Pipelines 2022: Multidiscipline and Utility Engineering and Surveying

The success of the machine learning (ML) approach for main break predictions offers several benefits to water utilities. Successful predictions supported by proper utility responses like replacement and monitoring offer the best chance to reduce the number of main breaks and the magnitude of leakage, a form of nonrevenue water (NRW). This presentation focuses on improvements on the consequence portion of the risk assessment, emphasizing water loss reduction. Individual pipe segments (usually defined by GIS) are given both probability and consequence of failure values. Machine learning has proven effective in prediction, especially identifying pipes with no prior break history with a high chance of failing. It is possible to focus on pipes most likely to break and subsequently look at the attributes of those pipes (material, size, vintage) and project the type of failure that is likely to occur based on the nature of the pipe, including the utility break history with that type of pipe. Some pipes with a high likelihood of failure will fail with a sudden potentially damaging break (like bell fractures of cast iron or longitudinal cracks of PVC). Other pipes will break with a lower flow with the potential to be long-running hidden breaks (like pinhole leaks or circumferential breaks). The analysis can then be advanced to identify geographical clusters of such breaks that might suggest acoustic monitoring in areas of hidden breaks or a renewal program in regions of potentially damaging breaks. Alternatively, anticipated clusters of breaks may be the sudden high flow, relatively low total volume type of break that causes significant damage to merit a high priority. The decisions to address either of these types of breaks remain at the discretion of the utility, but with a better prediction of not only which pipes will break but how they might break, the utility can strike an appropriate balance between NRW and main break reduction in both the short-term and the long-term. The applications of machine learning to forecast and address water system issues have two basic requirements: (1) a problem that involves assessing multiple elements that contribute to its occurrences and (2) the availability of pertinent and accurate data that can be applied to an analysis of past events to predict and as input to forecasting. This methodology has been successfully applied to predicting main failures, but this paper will look at other potential non-plant applications. This presentation will highlight how machine learning main break prediction is enhanced by predicting how pipe might fail. This step will advance a utility’s ability to address anticipated breaks by being aware of the probability the leak has the potential to be a highly damaging leak. Some types of leaks can produce service interruptions. Other leaks can be low flow, long-running leaks that generate significant water loss. Other opportunities to enhance asset management through machine learning processes are highlighted. These opportunities include the practical organization of main replacement projects, pressure management, and leak detection strategies designed to mitigate water losses more effectively.

It is critical for society that we transform our siloed water management and infrastructure systems into smart, connected, sustainable, and resilient systems. This transformation can help us to address the effects of increasing extreme climate events, ecosystem demands, rapid global urbanization, and infrastructure deterioration from age and neglect. As water utilities improve their asset management programs, it is imperative that the data-driven decision support systems represent the complexities within water pipeline infrastructure systems. Artificial intelligence (AI) techniques can enable modelers to train mathematical algorithms to learn complex patterns from data and represent the water pipeline infrastructure systems accurately. PIPEiD is a national database platform that uses artificial intelligence (AI) and machine learning techniques to assess the performance and risk of water pipelines to help utilities better assess pipe replacement decisions and allocate funding. PIPEiD (Pipeline Infrastructure Database) will assist water sector utilities to manage water pipeline infrastructure systems more effectively for performance, resiliency, and sustainability. PIPEiD will provide the secure, robust, and centralized web-based database platform to address all three major infrastructure asset management levels: strategic, tactical, and operational for utilities of all sizes (small, medium, and large) across the country. The research team collected field performance data for potable, raw, and reuse water pipelines made from materials reflecting the wide range of pipes currently in the ground throughout the US, including cast and ductile iron, prestressed concrete cylinder pipe, reinforced concrete, steel, thermoplastic, PVC, and asbestos. The researchers worked to collect data distributed across different ecological areas, or cohorts, organized based on the climatic conditions of the 500 water utilities’ locations. These cohorts included coastal, arid, Arctic, and mountainous regions. Researchers factored in environmental conditions, such as soil corrosivity, traffic loading, and frost action, that can affect pipelines. The team enhanced the data set out further with the help of external sources like the United States Geological Survey (USGS), the Soil Survey Geographic Database from the United States Department of Agriculture (USDA), and additional field data collected by a group of 25 water utilities across the country that were selected to validate the models and tools. This paper will present various applications of artificial intelligence and machine learning algorithms for advanced asset management of water pipeline infrastructure systems.

