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http://dx.doi.org/10.4334/JKCI.2005.17.6.1075

Estimation of Concrete Strength Using Improved Probabilistic Neural Network Method  

Kim Doo-Kie (Dept. of Civil Engineering, Kunsan National University)
Lee Jong-Jae (Dept. of Civil Engineering, Korea Adv. Inst. Of Sci. and Tech.,)
Chang Seong-Kyu (Dept. of Civil Engineering, Kunsan National University)
Publication Information
Journal of the Korea Concrete Institute / v.17, no.6, 2005 , pp. 1075-1084 More about this Journal
Abstract
The compressive strength of concrete is commonly used criterion in producing concrete. However, the tests on the compressive strength are complicated and time-consuming. More importantly, it is too late to make improvement even if the test result does not satisfy the required strength, since the test is usually performed at the 28th day after the placement of concrete at the construction site. Therefore, accurate and realistic strength estimation before the placement of concrete is being highly required. In this study, the estimation of the compressive strength of concrete was performed by probabilistic neural network(PNN) on the basis of concrete mix proportions. The estimation performance of PNN was improved by considering the correlation between input data and targeted output value. Improved probabilistic neural network was proposed to automatically calculate the smoothing parameter in the conventional PNN by using the scheme of dynamic decay adjustment (DDA) algorithm. The conventional PNN and the PNN with DDA algorithm(IPNN) were applied to predict the compressive strength of concrete using actual test data of two concrete companies. IPNN showed better results than the conventional PNN in predicting the compressive strength of concrete.
Keywords
concrete compressive strength; strength prediction; correlation; dynamic decay adjustment algorithm(DDA); probabilistic neural network(PNN);
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1 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   DOI   ScienceOn
2 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   DOI   ScienceOn
3 Chtioui, Y., Bertrand, D., Devaux, M.E 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   DOI   ScienceOn
4 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   DOI   ScienceOn
5 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 Processing Society, Vol. 46, No.7, 1990, pp.1980-1990
6 Parzen, E., 'On estimation of a probability density function and mode', Annals of Mathematical Statistics, Vol.33, 1962, pp1065-1076   DOI   ScienceOn
7 Cacoullos, T. 'Estimation of a multivariate density', Annals of the Institute of Statistical Mathematics, Tokyo, Vol.18, No.2, 1966, pp.179-189   DOI
8 M.R. Berthold and J. Diamond, Constructive training of probabilistic neural networks, Neurocomputing, 1998, pp. 167-183
9 Jin, X., Cheu, R.L., and Srinivasan, D, 'Development and adaptation of constructive probabilistic neural network in freeway incident detection', Transportation Research Part C, Vol.10, 2002, pp.121-147   DOI   ScienceOn
10 Snell, L.M., Van Roekel, J.V., and Wallace, N.D., 'Predicting Early Concrete Strength', Concrete International, VoI.11, No.12, 1989, pp.43-47
11 M.R. Berthold, 'A probabilistic extension for the DDA algorithm, in: Int. Conf. on Neural Network', IEEE, New York, Vol.1, 1996, pp. 341-346
12 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
13 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
14 Lee, S.C., Prediction of Concrete Strength Using Artificial Neural Networks, Engineering Structures, Vol. 25, 2003, pp. 849-857   DOI   ScienceOn
15 Specht, D. F., Probabilistic Neural Networks, Neural Networks 3, 1990, pp. 109-118
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   DOI   ScienceOn
17 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   DOI   ScienceOn
18 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   DOI   ScienceOn
19 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   DOI   ScienceOn
20 Goh, A. T. C., 'Probabilistic neural network for evaluating seismic liquefaction potential', Canadian geotechnical journal: Revue canadienne de geotechnique, Vol.39, No.1, 2002, pp.219-232   DOI   ScienceOn
21 M.R. Berthold and J.Diamond, 'Boosting the performance of RBF networks with dynamic decay adjustment', Advances in Neural Information Processing Systems, No.7, 1995, pp.521-528
22 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
23 Korean Standard Association, Stand Test Method for Compressive Strength of Cylindrical Concrete Specimens, KS F2405, 1997, 199pp
24 Rumelhart, D. E., McClelland, J. L., & the PDP Research Group, Parallel distributed processing, Vol.1: Foudations. Cambridge, MA: The MIT Press., 1986
25 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   DOI   ScienceOn
26 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   DOI   ScienceOn