Computers and Concrete

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Use of partial least squares analysis in concrete technology

  • Received : 2013.03.06
  • Accepted : 2013.09.16
  • Published : 2014.02.25
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Abstract

Multivariate analysis is a statistical technique that investigates relationship between multiple predictor variables and response variable and it is a very commonly used statistical approach in cement and concrete industry. During model building stage, however, many predictor variables are included in the model and possible collinearity problems between these predictors are generally ignored. In this study, use of partial least squares (PLS) analysis for evaluating the relationships among the cement and concrete properties is investigated. This regression method is known to decrease the model complexity by reducing the number of predictor variables as well as to result in accurate and reliable predictions. The experimental studies showed that the method can be used in the multivariate problems of cement and concrete industry effectively.

Keywords

References

  1. Ahangar-Asr, A., Faramarzi, A. and Javadi, A.A. (2011), "Modelling mechanical behavior of rubber concrete using evolutionary polynominal regression", Eng. Comput., 28(3-4), 492-507. https://doi.org/10.1108/02644401111131902
  2. Chen, G.J. (2012), "A simple way to deal with multicollinearity", J. Appl. Statistics, 39(9), 1893-1909. https://doi.org/10.1080/02664763.2012.690857
  3. De Jong, S. (1993), "SIMPLS: an alternative approach to partial least squares regression", Chemom. Intell. Lab. Syst., 18(3), 251-253. https://doi.org/10.1016/0169-7439(93)85002-X
  4. Dobrska, M., Wang, H. and Blackburn, W. (2012), "Ordinal regression with continuous pairwise preferences", Int. J. Mach. Learning Cybernetics, 3(1), 59-70. https://doi.org/10.1007/s13042-011-0036-x
  5. Draper, N.R. and Smith, H. (1998), Applied Regression Analysis, John Wiley and Sons, USA.
  6. Eswari, S., Raghunath, P.N. and Kothandaraman, S. (2011), "Regression modeling for strength and thoughness evaluation of hybrid fibre reinforced concrete", ARPN J. Eng. Appl. Sci., 6(5), 1-8.
  7. Li, Z. (2011), Advanced Concrete Technology, John Wiley & Sons, 528.
  8. Liebmann, B., Filzmoser, P. and Varmuza, K. (2010), "Robust and classical PLS regression compared", J. Chem., 24, 111-120. https://doi.org/10.1002/cem.1279
  9. Martens, H. and Naes, T. (1989), Multivariate Calibration, Wiley, Chichester, UK.
  10. Martins, J.P.A., Teofilo, R.F. and Ferreira, M.M.C. (2010), "Computational performance and cross-validation error precision of five PLS algorithms using designed and real data sets", J. Chem., 24, 320-332.
  11. Mehta, P.K. and Monteiro, P.K. (1993), Concrete. Structure, Properties and Materials, Prentice-Hall, New York, 496p.
  12. Mevik, B.H. and Wehrens, R. (2007), "The pls package: principal component and partial least squares regression in R", J. Statistical Softw., 18(2), 1-24. https://doi.org/10.1360/jos180001
  13. Miled, K., Limam, O. and Sab, K. (2012), "A probabilistic mechanical model for prediction of aggregates' size distribution effect on concrete compressive strength", PHYSICA A - Statistical Mechanics and Its Applications, 391(12), 3366-3378. https://doi.org/10.1016/j.physa.2012.01.051
  14. Mohammadhassani, M., Nezamabadi-Pour, H., Jumaat, MZ., Jameel, M., Hakim, S.J.S. and Zargar, M. (2013), "Application of the ANFIS model in deflection prediction of concrete deep beam", Struct. Eng. Mech., 45(3), 319-332.
  15. Musa, A.B. (2013), "Comparative study on classification performance between support vector machine and logistic regression", Int. J. Machine Learn. Cybernet., 4(1), 13-24. https://doi.org/10.1007/s13042-012-0068-x
  16. Neter, J., Kutner, H.M., Nachtshein, C.J. and Wasserman, W. (1996), Applied Linear Statistical Models, McGraw-Hill, Boston, Mass.
  17. Rasa, E., Ketabchi, H. and Afshar, M.H. (2009), "Predicting density and compressive strength of concrete cement paste containing silica fume using artificial neural networks", Sci. Iranica Transact. A - Civil Eng., 16(1), 33-42.
  18. R Development Core Team (2008), R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria, ISBN 3-900051-07-0
  19. Rosipal, R. and Kramer, N. (2006), "Overview and recent advances in partial least squares", in Subspace, Latent Structure and Feature Selection, Statistical and Optimization, Perspectives Workshop, SLSFS 2005 ,Eds. C. Sauners, Lecture Notes in Computer Science, Springer.
  20. Sahin, F. (2009), "Using of soft computing techniques in raw material and cement production processes", Msc Thesis, Cukurova University, Adana, Turkey (in Turkish).
  21. Taha, R.O.H. (2012), "The possibility of using artificial neural networks in auditing-theoretical analytical paper", European J. Economics, Finance and Administrative Sciences, 47, 43-56.
  22. Tutmez, B. (2009), "Clustering-based identification for the prediction of splitting tensile strength of concrete", Comput. Concr., 6(2), 155-165. https://doi.org/10.12989/cac.2009.6.2.155
  23. Tutmez, B. and Dag, A. (2012), "Regression-based algorithms for exploring the relationships in a cement raw material quarry", Comput. Concr., 10(5), 459-469.
  24. Varmuza, K. and Filzmoser, P. (2009), Introduction to multivariate statistical analysis in chemometrics, CRC Press, Boca Raton.
  25. Wold, S., Sjostromi, M. and Eriksson, L. (2001), "PLS-regression: a basic tool of chemometrics", Chem. Intell. laboratory Syst., 58, 109-130. https://doi.org/10.1016/S0169-7439(01)00155-1
  26. Yeniay, O. and Goktas, A. (2002). "A comparison of partial least squares regression with other prediction methods", Hacettepe J. Math. Statistics, 31, 99-111.
  27. Yeh, I.C. (2007), "Modeling slump flow of concrete using second-order regressions and artificial neural networks", Cement Concrete Compos., 29, 474-480. https://doi.org/10.1016/j.cemconcomp.2007.02.001
  28. Zarandi, M.H.F. (2008), "Fuzzy polynominal neural networks for approximation of the compressive strength of concrete", Appl. Soft Comput., 8(1), 488-498. https://doi.org/10.1016/j.asoc.2007.02.010

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