Temporal convolution network‐based time frequency domain integrated model of multiple arch dam deformation and quantification of the load impact

Abstract

Deformation is an intuitive reflection of the safety status of a dam. The construction of a dam deformation prediction model can predict the deformation and interpret the effects of environmental loads. The current research mainly focuses on the predictive ability of the model and rarely involves the interpretation of the load impact on deformation. Meanwhile, the selection of the model factors, such as water pressure factors and temperature factors, mostly relies on prior knowledge. In addition, the complex structure of multiple arch dams makes it difficult to capture the relationship between deformation and environmental loads. Consequently, the performance of conventional models based only on time domain information may be insufficient. In this paper, a deformation prediction model is established by integrating time frequency domain information. First, the deformation and load monitoring data are decomposed and regrouped according to the frequency characteristic of signals via kurtosis index‐based VMD. Second, the sequence relationship between the dam deformation and loads under different frequency characteristics is automatically captured based on the temporal convolution network (TCN). Finally, a quantitative method of the load impact is proposed based on the network parameters. The case results show that the proposed modeling paradigm has significantly improved the prediction accuracy. The quantification result of the load impact on the horizontal displacement change of the dam conforms to the actual state of the project during the analysis period. The work effectively supplements the research on the prediction of ML‐based models and interpretation of the load impact on deformation.