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Modified Probabilistic Neural Network of Heterogeneous Probabilistic Density Functions for the Estimation of Concrete Strength

  • Kim, Doo-Kie (Dept. of Civil and Environmental Engineering, Kunsan National University) ;
  • Kim, Hee-Joong (Dept. of Civil Engineering, Keimyung University) ;
  • Chang, Sang-Kil (Dept. of Civil and Environmental Engineering, Kunsan National University) ;
  • Chang, Seong-Kyu (Dept. of Civil and Environmental Engineering, Kunsan National University)
  • Published : 2007.03.31

Abstract

Recently, probabilistic neural network (PNN) has been proposed to predict the compressive strength of concrete for the known effect of improvement on PNN by the iteration method. However, an empirical method has been incorporated in the PNN technique to specify its smoothing parameter, which causes significant uncertainty in predicting the compressive strength of concrete. In this study, a modified probabilistic neural network (MPNN) approach is hence proposed. The global probability density function (PDF) of variables is reflected by summing the heterogeneous local PDFs which are automatically determined by the individual standard deviation of each variable. The proposed MPNN is applied to predict the compressive strength of concrete using actual test data from a concrete company. The estimated results of MPNN are compared with those of the conventional PNN. MPNN showed better results than the conventional PNN in predicting the compressive strength of concrete and provided promising results for the probabilistic approach to predict the concrete strength by using the individual standard deviation of a variable.

Keywords

References

  1. Snell, L. M., Van Roekel, J. V., and Wallace, N. D., "Predicting Early Concrete Strength," Concrete International, Vol.11, No.12, 1989, pp.43-47
  2. Popovics, S., "History of a Mathematical Model for Strength Development of Portland Cement Concrete," ACI Materials Journal, Vol.95, No.5, 1998, pp.593-600
  3. Oh, J. W, Lee, I.W, Kim, J. T., and Lee, G W, "Application of Neural Networks for Proportioning of Concrete Mixes," ACI Material Journal, Vol.96, No.1, 1999, pp.61-67.
  4. Lee, S. C., "Prediction of Concrete Strength Using Artificial Neural Networks", Engineering Structures, Vol.25, 2003, pp.849-857 https://doi.org/10.1016/S0141-0296(03)00004-X
  5. Kim, J. I., Kim, D. K., Feng, M. Q., and Yazdani, F., "Application of Neural Networks for Estimation of Concrete Strength," Journal of Materials in Civil Engineering, ASCE, Vol.16, No.3, 2004, pp.257-264 https://doi.org/10.1061/(ASCE)0899-1561(2004)16:3(257)
  6. Touretzky, D. S., Thibadeau, R. H., and Romero, R. D., "Optical Chinese Character Recognition Using Probabilistic Neural Networks," Pattern Recognition, Vol.30, No.8, 1997, pp.1279-1292 https://doi.org/10.1016/S0031-3203(96)00166-5
  7. Raghu, P. P. and Yegnanarayana, B., "Supervised Texture Classification Using a Probabilistic Neural Network and Constraint Satisfaction Model," IEEE Transactions on Neural Networks, Vol.9, No.3,1998, pp.516-522 https://doi.org/10.1109/72.668893
  8. Lin, S. H., Kung, S. Y., and Lin, L. J., "Face Recognition/Detection by Probabilistic Decision-Based Neural Network," IEEE Transactions on Neural Networks, Vol.8, No.1, 1997, pp.114-132 https://doi.org/10.1109/72.554196
  9. Chtioui, Y., Bertrand, D., Devaux, M. F., and Barba, D., "Comparison of Multilayer Perceptron and Probabilistic Neural Networks in Artificial Vision. Application to the Discrimination of Seeds," Journal of Chemometrics, Vol.11, No.2, 1997, pp.111-129 https://doi.org/10.1002/(SICI)1099-128X(199703)11:2<111::AID-CEM455>3.0.CO;2-V
  10. Wang, Y., Adali, T., Kung, S. Y., and Szabo, Z., "Quantification and Segmentation of Brain Tissues from MR Images: a Probabilistic Neural Network Approach," IEEE Transactions on Image Processing, Vol.7, No.8, 1998, pp.1165-1181 https://doi.org/10.1109/83.704309
  11. Holmes, E., Nicholson, J. K., and Tranter, G., "Metabonomic Characterization of Genetic Variations in Toxicological and Metabolic Responses Using Probabilistic Neural Networks," Chemical Research in Toxicology, Vol.14, No.2, 2001, pp.182-191 https://doi.org/10.1021/tx000158x
  12. Zaknich, A., "Introduction to the Modified Probabilistic Neural Network for General Signal Processing Applications," IEEE Transactions on Signal Processing : a Publication of the IEEE Signal ProcessingSociety, Vol.46, No.7, 1990, pp.1980-1990
  13. Yang, Z. R., Platt, M. B., and Platt, H. D., "Probabilistic Neural Networks in Bankruptcy Prediction," Journal of Business Research, Vol.44, No.2, 1999, pp.67-74 https://doi.org/10.1016/S0148-2963(97)00242-7
  14. Goh, A. T. C., "Probabilistic Neural Network for Eval-Uating Seismic Liquefaction Potential," Canadian Geotechnical Journal: Revue Canadienne de Geotechnique, Vol.39, No.1, 2002, pp.219-232 https://doi.org/10.1139/t01-073
  15. Aoki, T., Ceravolo, R., De Stefano, A., Genovese, C., and Sabia, D., "Seismic Vulnerability Assessment of Chemical Plants Through Probabilistic Neural Networks," Reliability Engineering & System Safety, Vol.77, No.3, 2002, pp.263-268 https://doi.org/10.1016/S0951-8320(02)00059-5
  16. Sinha, S. K. and Pandey, M. D., "Probabilistic Neural Network for Reliability Assessment of Oil and Gas Pipelines," Computer-Aided Civil and Infrastructure Engineering, Vol.17, No.5, 2002, pp.320-329 https://doi.org/10.1111/1467-8667.00279
  17. Kim, D. K., Lee, J. J., Lee, J. H., and Chang, S. K., "Estimation of Concrete Strength Using Improved Probabilistic Neural Network Method," Journal of the Korea Concrete Institute, Vol.17, No.6, 2005, pp.1075-1084 https://doi.org/10.4334/JKCI.2005.17.6.1075
  18. Specht, D. F., "Probabilistic Neural Networks," Neural Networks, Vol.3, 1990, pp.109-118 https://doi.org/10.1016/0893-6080(90)90049-Q
  19. Parzen, E., "On Estimation ofa Probability Density Function and Mode," Annals of Mathematical Statistics, Vol.33, 1962, pp.1065-1076 https://doi.org/10.1214/aoms/1177704472
  20. Cacoullos, T. "Estimation of a Multivariate Density," Annals of the Institute of Statistical Mathematics, Tokyo, Vol.18, No.2, 1966, pp.179-189 https://doi.org/10.1007/BF02869528
  21. ASTM. "Standard Test Method for Compressive Strength of Cylindrical Concrete Specimens," Annual Book of ASTM Standards: ASTM39-93a, Vol.4, No.2, 1992, pp.22-24.
  22. Korean Standard Association, Stand Test Method for Compressive Strength of Cylindrical Concrete Specimens, KS F2405, 1997