Volume 5 Number 3 (Mar. 2010)
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JSW 2010 Vol.5(3): 296-303 ISSN: 1796-217X
doi: 10.4304/jsw.5.3.296-303

Estimating Model Parameters of Conditioned Soils by using artificial network

Zichang Shangguan1, 2, Shouju Li3, Wei Sun4, Maotian Luan1
1School of Civil and Hydraulic Engineering, Dalian University of Technology, Dalian 16023,China
2Institute of Civil Engineering, Dalian Fishery University, Dalian 116023, P R China
3State Key Lab. of Struct. Anal. for Ind. Equip., Dalian University of Technology, Dalian, 116024, China
4School of Mechanical Engineering, Dalian University of Technology, Dalian 116024, China

Abstract—The parameter identification of nonlinear constitutive model of soil mass is based on an inverse analysis procedure, which consists of minimizing the objective function representing the difference between the experimental data and the calculated data of the mechanical model. The ill-poseness of inverse problem is discussed. The classical gradient-based optimization algorithm for parameter identification is also investigated. Neural network models are developed for estimating model parameters of conditioned soils in EBP shield. The weights of neural network are trained by using the Levenberg-Marquardt approximation which has a fast convergent ability. The parameter identification results illustrate that the proposed neural network has not only higher computing efficiency but also better identification accuracy. The results from the model are compared with simulated observations. The models are found to have good predictive ability and are expected to be very useful for estimating model parameters of conditioned soils in EBP shield.

Index Terms—parameter estimation, neural network, inverse problem, shield machine.

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Cite: Zichang Shangguan, Shouju Li, Wei Sun, Maotian Luan, "Estimating Model Parameters of Conditioned Soils by using artificial network," Journal of Software vol. 5, no. 3, pp. 296-303, 2010.

General Information

ISSN: 1796-217X (Online)
Frequency: Monthly
Editor-in-Chief: Prof. Antanas Verikas
Executive Editor: Ms. Yoyo Y. Zhou
Abstracting/ Indexing: DBLP, EBSCO, ProQuest, INSPEC, ULRICH's Periodicals Directory, WorldCat, etc
E-mail: jsw@iap.org
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