To mitigate the disruptions caused by pipe failures, water utility managers must be able to anticipate network degradation in the short to medium term. Unfortunately, predicting this deterioration can be a highly intricate and uncertain endeavor. The main culprit for this inherent complexity is the fact that a water main wear rate depends on its physical and structural characteristics but also on environmental and operational factors. In nearly all cases, the number of possible parameter combinations makes highly vulnerable pipes extremely difficult, if not impossible, to find with a manual approach. Furthermore, many studies have shown that modeling a group of pipes, or cohorts, which share similar characteristics improves the prediction of a distribution network’s deterioration. A more computational and data-driven solution seems to represent the best way to extract this valuable information efficiently. Artificial intelligence and unsupervised learning algorithms possess the advantage to identify the pipe cohorts that are most at risk of failure and the conditions under which network failures occur from historical data. Once these vulnerable groups are identified, it allows utility managers (1) to have a better understanding of the network’s degradation over time, (2) to tailor inspection plans and replacement programs, and (3) to optimize water main investments in order to provide an improved level of service. The Region of Peel (Canada) has made investments in the past to collect good quality data for water main breaks and associated factors. And as such, the Region of Peel is faced with the increasing challenge of water main breaks and the resulting disruption to Peel residents and businesses and, in an attempt to meet council-approved service levels, the Region intends to use innovative methods to plan and optimize strategic investment in the water distribution network. Staff plans to use this predictive modeling information to plan water main inspection and replacement programs and optimize investments in the water main replacement program.

Proactive asset management of prestressed concrete cylinder pipelines (PCCP) begins with an understanding of system parameters, operational history, design limits, and distress levels. This is especially true when these pipes are subject to environmental or system operating conditions that are different from the original design. When performing a condition assessment for aging PCCP infrastructure, including critical stakeholders such as the repair designer and rehabilitation contractor during the initial inspections can lead to efficiency gains such as shortening the project’s timeline, and lowering overall investment and the ability to address emergent repairs that may be identified without impacts to operational performance. This approach to project procurement and management, known in the industry as investigate-design-build (IDB), has become a best practice for ongoing asset management. This paper provides relevant background on the common failure modes of PCCP and describes an effective asset management program that includes the benefits of an IDB project delivery for periodic and long-term inspection programs. Incorporated in this paper is a case study of an American power generation facility that adopted the IDB approach to manage the first-time condition assessment, risk-based prioritization, and implementation of emergent repairs for a large-diameter PCCP.

Many water utilities are facing difficulties managing and financing their operational and capital programs. A proactive and sustainable asset management program is vital for utilities to maintain their capital assets at the required level of service while ensuring that adequate funds are available for intervening when needed at their riskiest assets. Existing asset management plans and capital improvement programs are mainly being built around qualitative risk models. Such models are suitable for cases where there is a lack of system-related quantitative data to perform a data-intensive quantitative risk assessment. In the water industry, this method is widely followed as well in the form of risk matrices, which are susceptible to human biases and inadequacy for accounting uncertainty. Quantitative risk models provide an objective assessment of the network’s risk through data-driven advanced risk assessment methods. This paper evaluates both a qualitative and quantitative risk assessment to prioritize assets for optimizing capital budget allocations and long term-asset management in a system dynamics environment. The goal is to provide insights into quantifying a more practical risk avoidance in both models to help the utility best plan for short and long-term capital operations.

Utilities develop asset management plans that pertain to physical assets within their organization. However, are engineering and utility companies focusing upon those future individuals, or potential individuals, who are their key resource in developing such plans? This presentation takes the key components of an asset management plan and applies those to the organizational structure of the engineering firm or utility by coupling the findings of an internationally published research study, which highlights motivational traits and value systems of existing and incoming generations. Obviously to attract and retain the best and the brightest engineers and utility operating personnel, an environment must be developed and maintained in order for such individuals to be productive.

