Frequency data driven damage detection of polymeric composite structural health using machine learning models

Abstract Firstly, the effect of damages (crack and delamination) on frequency responses of the polymeric composite structures is predicted numerically in this research. The responses are computed numerically using the finite element technique associated with a higher‐order deformation kinematic model. The model accuracy has been verified by comparing the published frequency responses and in‐house experimental data. The verified model is extended to generate the desired data (frequencies) utilizing various input parameters related to the geometrical forms and damage types (shapes, sizes, and positions). Further, different machine learning models (MLMs) are developed using Python algorithms for the identification of structural health. In this regard, the extracted data sets are initially used to train the MLM, detect the damages, and identify types of damage and damage‐related data of polymeric structures. Out of all kinds of MLMs, it is understood that the Random Forest Classifier provides the best result, which had an accuracy of 94.66% with the optimal parameters. The precision accomplished is 97% for intact and 94% for damaged structures. The proposed algorithm is also capable of identifying the damage‐related parameters (shape, size, type, and position) and predicting the defects early to prevent unintended mishaps.