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

Failure Rate Prediction Models of Water Distribution Networks

The economic, social and environmental impacts of water main failures impose a great pressure on utility managers and municipalities to develop reliable rehabilitation/replacement plans. The Canadian Infrastructure Report Card 2012 stated that 15.4% of Canadian water distribution systems was in a “fair” to “very poor” condition with a replacement cost of CAD 25.9 billion. The “fair”, “poor” and “very poor” conditions represent the beginning of deterioration, nearing the end of useful life and no residual life expectancy, respectively. The majority of municipalities in Canada do not possess complete dataset of water distribution networks. The annual number of breaks or breakage rate of each pipe segment is known as one of the most important criteria in condition assessment of water pipelines. The main objective of this research is to develop a research framework that circumvent the limitations of existing studies by: 1) identifying the most critical factors affecting water pipe failure rates, 2) determining the best mathematical expression for predicting water pipeline failure rate 3) developing deterioration curves, and 4) deploying sensitivity analysis to recognize the effect of each input change on the breakage rate. The proposed research framework utilizes Best Subset regression to recognize the most effective factors on water pipelines. Best-Subset Algorithm is a procedure to find the best combination of variables to predict the water pipe failure rate among all possible candidates. Once the process of critical factor selection is performed, selected variables are employed to predict the number of breaks of water pipes using Evolutionary Polynomial Regression (EPR). The EPR is an intuitive data mining technique performed in two stages: 1) the search for the best model using Multi-Objective Genetic Algorithm (MOGA), and 2) the parameter estimation for the model using Least Square Method. The predicted number of breaks, computed by EPR, is utilized to develop deterioration curves by applying Weibull distribution function. Finally, sensitivity analysis is performed to: 1) recognize the effect of changing each input on the failure rate, and 2) study the relationship between the selected inputs and the output. The developed research framework is applied into two case studies to test its effectiveness. The case considers the water distribution networks in the City of Montréal, Canada and the City of Doha, Qatar. Physical factors, such as age, length, diameter and pipe material were identified as the most critical factors to affect the failure rate of pipes. The results indicate that the developed models successfully estimated the number of breaks for the City of Montreal and City of Doha with a maximum R-Squared of 89.35% and 96.27%, respectively. Also, it is tested by using 20% of each dataset and promising results were generated with a maximum R-Squared of 84.86% and 74.39% for dataset of Montreal and Doha respectively. This demonstrates the accuracy and robustness of the developed models in assessing and analyzing water distribution networks. The developed model is useful for municipalities and decision makers to prioritize the maintenance, repair, rehabilitation, and budget allocation of water distribution networks

The impact of soils, weather and trees on water infrastructure failure.

The uninterrupted supply and reliable distribution of drinking water is fundamental in a modern society; however, water pipelines are subject to a range of operational and environmental factors which can lead to asset failure. For the privatised water-sector in the UK, utility companies are moving towards the deployment of statistical models for proactive asset management. For some companies, statistical models have facilitated the migration away from static annual burst targets, to targets which are dynamic and adjusted to observed environmental conditions. There is an increasing need for the development of accurate pipeline failure prediction models to support asset management and regulatory reporting. This thesis evaluates several quantitative measures to improve current methods of pipeline failure prediction. The aim of this thesis is to establish the impact of soils, weather and trees on water infrastructure failure and to develop a series of material-specific drinking water pipeline failure models for an entire distribution network. A quantitative assessment investigating the impact of data cleaning on the attained model error of a series of previously developed models was conducted. Material-specific variable selection and step-wise modelling approaches was used to construct a series of improved statistical models, which have an increased representation of the environmental factors leading to pipeline failure. A detailed national tree inventory was investigated for its use in enhancing pipeline failure predictions and for calculating failure rates of different pipe materials under varying soil shrink swell and tree density conditions. The value in creating separate winter and summer pipeline failure models was also evaluated, to increase representation of the highly seasonal nature of pipeline failure. Finally, a satellite approach was used to generate soil-related land surface deformation measurements across a regional area was investigated. The result is a series of enhanced statistical models for the prediction of water pipeline failure and a greater understanding into the environmental drivers leading to asset failure.PhD in Water, including Desig

