Concrete Bridges Continuous SHM Using MEMS Sensors: Anomaly Detection for Preventive Maintenance

Bridge infrastructures in Europe are facing ageing, progressive damaging processes, change of traffic loads as well as climate change effects; as such, a sound diagnostics process based on the analysis of accurate information acquired from monitoring systems is a key enabler to support the application of preventive maintenance plans and to guide efficient decisions on repairs or strengthening. The fast-paced development of cheaper but reliable devices has allowed to collect a huge amount of data to deepen the knowledge of the structural behavior over time of existing structures under service conditions. This paper shows the use of MEMS sensors, both clinometers and accelerometers, for continuous structural health monitoring on concrete bridges. A dense sensing monitoring approach is applied, and data are analyzed and compared in near-real time with a threshold set based on an updated reference FE model of the bridges. A case study is presented, where anomaly detection algorithms based on key performance indicator evolution in time have efficiently identified and localized damages triggering repeated proactive maintenance interventions. Attention is given to the seasonal influence on both the static and dynamic response of the bridges, and on the misleading effects on the damage detection and diagnostics processes. This approach is part of a wider framework aimed at an industrial application of SHM, in which the specific aspects covered in this paper have been identified and analyzed on multiple similar concrete bridges under continuous monitoring, of which evidence is provided.