59 ニューラルネットワークを用いた損傷検出に関する研究

概要

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A neural network-based approach is presented for the detection of changes in the characteristics of structure-unknown systems. The approach relies on the use of vibration measurements from a 'healthy' system to train a neural network for identification purposes. Subsequently, the trained network is fed comparable vibration measurements from the same structure under different episodes of response in order to monitor the health of the structure. It is shown, through simulation studies with linear as well as nonlinear models typically encountered in the applied mechanics field, that the proposed damage detection methodology is capable of detecting relatively small changes in the structural parameters. The methodology is applied to actual data obtained from ambient vibration measurements on a steel building structure, which was damaged under strong seismic motion during the Hyogo-Ken Nanbu Earthquake of January 17, 1995. A neural network is trained with data after the repairs, which represents 'healthy' condition of the building. The trained network, which is subsequently fed data before the repairs, successfully identified the difference between damaged story and undamaged story. Through this study, it is shown that the proposed approach has the potential of being a practical tool for damage detection methodology, which leads to smart civil structures.
一般社団法人日本建築学会の論文
1997-03-20