Neural Network Approach for Availability Indicator Prediction

The principal aim of this research was to find out if artificial neuralnetworks could be employed to predict the availability factorfor water mains, distribution pipes and house connections.Modelling by means of artificial neural networks (ANNs) wascarried out using the Statistica 10.0 software package. Operatingdata from the years 1999–2005 were used to train the ANNswhile data from the next seven years of operation were usedto verify the model. The optimal model (characterized by thelowest mean-square error) contained 11 hidden neurons activatedby the exponential function. The linear function was usedto activate the 3 output neurons. 185 training epochs sufficed totrain the ANN, using the quasi-Newton method. The correlationbetween the availability indicator experimental values and themodelling results would remain high, amounting during modelverification to R2 = 0.740, R2 = 0.823, R2 = 0.992 for respectivelywater mains, distribution pipes and house connections. As theavailability indicator prediction example shows, the artificialneural networks are a promising tool enabling quick and easyanalysis of failure frequency. It is possible to train the ANN furtherand change the number of training epochs and hidden neuronsas well as the activation functions and training methods

Prediction of Failure Frequency of Water-Pipe Network in the Selected City

The paper presents the modelling results of failure rate of watermains, distribution pipes and house connections in one Polishcity. The prediction of failure frequency was performed usingartificial neural networks. Multilayer perceptron was chosen asthe most suitable for modelling purposes. Neural network architecturecontained 11 input signals (sale, production, consumptionand losses of water, number of water-meters, length andnumber of failures of water mains, distribution pipes and houseconnections). Three neurons (failure rates of three conduitstypes) were put to the output layer. One hidden layer, with hiddenneurons in the range 1-22, was used. Operating data fromyears 2005-2011 were used for training the network. Optimalmodel was verified using operational data from 2012. ModelMLP 11-10-3 was chosen as the best one for failure rate prediction.In this model hidden and output neurons were activatedby exponential function and the learning was done using quasi-Newton approach. During the learning process the correlation(R) and determination (R2) coefficients for water mains, distributionpipes and house connections equaled to 0.9921, 0.9842;0.8685, 0.7543 and 0.9945, 0.9891, respectively. The convergencesbetween real and predicted values seem to be, from engineeringpoint of view, satisfactory

Systematic Review for Water Network Failure Models and Cases

As estimated in the American Society of Civil Engineers 2017 report, in the United States, there are approximately 240,000 water main pipe breaks each year. To help estimate pipe breaks and maintenance frequency, a number of physically-based and statistically-based water main failure prediction models have been developed in the last 30 years. Precious review papers focused more on the evolution of failure models rather than modeling results. However, the modeling results of different models applied in case studies are worth reviewing as well. In this review, we focus on research papers after Year 2008 and collect latest cases without repetition. A total of 64 papers are qualified following the selection criteria. Detailed information on models and cases are summarized and compared. Chapter 2 provides a summary and review of failure models and discusses the limitation of current models. Chapter 3 provides a comprehensive review of collected cases, which include network characteristics and factors. Chapter 4 focuses on the main findings from collected papers. We conclude with insights and suggestions for future model selection for pipe failure analysis

Comparison of Two Types of Artificial Neural Networks for Predicting Failure Frequency of Water Conduits

This paper presents the results of a comparison between two artificial neural network structures, i.e. the multilayer perceptron and the ANN with radial basis functions, with regard to the prediction of the failure intensity (failure rate) indicator for water mains, distribution pipes and house connections. The artificial neural network architecture included seven input signals (the number of house connections, the length of water mains, distribution pipes and house connections and the number of their failures). There were three neurons (the failure frequency indicators for the three types of conduits) at the ANN’s output. Operating data from the years 1999-2013 were used to train the ANNs while the optimal model was verified using data from the year 2014. Two models (MLP 7-14-3 and RBF 7-4-3), characterized by the best agreement between the predicted results and the experimental ones, were selected from a few tens of models. The RBF ANNs would generate results showing poorer agreement with the experimental failure frequency indicator values

