Composite Structural Health Prediction Using Vibroacoustic Responses via Machine Learning Techniques

This research adopted five different machine learning (ML) algorithms to identify damage in the laminated structure utilizing the vibroacoustic responses obtained via coupled finite element (FE)–boundary element (BE) solutions. Initially, the composite structural model is derived through higher-order midplane kinematics and solved to compute the fluid–structure interactions in terms of vibroacoustic values via in-house computer code. Furthermore, the exactness and effectiveness of the suggested model have been verified with experimental responses of the intact and damaged shell structure. Subsequently, the sound pressure level data have been extracted for different excitation frequencies numerically using various input parameters via the established model. First, the said ML models are trained using 1500 extracted data sets (using the customized MATLAB code) and predicted the damage using 303 datasets by setting the numerals, i.e., ‘0’ and ‘1’. A total of 1803 datasets (crack: 902 datasets; intact: 901 datasets) have been utilized for the current analysis. The current predicted responses indicate that the Random Forest Classifier is capable of providing higher accuracy (ranges between 81% and 91%) in comparison to all types of ML algorithms (Random Forest Classifier, Logistics Regression, Linear Support Vector Classified, Kernel Support Vector Machines and Artificial Neural Network) adopted. The proposed method detects and forecasts defects in the composite structures early and helps prevent adverse effects.