Pipe Age Research Articles

A Lifetime Prediction Method of Pressured Gas Polyethylene Pipes by Strain-Hardening Modulus and Tensile Test

In recent years, polyethylene (PE) pipes have been widely utilized for urban natural gas transmission. However, as the use of high-density polyethylene (HDPE) pipes increases, their service life and long-term performance assessment have become one of the most significant issues to be addressed. There has been a ton of studies on PE pipe life prediction techniques both domestically and internationally, but very little has been carried out on PE pipe life prediction in actual gas working environments with varying acid and alkaline levels. This experiment accelerates the aging of PE pipes using acid and alkaline corrosion immersion experiments to determine the lifespan of PE pipes. This study aims to investigate the performance changes of HDPE under strong, weak, and neutral corrosion conditions using corrosion solutions with PH values of 1, 5, and 8, to propose the impact of corrosion caused by various acids and alkalies on the HDPE aging life for natural gas, and to develop a mathematical model between the aging life of polyethylene and the PH values of acid and alkali corrosion solutions. The studies involved soaking and corroding HDPE pipes with various acidity and alkalinity chemicals to speed up the aging process, and then the tensile test was used to determine the mechanical characteristics of the aged PE pipes. Based on our findings, the empirical equation between acidity and service life of PE pipes is obtained by the mathematical fitting method, and a life prediction model of buried city gas HDPE pipes is proposed. The actual life of the aged pipes is determined by the relationship between strain-hardening (SH) modulus and aging time. The findings demonstrate that the service life of PE pipes changes with different levels of acidity and alkalinity: 1.872 days, 1060.507 days, and 1128.58 days following corrosive solution-accelerated aging with solution acidities of PH1, PH5, and PH8, respectively. The life prediction method applies to various plastic pipes in comparable environments as well as HDPE city gas pipes that are subject to acid and alkali corrosion forces.

Comprehensive comparison of water quality risk and microbial ecology between new and old cast iron pipe distribution systems

The effects of cast iron pipe corrosion on water quality risk and microbial ecology in drinking water distribution systems (DWDSs) were investigated. It was found that trihalomethane (THMs) concentration and antibiotic resistance genes (ARGs) increased sharply in the old DWDSs. Under the same residual chlorine concentration conditions, the adenosine triphosphate concentration in the effluent of old DWDSs (Eff-old) was significantly higher than that in the effluent of new DWDSs. Moreover, stronger bioflocculation ability and weaker hydrophobicity coexisted in the extracellular polymeric substances of Eff-old, meanwhile, iron particles could be well inserted into the structure of the biofilms to enhance the mechanical strength and stability of the biofilms, hence enhancing the formation of THMs. Old DWDSs significantly influenced the microbial community of bulk water and triggered stronger microbial antioxidant systems response, resulting in higher ARGs abundance. Corroded cast iron pipes induced a unique interaction system of biofilms, chlorine, and corrosion products. Therefore, as the age of cast iron pipes increases, the fluctuation of water quality and microbial ecology should be paid more attention to maintain the safety of tap water.

Influence of Aboveground Pipeline and Associated Factors on Movement of Winter-Active Boreal Mammals in the Alberta In-Situ Oil Sands

