Field evaluation and statistical analysis of the dynamic response of semi-rigid pavement under full-scale moving truck load

Abstract

Mechanistic-based pavement distress prediction models or transfer functions are often used to assess damage or predict the service life of the pavement structure. The reliability of pavement structure design and performance predictions is highly dependent on the accuracy of the critical responses. The present study provides an experimental and statistical evaluation of the relationship between influencing factors such as traffic speed, vehicle axle load, and seasonal temperature, and the pavement mechanical response. In addition, the level of impact of each influencing factor and their two-way interaction or coupling effect on the pavement critical response was investigated. The results of the experimental investigation and the statistical analysis of the pavement mechanical response were presented, analyzed, and discussed. Besides, by combining the application of the orthogonal array test and multivariate regression analysis, the current study proposes a prediction model to predict the critical stress response at the bottom of the semi-rigid layer.

KEYWORDS:

• Field response evaluation
• full-scale pavement test track (RIOHTRACK)
• dynamic instrumentation
• semi-rigid base asphalt pavement
• statistical analysis
• critical response prediction model

Acknowledgements

The authors gratefully acknowledge the technical support (equipment, software, etc.) provided by the Key Laboratory of Road Structure and Materials, Research Institute of Highway Ministry of transport, Beijing, China, and Harbin Institute of Technology. The authors also express their sincere gratitude to all the people involved in this research project. Finally, the authors would like to thank the editors and reviewers of this work for their useful comments and improving this work.

Disclosure statement

No potential conflict of interest was reported by the author(s).

Data availability statement

The data supporting the plots and tables in this paper, as well as other findings of this study, can be retrieved from the corresponding author upon request.

Additional information

Funding

This work was funded by the National Key of Research and Development Plan [grant number 2016YFE0202400], the Natural Science Foundation of China [grant number U163320005], the Open Research Fund Program of Guangdong Key Laboratory of Urban Informatics [grant numbers SZU51029202005] and National Natural Science Foundation of China joint fund for regional innovation and development [grant numbers U20A20315].