Top

Bulletin of Engineering Geology and the Environment

Published in:

29-11-2016 | Original Paper

Intelligent modelling of sandstone deformation behaviour using fuzzy logic and neural network systems

Authors: Behnam Yazdani Bejarbaneh, Elham Yazdani Bejarbaneh, Mohd For Mohd Amin, Ahmad Fahimifar, Danial Jahed Armaghani, Muhd Zaimi Abd Majid

Published in: Bulletin of Engineering Geology and the Environment | Issue 1/2018

Activate our intelligent search to find suitable subject content or patents.

search-config

AI-assisted search

Off

Abstract

A realistic analysis of rock deformation in response to any change in stresses is heavily dependent on the reliable determination of the rock properties as analysis inputs. Young’s modulus (E) provides great insight into the magnitude and characteristics of the rock mass/material deformation, but direct determination of Young’s modulus in the laboratory is time-consuming and costly. Therefore, basic rock properties such as point load strength index, P-wave velocity and Schmidt hammer rebound number have been used to estimate Young’s modulus. These rock properties can be easily measured in the laboratory. The main aim of this study was to develop two intelligent models based upon fuzzy logic and biological nervous systems in order to estimate Young’s modulus of sandstone for a set of known index properties drawn from laboratory tests. The database required to construct these models comprised a series of drill cores (96 samples of sandstone) from site investigation operations for a hydroelectric roller-compacted concrete (RCC) dam located in the Malaysian state of Sarawak. In the final stage of the present study, using the same data sets, multiple regression (MR) analysis was also proposed for comparison with the prediction results of both the fuzzy inference system (FIS) and artificial neural network (ANN) models. The ANN model was found to be far superior to FIS and MR in terms of several performance indices including root-mean-square error and ranking. Thus, from the results of this study, it was concluded that the models proposed herein could be utilised to estimate the E of similar rock types in practice.

previous article Model tree approach for predicting uniaxial compressive strength and Young’s modulus of carbonate rocks

next article A new approach for estimation of the rock mass deformation modulus: a rock engineering systems-based model

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Technik"

Online-Abonnement

Mit Springer Professional "Technik" erhalten Sie Zugriff auf:

über 67.000 Bücher
über 390 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Maschinenbau + Werkstoffe

Jetzt Wissensvorsprung sichern!

inform now

Springer Professional "Wirtschaft+Technik"

Online-Abonnement

Mit Springer Professional "Wirtschaft+Technik" erhalten Sie Zugriff auf:

über 102.000 Bücher
über 537 Zeitschriften

aus folgenden Fachgebieten:

Automobil + Motoren
Bauwesen + Immobilien
Business IT + Informatik
Elektrotechnik + Elektronik
Energie + Nachhaltigkeit
Finance + Banking
Management + Führung
Marketing + Vertrieb
Maschinenbau + Werkstoffe
Versicherung + Risiko

Jetzt Wissensvorsprung sichern!

inform now

Armaghani DJ, Amin MFM, Yagiz S, Faradonbeh RS, Abdullah RA (2016) Prediction of the uniaxial compressive strength of sandstone using various modeling techniques. Int J Rock Mech Min Sci 85:174–186

Bai Y, Zhuang H, Wang D (2007) Advanced fuzzy logic technologies in industrial applications. Springer Science & Business Media, London

Barton N (2002) Some new Q value correlations to assist in site characterisation and tunnel design. Int J Rock Mech Min Sci 39:185–216CrossRef

Barton N, Lien R, Lunde J (1974) Engineering classification of rock masses for the design of tunnel support. Rock Mech 6(4):189–236CrossRef

Beiki M, Majdi A, Givshad AD (2013) Application of genetic programming to predict the uniaxial compressive strength and elastic modulus of carbonate rocks. Int J Rock Mech Min Sci 63:159–169

Bieniawski ZT (1973) Engineering classification of rock masses. Trans S African Inst Civ Engrs 15(12):335–344

Bieniawski ZT (1978) Determining rock mass deformability—experience from case histories. Int J Rock Mech Min Sci Geomech Abstr 15:237–247CrossRef

Chiu SL (1994) Fuzzy model identification based on cluster estimation. J Intell Fuzzy Syst 2:267–278CrossRef

Cybenko J (1989) Approximations by superpositions of a sigmoidal function. Math Control Signal Syst 2:303–314CrossRef

Dehghan S, Sattari GH, Chehreh CS, Aliabadi MA (2010) Prediction of unconfined compressive strength and modulus of elasticity for Travertine samples using regression and artificial neural. New Min Sci Technol 20:0041–0046

