from an existing feature class into a new feature class in the

same warehouse, and by attaching an external data source.

31

3.2 FUNCTIONS OF GEOMEDIA

The main functions of GeoMedia include digitisation, building queries,

performing joins, buffer analysis, overlays, creating special filters etc.,

3.2.1 Digitization

For digitizing first of all the images are to be registered. A raster image, such as a

scanned map sheet, an aerial photograph or a satellite image can be inserted into a

read/write warehouse. The image is not moved from its original location, but the path to

the image is saved in the warehouse. To edit or change the image, the source file must be

edited. Inserting multiple images with the same file name into a single warehouse must

be avoided, even if the images are stored in different directories. The file type and

information contained in the file determine whether the file can be inserted interactively

or automatically.

?? Interactive placement requires a fence to be drawn in the map window to define

the size and location of the image.

?? Automatic placement inserts geo-registered images directly into map window and

preserves image geometry. There are two types of automatic placement, Geo-

referenced and by header.

GeoMedia Professional 5.2 also provides tools that maintain data integrity by

reviewing geometry information, validating geometry and validating connectivity.

GeoMedia provides tools to correct data by trimming and extending geometry to

intersections, inserting intersections, fixing connectivity and fixing geometry.

3.2.2 Queries

A query is a request for information. When we display a query, we are requesting

to see features that meet specific criteria. With GeoMedia Professional, we can build a

query by making selections on a dialog box without needing to know SQL. Queries

present current information in the warehouse. This means that each time we display a

query; we get the current information in the warehouse. Queries are stored in the

GeoWorkspace so that, if a warehouse changes, all queries are updated each time they are

displayed. If a spatial filter is applied to the warehouse connection at the time the query

is defined, the query is limited to the geographic area defined by that spatial filter.

32

?? Attribute-filter query allows limiting the search to individual features whose

attributes contain values that meet the conditions specified by an operator. An

operator is a symbol or expression, such as = (equals) or > (is greater than), that

represents the relationship between two values.

?? Spatial query allows limiting the search to individual features whose geometry

has a spatial relationship to features from another feature class or query.

?? Combined attribute and spatial query requests features with certain attribute

values that meet specified spatial conditions, such as overlapping or being

contained by another feature class or query.

3.3 GEOMEDIA TRANSPORTATION MANAGER 5.2

?? Used for linear data analysis as well as routing analysis.

?? Easiest way to merge linear referencing and geospatial technology.

?? Increases the value of our data by turning it into business critical, decision

support information.

There are two major divisions in GeoMedia Transportation Manager;

??Linear Referencing System (LRS)

??Dynamic Segmentation

??Network Routing

3.3.1 Linear Referencing

Linear Referencing is simply the tracking and analysis of data that is associated

with locations along a linear network. For example, tracking the condition of signage,

condition of pavements, location and severity of accident occurrences etc.

As an example for Linear Referencing, the preceding diagram shows a portion of

road on the left and its geospatial representation on the right. The road has kilometer

posts that indicate cumulative linear measures along the road. It also has a road name,

Highway 6 in this example. A section of fencing along the road is also shown in both the

left and right views. Based on the kilometer posts, it can be determined in the field that

this stretch of fence runs along Highway 6 from kilometer measure 2.0 to 2.6. These LRS

Linear Features are the backbone of the LRS and are used in automating the mapping of

linearly referenced data, such as this stretch of fencing, onto the map view.

33

Fig 3.4 Illustrating Linear Referencing

3.3.2 Dynamic segmentation

Dynamic segmentation is the overlay and display of attributes describing a linear

referenced network. Dynamic segmentation can accommodate multiple attribute tables,

describing a highway network, without requiring duplication of network geometry or

data. Only a single, graphic representation of the highway network is required. The

locations of attribute records along the network are identified using the linear referencing

method. In the present work Linear Referencing and Dynamic segmentation analysis of

GeoMedia Transportation Manager is used to its full extent.

3.3.3 Routing Network

A routing network is a system of connected linear features that can be used to

support the simulated transportation of goods, services, or communications between

locations on the network. A network can be thought of as an abstract model that is

derived from a set of linear features and their relationships. Each network model is

primarily composed of a set of geographic features called Edges and a set of implied

features called Nodes. Each Edge in a network represents one component of the

transportation system that is being modeled.

3.3.4 Routing Analysis

The major routing analysis tools provided with GeoMedia Transportation Manager are:

34

Best Path – This command gives the ability to find the best path for a vehicle that

needs to make one or more stops. We select whether Paths will be optimized so as to

minimize distance traveled or to have the command minimize other “user costs,” such as

time, money, or even safety. It also has an option to optimize the order of stops to further

optimize the path. The timesaving for maintenance, delivery, or other vehicles can result

insignificant cost savings, or in the case of emergency vehicles, even saved lives.

Find Closest Stops – This command finds the closest n destination Stops to a set

of origin Stops. This is particularly useful for finding, for example, the two closest

hospitals to each of a set of sports facilities. Path optimization may be by distance or by

user cost.

3.4 CONCLUSIONS

This chapter gives a brief review about the software GeoMedia Professional and

GeoMedia Transportation Manager used for the work. Important terminologies associated

with GeoMedia are also briefly discussed. Besides this a brief description about the

functions of GeoMedia is also given.

35

Chapter 4

PAVEMENT MAINTENANCE & MANAGEMENT ASSISTANT

4.0 GENERAL

This chapter describes in detail about the plug-in software, GeoMedia Pavement

Maintenance and Management Assistant (GPMMA) for GeoMedia.

4.1 AN OVERVIEW OF GPMMA

For performing PMMS analysis in GeoMedia, it is essential to customize it based

on our needs. A comprehensive plug-in software, GeoMedia Pavement Maintenance and

Management Assistant (GPMMA) for GeoMedia was developed, which provides no

bounds for PMMS analysis in GeoMedia. Important features in GPMMA are

Deterioration prediction, Cost analysis, BBD overlay design, Prioritization, Overlay Cost

Calculator, Maintenance schedule etc

4.2 BEHIND GPMMA The GPMMA software is so developed that it can be used with any GeoMedia

application. GPMMA is developed in Visual Basic platform with direct link to Microsoft

Access database. So it can also be used separately for PMMS analysis, by directly linking

with Access database and the UGI developed.

GPMMA also links GeoMedia with other useful external softwares like,

RomdasRMS, MicroPAVER 5.2, STIP, SW-1, Real Cost LCCA etc.

