Application of generalized regression neural network in prediction of cement properties

Author

Ren, Shuxia ; Yang, Dan ; Ji, Fengqiu ; Tian, Xiushu

Author_Institution

Sch. of Mater. Sci. & Eng., Shijiazhuang TieDao Univ., Shijiazhuang, China

Volume

2

fYear

2010

fDate

25-27 June 2010

Abstract

Different modelling methods based on neural networks have become popular and been widely used in a great variety of fields. The properties of cements were predicted by neural network according to their chemical compositions, fineness, and other factors in this paper. The results showed that generalized regression neural network (GRNN) has higher accuracy and faster training speed compared with BP neural network. The maximum relative errors of the hydration heat and compressive strength predicted by GRNN were less than 5% and 9%, respectively.

Keywords

backpropagation; cements (building materials); compressive strength; mechanical engineering computing; neural nets; BP neural network; cement properties prediction; chemical compositions; chemical fineness; compressive strength; generalized regression neural network; hydration heat; Application software; Cement industry; Chemicals; Computer networks; Design engineering; Educational institutions; Materials science and technology; Neural networks; Predictive models; Transfer functions; compressive strength; generalized regression neural network; hydration heat;

fLanguage

English

Publisher

ieee

Conference_Titel

Computer Design and Applications (ICCDA), 2010 International Conference on

Conference_Location

Qinhuangdao

Print_ISBN

978-1-4244-7164-5

Electronic_ISBN

978-1-4244-7164-5

Type

conf

DOI

10.1109/ICCDA.2010.5541402

Filename

5541402