Predicting thermal response of bridges using regression models derived from measurement histories

• Presents a systematic approach to quantify thermal response in measurements from SHM.
• Employs regression models trained on historical data to reliably predict thermal response.
• Incorporates strategies to manage high dimensionality and thermal inertia effects in data.
• High prediction accuracy is illustrated using measurements from lab and full-scale bridges.

This study investigates the application of novel computational techniques for structural performance monitoring of bridges that enable quantification of temperature-induced response during the measurement interpretation process. The goal is to support evaluation of bridge response to diurnal and seasonal changes in environmental conditions, which have widely been cited to produce significantly large deformations that exceed even the effects of live loads and damage. This paper proposes a regression-based methodology to generate numerical models, which capture the relationships between temperature distributions and structural response, from distributed measurements collected during a reference period. It compares the performance of various regression algorithms such as multiple linear regression (MLR), robust regression (RR) and support vector regression (SVR) for application within the proposed methodology. The methodology is successfully validated on measurements collected from two structures – a laboratory truss and a concrete footbridge. Results show that the methodology is capable of accurately predicting thermal response and can therefore help with interpreting measurements from continuous bridge monitoring.