PUBLIC WORKS MAGAZINE, October 1987
DENNIS POLHILL, P.E.
Mr. Polhill is vice president with Pavement Management Systems, Denver, Colorado
DATA are critical to a pavement management system, and there are three categories of field performance measures that are important. In order of significance for network level pavement management they are: roughness, structural and visual distress data. A fourth category — safety or skid resistance data — isconsidered on occasion.
Roughness is the parameter of most interest to road users because it costs a user much more money in operating costs to drive on rough roads than on smooth ones. Any acceleration or force imparted to the riders of a vehicle are contributing factors to the driver’s perception of road roughness. To define a pavement roughness function completely, some evaluation of the roughness of the entire surface area of the pavement is required. For most purposes this roughness can be divided into three components of pavement alignment: transverse, longitudinal, and horizontal variations of alignment. Anyone or combination of these variables can make a road appear rough.
The wavelength of the roughness is also important. Vehicle suspension systems and speeds determine which wavelengths are important. Obviously, an aircraft taking off at 200 mph will feel a different roughness than a truck at 55 mph, an automobile at 25 mph, or a bicycle. Consequently, vehicle speeds and types should be considered in assessment of roughness.
Structure is the parameter of greatest importance to the pavement management engineer. A pavement’s performance will deteriorate more quickly because of poor strength than from any other design parameter. An important component of structure is traffic loadings, which are typically considered part of the structural component. A strong pavement with light traffic loads will perform longer than a weak pavement with heavy traffic loads.
Structure is a key variable in how pavements perform. The length and shape of pavement performance curves are significantly affected by pavement strength. Strength information is essential for accurate performance prediction.
Last But Not Least
Visual distress data is the third most important category of data to include in a pavement management system. Distresses are the symptoms of poor performance. Visual distress data are a fruit salad; a combination of many different and unrelated distresses such as raveling, bleeding, cracking, and distortion. Visual distress rating systems in use today vary with regard to the number of distresses used. Some use as few as 2 distresses and others use over 35.
Visual distresses are the result of a combination of variables acting on a pavement. Raveling, bleeding, transverse cracking, and longitudinal cracking are each caused by different sets of variables. Thus, the components of a visual distress index are largely unrelated.
Because visual distresses are caused by unrelated factors, their rates of change are not related. Visual distress data collected over a period of time tend to have significant scatter when plotted on a graph. It is very difficult to draw a curve through visual distress data. The statistical correlation of curves of different visual distresses is typically quite low. Additionally, the weighting factors used to combine distress into an index are important. By changing weighting factors, the shape of a visual distress curve can change dramatically.
Other factors of interest in considering pavement management data are the cost of data collection and the shelf-life of data. Table 1 illustrates the relative costs of various types of data and the annual cost to maintain a current database.
Table 1 – Pavement Management Data Costs
|Importance||Shelf Life||Initial Survey Cost||Cost per year of Useful Value|
|Visual Distress||3||1-3 years||Moderate to High||High|
Table 1 represents broad ranges. Obviously, there are many variables that affect actual costs. Some cost variables relate to thoroughness or amount of detail data collected, type of equipment used, numbers of tests, and depth of data analysis.
Many agencies that enter into pavement management do so without thoroughly considering the implications of their initial decisions. A visually based system trades off prediction model precision and long term data collection costs in favor of lower initial database setup costs.
The wisdom of visual-only based systems is certainly debated. However, visual-only systems have value. This isparticularly true with agencies that have no existing pavement management system.
Pavement management holds enormous potential savings for agencies. The most serious error an agency can make is not to have a pavement management system. Even a poor pavement management system will save more than the cost of its set up and operation.