Active learning methods for strength assessment of circular CFST under coupled long-term axial loading and random localized corrosion

Abstract

In harsh marine environments, the complex combination of loading and corrosion severely affects the performances of concrete-filled steel tubular (CFST) structures. Therefore, accurately assessing their strengths becomes both complicated and vital for structural safety design and maintenance. Previously, the random localized corrosion along the steel surface was often simplified as uniform corrosion, which failed to reflect its inherent randomness and uncertainty. This study proposes an efficient modeling technique that simulates the coupled effects of long-term loading and random localized corrosion. The developed finite element models are validated against a series of test data. By combining numerical simulation and literature collection, databases of CFST with non-corrosion, uniform corrosion and random localized corrosion are constructed, comprising a total of 4038 samples. Seven machine learning methods are employed to predict the strength of CFST under the above three corrosion scenarios, where the active learning approach is adopted to select a few yet valuable data for training models. A hyperparameter optimization method, considering both structural mechanism and prediction accuracy, is proposed to ensure that the mechanical mechanisms are accounted for. The results show that CFST columns with random localized corrosion exhibit considerably lower strengths compared to counterparts with uniform corrosion. Among the established machine learning models, extreme gradient boosting (XGBoost) and Gaussian process regression (GPR) perform the best. The active learning method achieves similar model performance using only 63 % of the data as that using the entire data, demonstrating its capability in effectively addressing challenges in the field of structural engineering such as limited data and costly data acquisition.