The Analysis of Building Subsidence Prediction Based on Grey Model Combined with Radial Basis Neural Network

Abstract:

Article Preview

In this paper, a new prediction model named RBNN-GM(1,1) (Radial Basis Neural Network-Grey Model) model was constructed and used for the analysis of building subsidence prediction for the Palms Together Dagoba in Famen Temple in Shaanxi Province in China. The constructed model can make full use of the advantages of few samples and little information predicting in Grey Theory and swift and self-learning in RBNN. The prediction results show that the combined model is more effective than the common grey model. The proposed combined model for building subsidence prediction may offer scientific rationale for estimating whether the building transmutation exceeds the criterion and provide reference for taking the corresponding safety measures.

Info:

Periodical:

Advanced Materials Research (Volumes 368-373)

Edited by:

Qing Yang, Li Hua Zhu, Jing Jing He, Zeng Feng Yan and Rui Ren

Pages:

2359-2363

DOI:

10.4028/www.scientific.net/AMR.368-373.2359

Citation:

Y. Bai et al., "The Analysis of Building Subsidence Prediction Based on Grey Model Combined with Radial Basis Neural Network", Advanced Materials Research, Vols. 368-373, pp. 2359-2363, 2012

Online since:

October 2011

Export:

Price:

$35.00

In order to see related information, you need to Login.

In order to see related information, you need to Login.