DEVELOPMENT OF SOFT COMPUTING-BASED PREDICTIVE TOOLS FOR ESTIMATING THE YOUNG MODULUS OF WEAK ROCKS
Özet
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.
