Machine Learning–Based Reliability Analysis of Structural Concrete Cracking Considering Realistic Nonuniform Corrosion Development

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.