Numerical identification of soil‐structure interaction pressures

Gioda, Giancarlo; Jurina, Lorenzo

doi:10.1002/nag.1610050105

Cited by 50 publications

(13 citation statements)

References 4 publications

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“…Where, P is the parameters vector, which is also the input to artificial neural network; D is the measured information vector of rock, which is also the output of the artificial neural network; n is the number of input nodes and m is the number of output nodes; k h h , , 1 are the hidden layers of neural network.…”

Section: A Coupled Optimization Algorithm Of Rrock Mechanics Parametersmentioning

confidence: 99%

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A Parameter Intelligent Recognition Method and Numerical Simulation of Large Underground Caverns

Jiang¹,

Feng²,

Xiang³

et al. 2009

2009 International Conference on Computational Intelligence and Natural Computing

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To gain the equivalent mechanical parameter of rock in underground engineering and to improve the precision of numerical simulation of excavation, a new multiinformation intelligent recognition method of rock mechanical parameter is put forward. By coupling artificial neural network and genetic algorithm as a whole algorithm and by building an associated fitness function to absorb multiinformation, a combinatorial intelligent optimization method is available for parameter recognition. The method realizes the global optimization efficiently and improves the uniqueness of searched parameter as far as possible. After recognizing the equivalent mechanical parameters of rock in Jinping II underground caverns by above method, the performance of surround rock in construction is calculated by 3D numerical simulation with the characters of 1168 thousand elements and 234 gridpoints. The practical application indicates that the intelligent method is reliable to analyze and predict the safety of underground engineering.

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Section: A Coupled Optimization Algorithm Of Rrock Mechanics Parametersmentioning

confidence: 99%

“…Thus, many professional numerical simulation means are used to analysis the performance of surround rock in underground caverns [1][2][3][4]. In fact, the precision of numerical calculated result does depend on the veracity of inputted parameters significantly [5,6].…”

Section: Introductionmentioning

confidence: 99%

A Parameter Intelligent Recognition Method and Numerical Simulation of Large Underground Caverns

Jiang¹,

Feng²,

Xiang³

et al. 2009

2009 International Conference on Computational Intelligence and Natural Computing

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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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“…Displacement back analysis is commonly used in establishing geomechanical parameters in rock mechanics and engineering [1][2][3][4][5][6][7]. There are mainly three types of displacement back analysis methods: inverse solving method, atlas method and direct (i.e.…”

Section: Introductionmentioning

confidence: 99%

Geomechanical parameters identification by particle swarm optimization and support vector machine

Zhao

Yin

2009

Applied Mathematical Modelling

121

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a b s t r a c tBack analysis is commonly used in identifying geomechanical parameters based on the monitored displacements. Conventional back analysis method is not capable of recognizing non-linear relationship involving displacements and mechanical parameters effectively. The new intelligent displacement back analysis method proposed in this paper is the combination of support vector machine, particle swarm optimization, and numerical analysis techniques. The non-linear relationship is efficiently represented by support vector machine. Numerical analysis is used to create training and testing samples for recognition of SVMs. Then, a global optimum search on the obtained SVMs by particle swarm optimization can lead to the geomechanical parameters identification effectively.

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Numerical identification of soil‐structure interaction pressures

Cited by 50 publications

References 4 publications

A Parameter Intelligent Recognition Method and Numerical Simulation of Large Underground Caverns

A Parameter Intelligent Recognition Method and Numerical Simulation of Large Underground Caverns

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

Geomechanical parameters identification by particle swarm optimization and support vector machine

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