Damage assessment and structural performance analysis of stitched and unstitched composite laminates (carbon jute FRP) using experimental, numerical, and machine learning approaches (ANN-PSO)

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This study presents a comprehensive investigation of damage behavior and structural performance in composite materials, with particular emphasis on stitched and unstitched laminates. A combined approach integrating experimental modal analysis, numerical modeling, and data-driven techniques is adopted to evaluate the influence of damage and reinforcement on dynamic characteristics. Vibration-based methods are employed to extract modal parameters, including natural frequencies, damping ratios, and mode shapes, which serve as sensitive indicators of structural integrity. A finite element model is developed to simulate the dynamic response and damage evolution of composite structures, and its predictions are validated against experimental results. In addition, machine learning techniques are explored as efficient tools for predicting damage effects and reducing computational cost. The role of through-thickness stitching is critically assessed, highlighting its effectiveness in suppressing delamination and improving damage tolerance, while also introducing local stress concentrations and stiffness variations. The results demonstrate that stitched composites exhibit improved damping and reduced damage propagation, with observable differences in modal behavior compared to unstitched laminates. The proposed integrated methodology using ANN-PSO for damage size prediction in Carbon Jute FRP composite materials provides a reliable framework for damage assessment and structural health monitoring. The regression coefficients obtained for the ANN-PSO models were 0.99737, 0.99843, 0.99802, and 0.99768 for H = 6, 8, 10, and 12 , respectively, indicating a strong agreement between predicted and actual outputs. In addition, the prediction errors remained low across all scenarios. This work contributes to the development of more efficient and accurate predictive tools for advanced composite structures.

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