Research on dynamic prediction model of surface subsidence in mining areas with thick unconsolidated layers

Chi, Shenshen; Wang, Lei; Xiang, Yu; Lv, Weicai; Fang, Xinjian

doi:10.1177/0144598720981645

Cited by 20 publications

(9 citation statements)

References 17 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Based on a substantial amount of measured data [21,[41][42][43][44][45], it was evident that during a complete subsidence process, a ground point typically undergoes three phases as depicted in Figure 1.…”

Section: Mining Subsidence Dynamic Process Analysismentioning

confidence: 99%

Application of a Variable Weight Time Function Combined Model in Surface Subsidence Prediction in Goaf Area: A Case Study in China

Chai,

Xu,

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

To attain precise forecasts of surface displacements and deformations in goaf areas (a void or cavity that remains underground after the extraction of mineral resources) following coal extraction, this study based on the limitations of individual time function models, conducted a thorough analysis of how the parameters of the model impact subsidence curves. Parameter estimation was conducted using the trust-region reflective algorithm (TRF), and the time function models were identified. Then we utilized a combined model approach and introduced the sliding window mechanism to assign variable weights to the model. Based on this, the combined model was used for prediction, followed by the application of this composite prediction to engineering scenarios for the dynamic forecasting of surface movements and deformations. The results indicated that, in comparison with DE, GA, PSO algorithms, the TRF exhibited superior stability and convergence. The parameter models obtained using this method demonstrated a higher level of predictive accuracy. Moreover, the predictive precision of the variable-weight time function combined model surpassed that of corresponding individual time function models. When employing six different variable-weight combination prediction models for point C22, the Weibull-MMF model demonstrated the most favorable fitting performance, featuring a root mean square error (RMSE) of 32.98 mm, a mean absolute error (MAE) of 25.66 mm, a mean absolute percentage error (MAPE) of 7.67%; the correlation coefficient R2 reached 0.99937. These metrics consistently outperformed their respective individual time function models. Additionally, in the validation process of the combined model at point C16, the residuals were notably smaller than those of individual models. This reaffirmed the accuracy and reliability of the proposed variable-weight combined model. Given that the variable-weight combination model was an evolution from individual time function models, its applicability extends to a broader range, offering valuable guidance for the dynamic prediction of surface movement and deformation in mining areas.

show abstract

Section: Mining Subsidence Dynamic Process Analysismentioning

confidence: 99%

Application of a Variable Weight Time Function Combined Model in Surface Subsidence Prediction in Goaf Area: A Case Study in China

Chai,

Xu,

et al. 2024

Applied Sciences

View full text Add to dashboard Cite

show abstract

“…In addition, the analogous hyperbola, analogous hyperbola-funnel, and analogous funnel models were described to predict surface subsidence under the inclined coal seam mining conditions [22]. A Knothe time function considering the influence coefficient of unconsolidated layers was established to improve the accuracy of surface subsidence prediction [23]. As for the surface subsidence of backfill-strip mining, a prediction method to predict the surface subsidence of backfill mining and strip mining separately was proposed [24].…”

Section: Introductionmentioning

confidence: 99%

Surface Subsidence Characteristics of Mining Panel Layout Configuration with Multi-Seam Longwall Mining

Zhu

Wang

Gao³

et al. 2023

Processes

View full text Add to dashboard Cite

Mining-induced subsidence is critical for ecological environment reconstruction and damage prevention in coal mining areas. Understanding the characteristics of surface subsidence with multi-seam mining is the first step. Surface subsidence of different mining panel layout configurations was investigated by means of UDEC numerical simulation. Based on the simulation results, it was indicated that mining panel layout configuration had a significant impact on surface subsidence, including ground surface subsidence, horizontal displacement, crack propagation, and ground surface fissure development. The overlapped region of the upper panel and the lower panel is the key region, where existing bedding separations and strata cracks close and activate, the integrity and strength of the interburden layer are reduced, and the subsidence magnitude is enhanced. The subsidence profile of the overlapped region for the stacked configuration, external staggered, the edge of the lower panel internal staggered, two edges of the lower panel internal staggered are steeper and deeper, and the corresponding values of ground surface subsidence and horizontal displacement are greater than other regions. The ground surface fissures with the types of stepped, slided, and graben developed on the ground surface above the edge of the mining panel, and the development location is closely related to the strata movement edge. Because of the support activities of the reserved coal pillar, the ground subsidence of the external staggered (internal staggered) of the upper panel with the coal pillar is slight. The external staggered (internal staggered) and external staggered (internal staggered) of the upper panel with the coal pillar can be selected as the preferred layout configuration. The proposed description of surface subsidence of different mining panel layout configurations can be applied in subsidence prediction.

show abstract

“…At present, research on the skewed distribution of surface subsidence focuses on two aspects: (1) the mechanism of the skewed distribution of surface movements from the aspects of geological structures, such as joints [24][25][26], and (2) the analysis of the skewness distribution law of the basin combined with the measured data of surface movements [27][28][29][30]. However, no research on surface subsidence prediction based on skewed distribution has been published.…”

Section: Introductionmentioning

confidence: 99%

A Novel Surface Subsidence Prediction Model Based on Stochastic Medium Theory for Inclined Coal Seam Mining

Yan,

Guo,

Yan

et al. 2023

Advances in Civil Engineering

View full text Add to dashboard Cite

Most coal resources are deposited in the form of inclined coal seams, and surface subsidence basin morphology induced by the mining of inclined coal seams is frequently skewed. The probability integral method with symmetrical distribution characteristics is widely used at present in surface subsidence prediction in coal mining in China. However, this method performs poorly when the inclined coal seam mining subsidence is predicted, and prediction accuracy decreases considerably with an increase in coal seam inclination. To solve this problem, this study first establishes three coordinate systems: a working surface rectangular coordinate system, a working face body-following coordinate system, and a surface rectangular coordinate system. Then, a random medium theory is applied to realize the superposition integral operation of the subsidence influence of unit mining in the working faces body-following coordinate system. Subsequently, the subsidence effect of a certain point on the surface is converted into the surface rectangular coordinate system. Finally, the inclined coal seam mining subsidence prediction model is constructed under the surface rectangular coordinate system. Results show that the surface subsidence caused by the mining of the inclined coal seam units conforms to the Weibull polar distribution law, and the effectiveness of the prediction model is verified through examples. The relative mean squared error of the prediction is less than 10%. The results of the study can provide theoretical and technical support for the subsidence prediction of similar mining areas.

show abstract

Research on dynamic prediction model of surface subsidence in mining areas with thick unconsolidated layers

Cited by 20 publications

References 17 publications

Application of a Variable Weight Time Function Combined Model in Surface Subsidence Prediction in Goaf Area: A Case Study in China

Application of a Variable Weight Time Function Combined Model in Surface Subsidence Prediction in Goaf Area: A Case Study in China

Surface Subsidence Characteristics of Mining Panel Layout Configuration with Multi-Seam Longwall Mining

A Novel Surface Subsidence Prediction Model Based on Stochastic Medium Theory for Inclined Coal Seam Mining

Contact Info

Product

Resources

About