基于多分形的隧道围岩质量等级分类新模型

IF 8.2 1区 工程技术 Q1 ENGINEERING, CIVIL
Junjie Ma , Tianbin Li , Zhen Zhang , Roohollah Shirani Faradonbeh , Mostafa Sharifzadeh , Chunchi Ma
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引用次数: 0

摘要

了解隧道掘进机(TBM)运行参数的变化规律对于评估隧道内的工程地质条件和围岩质量等级至关重要。研究隧道掘进机运行参数的多分形特征有助于识别其变化规律,但相关研究尚未开展。本文基于多分形分析理论,提出了一种新型的 TBM 隧道围岩质量等级分类模型。首先,探讨了不同围岩等级的 8 个 TBM 循环数据的统计特征。随后,推导并总结了 TBM 运行数据多分形特征参数的计算和分析方法。研究结果表明,刀盘扭矩、总推力、进尺率和刀盘转速等 TBM 运行参数具有显著的多分形特征。其多分形维度、广义分形谱中点斜率和奇异强度范围可用于评估隧道围岩等级。最后,利用多元线性回归方法提出了基于多分形特征参数的隧道围岩分类模型,并通过包含不同围岩等级的四个 TBM 循环数据对模型进行了验证。结果表明,所提出的基于多分形的隧道围岩分类模型具有较高的准确性和适用性。该研究不仅实现了 TBM 运行参数的多分形特征表示和围岩分类,还为 TBM 运行参数的自适应调整和自动化隧道应用提供了可能。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Novel multifractal-based classification model for the quality grades of surrounding rock within tunnels
Understanding the variation patterns of tunnel boring machine (TBM) operational parameters is crucial for assessing engineering geological conditions and quality grades of surrounding rock within tunnels. Studying the multifractal characteristics of the TBM operational parameters can help identify the patterns, but the relevant research has not yet been explored. This paper proposed a novel classification model for quality grades of surrounding rock in TBM tunnels based on multifractal analysis theory. Initially, the statistical characteristics of eight TBM cycle data with different grades of surrounding rock were explored. Subsequently, the method of calculating and analyzing the multifractal characteristic parameters of the TBM operational data was deduced and summarized. The research results showed that the TBM operational parameters of cutterhead torque, total thrust, advance rate, and cutterhead rotation speed have significant multifractal characteristics. Its multifractal dimension, midpoint slope of the generalized fractal spectrum, and singularity strength range can be used to evaluate the surrounding rock grades of the tunnel. Finally, a novel classification model for the tunnel surrounding rocks based on the multifractal characteristic parameters was proposed using the multiple linear regression method, and the model was verified through four TBM cycle data containing different surrounding rock grades. The results showed that the proposed multifractal-based classification model for tunnel surrounding rocks has high accuracy and applicability. This study not only achieves multifractal feature representation and surrounding rock classification for TBM operational parameters but also holds the potential for adaptive adjustment of TBM operational parameters and automated tunneling applications.
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来源期刊
Underground Space
Underground Space ENGINEERING, CIVIL-
CiteScore
10.20
自引率
14.10%
发文量
71
审稿时长
63 days
期刊介绍: Underground Space is an open access international journal without article processing charges (APC) committed to serving as a scientific forum for researchers and practitioners in the field of underground engineering. The journal welcomes manuscripts that deal with original theories, methods, technologies, and important applications throughout the life-cycle of underground projects, including planning, design, operation and maintenance, disaster prevention, and demolition. The journal is particularly interested in manuscripts related to the latest development of smart underground engineering from the perspectives of resilience, resources saving, environmental friendliness, humanity, and artificial intelligence. The manuscripts are expected to have significant innovation and potential impact in the field of underground engineering, and should have clear association with or application in underground projects.
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