陈沅江, 吴婷婷. 基于熵权正态云模型的软岩等级评价[J]. 煤田地质与勘探, 2017, 45(5): 121-126,134. DOI: 10.3969/j.issn.1001-1986.2017.05.021
引用本文: 陈沅江, 吴婷婷. 基于熵权正态云模型的软岩等级评价[J]. 煤田地质与勘探, 2017, 45(5): 121-126,134. DOI: 10.3969/j.issn.1001-1986.2017.05.021
CHEN Yuanjiang, WU Tingting. Entropy and normal cloud model-based grade evaluation of soft rock[J]. COAL GEOLOGY & EXPLORATION, 2017, 45(5): 121-126,134. DOI: 10.3969/j.issn.1001-1986.2017.05.021
Citation: CHEN Yuanjiang, WU Tingting. Entropy and normal cloud model-based grade evaluation of soft rock[J]. COAL GEOLOGY & EXPLORATION, 2017, 45(5): 121-126,134. DOI: 10.3969/j.issn.1001-1986.2017.05.021

基于熵权正态云模型的软岩等级评价

Entropy and normal cloud model-based grade evaluation of soft rock

  • 摘要: 软岩是地下工程施工中常见的复杂地质情况之一,科学准确的对其分类是进行安全施工的重要前提。针对软岩环境的复杂性和不确定性,选取单轴抗压强度σc、完整性系数Kv、黏聚力σn、软化系数Kf和软化指数fs 5项定量化指标建立软岩评价指标体系,采用熵权法确定各指标权重,结合云理论建立熵权-正态云模型,对软岩的类型进行分级评价。以4组软岩工程实例对所建立模型进行检验,并与未确知度法、模糊评价法和BQ法的判别结果进行对比。研究结果表明,熵权-正态云模型在软岩等级判别中具有良好的实用性和可靠性,可为软岩类型预测提供一种新思路。

     

    Abstract: Soft rock is one of the main engineering geological hazards occurring in the underground engineering construction. The classification of soft rock is an important premise for safe construction. Six quantitative indices including uniaxial compressive strength σc, integrity coefficient Kv, cohesion σn, softening coefficient Kf and softening index fs are chosen as the predictor variables of soft rock according to the unascertained factors of classification prediction of soft rock. The entropy method was adopted to determine the weighting coefficient for each evaluation index. Based on entropy method and the unascertained measurement theory, with 4 groups of typical engineering examples, an entropy and the normal cloud model to predict the possibility and classification of soft rock was established. Then the proposed model was validated with four typical soft rock projects and compared with the results of unascertained degree method, fuzzy evaluation method and BQ method. The obtained results show a practicability and reliability in the grading of soft rock. It may provide a new way to predict the type of soft rock.

     

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