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Efficient surrogate method for predicting pavement response to various tire configurations

  • Engineering Applications of Neural Networks
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Abstract

A computationally efficient surrogate model was developed based on artificial neural networks (ANN) to investigate the effect of the new generation of wide-base tires on pavement responses. Non-uniform tire contact stress measurements were obtained using a stress-in-motion instrument. The measured 3-D contact stresses were applied on two extreme 3-D flexible pavement finite element models representing low-volume (thin) and high-volume (thick) roads. Eleven critical pavement responses were modeled at two different material properties input levels—detailed and simplified—depending on data availability. The results rendered by the ANN surrogate models were highly accurate with average prediction error less than 5 % and R-square values higher than 0.95. In addition, two sensitivity analyses were performed to investigate the variables effect on pavement responses. It was found that the type of tire (wide-base vs. dual tire assembly) is more influential than the inflation pressure on pavement responses. However, the tire inflation pressure seemed to have a significant effect on near-surface responses. The developed models were incorporated into a tool to assist designers and engineers in investigating the effect of the pavement responses of wide-base versus dual tire assembly under typical loading conditions and pavement structures.

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Acknowledgments

The financial support provided by the Federal Highway Administration (FHWA) is greatly appreciated. This paper discusses some results from an ongoing pooled fund study, DTFH61-11-C-00025: The Impact of Wide-Base Tires on Pavement—A National Study. The project is being conducted in cooperation with the Illinois Center for Transportation, the U.S. Department of Transportation, Federal Highway Administration, Rubber Manufacturers Association, and the following state departments of transportation: Illinois, Minnesota, Montana, New York, Ohio, Oklahoma, Texas, and Virginia. The authors would also like to acknowledge the assistance of Jaime Hernandez and Angeli Gamez. This project is managed by Eric Weaver, who has been instrumental in providing directions and input. The contents of this paper reflect the view of the authors, who are responsible for the facts and the accuracy of the data presented herein. The contents do not necessarily reflect the official views or policies of the Illinois Center for Transportation, the Federal Highway Administration, or the participating partners. This paper does not constitute a standard, specification, or regulation.

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Authors and Affiliations

  1. Civil and Environmental Engineering Department, Illinois Center for Transportation, University of Illinois at Urbana-Champaign, 1611 Titan Dr, Rantoul, IL, 61866, USA

    Mojtaba Ziyadi & Imad L. Al-Qadi

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  1. Mojtaba Ziyadi
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  2. Imad L. Al-Qadi
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Correspondence to Mojtaba Ziyadi.

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Ziyadi, M., Al-Qadi, I.L. Efficient surrogate method for predicting pavement response to various tire configurations. Neural Comput & Applic 28, 1355–1367 (2017). https://doi.org/10.1007/s00521-016-2442-1

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