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
• DOI: 10.1007/s12205-017-1518-5
• ISSN: 1226-7988

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 data for structural health monitoring and damage detection, while how to effectively interpret these complex sensor data to technical information posts many challenges. This paper presents three optimization-algorithm based support vector machines for damage detection. The optimization algorithms, including grid-search, partial swarm optimization and genetic algorithm, are used to optimize the penalty parameters and Gaussian kernel function parameters. Two types of feature extraction methods in terms of time-series data are selected to capture effective damage characteristics. A benchmark experimental data with the 17 different scenarios in the literature were used for verifying the proposed data-driven methods. Numerical results revealed that all three optimized machine learning methods exhibited significantly improvement in sensitivity, accuracy and effectiveness over conventional methods. The genetic algorithm based SVM had a better prediction than other methods. Two different feature methods used in this study also demonstrated the appropriate features are crucial to improve the sensitivity in detecting damage and assessing structural health conditions. The findings of this study are expected to help engineers to process big data and effectively detect the damage/defects, and thus enable them to make timely decision for supporting civil infrastructure management practices.