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Effect of chemical modification on hydraulic conductivity of stratified porous media

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Understanding hydraulic conductivity is essential in porous media as it dictates the capacity of fluids to permeate through these substances. This research investigated the impact of chemical modification using fly ash on hydraulic conductivity. The coefficient of hydraulic conductivity along the bedding plane at different angles of orientations was predicted by employing machine learning techniques such as gene expression programming (GEP), artificial neural networks (ANNs), and regression tree (RT), considering various influencing parameters, as the amount of fly ash in the porous medium rises, the hydraulic conductivity decreases. The statistical results demonstrate that the proposed ANN model effectively forecasted the horizontal hydraulic conductivity, It achieved the highest R~2 value (0.989), the lowest mean absolute percentage error value (5.772), the lowest scatter index value (0.040), and the lowest Akaike Information Criterion value (-2,359.914) compared to the GEP, RT, and previously established approaches. Moreover, a novel equation has been proposed for determining horizontal hydraulic conductivity, which can be used for any stratified medium, distinguishing this work from previous ones. These findings can inform global practices in mitigating groundwater contamination, enhancing irrigation efficiency, and improving the design of subsurface fluid flow systems, making it a valuable contribution to both academic and practical applications worldwide.

bedding planefly ashhorizontal permeability coefficientmachine learning approachesstatistical analysis

Shailesh Kumar Gupta、Amit Kumar Shrivastava、Munendra Kumar

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Department of Civil Engineering, Delhi Technological University, Delhi 110042, India

2024

Water science & technology: Water supply

Water science & technology: Water supply

EI
ISSN:1606-9749
年,卷(期):2024.24(11/12)