Prediction models of mixtures’ dynamic modulus using gene expression programming

References

• AASHTO, 2004. Standard method of test for determining dynamic modulus of hot-mix asphalt concrete mixtures. Washington, DC: AASHTO TP62.
   Google Scholar
• Alavi, A.H. and Gandomi, A.H., 2011. A robust data mining approach for formulation of geotechnical engineering systems. Engineering Computations, 28 (3), 242–274(33).10.1108/02644401111118132
   Web of Science ®Google Scholar
• Alavi, A.H., et al., 2011. Formulation of flow number of asphalt mixes using a hybrid computational method. Construction & Building Materials, 25, 1338–1355.
   Web of Science ®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
• Alkroosh, I. and Nikraz, H., 2011. Correlation of pile axial capacity and CPT Data using gene expression programming. Geotechnical and Geological Engineering, 29, 725–748.10.1007/s10706-011-9413-1
   Google Scholar
• Alkroosh, I. and Nikraz, H., 2012. Predicting axial capacity of driven piles in cohesive soils using intelligent computing. Engineering Applications of Artificial Intelligence, 25, 618–627.10.1016/j.engappai.2011.08.009
   Web of Science ®Google Scholar
• Andrei, D., Witczak, M.W., and Mirza, M.W., 1999. Development of a revised predictive model for the dynamic (complex) modulus of asphalt mixtures. NCHRP 1-37, an Interim Rep., College Park, MD: University of Maryland.
   Google Scholar
• Baykasoğlu, A., et al., 2008. Prediction of compressive and tensile strength of limestone via genetic programming. Expert Systems with Applications, 35, 111–123.10.1016/j.eswa.2007.06.006
   Web of Science ®Google Scholar
• Cascione, A.A., et al., 2011. Laboratory evaluation of field produced hot mix asphalt containing postconsumer recycled asphalt shingles and fractionated recycled asphalt pavement. Journal of the Association of Asphalt Paving Technologists, 80, 377–418.
   Google Scholar
• Cevik, A. and Cabalar, A.F., 2008. A genetic-programming-based formulation for the strength enhancement of fiber-reinforced-polymer confined concrete cylinders. Journal of Applied Polymer Science, 110 (5), 3087–3095.10.1002/app.v110:5
   Web of Science ®Google Scholar
• Ceylan, H., Kim, S., and Gopalakrishnan, K., 2007. Hot mix asphalt dynamic modulus prediction models using neural network approach. Intelligent Engineering Systems Through Artificial Neural Networks, 17, ASME Press.
   Google Scholar
• Ceylan, H., Kasthurirangan, K., and Kim, S., 2008. Advanced approaches to hot-mix asphalt dynamic modulus prediction. The Canadian Journal of Civil Engineering, 35 (7), 699–707.
   Web of Science ®Google Scholar
• Ceylan, H., Kasthurirangan, 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.
   Web of Science ®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.
   Web of Science ®Google Scholar
• Charles Dougan, E., et al., 2003. E* – dynamic modulus (test protocol – problems and solutions). Report Number：CT-SPR-0003084-F-03-3.
   Google Scholar
• Cho, Y., Park, D., and Hwang, S., 2010. A predictive equation for dynamic modulus of asphalt mixtures used in Korea. Construction and Building Materials, 24, 513–519.10.1016/j.conbuildmat.2009.10.008
   Web of Science ®Google Scholar
• Dibike, Y.B., et al., 2001. Model induction with support vector machines: introduction and applications. Journal of Computing in Civil Engineering, 15 (3), 208–216.10.1061/(ASCE)0887-3801(2001)15:3(208)
   Web of Science ®Google Scholar
• Dongre, R., et al., 2006. Field evaluation of witczak and hirsch models for predicting dynamic modulus of hot-mix asphalt. Asphalt Paving Technology, 74, 381–442.
   Google Scholar
• Eddie Johnson, et al., 2010. Incorporation of recycled asphalt shingles in hot mixed asphalt pavement mixtures. Report No. MN/RC 2010-08. Minnesota Department of Transportation.
   Google Scholar
• Ercan, O., 2011. Artificial neural network based modeling of the marshall stability of asphalt concrete. Expert Systems with Applications, 38, 6025–6030.
   Web of Science ®Google Scholar
• Ferreira, C., 2001. Gene expression programming: a new adaptive algorithm for solving problems. Complex Systems, 13 (2), 87–129.
   Google Scholar
• Foo, K.Y., Hanson, D.I., and Lynn, T.A., 1999. Evaluation of roofing shingles in hot mix asphalt. Journal of Materials in Civil Engineering, 11 (1), 15–20.
   Web of Science ®Google Scholar
• Gandomi, A.H. and Alavi, A.H., 2012. A new multi-gene genetic programming approach to non-linear system modeling. Part ii: geotzechnical and earthquake engineering problems. Neural Computing & Applications, 21 (1), 189–201.
