Water pipe failure prediction and risk models: state-of-the-art review

Dawood, Thikra; Elwakil, Emad; Novoa, Hector Mayol; Delgado, José Fernando Gárate

doi:10.1139/cjce-2019-0481

Cited by 46 publications

(11 citation statements)

References 42 publications

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“…While there are many opportunities to increase the accuracy level of the model by having additional parameters and longer-term data, the advantage of a scorecard value is its simplicity. It conveys a clear and uncomplicated message to the decision maker unlike other complicated models (see Dawood et al 2020a for a comprehensive review). Scorecard modelling is extensively used in the banking industry to weed out 'good' versus 'bad' investors at the time of loan application.…”

Section: Discussionmentioning

confidence: 99%

“…Figure 2(b) also illustrates a clear distinction between these two groups as the mean and median values dropping significantly between younger and older pipes. Past research provides clear evidence for this observation as a large number of studies have done systematic observations of pipe deterioration with age (Harvey et al 2014;Chik et al 2017;Farmani et al 2017;Dawood et al 2020a). According to Christodoulou (2011) aged pipes must be replaced at approximately 30 years.…”

Section: Model Validationmentioning

confidence: 91%

“…Many studies have found in addition to age, pipe diameter, material type, external environmental conditions, and internal and external pressure loads are related to water pipe failure (Christodoulou et al 2010;Fares & Sayed 2010;Jafar et al 2010;Kleiner & Rajani 2012;Laucelli et al 2014;Aydogdu & Firat 2015;Kabir et al 2016a;Farmani et al 2017;Ward et al 2017;Fu et al 2019;Motiee & Ghasemnejad 2019;Sattar et al 2019). Researchers have analysed the elements of water pipe failure and built failure prediction models suitable for different regions based on historical data (Rezaei & Stoianov 2015;Dawood et al 2020a). The water pipe fault prediction model can effectively predict the time when the water pipes may fail in the future and alert water utilities to replace them in advance, to avoid a series of impacts.…”

Section: Introductionmentioning

confidence: 99%

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Monitoring the health status of water mains using a scorecard modelling approach

et al. 2021

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There has been considerable research into prediction of water mains failure, however, those models are very complex and fail to convey the message of the health status of an asset to the relevant stakeholders. The study developed a ‘pipe health scorecard’ based on historical failure data which could be used for operation, maintenance, refurbishment, or replacement decisions by a water utility. This scorecard model was developed by using 160,413 pipe-condition datasets from the South Australian Water Corporation over 10 years. Measures such as the Kolmogorov–Smirnov (KS) statistic, Area Under the ROC Curve (AUC), and Population Stability Index (PSI) showed the model is strong enough to predict the health status of water mains. The study found that the factors influencing water mains failure to be in the order of importance: length, material, age, location (road vs verge), diameter, and operating parameters. The development of a simple but reliable model for the assessment of the health status of water mains will have major benefits to the water utility with the ability to identify and potentially replace water pipes prior to failure. Additional benefits of flexible scheduling of the maintenance and replacement programs would contribute to cost savings.

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Section: Discussionmentioning

confidence: 99%

Section: Model Validationmentioning

confidence: 91%

Section: Introductionmentioning

confidence: 99%

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Monitoring the health status of water mains using a scorecard modelling approach

et al. 2021

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“…e size of the detection standard λ value determines the number of flow measurement points; the larger the value of λ taken, the more flow monitoring points need to be arranged the higher the accuracy of monitoring, while it will cause increased costs and operating expenses; conversely, the smaller the number of arrangements, the lower the corresponding accuracy [25]. e value of λ in the actual engineering calculation can be considered according to the requirements of accuracy and cost.…”

Section: Multivariate Statistical Model Design For Water Leakage Of U...mentioning

confidence: 99%

A Multivariate Statistical Model of Water Leakage in Urban Water Supply Networks Based on Random Matrix Theory

Chen¹,

Yang

2022

Mathematical Problems in Engineering

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In this paper, model construction of random matrix theory is carried out to analyze water leakage of urban water supply pipeline network, and a multivariate statistical model of water leakage of urban water supply pipeline network based on random matrix theory is proposed. This paper presents a three-level DMA, short-term water demand prediction method based on water use patterns, aiming to solve the problem of inaccurate water demand prediction caused by climate and social factors. Correlation analysis and factor analysis analyze the main factors affecting daily water demand changes. The extracted information of the main influencing factors and historical monitoring data are used to establish the Elman water demand prediction model. At the same time, the daily water consumption pattern was extracted by correlation analysis, matched with the water consumption pattern of the predicted day, and combined with the expected value of water demand for momentary water demand allocation, which improved the prediction accuracy of short-term water demand. This paper is based on the precise leakage location based on the real-time hydraulic model. The real-time hydraulic model under leakage conditions is established, GA and NSGA2 are designed, and the nondominated ranking algorithm with elite strategy is used to achieve accurate leakage location using hydraulic model calibration search. This paper uses a random matrix algorithm to establish the leakage estimation model. A laboratory case pipe network is built to simulate the water supply conditions in residential areas, and the operating data of the laboratory case pipe network are used to confirm that the random matrix leakage estimation model can estimate the leakage. The applicability of the random matrix leakage estimation model and the wavelet transform algorithm for leakage calculation are compared in an engineering example. The example confirms that the leakage amount calculated by the random matrix model has a less relative error and more similar trend than the wavelet transform algorithm.

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“…Most research on water main failure prediction is focused on three aspects: (1) development of models to predict water main failure; (2) predictive factors for failure; and (3) prediction model outputs -e.g., failure rate, probability of failure, etc. These efforts have been summarized and discussed in many literature reviews, which generally focus on existing types of prediction model and their evolution Clair & Sinha 2012;Nishiyama & Filion 2013b;Ogutu et al 2017;Wilson et al 2017;Wu & Liu 2017;Dawood et al 2020). Less effort has been expended on reviewing model factors and other aspects of this research (Gao 2017).…”

Section: Graphical Abstract Introductionmentioning

confidence: 99%

A review of water quality factors in water main failure prediction models

Monfared

Nojumi

Bayat

2021

Water Practice and Technology

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Water main failure can result from structural failure of the pipes, changes in water quality, or a combination. This paper is a review of articles evaluating water quality factors and subfactors in the development of water main failure prediction models since 2000. A systematic process was implemented to capture the most relevant current published papers. Of 4598 published papers, 304 were screened for water main failure prediction models. The resulting set was further screened for water quality factors and subfactors (e.g., pH, temperature, etc.). This led to the identification of 18 relevant research papers, and each of these was reviewed comprehensively. The water quality-related findings, as well as combinations with other information – such as type of prediction model and type of prediction – are summarized and discussed.

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Water pipe failure prediction and risk models: state-of-the-art review

Cited by 46 publications

References 42 publications

Monitoring the health status of water mains using a scorecard modelling approach

Monitoring the health status of water mains using a scorecard modelling approach

A Multivariate Statistical Model of Water Leakage in Urban Water Supply Networks Based on Random Matrix Theory

A review of water quality factors in water main failure prediction models

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