Water Main Breaks Research Articles

Analysis of factors driving water main breaks across 13 Canadian utilities

Deterioration of water infrastructure is a global challenge that jeopardizes water system ability to deliver water safely. While various factors affect watermain failure, previous studies have focused on common pipe attributes or general protection strategies. The main objective of this study is to examine the relationship between pipe break characteristics and system properties. Comprehensive data from thirteen Canadian water systems (over 60,000 failures) are examined with correlation and chi-squared analyses. Joint and fitting failures are most likely for pipes aged 20 years or less, and universal joints are most associated with joint failure. Pipes in clay and sand soils are more likely to break due to improper bedding and differential settlement, respectively. Furthermore, in the summer, accidental breaks of asbestos cement pipes are more likely, as are failures of pipes with collar joints and coal tar lined pipes. By exploring these relationships, the paper provides insights into opportunities for reducing water main failure, through improved design, maintenance and rehabilitation.

Pressure‐Sensing Technology Mitigates Nonrevenue Water Loss

For more than two years, Aqua Pennsylvania has used remote pressure‐sensing technology to identify pressure transients, reduce water main breaks, and mitigate nonrevenue water.

A review of climatic impacts on water main deterioration

Watermains are both directly and indirectly affected by climatic conditions such as temperature and precipitation. Research has been conducted to understand and model the effects of climatic conditions on watermain breaks. However, review studies to map the knowledge development in this area, to identify key achievements and limitations of previous studies are missing, and are addressed in this study. This review uses a mixed systematic and scientometric analysis to establish the research trends, contributions, methods, and covariates employed by previous studies related to climatic impacts on watermain deterioration. Web of Science and Scopus database is primarily employed to identify 70 relevant studies on the subject matter. These studies have been mostly conducted by institutions based in Canada, USA, and Europe. There is a general lack of collaboration among different institutions conducting research in this area. Studies in the subject matter are published from 1982 onwards, however, significant number of studies per year can only be noticed from 2005 onwards. Previous studies have been mostly focused on the modelling of watermain failure and have used statistical methods, and data-driven and artificial intelligence (AI) approaches for failure modelling of watermains under climatic conditions. For testing and validation of research data, studies have employed correlation analysis, performance evaluation metrics, and descriptive statistics. Typically, climate-related variables used in studies include temperature, moisture, and precipitation. Reviewed studies have considered cold (66%), hot (31%), and dry (13%) climatic conditions. Studies have investigated watermains made of metal (56%), plastics (43%), and Concrete and Asbestos cement (31%). Future studies are recommended to consider Data-driven and AI approaches in research design; pay attention to watermains in climatically vulnerable and massively populated regions; and consider climate risk assessment and the impact of climate change and extreme weather conditions on watermains.

Ensemble framework for causality learning with heterogeneous Directed Acyclic Graphs through the lens of optimization

Directed Acyclic Graphs (DAGs) are informative graphical outputs of causal learning algorithms to visualize the causal structure among variables. In practice, different causal learning algorithms are often used to establish a comprehensive analysis pool, which leads to the challenging problem of ensembling the heterogeneous DAGs with diverse and conflicting embedded information. While naive approaches such as MEDIAN and greedy heuristics are presented in the literature, a mathematical framework to build the ensemble DAG is not investigated. Therefore, we propose a two-stage ensemble framework for causality learning with heterogeneous DAGs. In the first stage, we implement a data partitioning procedure to categorize the input data. Then, we apply multiple causal learning algorithms to each class and ensemble the results across the partitions for each method. The results across different approaches in the second stage are then ensembled to produce the final DAG. We define a novel mathematical model based on the marginal contribution concept, reflecting the collective information for each edge from input graphs. We investigate the relationship between the proposed modeling approach and well-known problems such as rank aggregation and the Traveling Salesman Problem. We also present a parametric analysis for the distance function, enriched by insights from their asymptotic behavior. We design a simulation experiment to show the computational advantage of adopting a lazy constraints solution approach. In addition to the T-cell synthetic dataset, we use the dataset of water main breaks from 2017 to 2021 in the City of Tampa, Florida, to demonstrate the framework’s applicability. The results show that the optimal solution of the proposed framework outperforms current approaches in the literature based on causality-based Structural Intervention Distance performance measures.

