路面屋顶稳定性分级方法基于多指数优化优化方法
Xiaoqiang Xue1, Wei Du1, Jian Cui2
1Shaanxi Xiaobaodang Mining Co., Ltd., Yulin, 719000, China.
Scientific reports
|August 9, 2025
概括
一种新方法对道路岩石稳定性进行了分级,以优化支设计. 深度和屋顶强度等关键因素影响稳定性,BP神经网络模型在分类中达到91%以上的准确性.
科学领域:
- 采矿工程 采矿工程 采矿工程
- 地质技术工程 地质技术工程
- 岩石机械 岩石机械
背景情况:
- 目前的道路支持策略往往是统一的,导致不稳定区域的压力问题和稳定区域的过度支持.
- 需要一个可靠的岩石质量分类系统,以便在整个道路上定制支持设计.
- 该研究侧重于小宝第1号煤矿,以应对这些挑战.
研究的目的:
- 开发和验证一种可靠的方法来分类道路周边岩石的稳定性.
- 实现差异化和优化的支持设计,以提高道路安全和效率.
- 为采矿业务的后续差异化支持策略提供理论参考.
主要方法:
- 使用SPSS和等级分析方法进行数据分析,以确定十个影响因素的权重.
- 基于权重影响因素和规范钻井数据的岩石稳定性分类公式的开发.
- 构建和训练用于道路屋顶稳定性分类的反向传播 (BP) 神经网络模型.
主要成果:
- 确定了影响屋顶稳定的五个关键因素:道路深度,屋顶强度,直接屋顶厚度,采矿高度和岩石完整性.
- BP神经网络模型实现了高精度:92.8%的训练组和91.7%的测试组.
- 矿井压力数据验证了模型的分类,显示了与稳定性水平相对应的明显变化,数值模拟表明易崩的区域的应力更高.
结论:
- 拟议的岩石稳定性分类方法,利用BP神经网络,为差异化道路支持提供了可靠的方法.
- 了解关键地质和采矿参数的影响对于有效的支持设计至关重要.
- 根据应力分析,在被确定为高度容易发生屋顶倒塌的地区,需要加强支持.
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