Machine Learning and Linear Regression Approach to Model Unconfined Compressive Strength of Ceramic Waste Modified Soil as Subgrade Pavement Material

Abstract

An effective application of artificial intelligence involves artificial neural networks. Artificial neural networks and linear regression models were developed to simulate the effects of using discarded ceramic waste as a subgrade for pavement. The ceramic waste was used at 2.5%, 5%, 7.5%, 10%, 12.5%, and 15%. A sample with 0% ceramic waste was tested to serve as a reference sample. The dataset was produced from laboratory experimentation findings used to train, test, and evaluate the model. A training set, a target set, and a prediction set were created from the dataset. The artificial neural network MSE was 0.42-1.40, while the linear regression model range was 1.74 to 3.63 for ceramic modified samples. The R2 range for the ANN model was 0.85-0.92, and the linear regression model exhibited a range of 0.71-0.78. The ANN model was more accurate than the linear regression model. Future studies are required to compare different machine-learning approaches for predicting soil mechanical properties.