Proceedings of PhD Student Poster Session 2014 Construction Research Congress Proceedings edited by Mani Golparvar-Fard, Ph.D., and Youngjib Ham This proceedings is invaluable to all practitioners and researchers in the field of construction engineering and management.
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Proceedings of
PhD Student Poster
Session 2014 Construction Research Congress Proceedings edited by Mani Golparvar-Fard, Ph.D., and Youngjib Ham
This proceedings is invaluable to all practitioners and researchers in the field of
construction engineering and management.
2014 CRC PhD Student Poster Session
Page 1 of 84
2014 Construction Research Congress PhD Student Poster Sessions Proceedings edited by Mani Golparvar-Fard, Ph.D., and Youngjib Ham
Chair: Mani Golparvar-Fard, Ph.D., University of Illinois at Urbana-Champaign
The Technical Committee of CRC2014 PhD Student Poster Session: Alex Albert, Ph.D., North Carolina State University Amir Behzadan, Ph.D., Central Florida University Caroline Clevenger, Ph.D., Colorado State University Changbum Ahn, Ph.D., University of Nebraska- Lincoln Ken-Yu Liu, Ph.D., University of Washington Mani Golparvar-Fard, Ph.D., University of Illinois at Urbana-Champaign
Ming Liu, Ph.D., University of Alberta Mounir El-Asmar, Ph.D., Arizona State University Pardis Pishdad-Bozorgi, Ph.D., Georgia Tech SangUk Han, Ph.D., University of Alberta Tanyel Bulbul, Ph.D., Virginia Tech Thais Alves, Ph.D., San Diego State University Xinyi Song, Ph.D., Georgia Tech The Members of the Jury– CRC2014 PhD Student Poster Session: Ali Touran, Ph.D., Northeastern University Carrie Sturts Dossick, Ph.D., University of Washington Charles Jahern, Ph.D., Iowa State University Daniel Castro-Lacouture, Ph.D., Georgia Tech Eddy Rojas, Ph.D., University of Nebraska- Lincoln Iris Tommelein, Ph.D., University of California – Berkeley Jesus M. de la Garza, Ph.D., Virginia Tech Miroslaw Skibniewski, University of Maryland – College Park Mohamed Al-Hussein, Ph.D., University of Alberta Simaan Abourizk, Ph.D., University of Alberta
List of Posters
1. RFID AND BIM-ENABLED WORKER LOCATION TRACKING TO SUPPORT REAL-TIME BUILDING PROTOCOL CONTROL AND DATA VISUALIZATION ON A LARGE HOSPITAL PROJECT ............................. 9 Aaron M. Costin ([email protected]), Advisor: Dr. Jochen Teizer Georgia Institute of Technology
2. DEVELOPING CONTEXT SPECIFIC AND GENERALIZED CONSTRUCTION LABOUR PRODUCTIVITY MODELS ............................................................................................................................................................. 10 Abraham Assefa Tsehayae (tsehayae@ualberta .ca), Advisor: Dr. Aminah Robinson Fayek University of Alberta
3. AUTOMATED ASSESSMENT OF TORNADO-INDUCED BUILDING DAMAGE BASED ON LASER SCANNING ......................................................................................................................................................... 11 Alireza G. Kashani ([email protected]), Advisor: Dr. Andrew J. Graettinger University of Alabama
4. COST EVALUATION MODEL FOR HOUSING RETROFIT DECISION-MAKING: A CASE STUDY ............. 12 Amirhosein Jafari ([email protected]), Advisor: Dr. Vanessa Valentin University of New Mexico
5. SEGMENTATION AND NURBS FITTING OF UNORDERED BUILDING POINT CLOUDS .......................... 13 Andrey Dimitrov ([email protected]), Advisors: Dr. Feniosky Pena Mora, Dr. Mani Golparvar-Fard Columbia University
6. SAVES II: A MULTIPLE SIGNALS ENHANCED AUGMENTED VIRTUALITY TRAINING SYSTEM FOR CONSTRUCTION HAZARD RECOGNITION ..................................................................................................... 14 Ao Chen ([email protected]), Advisors: Dr. Brian Kleiner, Dr. Mani Golparvar-Fard Virginia Tech
7. QUANTIFYING ENERGY-USE BEHAVIOR IN COMMERCIAL BUILDINGS ................................................. 15 Ardalan Khosrowpour ([email protected]), Rimas Gulbinas ([email protected]), Advisor: Dr. John E. Taylor Virginia Tech
8. ADOPTION READINESS OF PREVENTION THROUGH DESIGN (PTD) CONTROLS IN CONCRETE, MASONRY, AND ASPHALT ROOFING ............................................................................................................. 16 Ari Goldberg ([email protected]), Advisor: Dr. Deborah Young-Corbett Virginia Tech
9. A BIO-INSPIRED VIRTUAL PEDAGOGICAL ENVIRONMENT TO STIMULATE BIO-INSPIRED THINKING ............................................................................................................................................................................ 17 Aruna Muthumanickam ([email protected]), Advisor: Dr. John E. Taylor Virginia Tech
10. OPTIMIZING THE SUSTAINABILITY OF SINGLE-FAMILY HOUSING UNITS ........................................... 18 Aslihan Karatas ([email protected]), Advisor: Dr. Khaled El-Rayes University of Illinois at Urbana-Champaign
11. THREE-TIERED DATA & INFORMATION INTEGRATION FRAMEWORK FOR HIGHWAY PROJECT DECISION- MAKINGS ........................................................................................................................................ 19 Asregedew Woldesenbet ([email protected]), Advisor: Dr. “David” Hyung Seok Jeong Iowa State University
12. MINIMIZING EFFECTS OF OVERFITTING AND COLLINEARITY IN CONSTRUCTION COST ESTIMATION: A NEW HYBRID APPROACH ..................................................................................................... 20 Bo Xiong ([email protected]), Advisor: Dr. Martin Skitmore, Dr. Bo Xia Queensland University of Technology
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13. AUTOMATED REAL-TIME TRACKING AND 3D VISUALIZATION OF CONSTRUCTION EQUIPMENT OPERATION USING HYBRID LIDAR SYSTEM ................................................................................................. 21 Chao Wang ([email protected]), Advisor: Dr. Yong K. Cho Georgia Institute of Technology
14. INTERDEPENDENT INFRASTRUCTURE NETWORK SYSTEM VULNERABILITY IDENTIFICATION ..... 22 Christopher Van Arsdale ([email protected]), Advisor: Dr. Amlan Mukherjee Michigan Technological University
15. VOLATILE ORGANIC COMPOUNDS EMISSIONS GENERATED IN HOT-MIX ASPHALT PAVEMENT CONSTRUCTION AND THEIR HEALTH EFFECTS ON PAVEMENT WORKERS ........................................... 23 Dan Chong ([email protected]), Advisor: Dr. Yuhong Wang The Hong Kong Polytechnic University
16. QUANTITATIVE PERFORMANCE ASSESSMENT OF SINGLE-STEP AND TWO-STEP DESIGN-BUILD PROCUREMENT ................................................................................................................................................ 24 David Ramsey ([email protected]), Advisor: Dr. Mounir El Asmar, Dr. G. Edward Gibson Arizona State University
17. RISK ALLOCATION IN PUBLIC-PRIVATE PARTNERSHIPS: ANALYSIS OF CONTRACTUAL PROVISIONS IN 18 U.S. HIGHWAY PROJECTS .............................................................................................. 25 Duc A. Nguyen ([email protected]) and Edwin Gonzalez ([email protected]), Advisor: Dr. Michael J. Garvin Virginia Tech
18. EXTENDING BUILDING INFORMATION MODELING (BIM) INTEROPERABILITY TO GEO-SPATIAL DOMAIN USING SEMANTIC WEB TECHNOLOGY .......................................................................................... 26 Ebrahim P. Karan ([email protected]), Advisor: Javier Irizarry Georgia Institute of Technology
19. QUANTIFYING THE RISKS OF WILDFIRE TO BUILDINGS IN WILDLAND URBAN INTERFACE: A FORWARD VIEW ............................................................................................................................................... 27 Elmira Kalhor ([email protected]), Advisor: Dr. Vanessa Valentin University of New Mexico
