RETRACTED: Stiffness performance of polyethylene terephthalate modified asphalt mixtures estimation using support vector machine-firefly algorithm

[1]  Ning Li,et al.  Investigation of the dynamic and fatigue properties of fiber-modified asphalt mixtures , 2009 .

[2]  M. Gilchrist,et al.  Development of a recycled polymer modified binder for use in stone mastic asphalt , 2008 .

[3]  Andrew H. Sung,et al.  Intrusion detection using neural networks and support vector machines , 2002, Proceedings of the 2002 International Joint Conference on Neural Networks. IJCNN'02 (Cat. No.02CH37290).

[4]  T.-L. Lee,et al.  Support vector regression methodology for storm surge predictions , 2008 .

[5]  Seong-Whan Lee,et al.  Editorial: Support Vector Machines for Computer Vision and Pattern Recognition , 2003, Int. J. Pattern Recognit. Artif. Intell..

[6]  Abdulkadir Çevik,et al.  Prediction of Marshall test results for polypropylene modified dense bituminous mixtures using neural networks , 2010, Expert Syst. Appl..

[7]  Mehmet Saltan Modeling Deflection Basin Using Neurofuzzy in Backcaluculating Flexible Pavement Layer Moduli , 2002 .

[8]  Yanjun Ren,et al.  An SVM based Algorithm for Road Disease Detection using Accelerometer , 2013 .

[9]  Vladimir Vapnik,et al.  Statistical learning theory , 1998 .

[10]  Ercan Özgan,et al.  Fuzzy logic and statistical-based modelling of the Marshall Stability of asphalt concrete under varying temperatures and exposure times , 2009, Adv. Eng. Softw..

[11]  A. Burak Göktepe,et al.  Advances in backcalculating the mechanical properties of flexible pavements , 2006, Adv. Eng. Softw..

[12]  A.H. Sung,et al.  Identifying important features for intrusion detection using support vector machines and neural networks , 2003, 2003 Symposium on Applications and the Internet, 2003. Proceedings..

[13]  Shiliang Sun,et al.  A survey of multi-view machine learning , 2013, Neural Computing and Applications.

[14]  Thorsten Joachims,et al.  Text Categorization with Support Vector Machines: Learning with Many Relevant Features , 1998, ECML.

[15]  Alexander J. Smola,et al.  Support Vector Method for Function Approximation, Regression Estimation and Signal Processing , 1996, NIPS.

[16]  Xin-She Yang,et al.  Firefly Algorithm, Lévy Flights and Global Optimization , 2010, SGAI Conf..

[17]  Mahyar Arabani,et al.  Experimental investigation of the fatigue behaviour of asphalt concrete mixtures containing waste iron powder , 2011 .

[18]  A. Ghanizadeh,et al.  Effect of Waveform, Duration and Rest Period on the Resilient Modulus of Asphalt Mixes , 2013 .

[19]  Halit Ozen,et al.  Laboratory performance comparison of the elastomer modified asphalt mixtures , 2008 .

[20]  Slawomir Zak,et al.  Firefly Algorithm for Continuous Constrained Optimization Tasks , 2009, ICCCI.

[21]  Paola Bandini,et al.  Prediction of Pavement Performance through Neuro‐Fuzzy Reasoning , 2010, Comput. Aided Civ. Infrastructure Eng..

[22]  S. M. Abtahi,et al.  Fiber-reinforced asphalt-concrete – A review , 2010 .

[23]  Michael R. Lyu,et al.  Localized support vector regression for time series prediction , 2009, Neurocomputing.

[24]  Piotr Radziszewski Modified asphalt mixtures resistance to permanent deformations , 2007 .

[25]  Sheng-De Wang,et al.  Choosing the kernel parameters for support vector machines by the inter-cluster distance in the feature space , 2009, Pattern Recognit..

[26]  L. S. Davis,et al.  An assessment of support vector machines for land cover classi(cid:142) cation , 2002 .

