Towards probabilistic data‐driven damage detection in SHM using sparse Bayesian learning scheme
| Author(s): |
Qi‐Ang Wang
(State Key Laboratory for Geomechanics and Deep Underground Engineering & School of Mechanics and Civil Engineering China University of Mining and Technology Xuzhou China)
Yang Dai (State Key Laboratory for Geomechanics and Deep Underground Engineering & School of Mechanics and Civil Engineering China University of Mining and Technology Xuzhou China) Zhan‐Guo Ma (State Key Laboratory for Geomechanics and Deep Underground Engineering & School of Mechanics and Civil Engineering China University of Mining and Technology Xuzhou China) Yi‐Qing Ni
(National Rail Transit Electrification and Automation Engineering Technology Research Center (Hong Kong Branch) and Department of Civil and Environmental Engineering The Hong Kong Polytechnic University Hong Kong)
Jia‐Qi Tang (School of Mechanics and Civil Engineering China University of Mining and Technology Xuzhou China) Xiao‐Qi Xu (School of Mechanics and Civil Engineering China University of Mining and Technology Xuzhou China) Zi‐Yan Wu (School of Mechanics, Civil Engineering and Architecture Northwestern Polytechnical University Xi'an China) |
|---|---|
| Medium: | journal article |
| Language(s): | English |
| Published in: | Structural Control and Health Monitoring, September 2022, n. 11, v. 29 |
| DOI: | 10.1002/stc.3070 |
- About this
data sheet - Reference-ID
10685886 - Published on:
13/08/2022 - Last updated on:
10/12/2022
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