深層学習を活用したヘルスモニタリングシステムの検討

抄録

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 indexes 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 seismic 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. To overcome this problem, the authors propose the method using the deep Convolutional Neural Network. This method using CNN has two features. The first is to suggest employing 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 feature values detected by CNN allow the structural integrity against the seismic events to accurately classifier even if containing the uncertainties in the structural specifications and the seismic ground motions.