   Web of Science ®Google Scholar
• Gandomi, A.H. and Roke, D.A., 2015. Assessment of artificial neural network and genetic programming as predictive tools. Advances in Engineering Software, 88, 63–72.10.1016/j.advengsoft.2015.05.007
   Web of Science ®Google Scholar
• Gandomi, A.H., et al., 2011a. Nonlinear genetic-based models for prediction of flow number of asphalt mixtures. Journal of Materials in Civil Engineering, 23 (3), 248–263.10.1061/(ASCE)MT.1943-5533.0000154
   Web of Science ®Google Scholar
• Gandomi, A.H., et al., 2011b. A new prediction model for load capacity of castellated steel beams. The Journal of Constructional Steel Research, 67 (7), 1096–1105.10.1016/j.jcsr.2011.01.014
   Web of Science ®Google Scholar
• Goh, S.W. and You, Z., 2011. Evaluation of recycled asphalt shingles in hot mix asphalt. In Proceedings of the 1st congress of the transportation and development institute of ASCE (pp. 638–645), Chicago IL.
   Google Scholar
• Gopalakrishnan, K. and Kim, S., 2014. Support vector machines approach to HMA stiffness prediction. Journal of Engineering Mechanics, 137, 138–146.10.1061/(ASCE)EM.1943-7889.0000214
   Web of Science ®Google Scholar
• Haykin, S., 1999. Neural networks: a comprehensive foundation. Englewood Cliffs, NJ: Prentice-Hall.
   Google Scholar
• Jain, A.K. and Dubes, R.C., 1988. Algorithms for clustering data. Englewood Cliffs, NJ: Prentice-Hall.
   Google Scholar
• Javed, B. and Witczak, M.W., 2006. Development of a new revised version of the Witczak E* predictive model for hot mix asphalt mixtures. Journal of the Association of Asphalt Paving Technologists, 75, 381–424.
   Google Scholar
• Kara, I.F., 2011. Prediction of shear strength of FRP-reinforced concrete beams without stirrups based on genetic programming. Advances in Engineering Software, 42, 295–304.
   Web of Science ®Google Scholar
• Kara, I.F., 2012, Empirical modeling of shear strength of steel fiber reinforced concrete beams by gene expression programming. Neural Computing & Applications, 23(3-4), 823–834, s00521-012-0999-x.
   Web of Science ®Google Scholar
• Koza, J.R., 1992. Genetic programming: on the programming of computers by means of natural selection. Cambridge: MIT Press.
   Google Scholar
• Mirzahosseini, M.R., et al., 2010. Evaluation of rutting potential of asphalt mixtures using linear genetic programming. Genetic Programming, 2, 1527–1536.
   Google Scholar
• Pellinen, T.K., 2001. Investigation of the use of dynamic modulus as an indicator of hot-mix asphalt performance. Thesis (PhD). Arizona State University.
   Google Scholar
• Robinette, C. and Williams, R.C., 2006. The effects of the testing history and preparation method on the Superpave simple performance test. Journal of the Association of Asphalt Paving Technologists, 75, 227–318.
   Google Scholar
• Sarıdemir, M., 2011. Empirical modeling of splitting tensile strength from cylinder compressive strength of concrete by genetic programming. Expert Systems with Applications, 38, 14257–14268.
   Web of Science ®Google Scholar
• Sarıdemir, M., 2014. Effect of specimen size and shape on compressive strength of concrete containing fly ash: application of genetic programming for design. Materials and Design, 56, 297–304.10.1016/j.matdes.2013.10.073
   Web of Science ®Google Scholar
• Singh, D., Zaman, M., and Commuri, S., 2012. Inclusion of aggregate angularity, texture, and form in estimating dynamic modulus of asphalt mixes. Road Materials & Pavement Design, 13 (2), 1–18.
   Web of Science ®Google Scholar
• Singh, D., Zaman, M., and Commuri, S., 2013. Artificial neural network modeling for dynamic modulus of hot mix asphalt using aggregate shape properties. Journal of Materials in Civil Engineering, 25 (1), 54–62.10.1061/(ASCE)MT.1943-5533.0000548
   Web of Science ®Google Scholar
• Xiao, F. and Amirkhanian, S.N., 2009. Artificial neural network approach to estimating stiffness behavior of rubberized asphalt concrete containing reclaimed asphalt pavement. Journal of Transportation Engineering, 135, 580–589.10.1061/(ASCE)TE.1943-5436.0000014
   Web of Science ®Google Scholar
• Yan, K., Xu, H.B., and Shen, G.H., 2014. Novel approach to resilient modulus using routine subgrade soil properties. International Journal of Geomechanics, 14 (6), 04014025.
   Web of Science ®Google Scholar
• Yu, J., 2012. Modification of dynamic modulus predictive models for asphalt mixtures containing recycled asphalt shingles. Dissertations & Thesis – Ames. IA: Iowa State University Ames.
   Google Scholar