The energy-based method has been widely employed to evaluate soil liquefaction potential. A recent trend is to develop machine learning (ML) models to predict capacity energy. However, there is still a lack of statistical perspectives on energy-based ML models for liquefaction risk analysis. This study develops a rigorous Bayesian framework for characterizing uncertainties of two typical energy-based ML models and developing probabilistic versions. Performance of ML models is evaluated and compared from a statistical perspective using Bayesian model comparison approach. The effects of test data quality and data quantity are also comprehensively investigated. The probabilistic model is finally verified against real cases. Results show that the occurrence probability of artificial neural network model is higher than that of support vector machine model. The model uncertainty is more sensitive to uncertainties in soil textural properties and fines content than inter-granular contact density. The predicted liquefaction probabilities are in good agreement with field observations and superior to the traditional method. This study can extend the use of the energy-based methods and provide performance-based risk evaluation of soil liquefaction potential.

中文翻译：

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用于土壤液化可靠性分析的基于概率容量能量的机器学习模型


基于能量的方法已广泛应用于土壤液化潜力的评估。最近的趋势是开发机器学习 (ML) 模型来预测容量能量。然而，用于液化风险分析的基于能源的机器学习模型仍然缺乏统计视角。本研究开发了严格的贝叶斯框架，用于表征两种典型的基于能量的机器学习模型的不确定性并开发概率版本。使用贝叶斯模型比较方法从统计角度评估和比较机器学习模型的性能。还全面研究了测试数据质量和数据数量的影响。最后通过实际案例验证了概率模型。结果表明，人工神经网络模型的出现概率高于支持向量机模型。与粒间接触密度相比，模型不确定性对土壤质地特性和细粒含量的不确定性更敏感。预测的液化概率与现场观测结果吻合较好，优于传统方法。这项研究可以扩展基于能量的方法的使用，并提供基于性能的土壤液化潜力风险评估。