Aboveground pipelines (AGP) associated with in-situ oil sands may restrict mammal movement, potentially increasing extinction probability and decreasing reproductive success. Our 12-year study used winter track count techniques to assess the response of winter-active mammals to AGP in northern Alberta, Canada. The primary questions were: which species were most prone to movement obstruction by AGP, facilities or natural factor(s)?; and which factor exerted the strongest influence on crossing likelihood? A total of 2,068 trails of 12 different species were observed. All species crossed more than half of the time. Focal species crossed on average 80% of the time. Crossing likelihood of white-tailed deer (Odocoileus virginianus), ermine (Mustela erminea), coyote (Canis latrans), lynx (Lynx canadensis), and fisher (Martes pennanti) were significantly influenced by predictors including pipeline height, pipeline corridor width, infrastructure age, vegetation type, and proximity to infrastructure. Deer and lynx crossing likelihood was positively affected by pipe height. Deer, coyote, and ermine crossing likelihood was positively affected by age of pipe. Fisher and deer crossing likelihood was negatively affected by pipeline corridor widths. Our investigations show that most species cross AGP with high crossing frequencies of pipe heights, ranging from 130 cm to 160 cm. These findings are important for impact mitigation because of the scarcity of published studies of wildlife movement responses to AGP, our inclusion of small and mid-sized carnivores, and our investigation of multiple factors. We highlight mitigation and design improvements, effects of pipeline corridor widths, and challenges posed by coupling infrastructure with pipelines, serving to reduce movement barriers and fragmentation.

Vulnerability hotspot mapping (VHM) of sewer pipes based on deterioration factors

ABSTRACT A decentralized, and sustainable sewer inspection plan at the city scale demands multiple inspection methods with different areas of impact. While sewer deterioration models offer great areas of impact, spatial mapping can assess the vulnerability of the system without prior knowledge of the pipe’s structural health condition. In this study, we assess and prioritize sewer deterioration factors such as pipe age, material, sewer type, node degree, flow velocity, and surface vegetation for vulnerability hotspot mapping of a sewer system in Dresden, Germany. The validation and sensitivity analyses revealed that flow velocity, pipe age, and surface vegetation are the most sensible factors to model, respectively. The linear model resulted in 76% efficiency and a mean squared error of 0.918 while it was improved with a random forest algorithm which points out vulnerability mapping potential as an early sewer inspection method at the city scale.

Assessment of the impacts of climat change on water supply system pipe failures

Climate change is projected to have profound impacts on the resilience and sustainability of built infrastructure. This study aims to understand the impacts of climate change on water supply systems and to facilitate adaptive actions. A premium database maintained by the Cleveland Water Division, Cleveland, Ohio, USA is analyzed. It contains 29,621 pipe failure records of 51,832 pipes over the past 30 years, representing one of the largest dataset in current literature. From the database, pipe failure rate models have been developed for water pipes made of different types of materials at different ages. The influence of climate (temperature and precipitation) on fragility of water pipes are obtained. Based on the developed climate-fragility failure rate models, the impacts of climate change on the water systems located in different geographic regions are evaluated by predicting the failure rate and number of failures in the water systems in the next 80 years (2020 to 2100). Climate models are used to predict weather under different climate change scenarios. The results demonstrate that the impacts of climate change on water supply system are likely complicated and are dependent upon factors such as the geographic location, pipe material, pipe age, and maintenance strategies. Water pipes in the cold regions may experience fewer number breaks due to the warmer weather and less severe winter, whereas those located in the hot regions may experience more failures associated with more corrosion. Different pipe replacement strategies are compared, which demonstrate the importance of considering the aging of water supply system in future maintenance decisions. This study enriches current understandings on the impacts of climate change on the water systems. The results will help water utilities to design climate change adaptation strategies.

Influencing factors analysis for drinking water steel pipe pitting corrosion using artificial neural network

ABSTRACT Steel is a metal, and thus, it undergoes corrosion over time. The comprehensive analysis of the factors influencing corrosion can aid in developing strategies, such as new ways to avoid corrosive environments. This study explored the factors influencing pitting corrosion in steel water pipes in South Korea between 1988–2020, using artificial neural networks. Partial dependence plots and variable importance are used to identify the degree of influence of the 12 corrosion-influencing factors. Pipe age had the highest importance and strongest influence on corrosion among the corrosion-influencing factors. Soil resistivity strongly influenced external corrosion, especially at values less than 5,000 Ω-cm, and the influence of sulfide concentration on external corrosion was also relatively strong. Water alkalinity exhibited the strongest influence on internal corrosion. This study will serve as reference data for developing corrosion depth prediction models and will contribute to understanding corrosive environments when laying new pipelines and improving existing ones.