Using advanced metering infrastructure data to evaluate consumer compliance with water advisories during a water service interruption

Water main breaks disrupt services provided by utilities and result in Water Service Interruptions (WSIs). Water utilities can manage WSIs through water advisories, which request that consumers limit their water use. The performance of water advisories depends on consumer compliance and decisions to conserve water. This research explores customer compliance with water advisories using water consumption data collected through Advanced Metering Infrastructure (AMI). AMI provides high temporal and spatial resolution of water consumption data, which is analyzed to identify changes in water use behaviors. This research explores water use changes during a major water main break in Orange County, North Carolina, that caused a significant WSI, limiting water supply for more than 80,000 people. Customers were asked to reduce water use to essential purposes only and to boil water over the course of two days in November 2018. This research analyzes hourly consumption data to evaluate water consumption trends during the WSI and in response to water advisories. Statistical analysis is used to estimate the number of consumers who complied with utility notifications and to evaluate the volume of water saved. Regression analysis is applied to explore compliance across different user segments. Results provide insight about the level and variation of water conservation that can be expected during a WSI.

How slow is slow? Managing fire hydrant operation for protecting water infrastructure

AbstractThere have been several water main breaks attributed to fire hydrant operations despite the persistent guidance from stakeholders to operate the hydrants “slowly.” Traditional water hammer simulations for determining the safe operational times for every hydrant can be tedious and impractical for most water utilities. While the rapid operation of certain hydrants can generate excessive pressures, most hydrants are benign and do not generate pressures capable of bursting water mains. Using certain simplified water hammer parameters, the authors propose automated methods that can identify benign hydrants at least 100 times faster than the traditional methods where the operation of each hydrant is simulated by manually modifying the baseline models. Detailed analysis with more accurate data can then be performed to determine the precise operational times for the critical hydrants and develop the necessary measures to safeguard their operation.

Rapid assessment of abrupt urban mega-gully and landslide events with structure-from-motion photogrammetric techniques validates link to water resources infrastructure failures in an urban periphery

Abstract. Mass movement hazards in the form of mega-gullies and landslides pose significant risks in urbanizing areas, yet they are poorly documented. To obtain primary data on the size, frequency, and triggers of abrupt mega-gullies and landslides in urban areas, rapid assessment methods based on structure-from-motion (SfM) photogrammetric techniques and watershed models were developed and deployed in the Los Laureles Canyon, a rapidly urbanizing watershed in Tijuana, Mexico. Three abrupt earth surface hazards were observed over a 5-year study period including two mega-gullies and one landslide, and all were linked to a combination of rainfall and water resources infrastructure failures (WRIFs): (1) water main breaks resulted from rainfall-driven gully erosion that undermined supply lines, and the resulting water jets caused abrupt mega-gully formation; this represents one of the first studies to document this process in an urban environment; (2) antecedent saturation of a hillslope from a leaking water supply pipe contributed to an abrupt landslide during a storm event. The return period of the storms that triggered the WRIF-based earth surface hazards was ∼1–2 years, suggesting that such triggering events occur frequently. WRIF-based hazards were also a non-negligible contributor to sediment generation at the watershed scale. While the number of observed events is small, these results suggest that WRIF can, in some cases, be the single most important process generating abrupt and life-threatening earth surface hazards on the poor urban periphery. Future studies of the triggers and mechanisms of abrupt urban mega-gullies and landslides should consider the role of WRIFs in antecedent saturation and erosion by broken water supply lines.