20. COLLABORATION THROUGH INNOVATION: A MULTI-LAYERED FRAMEWORK FOR THE AECM INDUSTRY .......................................................................................................................................................... 28 Erik A. Poirier ([email protected]), Advisor: Dr. Daniel Forgues, Dr. Sheryl Staub-French École de Technologie Supérieure
21. THE VIRTUAL CONSTRUCTION SIMULATOR: AN EDUCATIONAL GAME IN CONSTRUCTION ENGINEERING ................................................................................................................................................... 29 Fadi Castronovo ([email protected]), Advisor: Dr. John I. Messner The Pennsylvania State University
22. PREDICTIVE EMISSIONS MODELS FOR EXCAVATORS ......................................................................... 30 Heni Fitriani ([email protected]), Advisor: Dr. Phil Lewis Oklahoma State University
23. ESTIMATING EXTREME EVENT RECOVERY WITH CONSTRUCTION ACTIVITY CHANGE POINTS ... 31 Henry D. Lester ([email protected]), Advisor: Dr. Gary P. Moynihan University of Alabama
24. AN INTEGRATED SIMULATION AND OPTIMIZATION BASED RESIDENTIAL CONSTRUCTION CARBON FOOTPRINT AND EMISSION ASSESSMENT .................................................................................. 32 Hong Xian Li ([email protected]), Advisor: Dr. Mohamed Al-Hussein, Dr. Mustafa Gül University of Alberta
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25. 3D RECONSTRUCTION OF INDUSTRIAL EQUIPMENT USING COMBINED GEOMETRIC AND TOPOLOGICAL INFORMATION FROM LASER-SCANNED DATA .................................................................. 33 Hyojoo Son ([email protected]), Advisor: Dr. Changwan Kim Chung-Ang University
26. INFORMATION EXTRACTION AND AUTOMATED REASONING FOR AUTOMATED REGULATORY COMPLIANCE CHECKING IN THE CONSTRUCTION DOMAIN ...................................................................... 34 Jiansong Zhang ([email protected]), Advisor: Dr. Nora El-Gohary University of Illinois at Urbana-Champaign
27. EX-ANTE ASSESSMENT OF PERFORMANCE IN CONSTRUCTION PROJECTS: A SYSTEM-OF-SYSTEMS APPROACH ...................................................................................................................................... 35 Jin Zhu ([email protected]), Advisor: Dr. Ali Mostafavi Florida International University
28. A FRAMEWORK FOR PUBLIC PRIVATE PARTNERSHIP RISK MITIGATION IN RURAL POST CONFLICT ENVIRONMENTS– A SYSTEMS APPROACH ............................................................................... 36 John T. Mitchell ([email protected]), Advisor: Dr. Yvan Beliveau Virginia Tech
29. FRAMEWORK FOR ON-SITE BIOMECHANICAL ANALYSIS DURING CONSTRUCTION TASKS ........... 37 JoonOh Seo ([email protected]), Advisor: Dr. SangHyun Lee University of Michigan
30. DEVELOP A PRICE ESCALATION METHOD FOR SINGLE AWARD INDEFINITE DELIVERY/INDEFINITE QUANTITY CONTRACTS: AXE BIDDING ......................................................................................................... 38 Jorge A. Rueda ([email protected]), Advisor: Dr. Douglas D. Gransberg Iowa State University
31. CONSTRUCTION OPERATIONS AUTOMATION USING MODIFIED DISCRETE EVENT SIMULATION MODELS ............................................................................................................................................................. 39 Joseph Louis ([email protected]), Advisor: Dr. Phillip S. Dunston Purdue University
32. AUTONOMOUS NEAR-MISS FALL ACCIDENT DETECTION TECHNIQUE USING INERTIAL MEASUREMENT UNITS ON CONSTRUCTION IRON-WORKERS .................................................................. 40 Kanghyeok Yang ([email protected]) and Sepideh S. Aria ([email protected]), Advisor: Dr. Changbum Ahn University of Nebraska at Lincoln
33. MANAGING WATER AND WASTEWATER INFRASTRUCTURE IN SHRINKING CITIES ......................... 41 Kasey Faust ([email protected]), Advisor: Dr. Dulcy Abraham Purdue University
34. MONITORING CONSTRUCTION PROGRESS AT THE OPERATION-LEVEL USING 4D BIM AND SITE PHOTOLOGS ..................................................................................................................................................... 42 Kevin K. Han ([email protected]), Advisor: Dr. Mani Golparvar-Fard University of Illinois at Urbana-Champaign
35. ESTIMATING OPTIMAL LABOR PRODUCTIVITY: A TWO-PRONG STRATEGY ...................................... 43 Krishna Kisi ([email protected]) and Nirajan Mani ([email protected]), Advisor: Dr. Eddy Rojas University of Nebraska-Lincoln
36. AN INVESTIGATION OF OCCUPANT ENERGY USE BEHAVIOR AND INTERVENTIONS IN A RESIDENTIAL CONTEXT .................................................................................................................................. 44 Kyle Anderson ([email protected]), Advisor: Dr. SangHyun Lee
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University of Michigan
37. MEASURING THE COMPLEXITY OF MEGA CONSTRUCTION PROJECTS IN CHINA—A FUZZY ANALYTIC NETWORK PROCESS ..................................................................................................................... 45 Lan Luo, Advisor: Dr. Qinghua He Tongji University
38. DECISION SUPPORT SYSTEM FOR SUSTAINABLE LABOR MANAGEMENT IN MASONRY CONSTRUCTION ............................................................................................................................................... 46 Laura Florez ([email protected]), Advisor: Dr. Daniel Castro-Lacouture Georgia Institute of Technology
39. BIM-BASED INTEGRATED APPROACH FOR OPTIMIZED CONSTRUCTION SCHEDULING UNDER RESOURCE CONSTRAINTS ............................................................................................................................. 47 Hexu Liu ([email protected]), Advisor: Dr. Mohamed Al-Hussein, Dr. Ming Lu University of Alberta
40. INCREASING MINDFULNESS OF COORDINATION PRACTICES IN INNER CITY UTILITY PROJECTS: THE ROLE OF NEW (BIM) TECHNOLOGIES ................................................................................................... 48 Léon L. olde Scholtenhuis ([email protected]), Advisor: Dr. T. Hartmann University of Twente
41. A SYSTEMATIC RISK ANALYSIS APPROACH AGAINST TUNNEL-INDUCED BUILDING DAMAGES .... 49 Limao Zhang ([email protected]), Advisor: Dr. Xianguo Wu Huazhong University of Science and Technology
42. MODELING AND VISUALIZING THE FLOW OF TRADE CREWS IN CONSTRUCTION USING AGENTS AND BUILDING INFORMATION MODELS (BIM) .............................................................................................. 50 Lola Ben-Alon ([email protected]), Advisor: Dr. Rafael Sacks Technion IIT
43. MEASURING INTERDEPENDENT INFRASTRUCTURE RESILIENCE UNDER NORMAL AND EXTREME CONDITIONS ...................................................................................................................................................... 51 María E. Nieves-Meléndez ([email protected]), Advisor: Dr. Jesús M. de la Garza Virginia Tech
44. THERMALLY ACTIVATED CLAY BASED BIOMASS POZZOLANA INVESTIGATIONS FOR SUSTAINABLE CONSTRUCTION IN GHANA ................................................................................................... 52 Mark Bediako ([email protected]), Advisor: SKY Gawu and AA Adjaottor Kwame Nkrumah University of Science and Technology, Ghana
45. ASSESSMENT OF ACTIVITIES’ CRITICALITY TO CASH-FLOW PARAMETERS ..................................... 53 Marwa Hussein Ahmed ([email protected]), Advisor: Dr. Tarek Zayed, Dr. Ashraf Elazouni Concordia University
