Abstract
The cost of dedicated sensors has hampered the collection of the high-quality seismic response data required for real-time health monitoring and damage assessment. The emergence of crowdsensing technology, where a large number of mobile devices collectively share data and extract information of common interest, may help remove such obstacles and mitigate the seismic hazard. The present study proposes a crowdsensing-oriented vibration acquisition and identification method based on time–frequency characteristics and deep transfer learning. It can distinguish the responses during an earthquake event from vibration under serviceability conditions. The core classification process is performed using a combination of wavelet transforms and deep transfer networks. The latter were pre-trained using finite element models calibrated with the monitored seismic responses of the structures. The validation study confirmed the superior identification accuracy of the proposed method.
Highlights
The earthquake-induced damage and collapse of buildings and infrastructure have caused enormous economic losses and casualties
A novel identification method for structural seismic responses is proposed based on deep transfer neural networks with the time–frequency domain characteristic input
Structural Seismic Response Identification Method describes the framework of the method, and Vibration Acquisition, Extraction of Vibration Characteristics, Training and Evaluation of Deep Transfer Neural Networks, Case Studies show the implementation steps based on this framework
Summary
The earthquake-induced damage and collapse of buildings and infrastructure have caused enormous economic losses and casualties. The simulated vibration-trained neural networks were adopted to identify the real monitored structural seismic responses.
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