Predictive model of asphalt mixes’ theoretical maximum specific gravity using gene expression programming
Tài liệu tham khảo
2014
Roberts, 2009
Garcia, 2001
Brown, 2001, Performance testing for hot mix asphalt, NCAT Report, 1
Fundamentals, 2000
Brown, 2009
Anderson, 1997, Evaluation and selection of aggregate gradations for asphalt mixtures using Superpave, Transport. Res. Rec., 1583, 91, 10.3141/1583-11
Cooley Jr, 2013
Lee, 1990
Al-Bayati, 2020, Experimental assessment of mineral filler on the volumetric properties and mechanical performance of HMA mixtures, Civ. Eng. J, 6, 2312, 10.28991/cej-2020-03091619
Huang, 2007, Effects of mineral fillers on hot-mix asphalt laboratory-measured properties, Int. J. Pavement Eng., 8, 1, 10.1080/10298430600819170
Akbulut, 2012, Investigation of using granite sludge as filler in bituminous hot mixtures, Construct. Build. Mater., 36, 430, 10.1016/j.conbuildmat.2012.04.069
Jweihan, 2023, Performance of aged asphalt mixes containing waste oil shale filler, Int. J. Pavement Res. Tech., 1
Pavement Interactive
ASTM, 2011
AASHTO, 2015
Dalhat, 2022, Artificial neural network modeling of theoretical maximum specific gravity for asphalt concrete mix, Int. J. Pavement Res. Tech., 1
Dukatz, 2009
Sholar, 2005, Investigation of the CoreLok for maximum, aggregate, and bulk specific gravity tests, Transport. Res. Rec., 1907, 135, 10.1177/0361198105190700116
Momani, 2022, Data-driven machine learning prediction models for the tensile capacity of anchors in thin concrete, Innovat. Infrast. Sol., 7, 1
Almomani, 2022, Predictive models of behavior and capacity of frp reinforced concrete columns, J. Appl. Eng. Sci., 1
Murad, 2021, Predictive model for bidirectional shear strength of reinforced concrete columns subjected to biaxial cyclic loading, Eng. Struct., 244
Jweihan, 2022, Prediction of marshall test results for dense glasphalt mixtures using artificial neural networks, Front. Machine Learning Applicat. Civil Eng., 16648714, 55
Polo-Mendoza, 2023, Environmental optimization of warm mix asphalt (WMA) design with recycled concrete aggregates (RCA) inclusion through artificial intelligence (AI) techniques, Results Eng., 17, 10.1016/j.rineng.2023.100984
Rabi, 2023, Prediction of the cross-sectional capacity of cold-formed CHS using numerical modelling and machine learning, Results Eng., 17, 10.1016/j.rineng.2023.100902
Fadhil, 2022, Application of artificial neural networks as design tool for hot mix asphalt, Int. J. Pavement Res. Tech., 15, 269, 10.1007/s42947-021-00065-7
Tapkın, 2010, Prediction of Marshall test results for polypropylene modified dense bituminous mixtures using neural networks, Expert Syst. Appl., 37, 4660, 10.1016/j.eswa.2009.12.042
Tapkin, 2015, Modelling Marshall design test results of polypropylene modified asphalt by genetic programming techniques, Period. Polytech. Civ. Eng., 59, 249, 10.3311/PPci.7624
Azarhoosh, 2020, Prediction of Marshall mix design parameters in flexible pavements using genetic programming, Arabian J. Sci. Eng., 45, 8427, 10.1007/s13369-020-04776-0
Pasetto, 2019, Asphalt concrete mechanical behavior prediction by Artificial Neural Networks, 252
Leon, 2019, Gene expression programming for evaluation of aggregate angularity effects on permanent deformation of asphalt mixtures, Construct. Build. Mater., 211, 470, 10.1016/j.conbuildmat.2019.03.225
Ozturk, 2014, An artificial neural network model for virtual Superpave asphalt mixture design, Int. J. Pavement Eng., 15, 151, 10.1080/10298436.2013.808341
Ozturk, 2016, An artificial neural network base prediction model and sensitivity analysis for marshall mix design
Sebaaly, 2018, Optimizing asphalt mix design process using artificial neural network and genetic algorithm, Construct. Build. Mater., 168, 660, 10.1016/j.conbuildmat.2018.02.118
Tarawneh, 2023, Hybrid data-driven machine learning framework for determining prestressed concrete losses, Arabian J. Sci. Eng., 1
1991, Director of planning and development, the hashemite kingdom of Jordan, Specificat. Highway Bridge Construct. (II), 5
Miles, 2014
Ferreira, 2001
Aval, 2017, 12, 13
Ferreira, 2002, Gene expression programming in problem solving, Soft computing and industry: recent applications, 635, 10.1007/978-1-4471-0123-9_54
Kayadelen, 2009, Modeling of the angle of shearing resistance of soils using soft computing systems, Expert Syst. Appl., 36, 11814, 10.1016/j.eswa.2009.04.008
Kara, 2011, Prediction of shear strength of FRP-reinforced concrete beams without stirrups based on genetic programming, Adv. Eng. Software, 42, 295, 10.1016/j.advengsoft.2011.02.002
Teodorescu, 2008, High energy physics event selection with gene expression programming, Comput. Phys. Commun., 178, 409, 10.1016/j.cpc.2007.10.003
Murad, 2020, Prediction model for concrete carbonation depth using gene expression programming, Computers Concrete, Int. J., 26, 497
Imam, 2021, Predicting pavement condition index from international roughness index using gene expression programming, Innovat. Infrast. Sol., 6, 1
Leon, 2022, Prediction of stiffness modulus of bituminous mixtures using the applications of multi-expression programming and gene expression programming, Road Mater. Pavement Des., 1
Deng, 2022, Development of predictive models of asphalt pavement distresses in Idaho through gene expression programming, Neural Comput. Appl., 34, 14913, 10.1007/s00521-022-07305-2
Almasabha, 2022, Machine learning algorithm for shear strength prediction of short links for steel buildings, Buildings, 12, 775, 10.3390/buildings12060775
Tarawneh, 2022, ColumnsNet: neural network model for constructing interaction diagrams and slenderness limit for FRP-RC columns, J. Struct. Eng., 148, 10.1061/(ASCE)ST.1943-541X.0003389
Tarawneh, 2021, 32, 1015