Member Spotlight – March 2019

  • by Will Holbert
  • Mar 22, 2019
  • Newsletters

A worker helps prepare a pipe at an SFPUC project site, part of the utility’s ongoing replacement work to upgrade its 1,200-mile water main system.

Taking the Guesswork out of Infrastructure Replacement Work

In the San Francisco Bay Area, two water agencies applied machine learning and acoustic technology to save millions of dollars, minimizing the impact of infrastructure replacement costs on ratepayers. In Southern California, a third water agency avoided having to build a $20 million reservoir by employing a system similar to that used by Netflix and Amazon to zero in on customer characteristics. 

What’s called “big data” takes many forms, but some of its latest results showcase how California water agencies can innovate their way around the daunting financial challenges of replacing and upgrading infrastructure.

Log books dating back to the 19th Century are among many sources of data used by the SFPUC in building an algorithm that maximizes the efficiency of its infrastructure replacement program. Photo courtesy of SFPUC.

At the San Francisco Public Utilities Commission, this effort not only features acoustic technology, but data sets that include handwritten entries in 19th Century logbooks. 

“We have some pipes that were installed when Abraham Lincoln was president,” said SFPUC Water Distribution Division Manager Katie Miller.

For the SFPUC, predictive analysis is prioritizing water main replacements within a 1,200-mile system of water mains and approximately 180,000 service connections. Archival information is one of a myriad of data in algorithms that include soil chemistry, water pressure and the age of pipes, to name only a few. Approximately 150 miles of mains are more than 100 years old and pipe networks naturally leak over time due to age, ground settlement and other factors. 

In addition to the SFPUC’s main replacement program to replace 12 to 15 miles of pipeline per year, a proactive leak detection and repair program helps the SFPUC to rehabilitate the aging system, reducing system water losses and preventing destructive and costly main breaks. This is where Smart Hydrants come in.

While virtually indistinguishable from traditional fire hydrants, 158 hydrants within SFPUC’s city-wide system contain acoustic technology that monitors water noise flowing through pipes. They listen for tiny aberrations in wavelength based on the length of time it takes water to move between two points. 

Once the data indicates a potential leak, the SFPUC dispatches a leak detection crew to the site, and workers use a portable listening device that can confirm and narrow down the leak to within 10-to 20-feet. A leak that could have revealed itself when water bubbled to the surface, or as a complete break requiring emergency response, can be repaired at a minimum in employee time and cost, not to mention inconvenience to the surrounding neighborhood. In other words, the fix could require a slab saw instead of a backhoe.

This approach confirmed eight leaks from September 2018 to February 2019, with repair costs ranging between $5,000 to $10,000. If they’d grown into complete breaks, that cost range would be anywhere from $20,000 to millions of dollars, Miller said.

“It’s really an exciting time in our industry to be working,” Miller said. “It’s pretty new for government agencies to use this technology. Ultimately, it’s going to make for more efficient government, and that’s the best kind of government.”

One of 158 “smart hydrants” installed within SFPUC’s service area. The hydrants contain acoustic technology that listens for leaks, allowing workers to pinpoint the tiniest of leaks and repair them long before they grow into complete breaks. Photo courtesy of SFPUC.

The East Bay Municipal Utility District provides high-quality drinking water in Alameda and Contra Costa counties in the East San Francisco Bay Area of California. Approximately 4,200 miles of pipeline deliver water to its 1.4 million customers.

Historically EBMUD has replaced about 10 miles of distribution pipe per year. The approach to selecting the projects was reactive, replacing only pipes that had broken many times before. Although EBMUD’s main break rate was within industry standards, the failure rate was increasing. EBMUD now selects projects by evaluation risk and as a supplemental tool also uses machine learning algorithms to find correlations between hundreds of data variables that can visualize vulnerabilities within the pipeline system. Costs can range from $2 million to $2.5 million a mile to replace pipe, so a premium is placed on keeping pipe in the ground as long as possible. The machine learning system ultimately ends up with a simple 1-through-5 likelihood of failure, or LOF, scale, with five identifying a pipe that’s in the worst shape and in need of replacement. 

“We want to make sure we’re replacing the right pipe, pipe that’s truly reached the end of its useful life,” said EBMUD Senior Civil Engineer David Katzev.

Age is definitely not a deciding factor alone, Katzev said. A cast iron pipe dating to 1894 can have a LOF of 1, and another buried 40 years later a score closer to 5. The predictive analysis gives EBMUD the confidence to pursue its goal of replacing 15 miles of pipe a year, and accelerating to 20 miles in the near future, Katzev said.

At the Moulton Niguel Water District, plans had called for spending $20 million on a new reservoir for storing water for recycling during the wet season, much of it for irrigation during dry months. 

MNWD Director of Finance and Water Resources Drew Atwater brought in data experts he know from Netflix. If you can use technology to anticipate customer demand, how about applying it to recycled water use instead of movie tastes? Atwater said that when MNWD shared precise data on recycled water use with large users, such as golf courses, they arrived at agreements on water conservation that eliminated the need for building a reservoir. 

“We were able to bring down costs, so we could reinvest that money into different things,” said MNWD General Manager Joone Lopez. “That kind of knowledge is really what big data is all about.”

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