Elsevier

Engineering Geology

Volume 203, 25 March 2016, Pages 70-82
Engineering Geology

Modeling deformation modulus of a stratified sedimentary rock mass using neural network, fuzzy inference and genetic programming

https://doi.org/10.1016/j.enggeo.2015.12.002Get rights and content

Highlights

  • Engineers prefer to determine deformation modulus of rock mass (Em) indirectly.

  • Estimating the Em using new techniques will help us obtain more realistic Em values.

  • It is aimed to obtain indirectly more realistic and reliable Em values in this paper.

  • Artificial neural network, neuro fuzzy and genetic programming methods were used.

  • The performance of expressions obtained from methods used are quite satisfactory.

Abstract

This paper investigates a series of experimental results and numerical simulations employed to estimate the deformation modulus of a stratified rock mass. The deformation modulus of rock mass has a significant importance for some applications in engineering geology and geotechnical projects including foundation, slope, and tunnel designs. Deformation modulus of a rock mass can be determined using large scale in-situ tests. This large scale sophisticated in-situ testing equipments are sometimes difficult to install, plus time consuming to be employed in the field. Therefore, this study aims to estimate indirectly the deformation modulus values via empirical methods such as the neural network, neuro fuzzy and genetic programming approaches. A series of analyses have been developed for correlating various relationships between the deformation modulus of rock mass, rock mass rating, rock quality designation, uniaxial compressive strength, and elasticity modulus of intact rock parameters. The performance capacities of proposed models are assessed and found as quite satisfactory. At the completion of a comparative study on the accuracy of models, in the results, it is seen that overall genetic programming models yielded more precise results than neural network and neuro fuzzy models.

Introduction

The deformation modulus of a rock mass is one of the most significant properties used by a number of designers (Gurocak et al., 2007, Gurocak et al., 2008, Alemdag et al., 2008, Gurocak, 2011, Kaya et al., 2011, Singh et al., 2012, Aksoy et al., 2012, Ajalloeian and Mohammadi, 2014, Alemdag et al., 2014, Nejati et al., 2014, Alemdag, 2015, Feng and Jimenez, 2015) for estimating deformation behavior of rock masses encountered in civil engineering projects, such as design of foundations, tunnels and slopes. The deformation modulus of a rock mass can only be determined by employing large-scale in-situ tests on the rock mass, itself, for example, pressuremeter, plate loading, flat dilatometer, plate jacking, and Goodman jacking. It is sometimes difficult to apply the large scale in-situ tests because of time consuming processes and installation required. Therefore, the modulus of deformation may be determined from other rock properties using empirical estimations for the indirect estimations, which may be acquired more easily and cost effective application.

A number of researchers have developed various empirical relationships for estimating the deformation modulus, based on different classification schemes including Rock Mass Rating (RMR) (Bieniawski, 1974), Tunneling Quality Index (Q) (Barton et al., 1974), Geological Strength Index (GSI) (Hoek and Brown, 1997) and Rock Mass Index (RMi) (Palmstrom, 1995), and RQD (Rock Quality Designation) (Deere, 1964). The first empirical equation for the rocks with RMR > 50 was proposed by Bieniawski (1978). Later, Serafim and Pereira (1983) developed an equation for rock masses with RMR < 50. Nicholson and Bienawski (1990) released an equation based on both RMR and Ei values. Read et al. (1999), Gokceoglu et al. (2003), Chun et al. (2006) and Chun et al. (2009) suggested RMR based equations, whereas Mitri et al. (1994), Kayabasi et al. (2003), Ramamurthy (2004), Gokceoglu et al. (2004) and Sonmez et al. (2006) utilized both RMR and Ei values. RQD and Ei values were employed for the deformation modulus of rock by Zhang and Einstein (2004). Alemdag et al. (2015) evaluated different rock masses, results of in-situ test and existing empirical equations and suggested a new regression equation for estimating the deformation modulus of rock mass.

The present study investigates the applications of artificial neural network (ANN), neuro fuzzy (NF), and genetic programming (GP) for estimating the deformation modulus of a stratified sedimentary rock mass (Em). Success of ANN, NF, and GP applications in various engineering disciplines including geotechnical engineering have already been proven in the literature (Ying et al., 2006, Sawmliana et al., 2007, Singh et al., 2008, Gokceoglu et al., 2009, Cabalar et al., 2009, Cevik and Cabalar, 2009, Cabalar and Cevik, 2009a, Cabalar and Cevik, 2009b, Cabalar et al., 2010, Cevik et al., 2010, Verma and Singh, 2010, Cabalar and Cevik, 2011, Gurocak et al., 2012, Cabalar et al., 2012, Edincliler et al., 2010, Singh et al., 2013). In this study, these modeling techniques have been employed by proposing various equations to indicate the relationships between Em (deformation modulus of rock mass), RMR (rock mass rating) and Ei (elasticity modulus of intact rock), RMR and UCS (uniaxial compressive strength), RMR and Ei/UCS, RQD (rock quality designation) and Ei, RQD and UCS, and RQD and Ei/UCS.