Pipe failure prediction and impacts assessment in a water distribution network

Abstract Water distribution networks (WDNs) aim to provide water with desirable quantity, quality and pressure to the consumers. However, in case of pipe failure, which is the cumulative effect of physical, operational and weather-related factors, the WDN might fail to meet these objectives. Rehabilitation and replacement of some components of WDNs, such as pipes, is a common practice to improve the condition of the network to provide an acceptable level of service. The overall aim of this thesis is to predict—long-term, annually and short-term—the pipe failure propensity and assess the impacts of a single pipe failure on the level of service. The long-term and annual predictions facilitate the need for effective capital investment, whereas the short-term predictions have an operational use, enabling the water utilities to adjust the daily allocation and planning of resources to accommodate possible increase in pipe failure. The proposed methodology was implemented to the cast iron (CI) pipes in a UK WDN. The long-term and annual predictions are made using a novel combination of Evolutionary Polynomial Regression (EPR) and K-means clustering. The inclusion of K-means improves the predictions’ accuracy by using a set of models instead of a single model. The long-term predictive models consider physical factors, while the annual predictions also include weather-related factors. The analysis is conducted on a group level assuming that pipes with similar properties have similar breakage patterns. Soil type is another aggregation criterion since soil properties are associated with the corrosion of metallic pipes. The short-term predictions are based on a novel Artificial Neural Network (ANN) model that predicts the variations above a predefined threshold in the number of failures in the following days. The ANN model uses only existing weather data to make predictions reducing their uncertainty. The cross-validation technique is used to derive an accurate estimate of accuracy of EPR and ANN models by guaranteeing that all observations are used for both training and testing, and each observation is used for testing only once. The impact of pipe failure is assessed considering its duration, the topology of the network, the geographic location of the failed pipe and the time. The performance indicators used are the ratio of unsupplied demand and the number of customers with partial or no supply. Two scenarios are examined assuming that the failure occurs when there is a peak in either pressure or demand. The pressure-deficient conditions are simulated by introducing a sequence of artificial elements to all the demand nodes with pressure less than the required. This thesis proposes a new combination of a group-based method for deriving the failure rate and an individual-pipe method for evaluating the impacts on the level of service. Their conjunction indicates the most critical pipes. The long-term approach improves the accuracy of predictions, particularly for the groups with very low or very high failure frequency, considering diameter, age and length. The annual predictions accurately predict the fluctuation of failure frequency and its peak during the examined period. The EPR models indicate a strong direct relationship between low temperatures and failure frequency. The short-term predictions interpret the intra-year variation of failure frequency, with most failures occurring during the coldest months. The exhaustive trials led to the conclusion that the use of four consecutive days as input and the following two days as output results in the highest accuracy. The analysis of the relative significance of each input variable indicates that the variables that capture the intensity of low temperatures are the most influential. The outputs of the impact assessment indicate that the failure of most of the pipes in both scenarios (i.e. peak in pressure and demand) would have low impacts (i.e. low ratio of unsupplied demand and small number of affected nodes). This can be explained by the fact that the examined network is a large real-life network, and a single failure of a distribution pipe is likely to cause pressure-deficient conditions in a small part of it, whereas performance elsewhere is mostly satisfactory. Furthermore, the complex structure of the WDN allows them to recover from local pipe failures, exploiting the topological redundancy provided by closed loops, so that the flow could reach a given demand node through alternative paths

Models and explanatory variables in modelling failure for drinking water pipes to support asset management: a mixed literature review

There is an increasing demand to enhance infrastructure asset management within the drinking water sector. A key factor for achieving this is improving the accuracy of pipe failure prediction models. Machine learning-based models have emerged as a powerful tool in enhancing the predictive capabilities of water distribution network models. Extensive research has been conducted to explore the role of explanatory variables in optimizing model outputs. However, the underlying mechanisms of incorporating explanatory variable data into the models still need to be better understood. This review aims to expand our understanding of explanatory variables and their relationship with existing models through a comprehensive investigation of the explanatory variables employed in models over the past 15 years. The review underscores the importance of obtaining a substantial and reliable dataset directly from Water Utilities databases. Only with a sizeable dataset containing high-quality data can we better understand how all the variables interact, a crucial prerequisite before assessing the performance of pipe failure rate prediction models.EF-O acknowledges the financial support provided by the “Agencia de Gestió d’Ajust Universitaris I de Recerca” (https:// agaur. gencat. cat/ en/) through the Industrial Doctorate Plan of the Secretariat for Universities and Research of the Department of Business and Knowledge of the Government of Catalonia, under the Grant DI 093-2021. Additionally, EF-O appreciates the economic support received from the Water Utility Aigües de Barcelona, Empresa Metropolitana de Gestió del Cicle Integral de l'Aigua.Peer ReviewedPostprint (published version

