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

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)
Chapter
Chapter 5: Monitoring, Evaluation and Reinforcement Technique of Civil Engineering
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, Q. C. Ren, F. K. Zeng, "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
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Price
$32.00
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