An Intelligent Temporal Framework for Interval Prediction of Concrete Dam Deformation

Vollständiger Abstract

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The inherent uncertainty of concrete dam systems, together with the complex influence of environmental loads and measurement noise, makes it difficult for traditional deterministic point prediction models to provide reliable deformation forecasts. In particular, the prediction performance of conventional models is highly dependent on parameter settings, while the uncertainty and potential deviation of future displacement responses are often not fully quantified. To address these limitations, this study proposes an intelligent data-driven prediction framework for dam displacement based on the integration of convolutional neural networks and long short-term memory networks. In the proposed framework, convolutional neural networks are used to extract local feature information from monitoring data, while long short-term memory networks are employed to capture temporal dependencies in displacement sequences. The Black-winged Kite Algorithm is introduced to optimize the key parameters of the integrated multi-level network, thereby improving the accuracy and robustness of point prediction. Furthermore, quantile regression is embedded into the optimized learning framework to construct an interval prediction model for dam deformation, enabling the conditional predictive uncertainty associated with displacement evolution to be quantitatively characterized. The engineering case study and comparative analyses with other models demonstrate that the proposed model achieves improved prediction performance for the investigated monitoring point. The interval prediction results further show that, for the investigated dam and monitoring point, the proposed framework can effectively characterize conditional predictive uncertainty and provide additional information for deformation interpretation and safety assessment. Further studies involving additional monitoring points and dams are required to assess its broader applicability.