Exploring Bridge Structural Response similarities: Data-Driven SHM Through MEMS Clinometer Clustering over a Network of 25+ Reinforced Concrete Bridges

Structural Health Monitoring (SHM) employs a combination of mechanism analysis, monitoring technology, and data analytics to identify, classify, and assess the significance of structural conditions such as sudden or cumulative damages. In recent years, the spread of IoT-based continuous SHM systems has enhanced the control of the functionality and operational integrity of bridges supporting an informed proactive maintenance, management, and decision-making processes associated with bridges. With the growing availability of big data across a network of similarly monitored structures, there is an increased interest and possibility of conducting a more in-depth analysis to identify commonalities or differences between homogeneous structural schemes and typologies. This interest arises from the dual need to optimize the utilization of monitoring systems over a widespread network and to address the challenge that in-service bridges often lack data from damage scenarios, with existing methods struggling to achieve real-time detection. This goal, along with optimizing the monitoring strategy, could be achieved by grouping similar structures in the network into macro-classes, with a 'master' structure serving as a model for the others. This paper explores the feasibility of this approach and aims to establish a valid criterion for identifying macro-classes within an infrastructure network. The study involves the analysis of the static response of over 25 reinforced concrete and prestressed bridges/viaducts built in Italy, sharing similarities in static schemes and construction methods. The analysis relies on data gathered from the execution of static load tests, proposing a tool for optimizing the monitoring strategy across a network of bridges.