scholarly journals Assessing pipe failure rate and mechanical reliability of water distribution networks using data-driven modeling

2009 ◽  
Vol 11 (1) ◽  
pp. 1-17 ◽  
Author(s):  
M. Tabesh ◽  
J. Soltani ◽  
R. Farmani ◽  
D. Savic
Keyword(s):  
Neural Network ◽  
Multivariate Regression ◽  
Regression Models ◽  
Water Distribution ◽  
Distribution Networks ◽  
Data Driven ◽  
Mechanical Reliability ◽  
Pipe Failure ◽  
Neuro Fuzzy ◽  
Data Driven Modeling

In this paper two models are presented based on Data-Driven Modeling (DDM) techniques (Artificial Neural Network and neuro-fuzzy systems) for more comprehensive and more accurate prediction of the pipe failure rate and an improved assessment of the reliability of pipes. Furthermore, a multivariate regression approach has been developed to enable comparison with the DDM-based methods. Unlike the existing simple regression models for prediction of pipe failure rates in which only few factors of diameter, age and length of pipes are considered, in this paper other parameters such as pressure and pipe depth, are also included. Furthermore, an investigation is carried out on most commonly used mechanical reliability relationships and the results of incorporation of the proposed pipe failure models in the reliability index are compared. The proposed models are applied to a real case study involving a large water distribution network in Iran and the results of model predictions are compared with measured pipe failure data. Compared with the results of neuro-fuzzy and multivariate regression models, the outcomes of the artificial neural network model are more realistic and accurate in the prediction of pipe failure rates and evaluation of mechanical reliability in water distribution networks.

scholarly journals Prediction of pipe failure rate in Tehran water distribution networks by applying regression models

2018 ◽  
Vol 19 (3) ◽  
pp. 695-702 ◽  
Author(s):  
Homayoun Motiee ◽  
Sonya Ghasemnejad
Keyword(s):  
Logistic Regression ◽  
Statistical Models ◽  
Poisson Regression ◽  
Regression Models ◽  
Water Distribution ◽  
Water Distribution Network ◽  
Distribution Networks ◽  
Hydraulic Pressure ◽  
Pipe Failure ◽  
Exponential Regression

Abstract Four statistical models (linear regression, exponential regression, Poisson regression and logistic regression) applied to analyze the variables in pipe vulnerabilities with the objective of finding equations to predict probable future pipe accidents. The most effective variables in pipe failures are material, age, length, diameter and hydraulic pressure. To evaluate these models, the data collected in recent years in the water distribution network of district 1 in Tehran were used, with a total length of 582,702 m of pipes, and 48,500 consumers. The results demonstrate that among the four studied models, the logistic regression model is best able to give a good performance and is capable of predicting future accidents with a higher probability.

scholarly journals Graph Neural Network for Integrated Water Network Partitioning and Dynamic District Metered Areas

2021 ◽  
Author(s):  
KEZHEN RONG ◽  
Minglei Fu ◽  
JIAWEI CHEN ◽  
LEJIN ZHENG ◽  
JIANFENG ZHENG ◽  
...  
Keyword(s):  
Neural Network ◽  
Distribution Systems ◽  
Water Distribution ◽  
Distribution Networks ◽  
Pollution Monitoring ◽  
Practical Case ◽  
Network Partitioning ◽  
Water Network ◽  
Search Range ◽  
Water Network Partitioning

Abstract Water distribution systems (WDSs) are used to transmit and distribute water resources in cities. Water distribution networks (WDNs) are partitioned into district metered areas (DMAs) by water network partitioning (WNP), which can be used for leak control, pollution monitoring, and pressure optimization in WDS management. In order to overcome the limitations of optimal search range and the decrease of recovery ability caused by two-step WNP and fixed DMAs in previous studies, this study developed a new method combining a graph neural network to realize integrated WNP and dynamic DMAs to optimize WDS management and respond to emergencies. The proposed method was tested in a practical case study; the results showed that good hydraulic performance of the WDN was maintained and that dynamic DMAs demonstrated excellent stability in emergency situations, which proves the effectiveness of the method in WNP.

scholarly journals Management of Water Distribution Systems in PDA Condition with Isolation Valves

Proceedings ◽  
2018 ◽  
Vol 2 (11) ◽  
pp. 672 ◽  
Author(s):  
Attilio Fiorini Morosini ◽  
Olga Caruso ◽  
Paolo Veltri
Keyword(s):  
Distribution Systems ◽  
Water Distribution ◽  
Distribution Networks ◽  
Optimal Number ◽  
System Control ◽  
Pipe Failure ◽  
Reliable System ◽  
Network Nodes ◽  
Pipe Burst

The correct management of Water Distribution Networks (WDNs) allows to obtain a reliable system. When a pipe failure occurs in a network and it is necessary to isolate a zone, it is possible that some nodes do not guarantee service for the users due to inadequate heads. In these conditions a Pressure Driven Analysis (PDA) is the correct approach to evaluate network behavior. This analysis is more appropriate than the Demand Driven Analysis (DDA) because it is known that the effective delivered flow at each node is influenced by the pressure value. In this case, it is important to identify a subset of isolation valves to limit disrupting services in the network. For a real network, additional valves must be added to existing ones. In this paper a new methodological analysis is proposed: it defines an objective function (OF) to provide a measure of the system correct functioning. The network analysis using the OF helps to choose the optimal number of additional valves to obtain an adequate system control. In emergency conditions, the OF takes into account the new network topology obtained excluding the zone where the broken pipe is located. OF values depend on the demand deficit caused by the head decrement in the network nodes for each pipe burst considered. The results obtained for a case study confirm the efficiency of the methodology.

