Rule induction for structural damage identification

Author

Rao, Wenbi ; Boström, Henrik ; Xie, Songhua

Author_Institution

Sch. of Comput. Sci. & Technol., Wuhan Univ. of Technol., China

Volume

5

fYear

2004

fDate

26-29 Aug. 2004

Firstpage

2865

Abstract

Structural damage identification is becoming a worldwide research subject. Some machine learning methods have been used to solve this problem, and most of them are neural network methods. In this paper, three different rule inductive methods named as divide-and-conquer (DAC), bagging and separate-and-conquer (SAC) are investigated for predicting the damage position and extent of a concrete beam. Then radial basis function neural network (RBFNN) is used here for comparative purposes. The rule inductive methods, especially bagging is shown to obtain good prediction.

Keywords

beams (structures); concrete; fault diagnosis; learning by example; radial basis function networks; structural engineering computing; bagging method; concrete beam; damage position prediction; divide and conquer method; machine learning methods; radial basis function neural network; rule inductive method; separate and conquer method; structural damage identification; Artificial neural networks; Bagging; Concrete; Learning systems; Mathematical model; Multi-layer neural network; Neural networks; Predictive models; Radial basis function networks; Recurrent neural networks;

fLanguage

English

Publisher

ieee

Conference_Titel

Machine Learning and Cybernetics, 2004. Proceedings of 2004 International Conference on

Print_ISBN

0-7803-8403-2

Type

conf

DOI

10.1109/ICMLC.2004.1378520

Filename

1378520