A bilevel data-driven method for sewer deposit prediction under uncertainty

Deposit accumulation is one of the predominant causes of sewer blockage and overflow. Nevertheless, the traditional detection methods are costly and time-consuming, and the accuracy of the mathematical models for deposit prediction is usually affected by some uncertain factors (e.g., pipe properties and flow velocity of water). This paper proposes a framework of global sensitivity analysis (GSA) to identify the most sensitive indicators for sewer deposit prediction by (i) developing a data-driven bilevel (i.e., catchment level and segment level) model to map the relation between input and output indicators and (ii) employing three different GSA methods, namely, the Morris method, Sobol method, and Borgonovo index method to identify the indicators as important or unimportant (insensitive). The results show that the likelihood of combined sewer overflow occurrences (LCSOO), pipe age (PA), and pipe material (PM) are influential parameters for the thickness of deposits. Here, we pay close attention to the most influential parameters, which can help improve forecast prediction accuracy.

Impact of the surface aging of potable water plastic pipes on their lead deposition characteristics

The use of plastic potable water pipes to replace corroded metallic plumbing and construct new potable water plumbing systems is rapidly increasing due to the low cost, noncorrosive characteristics, and easy installation of plastic water pipes.

Understanding district metered area level leakage using explainable machine learning

Understanding the various interrelated effects that result in leakage is vital to the effort to reduce it. This paper aims to understand, at the district metered area (DMA) level, the relationship between leakage and static characteristics of a DMA, i.e. without considering pressure or flow. The characteristics used include the number of pipes and connections, total DMA volume and network density, as well as pipe diameter, length, age, and material statistics. Leakage, especially background and unreported leakage, can be difficult to accurately quantify. Here, the Average Weekly Minimum Night Flow (AWM) over the last 5 years is used as a proxy for leakage. While this may include some legitimate demand, it is generally assumed that minimum night flow, strongly correlates with leakage. A data-driven case study on over 800 real DMAs from UK networks is conducted. Two regression models, a decision tree model and an elastic net linear regression model, are created to predict the AWM of unseen DMAs. Reasonable accuracy was achieved, considering pressure is not an included feature, and the models are investigated for the most prominent features related to leakage.

Development of AI-Based Water Pipe Aging Condition Evaluation Algorithm

As interest in the development of artificial intelligence technology in the water supply field increases, an artificial neural network algorithm that can predict improved decision ratings through repetitive learning using results of aging pipe condition evaluation data should be developed and the most reliable prediction model should be presented through a verification process. An algorithm was developed to predict pipeline ratings by updating weights through backpropagation so that 12 items of indirect evaluation data according to the 2020 Han River Basin

Logistic pipe failure prediction models for an urban water distribution network in the developing world: a case study of Kampala water, Uganda

Abstract Statistical models can be used as proactive approaches to pipe failure management for the satisfactory and efficient functionality of a water distribution network (WDN). The study aimed to develop two logistic regression models using historical data and evaluated them based on prediction accuracy, receiver operator characteristics (ROC), and area under the curve (AUC). Pipe sizes ranging from 150 mm to 350 mm in the WDN were adequate to prevent pipe failure. However, a 250 mm pipe diameter had the lowest failure probability. Old pipes had a lower failure probability than new pipes. Although it was evident that the installation design of water pipes is changing from steel to unplasticized polyvinyl chloride (uPVC), steel pipes had a lower failure probability than uPVC at the same depth from the soil surface. Pipes buried in gravel with a small diameter had a lower failure probability than those in clay of a bigger diameter. With a median pipe age of 8 years in the WDN and greater class weight on pipe failures, the binomial logistic regression model had better performance (accuracy – 96.9%, AUC – 0.996) than the multinomial logistic model (accuracy – 90.9%, AUC – 0.992), representing reliable model predictions. The models can be used to modify data collection protocols to better identify potential water pipes that require maintenance or replacement.

