Estimating model parameters of conditioned soils by using artificial network

被引：3
作者：
Shangguan Z. [1 ,2 ]
Li S. [3 ]
Sun W. [4 ]
Luan M. [1 ]
机构：
[1] School of Civil and Hydraulic Engineering, Dalian University of Technology
[2] Institute of Civil Engineering, Dalian Fishery University
[3] State Key Lab. of Struct. Anal. for Ind. Equip, Dalian University of Technology, Dalian
[4] School of Mechanical Engineering, Dalian University of Technology
来源：
Journal of Software | 2010年 / 5卷 / 03期
关键词：
Inverse problem; Neural network; Parameter estimation; Shield machine;
D O I：
10.4304/jsw.5.3.296-303
中图分类号：
学科分类号：
摘要：
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. © 2010 ACADEMY PUBLISHER.
引用
收藏
页码：296 / 303
页数：7
相关论文
共 22 条
  • [1] Yeh W.W.-G., Review of parameter identification procedures in groundwater hydrology: The inverse problem, Water Resour. Res, 22, pp. 95-108, (1986)
  • [2] Poeter E.P., Hill M.C., Inverse models: A necessary next step in ground-water modeling, Ground Water, 35, pp. 250-260, (1997)
  • [3] Shevnin V., Rodriguez O.D., Mousatov A., Estimation of soil petrophysical parameters from resistivity data: Application to oil-contaminated site characterization, Geofísica Internacional, 45, pp. 179-193, (2006)
  • [4] Tan C.P., Zweiri Y.H., Althoefer K., Online Soil Parameter Estimation Scheme Basedon Newton-Raphson Method for Autonomous Excavation, IEEE/ASME Transactions On Mechatronics, 10, pp. 221-230, (2005)
  • [5] Mertens J., Stenger R., Barkle G.F., Multiobjective inverse modeling for soil parameter estimation and model verification, Vadose Zone Journal, 5, pp. 917-933, (2006)
  • [6] Kasper T., Meschke G., A numerical study of the effect of soil and grout material properties and cover depth in shield tunneling, Computers and Geotechnics, 33, pp. 234-247, (2006)
  • [7] Bernat S., Cambou B., Soil-structure Interaction in Shield Tunnelling in Soft Soil, Computers and Geotechnics, 22, pp. 221-242, (1998)
  • [8] Borja R.I., Sama K.M., Sanz P.F., On the numerical integration of three-invariant elastoplastic constitutive models, Comput. Methods Appl. Mech. Engrg, 192, pp. 1227-1258, (2003)
  • [9] Woodward P.K., Earth pressure coefficients based on the Lade-Duncan failure, Engineering Structures, 19, pp. 733-737, (1997)
  • [10] Duncan J.M., Chang C., Nonlinear analysis of stress and strain in soils, Journal of The Soil Mechanics and Foundations Division, Proceedings of ASCE, 96, pp. 1629-1653, (1970)
123