46. UNDERSTANDING CURRENT HORIZONTAL DIRECTIONAL DRILLING PRACTICES IN MAINLAND CHINA BEING USED FOR ENERGY PIPELINE CONSTRUCTION .................................................................. 54 Maureen Cassin ([email protected]), Advisor: Dr. Samuel Ariaratnam Arizona State University
47. OPTIMIZING THE SELECTION OF SUSTAINABILITY MEASURES FOR EXISTING BUILDINGS ............ 55 Moatassem Abdallah ([email protected]), Advisor: Dr. Khaled El-Rayes University of Illinois at Urbana-Champaign
48. COMPETENCIES AND PERFORMANCE IN CONSTRUCTION PROJECTS ............................................. 56 Moataz Nabil Omar ([email protected]), Advisor: Dr. Aminah Robinson Fayek University of Alberta
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49. IMPROVING CONSTRUCTION COST ESCALATION ESTIMATION USING MACROECONOMIC, ENERGY AND CONSTRUCTION MARKET VARIABLES ................................................................................. 57 Mohsen Shahandashti ([email protected]), Advisor: Dr. Baabak Ashuri Georgia Institute of Technology
50. EX-ANTE SIMULATION AND VISUALIZATION OF SUSTAINABILITY POLICIES IN INFRASTRUCTURE SYSTEMS: A HYBRID METHODOLOGY FOR MODELING AGENCY-USER-ASSET INTERACTIONS .......... 58 Mostafa Batouli ([email protected]), Advisor: Dr. Ali Mostafavi Florida International University
51. DYNAMIC FATIGUE MODEL FOR ASSESSING MUSCLE FATIGUE DURING CONSTRUCTION TASKS ............................................................................................................................................................................ 59 MyungGi Moon ([email protected]) Advisor: Dr. SangHyun Lee University of Michigan
52. TOWARD SUSTAINABLE CAPITAL TRANSPORTATION INFRASTRUCTURE: MAXIMIZING PERFORMANCE OF PREPLANNING PHASE .................................................................................................. 60 Nahid Vesali ([email protected]), Advisor: Dr. Mehmet Emre Bayraktar Florida International University
53. A QUANTITATIVE INVESTIGATION OF BUILDING MICRO-LEVEL POWER MANAGEMENT THROUGH ENERGY HARVESTING FROM OCCUPANT MOBILITY .................................................................................. 61 Neda Mohammadi ([email protected]), Advisor: Dr. Tanyel Bulbul, Dr. John E. Taylor Virginia Tech
54. ESTIMATING LABOR PRODUCTIVITY FRONTIER: A PILOT STUDY ....................................................... 62 Nirajan Mani ([email protected]) and Krishna P. Kisi ([email protected]), Advisor: Dr. Eddy M. Rojas University of Nebraska-Lincoln
55. A DECISION SUPPORT SYSTEM FOR SUSTAINABLE MULTI OBJECTIVE ROADWAY ASSET MANAGEMENT .................................................................................................................................................. 63 Omidreza Shoghli ([email protected]), Advisor: Dr. Jesus M. de la Garza Virginia Tech
56. SIMULEICON: A SIMULATION-BASED MULTI-OBJECTIVE DECISION-SUPPORT TOOL FOR SUSTAINABLE BUILDING DESIGN ................................................................................................................... 64 Peeraya Inyim ([email protected]), Advisor: Dr. Yimin Zhu, Dr. Wallied Orabi Florida International University
57. CONSTRUCTION OPERATIONS PROCESS DATA MODELING AND KNOWLEDGE DISCOVERY USING MACHINE LEARNING CLASSIFIERS ................................................................................................................ 65 Reza Akhavian ([email protected]), Advisor: Dr. Amir H. Behzadan University of Central Florida
58. THE DEVELOPMENT OF AN AUTOMATED PROGRESS MONITORING AND CONTROL SYSTEM FOR CONSTRUCTION PROJECTS ........................................................................................................................... 66 Reza Maalek ([email protected]), Advisor: Dr. Janaka Ruwanpura, Dr. Derek Lichti University of Calgary
59. OPTIMUM RESOURCE UTILIZATION PLANNING IN CONSTRUCTION PORTFOLIOS THROUGH MODELING OF EVERYDAY UNCERTAINTIES AT CERTAIN CONFIDENCE LEVEL ..................................... 67 Reza Sheykhi ([email protected]), Advisor: Dr. Wallied Orabi Florida International University
60. USING STEP APPROACH TO ACHIEVE SUCCESSFUL OUTCOMES ON COMPLEX PROJECTS ........ 68 Ron Patel ([email protected]), Advisor: Dr. Edward J. Jaselskis
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North Carolina State University
61. QUANTIFYING HUMAN MOBILITY PERTURBATION UNDER THE INFLUENCE OF TROPICAL CYCLONES ........................................................................................................................................................ 69 Qi Wang ([email protected]), Advisor: Dr. John E. Taylor Virginia Tech
62. CONSTRUCTION WORKERS’ BEHAVIOR INFLUENCED BY SOCIAL NORMS: A STUDY OF WORKERS’ BEHAVIOR USING AGENT-BASED SIMULATION INTEGRATED WITH EMPIRICAL METHODS ............................................................................................................................................................................ 70 Seungjun Ahn ([email protected]), Advisor: Dr. SangHyun Lee University of Michigan
63. CONSTRUCTION SITE LAYOUT PLANNING USING SIMULATION .......................................................... 71 SeyedReza RazaviAlavi ([email protected]), Advisor: Dr. Simaan AbouRizk University of Alberta
64. 4-DIMENSIONAL PROCESS-AWARE SITE-SPECIFIC CONSTRUCTION SAFETY PLANNING .............. 72 Sooyoung Choe ([email protected]), Advisor: Dr. Fernanda Leite The University of Texas at Austin
65. THE IMPACT OF BUSINESS-PROJECT INTERFACE ON CAPITAL PROJECT PERFORMANCE ........... 73 Sungmin Yun ([email protected]), Advisor: Dr. Stephen P. Mulva, Dr. William J. O’Brien University of Texas at Austin
66. EXPLORING A PREFERENTIAL FRAMEWORK FOR FUTURE PROJECT OPPORTUNITIES ................ 74 Timothy W. Gardiner ([email protected]), Advisor: Dr. Yvan J. Beliveau Virginia Tech
67. ENVISIONING MORE SUSTAINABLE INFRASTRUCTURE THROUGH CHOICE ARCHITECTURE ........ 75 Tripp Shealy ([email protected]), Advisor: Dr. Leidy Klotz Clemson University
68. SEGMENTATION AND RECOGNITION OF ROADWAY ASSETS FROM CAR-MOUNTED CAMERA VIDEO STREAMS USING A SCALABLE NON-PARAMETRIC IMAGE PARSING METHOD ........................... 76 Vahid Balali ([email protected]), Advisor: Dr. Mani Golparvar-Fard University of Illinois at Urbana-Champaign
69. INTEGRATED COMPUTATIONAL MODEL IN SUPPORT OF VALUE ENGINEERING ............................. 77 Yalda Ranjbaran ([email protected]), Advisor: Dr. Osama Moselhi Concordia University
70. IMPROVING CAMPUS BUILDING ENERGY EFFICIENCY AND OCCUPANTS SATISFACTION THROUGH APPLICATION OF ARTIFICIAL INTELLIGENCE INTO CAMPUS FACILITY MANAGEMENT ...... 78 Yang Cao ([email protected]), Advisor: Dr. Xinyi Song Georgia Institute of Technology
71. A BIO-INSPIRED SOLUTION TO MITIGATE URBAN HEAT ISLAND EFFECTS ....................................... 79 Yilong Han ([email protected]), Advisor: Dr. John E. Taylor Virginia Tech
72. FORECASTING LONG-TERM STAFFING REQUIREMENTS FOR STATE TRANSPORTATION AGENCIES .......................................................................................................................................................... 80 Ying Li ([email protected]), Advisor: Dr. Timothy R. B. Taylor University of Kentucky
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73. VISION-BASED BUILDING ENERGY DIAGNOSTICS AND RETROFIT ANALYSIS USING 3D THERMOGRAPHY AND BIM ............................................................................................................................. 81 Youngjib Ham ([email protected]), Advisor: Dr. Mani Golparvar-Fard University of Illinois at Urbana-Champaign
74. MULTI-TIERED SELECTION OF PROJECT DELIVERY SYSTEMS FOR CAPITAL PROJECTS .............. 82 Zorana Popić ([email protected]), Advisor: Dr. Osama Moselhi Concordia University
