References
- Ferreira, C. (2001), "Gene expression programming: a new adaptive algorithm for solving problems", Comput. Sys., 13, 87-129.
- Ferreira, C. (2002a), "Gene expression programming in problem solving", in Soft Computing and Industry: Recent Applications.
- Ferreira, C. (2002b), Gene expression programming: mathematical modelling by an artificial intelligence, Second Edition, Springer.
- Ghio, V.A., Monteiro, P.J.M. and Demsetz, L.A. (1994), "The rheology of fresh cement paste containing polysaccharide gums", Cement Concrete Res., 24, 243-249. https://doi.org/10.1016/0008-8846(94)90049-3
- Homrich, J. and Naaman, A.E. (1987b), "Stress-strain properties of SIFCON in compression, fibre reinforced concrete properties and applications", SP-105, American Concrete Institute, Detroit, 283-304.
- Homrich, J. and Naaman, A.E. (1989), "Tensile stress-strain properties of SIFCON", ACI Mater. J. 86, 244-251.
- ISO 10414-1 (2001), "Petroleum and natural gas industries -- Field testing of drilling fluids - Part 1: Water-based fluids".
- Kawai, T. (1987), "Non-dispersible underwater concrete using polymers", Marine Concrete, International Congress on Polymers in Concrete, Brighton, UK, Chapter 11.5.
- Kantro, D.L. (1980), "Influence of water reducing admixtures on properties of cement paste - A miniature slump test", Cement Concrete Aggregate CCAGDP 2(2), 95-102. https://doi.org/10.1520/CCA10190J
- Khayat, K.H. and Saric-Coric, M. (2000), "Effect of welan gum-superplasticizer on properties of cement grouts", Sixth CANMET/ACI International Conference on Superplasticizers and Other Chemical Admixtures in Concrete, ACI SP-195, Nice, 249-268.
- Koza, J.R. (1992), Genetic programming: on the programming of computers by means of natural selection, Cambridge, MA: MIT Press
- Lankard, D.R. (1984), "Slurry infiltrated fiber concrete (SIFCON)", Conc. Int. 6, 44-47.
- Lankard, D.R. and Newell, J.K. (1984), "Preparation of highly reinforced steel fibre reinforced concrete composites", Proceedings of Fiber Reinforced Concrete, SP-81, American Concrete Institute, Detroit, 286-306.
- Lombardi, G. (1985), "The role of cohesion in the cement grouting of rock", Proceedings of Fifteenth Congress on Large Dams. International Commission on Large Dams, Q.53, R.B., 3, Lausanne, 235-261.
- Mondragon, R. (1987), "SIFCON in compression, fibre reinforced concrete properties and applications", SP-105, American Concrete Institute, Detroit, 269-281.
- Nehdi, M., Mindess, S. and Aitcin, P-C. (1996), "Optimization of high strength limestone filler cement mortars", Cement Concrete Res., 26, 883-893. https://doi.org/10.1016/0008-8846(96)00071-3
- Nehdi, M., Mindess, S. and Aïtcin, P-C. (1997), "Statistical modelling of the microfiller effect on the rheology of composite cement pastes, Advances in Cement Research, 9(33), 37-46. https://doi.org/10.1680/adcr.1997.9.33.37
- Sonebi, M. (2002), "Experimental design to optimize high-volume of fly ash grout in the presence of welan gum and superplasticizer", Mater. Struct., 35, 373-380. https://doi.org/10.1007/BF02483157
- Sonebi, M., Svermova, L. and Bartos, P.J.M. (2005), "Statistical modelling of cement slurries for self-compacting SIFCON containing silica fume", Mater. Struct., 38(275), 79-86. https://doi.org/10.1007/BF02480578
- Svermova, L. (2004), "Development of self-compacting SIFCON", PhD Thesis, University of Paisley, 370p.
