Upscaling Complex Project-Level Infrastructure Intervention Planning to Network Assets
Publication: Journal of Construction Engineering and Management
Volume 148, Issue 1
Abstract
Probabilistic and nonlinear models have been used to accurately model various phenomena in asset management systems (AMS). With a commonly adopted framework using Monte Carlo simulation and heuristic algorithms, AMS proposed in the literature aim to maintain the functionality of assets in their life-cycle by optimally allocating limited resources to different intervention actions. However, due to their high computational costs, upscaling complex project-level AMS to a multitude of assets currently is far from practical. To address this gap between the literature and the practice of project-level AMS, this paper presents a new machine learning–based methodology to estimate (near-)optimal intervention timings which usually are derived by optimization algorithms. To illustrate, an ensemble of random forests models was trained on optimal maintenance timings of more than 1.6 million semisynthesized bridges. The trained model yielded optimized maintenance, rehabilitation, and reconstruction (MRR) plans with greater than 95% accuracy on the test set and greater than 89% accuracy on more than 4,600 highway bridges in Indiana, and did so 6 orders of magnitude faster than the conventional framework of complex MRR optimization. Practitioners can adopt the proposed methodology to enhance their decision-making systems, obtain optimal maintenance plans without sacrificing complex and accurate models, and take another step toward sustainability objectives.
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Data Availability Statement
Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.
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Published In
Journal of Construction Engineering and Management
Volume 148 • Issue 1 • January 2022
Copyright
© 2021 American Society of Civil Engineers.
History
Received: May 23, 2021
Accepted: Sep 30, 2021
Published online: Nov 10, 2021
Published in print: Jan 1, 2022
Discussion open until: Apr 10, 2022
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