范新宇, 贾志献, 白雪亮, 杨天俊, 刘勇. 2019: 熵权模糊综合评价模型在极软岩隧洞围岩分级中的应用. 工程地质学报, 27(6): 1236-1243. DOI: 10.13544/j.cnki.jeg.2018-310
    引用本文: 范新宇, 贾志献, 白雪亮, 杨天俊, 刘勇. 2019: 熵权模糊综合评价模型在极软岩隧洞围岩分级中的应用. 工程地质学报, 27(6): 1236-1243. DOI: 10.13544/j.cnki.jeg.2018-310
    FAN Xinyu, JIA Zhixian, BAI Xueliang, YANG Tianjun, LIU Yong. 2019: CLASSIFICATION OF EXTREMELY SOFT ROCK TUNNELS USING COMPREHENSIVE EVALUATION MODEL OF ENTROPY-WEIGHTED FUZZY. JOURNAL OF ENGINEERING GEOLOGY, 27(6): 1236-1243. DOI: 10.13544/j.cnki.jeg.2018-310
    Citation: FAN Xinyu, JIA Zhixian, BAI Xueliang, YANG Tianjun, LIU Yong. 2019: CLASSIFICATION OF EXTREMELY SOFT ROCK TUNNELS USING COMPREHENSIVE EVALUATION MODEL OF ENTROPY-WEIGHTED FUZZY. JOURNAL OF ENGINEERING GEOLOGY, 27(6): 1236-1243. DOI: 10.13544/j.cnki.jeg.2018-310

    熵权模糊综合评价模型在极软岩隧洞围岩分级中的应用

    CLASSIFICATION OF EXTREMELY SOFT ROCK TUNNELS USING COMPREHENSIVE EVALUATION MODEL OF ENTROPY-WEIGHTED FUZZY

    • 摘要: 为解决传统围岩分级方法对极软岩隧洞复杂性考虑不足的缺陷,引入模糊数学理论,选取单轴抗压强度、岩石完整性指标RQD、结构面间距、结构面特征、岩石含水量及干燥饱和吸水率6个因素作为评价指标,为避免评价指标赋值的主观性,利用熵权法计算各指标的权重,建立起围岩分级的熵权模糊综合评价模型。将该模型应用于印度尼西亚某在建水电站黏土岩隧洞围岩分级当中,选取15个待评洞段进行评价,结果表明,隧洞围岩以Ⅳ级或Ⅴ级为主,利用该模型计算的围岩分级结果与现场确定的围岩支护等级高度吻合,通过对比分析,认为该模型分级结果较传统RMR分级法更优,表明模糊数学理论可以解决极软岩隧洞影响因素的复杂性及模糊性问题,熵权法能够降低人为赋值的主观性,两者结合可实现对黏土岩隧洞的客观评价,该方法可为极软岩隧洞分级提供参考。

       

      Abstract: This paper introduces the fuzzy mathematics theory to solve the defects of traditional methods that are insufficient consideration of complexity in extremely soft rock classification. It establishes the evaluation indexes with total uniaxial compressive strength,drill core quality RQD,spacing of discontinuities,conditions of discontinuities,water content and dry saturated hydroscopic moisture ratio of rock. It adopts the entropy method to calculate index weight so as to avoid subjectivity of evaluation index assignment. Thus,an entropy weight fuzzy based comprehensive evaluation model for rock classification is established. This model is applied to a hydropower station tunnel in Indonesia. It chooses 15 tunnel sections for evaluation. The results show that the surrounding rock are mainly class Ⅳ or Ⅴ. The results are highly consistent with tunnel support types that are determined on site. It is considered that the classification results of this model are better than the traditional RMR classification method through comparison and analysis. It shows that the fuzzy mathematics can solve the complexity and fuzziness problems of extremely soft rock influencing factors. The entropy weight method can reduce the subjectivity of human factors. The combination of the two methods can achieve objective evaluation. This approach can provide reference for the classification of extremely soft rock tunnel.

       

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