How Do Perceptions of Risk Communicator Attributes Affect Emergency Response? An Examination of a Water Contamination Emergency in Boston, USA

AbstractA water main break that contaminated the Boston area's water distribution system prompted a four‐day “boil water” order. To understand risk communication during this incident, 600 randomly sampled residents were mailed questionnaires, yielding 110 valid responses. This article describes how perceptions of different social stakeholders influenced whether respondents complied with the Protective Action Recommendation—PAR (i.e., drank boiled water), took alternative protective actions (i.e., drank bottled water or/and self‐chlorinated water), or ignored the threat (i.e., continued to drink untreated tap water). Respondents perceived technical authorities (i.e., water utility, public health, and emergency management) to be higher on three social influence attributes (hazard expertize, trustworthiness, and protection responsibility) than public (i.e., news media, elected officials) and private (i.e., self/family, peers, and personal physicians) intermediate sources. Furthermore, respondents were most likely to comply with the PAR if they perceived authorities and public intermediates to be high on all three attributes and if they had larger households and lower income. Contrarily, they were more likely to take alternative actions if they were younger and had higher levels of income, risk perception, and emergency preparedness. These results underscore the need for technical authorities to develop credibility with their potential audiences before a crisis occurs.

Time-Dependent Reliability Analysis of Buried Water Distribution Network: Combined Finite-Element and Probabilistic Approach

Corrosion deterioration is the predominant factor for a large number of cast iron (CI) water main breaks. The reliability of CI pipelines decreases overtime as corrosion growth on pipe walls significantly reduces the strength of pipeline materials. To predict the reliability of water pipelines accurately, this study used a finite-element analysis (FEA) approach to determine the maximum pipeline stress at various stages of the pipeline’s life. A series of FEAs was performed to consider uncertainties in parameters associated with stress analysis. The circumferential stress was found to be the most critical stress for pipelines. Time-variant circumferential stress was obtained by accounting for the time-dependent corrosion pit growth on the pipeline wall. The time-variant failure probability of pipelines was determined by comparing the circumferential stress and the tensile failure strength of the pipeline at the burst limit state. The FEA model developed in this study was validated with the results obtained from previous studies. Monte Carlo simulation was performed to generate the fragility curves in a probabilistic manner. This study also compared the fragility curves obtained utilizing FEA and conventional equations (elastic ring theory and the Spangler formula). The results showed that analytical equations are too conservative and provide a higher failure probability of pipeline. A sensitivity analysis was performed to identify the relative importance of parameters contributing to the pipeline’s performance. Additionally, the reliability of the water distribution network (WDN) was calculated based on the minimum cut set (MCS) and graph decomposition methods. The results showed that the two methods provide similar outcomes, however, MCS provides a greater safety margin. The proposed approach was illustrated for an example WDN.

Legionella: A Promising Supplementary Indicator of Microbial Drinking Water Quality in Municipal Engineered Water Systems.

Opportunistic pathogens (OPs) are natural inhabitants and the predominant disease causative biotic agents in municipal engineered water systems (EWSs). In EWSs, OPs occur at high frequencies and concentrations, cause drinking-water-related disease outbreaks, and are a major factor threatening public health. Therefore, the prevalence of OPs in EWSs represents microbial drinking water quality. Closely or routinely monitoring the dynamics of OPs in municipal EWSs is thus critical to ensuring drinking water quality and protecting public health. Monitoring the dynamics of conventional (fecal) indicators (e.g., total coliforms, fecal coliforms, and Escherichia coli) is the customary or even exclusive means of assessing microbial drinking water quality. However, those indicators infer only fecal contamination due to treatment (e.g., disinfection within water utilities) failure and EWS infrastructure issues (e.g., water main breaks and infiltration), whereas OPs are not contaminants in drinking water. In addition, those indicators appear in EWSs at low concentrations (often absent in well-maintained EWSs) and are uncorrelated with OPs. For instance, conventional indicators decay, while OPs regrow with increasing hydraulic residence time. As a result, conventional indicators are poor indicators of OPs (the major aspect of microbial drinking water quality) in EWSs. An additional or supplementary indicator that can well infer the prevalence of OPs in EWSs is highly needed. This systematic review argues that Legionella as a dominant OP-containing genus and natural inhabitant in EWSs is a promising candidate for such a supplementary indicator. Through comprehensively comparing the behavior (i.e., occurrence, growth and regrowth, spatiotemporal variations in concentrations, resistance to disinfectant residuals, and responses to physicochemical water quality parameters) of major OPs (e.g., Legionella especially L. pneumophila, Mycobacterium, and Pseudomonas especially P. aeruginosa), this review proves that Legionella is a promising supplementary indicator for the prevalence of OPs in EWSs while other OPs lack this indication feature. Legionella as a dominant natural inhabitant in EWSs occurs frequently, has a high concentration, and correlates with more microbial and physicochemical water quality parameters than other common OPs. Legionella and OPs in EWSs share multiple key features such as high disinfectant resistance, biofilm formation, proliferation within amoebae, and significant spatiotemporal variations in concentrations. Therefore, the presence and concentration of Legionella well indicate the presence and concentrations of OPs (especially L. pneumophila) and microbial drinking water quality in EWSs. In addition, Legionella concentration indicates the efficacies of disinfectant residuals in EWSs. Furthermore, with the development of modern Legionella quantification methods (especially quantitative polymerase chain reactions), monitoring Legionella in ESWs is becoming easier, more affordable, and less labor-intensive. Those features make Legionella a proper supplementary indicator for microbial drinking water quality (especially the prevalence of OPs) in EWSs. Water authorities may use Legionella and conventional indicators in combination to more comprehensively assess microbial drinking water quality in municipal EWSs. Future work should further explore the indication role of Legionella in EWSs and propose drinking water Legionella concentration limits that indicate serious public health effects and require enhanced treatment (e.g., booster disinfection).

