Harsha Vardhan

TL;DR: In this article, an attempt has been made to experimentally investigate the estimation of rock properties like compressive strength and abrasivity using sound levels produced during drilling using small portable pneumatic drilling equipment used in hard rock drilling.

TL;DR: In this paper, the authors used multiple regression, artificial neural network (MLP) and RBF models to predict rock properties using soft computing techniques such as multiple regression and artificial neural networks.

TL;DR: In this article, the authors developed a general prediction model and investigated the relationships between sound level produced during drilling and physical properties such as uniaxial compressive strength, tensile strength and percentage porosity of sedimentary rocks.

TL;DR: In this article, an attempt has been made to estimate rock properties such as uniaxial compressive strength, Schmidt rebound number and Young's modulus using sound level produced during rotary drilling.

TL;DR: In this paper, a new technique was developed based on the estimation of rock properties using dominant frequencies from sound pressure level generated during diamond core drilling operations, which can be used at early stage of design to predict rock properties.