DocumentCode
2270223
Title
The Forecast of Carbonation Depth of Concrete Based on RBF Neural Network
Author
Liu, Yan ; Zhao, Shengli ; Yi, Cheng
Author_Institution
Coll. of Urban & Rural Constr., Agric. Univ. of Hebei, Baoding
Volume
3
fYear
2008
fDate
20-22 Dec. 2008
Firstpage
544
Lastpage
548
Abstract
By analyzing the causes and influencing factors of carbonation of concrete, the RBF neural network model for predicting carbonation depth of concrete is founded. And actual data is analyzed through an example and results are compared with the BP network model. The testing results show that RBF network model for predicting carbonation depth of concrete can become a new effective assessment model with better prediction results and higher recognition precision.
Keywords
concrete; radial basis function networks; reinforced concrete; structural engineering computing; BP network model; RBF neural network; assessment model; concrete carbonation depth forecast; Chemical elements; Concrete; Data analysis; Intelligent networks; Laboratories; Neural networks; Predictive models; Protection; Steel; Testing; RBF neural network; carbonation depth; durability; forecast;
fLanguage
English
Publisher
ieee
Conference_Titel
Intelligent Information Technology Application, 2008. IITA '08. Second International Symposium on
Conference_Location
Shanghai
Print_ISBN
978-0-7695-3497-8
Type
conf
DOI
10.1109/IITA.2008.402
Filename
4740057
Link To Document