Towards high-precision data modeling of SHM measurements using an improved sparse Bayesian learning scheme with strong generalization ability
| Auteur(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) Jun-Fang Wang
(MOE Key Laboratory for Resilient Infrastructures of Coastal Cities, College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, China)
Jian-Fu Lin
(Center of Safety Monitoring of Engineering Structures, Shenzhen Academy of Disaster Prevention and Reduction, China Earthquake Administration, Shenzhen, 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)
Wei-Xin Ren (MOE Key Laboratory for Resilient Infrastructures of Coastal Cities, College of Civil and Transportation Engineering, Shenzhen University, Shenzhen, China) Jian Jiang (School of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou, China) Xuan Yang (School of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou, China) Jia-Ru Yan (School of Mechanics and Civil Engineering, China University of Mining and Technology, Xuzhou, China) |
|---|---|
| Médium: | article de revue |
| Langue(s): | anglais |
| Publié dans: | Structural Health Monitoring, mai 2023, n. 1, v. 23 |
| Page(s): | 147592172311703 |
| DOI: | 10.1177/14759217231170316 |
- Informations
sur cette fiche - Reference-ID
10730042 - Publié(e) le:
30.05.2023 - Modifié(e) le:
14.01.2024
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