Abstract
The knowledge of geomechanical properties of rocks is essential for planning and design of structures in civil, mining, and other allied industries. Direct assessment of these properties is quite expensive and time consuming. It also requires fine precision in sample preparation and high-end testing instruments. To overcome these problems, 597 rock samples of four different Himalayan rock types have been tested for their strength attributes in a laboratory. The test results were correlated with their P-wave velocity using linear regression analysis for estimation of compressive, tensile, cohesive strengths and angle of internal friction. Subsequently, an adaptive neuro-fuzzy inference system (ANFIS) technique was applied to find its efficiency as a predictor against normal regression system. The results indicated that the ANFIS is comparatively better at prediction of geotechnical strength parameters when pitted against regression techniques.
Published Version
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