Temperature-based Damage Detection for the Commissioning Dataset of the MX3D Bridge

Environmental and operational variations (EOV) remain a major obstacle for the successful transfer of structural health monitoring (SHM) techniques from laboratory experiments to full-scale structures. 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 is therefore of high priority within the civil engineering community. The temperature-based measurement-interpretation (TB-MI) approach monitors the thermal response of an instrumented structure and detects changes linked to damage by minimising the impact that temperature variations have on anomaly detection techniques. An iterative regression-based thermal response prediction (IRBTRP) methodology is utilised in the TB-MI approach, and is trained on the healthy condition of the structure to predict its response to temperature fluctuations. The difference between the measured and predicted response provides temperature-corrected signals that are used for damage detection. The TB-MI approach and the IRBTRP methodology are applied to detect damage on the MX3D Bridge, the world's first structure produced through metal additive manufacturing. This study demonstrates that the TB-MI approach enables earlier and more widespread damage detection amongst multiple sensor groups, compared to when no temperature effects are considered. The adoption of the TB-MI approach can therefore greatly increase the reliability and our reliance on SHM techniques for critical infrastructure.