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Hydraulic Conductivity plays a vital role in the studies encompassing explorations on flow and porous media. The study investigates the compaction characteristics of river sand (Beas, Sutlej, and Ghaggar) fly ash mix in different proportions and evaluates four empirical equations for estimating hydraulic conductivity. Experiments show that an increase in the fly ash content results in a decrease in the Maximum Dry Density (MDD) and an increase in the corresponding Optimum Moisture Content (OMC) of sand-fly ash samples. MDD at optimum fly ash content was achieved at low water content, which resulted in lesser dry unit weight than that of typical conventional fill. In Beas, Sutlej, and Ghaggar sands the optimum fly ash content up to which the hydraulic conductivity value reduced uniformly was found to be 30, 45, and 40% respectively. Any further, increase in the fly ash content results in a negligible decrease in hydraulic conductivity value. The observed hydraulic conductivity of sand-fly ash mix lies in the range of silts, which emboldens the use of sand-fly ash mix as embankment material. Further, the evaluation of empirical equations considered in the study substantiates the efficacy of Terzaghi equation in estimating the hydraulic conductivity of river sand-fly ash mix. HIGHLIGHT In India, with the advancement in technology, we are being more focused on utilizing waste products for specific purposes. In the present study, flyash is a waste product that is used for making impermeable embankments, fills, and groynes. This study, provides a systematic approach i.e., how this waste product (flyash) being used for specific civil engineering purposes.

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Knowledge of hydraulic conductivity (K) is inevitable for sub-surface flow and aquifer studies. Hydrologists and groundwater researchers are employing data-driven techniques to indirectly evaluate K using porous media characteristics as an alternative to direct measurement. The study examines the ability of the Adaptive Neuro-Fuzzy Inference System (ANFIS) to predict the K of porous media using two membership functions (MFs), i.e., triangular and Gaussian, and support vector machine (SVM) via four kernel functions, i.e., linear, quadratic, cubic, and Gaussian. The techniques used easily measurable parameters namely effective and mean grain size, uniformity coefficient, and porosity as input variables. A 70 and 30% dataset is used for the training and testing of models, respectively. The correlation coefficient (R) and root mean square error (RMSE) were used to evaluate the models. The Gaussian MF-based ANFIS model outperformed the triangular model having R and RMSE values of 0.9661 & 0.0010 and 0.9532 & 0.0015, respectively, whereas the quadratic kernel-based SVM model with R and RMSE values of 0.9520 and 0.0015 performs better than the other SVM models. Based on the evaluation of ANFIS and SVM models, the study establishes the efficacy of the Gaussian MF-based ANFIS model in estimating the K of porous media.