SHM-based probabilistic model updating for performance assessment of welded joints in steel decks considering strain, temperature and traffic data

Abstract

Structural monitoring has the potential to reduce the uncertainties associated with the prediction of performance profiles of deteriorating civil infrastructures. However, there is still a need to further develop the theoretical framework to extract relevant information from monitoring data to support management strategies. The present paper focuses on the use of monitoring data to assess the performance of welded joints in orthotropic steel decks. Firstly, a probabilistic model to predict on an hourly basis a strain-related performance indicator based on pavement temperatures and heavy traffic intensities is presented. Model parameters are then estimated/updated using monitoring outcomes from the Great Belt Bridge (Denmark) through Bayesian inference, using Markov Chain Monte Carlo (MCMC) simulation. Including more monitoring data in the estimation process may result in a reduction of the uncertainty of the estimated model parameters, which can be used to determine appropriate monitoring durations. Model-based performance predictions are benchmarked with real monitoring outcomes. Good agreement is found for a short-termprediction time interval of eight consecutive days with similar ambient conditions. The adequacy of the proposed model is discussed based on the obtained results and an overview of the ongoing research is finally given. © 2013 Taylor & Francis Group, London.