A case study: Comparison of disinfection by-product formations between LEED-certified and non-certified buildings

In a few studies focusing on premise plumbing, water quality parameters were compared between LEED-certified and non-certified buildings. However, in those studies, the building characteristics were different, which can cause variations in comparison attempts. This study's purpose is to identify the effects of building type on premise plumbing water quality. For this purpose, temperature, pH, free chlorine residuals, total organic carbon, and trihalomethanes (THMs) were sampled from four drinking fountains of a combined LEED-certified and non-certified building. Although some influential factors on water quality (e.g., location of buildings, distributed water quality) were isolated for a better comparison, variations in parameters and total THMs were observed resulting from the plumbing age differences in the buildings. The findings of this study point out the impacts of the plumbing pipe age on water chemistry and emphasize the consideration of piping age as a factor in water quality comparisons between LEED-certified and non-certified buildings.

CFD-DEM simulation of sand erosion into defective gravity pipes under constant groundwater table

As sewer pipes age, cracks and other forms of structural damage may develop leading to soil infiltration into the pipes that can cause sinkholes and damage the overlying roads and moving vehicles. The fluctuations in the groundwater table following snow melt or heavy rainfall events can exacerbate soil erosion posing risk to the surrounding infrastructure. To understand and adequately characterize erosion in such cases, microscale analysis of both sand and water is needed to resolve the complex interactions around the pipe defects. In this study, a detailed numerical investigation is carried out using a series of coupled Computational Fluid Dynamics-Discrete Element method (CFD-DEM) simulations to evaluate the effects of groundwater table, crack size, defect angle, and the height of sand fill above the pipe on erosion. It is found that raising the groundwater table accelerates erosion only to a certain limit, above which, raising the groundwater level does not have a tangible effect on erosion. This helps explain some of the misunderstandings reported in previous investigations regarding the effect of water level on the rate of erosion. With respect to the location of defects, it was found that, when groundwater is involved, the erosion rate is fastest when the defect is located at the pipe crown. However, inclined defects ultimately resulted in a larger spatial extent of eroded mass. In addition, the thickness of the sand fill above the pipe was found to have little effect on the rate of erosion as a freefall arch regime was dominant in all simulations. Finally, simplified relationships were developed for estimating the total eroded mass for given sand properties, defect geometry, and location of the groundwater table.

Sonic assessment of physical ageing of plastic pipes

This article explores the potential of vibro-acoustics to detect physical ageing of plastic pipes. For this purpose, two different topics are combined: the ability of vibro-acoustics to estimate the storage modulus of a plastic pipe, and the sensitivity of the estimated storage modulus to changes due to ageing. Concerning the first topic, a vibro-acoustic method was applied to two water-filled HDPE pipes, one surrounded by air and another by sand. The excitation was achieved via an impact hammer and the propagating signal was recorded with the aid of hydrophones. Signal analysis led to the estimation of the axial wavenumber of the propagating axisymmetric fluid-borne wave. This value was used in the dispersion equation for the propagating mode to evaluate the storage modulus of the pipe material for a given experimental setup. Results revealed that the vibro-acoustic method gives consistent and reliable estimations of the storage modulus. Concerning the second topic, samples from two PVC pipes with an age difference of 41 years were subjected to dynamic mechanical analysis to study the behaviour of the storage modulus as a function of frequency. Results showed that it is feasible to distinguish discrepancies in the magnitude of the storage modulus due to ageing, provided that the measurement uncertainty is small. The uncertainty analysis highlighted the parameters that need to be more accurately known in order to lower the overall uncertainty of the estimated storage modulus when the proposed vibro-acoustic method is used. Irrespectively of the medium surrounding the pipe (air or soil), the distance between the points of the recording signals should be sufficiently long to measure the signal phase accurately. It was found that the accurate knowledge of the pipe’s geometry, i.e. the wall thickness and internal radius, was more or equally important for controlling the overall uncertainty than that of the parameters of surrounding soil.

