Bayesian-informed DNN and ensemble learning for predicting soil water characteristic curves from easily measurable parameters

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dc.contributor.authorLiu, Yifei
dc.contributor.authorNi, Junjun
dc.contributor.authorZhang, Fei
dc.contributor.authorKravchenko, Ekaterina
dc.contributor.authorKamchoom, Viroon
dc.date.accessioned2026-08-06T10:54:19Z
dc.date.available2026-08-06T10:54:19Z
dc.date.issued2026-01-01
dc.description.abstractSoil-water characteristic curve (SWCC) represents one of the important properties describing the hydraulic characteristics of unsaturated soil, with extensive application value in geotechnical engineering, but the experimental process for obtaining SWCC is complex and time-consuming. This research proposes a prediction framework based on Bayesian-informed machine learning for SWCC applicable to different soil types, using easily measurable soil parameters. This model uses quantified particle size distribution, bulk density, and saturated water content as input features, and employs Bayesian-Markov Chain Monte Carlo methods to inversely derive Fredlund-Xing (FX) model parameters as output features. Deep Neural Network (DNN) and stacking models based on ensemble learning of five regressors were constructed to establish the prediction framework. Results show that both DNN and stacking models effectively capture complex nonlinear relationships between the three easily measured soil parameters and FX parameters, demonstrating good prediction accuracy. The stacked model shows better prediction results, with R<sup>2</sup> exceeding 0.94 for all three parameters, outperforming the DNN model (R<sup>2</sup> = 0.93). Through feature engineering and SHAP (Shapley Additive Explanations)-based feature sensitivity analysis, the relationships between input features and the three FX model parameters were physically interpreted, providing physical interpretability for the machine learning models. The prediction method offers a new approach for fast and accurate acquisition of SWCC, expanding the application of machine learning methods in the field of unsaturated soils.
dc.identifier.citationTransportation Geotechnics, 56, 2026
dc.identifier.doi10.1016/j.trgeo.2025.101791
dc.identifier.issn22143912
dc.identifier.other2-s2.0-105021107759
dc.identifier.urihttps://dspace.kmitl.ac.th/handle/123456789/17797
dc.sourceTransportation Geotechnics
dc.subjectBayesian inference
dc.subjectEnsemble learning
dc.subjectMachine learning
dc.subjectSoil water characteristic curve
dc.titleBayesian-informed DNN and ensemble learning for predicting soil water characteristic curves from easily measurable parameters
dc.typeArticle

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