Indirect estimation of compressive and shear strength from simple index tests

• Published in: Engineering with computers Vol. 33; no. 1; pp. 1 - 11
• Main Authors: Singh, P. K., Tripathy, A., Kainthola, A., Mahanta, Bankim, Singh, V., Singh, T. N.
• Format: Journal Article
• Language: English
• Published: London Springer London 2017; Springer Nature B.V
• Subjects: Artificial neural networks; CAE) and Design; Calculus of Variations and Optimal Control; Optimization; Classical Mechanics; Compressive strength; Computer Science; Computer-Aided Engineering (CAD; Control; Correlation analysis; Design engineering; Failure modes; Math. Applications in Chemistry; Mathematical and Computational Engineering; Neural networks; Original Article; Predictions; Regression analysis; Reliability analysis; Rocks; Shear strength; Statistical analysis; Studies; Systems Theory; Tensile strength; Shear strength; Compressive strength; P-wave velocity; Regression analysis; Artificial neural network
• ISSN: 0177-0667; 1435-5663
• DOI: 10.1007/s00366-016-0451-4

Uniaxial compressive and shear strength are two of the very important parameters, commonly required in the initial stages of planning and design of rock engineering projects. So, an attempt has been made in this study to predict compressive and shear strength (output) of rocks from some simple and easily determinable parameters in laboratory viz., point load index, tensile strength, unit weight and ultrasonic velocity (input). Failure modes have also been studied and correlated with their ultrasonic velocity. The study uses two of the most commonly used predictive mathematical techniques: statistical analysis and neural networks to predict the strength parameters. The regression analysis shows that the rock quality parameters are very well correlated with the ultrasonic velocity except for the unit weight. Unlike few researchers in the past, a linear correlation was best suited for the rock quality parameters in this study. On the other hand, Artificial Neural Network (ANN) was able to predict the same strength parameters with a better reliability than regression analysis. The study shows that the proposed method for the prediction of UCS and shear strength is acceptable and can be reliably applied in various rock engineering problems.