2014
DOI: 10.1080/1064119x.2013.836258
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A Practical Indirect Back Analysis Approach for Geomechanical Parameters Identification

Abstract: Back analysis is a powerful tool to determine geomechanical parameters. In this article, a practical approach that employs the response surface method and MS Excel solver for back analysis is presented. The least square support vector machine (LSSVM)-based response surface is utilized to represent the nonlinear relationship between the geomechanical parameters and the monitored information and Excel solver is used to search the geomechanical parameters based on the monitoring information. We analyze the robust… Show more

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Cited by 18 publications
(5 citation statements)
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“…Several researchers built proxy models to replace numerical simulations in their inverse analysis problems using distinct machine learning methods: artificial neural network, 22,25,26 genetic programming, 34 and support vector machine. 27,28,35 In the literature, numerous parameter identification problems of petroleum geomechanics applications considered artificial neural networks and genetic algorithms as proxy models and stochastic optimizers, respectively. Zhang and Yin 36 presented an inverse analysis approach based on both techniques to identify geomechanical parameters of petroleum reservoirs considering measured ground surface displacements.…”
Section: Introductionmentioning
confidence: 99%
See 1 more Smart Citation
“…Several researchers built proxy models to replace numerical simulations in their inverse analysis problems using distinct machine learning methods: artificial neural network, 22,25,26 genetic programming, 34 and support vector machine. 27,28,35 In the literature, numerous parameter identification problems of petroleum geomechanics applications considered artificial neural networks and genetic algorithms as proxy models and stochastic optimizers, respectively. Zhang and Yin 36 presented an inverse analysis approach based on both techniques to identify geomechanical parameters of petroleum reservoirs considering measured ground surface displacements.…”
Section: Introductionmentioning
confidence: 99%
“…If the mathematical model is not a closed‐form expression, the objective function evaluation of a parameter identification problem may be computationally expensive. Several researchers built proxy models to replace numerical simulations in their inverse analysis problems using distinct machine learning methods: artificial neural network, 22,25,26 genetic programming, 34 and support vector machine 27,28,35 …”
Section: Introductionmentioning
confidence: 99%
“…The neural network method was utilized to construct the intelligent displacement back analysis model for identifying the mechanical property of the surrounding rock mass [12][13][14][15]. The support vector machine and the relevance vector machine were selected to build a displacement back analysis model to recognize the geomaterials parameters [16][17][18]. Machine learning provides an excellent tool for predicting the structural response and is selected as the surrogate model in the geotechnical back analysis.…”
Section: Introductionmentioning
confidence: 99%
“…Seepage parameters could be calibrated with seepage monitoring data, and numerical simulation using the calibrated parameters would yield more realistic results. Typical calibration methods for geotechnical parameters include simulated annealing technique [9,10], particle swarm optimization [11][12][13], neural network and genetic algorithm [14][15][16][17], Nelder-Mead algorithm [13,18], response surface method (RSM) [19][20][21], and support vector machine [22].…”
Section: Introductionmentioning
confidence: 99%