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Establishing Risk-based Bridge Management Models and Tools for Municipal Transportation Assets
Principal Investigator:
David Yang, Portland State University
Summary:
The Portland Bureau of Transportation (PBOT) manages a bridge inventory including National Bridge Inventory (NBI) bridges, non-NBI pedestrian bridges, and culverts. Historically, condition data has been stored in disparate and/or non-digital formats, hindering efficient management and decision-making for rehabilitation efforts. PBOT is addressing this by migrating to AASHTOWare BrM, a bridge manag...
The Portland Bureau of Transportation (PBOT) manages a bridge inventory including National Bridge Inventory (NBI) bridges, non-NBI pedestrian bridges, and culverts. Historically, condition data has been stored in disparate and/or non-digital formats, hindering efficient management and decision-making for rehabilitation efforts. PBOT is addressing this by migrating to AASHTOWare BrM, a bridge management system, to digitize and standardize infrastructure data.
However, this process faces significant technical and organizational challenges. Technical issues include difficulty setting up deterioration models for budget analysis, poorly defined deterioration and intervention models for non-NBI elements, and the need to improve resource allocation protocols under limited budget. Organizational hurdles involve limited experience with bridge management systems, data and information silo between agencies and jurisdictions, and costly data retrieval from FHWA via external vendors.
This research aims to overcome these issues by exploring alternative management systems, establishing an open-source, streamlined workflow for direct data retrieval from FHWA, calibrating deterioration models and intervention policies for NBI and non-NBI elements validated by PBOT data, and optimizing maintenance planning using risk-based cost-benefit analysis and reinforcement learning algorithms.
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Project Details
| Project Type: | Research |
|---|---|
| Project Status: | In Progress |
| End Date: | September 14, 2027 |
| UTC Funding: | $55,980 |