工程岩体亚分级的必要性与实施策略探讨

    Discussion on necessity and implementation strategy of sub-classification of engineering rock mass

    • 摘要: 针对现有工程岩体分级方法普遍存在的级间跨度大、分级指标模糊等不足,本文以BQ法为典型代表,系统论证了开展岩体质量亚分级的必要性。通过IV级岩体的参数敏感性分析发现,同一级别内上下限参数的选取会显著影响隧道工程的拱顶沉降、围岩塑性区范围以及边坡工程的安全系数与变形量级,导致工程响应呈现明显差异;工程经济性案例进一步表明,基于同一级别上下限参数设计的露天矿边坡,其最大安全边坡角相差可达31.56°,由此引发的剥岩成本差异可达近百万元每延米量级。在此基础上,本文从方法论层面系统探讨了精细化岩体质量亚分级的实施策略与技术构想:在数据采集环节,提出集成多源随钻测量数据、钻孔图像信息与计算机视觉识别功能的原位测量系统概念设计;在指标筛选环节,探讨融合相关性分析、主成分分析与专家经验的分级指标遴选与影响机制解析策略;在等级评定环节,构想将模糊数学等传统方法与机器学习、深度学习等智能算法相结合的多维度亚分级技术路径,从而整合形成“数据采集-机理分析-智能评价”的完整方法框架。本文工作旨在为建立科学、统一的工程岩体质量亚分级标准提供理论参考与方法思路。

       

      Abstract: Addressing the common deficiencies in existing engineering rock mass classification methods—such as wide intervals between grades and vague classification indicators—this paper takes the BQ method as a typical representative to systematically demonstrate the necessity of conducting rock mass quality sub-classification. Through parameter sensitivity analysis of Grade IV rock mass, it is found that the selection of upper and lower limit parameters within the same grade can significantly affect the crown settlement of tunnel engineering, the extent of the plastic zone in surrounding rock, as well as the safety factor and deformation magnitude of slope engineering, leading to notable differences in engineering response. An engineering economy case study further indicates that for an open-pit mine slope designed based on the upper and lower limit parameters of the same grade, the maximum safe slope angle can differ by up to 31.56°, resulting in a difference in stripping costs that can reach the order of nearly one million yuan per linear meter. On this basis, this paper systematically explores, from a methodological perspective, the implementation strategies and technical concepts for refined rock mass quality sub-classification: in the data acquisition stage, a conceptual design of an in-situ measurement system is proposed, integrating multi-source measurement-while-drilling data, borehole image information, and computer vision recognition functions; in the indicator screening stage, strategies for selecting sub-classification indicators and analyzing their influence mechanisms are discussed, combining correlation analysis, principal component analysis, and expert experience; in the grade evaluation stage, a multi-dimensional sub-classification technical pathway is conceived, integrating traditional methods such as fuzzy mathematics with intelligent algorithms like machine learning and deep learning, thereby forming a complete methodological framework of "data acquisition – mechanism analysis – intelligent evaluation." The work presented in this paper aims to provide theoretical reference and methodological insights for establishing a scientific and unified standard for engineering rock mass quality sub-classification.

       

    /

    返回文章
    返回