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<p>Reliable liquefaction prediction is crucial for the prevention and control of seismic hazards in earth-rock dams. However, the existing studies focus only on the accuracy of seismic liquefaction prediction and neglect the catastrophic consequences caused by underestimated liquefaction risk levels. Moreover, conventional liquefaction predictions are mostly conducted on a single-algorithm Machine Learning (ML)-based model, which can only achieve accurate classification in few specific regions, greatly limiting the applicability of liquefaction prediction models. This study thereby presents a Risk-Averse Hybrid Ensemble Learning (RAHEL) model for seismic liquefaction prediction, which is capable of classifying both Liquefaction States (LS) and Liquefaction Risk Grades (LRG) simultaneously. Specifically, a novel Performance-Weighted Voting (PWV) ensemble strategy is designed to capture complicated mappings from various influencing factors to LS or LRG. The GridSearchCV optimization algorithm simultaneously finds the optimal values of all hyperparameters of heterogeneous ML base-classifiers in the Voting ensemble to generate the most effective classification framework. Further considering the adverse effect of underestimated LRG, a cost function with the penalty mechanism is defined to guide the search direction of GridSearchCV, thus forcing the underestimated risk level to shift to an overestimation. RAHEL integrates the advantages of hybrid and Voting ensemble schemes by combining hyperparameter optimization with risk-averse adaptation with efficient ML models. The effectiveness and generalization ability of the proposed RAHEL is verified comprehensively with reference to four case studies of field data that cover different types of site conditions and levels of liquefaction risk, including three publicly available historical datasets and one real earth-rock dams engineering dataset. The classification performance of RAHEL is compared to that of other single ML models, conventional Voting ensemble, and hybrid models. The analytical results show that RAHEL is the most reliable approach, which can maintain a high prediction accuracy and reduce the underestimation rate of LRG, thus achieving adaptive avoidance of seismic liquefaction risks. Furthermore, the discussion on the category weight schemes confirms the reasonableness of RAHEL, and the analysis based on SHapley Additive exPlanations (SHAP) shows the contribution of each liquefaction factor to LS or LRG. This work develops a highly promising tool that greatly outperforms currently available methods to assist dam engineers in seismic liquefaction forecasting and early-warning.</p>

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Improving soil liquefaction prediction of earth-rock dams via performance-weighted ensemble learning with risk-averse adaptation

  • 期刊论文

  • 发表日期：

    2026年6月2日

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  • Yingbo Chen,
  • Qiubing Ren,
  • Mingchao Li,
  • Huihui Jia,
  • Donghua Chen

摘要

Reliable liquefaction prediction is crucial for the prevention and control of seismic hazards in earth-rock dams. However, the existing studies focus only on the accuracy of seismic liquefaction prediction and neglect the catastrophic consequences caused by underestimated liquefaction risk levels. Moreover, conventional liquefaction predictions are mostly conducted on a single-algorithm Machine Learning (ML)-based model, which can only achieve accurate classification in few specific regions, greatly limiting the applicability of liquefaction prediction models. This study thereby presents a Risk-Averse Hybrid Ensemble Learning (RAHEL) model for seismic liquefaction prediction, which is capable of classifying both Liquefaction States (LS) and Liquefaction Risk Grades (LRG) simultaneously. Specifically, a novel Performance-Weighted Voting (PWV) ensemble strategy is designed to capture complicated mappings from various influencing factors to LS or LRG. The GridSearchCV optimization algorithm simultaneously finds the optimal values of all hyperparameters of heterogeneous ML base-classifiers in the Voting ensemble to generate the most effective classification framework. Further considering the adverse effect of underestimated LRG, a cost function with the penalty mechanism is defined to guide the search direction of GridSearchCV, thus forcing the underestimated risk level to shift to an overestimation. RAHEL integrates the advantages of hybrid and Voting ensemble schemes by combining hyperparameter optimization with risk-averse adaptation with efficient ML models. The effectiveness and generalization ability of the proposed RAHEL is verified comprehensively with reference to four case studies of field data that cover different types of site conditions and levels of liquefaction risk, including three publicly available historical datasets and one real earth-rock dams engineering dataset. The classification performance of RAHEL is compared to that of other single ML models, conventional Voting ensemble, and hybrid models. The analytical results show that RAHEL is the most reliable approach, which can maintain a high prediction accuracy and reduce the underestimation rate of LRG, thus achieving adaptive avoidance of seismic liquefaction risks. Furthermore, the discussion on the category weight schemes confirms the reasonableness of RAHEL, and the analysis based on SHapley Additive exPlanations (SHAP) shows the contribution of each liquefaction factor to LS or LRG. This work develops a highly promising tool that greatly outperforms currently available methods to assist dam engineers in seismic liquefaction forecasting and early-warning.