Xiaoyu Gu
University of Technology, Sydney
26 Papers
72 Citations
Xiaoyu Gu is an academic researcher from University of Technology, Sydney. The author has contributed to research in topics: Magnetorheological elastomer & Base isolation. The author has an hindex of 10, co-authored 24 publications. Previous affiliations of Xiaoyu Gu include Nanjing University of Science and Technology & University of Sydney.
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Papers
A novel deep learning-based method for damage identification of smart building structures:
TL;DR: A novel method based on deep convolutional neural networks to identify and localise damages of building structures equipped with smart control devices that has outstanding generalisation capacity and higher identification accuracy than other commonly used machine learning methods is proposed.
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Experimental study of semi-active magnetorheological elastomer base isolation system using optimal neuro fuzzy logic control
TL;DR: It is proven that the smart MRE base isolation system is able to provide satisfactory protection for both structural and non-structural elements of the system over a wide range of hazard dynamic loadings.
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A hysteresis model for dynamic behaviour of magnetorheological elastomer base isolator
TL;DR: In this paper, a nonlinear hysteresis model is presented to characterize the nonlinear shear force of a magnetorheological elastomer (MRE) base isolator for structural vibration control.
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Investigations on response time of magnetorheological elastomer isolator for real-time control implementation
TL;DR: In this paper, two feasible approaches to minimize the response time delay of the MRE vibration isolator were explored to reduce the force response time from 421 ms to 52 ms at rising and from 400 ms to 48 ms falling edges respectively.
Self-adaptive step fruit fly algorithm optimized support vector regression model for dynamic response prediction of magnetorheological elastomer base isolator
TL;DR: The results demonstrate that the proposed SSFFOA-optimized SVR has perfect generalization ability and more accurate prediction accuracy than other machine learning models, and it is a suitable and effective method to predict the dynamic behaviour of MRE isolator.
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