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Yilun Qin, Qizhi Tang, Jingzhou Xin, Changxi Yang, Zixiang Zhang and Xianyi Yang
Rapid and accurate identification of moving load is crucial for bridge operation management and early warning of overload events. However, it is hard to obtain them rapidly via traditional machine learning methods, due to their massive model parameters a...
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Bo Wu, Jianting Zhou, Jingzhou Xin, Hong Zhang, Liangliang Zhang and Xianyi Yang
In the present study, multiple-fan active control wind tunnel tests are conducted to investigate the aerodynamic forces on a 5:1 rectangular cylinder in sinusoidal streamwise winds with different angles of attack (AoA). The effects of the frequency, ampl...
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Lei Fu, Qizhi Tang, Peng Gao, Jingzhou Xin and Jianting Zhou
The shallow features extracted by the traditional artificial intelligence algorithm-based damage identification methods pose low sensitivity and ignore the timing characteristics of vibration signals. Thus, this study uses the high-dimensional feature ex...
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