ContactGAN development – prediction of tire-pavement contact stresses using a generative and transfer learning model

References

• Abadi, M., et al., 2016. Tensorflow: A system for large-scale machine learning. 12th {USENIX} Symposium on Operating Systems Design and Implementation ({OSDI}, 16, 265–283.
   Google Scholar
• Akila Agnes, S., Anitha, J., and Dinesh Peter, J., 2020. Automatic lung segmentation in low-dose chest CT scans using convolutional deep and wide network (CDWN). Neural Computing and Applications, 32 (20), 15845–15855.
   Web of Science ®Google Scholar
• Assogba, O. C., et al., 2020. Finite-element simulation of instrumented asphalt pavement response under moving vehicular load. International Journal of Geomechanics, 20 (3), 04020006.
   Web of Science ®Google Scholar
• Bhaduri, A., Gupta, A., and Graham-Brady, L, 2021. Stress field prediction in fiber-reinforced composite materials using a deep learning approach. ArXiv Preprint ArXiv:2111.05271.
   Google Scholar
• Chen, J., Wang, H., and Xie, P, 2020. Finite element modeling of mechanical responses of concrete pavement with partial depth repair. Construction and Building Materials, 240, 117960.
   Web of Science ®Google Scholar
• Chun, S., et al., 2015. Evaluation of interlayer bonding condition on structural response characteristics of asphalt pavement using finite element analysis and full-scale field tests. Construction and Building Materials, 96, 307–318.
   Web of Science ®Google Scholar
• Creswell, A., et al., 2018. Generative adversarial networks: an overview. IEEE Signal Processing Magazine, 35 (1), 53–65.
   Web of Science ®Google Scholar
• Els, P. S., et al., 2016. Comparison of tire footprint measurement techniques. ASME 2016 International Design Engineering Technical Conferences and Computers and Information in Engineering Conference, V003T01A027.
   Google Scholar
• Gruber, P., and Sharp, R. S, 2012. Shear forces in the contact patch of a braked-racing tyre. Vehicle System Dynamics, 50 (12), 1761–1778.
   Web of Science ®Google Scholar
• Gungor, O. E., and Al-Qadi, I. L, 2018. Developing machine-learning models to predict airfield pavement responses. Transportation Research Record: Journal of the Transportation Research Board, 2672 (29), 23–34.
   Web of Science ®Google Scholar
• Hernandez, J. A., et al., 2017. Numerical prediction of three-dimensional tire-pavement contact stresses. No. ICT-17-004. 2015.
   Google Scholar
• Hernandez, J. A., and Al-Qadi, I. L, 2016. Hyperelastic modeling of wide-base tire and prediction of its contact stresses. Journal of Engineering Mechanics, 142 (2), 04015084.
   Web of Science ®Google Scholar
• Ivanov, V, 2010. Analysis of tire contact parameters using visual processing. Advances in Tribology, 2010, 1–11.
   Google Scholar
• Jayme, A., and Al-Qadi, I. L, 2021. Thermomechanical coupling of a hyper-viscoelastic truck tire and a pavement layer and its impact on three-dimensional contact stresses. Transportation Research Record, 2675 (11), 359–372.
   Web of Science ®Google Scholar
• Jiang, H., et al., 2021. StressGAN: a generative deep learning model for 2D stress distribution prediction. Journal of Applied Mechanics, 88 (5), 051005.
   Web of Science ®Google Scholar
• Kingma, D. P., and Ba, J., 2015. Adam: a method for stochastic optimization. The 3rd International Conference for Learning Representations, San Diego.
   Google Scholar
• Liu, X., et al., 2019. Evaluation of vehicle braking performance on wet pavement surface using an integrated tire-vehicle modeling approach. Transportation Research Record: Journal of the Transportation Research Board, 2673 (3), 295–307.
   Web of Science ®Google Scholar
• Liu, X., and Al-Qadi, I. L, 2021a. Development of a simulated three-dimensional truck model to predict excess fuel consumption resulting from pavement roughness. Transportation Research Record, 2675 (9), 1444–1456.
   Web of Science ®Google Scholar
• Liu, X., and Al-Qadi, I. L, 2021b. Three-dimensional tire-pavement contact stresses prediction by deep learning approach. International Journal of Pavement Engineering, Advance Online Publication, 1–12.
