Analyzing watermain breaks in the Region of Peel

April 7, 2020 Chaim Schwartz

Watermains are typically one of the most expensive assets owned by a municipality. Furthermore, as an underground utility, often the most feasible strategy for monitoring the health of these assets is by recording and analyzing failure records. With 10 years’ worth of watermain breaks data from the Region of Peel, Ontario, we used ArcGIS Insights to describe the failure patterns; assess the effectiveness of the Region’s watermain replacement program; and analyze the expected useful life of watermains to support future expenditure estimates.

The Region of Peel, Ontario has been managing their Asset Information through a mature Geographic Information System (GIS) along with their Enterprise Work Management system for several years. As such, the organization has a wealth of asset and maintenance data available for analysis. In this article, we will show how ArcGIS Insights was used to explore that data and answer three questions that lie at the heart of the water asset management program:

  1. How are our assets failing? Describing technical characteristics of watermain failures and how these affect customers.
  2. How effective is our watermain replacement program? Evaluating the effectiveness of the Region’s watermain replacement program over the years.
  3. What is our infrastructure’s useful life? Understanding how much life can be expected from the various cohorts of watermains to support estimates for capital reinvestment needs./strong>

How are our assets failing?

The set of Insights “cards” below was designed to describe some of the technical aspects of the watermain breaks. Where are breaks happening across the Region? What are the main characteristics in terms of material, diameter and soil type? And what are the seasonal patterns throughout the year?  Rather than having a fixed set of charts and maps created periodically, ArcGIS Insights allows us to build this kind of analysis with a live connection to both the authoritative GIS and the work history. The interactivity built into this Insights “page” also allows us to explore numerous combinations of these parameters which would have been unrealistic with a static set of charts and maps.

Beyond the technical analysis of main breaks, the Region has also been focusing on gaining a better understanding of the effect these outages have on customers. To support this kind of analysis, an extensive effort was undertaken by the Region to estimate the number of customers affected by each individual break. Having these data allowed the Region to calculate annual Key Performance Indicators such as the average disruption per break, and the total affected customer hours. Being able to visualize these KPIs by selected wards as shown below, allows a quick comparison between neighboring wards. It also allows citizens and public servants to explore their neighborhood and see where breaks are happening, how fast outages have been restored and how that has changed over the years.

How effective is our Watermain Replacement Program?

After gaining an understanding of where, when and which watermains are failing, and how customers have been affected by this, we chose to assess the effectiveness of the Region’s watermain replacement program. Over the years the Region has been investing significantly in replacing aging and failing watermains. To assess the effectiveness of these replacements we overlaid two datasets: The total kilometers of watermains in the ground per year (gray bars on the right graph below), and the total number of breaks per year (red line on the right graph below).

Again, it is through the interactivity built into these graphs that we were able to derive the insights we needed. While the total number of breaks were shown to decrease over the years, we were able to very quickly identify cohorts that reacted more consistently and strongly to the removal of aging assets compared to cohorts that reacted moderately or even insignificantly when choosing a specific pipe material, diameter or a combination thereof.

We must remember that the reasons for removing watermains are not solely based on failures. However, failure rate remains one of the main factors, and this tool allows us to explore how the different cohorts reacted to their removal at a detailed level that could never be achieved by relying on organizational knowledge alone.

What is our Infrastructure’s Useful Life?

Lastly, after reviewing the watermain replacement program, we investigated one of the main questions that lie at the heart of an asset management program: What is the useful life of our assets? Having a good understanding of the expected useful life of assets is essential for estimating future expenditure and budget requirements. However, there are no “industry standards” that can reliably be used since the same infrastructure might experience significantly different service lives in differing operating environments. In the case of watermains these can be parameters such as soil type, water pressure, temperature, installation standards and many more. It is therefore essential to base the expected useful life on local data.

In the graphs below, we displayed the watermains age on the X axis and a failure rate defined by breaks per 100KM on the Y axis. Based on the 10 years’ worth of history, we were able to clearly visualize the life cycle of Cast Iron (orange) watermains, Ductile Iron (Green) watermains and PVC (red) watermains. These life cycle curves display the expected failure rate throughout most of the asset’s life, as well as the end of life where that curve sharply rises into higher and unacceptable failure rates. It is at this point that we need to keep in mind that a “useful” life is not a definite or physical threshold that can be measured in a lab, rather it reflects the level of risk decision-makers are willing to take. In our case, the useful life will be roughly at that point where the curve starts to rise. However, the acceptable level of risk will determine if it is a few years earlier or later around that point. By testing different combinations of materials and diameters, cohorts with similar life cycle curves can be grouped to share the same expected life, thereby simplifying expenditure estimates.

These examples show how fundamental asset management questions can be answered by visualizing data coming from the GIS and Work Management Systems. They show the value of recording asset and maintenance data, but also demonstrate the importance of recording these data accurately and consistently to establish a reliable system of record.

If you would like to learn more about this subject, we invite you to watch the recording of our webinar Analyzing Main Breaks in the Region of Peel to Ensure Safe Reliable WaterThe webinar explores how the Region of Peel successfully analyzed over 10 years of water main break data to develop new KPIs and detailed water break analyses to support better asset replacement and rehabilitation decisions.

Watch the webinar

About the Author

Chaim Schwartz

Chaim Schwartz is a Technical Solutions Specialist (TSS) with the public works team at Esri Canada. Chaim’s efforts are focused on helping customers leverage the ArcGIS and Cityworks platforms for infrastructure management, specifically asset inventory management, work management, field mobility and asset life cycle analysis. In his role as a TSS, Chaim works on introducing new technologies and workflows for improved asset management. Passionate about everything GIS and driven by his experience with municipal infrastructure management, Chaim is motivated by customers being able to derive value from geospatial technology.

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