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Transport Performance Enhancement through Risk-Informed Bridge Scour Management

Lookup NU author(s): Dr Manuel HerreraORCiD

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This work is licensed under a Creative Commons Attribution 4.0 International License (CC BY 4.0).


Abstract

Bridges are vital connections within transport networks, but scour-induced failures can severely disrupt network connectivity, increase user travel delays, and reduce reliability. The goal of this paper is to prioritize bridge scour management actions to improve transport network performance, defined here by connectivity and delay. This paper introduces a novel risk-informed decision-support framework that aids long-term programming and real-time operational decision processes. This framework couples bridge-level monitoring with network-level prioritization based on predicted transport-user impacts and early-warning triggers. It quantifies expected travel delays and network connectivity under different flood scenarios, guiding maintenance and protection investments toward bridges with the largest performance consequences. The framework is applied to a case study on UK railway bridges where warning times to failure are estimated and proactive bridge closures are simulated to assess operational impacts. The results inform the risk-aware prioritization of bridges for operational measures. This risk-informed approach extends traditional scour management by explicitly tying asset interventions to user-oriented performance outcomes and by supporting long-term programming and real-time operational decisions under uncertainty.


Publication metadata

Author(s): Sasidharan M, Herrera M, Yilmaz G, Parlikad AK, Schooling J

Publication type: Article

Publication status: Published

Journal: Transportation Research Record

Year: 2026

Pages: epub ahead of print

Online publication date: 27/01/2026

Acceptance date: 01/11/2025

Date deposited: 28/01/2026

ISSN (print): 0361-1981

ISSN (electronic): 2169-4052

Publisher: Sage Publications, Inc.

URL: https://doi.org/10.1177/03611981251404348

DOI: 10.1177/03611981251404348

Data Access Statement: The data used for this paper has been provided by Network Rail.


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Funding

Funder referenceFunder name
EP/Y024257/1
EPSRC
EP/N021614/1
EP/T022566/1
Innovate UK (Grant No. 920035)
Next Stage Digital Economy Research Centre

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