   Web of Science ®Google Scholar
• Liu, X., and Al-Qadi, I, 2022. Integrated vehicle–tire–pavement approach for determining pavement structure–induced rolling resistance under dynamic loading. Transportation Research Record, 2676 (5), 398–409.
   Web of Science ®Google Scholar
• Moazami, D., and Muniandy, R, 2018. Determination of relative damage of asphalt pavement from reduced tire contact area. International Journal of Pavement Engineering, 19 (6), 553–563.
   Web of Science ®Google Scholar
• Nie, Z., Jiang, H., and Kara, L. B, 2020. Stress field prediction in cantilevered structures using convolutional neural networks. Journal of Computing and Information Science in Engineering, 20 (1), 011002.
   Web of Science ®Google Scholar
• Okte, E., and Al-Qadi, I. L, 2021. Prediction of flexible pavement 3-D finite element responses using Bayesian neural networks. International Journal of Pavement Engineering, Advance Online Publication, 1–11.
   Web of Science ®Google Scholar
• Oubahdou, Y., et al., 2021. Effect of the tire – pavement contact at the surface layer when the tire is tilted in bend. Construction and Building Materials, 305, 124765.
   Web of Science ®Google Scholar
• Pan, S. J., and Yang, Q, 2010. A survey on transfer learning. IEEE Transactions on Knowledge and Data Engineering, 22 (10), 1345–1359.
   Web of Science ®Google Scholar
• Pau, M., Leban, B., and Baldi, A, 2008. Ultrasonic measurements of contact area and pressure distribution of a pneumatic tire on a rigid surface. Tire Science and Technology, 36 (1), 43–62.
   Google Scholar
• Said, I. M., et al., 2019. Structural and environmental impact of new-generation wide-base tires in New Brunswick, Canada. Road Materials and Pavement Design, 21 (7), 1968–1984.
   Web of Science ®Google Scholar
• Shakiba, M., et al., 2017. Introducing realistic tire–pavement contact stresses into Pavement Analysis Using Nonlinear Damage Approach (PANDA). International Journal of Pavement Engineering, 18 (11), 1027–1038.
   Web of Science ®Google Scholar
• Soekhoe, D., et al., 2016. On the impact of data set size in transfer learning using deep neural networks. In: H. Boström, et al, ed. Advances in intelligent data analysis XV. vol. 9897, 50–60. Stockholm, Sweden: Springer International Publishing.
   Google Scholar
• Wang, H., et al., 2020. Flexible pavement response analysis under dynamic loading at different vehicle speeds and pavement surface roughness conditions. Journal of Transportation Engineering, Part B: Pavements, 146 (3), 04020040.
   Web of Science ®Google Scholar
• Wang, H., and Al-Qadi, I. L, 2009. Combined effect of moving wheel loading and three-dimensional contact stresses on perpetual pavement responses. Transportation Research Record, 2095 (1), 53–61.
   Google Scholar
• Wang, H., Al-Qadi, I. L., and Stanciulescu, I, 2012. Simulation of tyre–pavement interaction for predicting contact stresses at static and various rolling conditions. International Journal of Pavement Engineering, 13 (4), 310–321.
   Web of Science ®Google Scholar
• Wang, H., Al-Qadi, I. L., and Stanciulescu, I, 2014. Effect of surface friction on tire–pavement contact stresses during vehicle maneuvering. Journal of Engineering Mechanics, 140 (4), 04014001.
   Web of Science ®Google Scholar
• Yang, Z., Yu, C.-H., and Buehler, M. J, 2021. Deep learning model to predict complex stress and strain fields in hierarchical composites. Science Advances, 7, 15.
   Web of Science ®Google Scholar
• Zhu, S., et al., 2017. Numerical study of tire hydroplaning based on power spectrum of asphalt pavement and kinetic friction coefficient. Advances in Materials Science and Engineering, 2017, 1–11.
   Web of Science ®Google Scholar
• Ziyadi, M., and Al-Qadi, I. L, 2017. Efficient surrogate method for predicting pavement response to various tire configurations. Neural Computing and Applications, 28 (6), 1355–1367.
   Web of Science ®Google Scholar