摘要
Abstract
Aiming at the drawbacks of empirical classification adopted in the existing corrosion evaluation of steel structures,which fails to effectively assess the mechanical properties of structures,a quantitative evaluation method based on deep learning is proposed in this paper.Steel plate specimens with various corrosion degrees are fabricated,and a 3D laser scanner is utilized to acquire the surface topography of corroded specimens.The degree of volume loss(DOV)is calculated via volume integration,while the maximum post-corrosion stress is obtained through inverse finite element modeling.These two indices are taken as quantitative labels of corrosion images to establish a corrosion image dataset.An improved ResNet-18 model is adopted to predict DOV,and a pre-trained and fine-tuned ViT-B/16 model is employed to forecast the maximum post-corrosion stress.The experimental results demonstrate that the predic-tion error of DOV by the improved ResNet-18 model is controlled within 0.015.After stable training of the ViT-B/16 model,the coefficient of determination reaches approximately 0.65,and the validation loss remains steady at 0.01～0.02.It is verified that the proposed method can realize continuous quantitative evaluation of corrosion severity,offer-ing a novel technical route for corrosion inspection and operation-maintenance decision-making of steel structures.关键词
钢结构/锈蚀程度定量评估/深度学习/体积损失率/锈后最大应力Key words
steel structure/quantitative evaluation for corrosion degree/deep learning/degree of volume loss(DOV)/maximum post-corrosion stress分类
建筑与水利
