Comparative evaluation of artificial intelligence models for prediction of uniaxial compressive strength of travertine rocks, Case study: Azarshahr area, NW Iran

Abstract

Uniaxial compressive strength (UCS) is one of the most widely used and important rock mechanical parameters in rock engineering. The main objective of the present study was to evaluate the ability of artificial intelligence models including multi-layer perceptron (MLP), Sugeno fuzzy logic (SFL), Mamdani fuzzy logic (MFL), adaptive neuro-fuzzy inference system (ANFIS) and support vector machine (SVM) to predict the UCS of travertine rocks in the Azarshahr area (NW Iran). To attempt this objective, 85 core samples of travertine rock were collected from the study area and the laboratory tests were performed to determine the P-wave velocity [Vp (km/s)], porosity (n %), Schmidt rebound hardness (Rn) and UCS of the rocks at the Rock Mechanics Laboratory in the Tarbiat Modares University. The data set including Vp (km/s), n % and Rn as the inputs and UCS as the output were divided into training (80 % of dataset) and testing (20 % of dataset) subsets to construct the models. The coefficient of determination (R2), root mean square error (RMSE) and mean absolute error (MAE) were used to evaluate the models performance. The models accuracy followed the order SVM > ANFIS > SFL > MLP > MFL. The SVM model with RBF kernel function yielded the highest R2 (0.9516), and the lowest RMSE (2.14 MPa) and MAE (1.351 MPa) in the testing step. Accuracy results indicated that SVM model predictions were better than MLP, SFL, MFL and ANFIS models for prediction of UCS of travertine rocks.