Hybrid Computational Model (FE and ANN) for Prediction of Modal Frequency Response of Damaged Hyperelastic (Hydrogel) Panel Structure

This study presents an integrated finite element (FE) and artificial neural network (ANN) framework for predicting the modal frequency response of damaged agar-based hydrogel plates under varying geometry, boundary conditions, and damage severity. The aim of the work is to develop a computationally efficient hybrid methodology in which validated FE simulations are used to generate a reliable dataset, and ANN-based surrogate models are subsequently employed for rapid frequency prediction. The hydrogel material parameters were obtained from experimental compression data through curve fitting using an Ogden-type hyperelastic model. Thereafter, the frequency characteristics were computed using ABAQUS-based FE analysis, and the numerical consistency was ensured through mesh convergence and validation studies. A systematic parametric investigation was conducted by varying stiffness-sensitive parameters, namely aspect ratio, thickness ratio, boundary conditions, and damage length, to establish physically consistent frequency trends. Unlike earlier studies that mainly focus on either numerical simulation or data-driven prediction independently, the present work combines FE-based modal analysis with ANN surrogate modelling for frequency-based assessment of damage-induced stiffness degradation in hydrogel plates. Based on the generated dataset, five ANN surrogate configurations were trained and compared to identify the most efficient prediction model. The proposed FE-ANN workflow enables rapid frequency estimation and reduces the need for repeated FE simulations. The frequency increased from 6.4977 Hz to 30.093 Hz when the aspect ratio changed from 1 to 3.