Sediment transport in sewers has been extensively studied in the past. This paper aims to propose a new method for predicting the self-cleansing velocity required to avoid permanent deposition of material in sewer pipes. The new Random Forest (RF) based model was implemented using experimental data collected from the literature. The accuracy of the developed model was evaluated and compared with ten promising literature models using multiple observed datasets. The results obtained demonstrate that the RF model is able to make predictions with high accuracy for the whole dataset used. These predictions clearly outperform predictions made by other models, especially for the case of non-deposition with deposited bed criterion that is used for designing large sewer pipes. The volumetric sediment concentration was identified as the most important parameter for predicting self-cleansing velocity.

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This paper presents a novel model for predicting the sediment transport rate during flushing operation in sewers. The model was developed using the Evolutionary Polynomial Regression Multi-Objective Genetic Algorithm (EPR-MOGA) methodology applied to new experimental data collected. Using the new model, a series of design charts were developed to predict the sediment transport rate and the required flushing operation time for several pipe diameters. Accurate results (i.e. sediment transport rates) were obtained when applied to a case study in a combined sewer pipe in Marseille, as reported in the literature. The novelty of the model is the inclusion of the pipe slope, the inflow ‘dam break’ hydrograph, and the sediment properties as explanatory parameters. The new model can be used to predict flushing efficiency and design new flushing cleaning schedules in sewer systems.

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Multiple models from the literature and experimental datasets have been developed and collected to predict sediment transport in sewers. However, all these models were developed for smaller sewer pipes, i.e. using experimental data collected on pipes with diameters smaller than 500 mm. To address this issue, new experimental data were collected on a larger, 595 mm pipe located in a laboratory at the University of los Andes. Two new self-cleansing models were developed using this dataset. Both models predict the sewer self-cleansing velocity for the cases of non-deposition with and without deposited bed. The newly developed and existing models were then evaluated and compared on the basis of the most recently collected and previously published datasets. Models were compared in terms of prediction accuracy measured by the root mean squared error and mean absolute percentage error. The results obtained show that in the existing literature, self-cleansing models tend to be overfitted, i.e. have a rather high prediction accuracy when applied to the data collected by the authors, but this accuracy deteriorates quickly when applied to the datasets collected by other authors. The newly developed models can be used for designing both small and large sewer pipes with and without deposited bed condition.

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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 self-cleansing 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 self-cleansing velocity.

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This paper aims to analyze different sediment self-cleansing criteria and to find out what the corresponding implications are on the optimal design of sewer systems. A methodology based on enumeration is used to find the sewer network design that minimizes the costs of construction while fulfilling a number of design criteria including self-cleansing constraints. Three stormwater and wastewater sewer networks are used for the analyses. The results indicate that in cases where the terrain slopes and design flow rates are higher, the self-cleansing restrictions are irrelevant to the optimal design. However, when the terrain slopes and the design flow rates are lower, these restrictions affect the final design. Using the results obtained, a graph is constructed showing the limit at which self-cleansing restrictions become a constraining parameter in optimal design for sewer networks. It is expected that this graph will be useful for the design of future sewer networks in low-income areas, where the design of traditional, gravity-based sewer systems is essential.@en