For GPMMA to work properly three sets of installation procedure is needed. First

is the GPMMA & External component installation, second is GPMMA Command

Installation and External software installation.

4.2.1 GPMMA & External Component Installation This is the primary step for installing GPMMA. With out this preliminary

installation GPMMA can’t give hooks to external build softwares. The installation

process is as shown in fallowing figures:

36

Fig 4.1 GPMMA & External component installation setup files

Fig 4.2 GPMMA & External component installation setup-1

37

Fig 4.3 GPMMA & External component installation setup-2

4.2.2 GPMMA Command Installation To bring GPMMA as a plug-in command to GeoMedia, the command .dll files

had to be properly registered into the system registry. The GPMMA command

installation will take care of this. The procedure for command installation is as shown

below:

Fig 4.4 GPMMA Command Installation setup-1

38

Fig 4.5 GPMM Command Installation setup-2

Fig 4.6 GPMMA Command Installation setup-3

4.2.3 External Software Installation GPMMA won’t directly install the external softwares in to the system. External

softwares can be installed any time later from GPMMA GUI. The procedure for

command installation is as shown below:

Fig 4.7 External Software Installation linking form

39

4.3 GPMMA OVERVIEW & IMPORTANT FEATURES

For performing PMMS analysis in GeoMedia, it is essential to customize it based

on our needs. Important features in GPMMA are Deterioration prediction, Economic

analysis, BBD overlay design, Prioritisation, Overlay Cost Calculator, Maintenance

scheduler etc

Fig 4.8 PMMA command installed in menubar

Fig 4.9 PMMA command windows

40

4.3.1 PMMA Deterioration Prediction

The Deterioration Predictor is an analysis tool to predict the pavement

deterioration for any selected design life. Working from the data collected, the tool

analyses current road conditions, age, and pavement type. These data are factored with

aging of pavement and fix strategies to estimate the future condition of the road network.

The base for Indian pavement deterioration prediction models are mainly the

contributions from different research institutes and organizations like Central Road

Research Institute (CRRI) (Updating Road User Cost Data URUCS 1991, Pavement

Performance Study (PPS-EPS 1993), RITES (HDM Calibration Studies -1994),

Bangalore University (Transition Probability Matrices for Optimal maintenance

decisions-1995), & Indian Institute of Technology Kharagpur (Analytical Pavement

Design (999). These studies laid the basis for the pavement data analysis and

development of Pavement deterioration model for Indian roads. The following Models

are found to be well suited for Indian conditions and are in practice.

4.3.1.1 Prediction of Yearly Change in Cumulative Standard Axles (CSA)

n

6

365 x A[(1+r) -1] x vdf x tdfCSA=r x 10

Where

CSA = Cumulative number of standard axles to be catered for in the design in

millions.

A = initial traffic in the year of completion of construction in terms of the

number of vehicles commercial vehicles per day duly modified to

account for lane distribution.

r = growth rate of vehicles

n = design life in years

vdf = vehicle damage factor

tdf = transverse distribution factor

41

4.3.1.2 Prediction of Yearly Change in Deflection

d = do (1+0.125 CSA / log10H)

Where:

do = initial deflection value immediately after construction

CSA = Cumulative number of standard axles in millions.

d = yearly change in deflection value.

H = equivalent granular thickness in mm.

4.3.1.3 Prediction of Yearly Change in Unevenness

UI = Ulo (1+ 0.11 CSA / log10H)

Where:

UI = Unevenness after a given number of traffic load repetitions in mm/km

UI0 = initial unevenness soon after the construction of overlay in mm/km

CSA = cumulative standard axles in millions

H = Granular overlay thickness in mm

4.3.1.4 Prediction of Yearly Change in Present Serviceability Rating (PSR)

The following model developed as a part of PDM research project at Bangalore

University is used for predicting the yearly change in serviceability over the design life.

PSR = -1.9326 loge (UI) + 14.3765

Where:

PSR = Present Serviceability Rating

UI = Unevenness Index in cm/km

42

Fig 4.10 GUI for PMMA Deterioration predictor

4.3.2 PMMA Economic Analysis

The Life cycle cost of a pavement is the total highway transportation cost during

the design life of the pavement. The total cost of highway transportation is made up of

the highway cost and the cost of operating motor vehicles over the highways. The

highway cost is that cost borne by the people through their highway department and the

vehicle operation cost is the cost which is borne directly by the owners of the motor

vehicles. In order to get the total cost of highway transportation, it is therefore, necessary

to consider the two sources of basic costs.

43

4.3.2.1 Construction Cost

Construction cost depends upon the type and thickness of the overlay. In the

present work, the construction cost for Bituminous Concrete, Bituminous Macadam,

Mixed Seal Concrete, Asphalt concrete was calculated per kilometer length based on the

standard schedule of rates of Kerala P.W.D. and based on the rate of last ten tenders for

works.

Three different strategies was considered like,

?? BM & AC + MSC with Profile Correction (Once in 5yr)

?? BBD Overlay (BM&BC in 2:1) (Designed Overlay)

?? Periodic Repair 25BM+25BC (Once in 2yr)

4.3.2.2 Vehicle Operation Cost

The vehicle operation cost differs between different classes of vehicles under the

same roadway conditions. Hence, the total vehicle operation cost is the total operating

cost of all vehicles plying on the road. The vehicle operation cost for the same class of

vehicle depends upon the width of the road, unevenness of the road and the gradient of

the road. Kadiyali et.al has given different equations for different classes of vehicles for

calculating the vehicle operating cost per kilometer excluding the tax.

For determining the vehicle operating costs in future years, there are six different

methods of economic analysis. According to Winfrey the Net Present Value method is

reliable as a measure of the comparisons of alternatives. The concept of the Net Present

Value is that the decrease in value of the property in any given year, and therefore its

depreciation for the year, is equal to the decrease for that year in the present value of its

portable future returns. The vehicle operation cost for the design life of the pavement is

calculated using the Net Present Value method in the present study.

Net present value (Present worth) method is based on the discounted cash flow

(DCF) technique. In NPV method benefits are treated as positive and costs as negative

and the net present value are found. Any project with a positive Net Present Value is

acceptable. In comparing more than one project, a project with the highest net present

value should be accepted.

