Uncertainty is a commonplace and significant issue in geotechnical engineering. Unlike conventional statistical and machine learning methods, this study presents a novel approach to correlating soil properties that takes uncertainty into account using an artificial neural network with Monte Carlo dropout (ANN_MCD). An uncertainty model for two important soil properties, creep index C_α, and hydraulic conductivity k, that control the long-term performance of geotechnical structures is proposed in a function of three soil physical properties using ANN_MCD. Evaluation of the accuracy, uncertainty, and monotonicity of the predicted results for both C_α and k reveals the excellent performance of the proposed model, which is used to simulate the long-term settling and excess pore pressure of an embankment on soft clays. The predicted results show good agreement with observations, within a 95% confidence interval. All results indicate that the proposed ANN_MCD-based modelling approach can be used to rapidly correlate soil properties with an uncertainty evaluation and can be further combined with numerical modelling to analyze an engineering-scale problem and conduct risk assessment.

中文翻译：

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基于机器学习的软粘土力学性能不确定性建模与时变行为及其应用

不确定性是岩土工程中常见且重要的问题。与传统的统计和机器学习方法不同，本研究提出了一种关联土壤特性的新方法，该方法使用带有蒙特卡洛辍学的人工神经网络 (ANN_MCD) 来考虑不确定性。使用 ANN_MCD 在三个土壤物理特性的函数中提出了控制岩土结构长期性能的两个重要土壤特性的不确定性模型，蠕变指数C _α和水力传导率k。评估C _α和k预测结果的准确性、不确定性和单调性揭示了所提出模型的优异性能，该模型用于模拟软粘土上路堤的长期沉降和超孔隙压力。预测结果与观察结果非常吻合，置信区间为 95%。所有结果表明，所提出的基于 ANN_MCD 的建模方法可用于快速关联土壤特性与不确定性评估，并可进一步结合数值建模来分析工程规模问题并进行风险评估。