Regression models for structural health monitoring of welded bridge joints based on temperature,traffic and strain measurements

Abstract

A modelling platform based on regression analysis is developed as a novel approach to structural health monitoring of welded joints of orthotropic bridge steel decks. Monitoring outcomes from the Great Belt Bridge (Denmark) are used to develop regression models following a weighted least squares approach to characterize the normal correlation pattern between environmental conditions (daily-averaged pavement temperatures), operational loads (daily-aggregated heavy traffic counts) and a strain-based performance indicator. The developed models can be used within a structural health monitoring–based asset management framework for performance assessment (i.e. diagnosis of structural performance changes) and performance prediction (i.e. prognosis of structural performance leading to service life estimates). The main novelty of the work presented consists of the development of an algorithm based on statistical control charts related to the prediction bands of the regression models. The algorithm enables the interpretation of new monitoring data and the identification of potentially abnormal behaviours via outlier detection, as part of an envisaged ‘real-time’ performance assessment application. The proposed approach to outlier detection through structural health monitoring is finally illustrated considering actual monitoring outcomes from the bridge. This highlights the applicability of the developed modelling platform and contributes to bridging the gap between monitoring data and monitoring-based information that can lead to more effective asset management decisions.

Keywords

Structural health monitoring regression analysis outlier detection orthotropic steel deck data-based models asset management

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