A major challenge in water pipe infrastructure asset management is determining the deterioration process for water pipes. The deterioration processes are usually modeled with a decay curve, which describes how pipe performance changes with time. Decay curves can also be used to predict the future performance of a water pipe. The methodology used to develop decay curves should be interpretable, reproducible, and validated. For the pipes with abundant failure records (usually small diameter pipes), we use survival analysis to derive the resulting failure rate function, mean time to failure, and survival curves. In this study, we develop a software application to analyze, visualize, and compare survival curves for water pipelines. The resulting survival curves can then be translated into corresponding decay curves or performance curves. The results will be piloted with water pipe systems of participating utilities.

The water pipeline infrastructure in the US is aging, posing an economic burden on water utilities. The improvement in the areas of data collection technologies (new sensory technologies and open-source federal databases), modeling techniques, and computational capabilities have enabled water utilities to adopt advanced asset management techniques to alleviate this burden. Risk-based asset management helps water utilities use data-driven techniques to prioritize water pipelines for renewal decision making (repair, rehabilitation, and replacement). The current practices include using quantitative index-based methods and semi-quantitative matrix-based visualizations. This study found that water utilities have investigated and developed techniques for modeling the likelihood of failure (LoF) and consequence of failure (CoF), the critical functions needed for risk analysis. However, the development of such models has not translated into better risk-based decision support systems for water utilities, as they still use limited aspects of their risk analyses’ results to prioritize pipes for renewal. The authors also found that there are significant areas of improvement in the current methodology for risk matrices like removing risk ties, incorporating game-theoretic criteria like risk attitudes, poor resolution for risk categories for prioritization action, and resource allocation, among others. This study shows how risk analysis methodologies for water pipeline infrastructure could be improved based on current scientific paradigms in risk modeling, including game-theoretic approaches. The proposed improvements can help water utilities improve the resolution, accuracy, and categorization of water pipes in their risk models, improving the reliability and usefulness of risk models for water pipeline renewal prioritization.

As part of a five-year project funded by the United States Bureau of Reclamation (USBR) under the United States Congressional direction, a team of researchers at Virginia Tech has compiled and analyzed data from more than 500 US water utilities and 100 federal facilities across the country to provide a robust picture of the health of the nation’s water pipeline infrastructure systems. The image that has surfaced is one of 985,000 mi of transmission and distribution of water pipes in the United States. Installed water pipeline infrastructure in need of replacement is worth an estimated $3.6 trillion over the next 25 years. These findings are among dozens of key insights distilled into distinct reports on pipeline performance, risk, and economics, designed by the team for more detailed, drill-down reading by the country’s water utility managers and the water industry. The reports will also lay the foundation for a longer-term effort to bring the data and its analysis online. The team is building the Pipeline Infrastructure Database (PIPEiD) to create a secured, standardized, and easily-accessible online database that can help water utility managers better monitor pipeline infrastructure systems. PIPEiD can enable water utilities to learn from the local, regional, and national patterns it presents through modeling and visualization, using tools like artificial intelligence and GIS mapping. PIPEiD will allow users to run queries that provide helpful analysis for decision-making, such as estimating pipeline useful life. The team collected field performance data for potable, raw, and reuse water pipelines made from materials reflecting the wide range of pipes currently in the ground throughout the US, including cast and ductile iron, prestressed concrete cylinderpipe, reinforced concrete, steel, and thermoplastic. The researchers worked to collect data distributed across different ecological areas, or cohorts, organized based on the climatic conditions of the 500 water utilities’ locations. These cohorts included coastal, arid, Arctic, and mountainous regions. Researchers factored in environmental conditions, such as soil corrosivity, traffic loading, and frost action, that can affect pipelines. The purpose of this paper is to disseminate the significance and key contributions of the research work. PIPEiD will provide descriptive, predictive, and prescriptive data analytics to address all three major asset management levels: strategic, tactical, and operational, and for water utilities of all sizes across the country. This paper is aimed at the water utility managers of small, medium, and large community water systems.

