Computers and Concrete

Techno-Press (테크노프레스)
권호 표지
DOI QR코드

DOI QR Code

Prediction of workability of concrete using design of experiments for mixtures

  • Yeh, I-Cheng (Department of Information Management, Chung-Hua University)
  • Received : 2007.03.20
  • Accepted : 2007.11.30
  • Published : 2008.02.25
⟨ Previous Next ⟩

Abstract

In this study, the effects and the interactions of water content, SP-binder ratio, and water-binder ratio on the workability performance of concrete were investigated. The experiments were designed based on flatted simplex-centroid experiment design modified from standard simplex-centroid one. The data gotten from the design was used to build the concrete slump model using neural networks. Research reported in this paper shows that a small number of slump experiments can be performed and meaningful data obtained with the experiment design. Such data would be suitable for building slump model using neural networks. The trained network can be satisfactorily used for exploring the effects of the components and their interactions on the workability of concrete. It has found that a high water content and a high SP/b ratio is essential for high workability, but achieving this by increasing these parameters will not in itself guarantee high workability. The w/b played a very important role in producing workability and had rather profound effects; however, the medium value about 0.4 is the best w/b to reach high slump without too much effort on trying to find the appropriate water content and SP/b.

Keywords

Acknowledgement

Supported by : National Science Council

References

  1. Aitcin, P. C. and Neville, A. (1993), "High-performance concrete demystified", Concrete Int., 1993, 21-26.
  2. Basma, A. A., Barakat, S., and Al-Oraimi, S. (1999), "Prediction of cement degree of hydration using artificial neural networks", J. Mater. in Civ. Eng., 96(2), 167-172.
  3. Brown, D. A., Murthy, P. L. N., and Berke, L. (1991), "Computational simulation of composite ply micromechanics using artificial neural networks", Micro. in Civ. Eng., 6, 87-97.
  4. Faroug, F., Szwaborski, J., and Wild, S. (1999), "Influence of superplasticizers on workability of concrete", J. Mater. Civ. Eng., 11(2), 151-157. https://doi.org/10.1061/(ASCE)0899-1561(1999)11:2(151)
  5. Ghaboussi, J., Garrett, J. H., and Wu, X. (1991), "Knowledge-based modeling of material behavior with neural networks", J. Eng. Mech., 117(1), 132-153. https://doi.org/10.1061/(ASCE)0733-9399(1991)117:1(132)
  6. Goh, A. T. C. (1995), "Neural networks for evaluating CPT calibration chamber test data", Micro. in Civ. Eng., 10: 147-151. https://doi.org/10.1111/j.1467-8667.1995.tb00277.x
  7. Haj-Ali, R. M., Kurtis, K. E., and Akshay, R. (2001), "Neural network modeling of concrete expansion during long-term sulfate exposure", J. Mater. in Civ. Eng., 98(1), 36-43.
  8. Ji, T. and Lin, X. J. (2006), "A mortar mix proportion design algorithm based on artificial neural networks", Comput. Concrete, 3(5), 357-373. https://doi.org/10.12989/cac.2006.3.5.357
  9. Kim, J. I., Kim, D. K., Feng, M. Q., and Yazdani, F. (2004), "Application of neural networks for estimation of concrete strength", J. Mater. in Civ. Eng., 16(3), 257-264. https://doi.org/10.1061/(ASCE)0899-1561(2004)16:3(257)
  10. Kwan, A. K. H. (2000), "Use of condensed silica fume for making high-strength, self-consolidating concrete", Canadian J. Civ. Eng., 27, 620-627. https://doi.org/10.1139/l99-091
  11. Lippmann, R. P. (1987), "An introduction to computing with neural nets", IEEE ASSP Magazine, 4(2), 4-22. https://doi.org/10.1109/MASSP.1987.1165575
  12. Myers, R. H., and Montgomery, D. C. (1995), Response Surface Methodology, John Wiley & Sons, Inc., New York.
  13. Nehdi, M., El Chabib, H., and El Naggar, M. H. (2001), "Predicting performance of self-compacting concrete mixtures using artificial neural networks", J. Mater. in Civ. Eng., 98(5), 394-401.
  14. Nehdi, M., Djebbar, Y. and Khan, A. (2001), "Neural network model for preformed-foam cellular concrete", J. Mater. Civ. Eng., 98(5), 402-409.
  15. Oh, J.-W., Lee, I.-W., Kim, J.-T., and Lee, G.-W. (1999), "Application of neural networks for proportioning of concrete mixes", J. Mater. Civ. Eng., 96(1), 61-67.
  16. Olek, J. and Diamond, S. (1989), "Proportioning of constant paste composition fly ash concrete mixes", ACI Mater. J., 86(2), 159-166.
  17. Peng, J., Li, Z. and Ma, B. (2002), "Neural network analysis of chloride diffusion in concrete", Mater. Civ. Eng., 14(4), 327-333. https://doi.org/10.1061/(ASCE)0899-1561(2002)14:4(327)
  18. Punkki, J., Golaszewski, J., and Gjorv, O. E. (1996), "Workability loss of high-strength concrete", ACI Mater. J., 93(5), 427-431.
  19. Stegemann, J. A. and Buenfeld, N. R. (2004), "Mining of existing data for cement-solidified wastes using neural networks", J. Environ. Eng., 130(5), 508-515. https://doi.org/10.1061/(ASCE)0733-9372(2004)130:5(508)
  20. Yeh, I-Cheng (1998a), Modeling concrete strength with augment-neuron networks. J. Mater. Civ. Eng. 10(4), 263-268. https://doi.org/10.1061/(ASCE)0899-1561(1998)10:4(263)
  21. Yeh, I-Cheng (1998b), "Modeling of strength of high performance concrete using artificial neural networks", Cement Concrete Res., 28(12), 1797-1808. https://doi.org/10.1016/S0008-8846(98)00165-3
  22. Yeh, I-Cheng (1999), "Design of high-performance concrete mixture using neural networks and nonlinear programming", J. Comput. in Civ. Eng., 13(1), 36-42. https://doi.org/10.1061/(ASCE)0887-3801(1999)13:1(36)
  23. Yeh, I-Cheng, Chen, I-Cheng, Ko, Tai-Zi, Peng, Chao-Che, Gan, Chun-Cheng, and Chen, J. W. (2002), "Optimum mixture design of high performance concrete using artificial neural networks", J. Technology, 17(4), 583-591.
  24. Yen, T., Tang, C.-W., Chang, C.-S., and Chen K.-H. (1999), "Flow behaviour of high strength high-performance concrete", Cement Concrete Compos., 21, 413-424. https://doi.org/10.1016/S0958-9465(99)00026-8

