Machine learning-based strain response prediction method for structural members of a building using ground motion data and intensity measures

This study presents a strain response prediction method for structural members of a building using ground motion (GM) data. The relationship between the strain response and the GM data is determined by a convolutional neural network (CNN), which is a machine learning technique. A CNN model with the time history GM data set as input and the strain response of structural members to earthquakes set as output is trained using the measurement data. The constructed CNN model is used to predict strain based on the recorded GM data when a seismic event occurs afterwards. In the proposed method, three seismic intensity measures such as singular value matrix, Arias Intensity, and cumulative absolute velocity extracted from the GM data are used as input data for the CNN in addition to time history GM data. Each seismic intensity measure with the GM data is employed in each CNN. Thus, a total four CNN models are presented. A comparison of strain prediction performance when subjected to seismic loads is made between the presented CNN models. The CNN model’s prediction performance is examined using the measured strain of structural members obtained from a shaking table test of a 3-story reinforced concrete frame specimen. Through the experimental study, intensity measures that are effective at predicting strain are discussed in detail.