UCS Prediction of Carbonate Rocks using Deep Feedforward Neural Networks with Aquila Algorithm Model
Abstract
For safe and cost effective design of geotechnical and rock engineering schemes, accurate Uniaxial Compressive Strength (UCS) prediction is crucial, especially in carbonate rock formations with considerable heterogeneity. For enhancing the precision and resilience of UCS prediction, this study proposes a hybrid intelligent modelling framework that combines Aquila Optimization (AO) with a Deep Feedforward Neural Network (DFNN). Comprehensive data collected from laboratory-tested carbonate rock samples are preprocessed, which includes handling missing values and data cleaning. To comprehend the behavior and correlations of input variables, exploratory data visualization and feature distribution analysis are carried out. The regression model is a deep feedforward neural network and important hyperparameters like number of hidden layers, neurons, learning rate and activation functions are optimally tuned using Aquila Optimisation Algorithm (AOA). Multiple regression metrics are used to quantitatively assess model performance from python software. The findings show that proposed framework attains minimum RMSE and MSE value of 0.0425,0.0018 is useful for rock mechanics and mining applications and gives a dependable, data-driven tool for UCS prediction in carbonate rocks.