Damage Modeling, Monitoring, and Assessment of Bridge Scour and Water Borne Debris Effects for Enhanced Structural Life

Details

• Creators:
  Sharma, Alok ; Hughes, William ; Matile, Steven ; Chattopadhyay, Indrani ; Gautam, Binit ; Anantharaman, Sreeram ; Zhang, Wei ; Malla, Ramesh B. ; Ravishanker, Nalini
• Corporate Creators:
  University of Connecticut. Department of Civil and Environmental Engineering ; University of Maine. Transportation Infrastructure Durability Center (TIDC)
• Corporate Contributors:
  United States. Department of Transportation. University Transportation Centers (UTC) Program ; United States. Department of Transportation. Office of the Assistant Secretary for Research and Technology
• Subject/TRT Terms:
  Bridge Management Systems Bridges Concrete Bridges Datasets Debris Flows Evaluation Failure Floods Risk Assessment Scour Truss Bridges

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• Resource Type:
  Tech Report
• Geographical Coverage:
  United States
• Edition:
  Final Report
• Corporate Publisher:
  University of Maine. Transportation Infrastructure Durability Center (TIDC)
• Abstract:
  During severe storm events, bridge safety is threatened by flooding hazards, including increased hydraulic pressures and water-borne debris, such as large woody debris buildups, which could exacerbate the scour of bridge foundations. To evaluate and identify possible bridge damages and vulnerabilities during flooding events, it is necessary to assess bridge resilience on a regional level. However, existing bridge vulnerability models are typically focused on a single bridge type and cannot effectively scale to larger areas that could include various bridge types. To balance high-fidelity models with the need for simplified, scalable models, bridges need to be classified based on key characteristics. This project aims to access the flood fragility of various types of bridges in a region, and it was completed in two phases. In the first phase, a clustering-based approach is used to determine the optimal classification of bridges for vulnerability analysis to flood-related hazards. The K-prototype algorithm is utilized to consider both categorical data and numerical data. Our dataset contains all the multi-span bridges overactive waterways in Vermont. The results unveil the bridges can be optimally classified into six major clusters: short, medium, and long-span steel, concrete tee-beams and culverts, and covered wooden truss bridges. In the second phase, the most representative bridges are selected for further analysis of their vulnerability to flood. Modelling of bridges in the Python library- OpenSeesPy was done for fragility analysis. The final outputs as probability of failure of each bridge types are presented in few easily applicable tables that is believed to serve bridge asset managers, local and state governments, and other related decision-makers to take immediate actions and strategies on the operations and maintenance of bridges during severe flood in order to mitigate the possibility of economic and human life losses.
• Format:
  PDF
• Funding:
  69A3551847101
• Collection(s):
  University Transportation Centers
• Main Document Checksum:
  urn:sha-512:4da8faac0697204759718186114318d2e6a3dc947c7e7f889582e24b331ae778f101b3617e0e0cc7691f76c83a7b6bdc97f1541eb78ad526f8a01913c6576b0e
• Download URL:
  https://rosap.ntl.bts.gov/view/dot/88985/dot_88985_DS1.pdf
• File Type:
  [PDF - 6.45 MB ]