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Journal of Zhejiang University SCIENCE A 2010 Vol.11 No.3 P.212-222


Predicting the shrinkage of thermal insulation mortar by probabilistic neural networks

Author(s):  Yi-qun DENG, Pei-ming WANG

Affiliation(s):  Key Laboratory of Advanced Civil Engineering Materials, Ministry of Education, Tongji University, Shanghai 200092, China

Corresponding email(s):   tjwpm@126.com

Key Words:  Mortar, Shrinkage, Probabilistic neural networks (PNN), Thermal insulation

Yi-qun DENG, Pei-ming WANG. Predicting the shrinkage of thermal insulation mortar by probabilistic neural networks[J]. Journal of Zhejiang University Science A, 2010, 11(3): 212-222.

@article{title="Predicting the shrinkage of thermal insulation mortar by probabilistic neural networks",
author="Yi-qun DENG, Pei-ming WANG",
journal="Journal of Zhejiang University Science A",
publisher="Zhejiang University Press & Springer",

%0 Journal Article
%T Predicting the shrinkage of thermal insulation mortar by probabilistic neural networks
%A Yi-qun DENG
%A Pei-ming WANG
%J Journal of Zhejiang University SCIENCE A
%V 11
%N 3
%P 212-222
%@ 1673-565X
%D 2010
%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A0900441

T1 - Predicting the shrinkage of thermal insulation mortar by probabilistic neural networks
A1 - Yi-qun DENG
A1 - Pei-ming WANG
J0 - Journal of Zhejiang University Science A
VL - 11
IS - 3
SP - 212
EP - 222
%@ 1673-565X
Y1 - 2010
PB - Zhejiang University Press & Springer
ER -
DOI - 10.1631/jzus.A0900441

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.

Darkslateblue:Affiliate; Royal Blue:Author; Turquoise:Article


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