Condition Assessment Models for Sewer Pipelines

Underground pipeline system is a complex infrastructure system that has significant impact on social, environmental and economic aspects. Sewer pipeline networks are considered to be an extremely expensive asset. This study aims to develop condition assessment models for sewer pipeline networks. Seventeen factors affecting the condition of sewer network were considered for gravity pipelines in addition to the operating pressure for pressurized pipelines. Two different methodologies were adopted for models’ development. The first method by using an integrated Fuzzy Analytic Network Process (FANP) and Monte-Carlo simulation and the second method by using FANP, fuzzy set theory (FST) and Evidential Reasoning (ER). The models’ output is the assessed pipeline condition. In order to collect the necessary data for developing the models, questionnaires were distributed among experts in sewer pipelines in the state of Qatar. In addition, actual data for an existing sewage network in the state of Qatar was used to validate the models’ outputs. The “Ground Disturbance” factor was found to be the most influential factor followed by the “Location” factor with a weight of 10.6% and 9.3% for pipelines under gravity and 8.8% and 8.6% for pipelines under pressure, respectively. On the other hand, the least affecting factor was the “Length” followed by “Diameter” with weights of 2.2% and 2.5% for pipelines under gravity and 2.5% and 2.6% for pipelines under pressure. The developed models were able to satisfactorily assess the conditions of deteriorating sewer pipelines with an average validity of approximately 85% for the first approach and 86% for the second approach. The developed models are expected to be a useful tool for decision makers to properly plan for their inspections and provide effective rehabilitation of sewer networks.1)- NPRP grant # (NPRP6-357-2-150) from the QatarNational Research Fund (Member of Qatar Foundation) 2)-Tarek Zayed, Professor of Civil Engineeringat Concordia University for his support in the analysis part, the Public Works 3)-Authority of Qatar (ASHGAL) for their support in the data collection

Modelling of Failure Rate of Water-pipe Networks

The paper describes the reliability of selected water-pipe networks in Polish medium-sized cities X and Z. The main goal of this research was to compare the results of failure rate modelling with the experimental data. On average, in the analyzed time the main conduits and the distribution and service pipes in city X were characterized by failure rates (fail. / (km·a)) of 0.27, 0.40 and 0.59 while in city Z the failure rates amounted to 0.30, 0.32 and 0.78. The model described in the literature has been slightly modified (as a result, model M3 has been created) by the author to achieve better agreement with the experimental data. In the future, model M3 will be extended to ensure a larger prediction domain, which will make it more suitable for planning renovation schedules

Intelligent urban water infrastructure management

Copyright © 2013 Indian Institute of ScienceUrban population growth together with other pressures, such as climate change, create enormous challenges to provision of urban infrastructure services, including gas, electricity, transport, water, etc. Smartgrid technology is viewed as the way forward to ensure that infrastructure networks are fl exible, accessible, reliable and economical. “Intelligent water networks” take advantage of the latest information and communication technologies to gather and act on information to minimise waste and deliver more sustainable water services. The effective management of water distribution, urban drainage and sewerage infrastructure is likely to require increasingly sophisticated computational techniques to keep pace with the level of data that is collected from measurement instruments in the field. This paper describes two examples of intelligent systems developed to utilise this increasingly available real-time sensed information in the urban water environment. The first deals with the failure-management decision-support system for water distribution networks, NEPTUNE, that takes advantage of intelligent computational methods and tools applied to near real-time logger data providing pressures, flows and tank levels at selected points throughout the system. The second, called RAPIDS, deals with urban drainage systems and the utilisation of rainfall data to predict flooding of urban areas in near real-time. The two systems have the potential to provide early warning and scenario testing for decision makers within reasonable time, this being a key requirement of such systems. Computational methods that require hours or days to run will not be able to keep pace with fast-changing situations such as pipe bursts or manhole flooding and thus the systems developed are able to react in close to real time.Engineering and Physical Sciences Research CouncilUK Water Industry ResearchYorkshire Wate