Data-driven support vector machine with optimization techniques for structural health monitoring and damage detection

• Published in: KSCE journal of civil engineering Vol. 21; no. 2; pp. 523 - 534
• Main Authors: Gui, Guoqing, Pan, Hong, Lin, Zhibin, Li, Yonghua, Yuan, Zhijun
• Format: Journal Article
• Language: English
• Published: Seoul Korean Society of Civil Engineers 01.02.2017; Springer Nature B.V; 대한토목학회
• Subjects: Algorithms; Big Data; Civil Engineering; Damage; Damage assessment; Damage detection; Decision making; Defects; Design Optimization and Applications in Civil Engineering; Detection; Diagnostic systems; Engineering; Feature extraction; Genetic algorithms; Geotechnical Engineering & Applied Earth Sciences; Industrial Pollution Prevention; Kernel functions; Machine learning; Mathematical models; Numerical methods; Optimization; Optimization techniques; Parameters; Public safety; Sensors; Structural health monitoring; Support vector machines; Technical information; 토목공학; structural health monitoring and damage detection; support vector machine learning; optimization; data-driven modeling

Rapid detecting damages/defeats in the large-scale civil engineering structures, assessing their conditions and timely decision making are crucial to ensure their health and ultimately enhance the level of public safety. Advanced sensor network techniques recently allow collecting large amounts of d...