Estimation of Weigh-in-Motion System Accuracy from Axle Load Spectra Data
Publication: Airfield and Highway Pavements 2021
ABSTRACT
Inaccurate weigh-in-motion (WIM) data may result in significant over-or under-estimation of the pavement performance period, leading to over-or under-design pavements. Therefore, the data collected at WIM systems must be accurate and consistent. The paper presents an approach to estimate WIM system accuracy based on axle load spectra attributes [normalized axle load spectra (NALS) shape factors]. This alternative approach to assess WIM system accuracy is needed to characterize temporal changes in WIM data consistency. The WIM error data collected before and after calibration were related to NALS shape factors for Class 9 vehicles. This analysis’s main objective is to determine WIM system errors based on axle loading without physically performing equipment calibration. This approach can help highway agencies select optimum timings for routine maintenance and calibration of WIM equipment without compromising its accuracy. The results show that the WIM accuracy for the tandem axle (TA) can be estimated with TA NALS shape factors with an acceptable degree of error for bending plate (BP) and quartz piezo (QP) sensors. Further, the results obtained using different statistical methods for model development and validation show reasonable goodness of fit. The use of NALS to estimate the TA WIM accuracy can save a significant amount of time and resources, which are usually spent on equipment calibrations every year.
Get full access to this article
View all available purchase options and get full access to this chapter.
REFERENCES
ASTM. (2009). Standard Specification for Highway Weigh-In-Motion (WIM) Systems with User Requirements and Test Methods E 1318-09. 2007 Annual Book of ASTM Standards. Edited by ASTM Committee E17-52 on Traffic Monitoring. ASTM International, USA.
Burnos, P., Gajda, J., and Sroka, R. (2018). “Accuracy criteria for evaluation of Weigh-in-Motion Systems.” Metrology and Measurement Systems, 25(4).
Davis, J., and Goadrich, M. “The relationship between Precision-Recall and ROC curves.” Proc., Proceedings of the 23rd international conference on Machine learning, 233-240.
Gajda, J., Sroka, R., and Żegleń, T. (2007). “Accuracy analysis of WIM Systems Calibrated Using Pre-Weighed Vehicles Method.” Metrology and Measurement Systems, 14(4), 517-527.
Haider, S. W., and Harichandran, R. S. (2007). “Relating Axle Load Spectra to Truck Gross Vehicle Weights and Volumes.” ASCE Journal of Transportation Engineering, 133(12), 696-705.
Haider, S. W., and Masud, M. M. “Effect of moisture infiltration on flexible pavement performance using the AASHTOWare Pavement-ME.” Proc., Proc., Advances in Materials and Pavement Prediction: Papers from the International Conference on Advances in Materials and Pavement Performance Prediction (AM3P 2018), April 16-18, 2018, Doha, Qatar.
Haider, S. W., and Masud, M. M. “Accuracy Comparisons Between ASTM 1318-09 and COST-323 (European) WIM Standards Using LTPP WIM Data.” Proc., Proceedings of the 9th International Conference on Maintenance and Rehabilitation of Pavements—Mairepav9, Springer, 155-165.
Haider, S. W., and Masud, M. M. (2020). “Use of LTPP SMP Data to Quantify Moisture Impacts on Fatigue Cracking in Flexible Pavements [].” United States. Federal Highway Administration. Office of Research ….
Haider, S. W., Masud, M. M., and Chatti, K. (2020). “Influence of moisture infiltration on flexible pavement cracking and optimum timing for surface seals.” Canadian Journal of Civil Engineering, 47(5), 487-497.
Haider, S. W., Masud, M. M., and Musunuru, G. (2018). Effect of Water Infiltration Through Surface Cracks on Flexible Pavement Performance.
Haider, S. W., Masud, M. M., Selezneva, O., and Wolf, D. J. (2020). “Assessment of Factors Affecting Measurement Accuracy for High-Quality Weigh-in-Motion Sites in the Long-Term Pavement Performance Database.” Transportation Research Record, 2674(10), 269-284.
Jacob, B. (2000). “Assessment of the Accuracy and Classification of Weigh-in-Motion Systems Part 1: Statistical Background.” International Journal of Heavy Vehicle Systems, 7(2-3), 136-152.
Masud, M. M. (2018). Quantification of Moisture Related Damage in Flexible and Rigid Pavements and Incorporation of Pavement Preservation Treatments in AASHTOWare Pavement-ME Design and Analysis, Michigan State University.
Masud, M. M. IRF GLOBAL R2T Conference.
Masud, M. M., and Haider, S. W. (2020). Long-Term Pavement Performance: International Data Analysis Contest, 2017–2018 Graduate Category: Use of LTPP SMP Data to Quantify Moisture Impacts on Fatigue Cracking in Flexible Pavements.
Masud, M. M., Haider, S. W., Selezneva, O., and Wolf, D. J. “Impact of WIM Systematic Bias on Axle Load Spectra–A Case Study.” Proc., Advances in Materials and Pavement Performance Prediction II: Contributions to the 2nd International Conference on Advances in Materials and Pavement Performance Prediction (AM3P 2020), 27-29 May, 2020, San Antonio, TX, USA, CRC Press, 64.
Papagiannakis, A., Johnston, E., Alavi, S., and Mactutis, J. (2001). “Laboratory and field evaluation of piezoelectric Weigh-in-Motion sensors.” Journal of testing and evaluation, 29(6), 535-543.
Yoo, H.-S., and Kim, Y.-S. (2016). “Development of a crack recognition algorithm from non-routed pavement images using artificial neural network and binary logistic regression.” KSCE Journal of Civil Engineering, 20(4), 1151-1162.
Information & Authors
Information
Published In
Airfield and Highway Pavements 2021
Pages: 378 - 388
Copyright
© 2021 American Society of Civil Engineers.
History
Published online: Jun 4, 2021
Authors
Metrics & Citations
Metrics
Citations
Download citation
If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.