Section snippets

Field studies and testing

The study area is located at approximately 40 km southeast of Elazig in eastern Turkey. Maestrichtian–Late Paleocene aged siltstones, known as Simaki Formation, outcrop out in the region (Fig. 1).

The siltstones are light gray and well-bedded (Fig. 2), and the geotechnical properties of the siltstones were determined by field and laboratory studies. During the field studies, the weathering, spacing, aperture, persistence, filling and roughness of discontinuities were defined using the scan-line

Development of empirical equations

Neural network (NN), neuro fuzzy (NF), and genetic programming (GP) approaches have been employed to determine the relationships between the deformation modulus of rock mass (Em) and the rock mass rating (RMR), rock quality designation (RQD), uniaxial compressive strength (UCS), and modulus of elasticity (Ei) parameters. During the analyses, the pairs of RMR–Ei, RMR–UCS, RMR–Ei/UCS, RQD–Ei, RQD–UCS and RQD–Ei/UCS were considered as the independent variables.

Conclusions

Rock mass deformation modulus (Em) has a very important place in rock engineering design due to a parameter represents the best the mechanical behavior of pre-failure of rock mass. Therefore, design engineers need to design the parameter which can be determined using empirical methods or in-situ tests. The Em requires large scale sophisticated in-situ testing equipments. Thus, it is sometimes difficult, due to the high costs, time consuming, and having operational difficulties, to determine the

References (68)

  • C. Gokceoglu et al.

    Predicting deformation moduli of rock masses

    Int. J. Rock Mech. Min. Sci.

    (2003)
  • C. Gokceoglu et al.

    A neuro-fuzzy model for the deformation modulus of jointed rock masses

    Comput. Geotech.

    (2004)
  • Z. Gurocak et al.

    Empirical and numerical analyses of support requirements for a diversion tunnel at the Boztepe Dam Site, Eastern Turkey

    Eng. Geol.

    (2007)
  • Z. Gurocak et al.

    Rock slope stability and excavatability assessment of rocks at the Kapikaya Dam Site, eastern Turkey

    Eng. Geol.

    (2008)
  • Z. Gurocak et al.

    New considerations for empirical estimation of tensile strength of rocks

    Eng. Geol.

    (2012)
  • E. Hoek et al.

    Practical estimates or rock mass strength

    Int. J. Rock Mech. Min. Sci. Geomech. Abstr.

    (1997)
  • A. Kayabasi et al.

    Estimating the deformation modulus of rock masses — a comparative study

    Int. J. Rock Mech. Min. Sci.

    (2003)
  • T. Ramamurthy

    A geo-engineering classification for rocks and rock masses

    Int. J. Rock Mech. Min. Sci.

    (2004)
  • R. Singh et al.

    Estimation of elastic constant of rocks using an ANFIS approach

    Appl. Soft Comput.

    (2012)
  • H. Sonmez et al.

    Estimation of rock modulus

    Int. J. Rock Mech. Min. Sci.

    (2006)
  • L.A. Zadeh

    Fuzzy sets

    Inf. Control.

    (1965)
  • L. Zhang et al.

    Using RQD to estimate the deformation modulus of rock masses

    Int. J. Rock Mech. Min. Sci.

    (2004)
  • R. Ajalloeian et al.

    Estimation of limestone rock mass deformation modulus using empirical equations

    Bull. Eng. Geol. Environ.

    (2014)
  • S. Alemdag

    Properties of Rock Mass Deformation of Siltstone in the Simaki Formation (Elazig)

    (2010)
  • S. Alemdag

    Assessment of bearing capacity and permeability of foundation rocks at the Gumustas Waste Dam Site (NE Turkey) using empirical and numerical analysis

    Arab. J. Geosci.

    (2015)
  • S. Alemdag et al.

    Estimation of bearing capacity of basalts at Atasu Dam Site, Turkey

    Bull. Eng. Geol. Environ.

    (2008)
  • S. Alemdag et al.

    A large and rapid planar failure: causes, mechanism and consequences (Mordut, Gumushane, Turkey)

    Arab. J. Geosci.

    (2014)
  • ASTM

    Standard Test Method for Pressuremeter Testing in Soils. Annual Book of ASTM Standards

    (2000)
  • N.R. Barton et al.

    Engineering classification of rock masses for the design of tunnel support

    Rock Mech.

    (1974)
  • Z.T. Bieniawski

    Estimating the strength of rock materials

    J. South. Afr. Inst. Min. Metall.

    (1974)
  • Z.T. Bieniawski

    Engineering Rock Mass Classifications

    (1989)
  • A.F. Cabalar et al.

    Triaxial behaviour of sand–mica mixtures using Genetic Programming

    Expert Syst. Appl.

    (2011)
  • A.F. Cabalar et al.

    Neuro-Fuzzy based constitutive modelling of undrained response of Leighton Buzzard Sand mixtures

    Expert Syst. Appl.

    (2009)
  • A.F. Cabalar et al.

    Constitutive modeling of Leighton Buzzard sands using genetic programming

    Neural Comput. & Applic.

    (2010)
  • Cited by (0)

    View full text