- www.gepsoft.com
Cited by
- New variogram modeling method using MGGP and SVR vol.9, pp.2, 2016, https://doi.org/10.1007/s12145-016-0251-9
- Estimation of factor of safety of rooted slope using an evolutionary approach vol.64, 2014, https://doi.org/10.1016/j.ecoleng.2013.12.047
- Process characterisation of 3D-printed FDM components using improved evolutionary computational approach vol.78, pp.5-8, 2015, https://doi.org/10.1007/s00170-014-6679-5
- Computer-aided design of the effects of Cr2O3 nanoparticles on split tensile strength and water permeability of high strength concrete vol.54, pp.3, 2011, https://doi.org/10.1007/s11431-010-4266-z
- Prediction of Compressive Strength of Geopolymers with Seeded Fly Ash and Rice Husk–Bark Ash by Gene Expression Programming vol.21, pp.8, 2012, https://doi.org/10.1177/1056789511431991
- Prediction split tensile strength and water permeability of high strength concrete containing TiO2 nanoparticles by artificial neural network and genetic programming vol.42, pp.3, 2011, https://doi.org/10.1016/j.compositesb.2010.12.004
- Computer-aided Prediction of the ZrO2 Nanoparticles' Effects on Tensile Strength and Percentage of Water Absorption of Concrete Specimens vol.28, pp.1, 2012, https://doi.org/10.1016/S1005-0302(12)60027-9
- A new prediction method for the rheological behavior of grout with bottom ash for jet grouting columns vol.57, pp.3, 2017, https://doi.org/10.1016/j.sandf.2017.05.006
- Computer-aided prediction of the Al2O3 nanoparticles’ effects on tensile strength and percentage of water absorption of concrete specimens vol.21, pp.7, 2012, https://doi.org/10.1007/s00521-011-0700-9
- A new multi-gene genetic programming approach to nonlinear system modeling. Part I: materials and structural engineering problems vol.21, pp.1, 2012, https://doi.org/10.1007/s00521-011-0734-z
- Prediction of Physical and Mechanical Properties of High Strength Concrete Containing CuO Nanoparticles by Artificial Neural Network and Genetic Programming vol.21, pp.2, 2012, https://doi.org/10.1177/1056789510397079
- Artificial intelligence in nanotechnology vol.24, pp.45, 2013, https://doi.org/10.1088/0957-4484/24/45/452002
- Computational Investigations of the Impact Resistance of Aluminum–Epoxy–Laminated Composites vol.21, pp.5, 2012, https://doi.org/10.1177/1056789511411739
- Evolving Functional Expression of Permeability of Fly Ash by a New Evolutionary Approach vol.107, pp.2, 2015, https://doi.org/10.1007/s11242-015-0454-4
- Computer-aided design of the effects of Fe2O3 nanoparticles on split tensile strength and water permeability of high strength concrete vol.32, pp.7, 2011, https://doi.org/10.1016/j.matdes.2011.01.064
- A novel approach to prediction of rheological characteristics of jet grout cement mixtures via genetic expression programming vol.28, pp.S1, 2017, https://doi.org/10.1007/s00521-016-2360-2
- An ensemble evolutionary approach in evaluation of surface finish reduction of vibratory finishing process vol.32, pp.5, 2015, https://doi.org/10.1108/EC-03-2014-0047
- Application of gene expression programming to predict the compressive damage of lightweight aluminosilicate geopolymer 2012, https://doi.org/10.1007/s00521-012-1137-5
- An integrated SRM-multi-gene genetic programming approach for prediction of factor of safety of 3-D soil nailed slopes vol.30, 2014, https://doi.org/10.1016/j.engappai.2013.12.011
- Prediction the effects of ZnO2 nanoparticles on splitting tensile strength and water absorption of high strength concrete vol.15, pp.3, 2012, https://doi.org/10.1590/S1516-14392012005000038
- A Computational Intelligence-Based Genetic Programming Approach for the Simulation of Soil Water Retention Curves vol.103, pp.3, 2014, https://doi.org/10.1007/s11242-014-0313-8
- COMPUTER-AIDED PREDICTION OF PHYSICAL AND MECHANICAL PROPERTIES OF HIGH STRENGTH CEMENTITIOUS COMPOSITE CONTAINING Cr2O3 NANOPARTICLES vol.05, pp.05, 2010, https://doi.org/10.1142/S1793292010002219
- Modeling Ductile-to-Brittle Transition Temperature of Functionally Graded Steels by Gene Expression Programming vol.21, pp.4, 2012, https://doi.org/10.1177/1056789511406561
- Modeling mechanical performance of lightweight concrete containing silica fume exposed to high temperature using genetic programming vol.24, pp.12, 2010, https://doi.org/10.1016/j.conbuildmat.2010.05.001
- Predicting the effects of nanoparticles on compressive strength of ash-based geopolymers by gene expression programming vol.23, pp.6, 2013, https://doi.org/10.1007/s00521-012-1127-7
- A data mining approach to compressive strength of CFRP-confined concrete cylinders vol.36, pp.6, 2008, https://doi.org/10.12989/sem.2010.36.6.759
- An evolutionary system for the prediction of high performance concrete strength based on semantic genetic programming vol.19, pp.6, 2017, https://doi.org/10.12989/cac.2017.19.6.651
- PREDICTIVE MODEL TO THE BOND STRENGTH OF FRP-TO-CONCRETE UNDER DIRECT PULLOUT USING GENE EXPRESSION PROGRAMMING vol.25, pp.8, 2008, https://doi.org/10.3846/jcem.2019.10798
- COMPRESSIVE STRENGTH PREDICTION OF LIGHTWEIGHT SHORT COLUMNS AT ELEVATED TEMPERATURE USING GENE EXPRESSION PROGRAMING AND ARTIFICIAL NEURAL NETWORK vol.26, pp.2, 2008, https://doi.org/10.3846/jcem.2020.11931
- Predictive compressive strength models for green concrete vol.11, pp.2, 2020, https://doi.org/10.1108/ijsi-05-2019-0044
- Joint shear strength models for exterior RC beam-column connections exposed to biaxial and uniaxial cyclic loading vol.30, pp.None, 2008, https://doi.org/10.1016/j.jobe.2020.101225
- Exterior RC joints subjected to monotonic and cyclic loading vol.37, pp.7, 2020, https://doi.org/10.1108/ec-06-2019-0269
- Prediction model for concrete carbonation depth using gene expression programming vol.26, pp.6, 2008, https://doi.org/10.12989/cac.2020.26.6.497
- A new formulation for strength characteristics of steel slag aggregate concrete using an artificial intelligence-based approach vol.27, pp.4, 2008, https://doi.org/10.12989/cac.2021.27.4.333