Deformation Prediction Model of Gravity Dam Based on ZOA-BiLSTM

In order to solve the limited prediction accuracy existing in the traditional monitoring model in the deformation prediction of concrete gravity dams, This paper presents a model for predicting dam deformation, which includes the Zebra Optimization Algorithm and a Bidirectional Long Short-Term Memory network.First, typical measurement point data within the deformation region were selected. Subsequently, the model hyperparameters are iteratively optimized by the ZOA optimization algorithm to improve the results. Finally, the high-precision prediction of concrete gravity dam deformation is realized by combining the proposed ZOA-BiLSTM prediction process. The engineering examples show that the model prediction results match the characteristics of the spatial distribution of the dam deformation.For monitoring dams, new ideas and methods have been proposed.