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作 者:赵昕 苏怀智[1,2] 方正[1,2] ZHAO Xin;SU Huai-zhi;FANG Zheng(The National Key Laboratory of Water Disaster Prevention,Hohai University,Nanjing 210098,Jiangsu Province,China;College of Water Conservancy and Hydropower Engineering,Hohai University,Nanjing 210098,Jiangsu Province,China)
机构地区:[1]河海大学水灾害防御全国重点实验室,江苏南京210098 [2]河海大学水利水电学院,江苏南京210098
出 处:《中国农村水利水电》2025年第4期58-64,共7页China Rural Water and Hydropower
基 金:国家自然科学基金资助项目(52239009);中央高校基本科研业务费专项资金项目(B240201028)。
摘 要:为提高大坝安全监测数据库的数据挖掘效率,引入改进的ECLAT关联规则算法。利用数据挖掘技术分析处理数量庞大的大坝安全监测数据,并建立大坝坝顶垂直位移预测模型。首先筛选出对大坝坝顶垂直位移的主要影响因素环境温度、坝前水温和坝前水位,然后用Eclat算法对预处理后的基本资料进行数据挖掘,筛选出可以用于预测的强关联规则,最后利用坝顶垂直位移对温度变化反应的滞后性,建立坝顶垂直位移预测模型。将本模型应用于某混凝土拱坝中,试验表明该模型的本次预测结果具有一定的可靠性。To improve the data mining efficiency of dam safety monitoring database,an improved ECLAT association rule algorithm is introduced.The data mining techniques are used to analyze and process a large amount of dam safety monitoring data,and a vertical displacement prediction model for the dam crest is established.Firstly,the main influencing factors of the vertical displacement of the dam crest,including environmental temperature,water temperature in front of the dam,and water level in front of the dam,are selected.Then,the Eclat algorithm is used to mine the preprocessed basic data to screen out strong association rules that can be used for prediction.Finally,the lag of the vertical displacement of the dam crest in response to temperature changes is utilized to establish a dam crest displacement prediction model.The application of this model to a concrete arch dam has shown that the predicted results of the model have a certain degree of reliability.
关 键 词:大坝安全监测 坝顶垂直位移 关联规则 Eclat算法 数据挖掘
分 类 号:TV689.1[水利工程—水利水电工程]
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