Jacob Egede Andersen

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

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.

A heuristic model for performance assessment of welded joints in orthotropic steel decks based on monitoring data

Orthotropic steel decks can present fatigue issues at welded joints, the assessment of which turns out to be a complex task due to their intricate geometry, the uncertain nature of the primary live load (traffic flow) and the temperature-driven composite action between the pavement and the steel deck. Current assessment methods tend to disregard the joint effect of traffic levels and pavement temperatures and rely on relatively long-term monitoring periods to capture strain responses and the overall associated uncertainty. This limits the ability to interpret short-term monitoring data, since failure to quantify this temperature-traffic interaction makes it impossible to understand short-term variations in the monitored strain responses. To overcome current limitations, a heuristic model is presented to predict stress-related performance indicators monitored at welded joints in orthotropic steel decks considering pavement temperatures and traffic levels as input parameters. Model parameters are determined through multiple linear regression and can be regarded as the set of health-related features to perform local Structural Health Monitoring (SHM). Model simulations are benchmarked with real monitoring data from the Great Belt Bridge (Denmark). In general, good agreement is found between model simulations and monitoring data. Current research efforts to improve the limitations of the proposed model are outlined.