A probabilistic data-driven framework for modelling clay consolidation and settlement behavior
Predicting consolidation settlement in large-scale reclamation projects remains difficult because laboratory consolidation test data are sparse in space and natural marine clays show strongly non-linear structured behavior. This study develops a probabilistic data-driven framework to reconstruct spatially continuous e–log p′ and log k–e relationships from sparse consolidation test data. A deep neural network is combined with repeated K-fold cross-validation to estimate the ensemble mean response and its 95% confidence interval. The framework was trained using consolidation test data from 49 boreholes at Kobe Airport and was evaluated using two independent blind-test boreholes that were not used in model development. The predicted mean curves reproduced the main features of the observed compression and permeability responses, including depth-dependent yield behavior and post-yield changes in compressibility. The engineering applicability of the framework was examined through settlement analysis at monitoring point KC-1, where no site-specific borehole data were available. In this analysis, soil deformation was treated as one-dimensional, whereas pore-water flow was modeled as two-dimensional. The predicted material relationships were used as input. The calculated settlement history reproduced the main observed trend, while the late-stage difference from the measurements showed the importance of deeper strata outside the present modeling scope. The proposed framework provides a practical way to interpolate consolidation behavior in space with quantified ML-related uncertainty for large reclamation projects with similar geological and data conditions.