Prediction model of dam deformation based on attention mechanism
Year: 2025
Type: conference-paper
Abstract: In order to adapt to the development of automated dam monitoring systems, artificial intelligence (AI) models based on deep learning are used for dam deformation prediction, effectively overcoming the limitations of traditional statistical analysis methods. These AI models have demonstrated excellent performance in capturing the deformation characteristics and potential dependencies of dams during their long-term service. This study takes a certain rolled concrete gravity dam as an object and combines the dam deformation statistical model with the Informer deep learning model to propose an attention mechanism dam deformation prediction model that considers water pressure, temperature, and aging factors. The comparative analysis of model performance indicates that, compared with commonly used models, such as multiple linear regression, support vector machines, and long short-term memory neural networks, the proposed model can better adapt to the complex nonlinear relationship between dam deformation and influencing factors and has excellent predictive performance and generalization ability, effectively improving the accuracy of dam deformation prediction and providing practical engineering benefits for dam safety monitoring.
Authors: JiaYi Huang, TingBiao Liu, YanKun Zhan, ZeQin Chen, XianBiao Xiao, Qi Wu, YuJing Zheng, RongFeng Liu, Yan Su
Institution: Fuzhou University
Language: English
Topic: Dam Engineering and Safety
Subfield: Civil and Structural Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: diamond
OpenAlex ID: W4410395494