Machine Learning–Aided Rapid Estimation of Multilevel Capacity of Flexure-Identified Circular Concrete Bridge Columns with Corroded Reinforcement
Publication: Journal of Structural Engineering
Volume 150, Issue 3
Abstract
Corrosion has been recognized as one of the predominant causes of degradation in aging RC bridge columns. This problem may significantly affect flexural and shear capacities and even alter failure modes of corroded columns. Traditional physics-based simulations used to determine the deteriorating capacity are time-consuming and may experience convergence problems, which hinders rapid and efficient fragility analyses of region-scale bridge infrastructure for the facilitation of regional risk assessment. To address the issue, this study leveraged machine learning techniques to identify column failure modes (flexure or shear-associated), followed by estimating the capacities of corroded RC columns with flexure failure, because flexural failure was found to be dominant among the considered large parameter spaces of corroded RC columns. To create the database, a finite-element model of RC columns considering bond-slip effects and deteriorating material strengths due to reinforcement corrosion was developed and validated experimentally to capture the flexural and shear behavior, and pushover analyses were preformed to examine the failure mode and quantify multilevel capacities of corroded columns. The performance of two machine learning algorithms (i.e., logistic regression and support vector machine) was evaluated for failure mode identification, and four additional algorithms (i.e., multiple linear regression, modified multiple linear regression, artificial neural network, and gradient boosting decision tree) were examined for the multilevel drift ratio capacity prediction of the flexure-identified corroded RC columns. The results show that the support vector machine and artificial neural network outperformed the other algorithms in failure mode identification and the drift ratio capacity prediction, respectively. In addition, the aspect ratio of columns was identified as the most significant feature for the drift ratio capacity of corroded RC columns, followed by reinforcement corrosion levels.
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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. For the sake of reproduction and education, the created database can be accessed at GitHub via https://bit.ly/3L5bcIS.
Acknowledgments
The research described in this paper was supported in part by the National Science Foundation (NSF) under Grant no. NSF-1638320 and by the US Department of Transportation (USDOT) National Center for Transportation Infrastructure Durability & Life-Extension. The second author was partially supported by the National Natural Science Foundation of China (Grant nos. 52378183 and 15008155). However, the writers take sole responsibility for the views expressed in this paper, which may not represent the position of the funding agencies or their respective institutions. The authors extend their gratitude to Dr. Ji-Gang Xu for valuable discussions and insightful advice.
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Information
Published In
Journal of Structural Engineering
Volume 150 • Issue 3 • March 2024
Copyright
© 2024 American Society of Civil Engineers.
History
Received: Apr 24, 2023
Accepted: Oct 23, 2023
Published online: Jan 4, 2024
Published in print: Mar 1, 2024
Discussion open until: Jun 4, 2024
ASCE Technical Topics:
- Analysis (by type)
- Artificial intelligence and machine learning
- Bridge engineering
- Bridges
- Bridges (by material)
- Columns
- Computer programming
- Computing in civil engineering
- Concrete
- Concrete bridges
- Concrete columns
- Corrosion
- Deterioration
- Engineering fundamentals
- Engineering materials (by type)
- Failure analysis
- Failure modes
- Forensic engineering
- Materials characterization
- Materials engineering
- Reinforced concrete
- Structural engineering
- Structural failures
- Structural members
- Structural systems
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