Machine Learning-Based Service Life Prediction of Corroded Steel CHS Using Time-Dependent Reliability
TL;DR
TRA-DNN employs the DNN model to perform SRA via Crude Monte Carlo Simulation, yielding results that are in high agreement with conventional SFEA, yet with significantly reduced computational time (1,388,250 times faster).
Abstract
The aim of structural reliability assessment (SRA) is to guarantee the safety, durability, and performance of structures; however, traditional methods like stochastic finite element analysis (SFEA) can be computationally prohibitive to use in practical situations. This paper introduces a novel framework for SRA utilizing deep neural networks (DNNs) implemented in an open-source program called TRA-DNN, replacing the resource-intensive finite element (FE) analysis with a DNN model. The DNN is trained using 6874 FE column models, including factors like geometric imperfections, resulting in a training database with 419,314 data records. It accurately predicts axial load-deformation curves for corroded steel CHS columns, enabling the determination of the ultimate capacities for columns with varying properties. The model’s accuracy is confirmed through rigorous quantitative and qualitative validation, including various failure modes. TRA-DNN employs the DNN model to perform SRA via Crude Monte Carlo Simulation (MCS), yielding results that are in high agreement with conventional SFEA, yet with significantly reduced computational time (1,388,250 times faster). In addition, TRA-DNN can be used to estimate the service life of CHS columns considering both corrosion propagation and load increase with time. Future research can utilize TRA-DNN to optimize column design and maintenance to minimize both risk and cost.