Title: Application of artificial intelligence techniques for identifying rock mass quality in an underground tunnel

Authors: Sylvanus Sebbeh-Newton; Hareyani Binti Zabidi

Addresses: School of Materials and Mineral Resources Engineering, Universiti Sains Malaysia, 14300, Penang, Malaysia ' School of Materials and Mineral Resources Engineering, Universiti Sains Malaysia, 14300, Penang, Malaysia

Abstract: The horizontal probe drilling data from the Pahang-Selangor raw water transfer tunnel (PSRWT) was used to develop intelligence models that could be used to identify the rock mass quality ahead of the excavation. In this study, two common artificial intelligence techniques; artificial neural network (ANN) and adaptive neuro-fuzzy inference system (ANFIS) were used to achieve this aim. The input variables include rock quality designation, discontinuity spacing, groundwater inflow, discontinuity conditions, and penetration rate. The target variable was the rock mass rating index. Coefficient of determination (R2), root mean squared error (RMSE), and variance accounted for (VAF) were calculated to assess the developed models. The R2, VAF, and RMSE values of 0.922, 92.08%, and 2.284 respectively for ANN indicates a lower prediction output than the ANFIS model with R2, VAF, and RMSE values of 0.925, 92.27%, and 2.054 respectively. The results show that ANFIS outperformed ANN in predicting rock mass rating.

Keywords: artificial neural network; ANN; adaptive neuro-fuzzy inference system; ANFIS; penetration rate; rock mass rating system; RMR; tunnelling.

DOI: 10.1504/IJMME.2021.116885

International Journal of Mining and Mineral Engineering, 2021 Vol.12 No.2, pp.132 - 148

Received: 15 Aug 2020
Accepted: 28 Mar 2021
Published online: 05 Aug 2021 *

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