Earthquakes could have enormous destructive impacts on water distribution networks. Utility managers are challenged to make proactive rehabilitation decisions under seismic and network uncertainties. These utility managers have different risk appetites. However, existing seismic rehabilitation decision-making models of water distribution networks do not consider decision-makers’ attitudes toward risk making existing models practically limited. The objective of this research is to formulate a risk-averse stochastic combinatorial optimization model to identify the critical pipes of a water distribution network for proactive seismic rehabilitation with controllable risk aversion levels. The functionality of the water distribution system is quantified by the post-earthquake serviceability index, the expected value of which is maximized by the objective function. A value-at-risk (VaR) constraint is used to control risk levels. This methodology includes four steps: seismic repair rate calculations, integrated multi-physics modeling, Monte Carlo simulation, and risk-averse stochastic combinatorial optimization. The repair rate of each pipe subjected to seismic loads was calculated using empirical fragility curves. These curves were generated based on the locations of the pipes, soil corrosivity in different locations, pipe diameters, pipe materials, and pipe joint properties. Network’s hydraulic behavior and seismic vulnerability assessment were simulated using an integrated multi-physics model. Monte Carlo simulations were performed to consider the probabilistic nature of damages to the water distribution systems. These damages were represented by leaks as well as breaks in the individual pipes. The model used to ascertain the susceptibility of the water distribution system to earthquake hazard was fused with a stochastic formulation of combinatorial optimization to maximize the serviceability index of the distribution system while minimizing risk. The solution to the optimization problem of detecting the critical pipes for a given resource constraint was obtained through a risk-averse simulated annealing approach. The approach was implemented on a widely used benchmark network to detect the critical pipelines of that water network. The introduction of risk-averse stochastic combinatorial optimization models equips decision makers with a proper model to make rehabilitation decisions at a controllable risk aversion level.

Water bills, which are rising faster than inflation, are increasing across the US; and communities are grappling with aging water systems, fewer water resources, and extreme weather. The federal government’s share of capital investment for water infrastructure has fallen from 31% in 1977 to 4% in 2017. Regional and state expenditure has accounted for a much larger share as federal aid for water infrastructure capital needs has declined. This led that water rates are rising to cover the costs of replacing and upgrading water infrastructure in many communities across the country. They are struggling to meet them through local rates and fees. The major objectives of this research are to predict the economic benefits of additional federal support in water infrastructure among interdependent sectors within an economic system for facilitating the federal government’s share of capital investment. This research conducts ripple effects analysis that is the process of predicting the effectiveness of water infrastructure capital investment using historical economic data. This study attempts to explore how federal capital investment in water infrastructure spreads economic benefits within an interdependent system. It will be conducted at the federal level using the interindustry-macro model that analyzes macro-economic data, including over 400 sectors. Investments that will be coordinated at the federal, state, and local level will help control and stabilize rising water rates across the United States.

The potential risks and vulnerabilities of water infrastructure are significant issues in our society. Water infrastructure’s problems can have an economic impact of trillions of dollars on the US economy and its critical infrastructure systems. The primary objectives of this research are to analyze what initiatives states are taking in regard to water infrastructure and to make recommendations to raise the grade to capture the apparent inconsistency between current water infrastructure management and existing ineffective solutions and recommendations. In addition, this study attempts to leverage data-driven approaches based on analysis of existing ineffective improvement methods and recommendations. Therefore, this research tries to determine the causes affecting grades of the infrastructure report card for water infrastructure and contributing to prolonged risks. Minimizing the vulnerability of the water infrastructure and closing the mismatch of the water sector will protect public health for future generations and lead to a resilient, efficient, and reliable water future.

