Development of Soft Computing-based Predictive Tools for Estimating the Young Modulus of Weak Rocks

The deformation characteristics of rocks are of vital importance in addressing most geomechanical issues as they are one of the most critical input parameters in rock engineering analyses. For this reason, robust forecasting models are required when analysing the stability of tunnels, slopes, mine galleries, and other underground excavations. In this research, novel predictive models are proposed to estimate the tangential Young modulus (Eti) of weak rocks. To achieve this, an extensive literature review is performed to obtain a comprehensive database including critical physico-mechanical properties of various weak rocks. Thanks to the advantages of soft computing methods such as genetic algorithm (GA), adaptive neuro-fuzzy inference system (ANFIS), artificial neural networks (ANN) and multivariate adaptive regression splines (MARS), novel predictive models are established. The effectiveness of the developed predictive models is investigated using various statistical measures and it is concluded that empirical models utilizing ANN and ANFIS methodologies are the most effective tools for estimating the Eti of weak rocks. In addition, a practical design chart is also developed for assessing the Eti of weak rocks.