Displacement is one of the most intuitive indicators reflecting the influence of internal and external environments on dam structures. However, existing displacement monitoring models mostly rely on static fitting of single point and deterministic point value estimation, failing to deeply consider the similarity and uncertainty of displacement in dynamic changes under significant water level fluctuations. To address this issue, the displacements are first divided into three periods: ascending, descending, and stable periods. Second, based on the Ward criterion, the displacements of measuring points in different periods are clustered and partitioned, and the CRITIC method is applied to amalgamate the displacement of multiple points within each partition into comprehensive displacements, which reflect the displacement changes of the corresponding partition. Then, the comprehensive displacements are predicted utilizing deep learning model convolutional neural network-gated recurrent unit (CNN-GRU). Finally, to consider the uncertainty and randomness of displacement changes, prediction intervals based on Bootstrap method are employed to quantify the impact of uncertain factors. Engineering examples show that the proposed method can provide scientific basis and technical support for the safety monitoring and health diagnosis of super-high arch dams.
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