TabPFN Extensions for Interpretable Geotechnical Modelling

Abstract

Geotechnical site characterisation relies on sparse, heterogeneous borehole data, where uncertainty quantification and interpretability matter as much as predictive accuracy. We evaluate TabPFN~\citep{Hollmann2025}, a tabular foundation model, and its \texttt{tabpfn-extensions} library on two geotechnical tasks: (1) soil-type classification from N-value and shear-wave velocity data as a controlled illustrative case, and (2) iterative imputation of five mechanical parameters ($s_{\mathrm{u}}$, $E_{\mathrm{u}}$, ${\ddot{I}{ƒ}^{\prime }}_{\mathrm{p}}$, $C_{\mathrm{c}}$, $C_{\mathrm{v}}$) in BM/AirportSoilProperties/2/2025. Without retraining, we apply cosine-similarity analysis to TabPFN embeddings, visualise predictive distributions, and compute SHAP attributions. On the regression benchmark we compare TabPFN with mean imputation, linear regression, random forests, XGBoost, and HBM; introduce a proxy decomposition of predictive uncertainty across context-perturbation classes; and propagate marginal $C_{\mathrm{c}}$ and ${\ddot{I}{ƒ}^{\prime }}_{\mathrm{p}}$ distributions through a one-dimensional consolidation model to obtain the reliability index ${\hat{I}}^2$ and serviceability exceedance probability $P_{\mathrm{f}}$. Embeddings exhibit label-consistent Clay/Sand grouping; iterative imputation reduces RMSE for all five targets, with TabPFN lowest on four; SHAP attributions are consistent with the Skempton compression-index correlation and the inverse preconsolidation-pressure-water-content dependence; the within-posterior component is largest in the proxy decomposition. We position the contribution as a worked evaluation workflow that may complement established methods for data-scarce geotechnics, not as algorithmic innovation.