44

VEHICLE TYPE VECHICLE OPERATION COST EQUATION

New technology Cars LogeVOCNC=.2 175-. 1 149w+0.000098U1+.01075Rf

Old technology Cars LogeVOCOC 0.3589-.0916w+0.000009834U1+0.0l042Rf

Heavy Commercial vehicles 2-axle LogeVOCHCV = 1 .5004-.8 188 W+0.00003732UI+0.00 1 55Rf

Buses LogeVOCB=1 .4041 -.08462W+0.00003939U1+0.0 11 33Rf

Heavy commercial vehicles (Multi-axle) LogeVOCOCV= 1 .9989-.07709W+0.00003 856U1+0.0 1 OO3Rf

Light Commercial Vehicles LogeVOCLCV 1.5541 -0.07768W+0.00002833U1+0.00 1 55Rf

Two-Wheelers LogeVOCTW=- 1.1531-0.0591 6W+0.OOQ 1 076U1+0.008786Rf

Table 4.1 Equations for vehicle operating cost

VEHICLE TYPE % TOTAL GROWTH RATE New tech .cars 0.080 0.075 Old tech cars 0.070 0.075 Heavy commercial vehicle 0.2452 0.075 Buses 0.1362 0.075 Heavy commercial vehicle 0.0039 0.075 Light commercial vehicles 0.2097 0.075 Two wheelers 0.2545 0.075

Table 4.2 Percentage of vehicles and growth rate

Inputs for the Economic analysis will be taken directly from Microsoft Access

Database which intern is connected to GeoMedia as its Warehouse. Other varying inputs

are being given through the Graphical User Interface (GUI) developed. Snap shot of the

GUI are given below.

45

Fig 4.11 GUI for PMMA Economic analysis 1

Fig 4.12 GUI for PMMA Economic analysis 2

46

Fig 4.13 GUI for PMMA Economic analysis 3

4.3.3 PMMA BBD Overlay Design

The rebound deflection tests were carried out on permanent deflection observation

points at equal intervals along the outer wheel path at 90 cm from the edge of the two-

lane pavement. The tests were conducted using a Benkelman beam and a loaded truck

having rear axle weight of 8170 kg fitted with two pairs of dual wheels with inflation

pressure of 5.6 kg/cm2. About 20 measurements per pavement stretch were made during

the field studies. The deflection tests were carried out as per Canadian Good Roads

Association (CGRA) procedure as mentioned in the guidelines of the IRC: 81-1997. The

pavement temperature and the subgrade moisture data were collected during each cycle.

47

The temperature observations were made on the bituminous layers by digging a small pit

of 40 mm depth and filling it with glycerol. After the glycerol attained the pavement

temperature, the temperature measurements were made. When the pavement temperature

is different than the standard of 350 C needs to be corrected. The correction will be

positive if pavement temperature is less then 350 C and negative if pavement is higher

then 350 C. Correction for temperature is required for pavement having a substantial

thickness of bituminous construction.

Separate sub programs where written for finding moisture correction, temperature

correction, vehicle damaging factor (VDF), Transverse Distribution Factor (TDF) etc.

Most of the inputs where taken directly from Microsoft Access Database. Remaining

varying data are input through the GUI.

VDF =

TDF =

4.3.3.1 Conversion of Curves into Mathematical Forms

IRC recommends the moisture correction factors for different conditions of soil.

These were converted into mathematical equations for different conditions of plasticity

index (P1), type of subgrade soil, annual rainfall and field moisture content. For

development of mathematical equations, a curve was divided into a number of segments

depending upon its shape and variation in the slope. The best fit equations were obtained

by using the GRAPHER package. The converted equations are shown in Table 5.3.

To calculate the thickness of Bituminous Macadam overlay(mm), design curves

of IRC 81-1997 were converted into mathematical equations for different values of

characteristic deflection (mm) and the Cumulative number of standard axles. The results

of overlay thickness are shown in Table 5.4.

Total no. of commercial vehicles in both the directions during the period of survey.

Total no. of commercial vehicles traveling along 90 cm wheel path in both directions.

Number of Vehicles

Equivalent Single Axles (ESA).

48

Fig. No. of IRC:

81-1997

Moisture Contents (%)

(x) Equation for moisture correction factor (y)

For Sandy/Gravely soil subgrade for low rainfall areas

2 4 to 11 y = .0001x4 - 0.0051x3 + 0.0922x2 - 0.7039x + 2.9628

For Sandy/Gravely subgrade for low rainfall areas

3 4 to 13 y = -.0001x4 + 0.0036x3 - 0.0425x2 + 0.1448x + 1.2743

For clayey subgrade with low plasticity (P1 < 15) and low rainfall areas

4 4 to 22 y = .000006x4 - 0.0005x3 + 0.0171x2 - 0.2446x + 2.4393

For clayey subgrade with low plasticity (P1 < 15) and low rainfall areas

5 4 to 22 y = .00002x4 - 0.0012x3 + 0.0303x2 - 0.3546x + 2.8708

For clayey subgrade with low plasticity (P1 > 15) and low rainfall areas

6 4 to 20 y = .00002x4 - 0.0013x3 + 0.0333x2 - 0.4092x + 3.0917

For clayey subgrade with high plasticity (P1 > 15) and high rainfall areas

7 4 to 20 y = .00004x4 - 0.0026x3 + 0.0596x2 - 0.6317x + 3.8078

Table 4.3 Equations for moisture correction factor

CSA Characteristic

deflection (x) Equation for Overlay thickness (y)

0.1 3 to 6 y = 4.1667x3 - 70x2 + 415.83x - 730

0.5 2 to 6 y = -2.5x4 + 45x3 - 302.5x2 + 930x - 970

1.0 1.68 to 6 y = -2.1093x4 + 37.187x3 - 245.09x2 + 742.82x - 717.56

2 1.4 to 6 y = -1.6162x4 + 28.811x3 - 191.18x2 + 583.9x - 514.85

5 1.15 to 6 y = -1.6824x4 + 29.356x3 - 189.19x2 + 556.93x - 431.6

10 1 to 6 y = 0.4583x5 - 10.208x4 + 88.958x3 - 379.79x2 + 820.58x - 520

20 .8 to 6 y = -0.6306x6 + 13.867x5 - 122.43x4 + 554.52x3 - 1362x2 +

1765.7x - 779.01

100 .55 to 6 y = -0.6834x6 + 14.393x5 - 120.43x4 + 510.44x3 - 1159x2 +

1387.4x - 487.06

Table 4.4 Equations for moisture correction factor

49

Fig 4.14 GUI for PMMA BBD overlay design

Fig 4.15 Access Database for PMMA BBD overlay design

50

The output of the design will directly get saved in corresponding column of the

Access database table. From there it is directly called to GeoMedia for its graphical

representation in map.

