Affiliation:
1. MOE Key Laboratory of Deep Earth Science and Engineering College of Architecture and Environment Sichuan University Chengdu China
2. Department of Civil Engineering Tsinghua University Beijing China
3. Key Laboratory of Civil Engineering Safety and Durability of China Education Ministry Tsinghua University Beijing China
Abstract
AbstractLaminated rubber bearings are the key components of base isolated structures. It is important to effectively monitor the axial pressure and shear deformation in rubber bearings. In previous studies, the axial pressure and shear deformation of rubber bearings have been monitored separately using wavelet‐packet‐based method. However, when the axial pressure and deformation of rubber bearings change at the same time, the prediction error of the above method is large. Therefore, this paper proposed a new deep neural network architecture, named P&D‐NET to monitor the axial pressure and shear deformation simultaneously. The P&D‐NET combines wavelet packet decomposition method and fully connected neural network. In this study, the coupling effects of axial pressure and shear deformation monitoring are studied by full‐scale experiments. The details of P&D‐NET are also introduced. Three full‐scale smart rubber bearings (SRB) were tested under different axial pressure and shear deformation to form a real measured dataset. The influence of feature extraction methods, network structures, and weighting coefficient in loss function was investigated to optimize the network. By using the optimal hyperparameters, the root‐mean‐square error (RMSE) of axial pressure prediction and shear deformation prediction in the test set is 1.41 MPa and 17.6%, which has reduced the error by up to 68.8% compared to the previous wavelet‐packet‐based method.
Funder
National Natural Science Foundation of China
Subject
Earth and Planetary Sciences (miscellaneous),Geotechnical Engineering and Engineering Geology,Civil and Structural Engineering
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