A Data-driven Computational Framework For Rapid Post-event Damage Assessment Of Bridge Infrastructure Assets
Increasing awareness of severe community disruption and long-term economic impacts following major disruptive events have led to a shift in the infrastructure performance objectives, from ensuring life safety to achieving continuous safe occupancy and continuity of critical functions. From this perspective, the ability to rapidly assess the spatial distribution and severity of bridge network damage in a post-disaster environment has become a key requirement, and yet remains a challenge. Understanding that 150 million people in the United States are exposed to the risk of a damaging earthquake within 50 years has prompted the Federal Emergency Management Agency (FEMA) and the National Institute of Standards and Technology (NIST) to advance the technical and social discussion to support communities’ resilience goals by improving recovery time for different performance levels, as documented in the 2021 NIST-FEMA special publication (Sattar, 2021). This research will develop a novel and integrative computational framework that will enhance the capability of the bridge engineering community to effectively respond in a post-earthquake emergency. The core idea is to use machine learning (ML) techniques for predicting the performance of highway bridges subject to multi-directional near and far-field earthquake ground motions.
Language
- English
Project
- Status: Active
- Funding: $157125
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Sponsor Organizations:
Accelerated Bridge Construction University Transportation Center (ABC-UTC)
Florida International University
10555 W. Flagler Street
Miami, FL United States 33174Office of the Assistant Secretary for Research and Technology
University Transportation Centers Program
Department of Transportation
Washington, DC United States 20590 -
Performing Organizations:
University of Nevada, Reno
College of Engineering
Reno, NV United States 89557 - Start Date: 20240102
- Expected Completion Date: 20241231
- Actual Completion Date: 0
- USDOT Program: University Transportation Centers
Subject/Index Terms
- TRT Terms: Asset management; Bridges; Data analysis; Disaster resilience; Disasters and emergency operations; Earthquakes; Machine learning
- Subject Areas: Bridges and other structures; Data and Information Technology; Highways; Maintenance and Preservation; Planning and Forecasting; Security and Emergencies;
Filing Info
- Accession Number: 01924839
- Record Type: Research project
- Source Agency: Accelerated Bridge Construction University Transportation Center (ABC-UTC)
- Files: UTC, RIP
- Created Date: Jul 21 2024 3:06PM