Proposed Damage Estimation Module Expert Panel Open Meeting Austin, Texas May 29, 2014

1. Proposed Damage Estimation Module Expert Panel Open Meeting Austin, Texas May 29, 2014

2. Agenda • Introductions • Overview • Proposed Damage Estimation Module • Wind • Surge and Wave • Claims Data Review • Future Work • Q&A

3. Introductions • Sam Amoroso, Ph.D. P.E., S.E. Forte & Tablada, Inc. • Bob Bailey, Ph.D., P.E. Exponent, Inc. • Bill Coulbourne, P.E. Coulbourne Consulting • Andrew Kennedy, Ph.D. University of Notre Dame • Doug Smith, Ph.D., P.E. Texas Tech University

4. Overview • 1st Open Meeting • Austin, August 22, 2013 • Develop Framework Plan • 2nd Open Meeting • Corpus Christi, December 10, 2013 • 3rd Open Meeting • Austin, March 13, 2014 • 4th Open Meeting • Austin, May 29, 2014

5. 1st Open Meeting • Panel Member Backgrounds • The TWIA expert panel has been appointed under Insurance Code §2210.578 and 28 Texas Administrative Code §§5.4260-5.4268. The panel’s purpose is to develop ways of determining whether a loss to TWIA-insured property was caused by wind, waves, tidal surges, or rising waters not caused by waves or surges. • After the panel completes its work, the commissioner will consider the panel’s findings and publish guidelines that TWIA must use to settle claims.

6. 2nd Open Meeting • Present Preliminary Overall Methodology • Initial Focus: Residential Slab Only Claims

7. 3rd Open Meeting • Present Proposed Hazard Module Methodology • Goal: To provide a time history of wind, surge, and wave heights for a given property location.

8. Preliminary Overall Methodology

9. Proposed Damage Estimation Module • Wind • Dr. Sam Amoroso • Basis for Development of Damage Functions • Component Demand • Component Capacity • Examples • Surge and Wave • Dr. Andrew Kennedy • Process • Definition • Examples

10. Damage Estimation Module • Based on Probabilities of Component Failure (Wind) or Probability of Structural Collapse (Surge and Wave) • Coupling of Component Damages • Time Histories of Damage estimated from Hazard Time Histories

11. Wind

12. Wind Damage Estimation • Probability-Based: What is the likelihood that wind pressure exceeds resistance capacity?

13. Wind Damage Estimation • Develop Probability Distributions for Component Demands and Capacities • Probability Distribution defines likelihood of possible values of a variable • Component Demand (Wind Pressure) depends on: • Wind Speed • Wind Direction • Surrounding Terrain • Building Height and Shape • Location of Component on Building • Size of Component • Whether Building Remains Enclosed

14. Wind Damage Estimation

15. Wind Damage Estimation • Component Capacities are also uncertain due to: • Age • Material • Fasteners • Configuration Source: Florida Public Hurricane Loss Model

16. Wind Damage Estimation • Component Capacities are variable

17. Wind Damage Estimation • Preliminary Demand (Wind Load) PDF Parameters from “Wind Load Statistics for Probability Based Structural Design,” Ellingwood and Tekie, 1999, ASCE Journal of Structural Engineering • Preliminary Capacity PDF parameters from Florida Public Hurricane Loss Model engineering documentation • Additional sources and claim data will be used to refine PDF’s

18. Wind Damage Estimation • Required: Time history of likely wind damage • Method: Monte Carlo Simulation • Randomly sample values of demand and capacity from respective PDF’s • Simulate demand for every time step, using associated wind speed and direction from hazard module • Large number of simulations for the storm • Damage statistics at each time step can be extracted from simulations • Mean, median, quartiles, etc.

19. Wind Damage Estimation • Component damage is coupled: • Damage to roof panels triggers damage to roof covering • Loss of cladding element triggers higher internal pressure • Several Components Considered, Including • Roof Covering • Windows • Doors • Garage Doors • Relative Proportion of Component Damage Reported • Roof Panels/Decking • Roof Trusses/Rafters • Wall Studs/Wall Sheathing • Shear Walls

20. Wind Damage Estimation – Sample Structure • One-story residential structure • Gable roof • Length = 56 feet, Width = 35 feet • Eave height = 10 feet • Roof Slope = 6:12 • Roof ridge is oriented in N-S direction • Open Terrain (ASCE 7 Exposure Category C) • Overhead Garage Door • Attached Garage

21. Wind Damage Estimation – Sample Storm

22. Wind Damage Estimation – Sample Results

23. Wind Damage Estimation – Sample Results

24. Surge and Wave

25. Wave And Surge Failures for Slab Claims • In those cases when a house is completely destroyed, it is important to know at what point wind, waves, or surge caused failure. • The expert panel will develop methods to estimate timing of any wind, wave, or surge slab failure. • The relative timings of wind, wave, and surge damage will be compared. • Process is under development for wave and surge damage – will likely employ Wave Height, Freeboard, and House Age.

