Health Monitoring of Neighbored Structures Using the Probability Space of Correlation Function From Sparsely Distributed Sensors

As compared with large-scale infrastructures with much attention in the design, construction, and operation, there are a huge number of small-scale infrastructures with a relatively lower safety factor and under deficient conditions. These structures rarely own a complete structural health monitoring (SHM) system due to budget limitations. Given the fact that the neighbored infrastructures are subjected to correlated load effects, their responses are spatiotemporally correlated. In this study, the concept of neighbored SHM is thereby proposed for the neighbored small-scale infrastructures under a sparse, lightweight, and data-driven sensing framework. The cross-correlation coefficients (CCCs) of the responses from multiple neighbored structures are theoretically derived, and the associated probability space is subsequently modeled. An anomaly can be detected by estimating the deviations of CCCs from their baselines. A decision matrix is finally obtained to identify the abnormal elementary structure from a set of neighbored structures, where installing only a single sensor is required on each elementary structure. The proposed approach has a sound theoretical basis, light computational cost, and low implementing burdens; thus, it applies to small-scale infrastructures with large populations. Numerical and laboratory experiments are conducted using the mass-spring systems and simply-supported beams. Results have demonstrated that the developed neighbored SHM strategy is efficient and sensitive to structural anomaly.