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基于多指标优化的巷道顶板稳定性分级方法

Roadway roof stability grading method based on multi-index optimization.

作者信息

Xue Xiaoqiang, Du Wei, Cui Jian, Zhang Wenxing

机构信息

Shaanxi Xiaobaodang Mining Co., Ltd., Yulin, 719000, China.

Department of Emergency Management of Shaanxi Province, Xi'an, 710018, China.

出版信息

Sci Rep. 2025 Aug 10;15(1):29232. doi: 10.1038/s41598-025-14287-3.

Abstract

Currently, most of the roadways adopt one support design strategy, which leads to high stress and insufficient support parameters in some crushed areas of the roadways and excess support parameters in some stable regions. There is an urgent need for a reliable method of grading the roadway perimeter rock to realize a reasonable support design for the whole area and cycle of the roadways. Taking Xiaobaodang No.1 coal mine as the background, Based on previous research, we utilized SPSS to analyze the data and selected ten indicators that significantly influence roof stability and are easily obtainable. The relatIVe weights between the influencing factors were determined using the hierarchical analysis method. The results showed that fIVe key factors, namely, roadway depth, roof strength, direct roof thickness, mining height, and rock integrity, emphatically affect the roof's stability. Based on the borehole data in the study area of the mine, 40 sets of borehole data were processed using normalization, and based on the weights of the influencing factors, a classification formula for the stability of the roadway perimeter rock was proposed to classify the boreholes initially. The roadway roof stability classification model of the BP neural network is constructed. The accuracy of the training set of 40 sets of drill hole data is 92.8%, and the accuracy of the test set is 91.7%. The classification results of the model are verified by using the mine pressure data of the mined face, and the mine pressure data shows a noticeable step change with the classification results, which puts forward theoretical references for the subsequent differentiated support of the working face. Numerical simulation software is used to analyze the vertical stress of different types of roof layers and the vertical stress of coal pillars, and the vertical stress of coal pillars at the roof layers that are highly prone to collapse of the roof is higher than that at other roof layers, so it is necessary to strengthen the support.

摘要

目前,大多数巷道采用单一的支护设计策略,这导致巷道某些破碎区域应力过高、支护参数不足,而在一些稳定区域支护参数过剩。迫切需要一种可靠的巷道周边围岩分级方法,以实现巷道整个区域和循环的合理支护设计。以小保当一号煤矿为背景,在以往研究的基础上,利用SPSS对数据进行分析,选取了十个对顶板稳定性有显著影响且易于获取的指标。采用层次分析法确定影响因素之间的相对权重。结果表明,巷道深度、顶板强度、直接顶厚度、采高和岩石完整性这五个关键因素对顶板稳定性有显著影响。基于该煤矿研究区域的钻孔数据,对40组钻孔数据进行归一化处理,并根据影响因素的权重,提出了巷道周边围岩稳定性分类公式,对钻孔进行初步分类。构建了BP神经网络的巷道顶板稳定性分类模型。40组钻孔数据训练集的准确率为92.8%,测试集的准确率为91.7%。利用采面矿压数据对模型的分类结果进行验证,矿压数据与分类结果呈现出明显的阶跃变化,为后续工作面的差异化支护提供了理论参考。利用数值模拟软件分析了不同类型顶板岩层的垂直应力和煤柱的垂直应力,顶板极易垮落的岩层处煤柱的垂直应力高于其他顶板岩层处,因此有必要加强支护。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/85ea/12335503/5e32ae28d919/41598_2025_14287_Fig6_HTML.jpg

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