Optimization-based damage detection in composite structures using incomplete measurements

Abstract

Incomplete data poses challenges in accurately assessing structural health and detecting damage. It limits the ability to capture the complete behavior and response of the structure, which may hinder the identification and localization of potential damage or anomalies. Addressing the issue of incomplete data requires developing strategies and algorithms that can effectively handle missing or limited measurements. Using incomplete and noisy measurements, we propose an optimization-based damage detection method for laminated composite plates with closely-spaced eigenvalues. The proposed method consists of two stages. In the first stage, the most probable defective elements are identified by utilizing condensed mode shapes as incomplete noisy inputs for modal residual vectors. This approach significantly reduces the computational effort for damage estimation. The second stage introduces an objective function based on incomplete and noisy Condensed Frequency Response Functions (CFRFs). To optimize the damage quantification, the Improved Particle Swarm Optimization (IPSO) algorithm is employed to minimize errors in the proposed objective function based on CFRFs of damaged and intact laminates. The proposed method is demonstrated on laminated composite plates with different lamination schemes, ply orientations, and multiple damaged elements in different damage scenarios. By evaluating the method on numerical results and comparing it with previous studies, its superiority is demonstrated. Furthermore, the proposed method exhibits robustness to changes in mass distribution in the system investigated by retrofitting extra masses to the plate structures that lead to worsening closely-spaced eigenvalues.