Structural edge damage detection based on wavelet transform and immune genetic algorithm

The wavelet transform (WT) has gained significant attention for its ability to identify damage details within strain modes. However, edge damage in structures often remains obscured and unrecognizable when WT is applied, primarily due to edge effects. Intelligent algorithms used to assess structural damage severity often face challenges such as premature convergence and a tendency to settle on local optima. To address these challenges, damage location is analyzed using WT with a fitting extension of the original vibration signal, effectively mitigating edge effects. Additionally, an immune-genetic algorithm, integrating genetic and immune algorithms, is employed to overcome limitations of traditional intelligent algorithms in damage severity identification. The two-stage method's effectiveness was validated through finite element simulations of fixed beam and frame structures, as well as vibration tests of fixed and cantilever beams, for locating and assessing edge damage. This method showed clear advantages, including precise damage characterization, noise robustness, and high sensitivity to edge damage.