Application of survival analysis modeling in water pipelines failure in Cape Town

Includes bibliographical references.The statistical modelling of water pipelines failure has been widely adopted by water authorities in their pursuit to proactively manage their aging water distribution systems. In this thesis failures of the 100 mm FC pipes in Cape Town City have been modelled by using survival analysis techniques. Estimates of the Mean Cumulative Function (MCF) have been used to predict the failure rates of pipes in the network

A Framework for Coordinating Water Distribution System and Pavement Infrastructure M&R Based on LCCA

The disruptions the public faces daily around the world due to urban infrastructure Maintenance and Rehabilitation (M&R) activities are having significant social, economic, and environment impacts on communities. With respect to water distribution systems, there have been millions of water main breaks in the U.S. since January 2000, with an average of nearly 700 water main breaks every day. The majority of these water utilities lie under paved roads, and the Open Cut method is the most widely used technology for repairing water main breakages. Subsequently, this continually increasing pipe breakage requires the destruction of pavements that may be in good condition and thereby results in not only untimely inconveniences to stakeholders, but can have large cost implications as well. Hence, in order to reduce the impact of pipe breakage on pavements in good condition and to minimize the user disruptions, it is essential to find a way to coordinate the M&R activities for both of these infrastructure systems. Therefore, this thesis presents a framework for coordinating pavement infrastructure and water distribution system M&R activities based on life cycle cost analysis. The proposed framework considers the costs and benefits associated with each treatment in a candidate scenario. The costs of each scenario consist of the agency costs (construction and subsequent maintenance) and the user costs incurred due to work zone activities. The benefits of each scenario are measured using monetized (savings in annual maintenance costs and vehicle operation costs due to pavement treatment and pipe valuation) and nonmonetized (treatment service life) approaches. To demonstrate the framework, three scenarios (maintenance only, rehabilitation only, and a combination of both) are considered for pavement treatments, while only replacement is considered for water pipelines. The results were evaluated using the EZStrobe discrete event simulation system. Highway agencies and water utilities can use this methodology to evaluate different scenarios and enhance the robustness of their decision-making processes