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List of Participants
Student Name School Advisor Name
1 Aaron Costin Georgia Institute of Technology Jochen Teizer
2 Abraham Tsehayae University of Alberta Aminah Robinson Fayek
3 Alireza Geranmayeh Kashani University of Alabama Andrew J. Graettinger
4 Amirhosein Jafari University of New Mexico Vanessa Valentin
5 Andrey Dimitrov Columbia University Feniosky Pena Mora, Mani Golparvar-Fard
6 Ao Chen Virginia Tech Brian Kleiner, Mani Golparvar-Fard
7 Ardalan Khosrowpour Virginia Tech John E. Taylor
8 Ari Goldberg Virginia Tech Deborah Young-Corbett
9 Aruna Muthumanickam Virginia Tech John E. Taylor
10 Aslihan Karatas University of Illinois at Urbana-Champaign
Khaled El-Rayes
11 Asregedew Woldesenbet Iowa State University “David” Hyung Seok Jeong
12 Bo Peter Xiong Queensland University of Technology
Martin Skitmore, Bo Xia
13 Chao Wang Georgia Institute of Technology Yong K. Cho
14 Christopher Van Arsdale Michigan Technological University
Amlan Mukherjee
15 Dan Chong The Hong Kong Polytechnic University
Yuhong Wang
16 David Ramsey Arizona State University Mounir El Asmar, G. Edward Gibson
17 Duc Nguyen & Edwin Gonzales Virginia Tech Michael J. Garvin
18 Ebrahim Karan Georgia Institute of Technology Javier Irizarry
19 Elmira Kalhor University of New Mexico Vanessa Valentin
20 Erik Poirier École de Technologie Supérieure Daniel Forgues, Sheryl Staub-French
21 Fadi Castronovo The Pennsylvania State University
John I. Messner
22 Heni Fitriani Oklahoma State University Phil Lewis
23 Henry D. Lester University of Alabama Gary P. Moynihan
24 Hong Li University of Alberta Mohamed Al-Hussein, Mustafa Gül
25 Hyojoo Son Chung-Ang University Changwan Kim
26 Jiansong Zhang University of Illinois at Urbana-Champaign
Nora El-Gohary
27 Jin Zhu Florida International University Ali Mostafavi
28 John Mitchell Virginia Tech Yvan Beliveau
29 JoonOh Seo University of Michigan SangHyun Lee
30 Jorge A. Rueda Iowa State University Douglas D. Gransberg
31 Joseph Louis Purdue University Phillip S. Dunston
32 Kanghyeok Yang & Sepidek S. Aria
University of Nebraska at Lincoln Changbum Ahn
33 Kasey Faust Purdue University Dulcy Abraham
34 Kevin Han University of Illinois at Urbana-Champaign
Mani Golparvar-Fard
35 Krishna Kisi University of Nebraska-Lincoln Eddy Rojas
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36 Kyle Anderson University of Michigan SangHyun Lee
37 Lan Luo Tongji University Qinghua He
38 Laura Florez Georgia Institute of Technology Daniel Castro-Lacouture
39 Lio Liu University of Alberta Mohamed Al-Hussein, Ming Lu
40 Léon L. olde Scholtenhuis University of Twente T. Hartmann
41 Limao Zhang Huazhong University of Science and Technology
Xianguo Wu
42 Lola Ben-Alon Technion IIT Rafael Sacks
43 Maria Nieves Virginia Tech Jesús M. de la Garza
44 Mark Bediako Kwame Nkrumah University of Science and Technology
SKY Gawu and AA Adjaottor
45 Marwa Hussien Concordia University Tarek Zayed, Ashraf Elazouni
46 Maureen Cassin Arizona State University Samuel Ariaratnam
47 Moatassem Abdallah University of Illinois at Urbana-Champaign
Khaled El-Rayes
48 Moataz Omar University of Alberta Aminah Robinson Fayek
49 Mohsen Shahandashti Georgia Institute of Technology Baabak Ashuri
50 Mostafa Batouli Florida International University Ali Mostafavi
51 MyungGi Moon University of Michigan SangHyun Lee
52 Nahid Vesali Mahmoud Florida International University Mehmet Emre Bayraktar
53 Neda Mohammadi Virginia Tech Tanyel Bulbul, John E. Taylor
54 Nirajan Mani University of Nebraska-Lincoln Eddy M. Rojas
55 Omidreza Shoghli Virginia Tech Jesus M. de la Garza
56 Peeraya Inyim Florida International University Yimin Zhu, Wallied Orabi
57 Reza Akhavian University of Central Florida Amir H. Behzadan
58 Reza Maalek University of Calgary Janaka Ruwanpura, Derek Lichti
59 Reza Sheykhi Florida International University Wallied Orabi
60 Ron Patel North Carolina State University Edward J. Jaselskis
61 Ryan Qi Wang Virginia Tech John E. Taylor
62 Seungjun Ahn University of Michigan SangHyun Lee
63 SeyedReza RazaviAlavi University of Alberta Simaan AbouRizk
64 Sooyoung Choe University of Texas at Austin Fernanda Leite
65 Sungmin Yun University of Texas at Austin Stephen P. Mulva, William J. O’Brien
66 Timothy W. Gardiner Virginia Tech Yvan J. Beliveau
67 Tripp Shealy Clemson University Leidy Klotz
68 Vahid Balali University of Illinois at Urbana-Champaign
Mani Golparvar-Fard
69 Yalda Ranjbaran Concordia University Osama Moselhi
70 Yang Cao Georgia Institute of Technology Xinyi Song
71 Yilong Han Virginia Tech John E. Taylor
72 Ying Li University of Kentucky Timothy R. B. Taylor
73 Youngjib Ham University of Illinois at Urbana-Champaign
Mani Golparvar-Fard
74 Zorana Popić Concordia University Osama Moselhi
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1. RFID and BIM-Enabled Worker Location Tracking to Support Real-time Building Protocol
Control and Data Visualization on a Large Hospital Project
In construction management research there is often a need for experiments to assess the impacts of different production control methods and information flows on production on site. Simulation methods are useful for such experimentation because field experiments suffer difficulties with isolating cause and effect. Existing methods such as DES (Discrete Event Simulation) are limited in terms of their ability to model decision-making by individuals who have distinct behavior, context and knowledge representation (Brodetskaia et al. 2012, Sawhney et al. 2003, and Watkins et al. 2009). The main objective is to develop an agent-based simulation model for studying and improving production control in construction processes, which accounts for individuals' decision making process and acquired knowledge. The simulation will exhibit the interdependence of individual workers and crews as they interact with each other and share resources. The goal will be to make the model robust and valid by attempting to calibrate it with field observations. Unlike the few existing research models, the simulation will be situated in a realistic virtual environment modeled using BIM, allowing future experimental setups that can incorporate human subjects. The method employs agent-based simulation (ABS), with a “bottom-up” approach to model the interactions between individual agents. It uses BIM models to define the physical and the process environment for the simulation. We apply agents programmed with decision making rules and utility functions to a to-be-built-environment represented as a BIM. By varying parameters such as reliability between workers, thresholds for information gathering and approach to making-do in terms of risk, it is possible to generate aggregate system performance similar to those found in an actual building context in the construction site. The research has two main steps: 1. Development of an agent based model to simulate the process incorporated in the LEAPCON game (LEAPCON 2005). This step has been implemented using the Starlogo TNG tool for multi-agent simulations which provides a development environment within a 3D visual context. The agent-based simulation of the LEAPCON game was developed with agents for the four independent specialty subcontractors, the client representative and the quality controller, and for each of the 32 apartments considered. The results show good calibration with existing observed field data, and to the existing DES. The effects are shown by measuring Work In Progress (WIP), Cycle Time (CT), cash flow patterns and efficiency of the operations (Sacks et al. 2007).
