Elastic modulus prediction of limestone using multiple variable regression and artificial neural network

Document Type : Original Article

Authors

1 Professor, Department of Engineering Geology, Isfahan University

2 M.Sc. Student of Engineering Geology, Department of Geology, Isfajan University, Isfahan, Iran.

3 . Ph.D. student of Engineering Geology, Department of Geology, Kharazmi University, Tehran, Iran.

Abstract

It is important to determinate the elastic modulus of intact rock in many engineering projects relating to rock such as tunnels, slopes and foundations. Determination of this parameter need of high quality core samples and sophisticated test equipment because these make performing this test difficult and costly. There for in current years, researches have tried to produce relationships for prediction of this parameter using physical and index characteristics of rocks. So here an attempt has been made to predicate the elastic modulus of limestone by uniaxial compress strength, porosity and longitudinal wave velocity. So we used multiple variable regression method and perceptron artificial   neural network with 3-4-1 architecture. Data base contains 123 samples that 70% and 30% of them has been used for train and test respectively in ANN. The models have been compared using coefficients of R2, RMSE and VAF. The coefficient of determination ( R2) using multiple variable regression was 0.738. it was 0.805 and 0.832 for ANN train data set and ANN test data set respectively. This shows the ANN method have more accuracy than regression.

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References

قزوینیان ع.، رسولی و.، نورانی ر.، 1386. کاربرد روش‌های آماری چندمتغیره در تخمین مقاومت فشاری تک‌محوره با استفاده از آزمایش‌های غیرمستقیم. سومین کنفرانس مکانیک سنگ ایران، صفحات 199-204.
Karakus, M., Kumral, M., Kilic, O., 2005. Predicting elastic properties of intact rocks from index tests using multiple regression modellin. International Journal of Rock Mechanics and Mining Sciences, 42: 323–330.
Kahraman, S., 2001.  Evaluation of simple methods for assessing the uniaxial compressive strength of rock: International Journal of Rock Mechanics and Mining Sciences, 38: 981–994
Heidari, M., Khanlari, G., Momen A.A., 2010. Prediction of elastic modulus of intact rocks using artificial neural networks and non-linear regression methods. Australian Journal of Basic and Applied Sciences, 4(12): 5869-5879.
Khandelwal, M., Singh, T.N., 2011. Predicting elastic properties of schistose rocks from unconfined strength using intelligent approach. Arabian Journal of Geosciiences, 4: 435–442.
Sarkar, K., Tiwary A., Singh, T.N., 2010. Estimation of strength parameters of rock using artificial neural network. Bulletin of Engineering Geolology and the Environment, 69: 599–606.
Sonmez, H., Gokceoglu, C., Nefeslioglu, H.A.,  and Kayabasi. A., 2006. Estimation of rock modulus: For intact rocks with an artificial neural network and for rock masses with a new empirical equation. International Journal of Rock Mechanics and Mining Sciences, 43: 224-235.
Y lmaz, I., Yuksek, A., 2008. An example of artificial neural network (ANN) application for indirect estimation of rock parameters. Rock mechanics and rock engineering, 41(5): 781-795.
Yagiz, S., Sezer, E.A., 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. International Journal for Numerical and Analytical Methods in Geomechanics, 36(14): 1636-1650.
Scientific Quarterly Journal of Iranian Association of Engineering Geology
Volume 5, Number 3 & 4
March 2012
Pages 33-38

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History

  • Receive Date: 17 July 2012
  • Revise Date: 19 March 2013
  • Accept Date: 07 May 2013

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