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

2017 ◽  
Vol 21 (2) ◽  
pp. 523-534 ◽  
Author(s):  
Guoqing Gui ◽  
Hong Pan ◽  
Zhibin Lin ◽  
Yonghua Li ◽  
Zhijun Yuan
Keyword(s):  
Support Vector Machine ◽  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Optimization Techniques ◽  
Data Driven ◽  
Support Vector ◽  
Structural Health

scholarly journals A Time-Domain Structural Damage Detection Method Based on Improved Multiparticle Swarm Coevolution Optimization Algorithm

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Shao-Fei Jiang ◽  
Si-Yao Wu ◽  
Li-Qiang Dong
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Structural Damage ◽  
Damage Threshold ◽  
Optimization Techniques ◽  
Local Optimum ◽  
Structural Health ◽  
Civil Infrastructures ◽  
Standard Particle Swarm Optimization

Optimization techniques have been applied to structural health monitoring and damage detection of civil infrastructures for two decades. The standard particle swarm optimization (PSO) is easy to fall into the local optimum and such deficiency also exists in the multiparticle swarm coevolution optimization (MPSCO). This paper presents an improved MPSCO algorithm (IMPSCO) firstly and then integrates it with Newmark’s algorithm to localize and quantify the structural damage by using the damage threshold proposed. To validate the proposed method, a numerical simulation and an experimental study of a seven-story steel frame were employed finally, and a comparison was made between the proposed method and the genetic algorithm (GA). The results show threefold: (1) the proposed method not only is capable of localization and quantification of damage, but also has good noise-tolerance; (2) the damage location can be accurately detected using the damage threshold proposed in this paper; and (3) compared with the GA, the IMPSCO algorithm is more efficient and accurate for damage detection problems in general. This implies that the proposed method is applicable and effective in the community of damage detection and structural health monitoring.

Toward Data-Driven Structural Health Monitoring: Application of Machine Learning and Signal Processing to Damage Detection

2013 ◽  
Vol 27 (6) ◽  
pp. 667-680 ◽  
Author(s):  
Yujie Ying ◽  
James H. Garrett ◽  
Irving J. Oppenheim ◽  
Lucio Soibelman ◽  
Joel B. Harley ◽  
...  
Keyword(s):  
Machine Learning ◽  
Signal Processing ◽  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Data Driven ◽  
Structural Health ◽  
Monitoring Application

Fatigue damage detection from imbalanced inspection data of Lamb wave

2021 ◽  
pp. 147592172110152
Author(s):  
Jingjing He ◽  
Ziwei Fang ◽  
Jie Liu ◽  
Fei Gao ◽  
Jing Lin
Keyword(s):  
Structural Health Monitoring ◽  
Damage Detection ◽  
Health Monitoring ◽  
Lamb Wave ◽  
Sampling Technique ◽  
Support Vector ◽  
Small Scale ◽  
Detection Range ◽  
Structural Health ◽  
Inspection Data

The core of structural health monitoring is to provide a real-time monitoring, inspection, and damage detection of structures. As one of the most promising technology to structural health monitoring, the Lamb wave method has attracted interest because it is sensitive to small-scale damage with a long detection range. However, in many real-world structural health monitoring applications, the nature of the problem implies structures work under normal condition in most of its operating phases; therefore, classes of data collected are not equally represented. The predictive capability of damage detection algorithms may significantly be impaired by class imbalance. This article presents a damage detection method using imbalanced inspection data which is collected through Lamb wave detection. Aiming at maximizing detection accuracy, an improved synthetic minority over-sampling technique using three-point triangle (triangle synthetic minority over-sampling technique) is proposed to conduct the over-sampling procedure and increase the number of minority samples. The iterative-partitioning filter is employed to remove the noisy examples which may be introduced by triangle synthetic minority over-sampling technique. Three conventional classification methods, namely, support vector machine, decision tree, and k-nearest neighbor, are used to perform the damage detection. A fatigue crack detection test using Lamb wave is performed to demonstrate the overall procedure of the proposed method. Three damage sensitive features, namely, normalized amplitude, correlation coefficient, and normalized energy, are extracted from signals as datasets. A cross-validation is performed to verify the performance of the proposed method for crack size identification.

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