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The Arizona Department of Transportation (ADOT) plans to install new weigh‐in‐motion (WIM) stations with either piezo-polymer or piezo-quartz sensors. Recognizing some limitations of WIM sensor technologies, ADOT sponsored this study to ensure the accuracy of the future WIM data collection. The project tasks included (1) reviewing other highway agencies’ practices related to WIM data quality assurance through literature review and a survey; (2) developing a guidebook of clear recommendations for managing WIM installation, calibration, maintenance, and data quality assurance; and (3) developing a research report with recommendations on how to achieve successful implementation of a WIM program. Through reviewing available literature and surveying selected highway agencies, the project team determined that the piezo-quartz sensors perform much better than the piezo-polymer sensors due to their consistent reliability, reduced calibration requirements, and relative temperature insensitivity. With proper installation, piezo-quartz WIM sensors should provide accurate axle and truck weight measurements in Arizona. Findings also indicated that piezo-polymer sensors should perform well in Arizona for vehicle classification, traffic volume, and speed studies, but not for weight data collection. This is due to the temperature sensitivity of piezopolymer sensors and to the limitations of auto-calibration and temperature compensation technologies in environments where pavements undergo rapid day-to-night temperature changes and are subjected to high seasonal temperatures. Piezo-polymer sensor use with an auto-calibration feature for weight measurements should be evaluated on a case-by-case basis. Using findings from the literature review and the successful WIM practices survey, the research team developed a guidebook with recommendations and procedures for WIM site selection and qualification, installation, calibration, maintenance, data quality assurance, and personnel needed to support ADOT’s WIM program. These recommendations are specific to WIM systems that use piezo-quartz sensors and piezo-polymer sensors. The guidebook is included as Chapter 4 of this final report.
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.
Weigh-in-motion (WIM) is a process of measuring the dynamic tire forces of a moving vehicle and estimating the corresponding tire loads of the static vehicle. This collection of lectures from the International Conference on Weigh-in-Motion details applications such as: collection of statistical traffic data, support of commercial vehicle enforcement, roadway and bridge cost allocation, and traffic management.
Inspired from the legacy of the previous four 3DFEM conferences held in Delft and Athens as well as the successful 2018 AM3P conference held in Doha, the 2020 AM3P conference continues the pavement mechanics theme including pavement models, experimental methods to estimate model parameters, and their implementation in predicting pavement performance. The AM3P conference is organized by the Standing International Advisory Committee (SIAC), at the time of this publication chaired by Professors Tom Scarpas, Eyad Masad, and Amit Bhasin. Advances in Materials and Pavement Performance Prediction II includes over 111 papers presented at the 2020 AM3P Conference. The technical topics covered include: - rigid pavements - pavement geotechnics - statistical and data tools in pavement engineering - pavement structures - asphalt mixtures - asphalt binders The book will be invaluable to academics and engineers involved or interested in pavement engineering, pavement models, experimental methods to estimate model parameters, and their implementation in predicting pavement performance.
The objectives of this study were to review and analyze current procedures in order to implement a process for collecting and analyzing weigh-in-motion (WIM) data to insure an adequate and accurate representation of weights of vehicles using Kentucky's roadways. A literature review of WIM data collection equipment, practices and procedures indicated that a range of options are available and used by other agencies. Piezoelectric cable detection systems were most frequently used and provided adequate accuracy, if attention is given to monitoring and calibration. An overall assessment of Kentucky's WIM data collection program resulted in recommendations for: 1) increased use of cell modems for more efficient data download, 2) attention to quality control of data with a routine program of polling sites and monitoring consistency of front-axle weights, 3) expansion of WIM data collection program to capture a wider range of functional class coverage of truck traffic, 4) attention to weight data collection on coal-hauling routes, 5) consideration of using static weigh station data to increase coverage of truck weight monitoring, 6) initiation of a data collection plan to capture sufficient data to develop length-based classification factors, 7) continued review and evaluation of new software that has the capability of increasing the efficiency and accuracy of WIM data processing, and 8) evaluation of the cost-effectiveness of expanded and accurate WIM data collection and the impact on pavement thickness designs.
The objectives of this study were to review and analyze current procedures in order to implement a process for collecting and analyzing weigh-in-motion (WIM) data to insure an adequate and accurate representation of weights of vehicles using Kentucky's roadways. A literature review of WIM data collection equipment, practices and procedures indicated that a range of options are available and used by other agencies. Piezoelectric cable detection systems were most frequently used and provided adequate accuracy, if attention is given to monitoring and calibration. An overall assessment of Kentucky's WIM data collection program resulted in recommendations for: 1) increased use of cell modems for more efficient data download, 2) attention to quality control of data with a routine program of polling sites and monitoring consistency of front-axle weights, 3) expansion of WIM data collection program to capture a wider range of functional class coverage of truck traffic, 4) attention to weight data collection on coal-hauling routes, 5) consideration of using static weigh station data to increase coverage of truck weight monitoring, 6) initiation of a data collection plan to capture sufficient data to develop length-based classification factors, 7) continued review and evaluation of new software that has the capability of increasing the efficiency and accuracy of WIM data processing, and 8) evaluation of the cost-effectiveness of expanded and accurate WIM data collection and the impact on pavement thickness designs.