Deep learning-based rapid damage assessment of RC columns under blast loading

Abstract

Reinforced concrete (RC) columns may be exposed to blast loading. Assessment and prediction of the damage condition and failure mode of RC columns are necessary for ensuring the structural safety and taking protective measures. However, rapid damage assessment of RC columns to blasts is challenging due to the lack of real measurement data and time-consuming numerical simulation. In this study, two deep learning models are established and trained in a supervised end-to-end manner to evaluate the damage degree and predict the failure mode of RC columns. To solve the shortage of real data, over 3000 numerical simulation datasets are generated and integrated with over 200 measurement data from the literature as the training data. First, a multi-hidden-layer neural network model is constructed using PyTorch to assess the damage degree of the RC columns under blast loading. Eight RC column structural parameters and two blast load parameters are used as inputs, and the damage index of the RC column is the output. Second, the Keras framework is used to build a multi-layer long-short-term memory neural network model with the same eight parameters as the inputs and the failure mode of the RC columns as the output to predict the failure mode of the RC columns. The established models are also applied to the explosion risk assessment of the RC column in a frame structure. The zoning maps of the damage degree and the failure mode are established. The results show that the developed deep learning models can accurately and rapidly assess the damage degree and predict the failure mode of the RC column under blast loading, which provides guidance for the anti-explosion protection design of RC columns.