As the District of Columbia’s (DC) largest tributary to the Anacostia River, Watts Branch has 5,500 acres of watershed in DC, and an additional 6,000 acres in Maryland, where its headwater originates. From 2018 to 2020, climate changes brought record shattering torrential storms, converting a calm creek into a white water in a couple hours. In early 2021, the inspection and enforcement staff of District of Columbia Department of Energy and Environment (DOEE) notified a DC Water engineers that a sanitary sewer manhole M-41235 was exposed from a significant washout in Watts Branch Stream in a community park. The same sewer had been lined two years prior, as part of a capital improvement project (CIP), and at the time of the lining, the manhole and sewer appeared to be about 6 ft behind the embankment. Upon an in-depth field and record evaluation, it was determined that the 24″ sanitary sewer inlet and outlet of the manhole could have been further exposed if urgent action was not implemented. DOEE and DC Water collaborated on strategizing on an expedited action to build a protection wall, in the approximate 8-ft washout zone in front of the sewer, to prevent undercutting of the sewer pipe. This wall leaves sufficient space for a permanent embankment to be designed, and constructed, by the creek owner in the future. The DOEE associate director demonstrated genuine stewardship by advising DC Water not to wait for a full-scale permitting to kick off the mitigation. This helped to avert a potential sanitary sewer overflow, SSO, while implementing best management practices, BMP. DC Water’s geo-structural engineer and emergency contractor implemented a design-build mitigation plan within a couple of weeks. The timely, proactive work completed in the spring of 2021 has already protected the sewer from being undercut during several torrential storms, including the one induced by Hurricane Ida. This presentation focuses on limited embankment protective work to protect buried sewer assets in sensitive areas, as a non-owner of the creek. It also shows collaboration between a regulatory agency and non-governmental municipal agency, and how both agencies used a goal-oriented, joint action to avert a catastrophic event in the interest of public and environment.

Indianapolis, Indiana, currently has approximately 16,000 homes served by on-site septic systems. The soils in Indianapolis are not ideally suited for septic systems, which in turn lead to high failure rates and can result in health and environmental hazards. Citizens Energy Group (Citizens), the owner and operator of the wastewater system, is improving the environment for all Indianapolis residents by extending sewer systems into high priority neighborhoods through the Septic Tank Elimination Program (STEP). STEP has evolved significantly since its inception in 2005. The evolution has focused on increasing customer satisfaction, increasing customer connection rates, and reducing overall project cost. Resulting from the program’s improvements, Citizens has experienced increased voluntary connection rate by over 50% as well as overall project cost reductions, which has reduced the financial burden on customers. This paper discusses the evolution of programs in Indianapolis focused on mitigating septic systems and Citizens’ current method of providing new sanitary sewers to unsewered areas.

Underground pipeline assets (water mains, sewer lines, storm drain pipes, meter and valve vaults, etc.) are subject to impacts from adjacent construction activities, such as open-cut excavations, tunneling, grading, pile driving, blasting, and construction loadings from heavy construction equipment (i.e., cranes). These activities may cause additional stresses, deformations, and other risks that can lead to failures of underground utilities. An article on the San Francisco Examiner dated January 13, 2014, stated approximately 25% of all breaks were caused by nearby construction activities, which was significant. This paper presents the potential consequences of these impacts and discusses, from an owner’s perspective, the provisions for protecting pipeline assets, including steps for impact assessment, evaluation methods, damage criteria, risk mitigation measures, and contingency plans. Example projects also are included. Utility protection is a collaborative process between utility owners, project owners, engineers, contractors, and all other stakeholders. With proper provisions in place, the interests and concerns of all the stakeholders can be adequately addressed.

The Joukowsky equation has been used to estimate the surge pressure in a pipeline system for a rapid valve closure. This practice is validated for a metallic pipeline system but becomes problematic for a flexible pipeline system. Charts of dimensionless surge pressures versus dimensionless valve closure times, obtained from transient and parametric simulations on three hypothetical pipeline systems with steel, HDPE, and hose, showed that dimensionless pressures are equal to 1 for the steel pipeline system but larger than 1 for both flexible pipeline systems. The simulations also showed that the maximum transient pressures, including line packs, are decreased with larger dimensionless times due to transient wave attenuations. Although the Joukowsky equation can predict the surge pressure for any pipe materials in an instantaneous valve closure, it underestimates them for both the flexible pipeline systems in a rapid valve closure.

