Rutting prediction model for semi-rigid base asphalt pavement based on a data-mechanistic dual driven method

References

• Al-Rub, Abu, et al., 2012. Comparing finite element and constitutive modelling techniques for predicting rutting of asphalt pavements. International Journal of Pavement Engineering, 13 (4), 322–338. doi:10.1080/10298436.2011.566613.
   Web of Science ®Google Scholar
• Assogba, O. C., et al., 2021. Field evaluation and statistical analysis of the dynamic response of semi-rigid pavement under full-scale moving truck load. Road Materials and Pavement Design. Early Access. doi:10.1080/14680629.2021.1987304.
   Google Scholar
• Chatterjee, A., and Tsai, Y.C., 2021. Training and testing of smartphone-based pavement condition estimation models using 3D pavement data. Journal of Computing in Civil Engineering, 34(6). doi:10.1061/(ASCE)CP.1943-5487.0000925.
   Web of Science ®Google Scholar
• Chen, L.L., et al., 2021. Rutting prediction model for semirigid base asphalt pavement based on Hamburg wheel tracking test. International Journal of Geomechanics, 21 (11), 04021215. doi:10.1061/(ASCE)GM.1943-5622.0002194.
   Web of Science ®Google Scholar
• Darabi, M. K., et al., 2011. A thermo-viscoelastic–viscoplastic–viscodamage constitutive model for asphaltic materials. International Journal of Solids and Structures, 48 (1), 191–207. doi:10.1016/j.ijsolstr.2010.09.019.
   Web of Science ®Google Scholar
• Darabi, M.K., et al., 2019. Characterization and validation of the nonlinear viscoelastic-viscoplastic with hardening-relaxation constitutive relationship for asphalt mixtures. Construction and Building Materials, 216, 648–660. doi:10.1016/j.conbuildmat.2019.04.239.
   Web of Science ®Google Scholar
• Fang, M., et al., 2020. Prediction modelling of rutting depth index for asphalt pavement using de-noising method. International Journal of Pavement Engineering, 21 (7), 895–907. doi:10.1080/10298436.2018.1512712.
   Web of Science ®Google Scholar
• Fwa, T. F., Chu, L. J, and Tan, K. H., 2016. Rational procedure for determination of Rut depth intervention level in network-level pavement management. Transportation Research Record: Journal of the Transportation Research Board, 2589, 59–67. doi:10.3141/2589-07.
   Google Scholar
• Gao, J., et al., 2019. Cycling comfort on asphalt pavement: influence of the pavement-tyre interface on vibration. Journal of Cleaner Production, 223, 323–341. doi:10.1016/j.jclepro.2019.03.153.
   Web of Science ®Google Scholar
• Guo, R.H., Fu, D.L., and Sollazzo, G., 2021. An ensemble learning model for asphalt pavement performance prediction based on gradient boosting decision tree. International Journal of Pavement Engineering, 23 (10), 3633–3646. doi:10.1080/10298436.2021.1910825.
   Web of Science ®Google Scholar
• Haddad, A.J., Chehab, G.R., and Saad, G.A., 2021. The use of deep neural networks for developing generic pavement rutting predictive models. International Journal of Pavement Engineering. doi:10.1080/10298436.2021.1942466.
   Web of Science ®Google Scholar
• Han, C. J., et al., 2021b. Application of a hybrid neural network structure for FWD backcalculation based on LTPP database. International Journal of Pavement Engineering. Early Access. doi:10.1080/10298436.2021.1883016.
   Google Scholar
• Han, C.J., et al., 2021c. CrackW-Net: A novel pavement crack image segmentation convolutional neural network. IEEE Transactions on Intelligent Transportation Systems. doi:10.1109/TITS.2021.3095507.
   PubMed Web of Science ®Google Scholar
• Han, C. J., Zhang, W. G., and Ma, T., 2021a. Data cleaning framework for highway asphalt pavement inspection data based on artificial neural networks. International Journal of Pavement Engineering. Early Access. doi:10.1080/10298436.2021.2001813.
