This document summarizes a regional risk assessment of coastal bridges in the Galveston Bay area during hurricane events. It presents new probabilistic models for estimating bridge deck uplift and pier/abutment scour failure probabilities. A case study of over 100 bridges found failure probabilities increased for stronger hurricane scenarios. Implications include using results to prioritize bridge retrofits and post-hurricane recovery routes. Future work includes completing scour models and fully automating the risk assessment.

1. REGIONAL RISK ASSESSMENT OF
COASTAL BRIDGES DURING HURRICANE
EVENTS
Candase Arnold- Graduate Research Assistant
Dr. Jamie Padgett- Assistant Professor
ICWES15-July 21, 2011

2. OVERVIEW AND OBJECTIVES
 Motivation for Research
 Empirical evidence from past hurricanes
 Typical failure mechanisms
 Methodologies for Estimating Failure Probability
 Bride Deck Uplift
 Pier and Abutment Scour
 Galveston Bay Area Case Study
 Results from Hurricane Simulations
 Implications for Sustainability
 Conclusions and Future Work

3. MOTIVATION FOR RESEARCH
Bridges are among the
most critical and vulnerable
components of the
transportation system
during an extreme event
 Emergency Response
 “Lifeline” routes for goods and
supplies
 Long term sustainability of the
bridge network

4. TYPICAL FAILURE MECHANISMS

6. VULNERABILITY METHODOLOGIES
 Inundation of Bridge  Static Bridge Deck
Deck Uplift
 Conveys short-term  Conveys long-term
damage or structural functionality
impassability  Compares capacity of
 Compares elevation of bridge deck with
bridge with surge height demand of hurricane
 Previous method of forces
determining bridge  New method of
vulnerability assessing bridge
vulnerability

7. DECK UPLIFT ILLUSTRATION

8. BRIDGE DECK UPLIFT- VULNERABILITY
MODELING
Adapted from Ataei and Padgett, 2010¹
Static Reliability
Assessment for
Span Unseating
Probabilistic Demand Probabilistic Capacity
Estimate Estimate
Wave and surge parameter estimation
Weight Anchorage
and associated uncertainties
Joint pdf of wave period Uncertainties in materials
and wave height densities and
superstructure geometry
Uniform distribution for
surge elevation Uncertainties in
materials strengths
Maximum Demand pdf
Capacity pdf
P[Demand > Capacity | Hazard Intensity]
=
Probability of Failure (Pf)
ATAEI, N. & PADGETT, J. E. 2010. Probabilistic Modeling of Bridge Deck Unseating during
Hurricane Events. ASCE Journal of Bridge Engineering. In Review. November 2010

9. SCOUR VULNERABILITY MODELING
Pier Hydraulic Soil
 New probabilistic Parameters Parameters Parameters
approach
 Uses existing Account for uncertainties
deterministic HEC-18 in input data
clay method
 Applicable to pier and Pier scour depth
using SRICOS
abutment scour method
Account for uncertainty
in predictive model
Obtain PDF of Scour Depth

10. REGIONAL CASE STUDY- HOUSTON/
GALVESTON BAY AREA
Galveston

11. REGIONAL CASE STUDY- GALVESTON BAY
AREA
 Number of Bridges: Bay Area Bridges by Soil
Type
 155 total (excluding
culverts) 5%
 136 used in Uplift Modeling 9% 3% Sand
 123 used in Pier Scour Sandy Clay
 107 used in Abutment Scour Silty-Sand
25%
 Sources of Data 58% Clay-Silt
 National Bridge Inventory Clay
Database
 TxDOT inspection files
 SoilMart

12. REGIONAL CASE STUDY- GALVESTON BAY
AREA Bay Area Bridges by
Height Above Water
 Parameters Collected: 4%
 Bridge Type 18% 0-5 ft
28% 5-15 ft
 Year Built
15-30 ft
 Connection Details 50% 30-65 ft
 Number of Spans
 Bridge Dimensions Bay Area Bridges by
 Height above Water Structure Type
 Water Depth 3% MSC Steel
 Soil Type
29% MSSS
 Surge/ Wave Height Concrete
1% MSSS Steel
67%
MSSS- Multi-Span Simply Supported
MSC- Multi-Span Continuous SS Concrete
SS- Single Span

13. RESULTS FROM CASE STUDY
 Inundation and Bridge
Deck Uplift Only
 3 Hurricane Scenarios Simulation Failure Probability (%)
 Hurricane Ike 0-5 5-25 25-75 75-100
 Hurricane Ike with 30% Ike 127 5 1 3
stronger wind speeds
 “Mighty Ike”- Hurricane Ike 30% 106 4 7 19
Ike with 30% stronger Stronger
wind speeds and a “Mighty Ike” 69 7 8 52
southern landing
position- worst case
Failure Probability of Bridge Deck Uplift for
scenario hurricane scenarios

14. Hurricane Ike Scenario
Storm surge data courtesy of Dawson and Proft, UT Austin

15. 30% Stronger Ike Scenario
Storm surge data courtesy of Dawson and Proft, UT Austin

16. “Mighty Ike” Scenario
Storm surge data courtesy of Dawson and Proft, UT Austin

17. “Mighty Ike” Inundation
Storm surge data courtesy of Dawson and Proft, UT Austin

18. “Mighty Ike” Comparison
Storm surge data courtesy of Dawson and Proft, UT Austin

19. IMPLICATIONS FOR SUSTAINABILITY
 Predictive Failure Probabilities
 Can be utilized to predict damage as a hurricane moves
through the Gulf of Mexico
 Mitigation and Retrofit Efforts
 Testing various retrofit measures like increased
connection between sub and super-structure
 Prioritize bridges for retrofit or rebuilding
 Post Event Re-Entry and Recovery Efforts
 Assess “life-line” routes onto Galveston Island
 Prioritize supply and emergency services locations
based on spatial distribution of damage

21. CLOSING REMARKS
 Future Work:  Conclusions:
 Complete pier and  Coastal bridges are vulnerable to
abutment scour models both deck displacement and
 Assess soil erosion scour during hurricanes
potential at roadways  New probabilistic models in deck
 Full automation of all risk displacement and scour
assessment models determination are developed and
together for predictive applied to a regional risk
modeling assessment
 Case study shows that a future
worst case scenario storm could
devastate the bridge network.
 Results can be used to prioritize
bridge retrofits, emergency
services locations and post-event
re-entry routes

22. Acknowledgments:
NSF: Graduate Research Fellowship Program
Houston Endowment
Navid Ataei: Graduate Research Assistant