2. Development of an agent-based model in UNITY 3D game engine to simulate production control of a process in a full-scale building project. This step is being pursued in collaboration with a construction company. Data on workers' motivations, behavior and performance was collected using interviews and observations of a crew performing finishing works in a high-rise residential tower project. This step presented the following challenges: observation and formulation of the variables and target function (motivations) of the agents, modeling the behavior of the agents while classifying professions, validation by calibration with actual performance. The contribution of this research lies in the development and testing of the ABS simulation. No simulations of this kind exist: previous efforts with ABS systems for construction have been limited to simplified and virtual environments that use DES that cannot reliably model the complex, emergent patterns of production behavior that result from the interaction of the myriad subcontracting teams and suppliers that perform construction work on and off site. In particular, the influence of each participants' knowledge, context and motivations on their day to day decisions about resource allocation and work sequence can be modeled in the ABS simulations, while they could not be modeled using DES. To-date, there has been no simple and reliable way to test different ideas for production control paradigms in construction.
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43. Measuring Interdependent Infrastructure Resilience under Normal and Extreme
Conditions
María E. Nieves-Meléndez ([email protected]), Advisor: Dr. Jesús M. de la Garza
Virginia Tech
Infrastructure is one of the most important elements of our built society. Failure in the infrastructure
system translates to human and economic losses, and in some cases environmental impacts that
could last for decades. Therefore, it is imperative to put efforts in making sure that the lifeline systems
are reliable in moments of stress. From the impact of natural hazards like Hurricane Katrina, to system
failures like the 2003 Northeast blackout and component failures like the I-35W Minneapolis bridge
collapse, civil engineers have the responsibility to renew and build infrastructure that can resist the
impact of a disruptive event, respond to such impact in a timely manner, and recover to the normal
operating condition. These three aspects form the concept of Resilience. A comprehensive literature
review has revealed the necessity for further research that develop standard frameworks and
techniques for measuring and increasing the resilience of the infrastructure systems. Consequently,
the objective of this research effort is to develop a framework for measuring the resilience of a ground
transportation system under normal and extreme conditions. Two existing models found in the
literature will be combined with the purpose of measuring the resilience of roadways under
predicted/anticipated traffic loads, routine intrusions, and extreme intrusions. The framework will
integrate a probability approach with a three-stage (resistive, absorptive, and restorative) resilience
model. This research can contribute to the development of techniques that identify the weaknesses in
the system and find ways to increase the resilience. The results could help engineers, state DOTs and
policy makers make investment decisions based on the resilience condition assessed. It could also
help identify and bring awareness to the risks associated with failing to renew the infrastructure
Recently, the accuracy of construction cost estimates has been significantly affected by fluctuations in construction costs. Construction cost fluctuations have been larger and less predictable than was typical in the past. Cost escalation has become a major concern in all industry sectors, such as infrastructure, heavy industrial, light industrial, and building. Construction cost variations are problematic for cost estimation, bid preparation and investment planning. Inaccurate cost estimation can result in bid loss or profit loss for contractors and hidden price contingencies, delayed or cancelled projects, inconsistency in budgets and unsteady flow of projects for owner organizations. The major problem is that construction cost is subject to significant variations that are difficult to estimate. The objective of this research is to create multivariate time series models for improving the accuracy of construction cost escalation estimation through utilizing information available from several indicators of macroeconomic condition, energy price and construction market. An advanced statistical approach based on multivariate time series analysis is used as a main research methodology. The first step is to identify explanatory variables of construction cost variations. A pool of 16 candidate (potential) explanatory variables is initially selected based on a comprehensive literature review about construction cost variations. Then, the explanatory variables of construction cost variations are identified from the pool of candidate explanatory variables using empirical tests including correlation tests, unit root tests, Granger causality tests, and Johansen’s cointegration tests. The identified explanatory variables represent the macroeconomic and construction market context in which the construction cost is changing. Based on the results of statistical tests, several multivariate time series models are created and compared with existing models for estimating construction cost escalation. These models take advantage of contextual information about macroeconomic condition, energy price and construction market for estimating cost escalation accurately. These multivariate time series models are rigorously diagnosed using statistical tests including Breusch–Godfrey serial correlation Lagrange multiplier tests and Autoregressive conditional heteroskedasticity (ARCH) tests. They are also compared with each other and other existing models. Comparison is based on two typical error measures: out-of-sample mean absolute prediction error and out-of-sample mean squared error. Based on the correlation tests, unit root tests, and Granger causality tests, consumer price index, crude oil price, producer price index, housing starts and building permits are selected as explanatory variables of construction cost variations. In other words, past values of these variables contain information that is useful for estimating construction cost escalation. Based on the results of cointegration tests, Vector Error Correction models are created as proper multivariate time series models to estimate cost escalation. Our results show that the multivariate time series models pass diagnostic tests successfully. They are also more accurate than existing models for estimating cost escalation in terms of out-of-sample mean absolute prediction error and out-of-sample mean square error. These findings contribute to the body of knowledge in construction cost escalation estimation by rigorous identification of the explanatory variables of the escalation and creation of multivariate time series models that are more accurate than the univariate time series models for estimating the escalation. It is expected that proposed cost escalation estimation models enhance the theory and practice of cost escalation estimation and help cost engineers and capital planners prepare more accurate bids, cost estimates and budgets for capital projects in various industry sectors.
