Wind Load Factor Analysis: An Exploratory Approach Snohomish Wind Group Alexi Curelop, Kofi Petty, Sonya Prasertong Geography 469 Spring 2010.

Wind Load Factor Analysis: An Exploratory Approach

Snohomish Wind GroupAlexi Curelop, Kofi Petty, Sonya Prasertong

Geography 469Spring 2010

Goal and Purpose

• How can we calculate and visualize the wind velocity pressure constant (Kzt factor) using the topography of Snohomish County?

• Scott Beard, Plans Examiner for City of Tacoma, manually produced Kzt factors for King County and City of Tacoma

• Will Rugg, GIS Analyst for Snohomish County Planning and Development Services, interested in how to automate the process via ArcGIS

• Our group decided to take on this topic in order to challenge our knowledge of how to apply GIS to real world problems

Kzt Factor Significance

• Kzt factor when calculated is equivalent to specified wind speeds and is on a scale of 1-2 (eg. Kzt of 2 = 120mph)

• It is part of a larger calculation used by planners and architects for determining appropriate areas and materials when developing land

• Our project, if successful, could aid Snohomish County in reducing the costs of building and planning for their architects and planners

The Kzt Factor

• Kzt = (1 + K1 + K2 + K3)2

• K 1 = determined from table below

• K2 = (1 – (|x|/µLh))

• K3= e-γz/Lh

Parameters for Speed-Up over Hills and Escarpments

Hill Shape

Exposure

γµ

B C D Upwind of Crest Downwind of Crest

2-dimensional ridges

1.30

1.45 1.55 3 1.5 1.5

2-dimensional escarpments

0.75

0.85 0.95 2.5 1.5 4

3-dimensional axisym, hill

0.95

1.05 1.15 4 1.5 1.5

Source: American Society of Civil Engineers: Minimum Design Loads for Buildings and Other Structures, 2006

Need-to-Know Questions

1. What are the mid-points of the data?

2. Based off of the mid-points, what is the velocity pressure constant (Kzt factor) of the terrain?

3. How do we model the output information product?

1. What are the mid-points of the data?

• Using primarily Spatial Analyst, we were able to calculate the slope of our pilot area to identify base and peak elevations of landforms

• We could then create a map of depicting polygons representing these areas, with the centroids for each polygon identified

• Tools used: Slope Calculation, Raster Calculator, Convert Raster to Features, Neighborhood Statistics, Zonal Statistics

2. Based off of the mid-points, what is the velocity pressure constant (Kzt factor) of the

terrain?

• From here we used the Identify tool to find the exact elevations for the the base and peak polygons of our landforms

• We created lines and points to keep track of our calculations, which were recorded in an Excel spreadsheet

• Due to the specifications of the Kzt formula, only 4 landforms were able to be selected for calculation

• Tools used: Identify tool, Measurement tool, Editor toolbar, Microsoft Excel

3. How do we model the output information product?

• We decided the best method of visualization would be colored polygons around our selected landforms, with each color representing a range of Kzt

• We decided to use the Waterbodies and Major Roads layers as a means of orienting the audience to our pilot area location

• Tools used: Editor toolbar, Add Field tool, Transparency setting

Analysis/Process

Findings

• Due to the specifications of what landforms can be calculated for Kzt, there were only 4 landforms that could be selected

• Of these 4 landforms, each seemed to fall on the two extremes of the Kzt factor scale (either 1 or very close to 2).

• Kzt Factor Results:Landform A = 2.43Landform B = 1.99Landform C = 1.00Landform D = 1.93

Conclusions

• The area we selected contained landforms on both extremes of the Kzt factor scale of 1-2

• We were able to automate the process of calculating peak and base elevations

• More time is needed in order to create a more complex pilot study

• Python scripting could assist in automating our manual Kzt calculations

• Due to our lack of comprehensive knowledge of the particulars of civil engineering formulas, errors in calculations were a possibility

Recommendations

• Creating a Python script that can automatically run the manual calculations we performed

• Work with individuals that have an in-depth knowledge of both the capabilities of GIS as well as civil engineering background to limit possible errors in calculations

• Allow for a study with a longer timeline