用于不排水抗剪强度的概率间接模型：采用高级插补和机器学习技术解决大量数据缺失与变异性问题
Probabilistic indirect models for undrained shear strength: addressing significant data missing and variability with advanced imputation and machine learning techniques
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中文总结 AI 辅助
本研究针对岩土工程中不排水抗剪强度预测的数据缺失与变异性问题，采用高级插补法结合机器学习技术，构建MN增强型MHA-PNN概率间接模型，显著提升了预测精度与不确定性量化效果。
中文摘要 AI 辅助
准确预测不排水抗剪强度（su）对岩土工程设计至关重要，但传统经验方法常受限于显著不确定性。本研究利用CLAY/10/7490全球数据库，开发基于阿特伯格极限和孔压静力触探（CPTU）测量值的su概率间接模型。首先，该数据集存在高缺失率与变异性，测试三种插补方法——多元正态（MN）、链式方程多重插补（MICE）和miss森林（MF）以填充缺失值；为验证其有效性，构建概率极端梯度提升（PXGB）模型，通过对比在插补后数据集上训练的PXGB性能与在原始不完整数据上训练的PXGB性能，评估各插补方法。其次，将多头注意力（MHA）机制集成至人工神经网络（ANN），构建基于MHA的概率神经网络（MHA-PNN）模型，以增强从有限数据中提取信息的能力。采用均方根误差（RMSE）、决定系数（R²）、平均绝对百分比误差（MAPE）、条件区间宽度（wCI）和覆盖率（CR），评估所提模型及传统基于MN的预测模型的性能。结果表明，所提MN增强型MHA-PNN模型在预测精度和不确定性量化方面均显著优于其他模型。这些发现凸显了该集成策略在岩土工程应用中构建稳健概率间接模型的潜力，尤其适用于面临稀疏与不完整数据集的场景。
英文摘要
Accurate prediction of undrained shear strength (su) is crucial for geotechnical design, but is often hampered by substantial uncertainty in traditional empirical methods. This study uses the CLAY/10/7490 global database to develop probabilistic indirect models to predict su based on Atterberg limits and piezocone cone penetration (CPTU) measurements. Firstly, the dataset has a high missing data rate and variability. We test three imputation methods - multivariate normal (MN), multiple imputation by chained equations (MICE), and miss forest (MF) - to fill the missing values. To validate their effectiveness, a Probabilistic Extreme Gradient Boosting (PXGB) model is developed, and the imputation methods are evaluated by comparing the PXGB's performance when trained on the imputed datasets against that on the original incomplete data. Secondly, the indirect model is built by integrating a multi-head attention (MHA) mechanism into an artificial neural network (ANN) to enhance information extraction from limited data, which leads to the MHA-based probabilistic neural networks (MHA-PNN) model. The models' performance, alongside a conventional MN-based prediction model, was evaluated using root mean square error (RMSE), coefficient of determination (R2), mean absolute percentage error (MAPE), conditional interval width (wCI), and coverage rate (CR). Results demonstrate that the proposed MN-enhanced MHA-PNN model substantially outperforms other models in both prediction accuracy and uncertainty quantification. These findings highlight the potential of this integrated strategy for building robust probabilistic indirect models in geotechnical applications, particularly when confronted with sparse and incomplete datasets.