The research presented in this poster focuses on the creation and testing of a new paradigm for sustainability assessment in urban infrastructure System-of-Systems (SoS). The National Academy of Engineering recently listed “restoring and improving urban infrastructure” through sustainable approaches as one of the global challenges of engineering in the 21st century. Assessment of sustainability in infrastructure systems is complex due to the existence of various actors whose adaptive behaviors and interactions affect the performance of asset networks. However, the existing methodologies (e.g., urban metabolism and life cycle analysis) for assessment of sustainability in infrastructure systems do not capture the existing complex adaptive behaviors and uncertainties, and thus, could not provide a robust basis for policy analysis and decision-making. The key missing element is an integrated methodology that captures the complex interactions at the interface between agency, asset, and user behaviors for ex-ante analysis of sustainability in infrastructure systems. The objective of this study was to create and test an ex-ante analytical framework for micro-simulation of policies related to the sustainability of urban infrastructure under uncertain conditions. This research investigated the hypothesis that sustainability in infrastructure System-of-Systems is an emergent property as a result of the coupling effects between: (1) the strategic and operational decision-making processes of the asset owners, (2) the performance of infrastructure assets, and (3) the user behaviors. A System-of-Systems approach was adopted in this study to provide an integrated framework for analysis of sustainability in infrastructure systems. This framework was based on the abstraction and micro-simulation of the interactions between the dynamic behaviors of infrastructure agencies, users, and assets, and its application was demonstrated in assessment of the sustainability in highway transportation infrastructure. Using the framework and data obtained from different sources ranging from historical records and literature reviews to case studies, the interdependencies between agency, asset, and user behaviors were explored. These interdependencies (e.g., the effects of maintenance/rehabilitation decisions of agencies on asset performance and user behaviors) were then used to develop an integrated model for micro-simulation of policies related to sustainable infrastructure management. In the integrated model, the micro-behaviors of infrastructure agencies and users were captured using agent-based modeling, the dynamic performance of infrastructure assets were modelled using system dynamics, and the environmental impacts were considered using a performance-adjusted life cycle analysis model. Using this model and Monte-Carlo experimentation, the policy landscape pertaining to the sustainability of a highway transportation network was simulated in a case study. The model was verified and validated by using sensitivity analysis and uncertainty propagation analysis. The results revealed the optimal policy scenarios based on different levels of budget, pavement types, and maintenance/rehabilitation strategies that enhance the sustainability of infrastructure systems at the network level and under different uncertain conditions. This distinctive approach is the first of its kind to simulate and visualize the policy landscape pertaining to the sustainability of infrastructure systems by simulating the dynamic behaviors at the interface between agencies, users, and assets. The framework and simulation model have the following benefits for policy analysis: (i) simulation and visualization of the outcomes of policies on the sustainability of infrastructure at the network level and at various policy horizons, (ii) comparison of the outcomes of different policies based on different infrastructure characteristics, agency priorities, and user behaviors, (iii) creation of the landscape of sustainable policies for infrastructure management, (iv) identification of the desired scenarios, and (v) quantification of the likelihood of desirable outcomes as a result of different policies.
58. The Development of an Automated Progress Monitoring and Control System for
Construction Projects
Reza Maalek ([email protected]), Advisor: Dr. Janaka Ruwanpura, Dr. Derek Lichti
University of Calgary
Project Monitoring and Control are vital to facilitate decision makers identify deviations between the as-planned vs. as-built state of the project and take timely measures where required. Monitoring is the process of collecting onsite data as a means of measuring the performance of the project. Traditionally, onsite data are collected manually, a time consuming and labor intensive task particularly in large scale projects. In practice, to justify the time and cost associated with such manual approaches, a limited amount (or frequency) of onsite data are collected, which diminishes the ability of the project manager to identify the causes of delays and cost overruns on time. Currently, site supervisory personnel spend 30-50% of their time on manually monitoring and controlling onsite data. Therefore, a novel approach towards data collection and analysis is required to help overcome the aforementioned limitations of current manual monitoring and control practices. Here, the main objective is the development of an automated monitoring and control system to assess the performance of construction work as-progressed and to predict the stochastic outcomes of the project. The proposed automated monitoring and control system consists of the following stages: 1. Automated Monitoring System: The technology capable of collecting the “scope of the work performed” is of interest. Based on the comparative evaluation of the applicable remote sensing technologies presented in, LiDAR (Light Detection And Ranging) is recommended for construction site monitoring. With respect to the nature of LiDAR data, the following three concerns are required to be addressed: (i) Optimization of the Location of the Scan-Stations: One of the goals of this research is to reduce the time and cost of manual monitoring. Therefore, the minimum number of scan stations capable of providing 3D point clouds of every structural element onsite is of the essence. The as-planned 4D model is used to simulate point clouds starting from a scan station positioned at an arbitrary location. The scanner is then moved in increments of “fuzzy” terminology and the expected point clouds are simulated. The location where the maximum number of structural facets are detected is considered as the initial scan station. The facets corresponding to the initial scan station are then removed from the as-planned model and the process is repeated for the remaining surfaces until a point cloud is assigned to every surface. (ii) Automated Feature Recognition: This stage involves the automatic identification of structural elements (i.e. Column, Slab) from the collected unorganized LiDAR point clouds. Current object-based recognition models use the planned model as a-priori knowledge to assign 3D point clouds to a structural element [6-9], which may not be reliable in cases where the location of the as-built structure differs from the planned location. In order to eliminate the dependency of the feature extraction model on the as-planned data, the “Geometric Primitives” are used to detect planar (Wall, Beam, Floor and Ceiling Slabs) and cylindrical (Column, Pipe, Cable, Reinforcement) surfaces. To reduce the effects of outliers caused by occlusions, moving objects and dust, a Robust method of Principal Component Analysis (PCA) is proposed to extract planar features through a robust estimate of the covariance matrix of a neighborhood of each point cloud (iii) Automated Feature-based Registration: Since the as-planned model is geo-referenced to a specific coordinate system during the feasibility stages, it is important to register the as-built data to the same coordinate system. For this matter, at least three (3) non-collinear point correspondences between the as-planned and as-built models are required. The extracted features are used to identify the point to point correspondences in order to perform a rigid body transformation from the scanner space to the as-planned space. 3. Automated Control System: The identified “scope of the work performed” is compared to the “scope of the work planned to be performed” in order to determine deviations between the planned and the actual state of the project. Two types of analysis are then performed where significant differences are detected. Initially a Stochastic Neural Network based Earned Value (EV) analysis is carried out to well predict the expected time and cost of completion of the project. Through project management performance enhancement tools such as Crashing, an optimized decision support solution is introduced to improve productivity. To evaluate the feasibility the proposed method for automatic development of 3D as-built models, one set of LiDAR data from a laboratory at the University of Calgary was collected. Our proposed method was able to detect the 22 walls in laboratory with a Mean Radial Spherical Error (MRSE) of 9 cm. Another set of experiment is also designed to monitor and control the expansion of the School of Engineering project for a duration of one year. The main contribution of this research is an automated construction progress monitoring and control system to improve time, cost and quality of the state-of-the-art onsite monitoring practices and to produce the opportunity for timely identification of deviations between the as-planned and as-built state of the project. The following benefits to the construction industry are denoted with the efficient implementation of the aforementioned system: (i)- reduction of the project managers' time and cost of travel to and within the site; (ii)- improving time, cost and reliability of data collection and analysis; (iii)- reduction of time and cost of preparation and analysis of progress reports; (iv)- improvement of quality inspection and management in order to minimize rework early in the project lifecycle; (v)- minimizing construction claims (vi)- development of 3D/ 4D as-built models of the construction site; and (vii)- stability control and Health monitoring of structural elements.
