[3K2-OS-18b] [Organized Session] OS-18

[3K2-OS-18b-02] Damage Detection of Wooden Structural Members Using Piezoelectric Sensor and Autoencoder for Structural Health Monitoring

〇Natsuhiko Sakiyama¹, Ayumu Ushigome¹, Sakuya Kishi¹, Akihiro Kishi¹, Yoichiro Hashizume¹, Takashi Nakajima¹, Takumi Ito¹ (1. Tokyo University of Science)

Keywords:Building, Machine Learning, Piezoelectric Sensor

We tried to identify the state of members injured by natural disasters by machine learning using the vibration response waveform of a piezoelectric sensor attached a wooden element. We destroyed step by step a wooden wall connected to columns. In each destruction stage, the vibrational characteristic is measured by a piezoelectric sensor. The oscillating source to obtain data models vibrations of natural vibration coming from a wind and so on. We detected the injury of a member by using autoencoder which learned only the waveform of the element which is not injured.