Modeling of temperature-frequency correlation using combined principal component analysis and support vector regression technique

X. G. Hua, Yiqing Ni, J. M. Ko, K. Y. Wong

Research output: Journal article publicationJournal articleAcademic researchpeer-review

120 Citations (Scopus)

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.
Original languageEnglish
Pages (from-to)122-135
Number of pages14
JournalJournal of Computing in Civil Engineering
Volume21
Issue number2
DOIs
Publication statusPublished - 26 Feb 2007

Keywords

  • Bridges, cable-stayed
  • Correlation
  • Frequency
  • Monitoring
  • Regression models
  • Temperature effects

ASJC Scopus subject areas

  • Civil and Structural Engineering
  • Computer Science Applications

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