ANN-based dynamic modulus models of asphalt mixtures with similar input variables as Hirsch and Witczak models

References

• AASHTO T 342-11, 2019. Standard method of test for determining dynamic modulus of hot-mix asphalt concrete mixtures.
   Google Scholar
• Abdo, A.A., et al., 2009. Prediction of the dynamic modulus of Superpave mixes. In: Proceedings of the 8th international conference on the Bearing Capacity of Roads, Railways and Airfields (BCR2A’09).
   Google Scholar
• Al-Khateeb, G., et al., 2006. A new simplistic model for dynamic modulus predictions of asphalt paving mixtures. Journal of the Association of Asphalt Paving Technologists, 75, 1254–1293.
   Google Scholar
• Amirkhanian, S., Xiao, F., and Corley, M., 2018. Characterization of asphalt concrete dynamic modulus in South Carolina. South Carolina Dept. of Transportation. FHWA-SC-18-04.
   Google Scholar
• Bari, J., 2005. Development of a new revised version of the Witczak E* predictive models for hot mix asphalt mixtures. Ph.D. thesis. Arizona State University Tempe, AZ.
   Google Scholar
• Bennert, T.A., 2009. Dynamic modulus of hot mix asphalt. New Jersey Department of Transportation. FHWA-NJ-2009-011.
   Google Scholar
• Birgisson, B., Sholar, G., and Roque, R., 2005. Evaluation of a predicted dynamic modulus for Florida mixtures. Transportation Research Record, 1929 (1), 200–207. doi: https://doi.org/10.1177/0361198105192900124
   Google Scholar
• Ceylan, H., et al., 2009b. Accuracy of predictive models for dynamic modulus of hot-mix asphalt. Journal of Materials in Civil Engineering, 21 (6), 286–293. doi: https://doi.org/10.1061/(ASCE)0899-1561(2009)21:6(286)
   Web of Science ®Google Scholar
• Ceylan, H., Gopalakrishnan, K., and Kim, S., 2008. Advanced approaches to hot-mix asphalt dynamic modulus prediction. Canadian Journal of Civil Engineering, 35 (7), 699–707. doi: https://doi.org/10.1139/L08-016
   Web of Science ®Google Scholar
• Ceylan, H., Gopalakrishnan, K., and Kim, S., 2009a. Looking to the future: the next-generation hot mix asphalt dynamic modulus prediction models. International Journal of Pavement Engineering, 10 (5), 341–352. doi: https://doi.org/10.1080/10298430802342690
   Web of Science ®Google Scholar
• Ceylan, H., Kim, S., and Gopalakrishnan, K., 2007. Hot mix asphalt dynamic modulus prediction models using neural networks approach. Civil, Construction and Environmental Engineering Conference Presentations and Proceedings, 29. Vol. 29.
   Google Scholar
• Chou, J.-S., et al., 2014. Machine learning in concrete strength simulations: Multi-nation data analytics. Construction and Building Materials, 73, 771–780. doi: https://doi.org/10.1016/j.conbuildmat.2014.09.054
   Web of Science ®Google Scholar
• Christensen Jr, D., Pellinen, T., and Bonaquist, R., 2003. Hirsch model for estimating the modulus of asphalt concrete. Journal of the Association of Asphalt Paving Technologists, 72, 97–121.
   Google Scholar
• Clyne, T.R., et al., 2003. Dynamic and resilient modulus of Mn/DOT asphalt mixtures. Department of Civil Engineering, University of Minnesota. MN/RC – 2003-09.
   Google Scholar
• Cross, S.A., Jakatimath, Y., and Sumesh, K.C., 2007. Determination of dynamic modulus master curves for Oklahoma HMA mixtures. Oklahoma City: Oklahoma State University. FHWA/OK 07 (05).
   Google Scholar
• Dong, Q., et al., 2018. Analysis of the influence of materials and construction practices on slurry seal performance using LTPP data. Journal of Transportation Engineering, Part B: Pavements, 144 (4), 04018046. doi: https://doi.org/10.1061/JPEODX.0000069
   Web of Science ®Google Scholar
• Dongre, R., et al., 2005. Field evaluation of Witczak and Hirsch models for predicting dynamic modulus of hot-mix asphalt (with discussion). Journal of the Association of Asphalt Paving Technologists, 74, 381–442.
   Google Scholar
• El-Badawy, S., Abd El-Hakim, R., and Awed, A., 2018. Comparing artificial neural networks with regression models for hot-mix asphalt dynamic modulus prediction. Journal of Materials in Civil Engineering, 30 (7), 04018128. doi: https://doi.org/10.1061/(ASCE)MT.1943-5533.0002282
   Web of Science ®Google Scholar
• El-Badawy, S., Bayomy, F., and Awed, A., 2012. Performance of MEPDG dynamic modulus predictive models for asphalt concrete mixtures: local calibration for Idaho. Journal of Materials in Civil Engineering, 24 (11), 1412–1421. doi: https://doi.org/10.1061/(ASCE)MT.1943-5533.0000518
   Web of Science ®Google Scholar
• Far, M.S.S., et al., 2009. Application of artificial neural networks for estimating dynamic modulus of asphalt concrete. Transportation Research Record, 2127 (1), 173–186. doi: https://doi.org/10.3141/2127-20
   Web of Science ®Google Scholar
• Far, M.S.S., 2011. Development of new dynamic modulus (E*) predictive models for Hot Mix Asphalt mixtures. Ph.D. thesis. North Carolina State University.
