基于动态跟踪与集对理论的地下病害风险评估云模型研究
Research on cloud model of risk assessment of urban underground
diseases based on dynamic tracking and set pair theory
Research on cloud model of risk assessment of urban underground
diseases based on dynamic tracking and set pair theory
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摘要: 本文提出一种融入动态跟踪思想和集对分析理论的城市地下病害风险评估云模型。首先，考虑到病害的动态发育特性，提出一种基于动态跟踪思想的风险评估方法，将病害属性变化率纳入风险评估体系；其次，利用基于二阶和四阶中心矩的逆向高斯云算法将评估指标转化为指标云，并使用最小二乘法将主观云权重和客观Critic权重耦合并优化，为指标云科学分配权重；最后，引入集对分析理论，将高斯云模型的“3En”规则与集对理论中的集对势相结合，计算云模型相似度，提高病害风险等级判别准确性。通过对贵阳市地下病害检测工程及北京东四环道路的检测评估，并与其他三种评估模型对比分析，验证了模型的有效性。Abstract: A cloud model for the risk assessment of urban underground diseases was proposed by integrating dynamic tracking theory and set pair analysis theoryFirstly，taking into account the dynamic development characteristics of diseases，a risk assessment method based on dynamic tracking thoughts was proposed by including the change rate of disease properties into risk assessment systemSecondly，assessment indicators were transformed into indicator clouds by inverse Gauss cloud algorithm based on the secondorder and fourthorder center distance，and the least squares method was used to combine and optimize the subjective cloud weight and objective critic weight to distribute weights for indicators scientificallyFinally，by introducing the set pair analysis theory，the “3En” rule of Gauss cloud model was combined with the set pair potential in set pair theory to calculate similarity of the cloud model and improve the accuracy of risk level discriminationThrough the underground disease detection project in Guiyang City and the East Fourth Ring of Beijing，the model proposed was compared with the other three assessment models and actual excavation results were used to verify the validity of the model
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