From Theory to Reality: Advanced SHM Methods to the Tadcaster Bridge

Experimental validation of SHM methods is a critical process in evaluating the accuracy and reliability of such methods for damage assessment, especially in long-term monitoring programs. In this chapter, real-world applications of the proposed unsupervised learning methods (i.e., HMC-DAE-MD, HMC-UTSL-MD, HMC-DTL-MD, HMC-ODTL-EMD and SLS-ODTL-EMD) developed for coping with the limitation of small data are investigated by using a small set of displacement responses of a masonry bridge called the Tadcaster Bridge located in Tadcaster, UK. Because this bridge experienced a partial collapse, it is an appropriate case study for verifying the proposed methods. This chapter also indicates how a data augmentation process can help to better observe the EOCs in augmented displacement responses.