The 2002 Status of the Nation's Highways, Bridges, and Transit: Conditions and Performance Report to Congress indicates that $594 billion in highway expenditures, exclusive of those related to bridges, will be required nationwide between 2001 and 2020 just to preserve the physical condition of the existing highway system. Changes in truck size and weight (TS&W) policy could have a major impact on pavement quality and performance characteristics and, therefore, future investment requirements.
As discussed in Chapter 3, uniform TS&W regulations in the Western States would change the distribution of freight to the more productive LCVs, and thus change the configuration characteristics of trucks traveling over the region's roadway pavements. Pavement wear occurs from load related, as well as non-load related factors. Non-load related factors include current pavement condition and environmental factors such as climate, subsoil type, and drainage.
Load related factors include axle weight, number, and width of tires on the axle, and spacing between axles. Because pavement deterioration increases with axle weight, the number of axle loadings and the spacing within axle groups, changes to the distribution of truck configurations traveling in the region could affect pavement wear.
The primary determinant of vehicle induced pavement wear severity is the load carried on axles and axle groups. Gross vehicle weight (GVW), in and of itself, has little impact on how much pavement wear a vehicle will cause. The number of axles and spacings between axles on the vehicle, and how the GVW is distributed over those axles, are the key determinants of the amount of stress a vehicle applies to the pavement.
Axle groups, such as tandems or tridems, distribute the load along the pavement, allowing greater weights to be carried and resulting in the same or less pavement distress than that occasioned by a single axle at a lower weight. The spread between two consecutive axles also affects pavement life or performance - the greater the spread, the more each axle in a group acts as a single axle. For example, a spread of 9 to 10 feet results in no apparent interaction of one axle with another, and each axle is considered a separate loading for pavement impact analysis or design purposes. Conversely, the closer the axles in a group are, the greater the weight they may carry without increasing pavement deterioration beyond that occasioned by the same number of single axles.
A common metric used to measure the amount of stress an axle or group of axles applies to pavement is the Equivalent Single Axle Load (ESAL). The ESAL unit expresses the amount of pavement stress occasioned by an 18,000 pound axle. Although ESALs were not used as the basis for estimating pavement impacts for this Study,12 they are widely understood by those concerned with the pavement impacts of TS&W scenarios, and provide a convenient metric for comparisons of pavement stresses between vehicles of different weights and axle configurations.
Table IV-1 shows payload tons per ESAL for study configurations at key weights, indexed to a 5-axle tractor-semitrailer weighing 80,000 pounds. Configurations at weights with an index number over 100 carry more payload per unit of pavement damage than the 5-axle tractor semitrailer, those with an index under 100 carry less.
|Configuration - Axles1||GVW||Payload Tons Per ESAL (indexed: 80,000 pound CS-5=100)|
(10 inch thickness)
(structural number 5,
terminal PSI 2.5)
|5-axle Tractor Semitrailer||80,0001||100||100|
|6-axle Tractor Semitrailer||85,000||145||160|
|7-axle Tractor Semitrailer||85,000 1||238||290|
|5-axle Double-Trailer Combination||80,000 1||126||72|
|6-axle Double-Trailer Combination||80,000||221||148|
|7-axle Double-Trailer Combination||95,000 1||202||158|
|8-axle Double-Trailer Combination||95,000 1||255||263|
|9-axle Double-Trailer Combination||95,000 1||380||333|
|7-axle Triple-Trailer Combination||90,000 1||261||142|
|8-axle Triple-Trailer Combination||90,000 1||369||229|
The ESAL calculations are based on hypothetical highway sections - rigid pavement of 10 inch thickness and flexible pavement with a structural number of 5 and terminal pavement serviceability index value of 2.5.13 Actual ESALs (and LEFs) vary by several factors including pavement type, thickness and sub-grade type, as well as the distribution of GVW over the vehicle's axle groups. These theoretical values show relative relationships among axle load, axle type, pavement type, and pavement characteristics, but they do not show the influence of environmental factors and thus should not be used in specific applications.
The payload per ESAL measure reflects the volume of freight that can be moved per unit of pavement distress relative to the distress occasioned by a 5-axle tractor semitrailer at the Federal weight limit of 80,000 pounds. As the table shows, all of the configurations considered for uniform size and weight throughout the region, with the exceptions of the 7-axle double and 7-axle triple on flexible pavement, are less damaging than the 5-axle tractor semitrailer comparison vehicle. In the case of the 7-axle triple, it is important to note that this configuration is expected to divert traffic from the 5-axle double configuration, which is more damaging at 80,000 pounds than the 7-axle triple is at 110,000.
The National Pavement Cost Model (NAPCOM) was used to estimate potential pavement impacts resulting from changes in vehicle size and weight limits in the region.14 NAPCOM is a complex simulation model initially developed in 1992 and subsequently improved for use in the 1997 Highway Cost Allocation Study (HCA Study) and 2000 CTS&W Studies. The key output of NAPCOM for truck size and weight analysis is the change in overall pavement improvement needs under alternative size and weight policy scenarios. The model is sensitive to different weight policies, depending on truck configuration, including the number of axles. Changes in pavement rehabilitation costs between successive runs of NAPCOM with changed assumptions about the distribution of freight among truck configurations and operating weights are attributed to specific groups of vehicles.
Axle load and frequency information have been estimated based on vehicle-miles-of-travel (VMT) information for various classes of highway vehicles from the 1997 HCA Study. HCA Study's VMT estimates by vehicle class and weight group have been updated and modified according to the policy options evaluated in this Study as analyzed in the freight distribution phase of the study described in Chapter III.
Scenario pavement impacts are measured by comparing NAPCOM results from a scenario run of the model against those from a base case run of the model. A base case run of the model uses the distribution of VMT by vehicle class and operating weight under existing TS&W regulations to estimate the level of pavement damage under the assumption of no change to TS&W policy. A scenario run of the model uses the VMT distribution estimated in the freight distribution phase of the study to estimate the level of pavement damage under the assumed change to TS&W policy. The difference between the scenario result and the base case result is the impact of the policy change.
Table IV-2 shows the impacts for the two scenarios analyzed in this study. Neither scenario changed pavement cost nor the cost share attributable to the study vehicles significantly over the 20-year time horizon modeled by NAPCOM. The low cube scenario's 9.5 percent decline of study vehicle VMT decreases pavement cost by $258 million, 0.4 percent, over the 20-year period. The 25.5 percent decline in vehicle VMT in the high cube scenario decreases pavement cost by $2,787 million, or 4.2 percent.
The relatively small percent change in pavement cost seen in the two scenarios is not surprising. Neither scenario proposes changes to axle weight limits, the primary driver in pavement damage. Overall, the increased size and weight of each scenario moves the same amount of freight ton-miles generating essentially the same axle load equivalents as the base case, but with fewer VMT.
|Analytical Case||VMT in Region (millions)||Impacts (millions of 2000 $)|
|All Highway Vehicles||Study Vehicles||Annual Pavement Cost||20-Year Pavement Cost||Change from Base Case||Study Vehicles' Share|
|2010 Base Case||381,801||18,823||$3,297||$65,934||--||76.0%|
|Low Cube Scenario||380,008||17,029||$3,284||$65,676||-0.4%||75.4%|
|High Cube Scenario||377,006||14,028||$3,157||$63,147||-4.2%||73.0%|