›› 2012, Vol. 33 ›› Issue (3): 799-804.

• Geotechnical Engineering • Previous Articles     Next Articles

Application of radial basis function neural network to geostress field back analysis

ZHANG Le-wen1, ZHANG De-yong2, QIU Dao-hong1   

  1. 1. Geotechnical and Structural Engineering Research Center, Shandong University, Jinan 250061, China; 2. China Petroleum Engineering & Construction Corporation, Qingdao, Shandong 266071, China
  • Received:2010-09-26 Online:2012-03-10 Published:2012-03-12

Abstract: Initial geostress field is a basic index for underground engineering design. According to geological data of Jiangbian hydropower station diversion tunnel area, a numerical model of this area is established by fast Lagrangian analysis of continua in three dimensions (FLAC3D). The mixed boundary conditions and lateral pressure coefficients which change with depth are applied to simulate different kinds of rocks, fault fracture zone and alteration zone along the diversion tunnel, so as to conduct positive analysis. According to the measured principal stress data of the engineering site, based on the principle of radial basis function (RBF) neural network, back analyses of rock mechanical parameters and initial stress field are made. The calculated results are consistent well with the measured results and meet accuracy requirements. The results show that the back analysis results are in line with the actual project; and back analysis method is reasonable.

Key words: RBF neural network, FLAC3D, rock mechanical parameters, boundary condition, back analysis of geostress field

CLC Number: 

  • TU 443
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