Simulating Reality: Numerical Assessments of a Bridge Health Monitoring

In contrast to conventional sensing technologies, the preparation and extraction of displacement data from a large number of SAR images are not trivial. Notably, this process is difficult and time-consuming for long-term monitoring. For this issue, this chapter aims to validate the proposed probabilistic unsupervised learning method via simulated displacement responses of a numerical model of a real-world bridge structure. On this basis, the numerical model of this bridge was developed in MATLAB environment. To simulate remote sensing-based SHM, a few points in the bridge deck are selected as the target areas for extracting structural responses. Three damage scenarios in the vicinity of one the bridge piers are simulated to assess whether the proposed probabilistic unsupervised learning method can effectively detect such scenarios.