Two-Stage Damage Identification in Beam Structures Using Residual-Based Wavelet Contrast Index and Improved Dung Beetle Optimizer

Accurately identifying damage in beam structures remains a tough challenge, the global trend of wavelet coefficients easily swallows faint local defect signatures, and high-dimensional model updating is computationally inefficient. To tackle these problems, this paper introduces a robust two-stage framework for damage identification that combines a residual-based wavelet strategy with an Improved t-distribution Dung Beetle Optimizer (ITDBO). Rather than relying on guesswork for wavelet selection, we introduce the Residual-based Wavelet Contrast Index (RWCI). By actively stripping away the global trend embedded within the wavelet coefficients, RWCI isolates the pure residual data, drastically amplifying the contrast between genuine stiffness loss and ambient noise for precise damage localization. With the search zone narrowed down, we deploy the ITDBO to quantify the severity. Powered by Bernoulli chaotic mapping and a t-distribution perturbation mechanism, ITDBO effectively bypasses the curse of dimensionality and entirely avoids the premature convergence traps that plague standard metaheuristics. Validated through both numerical simulations and physical experiments on a one-dimensional fixed-fixed steel beam, this hybrid approach proves its mettle. The framework not only accurately flags the defects through heavy noise but also locks onto the exact damage severity with unprecedented efficiency and stability.