Genetic Algorithm-Markovian Model for Predictive Bridge Asset Management
Publication: Journal of Bridge Engineering
Volume 26, Issue 8
Abstract
Rapid or unexpected bridge deterioration can lead to partial collapse, which can subsequently hinder transportation activities and result in economic and human losses. Heavily adopted by the research community, Markov chain-based deterioration models assume that bridge conditions exhibit stationary transitions over time. This assumption requires a significantly large, and often difficult to obtain, number of historical records. As such, Markov chain-based deterioration models have been developed within classical nonlinear optimization frameworks that might result in local optimal solutions. Therefore, to enhance the model capability to simulate the temporal state transition, this study develops a Markovian-based deterioration model embedded within a genetic algorithm (GA) framework—a class of evolutionary computing techniques, to overcome local optimality issues. To demonstrate its applicability, the developed model was applied to a relevant data set of previously rehabilitated and unrehabilitated concrete and steel bridges. The developed GA-Markovian model was able to replicate the actual state probabilities for the unrehabilitated bridges within both the calibration and validation periods. The model performance was slightly lower for the previously rehabilitated bridges due to the inherited nonstationary transition. The model developed in the present study can be used to guide effective rehabilitation and replacement strategies, prioritize available resources, and devise data-driven predictive bridge asset management policies and standards.
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Acknowledgments
The authors would like to acknowledge the support from the INTERFACE Institute for Multi-hazard Systemic Risk Studies in McMaster University.
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Published In
Journal of Bridge Engineering
Volume 26 • Issue 8 • August 2021
Copyright
© 2021 American Society of Civil Engineers.
History
Received: Nov 17, 2020
Accepted: Apr 6, 2021
Published online: Jun 3, 2021
Published in print: Aug 1, 2021
Discussion open until: Nov 3, 2021
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