Joshua Dill: Please tell us about your backgrounds and how you came to be in your current positions.
Allan Scott: I am HDR’s lead for utility management services in Northern California. My work focuses on projects concerning asset management, maintenance and operations, and business consulting for water and wastewater utilities. I’ve been working in this area for about 20 years. Before that, I worked on other water resource projects, both in consulting and for a couple of different municipalities.
Trent Stober: I’m HDR’s national director for utility management services. I have been in the field for 25 years, working on utility planning for water and wastewater utilities across the country. This work has been focused on a variety of issues that utilities face today, including asset management, capital planning, regulatory issues, and financial effects.
Joshua Dill: Please tell us about HDR’s utility management services division.
Trent Stober: We have about 150 technical practitioners in utilities management services, located across the United States. The municipal water service lines include utility planning, master planning, asset management, hydraulic modeling, rates and finance, operations consulting, and regulatory support for municipal utilities’ Safe Drinking Water Act and Clean Water Act obligations.
Joshua Dill: What are the main asset management problems that municipal water districts and other water utility managers are facing?
Trent Stober: Our nation’s utilities own and operate a vast amount of infrastructure that is nearing the end of its useful life. The funding needed to manage these assets to meet customer service goals can be daunting. We’re at a point in the industry where we have to apply a decision process for how to maintain, rehabilitate, and replace our buried infrastructure, including facilities like pump stations and treatment plants, in a cost-effective, defensible, and data-driven manner. Our ultimate goal is to use a proactive and repeatable approach to gain the highest value out of our infrastructure investments.
Allan Scott: Water and wastewater utilities have a significant amount of aging infrastructure. The value of their buried infrastructure can range from hundreds of millions to over a billion dollars and may represent the majority of their capital investment. They have to decide how much and where they’re going to spend their capital dollars on replacing or rehabilitating this aging infrastructure. Of course, the overall goal is to keep the system viable and to continue to meet the needs and expectations of their customers and stakeholders. That hasn’t changed over time: They’ve always wanted to put the investments where they’re going to do the most to maintain or improve their services. But how exactly to do that is difficult to figure out, especially for water utilities. Water distribution systems are closed systems, so you can’t run a camera inside them or physically inspect them. In the past, utilities have relied largely on indirect engineering assessments, intuition, and on the past experience of the operators regarding what needs the most attention. This approach doesn’t always align with the actual highest-priority needs of the system.
One of the things that we’re helping utilities pivot toward is the data-driven assessment of risk and vulnerability. That provides a transparent and consistent approach that helps them decide how much to invest in infrastructure to achieve certain service levels and where to make those investments. For example, how many main breaks are acceptable in a given year, and do we want to maintain the current number or drive it down? Where will our replacement dollars do the most good this year?
Joshua Dill: One of the ways districts have done this in the past is by relying on industry-established average useful lives. How are those average lives calculated?
Allan Scott: That information is typically estimated by pipe manufacturers, consultants, and utility managers based on various industry studies or experience. The reality is that the useful life of an asset varies greatly depending on its location, environmental factors, and the characteristics of the system it is part of. Especially in a water distribution system, the same material could be subjected to different pressures and stresses in one system or another. There is also variation dependent on the design specifications and construction methods applied at the time of installation and seasonal operational variations in pressure and flow. There are also environmental factors, like soil shrink/swell potential and corrosion potential. The average useful life that is provided by the industry or vendor is usually a pretty unrealistic estimate given the actual conditions that the pipes are experiencing. There are plenty of examples of water mains in excess of 100 years old—well beyond anybody’s calculated average useful life—that are still performing well. There are other examples of mains that are failing within just a few years of installation.
We’ve found that if utilities just use the industry-provided average useful life, they usually end up with annual replacement cost calculations that don’t line up with actual replacement needs and are much higher than they can realistically deal with. It is difficult for utility managers to justify these estimates because they are not based on data that reflect the performance of their systems. We’ve done some comparisons of replacement cost calculations based on average useful life– based estimates versus those based on the data-driven approach. We’re seeing that the average useful life approach many times yields unrealistic overestimates. Additionally, as time goes on and these estimates are shown to be inaccurate, decisionmakers’ confidence in the analysis goes way down. It has always been a challenge for utilities to justify industry-provided average useful lives to the board, the city council, or whoever else they’re accountable to.
Joshua Dill: How does HDR’s data-driven water main renewal and replacement analysis work?
Allan Scott: The foundation of our data-driven analysis is leveraging historical main-break data from the utility. When we propose doing that, the initial response of most utilities is that they don’t have very good main-break data. However, we can almost always take the data they have, and through some initial analysis, enhance the data and help them fill data gaps to get a solid, main-break history dataset. That dataset can help determine the characteristics of the system and help the utility hone in on what factors are driving the breaks.
