PROPOSAL OF METHOD USED DEEPLEARNING FOR BUILDING DAMAGE SHORTLY AFTER AN EARTHQUAKE AND EVALUATION OF METHOD BY RESULT OF LARGE SHAKING TABLE TEST OF A 18 STORY STEEL BUILDING AND SIMULATION

Abstract

After-earthquake assessment of building in terms of safety and usability is performed by technicians who give their judgement based on in-field surveys and visual inspections. In this study, an efficient method is proposed for building damage detection and structural health monitoring system is introduced. First, the data of acceleration sensors that are placed each floor is processed for creating images which include shaking information of building during earthquake. Second, all images are divided into train set and test set. Train set is used to train a neural network by using deep learning and test set is used for evaluating model. Five different way of creating images and three neural network models are compared. Finally, quantity of data is extended by simulation and it is possible to determine the damage of the building in more detail. The result illustrates that two way of creating image and one model can be effectively used to detect building damage.