Regression models presented in the American Society of Civil Engineers’ Journal of Infrastructure Systems
by researchers at Syracuse University’s L.C. Smith College of
Engineering and Computer Science are expected to help utility companies
predict the service life of wastewater pipeline infrastructure and take a
proactive approach to pipeline replacements and maintenance.
Ossama
(Sam) Salem, Yabroudi Chair of Sustainable Civil Infrastructures and
professor of construction engineering and management at L.C. Smith, and
his Ph.D. student Baris Salman, developed various statistical prediction
models using data obtained from the Metropolitan Sewer District of
Greater Cincinnati, Ohio, to generate deterioration models that will
help in the decision-making process regarding future infrastructure
development.
“The
models presented in this paper allow utility and wastewater management
companies to develop a sound maintenance plan and predict potential
failures,” Salem says. “This has impact not only economically, but
socially and environmentally as well.”
As
wastewater utilities seek to implement asset management strategies to
help justify and optimize their expenditures, understanding the current
and future behavior of wastewater lines may help utilities mitigate
costly emergency repairs. The deterioration models developed by Salem
and Salman are expected to assist utility officials in assessing risk
and identifying pipes that have the highest probability and consequences
of failure. Doing so will allow utilities to proactively prevent
problems, rather than simply reacting to fix problems after they occur.
While
the presented models are useful for the data set provided, their
applicability to different sewer systems depends on the characteristics
of those particular networks. Since weather conditions, soil properties
and construction methods vary among cities and among infrastructure
systems, different deterioration patterns may be observed in different
regions.
Modeling Failure of Wastewater Collection Lines Using Various Section-Level Regression Models
More information on other work by Salem and his research group