Roy, L B and Lahiri , Sivaji and Rabidas, Pappu (2026) Predicting intact rock strength for mechanical excavation in dry and saturated condition using multivariate statistics and artificial neural networks optimized using genetic algorithm. Journal of Earth System Science, 135. ISSN 0253-4126

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Abstract

The present study examines the mechanical properties of intact sandstone and shale from the Banhardi Coal Block (48–819 m depth) under both dry and saturated conditions, which are essential for deep mining applications. Laboratory analyses quantified uniaxial compressive strength (UCS), elastic modulus, density, porosity, and water absorption, while statistical and machine learning methodologies examined the interrelationships among these properties. Results indicate that water saturation consistently diminishes rock strength and stiffness. Depth-property analysis indicated: (1) UCS exhibits linear trends in shale (R2 = 0.327) and power–law behaviour in sandstone (R2 = 0.3698); (2) Elastic modulus demonstrates a more pronounced depth-dependence in saturated sandstone (R2 = 0.4161); (3) Porosity consistently diminishes with depth (R2 = 0.42–0.48). Although multivariate regression revealed significant connections, nonlinearities constrained its predictive efficacy. A comparative investigation indicated that genetic algorithm–optimized artificial neural network (GA-ANN) models (R > 0.85) surpassed both KNN and regression methods, exhibiting enhanced accuracy in representing saturation effects and depth-dependent behaviour with minimum error (low RMSE/WMAPE). The research identifies GA-ANN as an effective instrument for geomechanical forecasting in heterogeneous layers, especially for evaluating water-weakening impacts in deep formations. These findings enhance predictive modelling for subterranean construction, emphasising the joint impact of depth and saturation on rock characteristics.

Item Type:	Article
Subjects:	Material Testing
Divisions:	UNSPECIFIED
Depositing User:	Mr. B. R. Panduranga
Date Deposited:	07 Jul 2026 04:27
Last Modified:	07 Jul 2026 04:27
URI:	https://cimfr.csircentral.net/id/eprint/3034

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