Mix proportion design of SCC based on BP network

Title: Mix proportion design of SCC based on BP network
Author(s): Q. Zhao, J. Wang, J. Ji, H. Li
Paper category : conference
Book title: The 50-year Teaching and Research Anniversary of Prof. Sun Wei on Advances in Civil Engineering Materials
Editor(s): C. Miao, G. Ye and H. Chen
Print-ISBN: 978-2-35158-098-1
e-ISBN: 978-2-35158-099-8
Publisher: RILEM Publications SARL
Publication year: 2010
Pages: 173 - 178
Total Pages: 6
Nb references: 5
Language: English

Abstract: Many parameters of raw materials, such as, strength of cement, mud content and fineness modulus of river sand, maximum size and content of needle-like/sheet-like of crushed stone, loss of ignition and fineness of fly ash, may exert significant influence on the workability and mechanical properties of self compacting concrete(SCC). The optimal mix proportion of SCC alters along with the parameters of raw materials. By virtue of BP neural network approach, taking strength of cement, mud content and fineness modulus of river sand, maximum size and content of needle-like/sheet-like of crushed stone, loss of ignition and fineness of fly ash as the input parameters, and the corresponding optimized mix proportion as the output to describe the nonlinear relationship between them. And the orthogonal experiment was designed for the purpose of training of network. The results demonstrated that the BP neural network trained by orthogonal test data may employ to predict the optimal concrete mix proportion. This approach may replace some waste-time and heavy laboratory tests. In addition, such method may real-time optimize mix proportion of SCC, which has great effect on the quality control of manufacturing SCC.

Online publication: 2010-04-22
Publication type : full_text
Public price (Euros): 0.00