Using a digital twin to explore water infrastructure impacts during the COVID-19 pandemic.

During the coronavirus disease 2019 (COVID-19) pandemic, the daily pattern of activities changed dramatically for people across the globe, as they socially distanced and worked remotely. Changes in daily routines created changes in water consumption patterns. Significant changes in water demands can affect the operation of water distribution systems, resulting in new patterns of flow, with implications for water age, pressure, and energy consumption. This research develops a digital twin to couple Advanced Metering Infrastructure (AMI) data with a hydraulic model to assess impacts on infrastructure due to changes in water demands associated with the COVID-19 pandemic for a case study. Using 2019 and COVID-19 modeling scenarios, the hydraulic model was executed to evaluate changes to water quality based on water age, pressure across nodes in the network, and the energy required by the system to distribute potable water. A water supply interruption event was modeled as a water main break to assess network resiliency for 2019 and COVID-19 demands. A digital twin provides the capabilities to explore and visualize emerging consumption patterns and their effects on the functioning of water systems, providing valuable analyses for water utility managers and insight for optimizing infrastructure operations and planning for long-term impacts.

Accounting for Source Location on the Vulnerability Assessment of Water Distribution Network

AbstractThe vulnerability of water distribution networks (WDNs) to water-main breaks is used for prioritizing pipes in the network for maintenance planning. In this paper, a parameter of graph theo...

A Proof-of-Concept Study for Hydraulic Model-Based Leakage Detection in Water Pipelines Using Pressure Monitoring Data

It is estimated that about 20% of treated drinking water is lost through distribution pipeline leakages in the United States. Pipeline leakage detection is a top priority for water utilities across the globe as leaks increase operational energy consumption and could also develop into potentially catastrophic water main breaks, if left unaddressed. Leakage detection is a laborious task often limited by the financial and human resources that utilities can afford. Many conventional leak detection techniques also only offer a snapshot indication of leakage presence. Furthermore, the reliability of many leakage detection techniques on plastic pipelines that are increasingly preferred for drinking water applications is questionable. As part of a smart water utility framework, this paper proposes and validates a hydraulic model-based technique for detecting and assessing the severity of leakages in buried water pipelines through monitoring of pressure from across the water distribution system (WDS). The envisioned smart water utility framework entails the capabilities to collect water consumption data from a limited number of WDS nodes and pressure data from a limited number of pressure monitoring stations placed across the WDS. A popular benchmark WDS is initially modified by inducing leakages through addition of orifice nodes. The leakage severity is controlled using emitter coefficients of the orifice nodes. WDS pressure data for various sets of demands is subsequently gathered from locations where pressure monitoring stations are to be placed in that modified distribution network. An evolutionary optimization algorithm is subsequently used to predict the emitter coefficients so as to determine the leakage severities based on the hydraulic dependency of the monitored pressure data on various sets of nodal demands. Artificial neural networks (ANNs) are employed to mimic the popular hydraulic solver EPANET 2.2 for high computational efficiency. The goals of this study are to: (1) validate the proof of concept of the proposed modeling approach for detecting and assessing the severity of leakages and (2) evaluate the sensitivity of the prediction accuracy to number of pressure monitoring stations and number of demand nodes at which consumption data is gathered and used. This study offers new value to prioritize pipes for rehabilitation by predicting leakages through a hydraulic model-based approach.