Impact of New Madrid Seismic Zone Earthquakes on the Central USA, Vol. 1 and 2

The information presented in this report has been developed to support the Catastrophic Earthquake Planning Scenario workshops held by the Federal Emergency Management Agency. Four FEMA Regions (Regions IV, V, VI and VII) were involved in the New Madrid Seismic Zone (NMSZ) scenario workshops. The four FEMA Regions include eight states, namely Illinois, Indiana, Kentucky, Tennessee, Alabama, Mississippi, Arkansas and Missouri. The earthquake impact assessment presented hereafter employs an analysis methodology comprising three major components: hazard, inventory and fragility (or vulnerability). The hazard characterizes not only the shaking of the ground but also the consequential transient and permanent deformation of the ground due to strong ground shaking as well as fire and flooding. The inventory comprises all assets in a specific region, including the built environment and population data. Fragility or vulnerability functions relate the severity of shaking to the likelihood of reaching or exceeding damage states (light, moderate, extensive and near-collapse, for example). Social impact models are also included and employ physical infrastructure damage results to estimate the effects on exposed communities. Whereas the modeling software packages used (HAZUS MR3; FEMA, 2008; and MAEviz, Mid-America Earthquake Center, 2008) provide default values for all of the above, most of these default values were replaced by components of traceable provenance and higher reliability than the default data, as described below. The hazard employed in this investigation includes ground shaking for a single scenario event representing the rupture of all three New Madrid fault segments. The NMSZ consists of three fault segments: the northeast segment, the reelfoot thrust or central segment, and the southwest segment. Each segment is assumed to generate a deterministic magnitude 7.7 (Mw7.7) earthquake caused by a rupture over the entire length of the segment. US Geological Survey (USGS) approved the employed magnitude and hazard approach. The combined rupture of all three segments simultaneously is designed to approximate the sequential rupture of all three segments over time. The magnitude of Mw7.7 is retained for the combined rupture. Full liquefaction susceptibility maps for the entire region have been developed and are used in this study. Inventory is enhanced through the use of the Homeland Security Infrastructure Program (HSIP) 2007 and 2008 Gold Datasets (NGA Office of America, 2007). These datasets contain various types of critical infrastructure that are key inventory components for earthquake impact assessment. Transportation and utility facility inventories are improved while regional natural gas and oil pipelines are added to the inventory, alongside high potential loss facility inventories. The National Bridge Inventory (NBI, 2008) and other state and independent data sources are utilized to improve the inventory. New fragility functions derived by the MAE Center are employed in this study for both buildings and bridges providing more regionally-applicable estimations of damage for these infrastructure components. Default fragility values are used to determine damage likelihoods for all other infrastructure components. The study reports new analysis using MAE Center-developed transportation network flow models that estimate changes in traffic flow and travel time due to earthquake damage. Utility network modeling was also undertaken to provide damage estimates for facilities and pipelines. An approximate flood risk model was assembled to identify areas that are likely to be flooded as a result of dam or levee failure. Social vulnerability identifies portions of the eight-state study region that are especially vulnerable due to various factors such as age, income, disability, and language proficiency. Social impact models include estimates of displaced and shelter-seeking populations as well as commodities and medical requirements. Lastly, search and rescue requirements quantify the number of teams and personnel required to clear debris and search for trapped victims. The results indicate that Tennessee, Arkansas, and Missouri are most severely impacted. Illinois and Kentucky are also impacted, though not as severely as the previous three states. Nearly 715,000 buildings are damaged in the eight-state study region. About 42,000 search and rescue personnel working in 1,500 teams are required to respond to the earthquakes. Damage to critical infrastructure (essential facilities, transportation and utility lifelines) is substantial in the 140 impacted counties near the rupture zone, including 3,500 damaged bridges and nearly 425,000 breaks and leaks to both local and interstate pipelines. Approximately 2.6 million households are without power after the earthquake. Nearly 86,000 injuries and fatalities result from damage to infrastructure. Nearly 130 hospitals are damaged and most are located in the impacted counties near the rupture zone. There is extensive damage and substantial travel delays in both Memphis, Tennessee, and St. Louis, Missouri, thus hampering search and rescue as well as evacuation. Moreover roughly 15 major bridges are unusable. Three days after the earthquake, 7.2 million people are still displaced and 2 million people seek temporary shelter. Direct economic losses for the eight states total nearly $300 billion, while indirect losses may be at least twice this amount. The contents of this report provide the various assumptions used to arrive at the impact estimates, detailed background on the above quantitative consequences, and a breakdown of the figures per sector at the FEMA region and state levels. The information is presented in a manner suitable for personnel and agencies responsible for establishing response plans based on likely impacts of plausible earthquakes in the central USA.Armu W0132T-06-02unpublishednot peer reviewe

