闫伟涛, 陈俊杰, 柴华彬, 颜少鸽. 矿区高强度开采地表损坏动态预测模型[J]. 农业工程学报, 2019, 35(19): 267-273. DOI: 10.11975/j.issn.1002-6819.2019.19.033
    引用本文: 闫伟涛, 陈俊杰, 柴华彬, 颜少鸽. 矿区高强度开采地表损坏动态预测模型[J]. 农业工程学报, 2019, 35(19): 267-273. DOI: 10.11975/j.issn.1002-6819.2019.19.033
    Yan Weitao, Chen Junjie, Chai Huabin, Yan Shaoge. Ground surface dynamic damage prediction model with high-strength mining in mining area[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2019, 35(19): 267-273. DOI: 10.11975/j.issn.1002-6819.2019.19.033
    Citation: Yan Weitao, Chen Junjie, Chai Huabin, Yan Shaoge. Ground surface dynamic damage prediction model with high-strength mining in mining area[J]. Transactions of the Chinese Society of Agricultural Engineering (Transactions of the CSAE), 2019, 35(19): 267-273. DOI: 10.11975/j.issn.1002-6819.2019.19.033

    矿区高强度开采地表损坏动态预测模型

    Ground surface dynamic damage prediction model with high-strength mining in mining area

    • 摘要: 矿区地下资源的高强度开采势必引起耕地等地表附着物的严重破坏,尤以动态破坏影响较大。为对矿区高强度开采条件下地表动态损坏进行预测,该文首先通过相似模拟试验,揭示了高强度开采下覆岩和地表沿工作面推进方向周期性破坏的特征。然后,根据充分采动阶段的实测资料,检验分析了高强度开采地表下沉速度的右偏偏态分布规律,并总结了下沉速度动态分布的周期性,及其与工作面推进位置的相对位置关系。最后,据此构建了基于对数正态密度函数的地表损坏动态预测模型,并采用实例对预测模型的预测精度进行了验证,结果显示预测和实测曲线的决定系数在0.9以上,标准差与实测最大下沉速度值的比值小于7.0%,表明预测模型具有较高的精度,较为符合现场实际。研究结果可对类似条件矿区开采地表损坏动态预测提供指导。

       

      Abstract: Abstract: China has the third largest coal reserves and the first coal consumption in the world. China's coal resource areas are highly coincident with major grain producing areas or ecologically fragile areas. Under high-intensity mining, overburden strata move violently and surface damage is serious. The large-scale exploitation of underground coal resources will inevitably cause the destruction of surface attachments such as cultivated land, and disturbance of ecological environment in mining area, which will seriously affect the sustainable development of mining area. The surface damage induced by high-intensity mining includes static damage and dynamic damage, but the dynamic damage is more serious. Surface subsidence velocity can reflect the severity of surface movement and deformation. So, in this paper, we chose subsidence velocity as the dynamic deformation index to reveal the dynamic development law of surface damage induced by high-intensity mining in mining area. First, we took the field observed data of ground movement observation station set on the side of terminal line of 2407 working area as an example, and selected several periods of data in the full mining stage as samples. Through the analysis of mathematical statistics, we concluded that the subsidence velocity curve has the distribution characteristics of right skewness. Second, we established a physical similarity model test according to the mining and geological conditions of 2407 working area. Combined with this experiment, we found that the average of bedrock break angle was 56°, which revealed the formation mechanism of stepped crack. Under high-intensity mining, the bedrock was full-thickness fractured along the break angle of bedrock, the main failure type of loose layer was shear failure and the direction of failure was upward in the vertical direction. When the failure reached the surface, the stepped crack appeared on the ground surface. According to the analysis of the measured data of 2407 working area, it was found that overlying strata and surface had the periodic failure characteristics along the advancing direction of working area under high-intensity mining, the period of roof caving was the same as that of stepped crack, and the maximum subsidence velocity was located at stepped crack. The position of maximum subsidence velocity can be determined by the break angle of bedrock in real time. Then, we selected the lognormal density function with right skewness and normality, and combined with the real-time location distribution characteristics of maximum surface subsidence velocity to construct the dynamic prediction model of subsidence velocity. The reliability of the dynamic prediction model was analyzed by selecting the field measured data of subsidence velocity from adjacent high-intensity mining working face of Bulian Tower coal mine. The results showed that the correlation coefficient between predicted data and measured data was over 0.9, and the ratio of standard deviation to measured maximum subsidence velocity was less than7.0%. It showed the high reliability of the model. This research can provide guidance for dynamic prediction of mining subsidence in similar mining conditions.

       

    /

    返回文章
    返回