Evaluation of the impact of fly ash on infiltration characteristics using different soft computing techniques

The aim of this paper was to investigate the impact of the fly ash concentration on the infiltration process and to assess the potential of five soft computing techniques such as artificial neural network, Gaussian process, support vector machine (SVM), random forest, and M5P model tree and compare with two popular conventional models, SCS and Kostiakov mode, to estimate the cumulative infiltration of fly-ash-mixed soils. Laboratory experiment was carried out with the different combinations of the sand, clay, and fly ash by using mini disk infiltrometer. The combination consists of the different concentrations of sand (25–45%), clay (25–45%), and fly ash (10–50%). The total observation consists of the 138 field measurement. The cumulative infiltration increase with an increment in the concentration of the fly ash, but it decreases when fly ash concentration increases 40–50% in the soil. On the other hand, the cumulative infiltration increases with the decrease in the concentration of clay in samples. The predictive modeling technique, SVM with RBF kernel, is the best technique to predict the cumulative infiltration with minimum error. Results suggest that SVM with RBF kernel is the best-fit modeling technique among other soft computing techniques as well as conventional models to find the impact of fly ash on infiltration characteristics for the given combination of the sand, clay and fly ash.

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