The aim of this research is to investigate the failure mechanisms of the filament-wound composite tubes under axial compressional loading by using an acoustic emission approach. First, the mechanical properties of ±45°C composite tubes were obtained experimentally. Then, failure due to the buckling phenomenon and crashworthiness characteristics were studied utilizing numerical simulation and experimental methods. Tubes were next simulated in ABAQUS software, and a continuum damage mechanics model was implemented in a progressive framework to assess the failure modes. From the macroscale view, results showed that the damage behavior of composite tubes turned out to be dominated by local buckling followed by a post-buckling field, which is generated by longitudinal cracks along the winding direction. On the micro-scale, the acoustic emission-based procedure based on the wavelet packet transform method was adopted. The hierarchical modeled assessment resulted in the identity of four clusters of AE signals. In GFRP tubes, the fiber breakage and fiber/matrix separation could mostly control the higher percentage of damage and cause to increase the energy absorption. Finally, by comparing the results obtained from micro and macro scales, the local buckling failure mode was attributed to the low content of fiber/matrix debonding in the structure.

Composite structures in transportation industries have gained significant attention due to their unique characteristics, including high energy absorption. Non-destructive testing methods coupled with machine learning techniques offer valuable insights into failure mechanisms by analyzing basic parameters. In this study, damage monitoring technologies for composite tubes experiencing progressive damage were investigated. The challenges associated with quantitative failure monitoring were addressed, and the Genetic K-means algorithm, hierarchical clustering, and artificial neural network (ANN) methods were employed along with other three alternative methods. The impact characteristics and damage mechanisms of composite tubes under axial compressive load were assessed using Acoustic Emission (AE) monitoring and machine learning.Various failure modes such as matrix cracking, delamination, debonding, and fiber breakage were induced by layer bending. An increase in fibers/matrix separation and fiber breakage was observed with altered failure modes, while matrix cracking decreased Signal classification was achieved using hierarchical and K-means genetic clustering methods, providing insights into failure mode frequency ranges and corresponding amplitude ranges. The ANN model, trained with labeled data, demonstrated high accuracy in classifying data and identifying specific failure mechanisms. Comparative analysis revealed that the Random Forest model consistently outperformed the ANN and Support Vector Machine (SVM) models, exhibiting superior predictive accuracy and classification using ACC, MCC and F1-Score metrics. Moreover, our evaluation emphasized the Random Forest model's higher true positive rates and lower false positive rates. Overall, this study contributes to the understanding of model selection, performance assessment in machine learning, and failure detection in composite structures.