4.3.4 PMMA Maintenance Prioritisation

Timely maintenance or rehabilitation of all pavement stretch may not be possible

due to limited funds, material shortage, environmental restriction and so-forth. So the

questions such as what, where and when to maintain and rehabilitate have been common,

but the solution used have not always been the right one. The need for prioritising the

stretches is of principal importance. The various techniques for prioritising projects for

maintenance and rehabilitation works are listed below:

?? Univariate Time Series of Serviceability Index Model

?? Dynamic Decision Model

?? Index Ranking Method

?? Percentile Ranking Method

?? Successive Subsetting method

In this work prioritising techniques like Index Ranking Method and Percentile

Ranking Method are considered.

4.3.4.1 Index Ranking Method

The index uses ranking the proportion of distance that a given segment factor

value lies between the best and worst factor values. The total distance between the best

and the worst factor values in the needs list is called the “range.” A better value is the one

that would place a segment lower in the priority list than the segment currently under

consideration. The process is illustrated in the Fig 5.13

Fig 4.16 Index Ranking Method process

R

Most needy segment, Fw

Segment under consideration

Least needy segment, Fb

Range

X

51

For every segment, an index value is evaluated by using the formula.

j

XI = × 1 0 0

R? ?? ?? ?

Then composite index for each segment (Ic) is calculated as:

n

j jj = 1

C n

jj = 1

I × WI =

W

?

?

Where:

Fw = Worst value of factor for segments in needs list

Fb = Best value of factor for segment in needs list

X = Difference between Fw and the factor value

R = Difference between Fb and Fw the range of values

Ii = Segment index value, based on its value for factor j

Ic = Composite factor index of the segment under consideration

Wj = Weight for jth factor

4.3.4.2 Percentile Ranking Method

The segments percentile ranking represents that proportion of the other segments

in the needs list that fail to be as deserving of road funds as measured by the value of the

factor under consideration. For a single factor Percentile rank is given by,

Where:

P = Percentile rank of the segment

B = Number of segments with better values

W = Number of segments with worse values

As in the index method, a better value is one that would place a segment lower in

the priority list than the segment currently under consideration. The percentile ranking is

52

done separately for each factor, and then combined into a weighted sum of the weights W

to produce the composite percentile Pc.

4.3.4.3 Weightages Given For Various Parameters

In this work, parameters average daily traffic, deflection was given a weightage of

1.0 as these are the variable mostly leading to pavement structural failure. Variables

Unevenness Index is given a weightage of 0.75 as it represents functional condition of the

pavement. Pavement serviceability rating as it is not a measured quantity but is a

assumed value, weightage given for it is 0.5 only. If the pavement is not structurally

satisfactory, naturally it deteriorates at a faster rate then the pavement, which fails

functionally. Hence, the factors contributing to structural behavior of pavement are given

more weightage than factors representing functional behavior of the road.

Fig 4.17 GUI for Priority Ranking

53

4.3.5 PMMA Overlay Cost Calculator

For economical planning under restricted funds and materials, Overlay cost

calculator will be of great help to serve the need. We can calculate the cost of overlay

with varying materials and varying overlay thickness. Construction cost depends upon the

type and thickness of the overlay. In the present work, the construction cost for

Bituminous Concrete, Bituminous Macadam, Mixed Seal Concrete, Asphalt concrete was

calculated per kilometer length based on the standard schedule of rates of Kerala P.W.D.

and based on the rate of last ten tenders for works.

Fig 4.18 GUI for PMMA Overlay Cost Calculator

4.3.6 PMMA Maintenance Scheduler

The questions such as what, where and when to maintain and rehabilitate have

been very general in pavement management system. The PMMA Maintenance scheduler

will give you a fair answer for the question “when? & what?”.

54

Fig 4.19 Graph showing best time for Maintenance scheduling

Fig 4.20 GUI for PMMA Maintenance scheduler

4.3.7 Hooks to External Softwares

GPMMA links GeoMedia with other useful external softwares like, RomdasRMS,

MicroPAVER 5.2, STIP, MnPAVE, SW-1, Real Cost LCCA etc.

55

Fig 4.21 Snap Shots of External Softwares windows

4.4 CONCLUSIONS

This chapter gives a brief review about the plug-in software, GeoMedia

Pavement Maintenance and Management Assistant (GPMMA) for GeoMedia. Important

functions of GPMMA are also briefly discussed. Besides this a brief description about the

methodology fallowed in each function is also briefly described.

56

Chapter 5

GPMMS INPUTS

5.0 GENERAL

This chapter deals with the description of inputs and the techniques used to feed

the inputs for a standard GeoMedia based Pavement Maintenance and Management

System.

5.1 GPMMS INPUTS

The main inputs of GeoMedia based Pavement Maintenance and Management

System are:

?? Well Prepared Database

?? Georeferenced Digitized Base Map

?? PMMS Analysis Tools (GPMMA)

5.2 GPMMS DATABASE

The heart of a pavement management system is its database, which is the

storehouse for all pavement-related information collected. A program is only as good as

the data stored in it. A PMS must have usable, accurate, and timely data to produce

credible outputs. The effectiveness of data analysis will increase if the data entered is

accurate. This database possesses several features, including: a large capacity, user

friendly access, flexibility for future expansion, security features, and compatibility with

other databases that store related information. Every piece of information in the database

is referenced to the particular section of pavement which it describes. Table 5.1 shows the

data collected from 12th Mile to Kattankal of Kunnamangalam-Augustianmuzhi road.

The information collected and stored in the database can be divided into five categories:

?? Inventory data, ?? Construction data, ?? Traffic data, ?? Condition data (physical distress, roughness, structural capacity, friction), ?? Treatment data.

