REN H W, LIU K, LI J, et al. Stability analysis and rib spalling prediction of coal wall in ultra-high mining working faces[J]. Journal of Henan Polytechnic University(Natural Science) , 2025, 44(3): 1-11.

doi: 10.16186/j.cnki.1673-9787.2024110015

Received:2024/11/08

Revised:2025/01/25

Online:2025-04-18

Stability analysis and rib spalling prediction of coal wall in ultra-high mining working faces

REN Huaiwei1,2, LIU Kai3, LI Jian1,2, ZHAO Shuji1, HAN Cundi4, WANG Long4, LI Jiyang1

1.Smart Mining Branch， CCTEG Coal Mining Research Institute Co.， Ltd.， Beijing  100013， China；2.Coal Mining and Designing Branch， China Coal Research Institute， Beijing  100013， China；3.China University of Mining and Technology-Beijing， School of Energy and Mining Engineering， Beijing  100083， China；4.Shaanxi Caojiatan Coal Mining Co.， Ltd.， Shaan Mei Coal Group Corp.， Yulin  719099， Shaanxi， China

Abstract: Objectives The problem of coal wall rib spalling in ultra-high mining height working faces was addressed. Methods The mechanism of rib spalling was studied through theoretical analysis and field measurements. Numerical simulations were conducted to model coal wall failure under different mining height conditions. The relationship between mining height， support pressure， and rib spalling was analyzed. Machine learning methods were applied to predict rib spalling based on field data. Several prediction models were compared to select the most optimal one.  Results It was found that rib spalling occurred mainly in the middle and upper parts of the coal wall. As mining height and support pressure increased， rib spalling worsened. A stability coefficient formula for rib spalling was derived. It was found that the stability coefficient increased with the increase of the roof pressure and mining height. Numerical simulations showed that as mining height increased from 6m to 10m， the degree of coal wall fragmentation and rib spalling depth increased significantly， and the depth of rib spalling reached the peak point when the roof was pressed. Data analysis revealed that rib spalling resulted from the interaction between the coal wall， hydraulic supports， and the roof. Finally， maintaining proper support height and sufficient hydraulic support force was critical to reduce rib spalling risk. Machine learning methods were applied to predict rib spalling. Among the common machine learning algorithms， the KNN method achieved the highest prediction accuracy， 77.46%. However， the prediction accuracy of current machine learning methods still required improvement.  Conclusions The mechanism of rib spalling in ultra-high mining height working faces was revealed. A new approach to predicting rib spalling was proposed. As mining height and roof pressure increased， the risk of rib spalling grew. Machine learning methods could effectively predict rib spalling risks， providing theoretical support and technical assurance for the safe operation of ultra-high mining height coal mines.

Key words: ultra-high mining height working face; coal wall rib spalling; stability analysis; machine learning; rib spalling prediction