Cited by

  1. Modeling properties of self-compacting concrete: support vector machines approach vol.5, pp.5, 2008, https://doi.org/10.12989/cac.2008.5.5.461
  2. Properties of pervious concrete containing high-calcium fly ash vol.17, pp.3, 2016, https://doi.org/10.12989/cac.2016.17.3.337
  3. Modeling of Thermal Conductivity of Concrete with Vermiculite by Using Artificial Neural Networks Approaches vol.26, pp.4, 2013, https://doi.org/10.1080/08916152.2012.669810
  4. A comparative modeling study to estimate wear of concrete vol.24, pp.3-4, 2014, https://doi.org/10.1007/s00521-012-1277-7
  5. Comparison of artificial neural networks and general linear model approaches for the analysis of abrasive wear of concrete vol.25, pp.8, 2011, https://doi.org/10.1016/j.conbuildmat.2011.03.040
  6. Modelling the minislump spread of superplasticized PPC paste using RLS with the application of Random Kitchen sink vol.310, pp.1757-899X, 2018, https://doi.org/10.1088/1757-899X/310/1/012035
  7. Modeling the Fresh and Hardened Stage Properties of Self-Compacting Concrete using Random Kitchen Sink Algorithm vol.12, pp.1, 2018, https://doi.org/10.1186/s40069-018-0246-7
  8. Modeling the effects of additives on rheological properties of fresh self-consolidating cement paste using artificial neural network vol.8, pp.3, 2008, https://doi.org/10.12989/cac.2011.8.3.279
  9. Efficiency factor of high calcium Class F fly ash in concrete vol.8, pp.5, 2011, https://doi.org/10.12989/cac.2011.8.5.583
  10. Modeling the compressive strength of cement mortar nano-composites vol.10, pp.1, 2012, https://doi.org/10.12989/cac.2012.10.1.049
  11. Application of Fuller's ideal curve and error function to making high performance concrete using rice husk ash vol.10, pp.6, 2008, https://doi.org/10.12989/cac.2012.10.6.631
  12. Probabilistic modeling of geopolymer concrete using response surface methodology vol.19, pp.6, 2008, https://doi.org/10.12989/cac.2017.19.6.737
  13. Hybrid Gaussian Process Inference Model for Construction Management Decision Making vol.19, pp.4, 2008, https://doi.org/10.1142/s0219622020500212
  14. Artificial intelligence for the compressive strength prediction of novel ductile geopolymer composites vol.28, pp.1, 2008, https://doi.org/10.12989/cac.2021.28.1.055