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

http://doi.org/10.1631/jzus.A0900441


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.

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author="Yi-qun DENG, Pei-ming WANG",
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year="2010",
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doi="10.1631/jzus.A0900441"
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%A Yi-qun DENG
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%P 212-222
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%I Zhejiang University Press & Springer
%DOI 10.1631/jzus.A0900441

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T1 - Predicting the shrinkage of thermal insulation mortar by probabilistic neural networks
A1 - Yi-qun DENG
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J0 - Journal of Zhejiang University Science A
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SP - 212
EP - 222
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PB - Zhejiang University Press & Springer
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DOI - 10.1631/jzus.A0900441


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.

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

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