Financial Remedies for Personal Property Damage in Water Main Breaks

AbstractAmerica’s infrastructure failure in water main breaks has led to personal property damage; in recent years, thousands of homes and businesses have been damaged by flood waters, and property...

Pipeline burst-related ground stability in blowout condition

Sinkhole incidents have increased rapidly in recent decades due to water main breaks. Although numerous researchers have recently conducted investigations in sinkhole phenomenon, most of the studies are related to natural sinkhole formation, underground cavity detection, and collapse analysis. Very few studies can be found in relation to the blowout stability of soils due to defective pipeline under high water main pressures, in spite the frequent media news about the water main bursts which enlighten the relevance of the problem. The present paper aims to study the soil's blowout stability above a damaged water main pipeline in three idealized stages of internal soil erosion, i.e. horizontal, semi-circular, and circular cavities using the latest finite element limit analysis technique. Dimensionless design parameters are used throughout the paper to present rigorous bounding solutions that can be used directly by designers to evaluate blowout stability of soils above defective pipelines. Design charts and tables are presented to cover a wide range of design parameters, and a practical example is introduced to illustrate their use in practice.

Predictive analytics for water main breaks using spatiotemporal data

ABSTRACT Water main breaks are a common recurring problem in water distribution networks, resulting in cascading effects in the whole system and the interconnected infrastructures such as transportation. Having integrated the physical features of pipes such as diameter and environmental factors like precipitation, we propose predictive models based on spatiotemporal data and machine learning methods. In this study, the dataset is the main breaks recorded from 2015 to 2020 in the city of Tampa, Florida. First, a spatial clustering is conducted to identify vulnerable areas to breaks. A time series analysis is also carried out for the temporal data. The result of these analyses informed the machine learning algorithms as independent variables. We then compared the predictive models based on information-based and rank-based criteria. Obtained results indicated that Boosted Regression Tree (BRT) model was superior to the others. Finally, we present predicted normalized failure rates for the water distribution network to inform rehabilitation and fortification decisions at the municipality level.

Clustered into control: Heterogeneous causal impacts of water infrastructure failure

AbstractWe estimate economic impacts from decaying water infrastructure in the United States. Using water main breaks in Washington, DC, and a yearlong panel of hourly traffic speeds, we estimate causal effects of water main failures on traffic congestion. We use k‐means clustering to create clusters of streets that are similar to each other: treated observations are compared to other units in the cluster. We identify heterogeneous treatment effects algorithmically while retaining straightforward standard error calculations. We find strong evidence of heterogeneous treatment effects across clusters but small welfare impacts of water main breaks on traffic patterns overall.

Novel Use of Premixed Dialysate Bags during Water Supply Interruption in Acute Hospital Setting.

Patients on dialysis are exposed to large amounts of water during conventional intermittent hemodialysis; hence, there are strict regulations regarding the quality of water used to prepare dialysate. Occasionally, water systems fail due to natural disasters or structural supply issues, such as water-main breaks or unplanned changes in municipal or facility water quality. It is critical to regularly monitor and immediately recognize such a failure and take steps to avoid exposing the patients to contaminants. In addition to the recognition of the problem, the ability to pivot and continue to provide safe treatment to inpatients who are dependent on dialysis is essential, both from an ultrafiltration and a clearance standpoint. At our hospital, an unforeseen water disruption occurred and we were able to continue to provide KRT with premade, bagged dialysate to mitigate the effect on our patients on dialysis. This is a novel method using available machines and dialysate, which we normally stock for continuous KRT, for short dialysis sessions. The methodology is similar to that which has been widely used for short daily home hemodialysis with low dialysate flow rate. Because this situation occurred in the midst of the SARS-CoV-2 pandemic, we had to be mindful of dialysate volumes and staffing time. Here, we present our investigation into the cause of the water-system failure and how we quickly implemented the alternative dialysis method. Short dialysis with low-flow dialysate will not deliver the same Kt/V per session as standard dialysis; however, this method was successfully implemented and tailored with adjustments for patients requiring higher clearance for specific indications, such as severe hyperkalemia.

