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Structural Monitoring and Maintenance Volume 9, Number 2, June 2022 , pages 179-200 DOI: https://doi.org/10.12989/smm.2022.9.2.179 |
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Damage detection of bridges based on spectral sub-band features and hybrid modeling of PCA and KPCA methods |
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Hossein Babajanian Bisheh and Gholamreza Ghodrati Amiri
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| Abstract | ||
| This paper proposes a data-driven methodology for online early damage identification under changing environmental conditions. The proposed method relies on two data analysis methods: feature-based method and hybrid principal component analysis (PCA) and kernel PCA to separate damage from environmental influences. First, spectral sub-band features, namely, spectral sub-band centroids (SSCs) and log spectral sub-band energies (LSSEs), are proposed as damage-sensitive features to extract damage information from measured structural responses. Second, hybrid modeling by integrating PCA and kernel PCA is performed on the spectral sub-band feature matrix for data normalization to extract both linear and nonlinear features for nonlinear procedure monitoring. After feature normalization, suppressing environmental effects, the control charts (Hotelling T2 and SPE statistics) is implemented to novelty detection and distinguish damage in structures. The hybrid PCA-KPCA technique is compared to KPCA by applying support vector machine (SVM) to evaluate the effectiveness of its performance in detecting damage. The proposed method is verified through numerical and full-scale studies (a Bridge Health Monitoring (BHM) Benchmark Problem and a cable-stayed bridge in China). The results demonstrate that the proposed method can detect the structural damage accurately and reduce false alarms by suppressing the effects and interference of environmental variations. | ||
| Key Words | ||
| cable-stayed bridge; environmental effects; hybrid PCA-KPCA; spectral sub-band features; structural damage detection | ||
| Address | ||
| Hossein Babajanian Bisheh: School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran Gholamreza Ghodrati Amiri: Natural Disasters Prevention Research Center, School of Civil Engineering, Iran University of Science and Technology, Tehran, Iran | ||