57

Sl. No Chainage Item Value (Max)

1 6/330 - 7/240 Cracking % 0.0133 2 6/330 - 7/240 Pothole Area % Nil

3 6/330 - 7/240 Ravelling % Nil 4 6/330 - 7/240 Patch works % Nil 5 6/330 - 7/240 Edge break Nil

6 6/330 - 7/240 Roughness in UI - 7 6/330 - 7/240 Characteristic deflection (mm) - 8 6/330 - 7/240 Skid resistance value - 9 Shoulder

6/330 - 7/240 i. Paved/concreted 300 m one side

6/330 - 7/240 ii. Unpaved remaining 10 Predominant land use

6/330 - 7/240 i. Left Institution

6/330 - 7/240 ii. Right Institution 11 6/330 - 7/240 Terrain Plain 12 6/330 - 7/240 Pavement Type BT 13 6/330 - 7/240 Drainage Nil

14 6/330 - 7/240 Camber 2.5% 15 Level of adjacent land

6/330 - 7/240 i. Left Plain

6/330 - 7/240 ii. Right Plain

16 6/330 - 7/240 Traffic

17 6/330 - 7/240 Year of construction/repair/surfacing/strengthening

2005-06’ (widening and Overlaying)

18 Inventory 6/330 - 7/240 i. Carriage way 7.5 m

6/330 - 7/240 ii. Right of way 15 m

Table 5.1 Data collected from 12th Mile to Kattankal of Kunnamangalam-Augustianmuzhi road (MDR-2)

5.2.1 Inventory Data

Inventory data is a collection of the physical characteristics of the pavement, and

usually do not change between maintenance activities. The most basic information about

58

the road is included to reference the pavement, such as the road name or route number,

location (or referencing system), number of lanes, and pavement type. Other inventory

data may include:

?? Type of pavement (asphalt, concrete, composite),

?? Width of road,

?? Number of lanes,

?? Thickness of pavement layers, and ?? Drainage conditions.

Fig 5.1 Microsoft Access Database showing Inventory data

5.2.2 Construction Data

Related to inventory data, construction data contain information about the history

of the pavement. This information is important because roads can only be rehabilitated a

59

limited number of times before a full-scale reconstruction of the road is necessary. The

type of construction data collected includes:

?? Year built,

?? Design service life,

?? Date and type of rehabilitation and maintenance projects,

?? Materials used in construction activities, and

?? Cost of maintenance activities.

This set of data is lacking in the present work. If we don’t have a proper

knowledge about construction details of a pavement, it will be difficult to calculate its

remaining life as well as we can’t predict the deterioration correctly.

5.2.3 Traffic Data

The lifespan of a road is dependent on the amount of traffic that uses it. Traffic

count data are useful for calculating the remaining service life of a pavement. Estimating

traffic type is also important. Heavy loads, such as those generated by trucks, break down

pavement quicker than passenger cars. Knowing traffic volumes and type will be useful

for planning future pavement rehabilitation.

Fig 5.2 Microsoft Excel Database showing Traffic data

60

5.2.4 Condition Data

Condition data refers to information about the past and present surface condition

of a section of pavement. Accurate historical pavement condition information is

absolutely essential for operation of the pavement management system, because all

system recommendations are ultimately based on past and present condition data.

Sophisticated PMMS databases contain four different types of condition data: physical

distress data, roughness data, structural capacity data, and friction data.

5.2.4.1 Physical Distress Data

Physical distress is a measure of road surface deterioration caused by traffic,

environment, and aging. Distress can be measured by type, severity, and extent of

breakdown of pavement. The type of distress can be broken into three categories: fracture

(cracking), distortion (rutting), or surface wear (raveling). This information is the most

important information in the entire database.

Fig 5.3 Microsoft Access Database showing Physical distress data

61

5.2.4.2 Roughness Data

Roughness is a measure of ride quality on a particular pavement section. Studies

have consistently shown roughness can be directly related to both user satisfaction and

user costs. Therefore, road roughness measurements are important pieces of information

in a PMMS.

Unevenness index is the cumulative measure of the vertical undulations of the

pavement surface per unit horizontal length of the road. ,

UI = x R x 25.4

Where:

UI = Unevenness Index in mm/km

B = Bump Integrator reading

W = Number of wheel revolutions

R = Number of revolutions per km (460)

Fig 5.4 Microsoft Access Database showing Roughness data

W

B

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5.2.4.3 Structural Capacity Data

Structural capacity is the ability of a pavement to support traffic with little or no

structural damage. The structural capacity of a pavement is most commonly estimated

through the use of non-destructive testing equipment. This equipment measures the

deflection, curvature, and/or joint efficiency of the pavement under a specified loading.

Structural capacity data is normally used to evaluate past pavement designs or to select a

maintenance, rehabilitation, or reconstruction treatment for a specific project, not to

evaluate the performance of the entire highway network.

5.2.4.4 Friction Data

Friction data is collected using equipment that measures the skid resistance of the

pavement. Friction data are usually examined to determine if lack of friction is a probable

cause of traffic crashes at high-crash locations, or to identify potentially unsafe locations

that have yet to experience a large number of traffic crashes. In this respect, these data

may belong more to the area of safety management than pavement management

(although the two systems are certainly related).

5.3 UPDATING GPMMS DATABASE

The PMMS databases are usually updated by integrating the new set of database

and existing database by using Linear Referencing and Dynamic Segmentation. Linear

Referencing and Dynamic Segmentation are the process of merging new set of database

and existing database by using some primary key, say ID, Road Name or something.

5.3.1 Linear Referencing

Linear Referencing is one of the most important tools in GeoMedia for PMMS. It

helps to consider the actual road length of the Road network, rather than the airline

distance by which the whole network will get automated. It allows the real time tracking

of both linear and point features along the road. Linear Referencing is simply the tracking

and analysis of data that is associated with locations along a linear network. For example,

tracking the location of and condition of pavement, the location and severity of accident

occurrences etc.

63

Fig 5.5 Linear Referencing Command Toolbar & Work Flow

Fig 5.6 Linear Referenced Access Warehouse table & road network

64

5.3.2 Dynamic Segmentation

Dynamic Segmentation is the process of generating geometry for events based on

a LRS. Event features contain sufficient LRS information such that they can be

dynamically segmented to produce geometry based on an LRS feature class. The

flexibility of dynamic segmentation, with respect to data collection, management, and

integration, was the primary reason it was selected for the GPMMS database.

Fig 5.7 Basic Concepts of Dynamic Segmentation

65

Fig 5.8 Dynamic Segmentation Command Toolbar & Work Flow

5.4 DATA ENTRY

Pavement Management system needs a huge amount of data to meet its indented aim

properly. In this work Microsoft Access had been used as the Database as well as the

read/write warehouse for GeoMedia. For entering the relevant data in to the Access database

a user-friendly GUI had been developed using Microsoft InfoPath. The main motive behind

making such a data entry form is to reduce the work in data entry by auto filling the repeating

data; also it help in querying and editing of present data at any time.

