Adaptive Data Augmentation Deterioration Modeling for Spatiotemporal Reliability Analysis of Corroded Concretes in a Marine Environment

Abstract

Marine reinforced concrete (RC) structures suffer from corrosion-induced deterioration caused by the surrounding environment, which dramatically increases structural failure risk and reduces reliability. Although numerous studies have focused on the deterioration modeling and reliability assessment of existing RC structures using inspection data, many of these models still fail to adequately incorporate the spatiotemporal variability inherent in such data. Therefore, this paper investigates the influence of inherent variability in inspection data with different patterns on structural safety evaluation using two proposed spatiotemporal deterioration modeling methods: the data augmentation modeling (AUM) and the direct modeling (DM). It is found that the smoothness parameter of the gamma process and the correlation distance of the Gaussian random field, as well as the correlation among environmental and structural parameters, are not only time-variant but also exhibit spatial variability. The AUM method outperforms the DM method in terms of accuracy and effectiveness in spatiotemporal reliability analysis by better accounting for the spatiotemporal correlation of inspection data. Moreover, generating a spatiotemporal stochastic field of model parameters enhances the adaptability of the deterioration model to inspection data, leading to improved accuracy in reliability assessment.