基于自主开发的凿岩测试系统的围岩等级智能识别系统

Quanwei Liu, Junlong Yan, Hongzhao Li, Peiyuan Zhang, Yankai Liu, Linsheng Liu, Shoujie Ye, Haitao Liu
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引用次数: 0

摘要

围岩分类对于制定安全的隧道施工计划和支护措施至关重要。然而,隧道复杂的地质环境给获取准确的钻探参数以进行岩体分类带来了挑战。本文介绍了凿岩测试系统的开发情况,该系统包括推进速度采集系统、油压采集系统、气压采集系统和自动数据采集系统。该系统可对推进速度等参数进行实时、高精度的自动采集和存储,每个参数每秒采集两次数据。以青岛地铁 6 号线为例,我们进行了岩体钻探,并构建了岩体分类数据库。通过核密度估计和皮尔逊相关分析,我们量化了岩体分类与钻探参数之间的相关性。结果表明,依靠单一的钻探参数不足以准确确定岩体分类。冲击压力和旋转压力与岩体分类的相关性最强,两者的相关系数均低于-0.8(表明两者呈强负相关)。使用区间法排除了钻探参数的离群值。根据剩余数据,我们使用随机森林算法建立了智能岩体分类模型。该模型具有良好的准确性和泛化性能,平均准确率超过 0.9。所提出的凿岩测试系统与岩体智能分类模型相结合,形成了岩体等级智能识别的集成系统。该系统对钻爆隧道的智能安全施工具有重要意义。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Intelligent Identification of Surrounding Rock Grades Based on a Self-Developed Rock Drilling Test System
The classification of surrounding rock is crucial for formulating safe tunnel construction plans and support measures. However, the complex geological environment of tunnels presents a challenge in obtaining accurate drilling parameters for rock mass classification. This paper presents the development of a rock drilling testing system, which includes a propulsion speed acquisition system, oil pressure acquisition system, air pressure acquisition system, and an automatic data acquisition system. This system enables real-time, high-precision automatic collection and storage of parameters such as propulsion speed, with data collected twice per second for each parameter. Leveraging the Qingdao Metro Line 6 as a case study, we conducted rock mass drilling and constructed a rock mass classification database. By employing kernel density estimation and Pearson correlation analysis, we quantified the correlation between rock mass classification and the drilling parameters. The results indicated that relying on a single drilling parameter is insufficient for accurately determining rock mass classification. Both impact pressure and rotational pressure showed the strongest correlation with rock mass classification, each with a correlation coefficient below −0.8 (indicating a strong negative correlation). Outlier values of drilling parameters were excluded using the interval method. Based on the remaining data, we established an intelligent rock mass classification model using the random forest algorithm. This model demonstrated good accuracy and generalization performance, with an average accuracy exceeding 0.9. The proposed rock drilling testing system, combined with the intelligent rock mass classification model, forms an integrated system for the intelligent identification of rock mass grades. This system has significant implications for the intelligent and safe construction of drill-and-blast tunnels.
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