A two-stage concrete dam displacement prediction model based on VAE and INGO-ResNet-Informer

Abstract

[Objective] Displacement is the most intuitive and reliable reflection of the dam' s operating status and structural behavior under the dual effects of internal and external environments. Establishing a scientific and reasonable dam safety monitoring model is of great significance to ensure its safe operation. [Methods] To address the complex nonlinear, diverse, and time-varying characteristics of concrete dam displacement monitoring data, a two-stage concrete dam displacement prediction model based on Variational Autoencoder(VAE), Improved Northern Goshawk Optimization(INGO) algorithm, Residual Neural Network(ResNet) and Informer is proposed to improve the accuracy of dam behaviour analysis. Firstly, VAE is used to perform latent space modeling and nonlinear feature extraction on temperature monitoring data. Then, the improved Res Net is utilized to deeply characterize the multi-source input impact factors, the Informer model is introduced to capture the long-term dependency information in the data, and the INGO algorithm, enhanced with multiple strategies, adaptively tunes the key hyperparameters of the model to achieve precise modeling of dam displacement. [Results] Taking the displacement monitoring data of a concrete arch dam as an example, the proposed model is compared with the traditional prediction model. And the result demonstrate that the proposed model can accurately predict the dam displacement trend, achieving the best RMSE, MAE and R² values on the displacement data test set of each measuring point. [Conclusion] The proposed model exhibits significant advantages in prediction accuracy, stability and residual suppression ability, providing technical support for the safety monitoring and operation management of concrete dams.

Keywords

concrete dam displacement prediction / variational autoencoder / residual neural network / Informer / improved northern goshawk optimization algorithm / influencing factor / prediction model