Computers and Concrete

  • 제5권5호
  • /
  • Pages.475-490
  • /
  • 2008
  • /
  • 1598-8198(pISSN)
  • /
  • 1598-818X(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Modelling the performance of self-compacting SIFCON of cement slurries using genetic programming technique

  • Cevik, Abdulkadir (Department of Civil Engineering, University of Gaziantep) ;
  • Sonebi, Mohammed (School of Planning, Architecture, and Civil Engineering, Queen's University Belfast)
  • 투고 : 2007.12.20
  • 심사 : 2008.07.18
  • 발행 : 2008.10.25
⟨ 이전 논문 다음 논문 ⟩

초록

The paper explores the potential of applicability of Genetic programming approach (GP), adopted in this investigation, to model the combined effects of five independent variables to predict the mini-slump, the plate cohesion meter, the induced bleeding test, the J-fiber penetration value, and the compressive strength at 7 and 28 days of self-compacting slurry infiltrated fiber concrete (SIFCON). The variables investigated were the proportions of limestone powder (LSP) and sand, the dosage rates of superplasticiser (SP) and viscosity modifying agent (VMA), and water-to-binder ratio (W/B). Twenty eight mixtures were made with 10-50% LSP as replacement of cement, 0.02-0.06% VMA by mass of cement, 0.6-1.2% SP and 50-150% sand (% mass of binder) and 0.42-0.48 W/B. The proposed genetic models of the self-compacting SIFCON offer useful modelling approach regarding the mix optimisation in predicting the fluidity, the cohesion, the bleeding, the penetration, and the compressive strength.

키워드

참고문헌

  1. Ferreira, C. (2001), "Gene expression programming: a new adaptive algorithm for solving problems", Comput. Sys., 13, 87-129.
  2. Ferreira, C. (2002a), "Gene expression programming in problem solving", in Soft Computing and Industry: Recent Applications.
  3. Ferreira, C. (2002b), Gene expression programming: mathematical modelling by an artificial intelligence, Second Edition, Springer.
  4. 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
  5. 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.
  6. Homrich, J. and Naaman, A.E. (1989), "Tensile stress-strain properties of SIFCON", ACI Mater. J. 86, 244-251.
  7. ISO 10414-1 (2001), "Petroleum and natural gas industries -- Field testing of drilling fluids - Part 1: Water-based fluids".
  8. Kawai, T. (1987), "Non-dispersible underwater concrete using polymers", Marine Concrete, International Congress on Polymers in Concrete, Brighton, UK, Chapter 11.5.
  9. 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
  10. 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.
  11. Koza, J.R. (1992), Genetic programming: on the programming of computers by means of natural selection, Cambridge, MA: MIT Press
  12. Lankard, D.R. (1984), "Slurry infiltrated fiber concrete (SIFCON)", Conc. Int. 6, 44-47.
  13. 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.
  14. 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.
  15. Mondragon, R. (1987), "SIFCON in compression, fibre reinforced concrete properties and applications", SP-105, American Concrete Institute, Detroit, 269-281.
  16. 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
  17. 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
  18. 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
  19. 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
  20. Svermova, L. (2004), "Development of self-compacting SIFCON", PhD Thesis, University of Paisley, 370p.
  21. www.gepsoft.com

피인용 문헌

  1. New variogram modeling method using MGGP and SVR vol.9, pp.2, 2016, https://doi.org/10.1007/s12145-016-0251-9
  2. 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
  3. 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
  4. 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
  5. 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
  6. 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
  7. 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
  8. 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
  9. 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
  10. 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
  11. 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
  12. Artificial intelligence in nanotechnology vol.24, pp.45, 2013, https://doi.org/10.1088/0957-4484/24/45/452002
  13. Computational Investigations of the Impact Resistance of Aluminum–Epoxy–Laminated Composites vol.21, pp.5, 2012, https://doi.org/10.1177/1056789511411739
  14. 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
  15. 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
  16. 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
  17. 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
  18. Application of gene expression programming to predict the compressive damage of lightweight aluminosilicate geopolymer 2012, https://doi.org/10.1007/s00521-012-1137-5
  19. 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
  20. 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
  21. 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
  22. 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
  23. 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
  24. 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
  25. 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
  26. 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
  27. 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
  28. 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
  29. 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
  30. Predictive compressive strength models for green concrete vol.11, pp.2, 2020, https://doi.org/10.1108/ijsi-05-2019-0044
  31. 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
  32. Exterior RC joints subjected to monotonic and cyclic loading vol.37, pp.7, 2020, https://doi.org/10.1108/ec-06-2019-0269
  33. 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
  34. 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