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Prediction of strength parameters of sedimentary rocks using artificial neural networks and regression analysis

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Abstract

Accurate laboratory measurement of geo-engineering properties of intact rock including uniaxial compressive strength (UCS) and modulus of elasticity (E) involves high costs and a substantial amount of time. For this reason, it is of great necessity to develop some relationships and models for estimating these parameters in rock engineering. The present study was conducted to forecast UCS and E in the sedimentary rocks using artificial neural networks (ANNs) and multivariable regression analysis (MLR). For this purpose, a total of 196 rock samples from four rock types (i.e., sandstone, conglomerate, limestone, and marl) were cored and subjected to comprehensive laboratory tests. To develop the predictive models, physical properties of studied rocks such as P wave velocity (Vp), dry density (γd), porosity, and water absorption (Ab) were considered as model inputs, while UCS and E were the output parameters. We evaluated the performance of MLR and ANN models by calculating correlation coefficient (R), mean absolute error (MAE), and root-mean-square error (RMSE) indices. The comparison of the obtained results revealed that ANN outperforms MLR when predicting the UCS and E.

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References

  • Ahmadi MA, Ebadi M, Shokrollahi A, Majidi SMJ (2013) Evolving artificial neural network and imperialist competitive algorithm for prediction oil flow rate of the reservoir. Appl Soft Comput 13:1085–1098

    Article  Google Scholar 

  • Atkinson PM, Tatnall ARL (1997) Introduction neural networks in remote sensing. Int J Remote Sens 18:699–709

    Article  Google Scholar 

  • 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

    Article  Google Scholar 

  • Bejarbaneh BY, Bejarbaneh EY, Amin MFM, Fahimifar A, Jahed Armaghani D, Majid MZA (2018) Intelligent modelling of sandstone deformation behaviour using fuzzy logic and neural network systems. Bull Eng Geol Environ 77:345–361

    Article  Google Scholar 

  • Cabalar AF, Cevik A, Gokceoglu C (2012) Some applications of adaptive neuro-fuzzy inference system (ANFIS) in geotechnical engineering. Comput Geotech 40:14–33

    Article  Google Scholar 

  • Cevik A, Sezer EA, Cabalar AF, Gokceoglu C (2011) Modeling of the uniaxial compressive strength of some clay-bearing rocks using neural network. Appl Soft Comput 11:2587–2594

    Article  Google Scholar 

  • Dehghan S, Sattari GH, Chehre Chelgani S, Aliabadi MA (2010) Prediction of uniaxial compressive and modulus of elasticity for travertine sample using regression and artificial neural networks. Min Sci Technol 20:41–46

    Google Scholar 

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

    Article  Google Scholar 

  • Hajihassani M, Jahed Armaghani D, Sohaei H, Tonnizam Mohamad E, Marto A (2014) Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization. Appl Acoust 80:57–67

    Article  Google Scholar 

  • ISRM (1981) Rock characterization, testing and monitoring, ISRM suggested methods. Int Soc for Rock Mech pp. 211

  • 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 36:6874–6878

    Article  Google Scholar 

  • Lindquist ES, Goodman RE (1994) Strength and deformation properties of a physical model melange. In: Nelson PP, Laubach SE (eds) Proceedings of the 1st North American Rock Mechanics Symposium. Balkema, Rotterdam, pp 843–850

    Google Scholar 

  • 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 47:246–253

    Article  Google Scholar 

  • Majidi A, Rezaei M (2013) Prediction of unconfined compressive strength of rock surrounding a roadway using artificial neural network. Neural Comput & Applic 23:381–389

    Article  Google Scholar 

  • Mohamed MT (2009) Artificial neural network for prediction and control of blasting vibrations in Assiut (Egypt) limestone quarry. Int J Rock Mech Min Sci 46:426–431

    Article  Google Scholar 

  • Mohamad ET, Jahed Armaghani D, Momeni E, Alavi Nezhad Khalil Abad SV (2015) Prediction of the unconfined compressive strength of soft rocks: a PSO-based ANN approach. Bull Eng Geol Environ 74:745–757

    Article  Google Scholar 

  • Ozcelik Y, Bayram F, Yasitli NE (2013) Prediction of engineering properties of rocks from microscopic data. Arab J Geosci 6:3651–3668

    Article  Google Scholar 

  • Rajesh-Kumar B, Vardhan H, Govindaraj M, Vijay GS (2013) Regression analysis and ANN models to predict rock properties from sound levels produced during drilling. Int J Rock Mech Min Sci 58:61–72

    Article  Google Scholar 

  • Sarkar K, Tiwary A, Singh TN (2010) Estimation of strength parameters of rock using artificial neural networks. Bull Eng Geol Environ 69:599–606

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Singh TN, Dubey RK (2000) A study of transmission velocity of primary wave (P-wave) in coal measures sandstone. J Sci Ind Res India 59:482–486

    Google Scholar 

  • 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 43:224–235

    Article  Google Scholar 

  • Taheri-Garavand A, Ahmadi H, Omid M, Mohtasebi SS, Mollazade K, Russell Smith JR, Carlomagno GM (2015) An intelligent approach for cooling radiator fault diagnosis based on infrared thermal image processing technique. Appl Therm Eng 87:434–443

    Article  Google Scholar 

  • Tiryaki B (2008) Predicting intact rock strength for mechanical excavation using multivariate statistics, artificial neural networks and regression trees. Eng Geol 99:51–60

    Article  Google Scholar 

  • Torabi-Kaveh M, Naseri F, Sanei S, Sarshari B (2015) Application of artificial neural networks and multivariate statistics to predict UCS and E using physical properties of Asmari limestones. Arab J Geosci 8:2889–2897

    Article  Google Scholar 

  • 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. I J Numer Anal Method Geomech 36:1636–1650

    Article  Google Scholar 

  • Yesiloglu-Gultekin N, Gokceoglu C, Sezer EA (2013) Prediction of uniaxial compressive strength of granitic rocks by various nonlinear tools and comparison of their performances. Int J Rock Mech Min Sci 62:113–122

    Article  Google Scholar 

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

    Article  Google Scholar 

  • Yilmaz I, Yuksek AG (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):803–810

    Article  Google Scholar 

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Authors and Affiliations

  1. Department of Geology, Lorestan University, Khorramabad, Iran

    Yasin Abdi & Reza Zarei Sahamieh

  2. Department of Mechanical Engineering of Biosystems, Lorestan University, Khorramabad, Iran

    Amin Taheri Garavand

Authors
  1. Yasin Abdi
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  2. Amin Taheri Garavand
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  3. Reza Zarei Sahamieh
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Correspondence to Yasin Abdi.

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Abdi, Y., Garavand, A.T. & Sahamieh, R.Z. Prediction of strength parameters of sedimentary rocks using artificial neural networks and regression analysis. Arab J Geosci 11, 587 (2018). https://doi.org/10.1007/s12517-018-3929-0

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  • DOI: https://doi.org/10.1007/s12517-018-3929-0

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