Experimental and Machine Learning Models for Stress Amplitude Prediction in Damaged GFRP Composite Pipe

In this paper, a comprehensive modal analysis is conducted, utilizing experimental and numerical approaches to investigate and predict the influence of the damage size on structural behavior. A finite element (FE) model is created from different loading conditions of the glass fiber reinforced polymer (GFRP) composite pipe with a fixed damage size. The model is subjected to three pressure levels (10, 20, and 30 bar). Next, a dynamic frequency load is then added to the FE model to visualize the different frequency mode shapes and their values under different loading conditions on a pressurized and unpressurized composite pipe. The results were collected for the proposed training models. A Kolmogorov Arnold model is employed to develop a robust predictive model that accurately estimates these geometrical and mechanical parameters by analyzing vibration data from various scenarios. The results show a significant correlation between the actual and predicted results across a wide range of test settings and scenarios, and the precision of the predictions is increased by combining data-driven methodologies with classical modal analysis.