Study on structural health monitoring system using deep learning (Verification of effectiveness based on seismic response analysis using elastoplastic model)

Abstract

Monitoring structural integrity has been demanded to achieve a sustainable society against disasters, including seismic and extreme wind events, and thus Structural Health Monitoring (SHM) system is one of the significant technologies. The natural frequency and the yield displacement of structures will be the significant damage indices to assess the structural integrity. However, they would contain variances to the design values. The structural integrity assessments using the SHM should allow for the uncertainties in structural specifications, such as the yield displacement, to improve the assessment accuracy. Additionally, the earthquake ground motion will have uncertainties regarding the acceleration levels, spectral characteristics, and others. These uncertainties can lead to assessment difficulty, reducing the accuracy of the assessment. The authors propose the method using the deep Convolutional Neural Network to overcome this problem. This method using CNN has two features. The first is to suggest using the response spectra as the training data of CNN. The second is to create a simple CNN architecture with a high classifier accuracy using Bayesian optimization. This paper demonstrates that the image features detected by CNN enable us to accurately assess the structural integrity even when uncertainties in structural specifications and seismic motions are superimposed.