   Google Scholar
• Hassini, S., and Guo, Y.P., 2016. Exponentiality test procedures for large samples of rainfall event characteristics. Journal of Hydrologic Engineering, 21 (4). doi:10.1061/(ASCE)HE.1943-5584.0001352.
   Web of Science ®Google Scholar
• Hoegh, K, Khazanovich, L., and Jensen, M., 2010. Local calibration of mechanistic-empirical pavement design guide rutting model minnesota road research project test sections. Transportation Research Record: Journal of the Transportation Research Board, 2180, 130–141. doi:10.3141/2180-15.
   Web of Science ®Google Scholar
• Ji, X., et al., 2016. Development of a rutting prediction model for asphalt pavements with the use of an accelerated loading facility. Road Materials and Pavement Design, 17 (1), 15–31. doi:10.1080/14680629.2015.1055337.
   Web of Science ®Google Scholar
• Lethanh, N, Kaito, K., and Kobayashi, K., 2015. Infrastructure deterioration prediction with a Poisson hidden Markov model on time series data. Journal of Infrastructure Systems, 21 (3), 04014051. doi:10.1061/(ASCE)IS.1943-555X.0000242.
   Web of Science ®Google Scholar
• Li, S., et al., 2021. Rutting performance of semi-rigid base pavement in RIOHTrack and laboratory evaluation. Frontiers in Materials, 7 (395), 590604. doi:10.3389/fmats.2020.590604.
   Google Scholar
• Liu, G., et al., 2021. Rutting prediction models for asphalt pavements with different base types based on RIOHTrack full-scale track. Construction and Building Materials, 305, 124793. doi:10.1016/j.conbuildmat.2021.124793.
   Web of Science ®Google Scholar
• Liu, J., et al., 2022. Optimizing asphalt mix design through predicting the rut depth of asphalt pavement using machine learning. Construction and Building Materials, 356, 129211. doi:10.1016/j.conbuildmat.2022.129211.
   Web of Science ®Google Scholar
• Lytton, R. L., et al., 2018. Characteristics of damaged asphalt mixtures in tension and compression. International Journal of Pavement Engineering, 19 (3), 292–306. doi:10.1080/10298436.2017.1347439
   Web of Science ®Google Scholar
• Mirabdolazimi, S. M., and Shafabakhsh, G., 2017. Rutting depth prediction of hot mix asphalts modified with forta fiber using artificial neural networks and genetic programming technique. Construction and Building Materials, 148, 666–674. doi:10.1016/j.conbuildmat.2017.05.088.
   Web of Science ®Google Scholar
• Nguyen, H. T. T., 2017. Modelling the mechanical behaviour of asphalt concrete using the Perzyna viscoplastic theory and Drucker-Prager yield surface. Road Materials and Pavement Design, 18, 264–280. doi:10.1080/14680629.2017.1304255.
   Web of Science ®Google Scholar
• Pan, Y.Y., et al., 2020. Optimization of gradation design of recycled asphalt mixtures based on fractal and Mohr-Coulomb theories. Construction and Building Materials, 248, 118649. doi:10.1016/j.conbuildmat.2020.118649.
   Web of Science ®Google Scholar
• Shan, A., et al., 2022. Predicting the laboratory rutting response of asphalt mixtures using different neural network algorithms. International Journal of Pavement Engineering, 23 (6), 1948–1956. doi:10.1080/10298436.2020.1830282.
   Web of Science ®Google Scholar
• Sirhan, M., Bekhor, S., and Sidess, A., 2022. Implementation of deep neural networks for pavement condition index prediction. Journal of Transportation Engineering, Part B: Pavements, 148 (1), doi:10.1061/JPEODX.0000333.
   Web of Science ®Google Scholar
• Sun, Y.R., Chen, J.Y., and Huang, B.S., 2015. Characterization of asphalt concrete linear viscoelastic behavior utilizing Havriliak-Negami complex modulus model. Construction and Building Materials, 99, 226–234. doi:10.1016/j.conbuildmat.2015.09.016.