Planning of resource utilization can largely affect construction completion time and cost, especially when everyday uncertainties are taken into account as main sources of unexpected changes during projects. The impact is even more significant when managers should plan to supply limited pool of resources to a portfolio of concurrent projects, such as transportation network reconstruction. However existing studies in resource-constrained planning did not capture impact of day-to-day changes on time-related risk factors (e.g. weather, and trade coordination, etc.) and their associated uncertainties, and therefore, could not provide a robust and realistic basis for decision-making. On the other hand, planning of a group of project competing for limited pool of resources requires planners to examine their alternative resource sharing capabilities and policies under uncertainty, which is another important missing element in reported construction research. The objective of this research is to cover mentioned research gaps through developing a model to capture impacts of daily-changing uncertainties and alternative resource utilization policies on completion time and cost of construction project portfolios. This study investigates the hypothesis that such a model can result in (1) a more realistic trade-off between completion time and cost and (2) more distinctive and applicable optimal resource utilization solutions for resource-constrained portfolio planning in construction. Developed model aims to provide planners with a contemporary solution map of portfolio planning outcomes (time and cost) based on their resource sharing capabilities and restrictions, and with respect to their risk-taking confidence. This research adopts two main approaches to achieve mentioned objectives: (1) modeling of daily-changing uncertainties in construction network, through stochastic simulation of day-to-day changes in crew productivity instead of activity duration, and (2) finding optimal portfolio time-cost trade-off through a multi-objective optimization model. To this end, Monte Carlo Simulation Method has been employed to capture stochastic nature of crew productivity and to develop portfolio completion time and cost distributions. In addition, the NSGA-II multi-objective optimization has been implemented to find optimum solutions based on (1) alternative project prioritizations (in order to consume limited resources), and (2) alternative overtime working policies. The optimization model eventually intends to minimize overall time and cost of the portfolio, which are obtained from distributions based on planners predefined confidence level. The developed model has been employed for modeling reconstruction of a real case study of 5 roadway projects within a portfolio in United States. The resulting solution map (1) verifies model results being more reliable and realistic comparing to deterministic planning of construction, and (2) enables decision makers to pick their desired optimum solution for resource planning based on their actual availabilities and desired risk-taking confidence level. Results suggest that using alternative overtime policies may vary portfolio duration and cost up to 50% and 5%, respectively. However, using overtime policy alternatives with lower productivity adjustments does not necessarily result in longer projects, and thus the amount of weekly performance of crews should be considered in estimation of total duration. It is also found that higher weekly performance per unit expenditure (applying regular overtime working policies) results in longer portfolios with lower total cost under uncertainties. This research, in general, helps planners select their preferred combination of resource planning options to achieve optimal completion time and cost under uncertainty. To this end, the model provides planners with practical decision support material in order to answer the following questions: (1) how much of each resource should be available in work periods, such as weeks, and (2), how to share this limited available resource pool among projects. This research is the first of its kind (1) to capture impacts of daily-changing uncertainties and alternative resource utilization policies on completion time and cost of construction, and (2) to model planning of a group of project competing for limited pool of resources by examining alternative resource sharing capabilities and policies.
65. The Impact of Business-Project Interface on Capital Project Performance
Sungmin Yun ([email protected]), Advisor: Dr. Stephen P. Mulva, Dr. William J. O’Brien
University of Texas at Austin
A capital project represents a significant investment by a firm to create future economic benefits. Since the global economic recession, many corporate owners have been suffering from misaligned projects and a lack of systematic approach to align project management with business strategy. Corporate owners, therefore, have paid increased attention to business-project interfaces with the aim of improving alignment between business strategy and capital project planning and execution. Despite its importance, the interfaces between business and project functions has not been adequately identified and quantitatively measured. This study intends to identify which interface exists between business and project functions throughout capital project planning and execution process, and to quantify how the business functions get involved in the process and interact with project functions. Using the quantified interfaces, this study also aims to show how the business-project interface accounts for performance outcomes in terms of cost, schedule, change, and achievement of business objectives. To achieve these objectives, this study was conducted through a correlational study based on quantitative approach. A conceptual framework was developed to measure business-project interfaces throughout capital project lifecycle in terms of the interface components: management personnel, phase, work functions, and management efforts. Based on the framework, a questionnaire was designed to identify and quantify personnel involvement and task interaction on the interfaces between business and project functions. Survey was carried out targeting industrial capital projects which have been submitted in the Construction Industry Institute (CII) performance assessment database. The performance data of the projects which responded the survey were extracted from the CII database. Data analyses were conducted through categorical data analysis methods such as Pearson Chi-square test, Somers’ d test, Fisher’s exact test, and interaction effect analysis using factorial analysis of variance. The results of the data analyses indicate that project sponsor, finance, facility/maintenance, operations/production are major business functions which are highly involved in the capital project planning and execution process. Business units interact with project units in about 60% of the work functions. Funding requests during project execution received the highest level of interaction between business and project units among all work functions. Most work functions with higher levels of interaction belonged to front end planning phases such as project scoping, capital budgeting, business objective setting, manufacturing objectives criteria setting, economic feasibility study, and technical feasibility study. In addition to that, effective business-project interface has synergy effects on performance outcomes such as block-and-tackle system. The projects with high involvement of business functions and high interaction between business and project functions have better cost, schedule, and change performance. Moreover, the business-project interface has leverage effects on performance improvement when adequate management practices are highly implemented. The projects with high involvement of business functions and high implementation of the management practices show better performance outcomes. This study provides empirical evidences for the ontological arguments of the business-project interface throughout capital project lifecycle. The study provides assessment tools to quantitatively measure the level of involvement and interaction throughout capital project planning and execution. Industry practitioners now have a quantitative assessment tool that can be used to measure the business-project interface in terms of personnel involvement and task interaction. This tool enables industry practitioners to identify and quantify the current state of the business-project interface within their organizations during the development of a capital project. In addition, the assessment tool helps them understand the interfaces by which management personnel are involved in a capital project, and which tasks require interaction between the business and project unit. The descriptive statistics from the assessment can be used as benchmarks to compare their organization’s current level to others and will be used to examine the correlation between business-project interfaces with project performance outcomes.
