Using Deep Learning to Estimate Vibration Comfort of Large-Scale Shake Table During Operation

Abstract

Shake tables are useful earthquake simulation tools for structural seismic experiment, but they may also inadvertently induce vibrations to nearby buildings while in operation. Accelerating the comfort level quantification process of these vibrations before conducting a shake table test is necessary. To this end, this paper focuses on the influence of vibration introduced by a 6 × 9 m large-scale shake table at Southeast University and presents a one-dimensional convolutional neural network–based deep learning approach to efficiently estimate the vibration comfort of the shake table laboratory and surrounding buildings. Based on the on-site structural vibration monitoring of shake table test, a three-dimensional numerical model of the shake table–soil–surrounding building system is established and validated through the finite element method, and thus a dataset comprising 12,215 groups of input (i.e., peak acceleration values and time-history of the triaxial ground motion) and output (i.e., three-directional acceleration response for nine measuring points of surrounding buildings) data is simulated. Thereafter, the deep learning network is trained with 80% of the dataset and tested with the remaining 20%. The test results indicate that the approach enables the network to directly extract dynamic features from triaxial ground motion accelerations and to accurately estimate the weighted acceleration level (WAL) of nine different locations at the surrounding buildings. Finally, the optimized network is verified through an actual shake table experimental test on a self-centering concrete structure, which confirms the superior performance of the proposed approach on shake table–induced vibration comfort estimation. The approach is also beneficial for researchers to design reasonable loading scenarios before conducting shake table tests.