The Cedar Creek (CC) 72-inch (in.) prestressed concrete cylinder pipe is Tarrant Regional Water District’s (TRWD) oldest raw water transmission main, which was installed in the early 1970s. TRWD maintains a robust condition assessment and risk-based prioritization program in addition to the districts’ pressure transient-reduction measures in place since the mid-1990s. These measures have greatly extended the remaining useful life of the CC pipeline. However, despite this effective pipeline integrity program, high pressure rated areas of the pipeline continue to be at risk of failure, requiring a more substantial renewal program. The first large pipeline replacement project soon to be implemented by TRWD is the Cedar Creek Section 2 pipeline replacement project, which includes approximately 10 mi of the 72-in. pipe selected for replacement with 90-in. steel pipe. The remaining 15 mi of 72-in. pipe in between the Waxahachie Booster Pump Stations and the Kennedale Balancing Reservoir will stay as they are since those lower pressure portions of the pipe have a much lower risks of failing and also much a lower consequence of failure. Black & Veatch used PIPIT, a proprietary transient software, to conduct a hydraulic transient analysis of multiple operational scenarios (facility and pipeline configurations) using the Cedar Creek Pipeline Section 2 to convey water to and from the district’s Waxahachie and Kennedale facilities. The modeled scenarios were subjected to specific transient conditions. Specifically, conditions of pump start up, shutdown, power failures, and valve operations during water transfers were analyzed, as applicable, along the network of pipelines for nine different initial steady state scenarios. Power failures at Waxahachie were evaluated in six of nine scenarios. This paper identifies the methodology and software used to develop the hydraulic transient analysis; defines the different transient scenarios modeled and highlights the overall recommendations from the evaluations; and, explains TRWD’s use and implementation of these recommendations into their operational decision-based model for system wide operations.

Citizens Energy Group (Citizens) is responsible for over 3,300 mi of sanitary sewers and 4,500 mi of water mains within its service areas. Both systems have a total of over 3,200 stream crossings and because of the minimal design cover depth at most crossings, typically 3–5 ft or less, many have become exposed due to erosion or changing flow characteristics of the stream. A three-tier inspection and remediation stream crossing program was developed, which resulted in a defined risk based program that inspects all stream crossings on a recurring schedule and identifies, prioritizes, and remediates the exposed crossings. The paper explains the program’s three tiers in greater detail, including how the recurring inspections are done, the evaluating/prioritizing exposed crossing procedure, and when/if/how remediation is done.

With an ever-changing infrastructure environment, utility owners have faced increasing challenges in managing damage prevention. Until recently, little has evolved since the start of One-Call and the 811 system. Now, municipalities and states are beginning to integrate the technology and techniques used in subsurface utility engineering to provide an increased level of rigor of the utility existence and locations into the One-Call and damage prevention process; both utility owners and excavation contractors can benefit. This approach leads to a more sustainable infrastructure environment for utility owners. SUE incorporates the appropriate use of geophysical equipment, surveying, mapping, and improves the depiction of the underground assets, which has a positive effect on damage prevention as well as improvement to the utility records. By focusing more on completeness and less on assigning risk between the excavator and utility owner, both parties can benefit from fewer damages. This session will provide multiple case studies at the local and state level that show how SUE has been successfully integrated into One-Call/damage prevention programs as a risk mitigation strategy.

One aspect of the impacts of climate change that is constantly in the news is flooding, whether it be coastal or inland. The traditional way of moving storm or surge water is through large pipes, mostly under our developed areas like streets and parking lots. Increasing pipe capacity to move more water involves two solutions: more pipes, or larger pipes, or a combination thereof. Existing underground space is already burdened with millions of miles of other public and private utilities. One unique consideration of stormwater management is that gravity rather than pressurized pipes moves this water. Fitting new volume capacity constrained by elevation is going to be costly in terms of time and money. History documents that knowing the precise location and nature of all these other utilities—known and unknown, abandoned and active, repurposed or forgotten—is a prudent first step. Additionally, there are other buried items for which information may not be available until construction: thrust blocks, construction rubble, tanks, vaults, etc. Yet society’s records of what is under the ground are poor. Several new technologies, combined with traditional subsurface utility engineering investigation and documentation techniques, are the prudent engineering solution to characterizing the existing underground to eliminate costly construction surprises.