Understanding point-of-use tap water quality: From instrument measurement to intelligent analysis using sample filtration

In most cases, point-of-use tap water quality is not routinely monitored due to widely-dispersed sampling sites and the costly tests. Although previous studies have revealed the variation of drinking water quality during distribution in municipal networks, the influence of aging pipes in buildings on quality is still unknown and this makes it difficult for water utilities to conduct regular maintenance. Herein, we have undertaken a survey of tap water samples across 8 districts in Beijing (China) to evaluate the potential effects of pipe age on point-of-use water quality, including turbidity, organic matter characteristics, and bacterial community. By grouping the collected samples according to the pipe age and source water respectively, the results suggested that bacterial diversity is significantly influenced by the pipe age. However, bacterial community structure is clearly influenced by the source water. Similarly, aging pipes in buildings are also responsible for the deterioration of the final water quality, and their effects have been closely linked to selected water quality parameters by evaluating the relevant factors. Moreover, the interrelationships between physico-chemical parameters and bacteria abundance were identified. For example, pH, Ca2+, Mg2+, Na+ and K+ showed a positive relationship with Bacillus abundance. In addition, an intelligent analysis method for understanding pipe age, organic matter concentration, and hardness (i.e., Ca2+ and Mg2+ concentration), based on image analysis of filtered membranes has been developed. The accuracy of prediction was encouraging, but can be improved with the collection of more data from tap water samples. We expect that this method can be exploited by the public to monitor their tap water and provide a feasible and cost-effective approach for water suppliers to locate aging/deteriorating pipes which need to be replaced or maintained.

A comparison between multivariate adaptive regression splines and regressive convolution neural network with support vector regression for pipe burst rate prediction on limited dataset

ABSTRACT By accurate predicting of pipe bursts, it is possible to schedule pipe maintenance, rehabilitation and improve level of services in Water Distribution Networks (WDNs). In recent years, artificial intelligence techniques have been used vastly in pipe burst prediction. These techniques need big input dataset for deep learning but big dataset might not be available in old WDNs. This paper aimed to compare the performance of multivariate adaptive regression splines (MARS) and regressive convolution neural network with least square support vector regression (RCNN-LSSVR) for predicting of Pipe Burst Rate (PBR) with limited dataset. Pipes age, diameter, depth of installation, length, average and maximum hydraulic pressure are considered as effective parameters of PBR. Data collected from real-case study in Iran include 158 cases for polyethylene and 124 cases for asbestos cement pipes during 2012–2019. The results indicate that RCNN-LSSVR model has a great performance of PBR prediction on limited dataset.

EPANET MODEL CLEAN WATER NETWORK CALLIBRATION USING MODIFIED ROUGHNESS C NUMBER HAZEN WILLIAM METHOD

The Epanet model in clean water pipelines is intended for time efficiency in calculating hydraulic behaviour. Calibration of the water pipe network is to compare the simulation results of the model to actual field observations and to see that the model that has been made is truly valid and reliable as a tool to determine the hydraulic behaviour of the network system when a change input into the system is given (e.g. the addition of pipelines and number of customer tapping debits). The pipe roughness coefficient (C) is a number that indicates the amount of energy loss due to friction between the flowing fluid and the pipe wall. The increasing age of the pipe will cause the pipe wall roughness to increase, and the energy loss will be even more significant. This research aims to calibrate the pipe network model by adjusting the C value of the actual pipe roughness coefficient according to the installed age. An initial simulation model was carried out by entering the standard pipe C value in the Hazen Williams energy loss equation in the Epanet link/pipe properties. Then a simulation is carried out by modifying the value of C according to the pipe's service life. For 600 mm steel pipe, change the C value from 150 to 87 (pipe age >30 years), HDPE 315 mm pipe changes in C value from 140 to 100 (pipe age >10 years) and 25-50 mm PVC pipe from 140 to 100 (pipe age >10 years). From the simulation results of the Epanet model-field observations, the average compressive height was obtained for five observation nodes.of 2 tails was performed paired sample test the pressure height value of the model-observation. Before modifying the value of C, the paired mean difference test results showed a significant difference between the simulation results of the model on field observations with a model significance value of 0.004, which means <0.005. There is a considerable difference between the simulations and observations, and the model is considered invalid. However, after modifying the value of C according to the actual age of the pipe, a paired sample test T average value of the compression model-observation was 0.098 > 0.005), which means that there is no significant difference between the simulation and observation. This means that the model made is quite valid. Product moment correlation between the model simulation height - field observations, the value of R = 0.967, the relationship is robust between the two results.

