Geological modelling plays a critical role by improving subsurface characterization and supporting engineering decision-making. This study presents an integrated geostatistical modelling framework for spatial characterization of lithology, core recovery, rock quality designation (RQD) and percolation geoparameters at Bhama Askhed Irrigation Project site, Pune, India. Unlike conventional applications of geostatistical interpolation, the present work combines three-dimensional geological modelling, statistical validation, engineering performance assessment, and post-failure verification using Energy Dissipating Assembly (EDA) failure investigations by the Central Water and Power Research Station (CWPRS), Pune. Model performance was evaluated using correlation coefficient (R), Root Mean Square Error (RMSE), Nash-Sutcliffe Efficiency (NSE), and Ratio of Root Mean Square Error to Standard Deviation (RSR). The developed models exhibited satisfactory agreement between observed and predicted data with low residual errors and acceptable performance indices. Solid model residuals for the interpolated model are substantially less: 6.15, 8.29, 13.07 and -9.60 for lithology, recovery, RQD and percolation, respectively, thus indicating reasonable model fit. Statistical comparison of model-predicted parameters with the reserved data set also shows correlation coefficient greater than +0.70 indicates a strong uphill linear relationship between the observed and modelled data. The average correlation for lithology, core recovery, RQD and percolation was found to be 62.25%, 92.5%, 88.25% and 75.5%, respectively. The conducted tail channel erosion modelling can identify intensity of erosion-induced geological deterioration, enabling early recognition of vulnerable zones. The predicted weak zones showed consistency with field observations and post-failure investigations, highlighting the practical engineering applicability of the proposed framework. The study demonstrates that integrated geostatistical-geological modelling can, improve spatial prediction of geological parameters, produce missing spatial investigation data, support engineering design and risk assessment in infrastructure projects.