Diederichs MS, Kaiser PK (1999) Stability of large excavations in laminated hard rockmasses: the Voussoir analogue revisited. Int J Rock Mech Min Sci 36:97–117CrossRef

Dincer I, Acar A, Cobanoglu I, Uras Y (2004) Correlation between Schmidt hardness, uniaxial compressive strength and Young’s modulus for andesites, basalts and tuffs. Bull Eng Geol Environ 63:141–148CrossRef

Feng XT, Hudson JA (2004) The ways ahead for rock engineering design methodologies. Int J Rock Mech Min Sci 41(2):255–273CrossRef

Feng XT, Hudson JA (2010) Specifying the information required for rock mechanics modelling and rock engineering design. Int J Rock Mech Min Sci 47(2):179–194CrossRef

Gokceoglu C, Zorlu K (2004) A fuzzy model to predict the unconfined compressive strength and modulus of elasticity of a problematic rock. Eng Appl Artif Intell 17:61–72CrossRef

Hagan MT, Menhaj MB (1994) Training feed forward networks with the Marquardt algorithm. IEEE Trans Neural Netw 5:861–867CrossRef

Haykin S (1999) Neural networks, 2nd edn. Prentice-Hall, Englewood Cliffs

Hecht-Nielsen R (1987) Kolmogorov’s mapping neural network existence theorem. In: Proceedings of the first IEEE international conference on neural networks, San Diego, CA, USA, pp 11–14

Hoek E, Brown ET (1997) Practical estimates of rock mass strength. Int J Rock Mech Min Sci 34(8):1165–1186CrossRef

Hoek E, Diederichs MS (2006) Empirical estimation of rock mass modulus. Int J Rock Mech Min Sci 43:203–215CrossRef

Hornik K, Stinchcombe M, White H (1989) Multilayer feedforward networks are universal approximators. Neural Netw 2:359–366CrossRef

Hudson JA, Feng XT (2007) Updated flowcharts for rock mechanics modelling and rock engineering design. Int J Rock Mech Min Sci 44(2):174–195CrossRef

ISRM (2007) The complete ISRM suggested methods for rock characterization, testing and monitoring: 1974–2006. In: Ulusay R, Hudson JA (eds) Suggested methods prepared by the commission on testing methods, international society for rock mechanics, compilation arranged by the ISRM Turkish National Group, Ankara, Turkey

Jahed Armaghani D, Tonnizam Mohamad E, Momeni E, Narayanasamy MS, Mohd Amin MF (2014) An adaptive neuro-fuzzy inference system for predicting unconfined compressive strength and Young’s modulus: a study on Main Range granite. Bull Eng Geol Environ. doi:10.1007/s10064-014-0687-4

Jahed Armaghani D, Mohamad ET, Hajihassani M, Yagiz S, Motaghedi H (2015) Application of several non-linear prediction tools for estimating uniaxial compressive strength of granitic rocks and comparison of their performances. Eng Comput. doi:10.1007/s00366-015-0410-5

Jahed Armaghani D, Mohamad ET, Momeni E, Monjezi M, Narayanasamy MS (2016) Prediction of the strength and elasticity modulus of granite through an expert artificial neural network. Arab J Geosci 9(1):1–16. doi:10.1007/s12517-015-2057-3 CrossRef

Jain AK, Dubes RC (1988) Algorithms for clustering data. Prentice-Hall, Englewood Cliffs

Jain AK, Murty MN, Flynn PJ (1999) ACM Computing Surveys, Vol. 31, No. 3, September

Jang JSR, Sun CT, Mizutani E (1997) Neuro-fuzzy and soft computing: a computational approach to learning and machine intelligence. Prentice Hall, Inc, Englwood Cliffs, NJ

Kaastra I, Boyd M (1996) Designing a neural network for forecasting financial and economic time series. Neurocomputing 10:215–236CrossRef

Kahraman S, Gunaydin O, Alber M, Fener M (2009) Evaluating the strength and deformability properties of Misis Fault Breccia using Artificial Neural Networks. Expert Syst Appl 36(3):6874–6878CrossRef

Kanellopoulas I, Wilkinson GG (1997) Strategies and best practice for neural network image classification. Int J Remote Sens 18:711–725CrossRef

Lashkaripour GR (2002) Predicting mechanical properties of mudrock from index parameters. Bull Eng Geol Environ 61:73–77CrossRef

Leite MH, Ferland F (2001) Determination of unconfined compressive strength and Young’s modulus of porous materials by indentation tests. Eng Geol 59:267–280CrossRef

Looney CG (1996) Advances in feed-forward neural networks: demystifying knowledge acquiring black boxes. IEEE Trans Knowl Data Eng 8(2):211–226CrossRef

