(Received 24 April 2008; accepted 2 March 2010)
Published Online: 2010
| ||Format||Pages||Price|| |
|PDF Version||7||$25||  ADD TO CART|
This paper provides a summary of the weigh-in-motion (WIM) calibration practices used by state highway and load enforcement agencies in the United States. The detailed statistical data presented were collected through a web-based survey questionnaire. It covers three common WIM calibration practices, namely utilizing multiple passes of test trucks, utilizing traffic stream vehicles of known static weight, and employing only WIM data quality control (QC) techniques. To put the actual practice in perspective, an overview is provided of the current WIM calibration standard (ASTM E1318-02) and the new provisional standard for quantifying pavement roughness at the approach to WIM systems (AASHTO MP 14-05). Most agencies use a combination of two or more of these methods for WIM system calibration. The majority of agencies uses WIM data QC on a routine basis and they resort to one of the other two calibration methods when WIM data quality deteriorates. Test truck calibration typically involves one or two Class 9 trucks running at several speeds. Few of these agencies, however, perform actual pavement roughness measurements on the approach to the WIM sites. Agencies that use traffic stream vehicles of known static weight for WIM calibration obtain static weights manually using permanent static scales. The method involves up to 100 trucks selected by class, speed or both class and speed. Agencies use a variety of traffic elements and formulas for computing calibration factors. Similarly, a variety of traffic data element errors are computed and various approaches are used for computing calibration factors. In the light of these findings, the paper provides a number of recommendations for improving current WIM calibration practices.
Papagiannakis, A. T.
P.E.Professor and Dept. Chair, Dept. of Civil and Environmental Engineering, Univ. of Texas-San Antonio, San Antonio, TX
Stock #: JTE101836