基于贝叶斯优化1D-CNN的露天矿山斜坡的稳定性评估
Jinguo Lyu1,2, Taihong Hu3, Guangwei Liu1
1College of Mining, Liaoning Technical University, Fuxin, 123000, China.
Scientific reports
|June 17, 2024
概括
这项研究引入了贝叶斯优化的一维卷积神经网络 (B-1D MCNN),用于预测露天煤矿斜坡稳定性. B-1D MCNN 模型显著提高了比传统方法的准确性和精度,在采矿操作中提供了更高的安全性.
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
- 人工智能的人工智能
背景情况:
- 机械化开采煤炭采矿需要精确的斜坡稳定性评估.
- 倾斜稳定性分析的传统方法存在局限性.
- 开发先进的预测模型对于矿山安全至关重要.
研究的目的:
- 开发一种用于预测露天矿山坡度稳定的新型模型.
- 克服现有的机械,数值和实验技术的局限性.
- 确定影响斜坡稳定的关键因素,并创建一个全面的数据集.
主要方法:
- 开发了一个贝叶斯优化的一维卷积神经网络 (B-1D MCNN) 模型.
- 利用贝叶斯优化进行超参数调整,并结合了增强的卷积层.
- 在一维卷积神经网络 (1D-CNN) 中使用了带有dropout的Adam优化器来改进特征提取.
主要成果:
- B-1D MCNN 模型准确地描绘了影响因素和斜率稳定性之间的非线性相关性.
- 与其他模型相比,B-1D MCNN显示出显著的性能提升:精度为10.96-27.85%,精度为8.98-25.05%,F1-Score为10.26-28.55%.与其他模型相比,B-1D MCNN显示出显著的性能提升:精度为10.96-27.85%,精度为8.98-25.05%,F1-Score为10.26-28.55%.
- 随着训练数据集长度的增加,模型性能得到了改善,显示了87.5%的概括能力.
结论:
- B-1D MCNN 模型为预测开采矿坡度稳定性提供了一种优越的方法.
- 改进的模型提供比传统方法更准确和可靠的评估.
- 这些发现突出了人工智能驱动的模型在提高采矿操作的安全性和效率方面的潜力.
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