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.

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