Subscribe Now Subscribe Today
Research Article
 

Back Analysis Method of Foundation Pit Soil Mechanical Parameters Based on GA-BP Neural Network



Jiang Ping
 
ABSTRACT

In order to ensure the foundation pit construction safety, displacement monitoring was carried on the foundation pit soil. At the same time, according to the measured displacement, the displacement parameters inversion algorithm was taken to calculate the foundation pit soil mechanical parameters, then calculate the stability of foundation pit soil. According to the basic calculation method of genetic algorithm optimizing neural network, in this paper, use uniform design method to design the samples of the foundation pit soil displacement and mechanical parameters. The finite element analysis was used to calculate soil displacement with different parameters. The GA-BP neural network was trained to describe the sophisticated nonlinear relationship between displacement and mechanical parameters of the foundation pit soil. Finally, the actual displacement was input into the trained GA-BP neural network to obtain the soil mechanical parameters. As an example, the soil cohesion, friction angle, elastic modulus and Poisson ratio of Changchun West Railway Station foundation pit were back-analysis by the above method. The calculation displacement obtained by finite element analysis with inversion results and compared with measured data. The comparison result shows that GA-BP neural network has high precision in excavation soil parameters inversion which can meet the needs of engineering.

Services
Related Articles in ASCI
Similar Articles in this Journal
Search in Google Scholar
View Citation
Report Citation

 
  How to cite this article:

Jiang Ping , 2013. Back Analysis Method of Foundation Pit Soil Mechanical Parameters Based on GA-BP Neural Network. Journal of Applied Sciences, 13: 3099-3103.

DOI: 10.3923/jas.2013.3099.3103

URL: https://scialert.net/abstract/?doi=jas.2013.3099.3103

REFERENCES
Cheng, Y., Q. Ge, F. He and Q. Ge, 2010. Experimental research on critical depth of slip surface of soil slope in seasonal frozen area. Rock Soil Mechanics, 31: 1042-1046.

Gao, W. and Y. Zheng, 2001. Back analysis in geotechnical engineering based on fast-convergent genetic algorithm. Chinese J. Geotechnical Eng., 23: 120-122.
Direct Link  |  

Gao, W. and Y. Zheng, 2003. New evolutionary back analysis algorithm in geotechnical engineering. Chinese J. Rock Mechanics Eng., 22: 192-196.

Levasseur, S., Y. Malecot, M. Boulon and E. Flavigny, 2008. Soil parameter identification using a genetic algorithm. Int. J. Numerical Anal. Methods Geomechanics, 32: 189-213.
Direct Link  |  

Li, L., J. Wang and Z. Li, 1997. Application of neural network model in Non-linear dis-placement back analysis. Rock Soil Mechanics, 18: 62-66.

Li, N. and S. Yin, 1996. Back analysis of the safety factor for slope. Chinese J. Rock Mechanics Eng., 15: 9-18.

Liang, Y.C., D.P. Feng, G.R. Liu, X.W. Yang and X. Han, 2003. Neural identification of rock parameters using fuzzy adaptive learning parameters. Comput. Struct., 81: 2373-2382.
CrossRef  |  Direct Link  |  

Rechea, C., S. Levasseur and R. Finno, 2008. Inverse analysis techniques for parameter identification in simulation of excavation support systems. Comput. Geotech., 35: 331-345.
CrossRef  |  Direct Link  |  

Wang, W., 2007. The application of an improved particle swarm optimization in inversion of mechanical parameters of slope engineering. Ph.D. Thesis, HoHai University.

©  2019 Science Alert. All Rights Reserved