• 【标题】Deep learning-based evaluation for mechanical property degradation of seismically damaged RC columns
• 【摘要】The evaluation of mechanical property degradation (i.e., stiffness and strength degradation) for seismically damaged reinforced concrete (RC) components is a critical step in the post-earthquake assessment of the residual seismic capacity of buildings. In this study, a novel approach based on deep learning (DL) was proposed to evaluate the stiffness and strength degradation of RC columns according to visible seismic damage. A database was constructed by linking the test photos of RC column specimens with the loading points on the hysteretic curves, from which the stiffness and strength reduction factors (λ_K and λ_Q, respectively) were analyzed. Two novel convolutional network (CNN) modules were designed to enable feature extraction and integration of seismic damage with a reduced number of parameters, and multitask learning was introduced to enable adaptive feature fusion for stiffness and strength degradation individually. A deep convolutional network (DCNN) was therefore proposed to model the correlation between visible seismic damage and mechanical property degradation of flexural-dominated RC columns, which can integrate visual characteristics and spatial topologies of visible damage to estimate λ_K and λ_Q. The application to two test specimens validated the preferable accuracy and robustness of the proposed DL-based approach, and demonstrated its high potential for use in post-earthquake performance assessment of buildings.
• 【关键词】computer vision; deep convolutional network; mechanical property degradation; post-earthquake performance assessment; reinforced concrete columns
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