PMMS related data will be collected by various groups and organizations, so there is

a need to standardise and centralise these data. One of the main Challenges in present work

was to clean the whole data collected by various organization and to bring it in to a standard

environment. Microsoft InfoPath form can be used over internet to collect the relevant data

by publishing the form and the supporting Access database in to a subscribed webpage. At

any time later we can retrieve the data collected in the forms in web by various private

groups and organizations. These data can be directly used for Dynamic Segmentation.

66

Fig 5.9 Microsoft InfoPath form Query View and Data Entry View

5.5 GEOREFERENCED DIGITISED BASE MAP

The Scaled road map of Calicut district was scanned and brought to GeoMedia in

image feature classes as raster images. Image feature classes are distinguished from one

another based on the coordinate system of the feature class. You can only insert images into

an existing feature class when the coordinate systems of the image and the feature class are in

agreement. All the images in an image feature class can be represented by a either a single

legend entry or multiple legend entries. You can add images to existing feature classes as

needed, without the images being displayed, thereby managing system resources more

efficiently.

A minimum of four or more known coordinate points from the scanned map where

selected and was placed in the corresponding coordinate in the GeoMedia map window using

point feature. Now we have a set of known coordinate points both in scanned map and in

GeoMedia map window. Now the scanned map was brought to GeoMedia using the option

Insert > Interactive Image . Using the image registration option the corresponding points

in scanned map and GeoMedia map window was linked to get a Georeferenced map of

Calicut District.

67

Fig 5.11 Georeferenced Raster Images & Digitized Map

5.6 PMMS ANALYSIS TOOLS (GPMMA)

For performing PMMS analysis in GeoMedia, it is essential to customize it based on

our needs. A comprehensive plug-in software, GeoMedia Pavement Maintenance and

Management Assistant (GPMMA) for GeoMedia was developed, which provides no bounds

for PMMS analysis in GeoMedia. Important features in GPMMA are Deterioration

prediction, Cost analysis, BBD overlay design, Prioritization, Overlay Cost Calculator,

Maintenance schedule etc.

About GPMMA has already been briefly described in chapter.5. For More details

about the functions and working of GPMMA see its help.

5.7 CONCLUSIONS

This chapter gives a brief review about the inputs of GPMMS. Important functions of

GeoMedia like Dynamic Segmentation and Linear Referencing were also briefly discussed.

Besides this a brief description about the input features are also briefly described.

SSTTUUDDYY AARREEAA

CALICUT DISTRICT

Latitude: Between 110 04I 14II to 110 47I 51II Longitude: Between 750 31I 17II to 760 08I 45II

68

Chapter 6

GPMMS OUTPUTS & RESULTS

6.0 GENERAL

This chapter deals with the description of Work done and preferred Outputs &

Results of a standard GeoMedia based Pavement Maintenance and Management System. <

6.1 GPMMS OUTPUTS

GPMMS provides variable outputs that provide information for use in

implementing cost-effective reconstruction, rehabilitation, and preventive maintenance

programs and results in pavement design to accommodate current and forecasted traffic

in a safe, durable, and a cost-effective manner. This can help in reducing the time

allocated to the maintenance activity and facilitating the decision making process.

While the database is the “heart” of a pavement management system, data are not

useful unless they are presented in a meaningful way. It is the role of analysis procedures

to transform the raw collected data into products such as charts, graphs, and reports that

are helpful to decision-makers. A pavement management system can transform a

spreadsheet containing pavement condition data into a map. A map can be quickly and

easily used to examine the health of pavement in ways that are not readily apparent from

columns of numbers. Analytic procedures are grouped into four categories:

?? Thematic Maps ?? Deterioration prediction ?? Economic analysis ?? Maintenance scheduling ?? Maintenance prioritization ?? Overlay design ?? Overlay cost calculation ?? Other outputs

6.1.1 Thematic Maps

Thematic Maps (also called choropleth map) are the symbolised representation of the

geographic features according to nongraphic attribute data through the use of colors and

other user-defined display properties. We can create a thematic display from a feature class in

69

any open warehouse connection, or from a query in the active GeoWorkspace. In a

cartographic context, thematic mapping is the mapping that is based on the classification of

data of a common theme. Figure 6.1 to 6.8 are some examples of various outputs as Thematic

Maps. Almost all structural and functional attributes can be shown in thematic map form.

The condition data, Unevenness data inventory data are the most suited to represent through

thematic mapping. Thematic map will run on all features, feature classes, dynamic

segmented events, and quires etc. Thematic map can be drawn for a range of values or for

unique values. Range calculation and clustering techniques are also available with thematic

map option.

Fig 6.1 Thematic Map showing the lengthwise distribution of MDR

70

Fig 6.2 Thematic Map showing the bridge inventory details

Fig 6.3 Thematic Map showing the bridge attribute details

71

Fig 6.4 Thematic Map showing the ODR condition details

Fig 6.5 Thematic Map showing the SH condition details

72

Fig 6.6 Thematic Map showing the Culvert inventory details

Fig 6.7 Thematic Map showing the Culvert attribute details

73

Fig 6.8 Thematic Map showing the IRQP Phase of NH

6.1.2 Deterioration Prediction

This is an important output of PMMA analysis. These results will give an overall idea

about the future of the pavement. These predicted values are used as inputs for various other

PMMA analyses. A well equipped database is required to provide the input for Deterioration

prediction analysis.

The base for Indian pavement deterioration prediction models are mainly the

contributions from different research institutes and organizations like Central Road Research

Institute (CRRI) (Updating Road User Cost Data URUCS 1991, Pavement Performance

Study (PPS-EPS 1993), RITES (HDM Calibration Studies -1994), Bangalore University

(Transition Probability Matrices for Optimal maintenance decisions-1995), & Indian Institute

of Technology Kharagpur (Analytical Pavement Design (999).

In the following example (Fig 6.9) the road stretch from Arayadathupalam to

Eranhipalam of Calicut Mini-Bypass is taken for illustration. Apart from the input given

through the GUI, the other required data have been taken directly from corresponding

Microsoft Access Database. The methodology adopted for the calculation where as given by

74

IRC for predicting CSA, Deflection and Unevenness. The PSR value was predicted using the

model developed as a part of PDM research project at Bangalore University.

