Improving pipe failure predictions: Factors affecting pipe failure in drinking water networks

Barton, Neal Andrew; Farewell, Timothy S.; Hallett, Stephen R; Acland, Timothy Francis

doi:10.1016/j.watres.2019.114926

Cited by 146 publications

(96 citation statements)

References 62 publications

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…Commonly, the number of failures in small diameter pipes exceeds that of larger diameter pipes [5,49]. The main reason for the higher frequency of failure in small diameter pipes has been attributed to decreased pipe strength to ground movement and corrosion because of reduced wall thickness [50]. As presented in Figure 3b, the older pipes showed a higher number of failures.…”

Section: Case Studymentioning

confidence: 97%

“…Failure rates can be expected to be higher in the months following the installation. Then, the rates are lower for several years, before increasing with the age of the pipe [50]. Pipe deterioration with age is known to occur.…”

Section: Case Studymentioning

confidence: 99%

“…Failure records revealed that 67.8% of the failed pipes are made of asbestos-cement and 28.3% of PVC. The failure rate of pipe materials is determined by the material's flexibility, method of manufacture, and manufacturing processes [50]. It was expected that PVC pipes have a lower failure rate than asbestos-cement pipes because plastic materials were the last to be introduced, and their quality has improved significantly over the years.…”

Section: Case Studymentioning

confidence: 99%

See 2 more Smart Citations

Comparison of Statistical and Machine Learning Models for Pipe Failure Modeling in Water Distribution Networks

Giraldo-González

Rodríguez

2020

Water

View full text Add to dashboard Cite

The application of statistical and Machine Learning models plays a critical role in planning and decision support processes for efficient and reliable Water Distribution Network (WDN) management. Failure models can provide valuable information for prioritizing system rehabilitation even in data scarcity scenarios, such as developing countries. Few studies have analyzed the performance of more than two models, and examples of case studies in developing countries are insufficient. This study compares various statistical and Machine Learning models to provide useful information to practitioners for the selection of a suitable pipe failure model according to information availability and network characteristics. Three statistical models (i.e., Linear, Poisson, and Evolutionary Polynomial Regressions) were used for failure prediction in groups of pipes. Machine Learning approaches, particularly Gradient-Boosted Tree (GBT), Bayes, Support Vector Machines and Artificial Neuronal Networks (ANNs), were compared in predicting individual pipe failure rates. The proposed approach was applied to a WDN in Bogotá (Colombia). The statistical models showed an acceptable performance (R2 between 0.695 and 0.927), but the Poisson Regression was the most suitable for predicting failures in pipes with lower failure rates. Regarding Machine Learning models, Bayes and ANNs exhibited low performance in the prediction of pipe failure condition. The GBT approach had the best performing classifier.

show abstract

Section: Case Studymentioning

confidence: 97%

Section: Case Studymentioning

confidence: 99%

Section: Case Studymentioning

confidence: 99%

See 1 more Smart Citation

Comparison of Statistical and Machine Learning Models for Pipe Failure Modeling in Water Distribution Networks

Giraldo-González

Rodríguez

2020

Water

View full text Add to dashboard Cite

show abstract

“…Pipe failure mechanisms are unique for different pipe materials. The literature has reported a correlation between seasonal variation and pipe failures, as a consequence of changing weather (temperature, frost and rainfall deficit (RD)) and soil conditions (pH, ground hazards such as shrink swell, texture, moisture content) [6][7][8]11,12 . Failures during the winter are typically found in iron and to a lesser extent steel and ductile iron (SDI) pipes, and are associated with cold temperatures (typically below 3°C), internal water temperature, rapid temperature transit and prolonged periods of frost 8,9,13 .…”

Section: Introductionmentioning

confidence: 99%

Using generalized additive models to investigate the environmental effects on pipe failure in clean water networks

Barton

Farewell²,

Hallett

2020

npj Clean Water

Self Cite

View full text Add to dashboard Cite

Predicting pipe failures using statistical modelling benefits from detailed knowledge of the conditions and circumstances which influence such failures. Incorporating this knowledge into model building improves failure predictions. In this study, we model weather, soil and hydrogeological variables in a generalized additive model for five common pipe materials separately, using partial dependence plots to understand the partial effects of each variable on pipe failure. We show how severe temperatures are associated with high pipe failure. Cold temperatures and air frost and their interaction with soils represent the key factors for pipe failures during the winter for metal pipes. Warm temperatures, high soil moisture deficit and soil movement results in higher pipe failures in asbestos cement pipes during the summer. Warm temperatures, ground movement and soil wash out, and water demand are key factors for polyvinyl chloride pipe failure during the summer. Frost is a key factor influencing polyethylene pipes during winter. An understanding of the physical principals concerning pipe failures can enable the development of more accurate models, guiding network management plans to help reduce asset leakage through appropriate interventions.

show abstract

“…The technical condition of a system (facility) is understood as a matrix of features describing the physical properties of system components, taking into account the level of risk for loss of technical performance under the influence of operating time or external factors. Experience has shown that the quality of water and the physical and chemical processes taking place on the internal and external surfaces of water pipes determine the speed of their ageing [2,3]. Changes in technical condition are reflected in an increase in the number of failures, increased costs of pumping water and real losses during its distribution to customers.…”

Section: Introductionmentioning

confidence: 99%

The Analysis of Water Supply Operating Conditions Systems by Means of Empirical Exponents

Stańczyk

Burszta‐Adamiak

2019

Water

View full text Add to dashboard Cite

The stochastic character of water consumption by consumers and the technical condition of water supply systems are the main deterministic random factors influencing the observed changes in flow rate and pressure. The implementation of Supervisory Control and Data Acquisition (SCADA) systems resulted in the creation of dispersed data sets coming from the devices controlling the operation of the water supply system. Thanks to the use of metadata and advanced computer systems of analysis, data from various sources can be analyzed to detect the operating conditions of the water supply system. The aim of the research was to analyze an empirical exponent, determined on the basis of flow rate and pressure measurements for one of the District Metered Areas (DMAs). Modern supervised and unsupervised machine learning systems were implemented to classify the obtained results. The results of the research showed that on the basis of the established empirical exponent in the systems in which the pressure is reduced at night, it is possible to qualify the operating conditions of the water supply system in the DMA with accuracy of up to 90%. The conducted tests may be implemented as a component of expert diagnostic systems in water companies.

show abstract

Improving pipe failure predictions: Factors affecting pipe failure in drinking water networks

Cited by 146 publications

References 62 publications

Comparison of Statistical and Machine Learning Models for Pipe Failure Modeling in Water Distribution Networks

Comparison of Statistical and Machine Learning Models for Pipe Failure Modeling in Water Distribution Networks

Using generalized additive models to investigate the environmental effects on pipe failure in clean water networks

The Analysis of Water Supply Operating Conditions Systems by Means of Empirical Exponents

Contact Info

Product

Resources

About