Better Roads Magazine, September 1992, Interview of Dennis Polhill by Ruth W. Stidger, Editor-in-Chief
Is your PMS sophisticated enough?
The ability to predict future pavement conditions is what separates a leading-edge technology pavement management system from a database. Not only should such a system predict future conditions; it should also be able to tell you the impact of variously timed actions (such as specific maintenance steps). With these capabilities, PMS can help establish a more rational maintenance and rehabilitation policy — providing the most cost-effective solutions over the lifetime of the pavement.
Databases and aggregate index programs are system elements, says Pavement Management Systems’ Dennis Polhill, P.E. And, Polhill, previously a public works manager, believes that many of today’s systems rely on weak or incomplete technical approaches.
“Public policy makers should realize that PM is a fledgling technology without the benefit of standards, common definitions, and technologies, or a leadership entity that will oversee and facilitate its evolution,” Polhill says. “This lack of technical leadership causes compromises.”
“One of the benefits of a standardized aggregate index such as PCI [in the PAVER system] is to aid standardized, centralized control from top government. Thus, the FAA, military, and some state and federal highway agencies can use a common indicator to trade off need in one agency with another to allocate funding for the greatest benefit.
“However,” Polhill says, “not all PCIs are the same. The awkwardness and expense of using PCI as suggested by the Corps of Engineers has been met with dozens of creative approaches to minimize its difficulty.
“The result is that various PCIs represent different combinations of data, yielding the obvious compromise in the central authorities’ ability to compare these indexes.”
What level is the pavement management system you use? At the low end of the evolutionary scale are non-computerized methods focusing primarily on individual judgment. Politics often play an important role in repair and rehab decisions at this level.
Cycles are a part of the low level of sophistication. The approach assumes that cyclic variables such as traffic loads, materials, and climate affect pavement performance in a constant way. But, with many such variables to consider — one often affecting another
— the situation becomes too complex for the simple databases used at this level of sophistication.
Public policy financial options are equally simple — a 10% overlay rate over a 10-yr. period, for instance.
The database organizes collected data, but cannot account for quality of data or major changes in road use that render historical data inaccurate.
Ranking is the next logical step, Polhill says. To easily and quickly communicate about the masses of data, it is converted into various indexes. These allow simple ranking, moving the technology a further step.
An expert system is of medium sophistication, too. It converts PM data to a needs list using a simple decision tree. In other words, if condition A exists, treatment X is applied. Cost extensions are possible.
Expert systems and the resulting needs lists present new problems. The needs are often impossible to fund. At the next higher level of PM sophistication, software measures the cost benefits of each possible action. This allows agencies to spend money where it will be most effectively used.
Today’s leading-edge level of PM sophistication focuses on performance prediction. ‘Although many companies claim their system has this capability, few do,” Polhill says.
The missing consideration and benefit/cost and cost-effectiveness analysis is the time dimension. Because every pavement structure has a different performance curve, two pavements with the same index and calling for the same treatment may produce different benefits in time. One on a steep downward performance curve may quickly fail and need more expensive treatment than a road with a flat performance curve.
A leading-edge PMS uses reliable models and manipulates and maintains increasing volume of highly technical data to help form the most effective policy. The system links with various tools such as linear programming, decision theory, regression analysis, and dynamic programming to optimize the decisions made.