   Web of Science ®Google Scholar
• Tian, Y., et al., 2018. Calibrating the mechanistic-empirical pavement design guide rutting models using accelerated pavement testing. Transportation Research Record: Journal of the Transportation Research Board, 2672 (40), 304–314. doi:10.1177/0361198118788426.
   Web of Science ®Google Scholar
• Tong, J.S., et al., 2020. A criterion of asphalt pavement rutting based on the thermal-visco-elastic-plastic model. International Journal of Pavement Engineering, Early Access, doi:10.1080/10298436.2020.1792470.
   Google Scholar
• Wang, Y., et al., 2017. Finite element analysis for rutting prediction of asphalt concrete pavement under moving wheel load. International Journal of Simulation Modelling, 16 (2), 229–240. doi:10.2507/IJSIMM16(2)4.374.
   Web of Science ®Google Scholar
• Wu, J.F., et al., 2020. Temperature correction and analysis of pavement skid resistance performance based on RIOHTrack full-scale track. Coatings, 10(9). doi: 10.3390/coatings10090832.
   Web of Science ®Google Scholar
• Xu, Q., et al., 2013. Simulation of unbound material resilient modulus effects on mechanistic-empirical pavement designs. Materials and Structures, 46 (7), 1089–1100. doi:10.1617/s11527-012-9955-6.
   Web of Science ®Google Scholar
• Yang, J., Lu, H. Z., and Zhu, H. R., 2009. Approaches to Rut depth prediction in semirigid asphalt pavements. Journal of Engineering Mechanics, 135 (6), 510–516. doi:10.1061/(ASCE)0733-9399(2009)135:6(510).
   Web of Science ®Google Scholar
• Zhang, Y. Q., et al., 2015. A generalized Drucker–Prager viscoplastic yield surface model for asphalt concrete. Materials and Structures, 48 (11), 3585–3601. doi:10.1617/s11527-014-0425-1.
   Web of Science ®Google Scholar
• Zhang, W. G., et al., 2017a. Prediction model for field rut depth of asphalt pavement based on Hamburg wheel tracking test properties. Journal of Materials in Civil Engineering, 29 (9), 04017098. doi:10.1061/(ASCE)MT.1943-5533.0001946.
   Web of Science ®Google Scholar
• Zhang, Y., et al., 2017b. Viscoelastic-Plastic-Fracture modeling of asphalt mixtures under monotonic and repeated loads. Transportation Research Record: Journal of the Transportation Research Board, 20–29. doi:10.3141/2631-03.
   Web of Science ®Google Scholar
• Zhao, Y., et al., 2021. Establishment of rutting model of wheel-tracking test for real-time prediction of rut depth of asphalt layers. Advances in Civil Engineering, 2021, 5549891. doi:10.1155/2021/5549891.
   Web of Science ®Google Scholar
• Zhao, Y.Q, Ni, Y.B., and Zeng, W.Q., 2016. A consistent approach for characterising asphalt concrete based on generalised Maxwell or Kelvin model. Road Materials and Pavement Design, 15 (3), 674–690. doi:10.1080/14680629.2014.889030.
   Web of Science ®Google Scholar
• Zhu, J.W., et al., 2017. Development and calibration of shear-based rutting model for asphalt concrete layers. International Journal of Pavement Engineering, 18 (10), 937–944. doi:10.1080/10298436.2016.1138111.
   Web of Science ®Google Scholar
• Zhu, H., and Sun, L., 2013. Mechanistic rutting prediction using a Two-stage viscoelastic-viscoplastic damage constitutive model of asphalt mixtures. Journal of Engineering Mechanics, 139, 1577–1591. doi:10.1061/(ASCE)EM.1943-7889.0000598.
   Web of Science ®Google Scholar
• Ziari, H., et al., 2018. Predicting rutting performance of carbon nano tube (CNT) asphalt binders using regression models and neural networks. Construction and Building Materials, 160, 415–426. doi:10.1016/j.conbuildmat.2017.11.071.
   Web of Science ®Google Scholar