Hua Ping Wan
Zhejiang University
7 Papers
12 Citations
Hua Ping Wan is an academic researcher from Zhejiang University. The author has contributed to research in topics: Structural health monitoring & Computer science. The author has an hindex of 4, co-authored 7 publications. Previous affiliations of Hua Ping Wan include Hong Kong Polytechnic University.
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Papers
Bayesian multi-task learning methodology for reconstruction of structural health monitoring data:
Hua Ping Wan,Yiqing Ni +1 more
TL;DR: The reconstruction results of the structural health monitoring data show that the proposed Bayesian multi-task learning methodology affords an excellent performance, while the Bayesian single- task learning method is unreliable in certain cases; yet, the selection of covariance function has a significant impact on the reconstruction performance of the proposed methodology.
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Bayesian Modeling Approach for Forecast of Structural Stress Response Using Structural Health Monitoring Data
Hua Ping Wan,Yiqing Ni +1 more
TL;DR: The advancement in structural health monitoring technology has been evolving from monitoring-based diagnosis to monitoring- based prognosis, and the structural stress response derived bySHM technology is being studied.
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An efficient approach for dynamic global sensitivity analysis of stochastic train-track-bridge system
Hua Ping Wan,Yiqing Ni +1 more
TL;DR: An investigation as to how uncertainty in the parameters influences the dynamic responses of time-varying TTBS is provided, which refers to dynamic sensitivity analysis in the context of stochastic dynamic system.
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An improved complex multi-task Bayesian compressive sensing approach for compression and reconstruction of SHM data
TL;DR: An improved complex multi-task Bayesian CS (CMT-BCS) method is developed for compression and reconstruction of SHM data requiring a high sampling rate and is evaluated using the shaking table test data of a scale-down frame model and the real-worldSHM data acquired from a supertall building.
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Binary Segmentation for Structural Condition Classification Using Structural Health Monitoring Data
Hua Ping Wan,Yiqing Ni +1 more
TL;DR: This data indicates that direct assessment of structural condition diagnosis and prognosis based on appropriate analyses of in situ measurement data in patients with known structural condition problems is more beneficial than either of the other approaches.
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