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A new approach for modeling of flow number of asphalt mixtures

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Warianty tytułu
Języki publikacji
EN
Abstrakty
EN
Flow number of asphalt-aggregate mixtures is an explanatory parameter for the analysis of rutting potential of asphalt mixtures. In this study, a new model is proposed for the determination of flow number using a robust computational intelligence technique, called multi-gene genetic programming (MGGP). MGGP integrates genetic programming and classical regression to formulate the flow number of Marshall Specimens. A reliable experimental database is used to develop the proposed model. Different analyses are performed for the performance evaluation of the model. On the basis of a comparison study, the MGGP model performs superior to the models found in the literature.
Rocznik
Strony
326--335
Opis fizyczny
Bibliogr. 40 poz., rys., tab., wykr.
Twórcy
autor
  • Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI 48824, USA
autor
  • Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI 48824, USA
autor
  • Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI 48824, USA
autor
  • Department of Civil and Environmental Engineering, Michigan State University, East Lansing, MI 48824, USA
Bibliografia
  • [1] A.H. Alavi, A.H. Gandomi, A robust data mining approach for formulation of geotechnical engineering systems, Engineering Computations 28 (3) (2011) 242–274.
  • [2] A.H. Alavi, M. Ameri, A.H. Gandomi, M.R. Mirzahosseini, Formulation of flow number of asphalt mixes using a hybrid computational method, Construction and Building Materials 25 (3) (2011) 1338–1355.
  • [3] H.M. Azamathulla, Gene expression programming for prediction of scour depth downstream of sills, Journal of Hydrology 460–461 (2012) 156–159.
  • [4] H.M. Azamathulla, A. Guven, Y.K. Demir, Linear genetic programming to scour below submerged pipeline, Ocean Engineering 38 (8–9) (2011) 995–1000.
  • [5] H. Ceylan, C.W. Schwartz, S. Kim, K. Gopalakrishnan, Accuracy of predictive models for dynamic modulus of hot- mix asphalt, Journal of Materials in Civil Engineering 21 (2009) 286–293.
  • [6] G.R. Chehab, Y.R. Kim, M.W. Witczak, R. Bonaquist, Prediction of thermal cracking behavior of asphalt concrete using the viscoelastoplastic continuum damage model, in: Proceedings of the 83rd Annual Meeting of the Transportation Research Board, Washington, D.C., 2004.
  • [7] C.C.A. Coello, G.B. Lamont, D.A. Van Veldhuizen, Evolutionary Algorithms for Solving Multi-Objective Problems. Genetic and Evolutionary Computation, 2nd ed., Springer, New York, 2007.
  • [8] S.K. Das, P. Samui, A.K. Sabat, Application of artificial intelligence to maximum dry density and unconfined compressive strength of cement stabilized soil, Geotechnical and Geological Engineering 29 (3) (2011) 329–342.
  • [9] S.K. Das, P.K. Basudhar, Prediction of residual friction angle of clays using artifical neural network? Engineering Geology 100 (3–4) (2008) l142–l214.
  • [10] R.P. Elliott, M.C. Ford Jr., M. Ghanim, Y.F. Tu, Effect of aggregate gradation variation on asphalt concrete mix properties, Transportation Research Record 1317 (1991) 1–12.
  • [11] L.J. Fogel, A.J. Owens, M.J. Walsh, Artificial Intelligence Through Simulated Evolution, Wiley, New York, 1996.
  • [12] A.H. Gandomi, A.H. Alavi, M.R. Mirzahosseini, F. Moqhadas Nejad, Nonlinear genetic-based models for prediction of flow number of asphalt mixtures, Journal of Materials in Civil Engineering ASCE 23 (3) (2011) 248–263.
  • [13] A.H. Gandomi, G.J. Yun, A.H. Alavi, An evolutionary approach for modeling of shear strength of RC deep beams, Materials and Structures 46 (12) (2013) 2109–2119.
  • [14] A.H. Gandomi, A.H. Alavi, A new multi-gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems, Neural Computing & Applications 21 (2012) 171–187.
  • [15] A. Garg, K. Garg Ankit Tai, S. Sreedeep, An integrated SRM- multi-gene genetic programming approach for prediction of factor of safety of 3-D soil nailed slopes, Engineering Applications of Artificial Intelligence 30 (2014) 30–40.
  • [16] A. Garg, K. Tai, A.K. Gupta, A modified multi-gene genetic programming approach for modelling true stress of dynamic strain aging regime of austenitic stainless steel 304, Meccanica 49 (5) (2014) 1193–1209.
  • [17] N.H. Gibson, A Viscoelastoplastic Continuum Damage Model for the Compressive Behavior of Asphalt Concrete, Ph.D. dissertation, University of Maryland, College Park, MD, 2006.
  • [18] A. Golbraikh, A. Tropsha, Beware of q2, Journal of Molecular Graphics and Modelling 20 (4) (2002) 269–276.
