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

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...

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Veröffentlicht in:	KSCE Journal of Civil Engineering Jg. 21; H. 2; S. 523 - 534
Hauptverfasser:	Gui, Guoqing, Pan, Hong, Lin, Zhibin, Li, Yonghua, Yuan, Zhijun
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Seoul Korean Society of Civil Engineers 01.02.2017 Springer Nature B.V 대한토목학회
Schlagworte:	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
ISSN:	1226-7988, 1976-3808
Online-Zugang:	Volltext
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