基于使用多层感知子的点负载指数 (Is) 数据的Azarshahr梯形压缩强度预测
Yimin Mao1, Zhu Licai2, Li Feng3
1School of Information and Engineering, Shaoguan University, Shaoguan, 512005, Guangdong, China. mymlyc@163.com.
Scientific reports
|November 27, 2023
概括
人工智能,特别是多层感知器 (MLP),使用点负载指数数据准确预测石灰岩的压力强度. 这有助于加强阿扎尔沙赫尔矿业开采的挖掘规划和钻探能力评估.
科学领域:
- 地质技术工程 地质技术工程
- 采矿领域的人工智能
- 岩石力学 岩石力学 岩石力学
背景情况:
- 阿扎尔沙赫尔县的地质主要由石灰岩占据主导地位,导致了广泛的露天采矿.
- 岩石钻孔能力和挖掘阻力是与压力强度相关的关键采矿因素.
- 传统的岩石强度评估方法缺乏精度,阻碍了可靠的挖掘规划.
研究的目的:
- 开发一种人工智能模型,以更好地预测阿扎尔沙赫尔岩的压力强度.
- 使用多层感知器 (MLP) 进行精确的强度估计.
- 改进挖掘方法和钻探能力评估在石灰岩采矿.
主要方法:
- 编制了150个点负载指数 (Is) 测试的数据库,用于阿扎尔沙赫尔石.
- 使用编译的数据集开发和训练了一个多层感知器 (MLP) 模型.
- 使用接收器运行特征 (ROC) 曲线和曲线下的面积 (AUC) 度量,验证了模型准确性.
主要成果:
- 该MLP模型在预测轴向和直径压力强度方面取得了很高的准确性.
- 对于轴向和直径强度预测,获得了0.975的R平方系数.
- 总体准确度为0.968 (AUC) 证明了该模型的有效性.
结论:
- 基于MLP的模型从点负载指数数据准确地预测了石灰岩的压力强度.
- 这种人工智能方法比岩石强度分析的传统方法提供了显著的改进.
- 该预测模型为优化阿扎尔沙赫尔岩矿区挖掘规划和钻孔能力提供了宝贵的见解.
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