Adaptive signal analysis-based method for debonding defect identification on concrete-filled steel tubes

ObjectiveTo address the problems of insufficient identification accuracy, strong empirical dependence of parameters, and weak anti-noise performance in debonding defect identification on concrete-filled steel tubular arch bridges, an intelligent debonding detection method was proposed.MethodFirst, a transient rectangular pulse load was applied to simulate impact excitation, based on the numerical model of debonding defects in a concrete-filled steel tubular arch bridge. Subsequently, the corresponding acceleration response signals were obtained. The grasshopper optimization algorithm (GOA) was employed to optimize the parameters of variational mode decomposition (VMD), thereby establishing an adaptive GOA-VMD signal decomposition framework. The weak debonding-related features were enhanced and represented in the time-frequency domain, combining with Teager energy operator and synchrosqueezed wavelet transform. Furthermore, a comprehensive cyclic kurtosis entropy (CKE) index was constructed. Finally, the multifactor analysis of variance and gradient boosting tree model were adopted to identify and evaluate debonding states.ResultSignificant differences were observed in the distributions of CKE values between intact condition and debonding condition, i.e., significance probability was 0.048, indicating that the CKE index has good capability for identifying debonding damage. The measurement position was found to be the most significant factor affecting CKE response, and the interaction between thickness and measurement location was also statistically significant. When the debonding thickness reached 0.4 mm, the standard deviation of CKE increased by approximately 15% compared with those in conditions of 0.2 mm and 0.3 mm. The standard deviation of CKE was reduced by about 72% with the co-located impact-measurement scheme compared with other layout schemes.ConclusionThe proposed method can effectively identify debonding defects at different locations and of different severities in concrete-filled steel tubular arch bridges, and exhibit good stability, noise immunity, and engineering applicability. It can provide technical support for debonding detection and condition assessment in bridge structural health monitoring systems.