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大坝变形监测数据异常值判定方法研究

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本文通过对混凝土大坝变形与环境温度、水位变化以及时效变形等因素之间的关系进行讨论,采用支持向量机(SVM)和最小协方差矩阵(MCD)稳健估计对监测数据中的异常值进行识别.通过某水库大坝安全监测项目对基于SVM-MCD的异常值识别方法进行应用研究,与传统回归方法结合 3 准则异常值判定结果进行对比分析,验证了基于SVM-MCD算法的异常值识别方法的可靠性,能够有效解决传统异常值识别方法的漏判误判问题,准确识别监测数据中的异常值,从而提升混凝土大坝监测数据的可靠性.
Research on Determination Method of Outlier in Dam Deformation Monitoring Data
In this paper,by discussing the relationship between concrete dam deformation and environmental temperature,water level change,aging deformation and other factors,support vector machine(SVM)and minimum covariance matrix(MCD)robust estimation are used to identify outliers in the monitoring data.Through a dam safety monitoring project of a reservoir,the application of SVM-MCD algorithm based outlier recognition method is studied,and the traditional regression method is combined with the 3σ criteria of outlier judgment results are compared and analyzed,which verifies the reliability of the SVM-MCD algorithm based outlier recognition method,and can effectively solve the problem of missing judgment and misjudgment of the traditional outlier recognition method.The outliers in monitoring data can be accurately identified,to improve the reliability of concrete dam monitoring data.

concrete damoutliersafety monitoringsupport vector machine

李继宏

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广东省水文地质大队,广东 广州 510599

混凝土大坝 异常值 安全监测 支持向量机

2024

科技创新与生产力
太原科技战略研究院

科技创新与生产力

影响因子:0.271
ISSN:1674-9146
年,卷(期):2024.45(12)