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.
Hasan Gul, Hasan Gul, v. (2016) Empirical Estimation of Unconfined Compressive Strength and Modulus of Elasticity Using ANN, Pakistan Journal of Engineering and Applied Sciences, VOLUME 18, Issue 1.
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