Asset Management Tools for Sustainable Water Distribution Networks

Water Distribution Network (WDN) is the most important element in water supply systems. According to the Canadian Water and Wastewater Association (CWWA), there are more than 112,000 kilometers of water mains in Canada and their replacement cost is estimated to be $34 billion. Since majority of pipelines are frequently above 100 years old, they are prone to failure and outbreaks of disease derivable to drinking water are inevitable. Breakage in water infrastructure can result in disruptions and damage to other surrounding infrastructure such as road networks or structures. Moreover, unscheduled emergency rehabilitation works can cause interruption to traffic, households and businesses. Therefore, it is important to assess the unknown condition of WDNs to find their respective rate of deterioration in order to prevent disastrous failures or sudden shutdowns. Determining pipe condition through cost-effective assessments will grant very poor condition pipes to be considered first in order to avoid related risk and devastating failures. The problem here is that in most cases, there are limited data about condition of water mains due to the underground location of the pipelines and their restricted access. Several pipes were installed 100 years ago and they have not been examined until a problem occurred. An extensive literature review shows the absence of comprehensive and generalized maintenance model for scheduling the rehabilitation and replacement of individual pipelines in the whole network based on their remaining useful life. Previous research efforts concentrated mostly on developing models, which utilize long-term data and consider solely the pipe segments not the whole network. Since pipe segments are connected together, the performance of one pipe affect the performance of other pipes in the neighborhood. This is the reason that pipes should be considered as a network rather than individual pipeline. This shows the need for a model which could forecast the behavior of each pipeline and the whole network based on available data simultaneously. This study aims to develop a model that can predict remaining useful life to optimize the needed intervention plans based on the available budget. For this purpose, a statistical condition model is developed which utilizes characteristics of a pipeline to predict its condition. In this model, Delphi study identifies the most important factors affecting deterioration of water pipelines at first, through three rounds of questionnaires sent to selected experts. The findings show that important factors are mainly physical factors such as pipe age, pipe material, etc. After that, Fuzzy Analytical Hierarchy Process (FAHP) and Entropy Shannon are employed to prioritize the selected factors in previous step and calculate their weights based on their relative importance. Results reveal that pipe installation, age and material are the most effective parameters in deterioration. These weights are used to find the condition index of the pipeline from pipe characteristics, soil and water properties. Upon determining the condition index, the remaining useful life is estimated using the developed artificial neural network (ANN). Ultimately, the budget is allocated efficiently and different repair and replacement strategies are scheduled based on the remaining useful life and breakage rate of the pipelines utilizing the developed near optimum Genetic Algorithm (GA)-based model. Data of the water distribution network of the city of Montréal is used to develop, train and validate the developed models. Results indicate that 30.7 km of the pipelines of Montreal should be replaced in the next 20 years and 2610 km are in need of both major and minor rehabilitations. This research proposes a framework for optimized replacement and maintenance plans based on the remaining useful life and condition of the pipelines which will help operators for efficient budget allocation and better management of needed intervention plans

Soil-related geohazard assessment for climate-resilient UK infrastructure

UK (United Kingdom) infrastructure networks are fundamental for maintaining societal and economic wellbeing. With infrastructure assets predominantly founded in the soil layer (< 1.5m below ground level) they are subject to a range of soil-related geohazards. A literature review identified that geohazards including, clay-related subsidence, sand erosion and soil corrosivity have exerted significant impacts on UK infrastructure to date; often resulting in both long-term degradation and ultimately structural failure of particular assets. Climate change projections suggest that these geohazards, which are themselves driven by antecedent weather conditions, are likely to increase in magnitude and frequency for certain areas of the UK through the 21st century. Despite this, the incorporation of climate data into geohazard models has seldom been undertaken and never on a national scale for the UK. Furthermore, geohazard risk assessment in UK infrastructure planning policy is fragmented and knowledge is often lacking due to the complexity of modelling chronic hazards in comparison to acute phenomenon such as flooding. With HM Government's recent announcement of £50 million planned infrastructure investment and capital projects, the place of climate resilient infrastructure is increasingly pertinent. The aim of this thesis is therefore to establish whether soil-related geohazard assessments have a role in ensuring climate-resilient UK infrastructure. Soil moisture projections were calculated using probabilistic weather variables derived from a high-resolution version of the UKCP09 (UK Climate Projections2009) weather generator. These were then incorporated into a geohazard model to predict Great Britain's (GB) subsidence hazard for the future scenarios of 2030 (2020-2049) and 2050 (2040-2069) as well as the existing climatic baseline (1961-1990). Results suggest that GB is likely to be subject to increased clay-related subsidence in future, particularly in the south east of England. This thesis has added to scientific understanding through the creation of a novel, national-scale assessment of clay subsidence risk, with future assessments undertaken to 2050. This has been used to help create a soil- informed maintenance strategy for improving the climate resilience of UK local roads, based on an extended case study utilising road condition data for the county of Lincolnshire, UK. Finally, a methodological framework has been created, providing a range of infrastructure climate adaptation stakeholders with a method for incorporating geohazard assessments, informed by climate change projections, into asset management planning and design of new infrastructure. This research also highlights how infrastructure networks are becoming increasingly interconnected, particularly geographically, and therefore even minor environmental shocks arising from soil-related geohazards can cause significant cascading failures of multiple infrastructure networks. A local infrastructure hotspot analysis methodology and case-study is provided

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