   Google Scholar
• Kaya, O., et al., 2018. Development of artificial neural networks based predictive models for dynamic modulus of airfield pavement asphalt mixtures. In: International conference on Transportation and Development 2018: Airfield and Highway Pavements. Reston, VA: American Society of Civil Engineers.
   Google Scholar
• Khattab, A., et al., 2015. Comparing Witczak NCHRP 1-40D with Hirsh E* predictive models for Kingdom of Saudi Arabia asphalt mixtures. In: 3rd Middle East Society of Asphalt Technologists (MESAT) conference, American University in Dubai, UAE.
   Google Scholar
• Liu, J., et al., 2017. Prediction models of mixtures’ dynamic modulus using gene expression programming. International Journal of Pavement Engineering, 18 (11), 971–980. doi: https://doi.org/10.1080/10298436.2016.1138113
   Web of Science ®Google Scholar
• Liu, J., et al., 2018. Using artificial neural networks to predict the dynamic modulus of asphalt mixtures containing recycled asphalt shingles. Journal of Materials in Civil Engineering, 30 (4), 04018051. doi: https://doi.org/10.1061/(ASCE)MT.1943-5533.0002242
   Web of Science ®Google Scholar
• Majidifard, H., et al., 2019. New machine learning-based prediction models for fracture energy of asphalt mixtures. Measurement, 135, 438–451. doi: https://doi.org/10.1016/j.measurement.2018.11.081
   Web of Science ®Google Scholar
• Mohammad, L.N., et al., 2014. Characterization of Louisiana asphalt mixtures using simple performance tests and MEPDG. Louisiana: Dept. of Transportation and Development. FHWA/LA.11/499.
   Google Scholar
• Pellinen, T.K., 2002. Investigation of the use of dynamic modulus as an indicator of hot-mix asphalt performance. Ph.D. thesis. Arizona State University.
   Google Scholar
• Rahman, A. and Tarefder, R.A., 2017. Dynamic modulus predictive model based on artificial neural network for the superpave asphalt mixtures of New Mexico. In: ASME 2017 international mechanical engineering congress and exposition. Vol. 14. American Society of Mechanical Engineers Digital Collection.
   Google Scholar
• Rahman, A.A., Islam, M.R., and Tarefder, R.A., 2018. Assessment and modification of nationally-calibrated dynamic modulus predictive model for the implementation of mechanistic-empirical design. International Journal of Pavement Research and Technology, 11 (5), 502–508. doi: https://doi.org/10.1016/j.ijprt.2018.03.008
   Google Scholar
• Rahman, A.A., Islam, M.R., and Tarefder, R.A., 2019. Dynamic modulus and phase angle models for New Mexico’s Superpave mixtures. Road Materials and Pavement Design, 20 (3), 740–753. doi: https://doi.org/10.1080/14680629.2017.1422002
   Web of Science ®Google Scholar
• Sakhaeifar, M.S., Kim, Y.R., and Kabir, P., 2015. New predictive models for the dynamic modulus of hot mix asphalt. Construction and Building Materials, 76, 221–231. doi: https://doi.org/10.1016/j.conbuildmat.2014.11.011
   Web of Science ®Google Scholar
• Schwartz, C.W., 2005. Evaluation of the Witczak dynamic modulus prediction model. In: Proceedings of the 84th annual meeting of the Transportation Research Board, Washington, DC, USA.
   Google Scholar
• Witczak, M., El-Basyouny, M., and El-Badawy, S., 2007. Incorporation of the New (2005) E* predictive model in the MEPDG. NCHRP 1-40D Final Report.
   Google Scholar
• Yang, E., et al., 2011. Simplified predictive model of dynamic modulus for characterizing Florida hot-mix asphalt mixtures. In: Transportation Research Board 90th annual meeting, Washington, DC, USA.
   Google Scholar
• Yao, L., et al., 2019. Establishment of prediction models of asphalt pavement performance based on a novel data calibration method and neural network. Transportation Research Record, 2673 (1), 66–82. doi: https://doi.org/10.1177/0361198118822501
   Web of Science ®Google Scholar
• Yu, H. and Shen, S., 2012. An investigation of dynamic modulus and flow number properties of asphalt mixtures in Washington State. Transportation Northwest Regional Center X (TransNow). TNW2012-02.
   Google Scholar
• Zhao, S., et al., 2017. Dynamic modulus characterization of Alaskan asphalt mixtures for mechanistic-empirical pavement design. Journal of Materials in Civil Engineering, 29 (11), 04017213. doi: https://doi.org/10.1061/(ASCE)MT.1943-5533.0002069
   Web of Science ®Google Scholar