The Effect of Truck Permitting Policy on US Bridge Loading

Accurate estimates of characteristic traffic load effects are greatly beneficial in prioritizing bridges for repair and replacement. The extreme loading events likely to cause characteristic load effects are dominated by very heavy permit trucks. As these trucks are significantly heavier and are subject to stricter controls than standard trucks, they may be treated separately from the general truck population. This paper examines truck loading at 3 Weigh-in-Motion (WIM) sites in the United States and develops filtering rules to identify permit trucks based on the available axle spacing information. Once all trucks have been classified, permit and standard trucks are examined separately to get a better understanding of their importance for bridge loading. A Monte Carlo simulation model is developed which allows permit trucks to be simulated independently of the standard truck population. The truck simulation model is used to investigate the changes in characteristic load effects resulting from changes in permit-issuing policy. normal trucks and routine permits, and special design vehicles, which are trucks above the limits for routine permits that require individual analysis. It can be argued that standard vehicles are not well controlled and should have a higher factor of safety or return period. Permit vehicles, on the other hand, are subject to a greater degree of control which may justify a lesser factor of safety or return period. Previous work has shown that characteristic load effects are caused predominantly by permit vehicles (Enright & OBrien, 2012). In this paper, WIM data from three states in the United States are filtered to separate apparent permit vehicles from standard vehicles. The two data subsets – apparent standard and apparent permit – are examined separately. 2. WEIGH-IN-MOTION DATABASE For this study, data from three WIM sites in the United States is analyzed. This WIM data has been collected as part of a follow-on project of the Federal Highway Administration’s Long Term Pavement Performance (LTPP) program for traffic data collection. In the early years of the LTPP, traffic data was collected with inconsistent quality control measures (Walker & Cebon 2012). A plan was developed in 1999 under which, among other things, quality control was improved and implemented centrally. This led to a significant improvement in WIM data reliability. Since 2003, ‘research quality’ WIM data is being collected at 28 of the Specific Pavement Studies LTPP sites. Research Quality is, for this purpose, defined as 210 days of data per year of known calibration, meeting LTPP’s accuracy requirements for steering and tandem axles, gross vehicle weight, vehicle length, speed, and axle spacing. The recommended WIM technologies include bending plate, load cell, and quartz sensors. The three sites used here all belong to this group of research-quality WIM sites. Table 1 shows the details of the WIM sites used in this work. At all sites, only one lane in one direction is measured, that being the slow lane. All data was collected between 1 st January 2008 and 31 st December 2011. Table 1. Details of WIM sites Site Road Weekdays of Data Average Trucks/day Arizona I-10 East 996 4988 Illinois I-57 North 1008 3139 Indiana US-93 North 87