Typically, in any given water distribution network, only a small percentage of the pipes are actually breaking. Most of the pipes are performing well and not breaking at all. The objective is to identify the pipes that are breaking and understand why, because this is the most likely place where future breaks will occur. The break history can help determine the average time between breaks. You may be able to determine that pipes that have experienced their first break will on average experience the second break within 8 years, but pipes that already have six breaks will on average experience the seventh break within 1 year. By building out that dataset and looking at the average time between breaks, you can analyze how quickly your system is deteriorating. The next step is to take a look at what parameters are contributing to that, for example, pipe vintage, which refers to pipe material and manufacturing date. Asbestos pipe that was manufactured and installed in the early 1960s or earlier has consistently exhibited much worse break characteristics than asbestos pipe that was manufactured and installed later. Metal pipe has also been manufactured differently over time, so metal pipe also has different break characteristics depending on its age. We will analyze 20–30 different factors and start to tease out which ones contribute most to main breaks. We focus on the high contributors by assigning higher scores and weighting factors to pipes with those characteristics and build the risk model based on that.
The risk model assigns a numerical risk score to each pipe based on two components: the likelihood of failure, which expresses how quickly the pipe
is expected to break again, and the consequence of failure, which indicates what the effect of a break would be. Would it inconvenience a couple of folks on cul-de-sac, or is it going to make the nightly news? Obviously, the pipes with the higher risk scores are the ones you want to focus on the most because these may break sooner or have bigger consequences if they do.
Trent Stober: We can use some of the utility’s existing tools, like its hydraulic models, to evaluate the potential effect on customer service of a break in each pipe in the system. We can simulate the effect on the public or on critical customers like hospitals and large industrial users to understand the consequences of breaks for each pipe and to provide greater insights into risk. Those with higher consequences may need a more aggressive approach to replacement or renewal over less important pipes that break at the same rate.
Joshua Dill: You draw the data in each case from the specific utility rather than from data gathered nationwide, correct?
Trent Stober: Correct. We see large differences in break history among regions of the country. Different pipe materials have historically been used in different parts of the country, and the environmental conditions that influence pipe breaks differ across the country as well. Many factors affect how those pipes age and what we can expect from them. We have a large national database that helps us understand those regional differences.
Joshua Dill: After you do your initial study and you create this model, what does the agency do with it? What are the next steps?
Allan Scott: There is a two-step process. We’ve been focusing on the first step, which is developing a risk model. That answers the question of which pipes are problem pipes and which should be addressed first. The second question is how the system should be managed in the long run through replacement and rehabilitation. That goes back to the question we discussed earlier regarding how much utilities spend on rehabilitation each year. How much should they apportion to replacing pipes versus their other assets? The second part of our analysis helps them answer that question. We go back to that break history, and, analyzing the pattern of breaks, we can start to do some forward-looking analysis and assess how many of those pipes are likely to break in any given year in the future based on their break history and the average break characteristics of the system. That analysis helps our utilities identify how many miles of pipe they should be replacing each year in order to keep breaks at a manageable number and achieve their goals—whether those involve maintaining the current break levels or reducing them—and helps them balance their annual capital investments between their water mains and other assets.
Trent Stober: This approach also arms the utility with information to explain its financial needs to the community and elected officials and collaboratively set level-of-service goals. This helps build the utility’s case for the resources it needs.
Joshua Dill: Who are your clients, and how big are they?
Trent Stober: We have clients in just about every region in the country. They are predominantly mid- to large-size utilities, those serving populations from thousands to millions. They have a good amount of data.
Allan Scott: I most recently completed work in Northern California for the Contra Costa Water District and the City of Santa Cruz. In both cases, we did a full analysis based on their break history and have helped them develop a consistent, data-driven process for replacement planning and a transparent framework to communicate and justify their plans to their board or the city council.
One of the other key drivers that motivates utilities to do this is the fact that they’re losing a lot of their more senior workforce, who know the system and intuitively understand where the problem areas are. That was the case with Contra Costa Water District. It had recently had several key folks retire who knew the system well and did most of the pipeline replacement planning. This prompted the district to establish a more formal, repeatable process that was independent of individual staff members’ institutional knowledge or system experience. That’s why the district developed its current approach to pipeline renewal and replacement planning, which relies on its main break history and our data-driven methodology.
Trent Stober: There’s also a really good opportunity to coordinate with other municipal functions. We’re increasingly seeing that utilities are partnering with other agencies within the city, like public works, so that community disruption due to infrastructure improvements is minimized. For example, if a major road project exposes a buried asset, should a utility proactively replace or rehabilitate that asset, even if it wouldn’t be scheduled for maintenance for a few years? Doing both projects at the same time may be less disruptive to the community in the long run.