Application of model tree and Evolutionary Polynomial Regression for evaluation of sediment transport in pipes

Prediction of critical velocity for sediment deposition is a significant component in design of sewer pipes. Because of the abrupt changes in velocity and shear stress distributions, traditional equations based on regression analysis can fail in evaluating sediment transport efficiently. Therefore, different artificial intelligence approaches have been applied to investigate sediment transport in sewer pipes. This study proposes two different approaches to predict the critical velocity for sediment deposition in sewer networks: Model Tree (MT) and the Evolutionary Polynomial Regression (EPR), a hybrid data-driven technique that combines genetic algorithms with numerical regression. The hydraulic radius, average size of sediments, volumetric concentration, total friction factor, and non-dimensional sediment size were considered as input parameters to characterize sediment transport in clean sewer pipes. The present study implements data collected from different works in literature. The proposed modeling approaches are compared to some benchmark formulas from literature, and discussed from the accuracy and knowledge discovery points of view, highlighting the advantage of both proposed techniques. Results indicated that both techniques have similar accuracy in predictions, but EPR allows to physical validation of returned formulas, allowing identifying the most influent inputs on the phenomenon at stake.