Indirect estimation of compressive and shear strength from simple index tests

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 e...

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Bibliographic Details
Published in:Engineering with computers 2017, Vol.33 (1), p.1-11
Main Authors: Singh, P. K., Tripathy, A., Kainthola, A., Mahanta, Bankim, Singh, V., Singh, T. N.
Format: Article
Language:English
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
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Summary: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.
ISSN:0177-0667
1435-5663
DOI:10.1007/s00366-016-0451-4