Abstract

By accurate predicting of pipe bursts, it is possible to schedule pipe maintenance, rehabilitation and improve the level of services in water distribution networks (WDNs). In this study, we aimed to implement five artificial intelligence and machine learning regression models such as multivariate adaptive regression splines (MARS), M5' regression tree (M5'), Least square support vector regression (LS-SVR), fuzzy regression based on c-means clustering (FCMR) and regressive convolution neural network with support vector regression (RCNN-SVR) for predicting pipe burst rate and evaluating the performance of these models. The most effective parameters for regression models are pipes age, diameter, depth of installation, length, average and maximum hydraulic pressure. In the present study, collected data include 158 cases for polyethylene (PE) and 124 cases for asbestos cement (AC) pipes during 2012-2019. The results indicate that the RCNN-SVR model has a great performance of pipe burst rate (PBR) prediction.

Highlights

  • Water distribution networks (WDNs) are critical infrastructures

  • The results indicate that RCNN-Support Vector Regression (SVR) 15 model has a great performance of pipe burst rate (PBR) prediction

  • As can be seen from the 230 obtained results listed in table 1, it is evident that there is a correlation between PBR and the pipe burst variables

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Summary

Introduction

Water distribution networks (WDNs) are critical infrastructures. The objective of WDNs is to provide water with desirable quantity, quality and pressure for the consumers. In case of pipe failure which is the progressive effect of physical, operational and weather-related factors, might fail the WDN to achieve these goals (Kakoudakis, 2019). Pipe burst prediction can be used for budget allocation and cost analysis of dynamic or static designing of water distribution networks. Physical models are developed to 25 understand the physical process of pipe deterioration. In this models, the items that may affect the pipes burst, include environmental conditions, quality of manufacturing, installation procedure, internal and external loads, surrounding soil, ground traffic and etc.

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