Probabilistic outlier detection for robust regression modeling of structural response for high-speed railway track monitoring

Autor(en):	Qi Li (Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology, School of Civil Engineering, Harbin Institute of Technology, Harbin, China) Jingze Gao (Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology, School of Civil Engineering, Harbin Institute of Technology, Harbin, China) James L. Beck (Division of Engineering and Applied Science, California Institute of Technology, Pasadena, CA, USA) Chao Lin (China Railway Siyuan Survey and Design Group Co., Ltd., WuHan, China) Yong Huang (Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology, School of Civil Engineering, Harbin Institute of Technology, Harbin, China) Hui Li (Key Lab of Smart Prevention and Mitigation of Civil Engineering Disasters of the Ministry of Industry and Information Technology, School of Civil Engineering, Harbin Institute of Technology, Harbin, China)
Medium:	Fachartikel
Sprache(n):	Englisch
Veröffentlicht in:	Structural Health Monitoring, Juli 2023, n. 2, v. 23
Seite(n):	1280-1296
DOI:	10.1177/14759217231184584

Structurae kann Ihnen derzeit diese Veröffentlichung nicht im Volltext zur Verfügung stellen. Der Volltext ist beim Verlag erhältlich über die DOI: 10.1177/14759217231184584.

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