Predicting bedload sediment transport of non-cohesive material in sewer pipes using evolutionary polynomial regression – multi-objective genetic algorithm strategy

Abstract

2020)Predicting bedload sediment transport of non-cohesive material in sewer pipes using evolutionary polynomial regression -multi-objective genetic algorithm strategy, Urban Water Journal, 17:2, 154-162, ABSTRACT Sediment transport in sewer systems is an important issue of interest to engineering practice. Several models have been developed in the past to predict a threshold velocity or shear stress resulting in selfcleansing flow conditions in a sewer pipe. These models, however, could still be improved. This paper develops three new self-cleansing models using the Evolutionary Polynomial Regression-Multi-Objective Genetic Algorithm (EPR-MOGA) methodology applied to new experimental data collected on a 242 mm diameter acrylic pipe. The three new models are validated and compared to the literature models using both new and previously published data sets. The results obtained demonstrate that three new models have improved prediction accuracy when compared to the literature ones. The key feature of the new models is the inclusion of pipe slope as a significant explanatory factor in estimating the threshold selfcleansing velocity. ARTICLE HISTORY

A Two‐Stage Data‐Driven Spatiotemporal Analysis to Predict Failure Risk of Urban Sewer Systems Leveraging Machine Learning Algorithms

“…The classification of zones (cells or areas) into risk levels could be used to prioritize the sewer sections that require engineering interventions to increase their reliability. Examples of such interventions are expansions of the sewer system capacity or replacements of piped sections with self‐cleansing sewer pipes that guarantee sediment transport (Montes, Berardi, Kapelan, & Saldarriaga, 2020; Montes, Kapelan, & Saldarriaga, 2019). Alternatively, Sustainable Urban Drainage Systems (Ghodsi, Zahmatkesh, Goharian, Kerachian, & Zhu, 2020; Torres, Fontecha, Zhu, Walteros, & Rodríguez, 2020) can be placed to alleviate the sediments load from runoff (Maringanti, Chaubey, & Popp, 2009).…”

Using artificial neural networks to determine sediment concentration at limit of deposition in storm sewers

“…The EPR-MOGA model was trained to predict the Froude Number at the limit of deposition, validated with their own experimental data aggregated another data found in the literature. Using the sum of squared errors (SSE), R 2 and the Akaike Information Criterion (AIC), Montes et al (2020b) concluded that the EPR-MOGA model performs better than the equations analyzed in their work, which showed values of SSE equal to 0.06, R 2 of 0.98 and the lower AIC observed. One reason attributed to that is because the analyzed models do not considerate the longitudinal slope as a dependent variable, and, the EPR-MOGA have improved generalization capability.…”

“…In general, in all the presented studies, one can notice that artificial intelligence and soft computing models were developed in order to determine the limit of deposition using the Froude Number. In addition, with exception of Montes et al (2020b), the presented researches use a limited experimental data, restraining the range of the observed variables in the training and evaluation dataset. Also, the conclusions and assumptions of previous studies indicates that, equations present worse predictions of the Froude Number than the proposed models evaluated, but there are no discussions of which model should be used in extrapolations.…”

Model predictive control based on artificial intelligence and EPA-SWMM model to reduce CSOs impacts in sewer systems

“…GAs are widely utilized and present their efficiency to solve many optimization problems in many fields, specifically in water. We can cite the works of Tayfur et al (2009) for predicting peak flows, Li et al (2020) for water resource management, Bostan et al (2019) for the optimal design of shock dampers, Montes et al (2020) for predicting bedload sediment transport in sewer networks, and Hassan et al (2020) for the optimal design of sewer networks. Therefore, GAs are chosen to optimize the sewer system operating as part of this work.…”