Risk-Based Multi-Threat Decision-Support Methodology for Long-Term Bridge Asset Management — Volume 2: Network-level Decision Support

Ghavidel, Alireza; Mashrur, Naqib; Kameshwar, Sabarethinam; Du, Ao; University of Texas at San Antonio. Department of Civil and Environmental Engineering; Louisiana State University

doi:s://hdl.handle.net/20.500.12588/7358

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Risk-Based Multi-Threat Decision-Support Methodology for Long-Term Bridge Asset Management — Volume 2: Network-level Decision Support

2025-05-03
By Ghavidel, Alireza ; Mashrur, Naqib ; Kameshwar, Sabarethinam ; ...

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Creators:

Ghavidel, Alireza ; Mashrur, Naqib ; Kameshwar, Sabarethinam ; Du, Ao

Ghavidel, Alireza ; Mashrur, Naqib ; Kameshwar, Sabarethinam ; Du, Ao Less -
Corporate Creators:

University of Texas at San Antonio. Department of Civil and Environmental Engineering ; Louisiana State University

University of Texas at San Antonio. Department of Civil and Environmental Engineering ; Louisiana State University Less -
Corporate Contributors:

United States. Federal Highway Administration. Office of Bridges and Structures
Subject/TRT Terms:

[+]

Artificial Intelligence Asset Management Benefit Cost Analysis Bridge Management Systems Budgeting Decision Support Systems Financial Analysis Project Management
DOI:

https://hdl.handle.net/20.500.12588/7358
Resource Type:

Tech Report
Geographical Coverage:

United States
Edition:

Volume 2 Report (for Task 4 and 5) January 2024- June 2025
Corporate Publisher:

University of Texas at San Antonio. Dept. of Civil and Environmental Engineering
Abstract:

In the Volume 2 report, the bridge-level AI-based maintenance decision policy previously developed in the Volume 1 report is further integrated into a network-level decision support framework, by considering network-level budget and resource constraints. A Pareto Frontier based ranking approach is proposed to rank the maintenance projects suggested by the bridge-level maintenance policies by holistically considering multiple decision factors. The top ranked projects are then allocated with the funding and resources for actual implementation. A thorough comparative study is carried out by comparing the efficacy of the AI or other condition-based policies at the bridge level, under the proposed network-level decision framework. First, a sensitivity study is performed to investigate the influence of bridge related attributes in the Pareto Frontier ranking scheme. Next, the influence of using different bridge-level policies within the proposed network-level decision framework is examined by considering multiple network-level asset management performance measures such as the overall funding usage, indirect costs, bridge conditions, and travel time, under different funding scenarios. It is observed that the AI-based policy outperforms other traditional condition-based policies in almost all considered cases. Also from some exemplary annual network-level decision illustration, it is found that the AI-based bridge-level decision policy when deployed into the network-level decision framework can offer reasonable budget and resource allocation. Finally, the open-sourced computer codes are shared and related hands-on tutorials are provided for better result dissemination. In conclusion, the research tools developed from this entire research project can not only offer proactive and adaptive bridge maintenance decisions at the individual bridge level, but can also optimize the budget and resource allocation at the network level by better utilizing the limited resources, preserving the overall asset conditions, and reducing the socioeconomic impact due to deteriorating bridge assets.
Format:

PDF
Funding:

693JJ321C000027
Collection(s):

US Transportation Collection
Main Document Checksum:

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urn:sha-512:6dedce2ec743be928cce3db350a0d088b743c7866350e6953d5fd6fa74ec09d3a339452898700e8a05cc554d09d0430c59f60d3272244d298cce2b8c0954a150
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https://rosap.ntl.bts.gov/view/dot/84771/dot_84771_DS1.pdf
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Risk-Based Multi-Threat Decision-Support Methodology for Long-Term Bridge Asset Management — Volume 2: Network-level Decision Support

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