  • [19] K. Gopalakrishnan, H. Ceylan, S. Kim, S.K. Khaitan, Natural selection of asphalt mix stiffness predictive models with genetic programming, in: Proc. Intelligent Engineering Systems through Artificial Neural Networks, volume 20, paper 48, St. Louis, MO, USA, 2010.
  • [20] K.E. Kaloush, Sample Performance Test for Permanent Deformation of Asphalt Mixtures, Ph.D. thesis, Arizona State Univ., 2001.
  • [21] Y.R. Kim, Modeling of Asphalt Concrete, chapter 11, 1st ed., McGraw-Hill, New York, 2008.
  • [22] J. Koza, Genetic Programming, on the Programming of Computers by Means of Natural Selection, MIT Press, Cambridge, MA, 1992.
  • [23] M. Maalouf, N. Khoury, T.B. Trafalis, Support vector regression to predict asphalt mix performance, International Journal for Numerical and Analytical Methods in Geomechanics 32 (2008) 1989–1996.
  • [24] M.R. Mirzahosseini, A. Aghaeifar, A.H. Alavi, A.H. Gandomi, M. Seyednour, Permanent deformation analysis of asphalt mixtures using soft computing techniques, Expert Systems with Applications 38 (5) (2011) 6081–6100.
  • [25] P.K. Muduli, S.K. Das, CPT-based seismic liquefaction potential evaluation using multi-gene genetic programming approach, Indian Geotechnical Journal, 2013.
  • [26] P.P. Roy, K. Roy, On some aspects of variable selection for partial least squares regression models, QSAR & Combinatorial Science 27 (3) (2008) 302–313.
  • [27] H. Salehi, S. Das, S. Chakrabartty, S. Biswas, R. Burgueno, Structural assessment and damage identification algorithms using binary data, in: ASME 2015 conference on smart materials, adaptive structures and intelligent systems (SMASIS2015), Colorado Springs, Colorado, 2015.
  • [28] H. Salehi, S. Das, S. Chakrabartty, S. Biswas, R. Burgueno, Structural health monitoring from discrete binary data through pattern recognition, in: The 6th International Conference on Structural Engineering, Mechanics and Computation (SEMC 2016), Cape Town, South Africa, 2016.
  • [29] H. Salehi, T. Taghikhany, A.Y. Fallah, Seismic protection of vulnerable equipment with semi-active control by employing robust and clipped-optimal algorithms, International Journal of Civil Engineering 12 (4) (2014) 413–428.
  • [30] M. Saltan, S. Terzi, Comparative analysis of using artificial neural networks (ANN) and gene expression programming (GEP) in backcalculation of pavement layer thickness, Indian Journal of Engineering and Materials Sciences 12 (1) (2005) 42–50.
  • [31] P. Samui, T.G. Sitharam, Machine learning modelling for predicting soil liquefaction susceptibility, Natural Hazards and Earth System Sciences 11 (2011) 1–9.
  • [32] D.P. Searson, GPTIPS: Genetic Programming & Symbolic Regression for MATLAB, 2009.
  • [33] D.P. Searson, D.E. Leahy, M.J. Willis, GPTIPS: an open source genetic programming toolbox for multigene symbolic regression, in: Proc. Int. Multi Conf. Eng. Comput. Sci. Hong Kong, 2010.
  • [34] D.P. Searson, M.J. Willis, G.A. Montague, Co-evolution of nonlinear PLS model components, Journal of Chemometrics 2 (2007) 592–603.
  • [35] G.N. Smith, Probability and Statistics in Civil Engineering, Collins, London, 1986.
  • [36] J.B. Sousa, J. Craus, C.L. Monismith, Summary Report on Permanent Deformation in Asphalt Concrete, Strategic Highway Research Program (SHRP), National Research Council, Institute of Transportation Studies, University of California, Berkeley, 1991.
  • [37] S. Tapkin, A. Çevik, Ş. Özcan, Utilising neural networks and closed form solutions to determine static creep behaviourand optimal polypropylene amount in bituminous mixtures, Materials Research 15 (6) (2012) 865–883.
  • [38] S. Tapkin, B. Şengöz, G. Şengül, A. Topal, E. Özçelik, Estimation of polypropylene concentration of modified bitumen images by using k-NN and SVM classifiers, ASCE Journal of Computing in Civil Engineering (2013) 04014055, http://dx.doi.org/10.1061/(ASCE)CP.1943-5487.0000353.
  • [39] S. Terzi, Modeling the deflection basin of flexible highway pavements by gene expression programming, Journal of Applied Sciences 5 (2) (2005) 309–314.
  • [40] M.W. Witczak, K. Kaloush, T. Pellinen, M. El-Basyouny, H. Von Quintus, Simple Performance Test for Superpave Mix Design. NCHRP Rep. 465, National Research Council, Transportation Research Board, Washington, DC, 2002.
Uwagi
PL
Opracowanie ze środków MNiSW w ramach umowy 812/P-DUN/2016 na działalność upowszechniającą naukę (zadania 2017)
Typ dokumentu
Bibliografia
Identyfikator YADDA
bwmeta1.element.baztech-b68e90e7-2df6-4408-86f0-47f0e61a3502
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