Machine Learning–Based Reliability Analysis of Structural Concrete Cracking Considering Realistic Nonuniform Corrosion Development
Publication: Journal of Structural Engineering
Volume 150, Issue 1
Abstract
Corrosion-induced concrete cracking significantly weakens the integrity, serviceability and durability of reinforced concrete (RC) structures. Existing reliability analysis of corrosion-induced concrete cracking often considers the corrosion of reinforcement as a uniform process, in favor of implementing the analytical formulation of corrosion rust progression. However, corrosion distribution in RC structures is seldom uniform around the steel reinforcement, hence the corrosion-induced pressure. Thus, considering the nonuniform corrosion process in the reliability analysis becomes important. This paper develops a time-dependent reliability methodology, combining mesoscale heterogeneous fracture modeling and a state-of-the-art machine learning algorithm, to assess the serviceability of the RC structures subjected to nonuniform development of corrosion. The effects of critical crack width, corrosion nonuniformity, chloride content, temperature, and relative humidity on the failure probability are investigated. The worked example demonstrates the importance of considering the nonuniformity of the corrosion product distribution, which provides reliable evaluation of the remaining safe life of RC structures compared with the use of a uniform corrosion model. The developed unified assessing methodology for corrosion of RC structures can serve as a useful tool for engineers, designers, and asset managers for their decision making with regard to repair and maintenance of corrosion-affected RC structures.
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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.
Acknowledgments
Financial support from the UK Engineering and Physical Sciences Research Council (EP/S005560/1 and EP/W027763/1) is thankfully acknowledged. Contributions and useful discussions from Dr. Shen Li and Professor Feargal Brennan from the Department of Navel Architecture, Ocean and Marine Engineering, University of Strathclyde, through the Strathwide funding scheme, are also thankfully acknowledged. This work is also supported by the Fundamental Research Funds for the Central Universities in China (No. 00007738).
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Published In
Journal of Structural Engineering
Volume 150 • Issue 1 • January 2024
Copyright
© 2023 American Society of Civil Engineers.
History
Received: Nov 15, 2022
Accepted: Sep 6, 2023
Published online: Nov 7, 2023
Published in print: Jan 1, 2024
Discussion open until: Apr 7, 2024
ASCE Technical Topics:
- Artificial intelligence and machine learning
- Computer programming
- Computing in civil engineering
- Concrete
- Concrete structures
- Continuum mechanics
- Corrosion
- Cracking
- Design (by type)
- Deterioration
- Engineering fundamentals
- Engineering materials (by type)
- Engineering mechanics
- Fracture mechanics
- Materials characterization
- Materials engineering
- Reinforced concrete
- Solid mechanics
- Structural analysis
- Structural design
- Structural engineering
- Structural reliability
- Structure reinforcement
- Structures (by type)
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