Faculty Publications

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    Regression analysis and ANN models to predict rock properties from sound levels produced during drilling
    (Elsevier Ltd, 2013) Rajesh Kumar, B.; Vardhan, H.; Govindaraj, M.; Vijay, G.S.
    This study aims to predict rock properties using soft computing techniques such as multiple regression, artificial neural network (MLP and RBF) models, taking drill bit speed, penetration rate, drill bit diameter and equivalent sound level produced during drilling as the input parameters. A database of 448 cases were tested for determination of uniaxial compressive strength (UCS), Schmidt rebound number (SRN), dry density (?), P-wave velocity (Vp), tensile strength (TS), modulus of elasticity (E) and percentage porosity (n) and the prediction capabilities of the models were then analyzed. Results from the analysis demonstrate that neural network approach is efficient when compared to statistical analysis in predicting rock properties from the sound level produced during drilling. © 2012 Elsevier Ltd.
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    Solution of space–time fractional diffusion equation involving fractional Laplacian with a local radial basis function approximation
    (Springer Science and Business Media Deutschland GmbH, 2024) Revathy, J.M.; Godavarma, G.
    Radial basis function-based finite difference (RBF-FD) schemes generalize finite difference methods, providing flexibility in node distribution as well as the shape of the domain. In this paper, we consider a numerical formulation based on RBF-FD for solving a time–space fractional diffusion problem defined using a fractional Laplacian operator. The model problem is simplified into a local problem in space using the Caffarelli–Silvestre extension method. The space derivatives in the resulting problem are then discretized using a local RBF-based finite difference method, while L1 approximation is used for the fractional time derivative. Results obtained using the proposed scheme are then compared with that given in the existing literature. © The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature 2023. Springer Nature or its licensor (e.g. a society or other partner) holds exclusive rights to this article under a publishing agreement with the author(s) or other rightsholder(s); author self-archiving of the accepted manuscript version of this article is solely governed by the terms of such publishing agreement and applicable law.