节点文献
基于理想点-可拓云模型的顺层岩质路堑边坡稳定性评价
Stability Evaluation of Bedding Rock Cutting Slope based on Ideal Point-extension Cloud Model
【摘要】 针对顺层岩质路堑边坡稳定性评价存在的模糊性和随机性特征,通过公路边坡施工的风险辨识建立风险评估指标体系,采用理想点法和可拓云模型相耦合的评估方法对边坡稳定性进行评价。首先,基于层次分析法和熵权法确定边坡评估指标的主观和客观权重,采用理想点法对评价指标的权重进行优化;然后,基于可拓云理论,构建了顺层岩质路堑边坡风险评估模型,并引入置信因子和危险指标,用以获取评估结果的可信度及安全隐患因素;最后,通过所提出的模型对某高速6处顺层岩质路堑边坡进行风险评价,并与采用常规方法的评估结果进行对比。结果表明:基于理想点-可拓云模型的评估结果与常规方法的评估结果基本吻合,验证了该评估方法的有效性与可靠性。该模型可克服单一指标赋权方法的劣势,提供更多的综合评价信息,减少评价过程中的不确定关系。
【Abstract】 In view of the fuzziness and randomness of the stability evaluation of bedding rock cutting slope, the risk evaluation index system is established through the risk identification of highway slope construction, and the slope stability is evaluated by the ideal point method and the extension cloud model coupling evaluation method.Firstly, the subjective and objective weights of slope evaluation indexes are determined based on the analytic hierarchy process and entropy weight method, and the weights of evaluation indexes are optimized by the ideal point method.Then, based on the extension cloud theory, the risk assessment model of bedding rock cutting slope is constructed, and the confidence factor and risk index are introduced to obtain the reliability of the assessment results and the factors of potential safety hazards.Finally, the risk assessment of six bedding rock cutting slopes of a highway is carried out by the proposed model, and the results are compared with those of conventional methods.The results show that the evaluation results based on the ideal point-extension cloud model are basically consistent with the evaluation results of the conventional methods, which verifies the effectiveness and reliability of the evaluation method.The model can overcome the disadvantages of the single index weighting method, provide more comprehensive evaluation information, and reduce the uncertainty in the evaluation process.
【Key words】 high cutting slope; risk assessment; ideal point; extension cloud model;
- 【文献出处】 公路 ,Highway , 编辑部邮箱 ,2023年11期
- 【分类号】U416.1
- 【网络出版时间】2023-11-13 16:14:00
- 【被引频次】1
- 【下载频次】246