Modeling concrete strength using genetic operation trees

Author

Yeh, I-cheng ; Lien, Che-hui ; Peng, Chien-hua ; Lien, Li-chuan

Author_Institution

Chung Hua Univ., Hsinchu, Taiwan

Volume

3

fYear

2010

fDate

11-14 July 2010

Firstpage

1572

Lastpage

1576

Abstract

This study proposed to employ Cross-Validation (CV) to evaluate reliability of the strength models generated by nonlinear regression analysis (NLRA), artificial neural network (ANN), and genetic operation tree (GOT), to make more sound comparisons between them. It was found that (1) the ANN was the most accurate modeling tool for the Low, Medium, and High water-binder ratio (w/b) data sets; (2) using t-statistic, under 1% of level of significance, GOT was more accurate than NLRA for the Low and the Medium w/b data sets. (3) GOT can generate creative formulas consisting with domain knowledge.

Keywords

concrete; mechanical engineering computing; mechanical strength; neural nets; regression analysis; reliability; trees (mathematics); ANN; artificial neural network; concrete strength modeling; cross-validation; genetic operation trees; nonlinear regression analysis; reliability; t-statistic; Artificial neural networks; Biological system modeling; Concrete; Data models; Genetics; Mathematical model; Optimization; Concrete; genetic algorithms; nonlinear regression analysis; operation trees;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics (ICMLC), 2010 International Conference on

Conference_Location

Qingdao

Print_ISBN

978-1-4244-6526-2

Type

conf

DOI

10.1109/ICMLC.2010.5580800

Filename

5580800