Post-earthquake preliminary estimation of residual seismic capacity of RC buildings based on deep learning

Preliminary assessment of the seismic performance of reinforced concrete (RC) buildings with placards can reduce the number of buildings that require a detailed and costly assessment. Although existing image-processing-based techniques can detect the existence of cracks and spalling in concrete, it remains difficult to define with damage levels of the damaged vertical members based on these techniques. This study aims to fill this gap by exploiting convolutional neural network (CNN) techniques for damage level classification of vertical components in RC buildings. The preliminary seismic assessment approach of existing RC buildings developed by the National Center for Research on Earthquake Engineering, Taiwan is employed in this study, and the residual strength factors for damage levels of vertical members are identified. The proposed CNN technique can estimate the damage levels of the vertical members, and the seismic capacity reduction of these damaged vertical members can be graded accordingly. Hence, the seismic resistance of the RC buildings with damaged members caused by an earthquake can be estimated. The earthquake reconnaissance data collected after recent earthquakes are used to train and validate the CNN network. The performance of the proposed approach is verified using the earthquake data with the necessary information for the preliminary seismic assessment approach. In general, the precision and recall values that we obtain for the identification of the damage in vertical members are acceptable. Based on the results of this study, performing a seismic evaluation of RC buildings by calculating the residual seismic capacity ratio with the help of machine learning appears to be an effective strategy.