Improve Infrastructure Planning with Risk-Based Assessments

In 2017, the BBC referred to America’s infrastructure deficit as a “$4 trillion time bomb.” Harsh, but a fair characterization. In order to stave off disastrous and expensive repercussions of this lag, planning around the channels that support transportation, water, energy, and transfer of information require strong leadership, forethought, and innovation. South Portland, Maine, a community close to our home office, is embracing such a philosophy using a Computerized Maintenance Management System (CMMS).

Every year, cities around the U.S. spend hundreds of thousands of dollars repaving functional roadways after performing underground infrastructure repairs and upgrades. It’s a frustrating situation for both taxpayers and municipalities. The use of those finances to perform what is essentially a redundancy is fiscally undesirable and time-consuming. On top of that, when residents find that roadways where they navigated paving construction only a year or so before are being torn up again, it affects their confidence in their municipality’s management.

Prioritizing at-risk Infrastructure

Woodard & Curran worked with South Portland to perform risk-based assessments on roadway and wastewater assets in order to help the City build its CMMS dataset. Our approach identified the condition and age of this infrastructure, as well as its respective consequence of failure, to create an easy-to-use, risk-based, digital asset management system. This data, collected and applied in an accessible interface, helps the city plan and prioritize maintenance, repairs, and upgrades in a time- and cost-efficient manner.

Of course, there’s no perfect method to predict what infrastructure will fail and when, but it is possible to identify buried infrastructure and roadways that are at higher risk of presenting issues to the public and prioritize investments. Using calculations based on National Association of Sewer service Companies (NASSCO) pipeline condition assessment methodology and Pavement Condition Index (PCI), our engineers were able to rank buried and roadway infrastructure by a combination of likelihood of failure (LOF) and consequence of failure (COF) — a measure of what it means for the community were the infrastructure in question to fail.

While it sounds simple, analyzing infrastructure required inspection of 118 miles of roadway and 107 miles of piping. These inspections included assessing and scoring the condition of infrastructure and then correlating the condition to a score of 1 to 5, a measure of the LOF. For roadways, the city performed inspections and assigned values based on an in-house pavement condition index survey – specific ranges on that index were translated into five conditional categories, with pavement in the worst shape (greatest density and severity of distress) and highest LOF rating a 5, and pavement with little wear or tear rating a 1 and the lowest LOF.

Calculating Risk

For underground infrastructure, the ratings for likelihood of failure were based not only on age and condition, but also their overall functionality. For example, a pipe in perfect condition that is practically brand new could still present a higher likelihood of failure if it is undersized for the flow it needs to convey. Infrastructure that functioned as designed and were under ¼ life expended were labeled as low LOF (a score of 1); infrastructure that was not functional at all, needing repair, reconstruction, or replacement were labeled as high LOF (a score of 5). 

Yet another scoring system was used to determine how detrimental the failure of infrastructure — both above and below ground — would be in the context of the community. Infrastructure that upon failure would have no impact on operations, was unlikely to result in regulatory noncompliance, would present little to no environmental impact, and would likely not result in personal injury was rated low COF (a score of 1). Infrastructure that upon failure would result in significant disruption of services, leave a major impact on stakeholders, present a serious threat to the environment, or pose a serious threat of injury was rated high COF (a score of 5).

Then came the math. To determine risk (defined as the probability or likelihood of an event occurring and the consequences of that event) for each piece of infrastructure under consideration, we multiply likelihood of failure scores by their corresponding consequence of failure scores. For example, a low consequence of failure pipe in pristine, functional condition leaves us with the formula 1 x 1 = 1: low risk. At the other end of a spectrum, a high consequence of failure roadway in very poor condition gives us 5 x 5 = 25: high risk.

This data is useful on its own, but when formatted, as in this case, into an interactive georeferenced dashboard in the City’s CMMS, it’s even more powerful. The City of South Portland can now view its infrastructure and risk data by location in a color-coded heat map. As investments are made and infrastructure conditions change (pipe cleaning or crack-sealing), Public Works employees can update LOF scores in the CMMS in real time. The City’s CMMS provides a living tool for planning and prioritizing the use of public funds for infrastructure investments.


Barry Sheff

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