面向TBM掘进围岩类别预测的复杂数据处理分析方法研究

Data Processing and Analysis Methods for Predicting Surrounding Rock Types in TBM Excavation

现代隧道工程中使用全断面隧道掘进机(TBM)已成为一种常见且高效的掘进方法,TBM掘进过程中围岩类别预测也是当下的热门研究内容,但面对TBM现场掘进大量原始数据如何进行正确系统的处理是一个不可忽视的问题.TBM掘进数据的系统性处理分析对于优化施工过程、实现围岩识别预测进一步提升掘进效率具有重要意义.该研究以广花城际项目工程中施工采集的大量数据建立的数据库为基础,选取了能够反映TBM掘进状态的四个关键参数,即刀盘转速、刀盘扭矩、推进速度、总推进力,针对TBM掘进围岩类别预测的数据处理分析,提出了一种基于Python统计函数库的可视化为主的数据处理分析流程,并对掘进参数之间的相关性做出分析,以提高数据质量,为后续的TBM掘进预测模型提供良好的数据支撑.

In modern tunnel engineering,the Tunnel Boring Machine(TBM)has become a widely used and efficient method for tunnel excavation.The prediction of surrounding rock types during TBM excavation is currently a prominent research topic.However,the correct and systematic processing of the large amounts of raw data recorded during TBM on-site excavation is a critical issue.Systematic processing and analysis of TBM excavation data are crucial for optimizing the construction process,achieving surrounding rock identification and prediction,and further enhancing excavation efficiency.This paper utilizes the database system established from the extensive data collected during the construction of the Guanghua Intercity Project to select five key parameters that reflect the state of shield excavation.To process and analyze the data for predicting surrounding rock types during TBM excavation,a systematic method utilizing visualization through the Python statistical function library is proposed.The correlation between excavation parameters is analyzed to enhance data quality and provide robust support for the subsequent TBM excavation prediction model.