Interpolation of Missing Values in Temperature Field Monitoring Data of Arch Dams Based on Causal Mechanism and Neighboring Effects
The use of measured temperature fields is an important way to improve the performance of deformation monitoring model of arch dams.However,the failure of measurement instruments will result in the loss of monitoring data at some times.In this paper,the position of missing values in the measured temperature time series and the relative variation amplitude are both used to determine the interpolation method for a single temperature time series.Similarities between different temperature time series are quantified by the dynamic time warping method,and hierarchical criteria and priorities are defined for multiple temperature time series.On this basis,prediction models used for the interpolation of missing values are established,in which both the causal mechanism and neighboring effects are considered.The results of a high arch dam show that the proposed method can effectively achieve the systematic interpolation of dam temperature fields.The proposed interpolation prediction model has an improvement effect for 84.0%temperature monitoring points.
arch dammeasured temperature fieldmissing data interpolationcausal mechanismneighboring effectshierarchical priority
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