Using Similarity Distance Measures for Multiclass Damage Detection in Dynamically Monitored Structures

Domain adaptation (DA) techniques have recently been developed as a promising approach to enhance the performance of structural damage classification algorithms. Unlike traditional methods, DA imposes fewer constraints on the nature and completeness of datasets, although its effectiveness largely depends on the similarity between the datasets used for knowledge transfer. This paper proposes a novel approach for assessing structural similarity to improve DA in structural health monitoring (SHM). The identification of suitable source data for knowledge transfer in damage detection is an open issue in SHM, especially when dealing with important geometric, mechanical, and topological differences between the structures. To address this issue, damage detection accuracy is increased by investigating similarity in the modal features of different framed structures, with the aim of understanding their dynamic behavior through a similarity index based on divergence measures. In detail, this work proposes a novel modal sensitivity-based similarity index which relies on the Kullback-Leibler divergence computed from vibration-based dynamic features. This similarity index effectively reveals how structures differing in highly sensitive parameters exhibit greater divergence. When DA is applied, source datasets with higher similarity lead to improved multiclass damage classification accuracy on the target framed structure. The proposed index can be used to systematically rank candidate source structures before applying DA, allowing a more efficient selection process. Its applicability extends to large-scale structures, where managing heterogeneous structural datasets is essential, supporting data-driven SHM strategies with enhanced transferability and reliability in real-world monitoring scenarios.