Majdi A, Beiki M (2010) Evolving neural network using a genetic algorithm for predicting the deformation modulus of rock masses. Int J Rock Mech Min Sci 47(2):246–253CrossRef

Mamdani EH, Assilian S (1975) An experimental in linguistic synthesis with a fuzzy logic control. Int J Man-Mach Stud 7:1–13CrossRef

Martin DM, Stimpson B (1994) The effect of sample disturbance on laboratory properties of Lac du Bonnet granite. Can Geotechn J 31(5):692–702CrossRef

Masters T (1994) Practical neural network recipes in C++. Academic, Boston

MATLAB user’s guide (2007) Fuzzy logic Toolbox user’s guide (MATLAB CD-ROM). Mathworks Inc, Natick, p 299

McCulloch WS, Pitts W (1943) A logical calculus of the ideas immanent in nervous activity. Bull Math Biophys 5:115–133CrossRef

Mishra DA, Basu A (2013) Estimation of uniaxial compressive strength of rock materials by index tests using regression analysis and fuzzy inference system. Eng Geol 160:54–68CrossRef

Mishra DA, Srigyan M, Basu A, Rokade PJ (2015) Soft computing methods for estimating the uniaxial compressive strength of intact rock from index tests. Int J Rock Mech Min Sci 80:418–424

Mitri HS, Edrissi R, Henning J (1994) Finite element modeling of cable bolted stopes in hard rock ground mines. In: Presented at the SME annual meeting, New Mexico, Albuquerque, pp 94–116

Mohamad ET, Armaghani DJ, Momeni E, Abad SVANK (2015) Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach. Bull Eng Geol Environ 4:745–757CrossRef

Momeni E, Jahed Armaghani D, Hajihassani M, Amin MFM (2015) Prediction of uniaxial compressive strength of rock samples using hybrid particle swarm optimization-based artificial neural networks. Measurement 60:50–63CrossRef

Moradian ZA, Behnia M (2009) Predicting the uniaxial compressive strength and static Young’s modulus of intact sedimentary rocks using the ultrasonic test. Int J Geomech 9(1):14–19CrossRef

Nefeslioglu HA (2013) Evaluation of geo-mechanical properties of very weak and weak rock materials by using non-destructive techniques: ultrasonic pulse velocity measurements and reflectance spectroscopy. Eng Geol 160:8–20CrossRef

Nicholson GA, Bieniawski ZT (1990) A nonlinear deformation modulus based on rock mass classification. Int J Min Geol Eng 8:181–202CrossRef

Paola JD (1994) Neural network classification of multispectral imagery. MSc thesis, The University of Arizona, USA

Read SAL, Richards LR, Perrin ND (1999) Applicability of the Hoek–Brown failure criterion to New Zealand greywacke rocks, vol 2. In: Proceedings 9th Int. Society for Rock Mechanics Congress, Paris, pp 655–660

Rezaei M, Majdi A, Monjezi M (2012) An intelligent approach to predict unconfined compressive strength of rock surrounding access tunnels in longwall coal mining. Neural Comput Appl 24(1):233–241CrossRef

Ripley BD (1993) Statistical aspects of neural networks. In: Barndoff- Neilsen OE, Jensen JL, Kendall WS (eds) Networks and chaos-statistical and probabilistic aspects. Chapman and Hall, London, pp 40–123CrossRef

Rutkowski L (2004) Flexible neuro-fuzzy systems: structures, learning and performance evaluation. Kluwer Academic, New York

Sachpazis CI (1990) Correlating Schmidt hardness with compressive strength and Young’s modulus of carbonate rocks. Bull Int Assoc Eng Geol 42:75–84CrossRef

Serafim JL, Pereira JP (1983) Considerations of the geomechanics classification of Bieniawski. In: Proceedings of the International Symposium on Engineering Geology and Underground Construction, Laboratorio National De Engenharia Civil, Lisbon, Spain. A.A. Balkema, Rotterdam, the Netherlands, vol 1, pp 1133–1142

Shams S, Monjezi M, Johari Majd V, Jahed Armaghani D (2015) Application of fuzzy inference system for prediction of rock fragmentation induced by blasting. Arab J Geosci 8:10819–10832CrossRef

Simpson P (1990) Artificial neural system: foundation, paradigms, applications and implementations. Pergamon, New York

Singh R, Kainthola A, Singh TN (2012) Estimation of elastic constant of rocks using an ANFIS approach. Appl Soft Comput 12(1):40–45CrossRef

Sivanandam SN, Sumathi S, Deepa SN (2007) Introduction to fuzzy logic using MATLAB. Springer, Berlin, HeidelbergCrossRef

