Intelligent back analysis of geotechnical parameters for time-dependent rock mass surrounding mine openings using grey Verhulst model

Han, Un Chol; Choe, Chung Song; Hong, Kun Ui; Han, Hyon Il

doi:10.1007/s11771-021-4822-7

Cited by 10 publications

(8 citation statements)

References 48 publications

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“…GVM is a special kind of model within the grey system, which is first introduced by German biologist Pierre Franois Verhulst [31,32]. e main purpose of GVM is to limit the whole development for a real system, and it is effective in describing some increasing processes, such as an S-curve that has a saturation region [20].…”

Section: Nonequidistant Grey Verhulst Model (Ngvm)mentioning

confidence: 99%

“…Huang et al [18] used time-series analysis termed as autoregressive moving average (ARMA (n, m)) model and grey model (GM(1,1)) to predict rock mass displacement at the key measuring points of the permanent shiplock in the ree Gorges Project, and Guo et al [19] presented the nonequidistant grey model (NGM(1,1)) reduced origin error to predict final tunnel surrounding rock displacement based on the data of early 20 days. Han et al [20] predicted the final displacement of tunnel in time-dependent rock mass by using the traditional nonequidistant grey Verhulst model (NGVM). Also, the Gaussian process (GP), SVM, the wavelet neural network (WNN), and GM(1,1) are analyzed comparatively for predicting the surrounding rock nonlinear deformation [21].…”

Section: Introductionmentioning

confidence: 99%

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Prediction of Final Displacement of Tunnels in Time-Dependent Rock Mass Based on the Nonequidistant Grey Verhulst Model

Han¹,

Choe²,

Hong³

et al. 2022

Mathematical Problems in Engineering

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Time-dependent deformations of rock are important factors for the design and construction of tunnels in the rock masses with time-dependent strength and deformation properties. Therefore, determination displacement and stable time of tunnel are an indispensable task in geotechnical engineering, especially during the design and construction of underground structures. However, in some types of rock masses, the tunnel displacement may increase for months or years after the excavation owing to the rheological behavior of the surrounding rock masses, which greatly influences the selection of the initial tunnel support system, the excavation layout, and the determination of its load-carrying capacity. In this paper, we present a method to quickly predict the final displacement and stable time of tunnels in time-dependent rock mass using the improved nonequidistant grey Verhulst model (INGVM) with Fourier series, which is used to reduce the periodic residual error. The proposed method is validated fairly via a test for a newly excavated roadway of −600 m level in a coal mine, Democratic People’s Republic of Korea. These results are useful references for similar projects in the future.

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Section: Nonequidistant Grey Verhulst Model (Ngvm)mentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Prediction of Final Displacement of Tunnels in Time-Dependent Rock Mass Based on the Nonequidistant Grey Verhulst Model

Han¹,

Choe²,

Hong³

et al. 2022

Mathematical Problems in Engineering

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“…The stress-strain relationship of the fractional differential constitutive model can be described using Eqs (1), ( 2), ( 4), (6), and (17). The parameters of the constitutive model (11 parameters in total) include elastic modulus E 1 , viscoelastic parameters(E 2 , η 1 , q 1 ), plastic parameters (λ, κ, e 0 , φ r , p c ) and viscoplastic parameters (η 2 , q 2 ).…”

Section: Plos Onementioning

confidence: 99%

“…To improve the accuracy of parameter acquisition and simulation efficiency, intelligent algorithms, such as genetic algorithms, evolutionary algorithms, and ant colony algorithms, have been gradually introduced in the field of displacement back analysis [14,15]. Some artificial intelligence methods, such as neural networks, are also applicated in the parameters back analysis of soft clay [16,17].…”

Section: Introductionmentioning

confidence: 99%

Modeling consolidation of soft clay by developing a fractional differential constitutive model in conjunction with an intelligent displacement inversion method

Liu

Ming

et al. 2022

PLoS ONE

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Studying the constitutive relation of soft clays is of critical importance for fundamentally understanding their complex consolidation behavior. This study proposes a fractional differential constitutive model in conjunction with an intelligent displacement inversion method based on the classic particle swarm optimization for modeling the deformation behavior of soft clay. The model considered the rheological properties of soft clay at different consolidation stages. In addition, statistical adaptive dynamic particle swarm optimization-least squares support vector machines were implemented to identify the model parameters efficiently. The accuracy and effectiveness of the model were validated using available experimental results. Finally, the application results showed that the proposed model could efficiently simulate coupling properties of soft clay’s primary and secondary consolidations.

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“…The GM (1,1) model is primarily adopted to analyze the data with significant exponential law or similar filtering; the Verhulst model focuses on a process of presenting saturation state. HAN et al adopted the intelligent back analysis method of the gray Verhulst model (GVM) to effectively determine the design parameters and stability of roadways and stopes [ 16 ]. HE et al established a cloud-Verhulst hybrid prediction model by combining a cloud model with the Verhulst model, and this model achieved higher prediction accuracy compared to the traditional statistical model [ 17 ].…”

Section: Introductionmentioning

confidence: 99%

Prediction and forewarning of axial force of steel bracing in foundation pit based on verhulst model

Chen

Zhang

Jiang³

et al. 2022

PLoS ONE

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The axial force of steel bracing is one of the essential indexes to measure the stability of the bracing system of a foundation pit. The steel bracing system of a foundation pit in Ningbo City, China was taken as the research object to guarantee the stability of the steel bracing system of the foundation pit. Besides, the change of axial force between the two steel bracing structures was analyzed to predict the axial force data of the steel bracing and perform the safety forewarning of the steel bracing system. Firstly, GM (1,1) and Verhulst models in the gray model were selected for prediction based on the characteristics of poor information and the small sample size of original monitoring data of the steel bracing. Secondly, the precision of the GM (1,1) model and Verhulst model was compared to determine a more accurate prediction method. Finally, the safety forewarning model of the confidence interval estimation method was established based on the data obtained from the prediction model and the deformation characteristics and indexes of the steel bracing. With the significance levels α = 5% and α = 2% as the demarcating points, the forewarning grades of the steel bracing system of the deep foundation pit were divided, and then the operating state of the current steel bracing system was determined. The results demonstrated that the Verhulst model had better prediction precision compared with the ordinary GM (1, 1) model. Besides, the steel bracing system was in the safe operation range, and the judgment results of the model were consistent with the actual situation of the foundation pit of the steel bracing system. Thus, the Verhulst prediction model and the confidence interval security early forewarning model could be used to judge the stability of the steel bracing system.

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Intelligent back analysis of geotechnical parameters for time-dependent rock mass surrounding mine openings using grey Verhulst model

Cited by 10 publications

References 48 publications

Prediction of Final Displacement of Tunnels in Time-Dependent Rock Mass Based on the Nonequidistant Grey Verhulst Model

Prediction of Final Displacement of Tunnels in Time-Dependent Rock Mass Based on the Nonequidistant Grey Verhulst Model

Modeling consolidation of soft clay by developing a fractional differential constitutive model in conjunction with an intelligent displacement inversion method

Prediction and forewarning of axial force of steel bracing in foundation pit based on verhulst model

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