6 November 2006 Autogenous shrinkage prediction on high-performance concrete of fly ash based on BP neural network
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Proceedings Volume 6357, Sixth International Symposium on Instrumentation and Control Technology: Signal Analysis, Measurement Theory, Photo-Electronic Technology, and Artificial Intelligence; 63574O (2006); doi: 10.1117/12.717458
Event: Sixth International Symposium on Instrumentation and Control Technology, 2006, Beijing, China
Abstract
The article adopts test data of neural network for autogenous shrinkage to train and predict on the data which doesn't join training. The article's prediction is on the basis of common medium sand, 5-31.5mm limestone rubble, second class fly-ash, P.O42.5 silicate cement, considering factors include five ones such as ratio of water and cement, sand rate, content of cement, content of fly ash, etc.By adjusting various parameters of neural network structure, it obtains three optimized results of neural network simulation. The error between concrete autogtenous shrinkage value of neural network prediction and trial value is within 3%, which can meet requirement of the concrete engineering.
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Baomin Wang, Wenping Zhang, Lijiu Wang, "Autogenous shrinkage prediction on high-performance concrete of fly ash based on BP neural network", Proc. SPIE 6357, Sixth International Symposium on Instrumentation and Control Technology: Signal Analysis, Measurement Theory, Photo-Electronic Technology, and Artificial Intelligence, 63574O (6 November 2006); doi: 10.1117/12.717458; https://doi.org/10.1117/12.717458
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KEYWORDS
Cements

Neural networks

Humidity

Nerve

Minerals

Silicates

Temperature metrology

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