Similar articles

Abstract: This study explored the potential of using probabilistic neural networks (PNN) to predict shrinkage of thermal insulation mortar. Probabilistic results were obtained from the PNN model with the aid of Parzen non-parametric estimator of the probability density functions (PDF). Five variables, water-cementitious materials ratio, content of cement, fly ash, aggregate and plasticizer, were employed for input variables, while a category of 56-d shrinkage of mortar was used for the output variable. A total of 192 groups of experimental data from 64 mixtures designed using JMP7.0 software were collected, of which 120 groups of data were used for training the model and the other 72 groups of data for testing. The simulation results showed that the PNN model with an optimal smoothing parameter determined by the curves of the mean square error (MSE) and the number of unrecognized probability densities (UPDs) exhibited a promising capability of predicting shrinkage of mortar.

[1] ACI Committee 209, 1982. Prediction of Creep, Shrinkage and Temperature Effects in Concrete Structures. ACI-209-82, American Concrete Institute, Detroiti.

[2] Al-Omari, F.A., Al-Jarrah, O., 2004. Handwritten Indian numerals recognition system using probabilistic neural networks. Advanced Engineering Informatics, 18(1):9-16.

[3] Calderon, T.G., Cheh, J.J., 2002. A roadmap for future neural networks research in auditing and risk assessment. International Journal of Accounting Information Systems, 3(4):203-236.

[4] CEB-FIP, 1993. CEB-FIP Model Code 1990: Design Code 1994. Thomas Telford, London.

[5] Cevik, A., Guzelbey, I.H., 2008. Neural network modeling of strength enhancement for CFRP confined concrete cylinders. Building and Environment, 43(5):751-763.

[6] Fazel Zarandi, M.H., Türksen, I.B., Sobhani, J., Ramezanianpour, A.A., 2008. Fuzzy polynomial neural networks for approximation of the compressive strength of concrete. Applied Soft Computing, 8(1):488-498.

[7] Goel, R., Kumar, R., Paul, D.K., 2007. Comparative study of various creep and shrinkage prediction models for concrete. Journal of Materials in Civil Engineering, 19(3):249-260.

[8] Hamid, S.A., Iqbal, Z., 2004. Using neural networks for forecasting volatility of S&P 500 Index futures prices. Journal of Business Research, 57(10):1116-1125.

[9] Holmes, E, Nicholson, J.K., Tranter, G., 2001. Metabonomic characterization of genetic variations in toxicological and metabolic responses using probabilistic neural networks. Chemical Research in Toxicology, 14(2):182-191.

[10] Hsieh, S.H., Chen, C.H., 2009. Adaptive image interpolation using probabilistic neural network. Expert Systems with Applications, 36(3):6025-6029.

[11] JMP, 2005. Design of Experiments. SAS Institute Inc., Cary, NC, USA, p.80-81.

[12] Kim, S.H., Chun, S.H., 1998. Graded forecasting using an array of bipolar predictions: application of probabilistic neural networks to a stock market index. International Journal of Forecasting, 14(3):323-337.

[13] Kim, D.K., Lee, J.J., Lee, J.H., Chang, S.K., 2005. Application of probabilistic neural networks for prediction of concrete strength. Journal of Materials in Civil Engineering, 17(3):353-362.

[14] Kraaijveld, M.A., 1996. A Parzen classifier with an improved robustness against deviations between training and test data. Pattern Recognition Letters, 17(7):679-689.

[15] Lam, J.P., 2002. Evaluation of Concrete Shrinkage and Creep Prediction Models. MS Thesis, San Jose State University, USA.

[16] Lee, J.J., Kim, D., Chang, S.K., Nocete, C.F.M., 2009. An improved application technique of the adaptive probabilistic neural network for predicting concrete strength. Computational Materials Science, 44(3):988-998.

[17] Lilliefors, H., 1967. On the Kolmogorov-Smirnov test for normality with mean and variance unknown. Journal of the American Statistical Association, 62(318):399-402.

[18] Masters, T., 1995. Advanced Algorithms for Neural Networks. John Wiley, New York, p.55-56.

[19] MOC (Ministry of Construction of the People’s Republic of China), 1991. Standard for Test Method of Basic Properties of Construction Mortar. National Standards of People’s Republic of China (JGJ 70-90) (in Chinese).

[20] RILEM TC-107-GCS, 1995. Creep and shrinkage prediction models for analysis and design of concrete structures-Model B3. Material Structure, 28(6):357-365.

[21] Sarıdemir, M., Topçu, İ.B., Özcan, F., Severcan, M.H., 2009. Prediction of long-term effects of GGBFS on compressive strength of concrete by artificial neural networks and fuzzy logic. Construction and Building Materials, 23(3):1279-1286.

[22] Simon, L., Karim, M.N., 2001. Probabilistic neural networks using Bayesian decision strategies and a modified Gompertz model for growth phase classification in the batch culture of Bacillus subtilis. Biochemical Engineering Journal, 7(1):41-48.

[23] Shaffer, R.E., Rose-Pehrsson, S.L., 1999. Improved probabilistic neural network algorithm for chemical sensor array pattern recognition. Analytical Chemistry, 71(19):4263-4271.

[24] Specht, D.F., 1988. Probabilistic Neural Networks for Classification, Mapping, or Associative Memory. IEEE International Conference on Neural Networks, San Diego, CA, 1:525-532.

[25] Steenhoek., L.W., 1999. A Probabilistic Neural Network Computer Vision System for Corn Kernel Damage Evaluation. PhD Thesis, Iowa State University, USA, p.12-14.

[26] Thibault, J., Grandjean, B.P.A., 1991. A neural network methodology for heat transfer data analysis. International Journal of Heat and Mass Transfer, 34(8):2063-2070.

[27] Topçu, İ.B., Sarıdemir, M., 2007. Prediction of properties of waste AAC aggregate concrete using artificial neural network. Computational Materials Science, 41(1):117-125.

[28] Wang, J.Z., Ni, H.G., He, J.Y., 1999. The application of automatic acquisition of knowledge to mix design of concrete. Cement and Concrete Research, 29(12):1875-1880.

[29] Wang, Y., Adali, T., Kung, S.Y., Szabo, Z., 1998. Quantification and segmentation of brain tissues from MR images—a probabilistic neural network approach. IEEE Transactions on Image Processing, 7(8):1165-1181.

[30] Yan, P.F., Zhang, C.S., 2002. Artificial Neural Networks and Evolutionary Computing. Tsinghua University Press, Beijing, China, p.51-60 (in Chinese).

[31] Yang, Z.R., Platt, M.B., Platt, H.D., 1999. Probabilistic neural networks in bankruptcy prediction. Journal of Business Research, 44(2):67-74.