An output-only method for structural health monitoring via collaborative swarm optimisation algorithm and enhanced response reconstruction

The absence of excitation information and the presence of measurement noise pose significant challenges to accurate damage detection. To address these issues, this study introduces a robust output-only damage identification framework that integrates an enhanced response reconstruction scheme with a collaborative swarm optimisation algorithm (CSOA). In the proposed framework, the available structural responses are partitioned into two subsets. The second set measurements are reconstructed from the first set using the enhanced response reconstruction technique with an improved regularised method. The damage identification task is then formulated as an optimisation-based inverse problem, where the objective function is defined by the discrepancy between the measured and reconstructed responses. Subsequently, CSOA is employed as the search tool to identify local damages by minimising this objective function. The proposed approach is validated through numerical studies on a simply supported truss, a cantilever beam and the Guangzhou New TV Tower, as well as experimental verification on an eight-story steel frame. The effects of measurement noise and damage scenarios are systematically investigated. Results demonstrate that the method can reliably and efficiently identify both the location and severity of single and multiple damages, even with noisy output-only data.