Fig 6.9 Deterioration Prediction - output

6.1.3 Economic Analysis

Economic analysis has found a ready application in problem concern with the

evaluation of alternative transportation plans. International lending institutions, such as the

World Bank, attach the greatest important to assuring themselves that a particular project for

which a loan its required is not only economically and technically sound, but that its order of

priority in relation to other possible projects has been carefully determined in the light of the

overall development of the country concerned. The basic principle behind any method of

75

economic analysis is to measure the cost of the project, determine the benefits that are likely

to accrue and compare the two.

The objectives of the Economic Analysis are:

1. Whether the plan under consideration is worth investment at all.

2. To rank scheme competing for scarce resources in order of priority.

3. To compare mutually exclusive schemes and select the most economic.

4. To assist in phasing the programme over time period.

After having determined the costs and benefits of a scheme, a method has to be

evolved for relating these two so as to arrive at an assessment of the soundness of a scheme

in economic terms. The important methods are

Rate of Return Method

Benefit cost ratio method, First year rate of return method.

Discounting Cash Flow Method

Net present value method (NPV), Internal rate of return method

According to Winfrey (1994) the NPV method is reliable as a measure of the

comparisons of alternatives. In the present work NPV is selected as the evaluation method.

6.1.3.1 Net Present Value Method (NPV)

Net present value (Present worth) method is based on the discounted cash flow (DCF)

technique. In this method, the stream of costs/benefits associated with the project over an

extended period of time is calculated and is discounted at a selected discount rate to give the

present rate. Benefits are treated as positive and costs as negative and the net present value

are found. Any project with a positive Net Present Value is acceptable. In comparing more

than one project, a project with the highest net present value should be accepted. The net

present value is algebraically expressed as:

Where,

NPV = Net present value in the base year.

Bt = Value of benefits which occur in the year t .

Ct = Costs which occur in the year t i = Discount rate per annum.

N = The number of years for which the return is to be calculated.

? ?0

-NPV

1

Nt t

tt

B C

i?

??

?

76

In the following example (Fig 6.10) the road stretch from Chorode to Vadakara of

NH-17 (Vadakara Sub-division) is taken for illustration. Apart from the input given through

the GUI, the other required data have been taken directly from corresponding Microsoft

Access Database. The evaluation was done for a design life of 10 years, vehicle growth rate

was taken as 7.5% and a discount rate of 4.5%.

The methodology adopted for the deterioration prediction calculation where as given

by IRC for predicting CSA, Deflection and Unevenness. The PSR value was predicted using

the model developed as a part of PDM research project at Bangalore University.

Fig 6.10 Economic Analysis – Deterioration Prediction

77

Construction cost depends upon the type and thickness of the overlay. In the present

work (Fig 6.11), the construction cost for Bituminous Concrete (BC), Bituminous Macadam

(BM), Mixed Seal Concrete (MSC), Asphalt concrete (AC), was calculated per kilometer

length based on the standard schedule of rates of Kerala P.W.D. and based on the rate of last

ten tenders for works. Three different strategies was considered like,

1. BM & AC + MSC with Profile Correction Course (Once in 5years)

2. BBD Design Overlay (BM & BC in 2:1 ratio) (From Overlay Design)

3. Periodic Repair 25mm BM + 25mm BC (Once in every 2years)

Fig 6.11 Economic Analysis – Overlay Cost Calculation

78

The vehicle operation cost differs between different classes of vehicles under the

same roadway conditions. Hence, the total vehicle operation cost is the total operating cost of

all vehicles plying on the road. The vehicle operation cost for the same class of vehicle

depends upon the width of the road, unevenness of the road and the gradient of the road.

For determining the vehicle operating costs in future years, there are six different

methods of economic analysis. Among them the Net Present Value method is the reliable, as

a measure of the comparisons of alternatives. See Fig 6.12 for details.

Fig 6.12 Economic Analysis – NPV & Strategy Selection

79

6.1.4 Maintenance Scheduling

At the present day, the development of systematic procedure for scheduling

maintenance and rehabilitation activities is one of the major concerns of highway agencies

today. For every road link, there is a particular period in its life span, when it is most

effective to undertake particular type of maintenance measure, beyond which the

deterioration of the pavement increases rapidly.

Fig 6.13 Present Serviceability Index Vs Age of Pavement

The questions such as what, where and when to maintain and rehabilitate have been

very general in pavement management system. The PMMA Maintenance scheduler will give

you a fair answer for the question “when? & what?”.

The strategy where developed by using optimization techniques to maximize the

benefit with in a given set of constrains. In Fig 6.14 the road stretch from Arayadathupalam

to Eranhipalam of Calicut Mini-Bypass is taken for illustration. Apart from the input given

through the GUI, the other required data have been taken directly from database.

A B C

D

E

F Reconstruction Age of Pavement 0

10 20 30 40 50 60 70 80 90 100 Rehabilitation or

Deferred Action

Rehabilitation

Preventative Maintenance Routine Maintenance

PCI

60% of life 30% of life

75% of life

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Fig 6.14 Maintenance scheduling- output

6.1.5 Maintenance Prioritization

Timely maintenance or rehabilitation for every good link may not be possible because

of limited fund, material shortages, environmental restrictions and so forth. Hence the need

for prioritising the stretches is of principal importance. In this work prioritising techniques

like Index Ranking Method and Percentile Ranking Method are considered.

The prioritization was done for each road type separately. Based on the fund available

per year, the road stretches to be considered for Maintenance in each year will also be

calculated based on the total kilometerage of the considered stretches and the rate for

maintenance/kilometer length. In Fig 6.15 prioritisation of MDR’s are taken for illustration.

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Fig 6.15 Maintenance prioritisation of MDR’s

Fig 6.16 Maintenance prioritisation of SH’s

82

The vehicle operation cost is directly proportional to the distance traveled and the

travel time. So it is necessary to make sure that the vehicles are able to use the non circuitous

routes. Normally most of the vehicles will be using those path which are in good surface

conditions and having better serviceability (Say, NH’s, SH’s), even though the route may be

more circuitous. Till now there is not much effective methods used to include this fact in a

PMMS analysis. In this work an attempt had been made to incorporate this, utilizing the

functions in GeoMedia like buffer-zones, closest path and so on.

