Adaptive learning for reliability analysis using Support Vector Machines
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DOI: 10.1016/j.ress.2022.108635
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Cited by:
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- Wu, Jinhui & Tao, Yourui & Han, Xu, 2023. "Polynomial chaos expansion approximation for dimension-reduction model-based reliability analysis method and application to industrial robots," Reliability Engineering and System Safety, Elsevier, vol. 234(C).
- Zhang, Qiang & Zhao, Yan-Gang & Kolozvari, Kristijan & Xu, Lei, 2022. "Reliability analysis of reinforced concrete structure against progressive collapse," Reliability Engineering and System Safety, Elsevier, vol. 228(C).
- Van Huynh, Thu & Tangaramvong, Sawekchai & Do, Bach & Gao, Wei & Limkatanyu, Suchart, 2023. "Sequential most probable point update combining Gaussian process and comprehensive learning PSO for structural reliability-based design optimization," Reliability Engineering and System Safety, Elsevier, vol. 235(C).
- Yu, Ting & Lu, Zhenzhou & Yun, Wanying, 2023. "An efficient algorithm for analyzing multimode structure system reliability by a new learning function of most reducing average probability of misjudging system state," Reliability Engineering and System Safety, Elsevier, vol. 230(C).
- Chen, Zequan & He, Jialong & Li, Guofa & Yang, Zhaojun & Wang, Tianzhe & Du, Xuejiao, 2024. "Fast convergence strategy for adaptive structural reliability analysis based on kriging believer criterion and importance sampling," Reliability Engineering and System Safety, Elsevier, vol. 242(C).
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Keywords
Reliability analysis; Adaptive learning; Support Vector Machines; Failure probability;All these keywords.
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