A novel multimodal deep learning framework for predicting residual strength of corroded rectangular hollow-section columns

Abstract

Corrosion, recognized as a thermodynamically spontaneous process, is one of the key issues affecting the health of rectangular hollow steel section columns under working conditions, and has attracted much attention in recent years. Traditional approaches, such as multi-layer perceptron, often rely solely on the degree of volume loss to predict residual strength, overlooking the spatial complexity of actual corrosion patterns. To address these limitations, this study presents a novel multimodal deep learning network for accurately predicting the residual strength of corroded hollow steel section columns with random, nonuniform corrosion distributions. Our approach integrates (i) image‐based corrosion distributions on four steel walls, and (ii) tabular geometric parameters, through five distinct data-fusion methods proposed in this work, three employing Late Fusion (via a novel multi‐head attention module) and two using Early Fusion (via pixel−level merging). The image information extraction core is built upon a lightweight convolutional neural network and a channel−spatial attention block, while the tabular extraction module leverages a revised multi-layer perceptron architecture. After Bayesian hyperparameter optimization, the best‐performing model achieves a coefficient of determination of 0.971 on the test set, surpassing conventional machine learning and other multimodal fusion techniques by 0.01–0.161. Further analysis shows that the reverse visualization technique highlights corrosion−critical regions that closely coincide with the experimentally validated failure zones. Consequently, the proposed framework not only predicts residual strength with high accuracy but also localizes vulnerable areas for targeted reinforcement. This methodology holds promise for large‐scale corrosion monitoring and structural health assessment of steel infrastructure.