Back analysis of measured displacements of tunnels

Sakurai, Shunsuke; Takeuchi, Kunifumi

doi:10.1007/bf01033278

Cited by 219 publications

(74 citation statements)

References 3 publications

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“…Already in the 1980s, the use of data from in situ measurements as input for back analyses in geotechnical engineering was proposed in References [1,2]. Based on these measurements, values of unknown model parameters characterizing mechanical properties of the soil=rock mass can be determined.…”

Section: Introductionmentioning

confidence: 99%

Back analysis of model parameters in geotechnical engineering by means of soft computing

Pichler

Lackner

Mang

2003

Numerical Meth Engineering

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SUMMARYIn this paper, a parameter identiÿcation (PI) method for determination of unknown model parameters in geotechnical engineering is proposed. It is based on measurement data provided by the construction site. Model parameters for ÿnite element (FE) analyses are identiÿed such that the results of these calculations agree with the available measurement data as well as possible. For determination of the unknown model parameters, use of an artiÿcial neural network (ANN) is proposed. The network is trained to approximate the results of FE simulations. A genetic algorithm (GA) uses the trained ANN to provide an estimate of optimal model parameters which, ÿnally, has to be assessed by an additional FE analysis. The presented mode of PI renders back analysis of model parameters feasible even for largescale models as used in geotechnical engineering. The advantages of theoretical developments concerning both the structure and the training of the ANN are illustrated by the identiÿcation of material properties from experimental data. Finally, the performance of the proposed PI method is demonstrated by two problems taken from geotechnical engineering. The impact of back analysis on the actual construction process is outlined.

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Section: Introductionmentioning

confidence: 99%

Back analysis of model parameters in geotechnical engineering by means of soft computing

Pichler

Lackner

Mang

2003

Numerical Meth Engineering

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“…Alternatively, back analysis, associated with the "in situ" approach, has been widely used to determine the mechanical parameters of rock mass in rock engineering [15][16][17][18][19][20]. Zhang and Yin proposed a back analysis method which combined a neural network and a genetic algorithm to simultaneously identify the in situ stresses and elastic parameters [2]; however, this method did not consider the poroelastic effect.…”

Section: Introductionmentioning

confidence: 99%

Back Analysis of Rock Hydraulic Fracturing by Coupling Numerical Model and Computational Intelligent Technology

Zhao

Feng

et al. 2017

Geofluids

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Hydraulic fracturing is widely used to determine in situ stress of rock engineering. In this paper we propose a new method for simultaneously determining the in situ stress and elastic parameters of rock. The method utilizing the hydraulic fracturing numerical model and a computational intelligent method is proposed and verified. The hydraulic fracturing numerical model provides the samples which include borehole pressure, in situ stress, and elastic parameters. A computational intelligent method is applied in back analysis. A multioutput support vector machine is used to map the complex, nonlinear relationship between the in situ stress, elastic parameters, and borehole pressure. The artificial bee colony algorithm is applied in back analysis to find the optimal in situ stress and elastic parameters. The in situ stress is determined using the proposed method and the results are compared with those of the classic breakdown formula. The proposed method provides a good estimate of the relationship between the in situ stress and borehole pressure and predicts the maximum horizontal in situ stress with high precision while considering the influence of pore pressure without the need to estimate Biot's coefficient and other parameters.

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“…This is a classical 'inverse' problem, which in the past has been solved by many researchers using conventional back analyses for the identification of elastic and strength parameters for isotropic materials [1][2][3]. However, it is not a simple extension of known procedures if parameters for an anisotropic or even an orthotropic material were to be identified.…”

Section: Introductionmentioning

confidence: 99%

Neural Network Based Identification of Nine Elastic Constants of an Orthotropic Material From a Single Structural Test

Shin¹,

Lee²,

Kim³

et al. 2004

Proceedings of the 21st International Symposium on Automation and Robotics in Construction

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In this paper, a new methodology for identifying numerous elastic parameters of an orthotropic material from a single structural test is presented. At the heart of the methodology is the self-learning algorithm which is to extract various stress-strain relationships from a single structural test and train a neural network with the relationships in finite element framework. The constitutive matrix resulting from the trained neural network based constitutive model (NNCM) is compared with the conventional constitutive matrix for an orthotropic material to determine the nine independent elastic constants. An example is given for better understanding of the methodology proposed.

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Back analysis of measured displacements of tunnels

Cited by 219 publications

References 3 publications

Back analysis of model parameters in geotechnical engineering by means of soft computing

Back analysis of model parameters in geotechnical engineering by means of soft computing

Back Analysis of Rock Hydraulic Fracturing by Coupling Numerical Model and Computational Intelligent Technology

Neural Network Based Identification of Nine Elastic Constants of an Orthotropic Material From a Single Structural Test

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