Comparison of different statistical approaches for removing environmental/operational effects for massive data continuously collected from footbridges

Abstract

The implementation of continuous dynamic monitoring systems in two bridges, in Portugal, is enabled to detect the occurrence of very significant environmental and operational effects on the modal properties of these bridges, based on automated processing of massive amounts of monitoring data collected by a set of accelerometers and thermal sensors over several years. In order to remove or mitigate such environmental/operational effects with the purpose of damage detection, two different statistical methods have been adopted. One of them is the multiple linear regression by performing nonlinear correlation analysis between measured modal properties and environmental/operational variables. Another one is principal component regression based on the identification of the linear subspace within the modal properties without using measured values of environmental and operational variables. This paper presents a comparison of the performance of these two alternative approaches on the basis of continuous monitoring data acquired from two instrumented bridges and simulated damage scenarios. It is observed that different methods show similar capacity in removing environmental effects, and the multiple linear regression method is slightly more sensitive to structural damage. KEYWORDS damage detection, environmental/operational effect, modal property, multiple linear regression, principal component regression

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