Structural Health Monitoring as a Big-Data Problem

Structural health monitoring (SHM) has evolved over decades of continuous progress in measuring, processing, collecting and storing massive amounts of data that can provide valuable information for owners and managers in order to control and manage the integrity of their structures. The data sets acquired from SHM systems are undoubtedly of the big data type due to their sheer volume, complexity and diversity, and conducting relevant analyses of their content can help to identify damage or failure during operation through the relationships between the measurements taken by multiple sensors. A great deal can be learned from these large pools of data, resulting in significant advances in efficient integrity control. From banking to retail, many sectors have already embraced big data, which is often synonymous with big expectations; in the present case, it offers opportunities to apply data-processing research to the development of more efficient SHM systems with real-time capabilities. By presenting various examples of bridge monitoring systems, this paper contributes to the ongoing cross-disciplinary efforts in data science for the utilization and advancement of SHM.