基于大数据机器学习的成型混凝土码头的不稳定性预测,用于没有煤柱采矿的二级采矿
Yanhui Zhu1, Ye Tian2, Peilin Gong3
1College of Mining Engineering, Taiyuan University of Technology, Taiyuan, 030024, China.
Scientific reports
|May 16, 2025
概括
二次采矿对混凝土码头柱造成重大损害. 一个新的高斯过程回归模型准确地预测了应力和损伤,确保了更安全的采矿操作.
科学领域:
- 采矿工程 采矿工程 采矿工程
- 土木工程 土木工程是指土木工程.
- 计算力学 计算力学 计算力学
背景情况:
- 无支柱式煤矿的二次开采导致柔性成型混凝土码头柱的过度损坏.
- 了解压力迁移和不稳定机制对于确保结构完整性和运营安全至关重要.
研究的目的:
- 研究二次采矿下混凝土码头柱的应力迁移模式和不稳定机制.
- 开发一个准确的预测模型,用于列应力和损坏程度.
- 验证安全采矿操作的强化策略的有效性.
主要方法:
- 在州和州煤矿进行实地研究和测试.
- 数字模拟和压力分布的理论分析.
- 大数据机器学习,特别是高斯过程回归 (GPR),用于压力预测.
- 与线性回归和BP神经网络的比较.
主要成果:
- 确定压力度是造成过度柱损伤的主要原因.
- 与线性回归和BP网络相比,开发了一个GPR模型 (ARD-Rational-Quadratic-Kernel) 的精度更高 (MSE=1.3463).
- 确定了相对于采矿面距离的柱子的两阶段损伤模式:最初的线性增长,然后是稳定.
结论:
- 在二次采矿过程中,GPR模型提供了一个非常准确的工具,用于预测二次采矿期间混凝土码头柱的应力和损伤.
- 用Z6混凝土增强剂增强柔性码头柱子有效地确保了安全的采矿操作.
- 该研究验证了先进的建模和强化技术在采矿安全方面的实际应用.
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