Gaussian active learning algorithm for extreme event estimation
Civil Engineering|更新时间:2025-11-14
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Gaussian active learning algorithm for extreme event estimation
“In the field of structural safety, experts have proposed a new method that can accurately estimate the minimum failure probability of complex structures, providing a solution to the tail risk problem.”
Journal of Civil and Environmental EngineeringVol. 47, Issue 4, Pages: 148-156(2025)
作者机构:
1.西南交通大学,土木工程学院,成都 610031
2.西南交通大学,陆地交通地质灾害防治技术国家工程研究中心,成都 610031
3.中铁第一勘察设计院集团有限公司,西安 710043
作者简介:
YANG Haiting (2000- ), main research interest: reliability of engineering structure, E-mail: 1304173992@qq.com.
YANG Cheng (corresponding author), associate professor, doctorial supervisor, E-mail: yangcheng@swjtu.edu.cn.
基金信息:
National Natural Science Foundation of China(2019YFD1101005;2019YFD1101001;2021YFB2600501);Natural Science Foundation of Sichuan Province(2022NSFSC0458);Research and Development Project of China Railway First Survey and Design Institute Group Co., Ltd (Academy 20-53, Academy 20-21, CR2321718)(院科20-53;院科20-21;CR2321718)
YANG Haiting,YIN Weihao,HUANG Yanwen,et al.Gaussian active learning algorithm for extreme event estimation[J].Journal of Civil and Environmental Engineering,2025,47(04):148-156.
YANG Haiting,YIN Weihao,HUANG Yanwen,et al.Gaussian active learning algorithm for extreme event estimation[J].Journal of Civil and Environmental Engineering,2025,47(04):148-156. DOI: 10.11835/j.issn.2096-6717.2024.031.
Gaussian active learning algorithm for extreme event estimation