[27]  Ibrahim Korkmaz,et al.  Adaptive neuro-fuzzy inference approach for prediction the stiffness modulus on asphalt concrete , 2012, Adv. Eng. Softw..

[28]  Talat Sukru Ozsahin,et al.  Neural network model for resilient modulus of emulsified asphalt mixtures , 2008 .

[29]  Mac McKee,et al.  Multi-time scale stream flow predictions: The support vector machines approach , 2006 .

[30]  C. Schunn,et al.  Evaluating Goodness-of-Fit in Comparison of Models to Data , 2005 .

[31]  Vladimir N. Vapnik,et al.  The Nature of Statistical Learning Theory , 2000, Statistics for Engineering and Information Science.

[32]  Kasthurirangan Gopalakrishnan,et al.  Support vector machines for nonlinear pavement backanalysis , 2010 .

[33]  C. W. Tong,et al.  A new hybrid support vector machine–wavelet transform approach for estimation of horizontal global solar radiation , 2015 .

[34]  Shiliang Sun,et al.  Multitask multiclass support vector machines: Model and experiments , 2013, Pattern Recognit..

[35]  Xin-She Yang,et al.  Firefly algorithm, stochastic test functions and design optimisation , 2010, Int. J. Bio Inspired Comput..

[36]  Khubaib Amjad Alam,et al.  Support vector regression based prediction of global solar radiation on a horizontal surface , 2015 .

[37]  Mahyar Arabani,et al.  The effect of waste tire thread mesh on the dynamic behaviour of asphalt mixtures , 2010 .

[38]  S Hınıslıoglu,et al.  Use of waste high density polyethylene as bitumen modifier in asphalt concrete mix , 2004 .

[39]  Matthew W Witczak,et al.  Use of Stiffness of Hot-Mix Asphalt as a Simple Performance Test , 2002 .

[40]  Shan Suthaharan,et al.  Support Vector Machine , 2016 .

[41]  Zhang Yu-zhen,et al.  The research of GMA-g-LDPE modified Qinhuangdao bitumen , 2008 .

[42]  A. Aksoy,et al.  Investigation of rutting performance of asphalt mixtures containing polymer modifiers , 2007 .

[43]  Payam Shafigh,et al.  Using waste plastic bottles as additive for stone mastic asphalt , 2011 .

[44]  Dongdong Ge,et al.  Support Vector Machine Models for Prediction of Flow Number of Asphalt Mixtures , 2014 .

[45]  Özgür Kişi,et al.  Evapotranspiration modeling using a wavelet regression model , 2010, Irrigation Science.

[46]  Ali Khodaii,et al.  Evaluation of permanent deformation of unmodified and SBS modified asphalt mixtures using dynamic creep test , 2009 .

[47]  Ercan Özgan,et al.  Artificial neural network based modelling of the Marshall Stability of asphalt concrete , 2011, Expert Syst. Appl..

[48]  Wei-Zhen Lu,et al.  Potential assessment of the "support vector machine" method in forecasting ambient air pollutant trends. , 2005, Chemosphere.

[49]  Shoutao Li,et al.  A new road friction coefficient estimation method based on SVM , 2012, 2012 IEEE International Conference on Mechatronics and Automation.

[50]  Mohamed Rehan Karim,et al.  Evaluation of permanent deformation characteristics of unmodified and Polyethylene Terephthalate modified asphalt mixtures using dynamic creep test , 2014 .

[51]  Abolfazl Hassani,et al.  Use of plastic waste (poly-ethylene terephthalate) in asphalt concrete mixture as aggregate replacement , 2005, Waste management & research : the journal of the International Solid Wastes and Public Cleansing Association, ISWA.

[52]  Sunghwan Kim,et al.  Support Vector Machines Approach to HMA Stiffness Prediction , 2011 .

[53]  Amir Hossein Gandomi,et al.  Permanent deformation analysis of asphalt mixtures using soft computing techniques , 2011, Expert Syst. Appl..