Twenty-eight major centers in Calicut where identified, based on their population

density and expert opinions. A buffer-zone of around 10 to 12 km (based on their

importance) where made around each major center. All other major centers coming in the

buffer zone were found out and the closest routes from the major center considered to all

other major centers inside the bufferzone were also found out, see Fig 6.17. Likewise it was

repeated for all the 28centers. The roads coming in these closest paths were given priority for

widening activities, Preventive maintenance and other routine maintenance works.

Fig 6.17 BufferZones & Closest path around Koduvally

83

Based on the rankings values, a thematic map was made to show the priority rank for

maintenance of the stretch. For an available fund per maintenance works, the total length of

road that can be maintained with that fund was calculated, and the road network was made in

to different cluster priority groups. Stretches for maintenance where hence find out using the

priority. In Fig 6.18 a four level cluster prioritisation of MDR’s in Calicut.

Fig 6.18 BufferZones & Closest path around Koduvally

6.1.6 BBD Overlay Design

Benkelman Beam Deflection analysis is used to evaluate the strengthening

requirement of existing flexible road pavements. Performance of flexible pavements is

closely related to the elastic deflection of pavement under the wheel loads. The deformation

or elastic deflection under a given load depends upon subgrade soil type, its moisture content

and compaction, the thickness and quality of the pavement courses, drainage conditions,

pavement surface temperature etc.

In Fig 6.19 the road stretch from Arayadathupalam to Eranhipalam of Calicut Mini-

Bypass is taken for illustration. Apart from the input given through the GUI, the other

required data have been taken directly from corresponding Microsoft Access Database. The

methodology adopted for the calculation where as given by IRC: 81-1997.

84

Fig 6.19 BBD Overlay design-output

6.1.7 Overlay Cost Calculation

It is necessary to know the overlay cost of a selected stretch, for various combinations

of over lay materials. This GUI provides the user a flexible and friendly overlay cost

calculation method.

In Fig 6.20 the road stretch from Kunnamangalam to Augustianmuzhi (MDR No 2) is

taken for illustration. Apart from the input given through the GUI, the other required data

have been taken directly from corresponding Microsoft Access Database.

The result from Overlay cost calculator is not written to any access table it is only to

try and see the rate for various combination as needed. The rates entered are the prevailing

PWD rates. For more reliable calculation we had to update the rates periodically.

85

Fig 6.20 Overlay cost calculation -output

6.1.8 Other Outputs

The important other types of outputs are Charts and Reports. There are already so

many customised commands for GeoMedia available in the internet. Among them is one for

making chart from the data window entries which can be either an attribute or a dissected

value using GeoMedia analysis tools. Fig 6.21 shows the window for this command.

Fig 6.21 Customised Window for chart output

86

Fig 6.22 Bar Chart showing Raveling details along the stretch

Fig 6.23 Bar Chart showing Crack details along the stretch

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Fig 6.24 Bar Chart showing Potholes details along the stretch

Fig 6.25 Bar Chart showing Patchwork details along the stretch

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Figures 6.22 to 6.25 shows the various out puts from the attribute of the road stretch

from Arayadathupalam to Eranhipalam of Calicut Mini-Bypass. Likewise we can get outputs

in various forms of graphs 2D’s or 3D’s.

All the PMMA results can be directly given for printing and can be made in the form

of a report, which will be very useful for the decision makers to come across a conclusion.

6.2 CONCLUSIONS

This chapter gives a brief review about the outputs and other results of GPMMS.

Important functions of GeoMedia like Thematic Mapping and PMMA Analysis were also

briefly discussed. Besides this a brief description about the output features are also included.

89

Chapter 7

CONCLUSIONS, LIMITATIONS & SCOPE OF FUTURE WORK

7.0 GENERAL

This chapter contains the important features like summary, conclusions, limitations &

scope of future work.

7.1 SUMMARY AND CONCLUSIONS

A Pavement Maintenance & Management System for a the whole of Kozhikode

District was developed using Intergraph’s GeoMedia Professional, GeoMedia Transportation

Manager 5.2 version and GeoMedia Pavement Maintenance & Management Assistant.

?? Road Network as well as Water bodies and Rail of whole Kozhikode District were digitised.

?? Prepared map can be used as the base map for all present and future analysis like LRS

and Dynamic Segmentation.

?? Database was created with all the available details of the road network like Inventory

data, Construction data, Traffic data, Condition data, etc.

?? A plug-in software, GPMMA which contains user-friendly menus has been developed

to present PMMS results to justify the decisions made.

?? This work shows that a PMMS which is based on the direct integration between

PMMA and GeoMedia Professional can be used to facilitate the decision making

process for managing pavements.

?? The Management System developed is capable of handling a large network, and

hence it can even be used at the network level.

?? A centralized Calicut district PMMS data collection should be initiated involving all

the Sub-Divisions in Calicut.

?? The results of the present study can be used for the selection of type and thickness of

material for overlay on different subgrade soil economically without sacrificing the

safety of the road structure.

?? The VOC per year can be calculated, the program is capable of prediction of

appropriate period for strengthening and life cycle of .different alternatives and

choosing the best alternative for which the total transportation cost is the least.

90

?? The Net Present values (NPV) of benefits for the three strategies considered were

found out and the most economic one for various design life period was selected.

?? The best time for Preventive maintenance as well as for Regular maintenance was

also found out by optimizing the NPV of the corresponding benefits.

?? Thematic maps, Charts and Reports for various useful attributes were developed for

further decision making.

7.2 LIMITATIONS AND SCOPE OF FURTHER WORK

The present study is an essential requirement for project planning and budget

allocation.

Comprehensive data is not available for all stretches; even this data is not available

with the Public Works Departments. The data available is also spread over a number of

organisations.

As a scope of future work Data Entry forms can be published over internet and can be

get filled from those various organisations from there office itself. These data can be

retrieved at a later time.

In this study an attempt is made to give some of the methods for setting priority of the

projects for maintenance and rehabilitation. These methods can be still be improved by

combining subjective aspects with objective information more effectively by adopting goal

programming and Monrovian decisions process analytical hierarchy techniques.

The plug-in software developed for PMMS analysis is in the first phase. More

functions as well as modification for the present work can be made based on the needs. The data collected was old for most of the stretches and only one time data per stretch

was available for analysis.

7.3 CONCLUSIONS

This chapter gives a brief review about the Summary and other conclusions of

GPMMS. The limitation and the scope of future work were also included in this chapter.

91

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