Our Water Mains Contaminate Us with E. Coli, Lead and Copper – Preventable Illness and Death Follow

The title of this article may seem abrasive, but the stakes are human lives that are affected by E. coli infections and drinking water contaminations by lead and copper. First of all, this research targets a new and urgent understanding of a common cause for E. coli outbreaks, which results in many illnesses and deaths every year. As part of a world-wide problem, E. coli infects more than 73,000 people and kills more than 60 people every year in the U.S. alone. Also a connection exists between urinary tract infections and water main break transmissions of E. coli, where five to seven million cases strike each year in the U.S. Methods are confined to an extensive literature review and detailed studies of pertinent topics. Research concludes that a phenomenon referred to as water hammer breaks underground water mains, and underground E. coli enters water mains during power outages or pressure losses to distribute E. Coli to our homes, businesses and irrigation systems to drive E. coli outbreaks. People die from E. coli, and illnesses and deaths are preventable. New theory proves that water systems are infected during normal operations that crack water mains to permit E. coli ingress into pipes to infect homes, businesses and irrigation. E. Coli infection outbreaks can be stopped. Essentially, water hammer breaks water mains, E. coli enters piping during power outages and other system shutdowns, infected water is pushed to customers ahead of any disinfectants that are added prior to return to service, and infections spread to water consumers. The most important research conclusion is that E. coli infections will stop by controlling water main break destruction and controlling water operations after power outages and water pressure losses. A parallel health concern relates lead and copper contaminations of water supplies to water hammer induced water main breaks. Another important finding proves that lead and copper contamination of drinking water systems can be minimized. If water hammers are reduced, water main breaks will be reduced, and infections and contaminations will be reduced. Although facts explain observations of lethal E. coli outbreaks, experimental validation of theory following a future outbreak is required. There is more work required, but if people are unaware of this pandemic health hazard, no work will be done, and the hazard will continue. The learning curve to safe drinking water should not climb slowly, but should sharply leap to save lives and ensure water safety.

(Water) Public–Private Partnerships: The Economics

With frequent water main breaks, leaking pipes, and lead contamination, the water infrastructure of many countries is in bad shape, and improvements are urgent in light of climate change and increasing water stress. Discussion in recent years has focused on the role of private companies in a sector that has traditionally been in public hands. Since the privatization of the United Kingdom's water infrastructure in the late 1980s, a whole range of novel modes of cooperation have been identified, from 100% private to 100% public ownership. This note focuses on the economics of public-private partnerships (PPPs). PPPs offer a contractual approach to the delivery of infrastructure goods and services that are typically provided by the public sector or by heavily regulated private operators such as public utilities. They tend to involve relatively long-term contracts, in which the public sector shares some decision-making authority and property rights with private companies. This overlap of ownership and decision-making powers between the public authorities and private operators is both the appeal and the challenge of water PPPs. Excerpt UVA-GEM-0183 Dec. 14, 2020 (Water) Public-Private Partnerships: The Economics With frequent water main breaks, leaking pipes, and lead contamination, the water infrastructure of many countries is not in good shape. In light of climate change and increasing water stress, calls for repairs and upgrades are getting louder. Beyond focusing on the specific upgrades needed, the discussion in recent years has zoomed in on the roles private companies can play in a sector that has traditionally been in public hands. Since the privatization of the United Kingdom's water infrastructure in the late 1980s, a whole range of novel modes of cooperation have been identified, from 100% private to 100% public ownership. This note focuses on those forms of collaboration between the public and private sector that are fashionably labeled public-private partnerships (PPPs); it lays out the economics of how PPPs in the water sector function. PPPs offer a contractual approach to the delivery of infrastructure goods and services that are typically provided by the public sector or by heavily regulated private operators such as public utilities. PPPs tend to involve relatively long-term contracts, in which the public sector shares some decision-making authority and property rights with private companies. This overlap of ownership and decision-making powers between the public authorities and private operators is both the appeal and the challenge of water PPPs. . . .