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 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.

摘要

下水道系统中的泥沙运输是工程实践中关注的重要问题。过去已经开发了几种模型来预测阈值速度或剪切应力，从而导致下水道中的自清洁流动情况。但是，这些模型仍可以改进。本文使用进化多项式回归多目标遗传算法（EPR-MOGA）方法开发了三种新的自清洁模型，这些方法适用于在242毫米直径的丙烯酸管上收集的新实验数据。验证了这三个新模型，并使用新数据集和先​​前发布的数据集将其与文献模型进行了比较。获得的结果表明，与文献模型相比，三种新模型具有更高的预测精度。