Prediction of Young's modulus of weathered igneous rocks using GRNN, RVM, and MPMR models with a new index

Abstract

Young's modulus (YM) of intact rock is an important parameter in the assessment of engineering behaviours of rock masses, and it cannot always be obtained in an economical and practical manner in laboratory experiments. The main purpose of this study is to examine the capability of the minimax probability machine regression (MPMR), relevance vector machine (RVM), and generalised regression neural network (GRNN) models for the prediction of YM. The other aim is to determine the usefulness of a new index, the n-durability index (n(drb)), which is based on porosity and the slake durability index. According to the regression analysis performed in this study, the n-durability index as an explanatory parameter performs better than the P-wave velocity (V-p), porosity, and slake durability index in the models, considering the results herein as well as the existing literature. According to regression error characteristic curves, Taylor diagrams, and performance indices, the best prediction model is MPMR, while the worst is the GRNN model. Although GRNN is the worst of the soft computing models, its performance is slightly better than that of the multiple linear regression (MLR) model. According to the results of the study, the MPMR and RVM models with n(drb) and V-p are successful tools that can predict the YM of igneous rock materials to different degrees.