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<p>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, <i>p</i> &lt; 0.05) and provided an early-warning lead time of up to 3.5 weeks 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.</p>

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Adaptive multihazard modeling predicts rainfall-driven dam failure: a case study

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年2月23日

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  • Mohammed Nasser,
  • Eleyas Assefa,
  • Siraj M. Assefa,
  • Constantinos C. Sachpazis,
  • Lysandros Pantelidis

摘要

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 an early-warning lead time of up to 3.5 weeks 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.