Y
Yong Xia
Researcher at Hong Kong Polytechnic University
Publications - 192
Citations - 5646
Yong Xia is an academic researcher from Hong Kong Polytechnic University. The author has contributed to research in topics: Finite element method & Structural health monitoring. The author has an hindex of 37, co-authored 171 publications receiving 4343 citations. Previous affiliations of Yong Xia include Nanyang Technological University & Main Roads Western Australia.
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Structural Analysis of Large-Scale Vertical-Axis Wind Turbines, Part I: Wind Load Simulation
TL;DR: In this paper, a 2D shear stress transport (SST) k-ω model is proposed to simulate wind loads on a VAWT. And the wind loads obtained in this study will be applied to the fatigue and ultimate strength analyses of the vertical-axis wind turbines in the companion paper.
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SHM-based F-AHP bridge rating system with application to Tsing Ma Bridge
TL;DR: In this article, a structural health monitoring (SHM)-based bridge rating method for bridge inspection of long-span cable-supported bridges is proposed, where the fuzzy based analytic hierarchy approach is employed, and the hierarchical structure for synthetic rating of each structural component of the bridge is proposed.
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Kron's substructuring method to the calculation of structural responses and response sensitivities of nonlinear systems
Wei Tian,Shun Weng,Yong Xia +2 more
TL;DR: In this article, the structural responses and response sensitivities are calculated from the reduced vibration equation of a relatively large-scale nonlinear frame and the precision and efficiency of the proposed method are verified by a nonlinear spring-mass system and a relatively small-scale structural frame.
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Multi-scale stochastic dynamic response analysis of offshore risers with lognormal uncertainties
TL;DR: In this article, a multi-scale stochastic dynamic analysis method for offshore structures is presented, where the uncertainties in the structural material parameters, such as mass density and Young's modulus, are considered.
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Anomaly detection of sensor faults and extreme events based on support vector data description
TL;DR: An automatic and efficient anomaly detection methodology based on support vector data description (SVDD) to simultaneously detect anomalies caused by sensor faults and extreme events is proposed and applied to datasets collected from two SHM systems.