Data driven modeling based on dynamic parsimonious fuzzy neural network

2013 ◽  
Vol 110 ◽  
pp. 18-28 ◽  
Author(s):  
Mahardhika Pratama ◽  
Meng Joo Er ◽  
Xiang Li ◽  
Richard J. Oentaryo ◽  
Edwin Lughofer ◽  
...  
Keyword(s):  
Neural Network ◽  
Fuzzy Neural Network ◽  
Data Driven ◽  
Fuzzy Neural ◽  
Data Driven Modeling

scholarly journals Data-driven modeling to optimize the injection well placement for waterflooding in heterogeneous reservoirs applying artificial neural networks and reducing observation cost

2020 ◽  
Vol 38 (6) ◽  
pp. 2413-2435 ◽  
Author(s):  
Xinwei Xiong ◽  
Kyung Jae Lee
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Injection Well ◽  
Data Driven ◽  
Well Placement ◽  
Heterogeneous Reservoirs ◽  
Modeling Approach ◽  
Artificial Neural ◽  
Well Data ◽  
Data Driven Modeling

Secondary recovery methods such as waterflooding are often applied to depleted reservoirs for enhancing oil and gas production. Given that a large number of discretized elements are required in the numerical simulations of heterogeneous reservoirs, it is not feasible to run multiple full-physics simulations. In this regard, we propose a data-driven modeling approach to efficiently predict the hydrocarbon production and greatly reduce the computational and observation cost in such problems. We predict the fluid productions as a function of heterogeneity and injection well placement by applying artificial neural network with small number of training dataset, which are obtained with full-physics simulation models. To improve the accuracy of predictions, we utilize well data at producer and injector to achieve economic and efficient prediction without requiring any geological information on reservoir. The suggested artificial neural network modeling approach only utilizing well data enables the efficient decision making with reduced computational and observation cost.

scholarly journals Complex network and fractal theory for the assessment of water distribution network resilience to pipe failures

2017 ◽  
Vol 18 (3) ◽  
pp. 767-777 ◽  
Author(s):  
Armando Di Nardo ◽  
Michele Di Natale ◽  
Carlo Giudicianni ◽  
Roberto Greco ◽  
Giovanni Francesco Santonastaso
Keyword(s):  
Complex Network ◽  
Water Distribution ◽  
Fractal Theory ◽  
Distribution Networks ◽  
Pipe Failure ◽  
Operational Conditions ◽  
Complex Network Theory ◽  
Topological Features ◽  
Wide Range ◽  
The Impact

AbstractWater distribution networks (WDNs) must keep a proper level of service under a wide range of operational conditions, and, in particular, the analysis of their resilience to pipe failures is essential to improve their design and management. WDNs can be regarded as large sparse planar graphs showing fractal and complex network properties. In this paper, the relationship linking the geometrical and topological features of a WDN to its resilience to the failure of a pipe is investigated. Some innovative indices have been borrowed from fractal geometry and complex network theory to study WDNs. Considering all possible network configurations obtained by suppressing one link, the proposed indices are used to quantify the impact of pipe failure on the system's resilience. This approach aims to identify critical links, in terms of resilience, with the help of topological metrics only, and without recourse to hydraulic simulations, which require complex calibration processes and come with a computational burden. It is concluded that the proposed procedure, which has been successfully tested on two real WDNs located in southern Italy, can provide valuable information to water utilities about which pipes have a significant role in network performance, thus helping in their design, planning and management.

scholarly journals Robust Data-Driven Leak Localization in Water Distribution Networks Using Pressure Measurements and Topological Information

Sensors ◽  
2021 ◽  
Vol 21 (22) ◽  
pp. 7551
Author(s):  
Débora Alves ◽  
Joaquim Blesa ◽  
Eric Duviella ◽  
Lala Rajaoarisoa
Keyword(s):  
Water Distribution ◽  
Pressure Sensors ◽  
Distribution Networks ◽  
Water Distribution Networks ◽  
Data Driven ◽  
Pressure Measurements ◽  
Leak Pressure ◽  
Flow Measurements ◽  
Topological Information ◽  
Probable Path

This article presents a new data-driven method for locating leaks in water distribution networks (WDNs). It is triggered after a leak has been detected in the WDN. The proposed approach is based on the use of inlet pressure and flow measurements, other pressure measurements available at some selected inner nodes of the WDN, and the topological information of the network. A reduced-order model structure is used to calculate non-leak pressure estimations at sensed inner nodes. Residuals are generated using the comparison between these estimations and leak pressure measurements. In a leak scenario, it is possible to determine the relative incidence of a leak in a node by using the network topology and what it means to correlate the probable leaking nodes with the available residual information. Topological information and residual information can be integrated into a likelihood index used to determine the most probable leak node in the WDN at a given instant k or, through applying the Bayes’ rule, in a time horizon. The likelihood index is based on a new incidence factor that considers the most probable path of water from reservoirs to pressure sensors and potential leak nodes. In addition, a pressure sensor validation method based on pressure residuals that allows the detection of sensor faults is proposed.

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