The safety of earth-rockfill dams during construction is critically challenged by transient hydrological loads and evolving boundary conditions. While numerical and data-driven models exist, their standalone application is limited; the former is computationally prohibitive for real-time forecasting, and the latter often lacks physical interpretability. To bridge this gap, we introduce a novel hybrid framework that tightly couples Finite Element analysis (FEM) with deep learning (ANN-LSTM) for the physics-constrained forecasting of rainfall-induced instability. The model leverages FEM to simulate the hydro-mechanical response, the LSTM to capture temporal patterns in monitoring data, and an ANN to map strength degradation, with an attention mechanism identifying critical antecedent failure sequences. Validated on a two-year monitoring dataset from the Megech Dam, which experienced documented instabilities, our framework significantly outperformed established baselines. It achieved a superior MAE of 0.027 (vs. 0.081 for SVM, 0.067 for Random Forest, and 0.052 for standalone LSTM, p < 0.05) and provided a 2–3 week early-warning lead time by identifying the critical lag between rainfall peaks and pore-pressure buildup. The integrated attention mechanism autonomously highlighted weeks 25–30 and 75–80 as high-risk periods, aligning with field observations. This work demonstrates that a physics-informed hybrid approach offers a more reliable and interpretable tool for early-warning systems than purely data-driven methods. The proposed framework is adaptable to other earth-rockfill dams, providing a pathway from reactive monitoring to proactive risk management during critical construction phases.
Nasser et al. (Mon,) studied this question.