Empirical Estimation of Unconfined Compressive Strength and Modulus of Elasticity Using ANN

Hasan Gul, Khalid Farooq, Hassan Mujtaba

Abstract


The strength parameters such as unconfined compressive strength (UCS) and Modulus of Elasticity (E) of rocks are important for design of foundations. Both the parameters are determined in laboratory after rigorous and destructive testing. In this study Artificial Neural Network (ANN) models are developed for prediction of UCS and E from index test parameters such as Unit Weight (γ), porosity (n) and point load index Is(50). Multi variable regression models are also developed to compare the accuracy of prediction from different models. Coefficient of determination (R2 ), Root Mean Squared error (RMSE) and Standard Error of Estimate (SEE) has been used as the controlling factor to determine the prediction accuracy of both ANN and multivariable regression. The ANN models increased the R2 values from 0.53 to 0.72 and 0.51 to 0.75 for UCS and E respectively. The variation between experimental and predicted values of UCS and E for ANN model are ± 23% and ± 29% and for regression model are ± 40% and ± 31% respectively.

Full Text:

PDF

References


Neural Networks: A Comprehensive Foundation; Haykin, S., Prentice Hall, N.J, 2nd Edition, (1999).

Liu, S. W., Huang, J. H., Sung, J. C., Lee, C. C. 2002. Detection of cracks using Neural Networks and Computational Mechanics. Computer Methods in Applied Mechanics and Engineering. Vol. 191. pp. 2831 – 2845.

Yilmaz, I., Yuksek, A. G. 2008. Technical Note an Example of Artificial Neural Network (ANN) Application for Indirect Estimation of Rock Parameters. Rock Mechanics and Rock Engineering. Vol. 41 (5). pp. 781-795.

Yurdakul M, Ceylan H, Akdas H (2011). “A Predictive Model for Uniaxial Compressive Strength of Carbonate Rocks from Schmidt Hardness”. ‘Civil, Construction and Environmental Engineering Conference Presentation and Proceedings’, Paper 7. http://lib.dr.iastate.edu/ccee_conf/7

Yagzi, S., Sezer, E. A., Gokceoglu, C. 2012. Artificial Neural Networks and Non-linear Regression Techniques to Assess the Influence of Slake Durability Cycles on the Prediction of Unconfined Compressive Strength and Modulus of Elasticity for Carbonate Rocks. International Journal for Numerical and Analytical Methods in Geomechanics. Vol. 36. pp. 1636-1650.

Majdi, A., Rezaei, M. 2013. Prediction of Unconfined Compressive Strength of Rock surrounding a Roadway using Artificial Neural Network. Neural Computing & Applications Vol. 23. pp. 381-389.

Munir, K.; Development of Correlation between Rock Classification System and Modulus of Deformation, Ph.D. Thesis, University of Engineering & Technology, Lahore, Pakistan, (2014).

Annual Book of ASTM Standards; D2938-95, D5731-08, ASTM International, West Conshohocken, PA, USA, (2008).

Gul, H.; Prediction Models for Estimation of Unconfined Compressive Strength and Modulus of Elasticity from Index Tests of Rocks, M.Sc. Thesis, University of Engineering & Technology, Lahore, Pakistan, (2015).

Haghnejad, A., Ahangari, K., Noorzad, A. 2014. Investigation on various relations between Uniaxial Compressive Strength, Elasticity and Deformation Modulus of Asmari Formation in Iran. Arabian Journal for Science and Engineering. Vol. 39. pp. 2677 – 2682.

Khuntia, S., Mujtaba, H., Patra, C., Farooq, K., Sivakugan, N., Das, B. M. 2015. Prediction of Compaction Parameters of Coarse Grained Soils using Multivariate Adaptive Regression splines (MARS). International Journal of Geotechnical Engineering. Vol. 9 (1). pp. 79 -88.

Sulewska M. J. 2010. Prediction Model for Minimum and Maximum Dry Density of NonCohesive Soils. Polish Journal of Environmental Studies. Vol. 19 (4), pp. 797 - 804.

Guven, A., Gunal, M. 2008. Prediction of Scour Downstream of Grade-Control Structures Using Neural Networks. Journal of Hydraulic Engineering, ASCE. Vol. 134 (11). pp. 1656 – 1660.

Mohammadi, H., Rahmannejad, R. 2010. The Estimation of Rock Mass Deformation Modulus Using Regression and Artificial Neural Network Analysis. Arabian Journal for Science and Engineering. Vol. 35 (1A). pp. 205-217.

Kabuba, J., Bafbiandi, A. M., Battle, K. 2014. Neural Network Technique for modeling of Cu (II) removal from aqueous solution by Clinoptilolite. Arabian Journal for Science and Engineering. Vol. 39. pp. 6793 – 6803.

Khandelwal, M., Singh, T. N. 2011. Predicting elastic properties of schistose rocks from unconfined strength using intelligent approach. Arabian Journal for Science and Engineering. Vol. 4. pp. 435 – 442.

Gurocak, Z., Solanki, P., Alemdag, S., Zaman, M. M. 2012. New Considerations for Empirical Estimation of Tensile Strength of Rocks. Engineering Geology. Vol. 145 – 146. pp. 1-8.






Copyright (c) 2016 Hasan Gul

Powered By KICS