Document Type

Presentation

Publication Date

7-1-2026

Subjects

Bridges, Risk assessment -- Methodology, Bridge retrofit prioritization

Abstract

Bridge retrofit prioritization is commonly guided by structural vulnerability, traffic conditions, or indirect consequences following a single bridge failure. However, these metrics often fail to identify retrofit projects that maximally reduce system-level functionality risk, because they do not account for the consequences of joint failures among multiple bridges. This presentation develops a mathematically rigorous, gradient-based approach to bridge retrofit prioritization for achieving a near-optimal reduction in functionality risk. This risk is formulated as the expected increase in total travel time given probabilistic bridge survival or failure under external stressors such as earthquakes. By reinterpreting risk as the evidence term in an auxiliary Bayesian updating problem, the marginal posterior failure probabilities are leveraged to construct an asset importance factor. This factor is shown to be proportional to the gradient of system-level functionality risk relative to the failure probability of an asset. Using this connection between the proposed importance factor and the risk gradient, risk minimization under budgetary constraints is approximated as a classical knapsack problem. Accordingly, a near-optimal set of retrofit projects can be selected using simple heuristics based on benefit-to-cost ratios, with benefit defined using the proposed importance factor. The new approach, as well as the associated novel importance factor, is validated on the Sioux Falls bridge network against ground-truth retrofit selections for risk minimization and those prioritized using several existing decision-making analytics. Results demonstrate that project prioritization based on the proposed importance factor consistently outperforms existing analytics and closely matches the exact optimal solutions across varying budget levels and retrofit effectiveness. The presentation was given during the ASCE Engineering Mechanics Institute Conference (EMI 2026), June 2-5, 2026, at the University of Colorado Boulder, Boulder, Colorado.

DOI

10.15760/cee-presentations.01

Persistent Identifier

https://archives.pdx.edu/ds/psu/44946

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