Height identification of water-permeable fractured zone based on synchronous movement in overlying strata

Wang, Zhiqiang; Li, Jingkai; Qin, Zhangrong; Y, Su; Sidikovna, Shermatova Sayyora

doi:10.1038/s41598-022-11752-1

Cited by 2 publications

References 35 publications

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Research on the prediction of the height of water-conducting fissure zones in a Jurassic coalfield based on AdaBoost-WOA-BPNN

Hou,

Bi,

Long

et al. 2024

Preprint

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Understanding the laws behind the development of water-conducting fissure zones in the Huanglong Jurassic coalfield and accurately predicting the height of these water-conducting fissures are crucial to prevent and control the water damage in the overlying thick sandstone aquifer of the Luohe Formation. To develop a prediction model applicable to mining in the Huanglong Jurassic coalfield, data from measurements of 27 water-conducting fissure zones in the coalfield were used as samples, and coal seam burial depth, coal seam mining thickness and the oblique length of the working face were used as training indicators. The whale optimisation algorithm (WOA), back-propagation neural network (BPNN) and AdaBoost algorithm were combined to develop the AdaBoost–WOA–BPNN model for predicting the height of water-conducting fissure zones. The accuracies of the BPNN, WOA–BPNN and AdaBoost–WOA–BPNN models were compared, and the height of the water-conducting fissure zone in the 4105 working face of Wenjiapo coal mine was predicted. The AdaBoost–WOA–BPNN model outperformed the other models in terms of error, prediction accuracy and applicability. Compared with the traditional BPNN model, the WOA–BPNN model improved accuracy by 2.4%, while the AdaBoost–WOA–BPNN model improved accuracy by 3.64%. The measured heights of the water-conducting fissure zone in the 4105 working face were 168.2 m (SD1 hole) and 222.3 m (SD2 hole). The corresponding predicted heights by the AdaBoost–WOA–BPNN model were 162.75 m and 213.48 m, respectively. The absolute errors between the predicted and measured values from holes SD1 and SD2 were respectively 5.45 m and 8.82 m, with relative errors of 3.17% and 4.05%. The prediction accuracy meets the requirements of engineering practice. The results of this study provide a valuable reference for predicting the height of the water-conducting fissure zone and for the prevention and control of roof water hazards in the Huanglong Jurassic coalfield.

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Research on the prediction of the height of water-conducting fissure zones in a Jurassic coalfield based on AdaBoost-WOA-BPNN

Hou,

Bi,

Long

et al. 2024

Preprint

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Analysis of Factors Influencing Mining Damage Based on Engineering Detection and Machine Learning

et al. 2022

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The direct results of mining damage are overburden fracture and surface subsidence, which may induce groundwater seepage and surface vegetation degradation. Therefore, it is essential to research the factors and mechanisms influencing mining damage. Based on the geological characteristics of the Xiaobaodang minefield in the Yushen Mine area in China, the engineering detection of fractured zone height (FZH), sampling tests of rock mechanical properties, and field measurements of the surface settlement were carried out. Firstly, the factors influencing the FZH were screened by correlation analysis and partial correlation analysis. Next, a model for predicting the maximum height of the fracture zone with the BP neural network (BPNN) was established and trained with Python. Finally, the FLAC3D numerical simulation experiment was adopted to reveal the variation law of overburden stress during coal mining, and the relationship between stress and overburden fracture was analyzed. The results show the following: When the average mining thickness in the study area is 5.8 m, the maximum height of the fractured zone is 157.46 m, and the maximum surface subsidence is 3715 mm. Further, the mining thickness, mining depth, the compressive strength of overburden, the width of the working face, and the mining velocity are the main factors affecting the maximum height of the fractured zone. Additionally, the goodness of fit of the BPNN model can reach 97.22%, meaning that it can effectively predict the maximum height of the fractured zone caused by coal mining. Finally, the area where the stress changes markedly above the goaf is the area where the fractures develop rapidly. Meanwhile, there is a positive correlation between the surface subsidence and the FZH. The research results obtained provide new ideas for reducing mining damage and will be helpful for the green and sustainable development of the mine.

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Height identification of water-permeable fractured zone based on synchronous movement in overlying strata

Cited by 2 publications

References 35 publications

Research on the prediction of the height of water-conducting fissure zones in a Jurassic coalfield based on AdaBoost-WOA-BPNN

Research on the prediction of the height of water-conducting fissure zones in a Jurassic coalfield based on AdaBoost-WOA-BPNN

Analysis of Factors Influencing Mining Damage Based on Engineering Detection and Machine Learning

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