Please use this identifier to cite or link to this item: http://hdl.handle.net/10397/17499
Title: Modeling of temperature-frequency correlation using combined principal component analysis and support vector regression technique
Authors: Hua, XG
Ni, YQ 
Ko, JM
Wong, KY
Keywords: Bridges, cable-stayed
Correlation
Frequency
Monitoring
Regression models
Temperature effects
Issue Date: 2007
Publisher: American Society of Civil Engineers
Source: Journal of computing in civil engineering, 2007, v. 21, no. 2, p. 122-135 How to cite?
Journal: Journal of computing in civil engineering 
Abstract: A good understanding of environmental effects on structural modal properties is essential for reliable performance of vibration-based damage diagnosis methods. In this paper, a method of combining principal component analysis (PCA) and support vector regression (SVR) technique is proposed for modeling temperature-caused variability of modal frequencies for structures instrumented with long-term monitoring systems. PCA is first applied to extract principal components from the measured temperatures for dimensionality reduction. The predominant feature vectors in conjunction with the measured modal frequencies are then fed into a support vector algorithm to formulate regression models that may take into account thermal inertia effect. The research is focused on proper selection of the hyperparameters to obtain SVR models with good generalization performance. A grid search method with cross validation and a heuristic method are utilized for determining the optimal values of SVR hyperparameters. The proposed method is compared with the method directly using measurement data to train SVR models and the multivariate linear regression (MLR) method through the use of long-term measurement data from a cable-stayed bridge. It is shown that PCA-compressed features make the training and validation of SVR models more efficient in both model accuracy and computational costs, and the formulated SVR model performs much better than the MLR model in generalization performance. When continuously measured data is available, the SVR model formulated taking into account thermal inertia effect can achieve more accurate prediction than that without considering thermal inertia effect.
URI: http://hdl.handle.net/10397/17499
ISSN: 0887-3801
EISSN: 1943-5487
DOI: 10.1061/(ASCE)0887-3801(2007)21:2(122)
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