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Monitoring displacements and weather impact of complex structures, such as a large cable-stayed footbridge, generates a large amount of data. To extract, visualize, and classify health-monitoring data for better comprehension, multivariate statistical analysis is a powerful tool. This paper describes screening to evaluate if principal component analysis is useful for health monitoring data. Principal component analysis (PCA) and projections to latent structures by means of partial least squares (PLS) modeling were used to achieve a better understanding of the complex interaction between bridge dynamics and weather effects. The results show that PCA gives a good overview of the collected data, and PLS modeling shows that winds from east and west best explain bridge movements.

Multivariate Screening of the Weather Effect on Timber Bridge Movements