Environmental-aware deformation prediction of water-related concrete structures using deep learning
Hao Gu, Yangtao Li, Yixiang Fang, Yiming Wang, Yang Yu, Yang Wei, Liqun Xu, Yijun Chen
Abstract
Accurate long-term deformation prediction is essential to ensure the structural security and ongoing stability of large water-related concrete structures like ultra-high arch dams. Traditional statistical regression and shallow machine learning approaches, due to their algorithmic constraints, often fail to comprehensively capture the complex temporal and spatial dependencies inherent in high-dimensional prototypical monitoring data, thereby limiting their predictive accuracy and robustness. To address these challenges, this study proposes a multi-point deformation forecasting model that incorporates both spatial and temporal correlations between environmental factors and deformation, utilizing advanced deep learning (DL) techniques. Specifically, we employ a Transformer-based convolutional long short-term memory (ConvLSTM) model to capture the spatiotemporal dependencies across numerous temperature and deformation monitoring sequences. Furthermore, the multi-objective bayesian optimization algorithm is utilized to ascertain the optimal model architecture and hyperparameters, concurrently maximizing the regression coefficient and minimizing the root mean square error (RMSE). The effectiveness of the proposed DL-based model for high-arch dam deformation prediction is validated using data from multiple monitoring points of ultra-high arch dams. Experimental results demonstrate that the TransformerConvLSTM method significantly outperforms other models at five monitoring points. Quantitatively, it consistently achieves lower RMSE and high correlation coefficient values, indicating its superior ability to provide accurate predictions with minimal error.