Effects of saltwater intrusion and sea level rise on aging and corrosion rates of iron pipes in water distribution and wastewater collection systems in coastal areas

Failure of water distribution and wastewater collection pipes can impact human health and environment due to potential contamination of water and soil in the surrounding areas, as well as service interruptions and road closures. This paper provides a quantitative assessment of projected shifts in failure rates of iron pipes due to saltwater intrusion and sea level rise in coastal areas. Probability of pipe failure depends on pipe characteristics, environmental parameters, and systems conditions. Analyses showed that saltwater intrusion and sea level rise can significantly affect the integrity of water and wastewater collection pipes due to potential changes in soil and groundwater characteristics that increase corrosion rates. Corrosion rate is intensified by increased conductivity of groundwater due to salinity. Coastal areas with subtropical climate have higher corrosion rates. The estimated lifetimes of ductile iron pipes are between 80 and 100 years. However, the actual service lifetimes can be significantly shorter. Pipes in subtropical areas corrode significantly faster than those in temperate climates. Presence of dissolved oxygen and chloride ion can significantly intensify the corrosion rates, shortening service times of iron pipes. Projections for pipe corrosion can help development of effective asset management strategies and for infrastructure maintenance planning in coastal areas that are vulnerable to sea level rise.

Application of the Harmony Search Algorithm for Optimization of WDN and Assessment of Pipe Deterioration

The relevance of the optimal design of water distribution networks lies in its applicability. Thus, the design must be as efficient and affordable as possible, meaning that it achieves a minimum level of serviceability using an appropriate amount of resources. In this context, an ideal water network requires the use of minimum-size elements to reach the minimum head pressure required for each node of the system with the lowest energy consumption. For this purpose, a relatively new meta-heuristic algorithm, called Harmony Search, was used in this study to optimize the water distribution network of Cheongna International City, Korea, in terms of the pipe diameter. Furthermore, using the same algorithm under a different approach, an assessment of the deterioration of conduits over time was conducted by analyzing the optimal roughness coefficient for the original pipe material, which provides a useful tool for decision making. Other heuristic and meta-heuristic algorithms have been applied to this type of problem; however, Harmony Search provides a convenient implementation at a reasonable computational cost. In this study, Harmony Search is demonstrated to be a valuable tool for water distribution network optimization as well as for pipe aging assessment.

Vulnerability of water distribution networks with real-life pipe failure statistics

Abstract Reasons for occasional, random pipe bursts in water distribution networks (WDNs) may come from numerous factors (e.g. pH value of the soil, pipeline material). Still, the isolation of the damaged section is inevitable. While the corresponding area is segregated by closing the isolation valves, there is a shortfall in drinking water service. This paper analyses the vulnerability of segments of WDNs from the viewpoint of the consumers that is the product of the failure rate and the relative demand loss. Real pipe failure database, pipe material and pipe age data are used to increase the accuracy of the failure rate estimation for 27 real-life WDNs from Hungary. The vulnerability analysis revealed the highly exposed nature of the local vulnerabilities; the distribution of local vulnerability values follows a power-law distribution. This phenomenon is also found by investigating the artificial WDNs from the literature using N rule in terms of isolation valve layout, namely the ky networks, with similar results.