66. Exploring a PREFERENTIAL Framework for Future Project Opportunities
Timothy W. Gardiner ([email protected]), Advisor: Dr. Yvan J. Beliveau
Virginia Tech
A project delivery method is defined as “a comprehensive process by which Designers (A/E), Constructors (GC), and various consultants provide services for design and construction to deliver a complete project to the Owner (O).” Design-bid-build (DBB) acts as the most common project delivery system in the United States (U.S.) today, followed by other transactional methods of construction management at risk (CM@R) and design-build (DB). The choice of delivery method has been found to have a defining impact on project results within the U.S. construction industry which still suffers from suboptimal performance including the lowest measured domestic productivity for (almost five (5)) decades. As a result, construction practitioners and academics are in endless search of alternative approaches such as Integrated Project Delivery (IPD) that might meet evolving needs and serve to counteract chronic industry challenges. In its “purest” form as a relational project delivery approach, IPD distinguishes itself from (aforementioned) transactional counterparts on a continuum of collaboration through recognizing all of the following attributes: (1) Early involvement of key participants, (2) Shared risk and reward, (3) Multi-party contract, (4) Collaborative decision-making, (5) Liability waivers, and (6) Jointly developed goals. IPD, with its trademark in 2005, has been considered an emerging delivery method with expected widespread use in the U.S. construction industry despite remaining limited in application. The research objective has been framed on the background of established “elements” found for (real estate and e-business) organizations as well as (integrated and “through-life”) project delivery approaches. With an enabler of a continuum of collaboration metric, these elements can be observed and form the basis for determining a “readiness” for transition to relational project delivery: • To evaluate the impact of “teamwork” (People); • To investigate the evolution of Information and Communication Technologies “ICT” (Technology); • To assess project life cycle “achievement” (Process); and • To critically appraise the established “language” (Legal and Commercial Structure). Current Research Question – How does each transactional project perform based on established metrics and do these (individual and collective) outcomes warrant a move to relational delivery for future work opportunities? The case study methodology has been selected to explore the potential for IPD (“contemporary phenomenon”) within a real-life context. With the organization acting as the primary unit of analysis, the case study will rely on sources of evidence from documentation and interviews of project stakeholders including O, A/E, GC as well as Specialty Contractors (SC) and Manufacturers (M). An instrument developed by literature and tested through pilot study will be employed to establish the readiness of an organization on subject projects in adopting relational principles. The findings will be verified for validity by an expert panel. Within the first decade of the 21st century, five (5) ground-up new construction projects have been completed within an approximately one hundred (100) acre business park, consisting of six (6) flex buildings and three (3) annex warehouse one-story buildings totaling 264,113 gross square feet (SF). The assemblage of five (5) projects has established the following results: • Estimated under a pre-construction arrangement and constructed under CM@R; • Designed, permitted and constructed by the same group of key stakeholders – O, A/E and GC; and • Engaged select SC and M either on multiple (more than one) or (in select instance) all (five) projects. The proposed contribution involves developing a framework for understanding the “limited in application” IPD against project delivery choices that remain prevalent today. This model identifies as a Project Readiness Evaluation Framework Emphasizing Relational Elements Named Teamwork, ICT, Achievement and Language (PREFERENTIAL) approach. Flex-type buildings offer one potential for proposed application as one of nine (9) primary property types in the domestic commercial real estate market, totaling nearly three (3) billion SF.
Accurate quantification of the energy cost savings associated with retrofitting performance problems in existing buildings can minimize the financial risks in retrofit investments. Nevertheless, the industry continues to face several technical challenges in identifying potential areas in building envelopes for retrofit and providing recommendations based on sound cost-benefit analysis of the retrofit alternatives. To address the current needs in energy diagnostics and retrofit decision-makings, infrared thermography and BIM-based energy analysis tools (e.g. EnergyPlus) are being widely used. However, current practices of manually interpreting large amounts of visual thermal data and leveraging as-designed building conditions declared in industry standard databases in the current BIM-authoring tools often lead to subjective and inaccurate assessments. For existing buildings, without considering the diminishing thermal properties of building elements caused by deteriorations and updating the associated material properties in BIM, the results from BIM-based energy analysis will not be trustworthy. This research aims to create and validate an easy-to-use tool and automated methods based on 3D thermography and BIM to support reliable cost-benefit analysis of building energy efficiency retrofits and improve the reliability of BIM-based building energy performance analysis. First, by using a consumer-level hand-held thermal camera, practitioners collect large numbers of unordered thermal images from building environments under inspection. Then, using a new computer vision based algorithm, 3D thermal models are generated wherein actual surface temperatures are modeled at the level of 3D points. 1) Building areas with potential thermal deteriorations are detected by comparing the actual measurements with the energy performance benchmark resulting from a numerical analysis. By using 3D thermal distribution and environmental assumptions that the indoor heat transfer is attributed to thermal convection and radiation under a quasi-steady-state condition, actual thermal resistances (R-value) are calculated at the level of 3D points. Then, based on the ‘degree days’ data, we estimate energy saving costs when thermal resistance of defective areas are increased to their recommended level. 2) We automatically map the actual thermal resistances at the level of 3D points to their corresponding BIM elements in gbXML schema. This is done by discretizing building elements in BIM into a mesh and using the nearest neighbor searching algorithm. We then derive a single actual R-value for each building element, and automatically update the corresponding entry for the thermal resistance in the as-designed BIM. The outcome can be used as an input of BIM-based energy analysis tools for more accurate analysis. We have conducted several experiments on two real-world residential and instructional buildings in Virginia and four hypothetical cases in Minnesota and Florida. Our findings on the difference between the actual thermal resistance measurements and the notional values declared in standard methods such as ISO 6946 were about 10%. Our experimental results for cost-benefit analysis show that the proposed method can reliably estimate ROI associated with retrofitting thermal performance problems and has potential to improve today’s practices of financial feasibility analysis on building retrofits. Also, by shortening the existing gaps in knowledge about energy performance modeling between the architectural information in the as-designed BIM and the actual building conditions, this research enables reliable BIM-based energy performance analysis. The primary scientific contributions are as follows: 1) an automated method for comparing actual and expected building energy performance in 3D and analyzing the deviations to detect potential performance problems; 2) a method for measuring actual thermal resistances of building assemblies in 3D; 3) a method for automated association of actual thermal property measurements to building elements in BIM; and 4) an automated method for updating their corresponding thermal properties in gbXML schema of BIM. Over the next 30 years, about 150 billion S.F. (roughly half of the U.S. building stock) will require retrofit to meet the new rigorous energy standards. Non-compliance with the new energy standards is not limited to existing buildings. About a quarter of the newly constructed and certified buildings do not also save as much energy as their designs had originally predicted. Construction companies can leverage the findings of this research and the developed tools to create new workflows for building commissioning –particularly for LEED certified buildings– and also new workflows for energy efficiency retrofit assessment processes.