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

Yan Bai; Qing Chang Ren; Fan Kui Zeng

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

Paper Titles

Cyclic Behavior of the Steel Plate Reinforce Concrete Shear Walls Built at the Aspect Ratio Is 2
p.2333

Application of Equal Strength Substitution Method on Concrete Beams Strengthened with Steel Plates
p.2341

Experimental Study of Pre-Stressed Concrete Beams with Fatigue Subjoining Freezing-Thawing Circle
p.2346

Study on Cracking Reasons for Continuous Box Girder Bridge with Secondary Concreting
p.2351

The Analysis of Building Subsidence Prediction Based on Grey Model Combined with Radial Basis Neural Network
p.2359

Research on Design Reference Period and Structural Design Method
p.2364

The Application of the Complete Decomposition Model in Analyzing the Incontrollable Construction Cost
p.2369

Experimental Study on Flexural of RC Beams Strengthened with Basaltic Fiber Sheets
p.2376

The Status and Prospect of Long Span Bridges Detection
p.2381

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 368-373The Analysis of Building Subsidence Prediction...

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.

You might also be interested in these eBooks

Advances in Civil Engineering and Architecture Innovation

View Preview

Info:

Periodical:

Advanced Materials Research (Volumes 368-373)

Pages:

2359-2363

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.368-373.2359

Citation:

Cite this paper

Online since:

October 2011

Authors:

Yan Bai, Qing Chang Ren, Fan Kui Zeng

Keywords:

Grey Model, Neural Network (NN), Prediction Model, Subsidence Predicting

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] M.M. Aris and K. Lorenz: Monitoring land subsidence in Semarang. Environmenta Geology Vol. 53 (2007), p.651

Google Scholar

[2] K.D. Kim, S. Lee and H.J. Oh: Prediction of ground subsidence in Samcheok City Korea using artificial neural networks and GIS. Environmental Geology Vol. 58 (2009), p.61

DOI: 10.1007/s00254-008-1492-9

Google Scholar

[3] Y. Bai and G.S. Ma: The analysis of combined prediction model of network traffic with grey theory and neural network. Computer engineering and science Vol. 30 (2008), p.122 (in Chinese)

Google Scholar

[4] J.K. Bae and J. Kim: Combining models from neural networks and inductive learning algorithms. Expert Systems with Applications Vol. 38 (2011), p.4839

DOI: 10.1016/j.eswa.2010.09.161

Google Scholar

[5] Liu Si-feng, Dang Yao-guo and Z.G. Fang: The Grey theory and application (Science Press Publishing, China 1999) (in Chinese).

Google Scholar

[6] M.Z. Hou and X.L. Han: The multidimensional function approximation based on constructive wavelet RBF neural network. Applied Soft Computing Vol. 11 (2011), p.2173

DOI: 10.1016/j.asoc.2010.07.016

Google Scholar

[7] F.K. Zeng, C.M. Hu, J.L. Che, Y.H. Li and X. Yan: Study on the crack treatment technology for Palms Together Pagoda in the Famen Monastery in Wenchuan Earthquake. Journal of Xi'an University of Architecture and Technology Vol. 40 (2008), p.667 (in Chinese)

Google Scholar