Diagnosis of pipe illicit connections and damaged points in urban stormwater system using an inversed optimization model

Abstract

Pipe illicit connection and pipe damage are common issues in urban stormwater systems. This challenge brings problems that sewage directly discharges into river and groundwater infiltration occupies drainage capacity. The major task of solving these critical issues is to locate these illicit connections and damaged points. However, the conventional method for direct detection of problem points is expensive and ineffective. Therefore, there is an urgent demand for a critical method to diagnose the illicit connections and damaged points of the pipes. This study developed an effective inversed optimization model to easily and precisely diagnose and locate their place. The inversed algorithm in the model is a trial-and-error method using automatic iteration to optimize its calculation. Firstly, a chemical mass balance model1 (CMB) based on Monte Carlo algorithm is built to calculate the overall illicit sewage and groundwater infiltration flow of stormwater system. Then, an inversed optimization model is established to locate the high-risk accumulation areas of illicit connection and pipe damage. The inversed optimization model consists of USEPA Stormwater Management Model2(SWMM), a Python language software package for SWMM3 (PySWMM) and microbial genetic algorithm4 (MGA), called SWMM-PySWMM-MGA.