Distinct element modelling of mining-induced instability of a heterogeneous fault

Li, Yingchun; Zhang, Fangxue; Hu, Li; Zhuang, Ziyu

doi:10.1007/s10064-023-03085-8

Cited by 5 publications

(4 citation statements)

References 47 publications

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“…In terms of functional expressions, the Weibull time function is actually improved from the Knothe time function. When b = 1, the Weibull time function is transformed into the Knothe time function The expression of the Gompertz time function is given by where a and b are variable morphological parameters, which are related to the mechanical and physical properties of the overlying rock in the settlement zone and could affect the geometry of the curve, and t is the deformation time.…”

Section: Improved Dynamic Prediction Functionmentioning

confidence: 99%

“…When b = 1, the Weibull time function is transformed into the Knothe time function. 32 (3) The expression of the Gompertz time function is given by…”

Section: Improved Dynamic Prediction Functionmentioning

confidence: 99%

“…When b = 1, the Weibull time function is transformed into the Knothe time function. 32 The expression of the Gompertz time function is given by where a and b are variable morphological parameters, which are related to the mechanical and physical properties of the overlying rock in the settlement zone and could affect the geometry of the curve, and t is the deformation time. The Gompertz function curve is plotted by MATLAB software, as shown in Figure 4 .…”

Section: Improved Dynamic Prediction Functionmentioning

confidence: 99%

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Monitoring and Prediction of Horizontal Displacement of Underground Enclosure Piles in Subway Foundation Pits

et al. 2023

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Urban rail transportation and underground space development and utilization are initiatives to solve urban traffic congestion. The monitoring and prediction of the stability of underground enclosure piles in foundation pits is a key index for the dynamic evaluation of the stability of underground space engineering. In this paper, we focused on the problem that the dynamic prediction accuracy and stability of foundation pit retaining piles in the Qingdao area are not high. Based on the analysis of various time function curves and the physical meaning of the parameters, we proposed the Adjusted-Logistic time function model, which introduces three physical parameters to adjust the deformation velocity and deformation acceleration in different stages for better accuracy. It could predict the deformation process of underground enclosure piles under different geological engineering conditions. It was verified in the field that the root-mean-square error (RMSE) of the Adjusted-Logistic function was 0.5316, MAE was 0.3752, and R 2 was 0.9937, which were better than those of Gompertz, Weibull, and Knothe time function models. Meanwhile, it showed that with the increase of excavation depth, the maximum horizontal displacement of the underground enclosure piles gradually decreased and finally stabilized at 0.62−0.71 H. We established a catastrophe model of the horizontal displacement cusp at the observation point of the underground enclosure piles by using the time series of the measured data. It could determine the weak location of the underground enclosure pile stability and realize the multipoint warning of the foundation pit stability, which would ensure safe construction.

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Section: Improved Dynamic Prediction Functionmentioning

confidence: 99%

“…When b = 1, the Weibull time function is transformed into the Knothe time function. 32 (3) The expression of the Gompertz time function is given by…”

Section: Improved Dynamic Prediction Functionmentioning

confidence: 99%

Section: Improved Dynamic Prediction Functionmentioning

confidence: 99%

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Monitoring and Prediction of Horizontal Displacement of Underground Enclosure Piles in Subway Foundation Pits

et al. 2023

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“…Research indicates that during the process of coal mining, faults within the mining area may become active, thereby increasing the risk of fault slip and mine disasters. Consequently, studying the faults in mining areas holds significant scientific and practical value for preventing geological disasters and ensuring miner safety [4][5][6][7].…”

Section: Introductionmentioning

confidence: 99%

Research on Prediction of Surface Deformation in Mining Areas Based on TPE-Optimized Integrated Models and Multi-Temporal InSAR

Long,

Liu,

Xiong

et al. 2023

Remote Sensing

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The prevailing research on forecasting surface deformations within mining territories predominantly hinges on parameter-centric numerical models, which manifest constraints concerning applicability and parameter reliability. Although Multi-Temporal InSAR (MT-InSAR) technology furnishes an abundance of data, the underlying information within these data has yet to be fully unearthed. Consequently, this paper advocates a novel methodology for prognosticating mining area surface deformation by integrating ensemble learning with MT-InSAR technology. Initially predicated upon the MT-InSAR monitoring outcomes, the target variables for the ensemble learning dataset were procured by melding distance-based features with spatial autocorrelation theory. In the ensuing phase, spatial stratified sampling alongside mutual information methodologies were deployed to select the features of the dataset. Utilizing the MT-InSAR monitoring data from the Zixing coal mine in Hunan, China, the relationship between fault slippage and coal extraction in the study area was rigorously analyzed using Granger causality tests and Johansen cointegration assays, thereby acquiring the dataset requisite for training the Bagging model. Subsequently, leveraging the Bagging technique, ensemble models were constructed employing Decision Trees, Support Vector Regression, and Multi-layer Perceptron as foundational estimators. Furthermore, the Tree-structured Parzen Estimator (TPE) optimization algorithm was applied to the Bagging model, resulting in an optimal model for predicting fault slip in mining areas. In comparison with the baseline model, the performance increased by 25.88%, confirming the effectiveness of the data preprocessing method outlined in this study. This result also demonstrates the innovation and feasibility of combining ensemble learning with MT-InSAR technology for predicting mining area surface deformation. This investigation is the first to integrate TPE-optimized ensemble models with MT-InSAR technology, offering a new perspective for predicting surface deformation in mining territories and providing valuable insights for further uncovering the hidden information in MT-InSAR monitoring data.

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DEM investigation on the shear mechanical behavior of rock fractures with the same roughness level

Huan,

He,

et al. 2024

Bull Eng Geol Environ

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Distinct element modelling of mining-induced instability of a heterogeneous fault

Cited by 5 publications

References 47 publications

Monitoring and Prediction of Horizontal Displacement of Underground Enclosure Piles in Subway Foundation Pits

Monitoring and Prediction of Horizontal Displacement of Underground Enclosure Piles in Subway Foundation Pits

Research on Prediction of Surface Deformation in Mining Areas Based on TPE-Optimized Integrated Models and Multi-Temporal InSAR

DEM investigation on the shear mechanical behavior of rock fractures with the same roughness level

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