26. Definitions for Wave/Surge Processes House Age(likely in ranges of years) Wave Crest Significant Wave Height Freeboard (negative here) Surge Elevation Water depth Ground Elevation Zero NAVD88 Datum

27. Surviving, higher elevation homes Destroyed, lowerelevation homes

28. Example of Surge/Wave Failure Prediction Different Freeboards Age Pre-1974 1974-1987 Significant Wave Height (m) Significant Wave Height (m) 1987-1995 1995-2008 Significant Wave Height (m) Significant Wave Height (m) • Failure increases strongly with increasing wave heights • Older houses significantly more fragile • Higher house elevations (higher Freeboard) survive better

29. Preliminary Overall Methodology

30. Claims Data Review • Methodology Used for Review • Determine fields of data of interest. • Select small sample and test difficulty in finding that data of interest. • Each panel member search sample files for data of interest. • Instruct panel member firms who will help with data search on what data is of interest and how to complete data fields. • Conduct data search of 500 claims files. • Current Status • Fields of data have been determined based on data needed for vulnerability model. • Sample files have been selected. • Panel members have searched sample files.

31. Fields of Data Fdn Type Roof sheathing type % glass area Window protection (Y, N) Wall sheathing type Exterior wall finish Fence (Y, N) Out building (Y, N) Canopy (Y, N) Tree w/in striking distance (Y, N) Total Ike loss ($) Total Ike Loss (% Value) % Roof Cover Damage % Roof sheathing damage % Roof framing damage % Window damage % Door damage % Garage door damage % shear wall damage % out-of-plane wall damage % wall sheathing damage % fence damage % out building damage % canopy damage tree fall damage Flooded Depth of Water (ft) Flood damage TWIA file no. Address City State Zip Code Latitude Longitude Policy Value Bldg Value Bldg SF Approx Length Approx Width Perimeter (ft) Plan Shape (R, T, U, L) Roof Cover Type Roof Shape (H, G) Age of Roof Cover Orientation of long axis w.r.t North Exposure Category O'hd garage door (Y, N) Garage Attached? (Y/N) Direction Garage Door Faces Eave height Roof slope Roof ridge height 1st floor elevation Ground elevation No. floors Year Built • Fdn Type • Roof sheathing type • % glass area • Window protection (Y, N) • Wall sheathing type • Exterior wall finish • Fence (Y, N) • Out building (Y, N) • Canopy (Y, N) • Tree w/in striking distance (Y, N) • Total Ike loss ($) • Total Ike Loss (% Value) • % Roof Cover Damage • % Roof sheathing damage • % Roof framing damage • % Window damage • % Door damage • % Garage door damage • % shear wall damage • % out-of-plane wall damage • % wall sheathing damage • % fence damage • % out building damage • % canopy damage • tree fall damage • Flooded • Depth of Water (ft) • Flood damage 57 Fields of Data

32. Data Collection

33. Claims Data Locations Mapped Location Colors show Roof Damage % Blue: 0 – 9% Yellow: 10 – 19% Red: 20% +

34. Data Issues • Must estimate component damage by “eye” or very rough measurements. • Some components are not visible and thus are unknown (wall sheathing, roof sheathing). • Some damage is collateral – tree fall damages building. • Some claim files cover multiple physical locations. • Need multiple resources to collect all data – not just claim files.

35. Calibration Methodology • From Hazard Data for wind and flood – determine time histories for highest winds and storm surge. • At a location, determine the highest wind speeds and storm surge levels. • Determine damage levels for components based on those highest wind speeds and storm surge levels. • Compare predicted damage levels obtained from models with claim file damage. • Adjust damage module where deemed appropriate.

36. Future Work • Calibration and Validation of Model • Compare Ike damage with model predictions for same location. • Adjust model as necessary. • Conduct randomized model validation using claim data from Hurricane Ike. • Present findings in a future Open Meeting. • Finalize recommendations and present to TDI. • Continue with development of a method or model to estimate damage to commercial properties starting with slab-only cases.

37. Q&A