Thermal response prediction of the MX3D bridge’s operational dataset

Environmental and operational variations (eov) remain a major obstacle for the successful transfer of structural health monitoring (shm) techniques from laboratory experiments, such as cracked beam experiments, to full-scale structures, such as long-span bridges. With evidence that timely interventions significantly increase the service-life and reduce the overall life-cycle cost of ageing infrastructure, carrying out shm in the presence of eov, such as temperature, is therefore of high priority within the bridge maintenance community. An iterative regression-based thermal response prediction (irbtrp) methodology is utilized within the temperature-based measurement interpretation (tb-mi) approach and is trained on the healthy condition of the bridge to predict its undamaged response to temperature fluctuations. The tb-mi approach and the irbtrp methodology are applied to the mx3d bridge in this study, demonstrating that their use enables earlier and more widespread damage detection amongst multiple sensor groups, compared to when no temperature effects are considered.