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基于配备RCM的传感器和多元化集体学习模型的岩石质量分类的实验研究

Feng Li^1,2, Huike Zeng³, Hongbin Xu^1,2

¹College of Civil and Transportation Engineering, Shenzhen University, Shenzhen 518060, China.

Sensors (Basel, Switzerland)

|October 16, 2024

概括

这项研究使用道钻机 (TBM) 上的传感器实时分类岩石状况. 一个集体学习模型准确地预测了岩石质量,提高了道建设的安全性和效率.

科学领域:

地质技术工程地质技术工程
机器学习应用机器学习应用
道技术道技术

背景情况:

地质状况监测对于使用道钻井机 (TBM) 进行安全高效的道建设至关重要.
实时岩石质量分类有助于调整TBM操作以适应不同的地质层.
现有的方法可能缺乏动态地下环境所需的准确性和稳定性.

研究的目的:

开发和验证使用TBM配备的传感器实时岩石质量分类系统.
评估一个堆叠集体学习模型的性能,用于地质识别.
通过改进地质监测,提高道建设的安全性和效率.

主要方法:

使用TBM光盘切割机进行了全面的旋转切割实验.
推力,扭矩和振动传感器与旋转切割机 (RCM) 集成.
一个堆叠集体学习模型利用了统计特征,包括基于振动谱图的新型局部放大特征,用于训练.

主要成果:

与单个模型相比,堆叠集体学习模型表现出更高的准确性和稳定性.
该模型有效地集成了用于岩石质量分类的推力,扭矩和振动数据.
拟议的方法显示了在道挖掘过程中实时地质识别的巨大潜力.

结论:

开发的堆叠集体学习模型为实时岩石质量分类提供了强大的解决方案.
装有TBM的传感器数据,特别是振动特征,可以有效地用于地质监测.
这种方法有望提高道钻井操作的安全性和效率.

关键词:

装有 TBM 的传感器.多个来源的数据数据.岩石质量分类的分类堆叠集体学习模型的模型.

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