Sonmez H, Gokceoglu C, Ulusay R (2004) Indirect determination of the modulus of deformation of rock masses based on the GSI system. Int J Rock Mech Min Sci 1:849–857CrossRef

Sonmez H, Gokceoglu C, Nefeslioglu HA, Kayabasi A (2006) Estimation of rock modulus: for intact rocks with an artificial neural network and for rock masses with a new empirical equation. Int J Rock Mech Min Sci 43:224–235CrossRef

SPSS Inc (2007) SPSS for Windows (Version 16.0). SPSS Inc, Chicago

Swingler K (1996) Applying neural networks: a practical guide. Academic, New York

Takagi T, Sugeno M (1985) Identification of systems and its application to modeling and control. IEEE Trans Syst Man Cybern 15:116–132CrossRef

Wang C (1994) A theory of generalization in learning machines with neural application. PhD thesis, The University of Pennsylvania, USA

Yager RR, Filev DP (1994a) Generation of fuzzy rules by mountain clustering. J Intell Fuzzy Syst 2:209–219

Yager RR, Filev DP (1994b) Essentials of fuzzy modeling and control. Wiley, New York

Yagiz S, Sezer EA, Gokceoglu C (2012) Artificial neural networks and nonlinear regression techniques to assess the influence of slake durability cycles on the prediction of uniaxial compressive strength and modulus of elasticity for carbonate rocks. Int J Numer Anal Method 36(14):1636–1650CrossRef

Yasar E, Erdogan Y (2004) Correlating sound velocity with the density, compressive strength and Young’s modulus of carbonate rocks. Int J Rock Mech Min Sci 41(5):871–875CrossRef

Yazdani Bejarbaneh B, Armaghani DJ, Amin MFM (2015) Strength characterisation of shale using Mohr-Coulomb and Hoek-Brown criteria. Measurement 63:269–281CrossRef

Yilmaz I, Sendir H (2002) Correlation of Schmidt hardness with unconfined compressive strength and Young’s modulus in gypsum from Sivas (Turkey). Eng Geol 66(3):211–219CrossRef

Yilmaz I, Yuksek AG (2008) An example of artificial neural network (ANN) application for indirect estimation of rock parameters. Rock Mech Rock Eng 41(5):781–795CrossRef

Yilmaz I, Yuksek G (2009) Prediction of the strength and elasticity modulus of gypsum using multiple regression, ANN, and ANFIS models. Int J Rock Mech Min Sci 46(4):803–810CrossRef

Zadeh LA (1965) Fuzzy sets. Inform. Control 8:338–353CrossRef

Zadeh LA (1973) Outline of a new approach to the analysis of complex systems and decision processes. IEEE Trans Syst Man Cybern 3:28–44CrossRef

Zorlu K, Gokceoglu C, Ocakoglu F, Nefeslioglu HA, Acikalin S (2008) Prediction of uniaxial compressive strength of sandstones using petrography-based models. Eng Geol 96(3):141–158CrossRef

Zurada JM (1992) Introduction to artificial neural systems. West, St Paul

Title: Intelligent modelling of sandstone deformation behaviour using fuzzy logic and neural network systems
Authors: Behnam Yazdani Bejarbaneh
Elham Yazdani Bejarbaneh
Mohd For Mohd Amin
Ahmad Fahimifar
Danial Jahed Armaghani
Muhd Zaimi Abd Majid
Publication date: 29-11-2016
Publisher: Springer Berlin Heidelberg
Published in: Bulletin of Engineering Geology and the Environment / Issue 1/2018
Print ISSN: 1435-9529
Electronic ISSN: 1435-9537
DOI: https://doi.org/10.1007/s10064-016-0983-2

Springer Professional

Abstract

Please log in to get access to your license.

Dont have a licence yet? Then find out more about our products and how to get one now:

Springer Professional "Technik"

Springer Professional "Wirtschaft+Technik"

Other articles of this Issue 1/2018

The influence of an abandoned goaf on surface subsidence in an adjacent working coal face: a prediction method

Slope failure mechanisms in dipping interbedded sandstone and mudstone revealed by model testing and distinct-element analysis

Predicting earthquake-induced soil liquefaction based on a hybridization of kernel Fisher discriminant analysis and a least squares support vector machine: a multi-dataset study

Landslide susceptibility of the Xiangjiaba Reservoir area associated with the Yaziba Fault

A new approach for estimation of the rock mass deformation modulus: a rock engineering systems-based model

Hydraulic-mechanical properties of loess and its behavior when subjected to infiltration-induced wetting