Using Probabilistic and Deterministic Modeling Techniques to Relate Funding to Service Levels to Drive a Water Main Rehabilitation Program in Toronto, Canada

Toronto Water (TW) manages an inventory of over 6,600 km (3,800 miles) of watermains that directly service a population of 2.81 million. The greater metropolitan area is over 5.5 million and it is the largest urban and metro area in Canada. While the age of the inventory dates back to the 1870s, the system experienced significant growth post-second World War. TW has maintained a comprehensive database of watermain failures (>78,500) and intervention techniques dating back to 1928 as well as a comprehensive failure watermain coupon dataset with detailed information on failure mode, material type, condition state, soil chemistry, and electrochemistry. Approximately 79% of the inventory is comprised of ferrous metals (largely cast iron with ductile iron and steel) which account for 98% of the historical failures. In 2016, TW desired to develop a comprehensive model to relate watermain replacement/rehabilitation funding and techniques to resulting service levels in the system to facilitate communication with policy makers the ramification of different funding levels and to optimize investment with respect to achieving stated service goals in the most cost effective manner possible. Using a balance of both probabilistic and deterministic modeling techniques the work has enabled the development of not only a global view of funding versus service level, but a very discrete “stick-by-stick” view of failure risk such that implementation of the watermain replacement/rehabilitation program can utilize risk-based decision logic to preferentially replace/rehabilitate higher risk mains. The review of failures in the program has provided unique insight into the “vulnerability of era” in the ferrous metal inventory as 150 mm centrifugally cast iron manufactured and installed from 1953 to 1975 is only 25% of the total pipe length but accounts for over 50% of the failures. This insight into era and exposure condition vulnerability allows the development of programs that preferentially target portions of the system that will produce the greater reduction in failures. The optimization work has utilized genetic algorithms to analyze literally thousands of alternatives with subtly different objective ranging from pure least total cost over time at different service level objectives, stabilized funding over time, stabilized failure reduction or service level rates (by adjusting funding to eliminate variability due to vulnerable era demographics), and variable time frames to reach service level objective goals.