Document Type: Original Research Paper

Author

Department of Earth Sciences Engineering, Arak University of Technology, Arak, Iran

10.22044/jme.2020.9775.1897

Abstract

The tensile strength (σt) of a rock plays an important role in the reliable construction of several civil structures such as dam foundations and types of tunnels and excavations. Determination of σt in the laboratory can be expensive, difficult, and time-consuming for certain projects. Due to the difficulties associated with the experimental procedure, it is usually preferred that the σt is evaluated in an indirect way. For these reasons, in this work, the adaptive network-based fuzzy inference system (ANFIS) is used to build a prediction model for the indirect prediction of σt of sandstone rock samples from their physical properties. Two ANFIS models are implemented, i.e. ANFIS-subtractive clustering method (SCM) and ANFIS-fuzzy c-means clustering method (FCM). The ANFIS models are applied to the data available in the open source literature. In these models, the porosity, specific gravity, dry unit weight, and saturated unit weight are utilized as the input parameters, while the measured σt is the output parameter. The performance of the proposed predictive models is examined according to two performance indices, i.e. mean square error (MSE) and coefficient of determination (R2). The results obtained from this work indicate that ANFIS-SCM is a reliable method to predict σt with a high degree of accuracy.

Keywords

[1]. Baykasoğlu, A., Güllü, H., Çanakçı, H. and Özbakır, L. (2008). Prediction of compressive and tensile strength of limestone via genetic programming. Expert Syst Appl 35 (1):111-123.

[2]. Ceryan, N., Okkan, U. and Kesimal, A. (2012). Application of generalized regression neural networks in predicting the unconfined compressive strength of carbonate rocks. Rock Mech Rock Eng 45 (6):1055-1072.

[3]. Kahraman, S. and Yeken, T. (2010). Electrical resistivity measurement to predict uniaxial compressive and tensile strength of igneous rocks. B Mater Sci 33 (6):731-735.

[4]. Singh, V. and Singh, D. and Singh, T. (2001). Prediction of strength properties of some schistose rocks from petrographic properties using artificial neural networks. Int J Rock Mech Min Sci 38 (2):269-284.

[5]. Cai, M. (2010). Practical estimates of tensile strength and Hoek–Brown strength parameter m i of brittle rocks. Rock Mech Rock Eng 43 (2):167-184.

[6]. Karakus, M. (2011). Function identification for the intrinsic strength and elastic properties of granitic rocks via genetic programming (GP). Comput Geosci 37 (9):1318-1323.

[7]. Chen, G., Jia, Z. and Ke, J. (1997). Probabilistic analysis of underground excavation stability. Int J Rock Mech Min Sci 34 (3-4):51. e51-51. e16.

[8]. Heidari, M., Khanlari, G., Torabi Kaveh, M. and Kargarian, S. (2012). Predicting the uniaxial compressive and tensile strengths of gypsum rock by point load testing. Rock Mech Rock Eng 45 (2):265-273.

[9]. Abolhosseini, H., Hashemi, M. and Ajalloeian, R. (2020). Evaluation of geotechnical parameters affecting the penetration rate of TBM using neural network (case study). Arab J Geosci 13 (4):183.

[10]. Iphar, M., Yavuz, M. and Ak, H. (2008). Prediction of ground vibrations resulting from the blasting operations in an open-pit mine by adaptive neuro-fuzzy inference system. Environ Geol 56 (1):97-107.

[11]. Sezer, E.A., Nefeslioglu, H.A. and Gokceoglu, C. (2014). An assessment on producing synthetic samples by fuzzy C-means for limited number of data in prediction models. Appl Soft Comput 24:126-134.

[12]. Fattahi, H. (2016). Adaptive neuro fuzzy inference system based on fuzzy C–means clustering algorithm, a technique for estimation of TBM peneteration rate. Int J Optim Civil Eng 6 (2):159-171.

[13]. Fattahi, H .(2016). Indirect estimation of deformation modulus of an in situ rock mass: an ANFIS model based on grid partitioning, fuzzy c-means clustering and subtractive clustering. J Geosci 20 (5):681–690.

[14]. Karimpouli, S. and Fattahi, H. (2018), Estimation of P-and S-wave impedances using Bayesian inversion and adaptive neuro-fuzzy inference system from a carbonate reservoir in Iran. Neural Comput Appl 29 (11):1059-1072.

[15]. Fattahi, H. and Karimpouli, S. (2016). Prediction of porosity and water saturation using pre-stack seismic attributes: a comparison of Bayesian inversion and computational intelligence methods. Computat Geosci 20 (5):1075-1094.

[16]. Jang, J-S .(1993). ANFIS: adaptive-network-based fuzzy inference system. IEEE T Syst Man Cyb 23 (3):665-685.

[17]. Weiling C, Lee J Fuzzy Logic for the Applications to Complex Systems. In: Proceedings of the International Joint Conference of CFSA/IFIS/SOFT on Fuzzy Theory and Applications. Singapore et al.: World Scientific, 1995.

[18]. Chiu, S.L. (1994). Fuzzy model identification based on cluster estimation. J Intell Fuzzy Syst 2 (3):267-278

[19]. Bezdek, J.C. (1973). Fuzzy mathematics in pattern classification. Cornell university, Ithaca.

[20]. Ghobadi, M.H., Mousavi, S., Heidari, M. and Rafie, B. (2015). The Prediction of the Tensile Strength of Sandstones from their petrographical properties using regression analysis and artificial neural network. Geopersia 5 (2):177-187.

[21]. Gholami, R., Moradzadeh, A., Maleki, S., Amiri, S. and Hanachi, J. (2014). Applications of artificial intelligence methods in prediction of permeability in hydrocarbon reservoirs. J  Pet Sci Eng 122:643-656.

[22]. Jayalakshmi, T. and Santhakumaran, A. (2011). Statistical normalization and back propagation for classification. International Journal of Computer Theory and Engineering 3 (1):1793-8201.

[23]. Fattahi, H. (2016). Application of improved support vector regression model for prediction of deformation modulus of a rock mass. Eng Comput 32 (4):567-580.

[24]. Fattahi, H. (2017). Applying soft computing methods to predict the uniaxial compressive strength of rocks from schmidt hammer rebound values. Computat Geosci 21 (4):665-681. doi:10.1007/s10596-017-9642-3.

[25]. Fattahi, H. and Bazdar, H. (2017). Applying improved artificial neural network models to evaluate drilling rate index. Tunn Undergr Sp Tech 70:114-124.

[26]. Karimpouli, S. and Fattahi, H .(2018). Estimation of P-and S-wave impedances using Bayesian inversion and adaptive neuro-fuzzy inference system from a carbonate reservoir in Iran. Neural Comput Appl 29 (11):1059–1072.