An Improved Knothe Time Function Model in the Prediction of Ground Mining Subsidence by Using the Kalman Filter Method

Zhang, Shifang; Zhang, Jin

doi:10.1155/2022/3567808

Cited by 5 publications

(9 citation statements)

References 24 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…This segmentation allows for tailored expressions for each stage, enabling better alignment with the nuanced patterns observed in subsidence data and thereby improving predictive accuracy. Furthermore, the introduction of additional parameters enables comparability with the segmentation function refined by Bing Zhang et al [14]. This multi-parameter approach facilitates parameter adjustments to accommodate varying geological conditions, enhancing the model's versatility and applicability across different scenarios.…”

Section: Analysis Of Resultsmentioning

confidence: 99%

“…Based on the time function proposed by Polish scholar Knothe in 1952 [12], Chang et al [13] proposed the segmented Knothe time function model for dealing with the deficiencies of the Knothe time function. Zhang et al [14] extended the application range of the segmented Knothe time function model and optimized its model parameterization method [15]. Miao et al [16] used the probability integration model-related theory to construct a model for solving the time function parameters c and τ, and further optimized the segmented Knothe time function model.…”

Section: Introductionmentioning

confidence: 99%

“…Miao et al [16] used the probability integration model-related theory to construct a model for solving the time function parameters c and τ, and further optimized the segmented Knothe time function model. Liu and Zhang [17,18] proposed an improved power exponential Knothe time function model. Jin et al [19] applied the monitoring results of InSAR to fit the parameters of the power exponential Knothe time function model.…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Prediction of Delayed Surface Subsidence Based on the Improved Knothe-n Model

Dong,

Tang,

Liu

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

The delayed surface subsidence caused by coal seam mining is a problem that cannot be ignored, while accurate prediction of the surface subsidence provides a guarantee of the safety and stability of the relevant areas. However, the traditional Knothe model has limitations in considering delayed surface subsidence. Because of this, the Knothe-n time function model is segmented and improved by using the data of the subsidence area obtained from a FLAC3D-based numerical model, and the maximum delayed surface subsidence in different periods is calculated. The analytical results are compared with the numerical results to validate the effectiveness of the improved segmented time function model in predicting delayed surface subsidence. The improved model is applied to predict the surface subsidence in the Yutianbao subsidence area. The root-mean-square error between the predicted and measured values for the maximum subsidence monitoring point is 1.12, and the root-mean-square error between the average predicted and measured values for the surface monitoring points is 0.37, which verifies the accuracy of the improved model. The prediction model provides a scientific basis for environmental protection and safety management after coal seam mining.

show abstract

Section: Analysis Of Resultsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Prediction of Delayed Surface Subsidence Based on the Improved Knothe-n Model

Dong,

Tang,

Liu

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…Cheng et al [22] proposed a stepwise prediction model based on the influence function method, which involved a dynamic iterative calculation process to derive the movement and deformation of each formation and modified the influence function method. Zhang and Cui [23] discussed the shortcomings of this time function and proposed corresponding improvement methods to address the existing problems, and then constructed a new segmented Knothe time function model with a wider range of application and higher prediction accuracy. Alternatively, subsidence prediction via numerical simulations using the finite element method, boundary element method, finite different method and distinct element method have been conducted [24][25][26][27][28][29][30].…”

Section: Introductionmentioning

confidence: 99%

A Novel Analytical Model of Mining Subsidence Considering Time Effect Based on the Probability Integral Theory

et al. 2022

Sustainability

View full text Add to dashboard Cite

Surface subsidence caused by underground coal mining has received wide attention due to its impact on the ecological environment. To obtain first-hand data on mining subsidence, we arranged line measurement stations in the mining area of the Weiqiang coalfield and implemented surface movement observation at its first mining working face and obtained the dynamic subsidence curve. The subsidence curves reflected the initial, active and declining stages of subsidence at each measurement point at a basic level, and were consistent with the general rule of surface subsidence. To accurately analyze the surface movement caused by coal mining, based on the probability integral method and considering the actual advancement progress of the working face, a new three-dimensional dynamic prediction model, namely the strip unit mining model, was proposed. The core assumption of this model was that the longwall mining is regarded as the result of superposition of many units of mining. Considering the influence of different mining times and periods after mining, the subsidence time function of two-time factors was introduced into the strip unit mining model, and the final formulation of the dynamic subsidence prediction of the three-dimensional surface is derived by considering the time factor. Based on the analysis of the measured data of the Weiqiang coalfield, the prediction parameters of surface movement were obtained, and the surface movement and deformation are predicted via the probability integral method. Finally, the measured surface subsidence was compared with the theoretically-predicted one. The good match showed that the parameters of the probability integral method, determined according to the measured data, meet the requirements of geological conditions and mining settings in the mining area, and the predicted subsidence curves agreed well with the field measurements, demonstrating the effectiveness of the newly-developed strip unit mining model.

show abstract

“…a certain moment. Zhang and Cui21 constructed a new segmented Knothe time function model with a wider application range and higher prediction accuracy, which expanded the applicability of its dynamic prediction and improved its accuracy in predicting the dynamic subsidence of the earth. Yu and Liu22 established a Gompertz model for embankment settlement prediction and proved the feasibility of the model with examples.…”

mentioning

confidence: 99%

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

et al. 2023

View full text Add to dashboard Cite

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.

show abstract

An Improved Knothe Time Function Model in the Prediction of Ground Mining Subsidence by Using the Kalman Filter Method

Cited by 5 publications

References 24 publications

Prediction of Delayed Surface Subsidence Based on the Improved Knothe-n Model

Prediction of Delayed Surface Subsidence Based on the Improved Knothe-n Model

A Novel Analytical Model of Mining Subsidence Considering Time Effect Based on the Probability Integral Theory

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

Contact Info

Product

Resources

About