Machine learning–based uncertainty modelling of mechanical properties of soft clays relating to time-dependent behavior and its application

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.