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
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;
Conference_Titel :
Intelligent Information Technology Application, 2008. IITA '08. Second International Symposium on
Conference_Location :
Shanghai
Print_ISBN :
978-0-7695-3497-8
DOI :
10.1109/IITA.2008.402
