Mechanics-based deep learning framework for predicting deflection of functionally graded composite plates using an enhanced whale optimization algorithm

Abstract

This paper introduces a novel deep learning framework for predicting the normalized and non-dimensional deflection of functionally graded composite plates subjected to sinusoidal loading. The proposed approach integrates a deep neural network (DNN) with a novel enhanced whale optimization algorithm (EWOA) to optimize deflection predictions considering mechanical parameters as input data, including stress, strain, plate geometry, and boundary conditions. The deflection outputs are expressed in both normalized and non-dimensional forms, demonstrating a robust and generalizable prediction model applicable to various structural configurations. During the training phase, the proposed EWOA significantly enhances convergence efficiency and prediction accuracy by introducing two key improvements: chaotic initialization and an adaptive leader mechanism. The EWOA-DNN model is trained using analytically derived deflection datasets, exhibiting strong adaptability to changes in material gradation and loading scenarios. Comparative studies confirm that the suggested hybrid framework outperforms conventional optimization-based models, creating an effective and reliable artificial intelligence (AI)-driven tool for structural design, computational mechanics, and the analysis of functionally graded composite materials.