Multi-expert attention network for long-term dam displacement prediction

作者：Zhou, Yuhang; Bao, Tengfei^*; Li, Guoli; Shu, Xiaosong; Li, Yangtao

来源：ADVANCED ENGINEERING INFORMATICS, 2023, 57: 102060.

DOI：10.1016/j.aei.2023.102060

• Multi -head attention
• Spatio-temporal feature
• Graph convolutional network
• Dam displacement prediction
• Long short-term memory networks

Monitoring and predicting the dam displacement of concrete dams has attracted increasing attention for ensuring the long-term health conditions. Most existing models focus on just temporal features and ignore the spatial features relations of monitoring data. To address these problems, a dam displacement prediction model based on multi-expert network is developed. In the proposed model, the long short-term memory network is employed to extract the temporal features of each monitoring sensor. Then the multi-head attention network is employed to obtain the spatial features and the adjacency values. The multi-expert graph is employed to describe the spatial relations between different monitoring sensors. The graph convolutional network is employed to integrate the spatio-temporal features and predict the long-term dam displacement. Through a real-world comparative study against eight prediction models, the proposed model performs better than other models in both cyclical and non -cyclical time series. Therefore, the proposed model is suitable for evaluating the dam health condition.

• 单位
  河海大学