Site-independent prediction of field vane undrained shear strength of Shanghai clay: machine-learning models versus a log-normal multivariate baseline under leave-one-site-out validation

Abstract Undrained shear strength (S u ) is often estimated from index properties, but reported machine-learning (ML) accuracies frequently rest on random splits that place rows from the same site in both training and test sets. Using the public SH-CLAY/11/4051 database of Shanghai clay, we evaluate eight regression models (ridge regression, support vector regression, Gaussian process regression, random forest, XGBoost, LightGBM, a perceptron and a perceptron ensemble) for predicting field vane S u from depth, plasticity index, void ratio and effective vertical stress (377 complete rows from 28 sites). Models are assessed by nested leave-one-site-out cross-validation, against log-normal multivariate (ln-MVN) and linear mixed-effects baselines using identical inputs, with site-cluster bootstrap intervals. All ML models outperform the ln-MVN baseline: pooled out-of-site root-mean-square error (RMSE) is 5.15 to 5.34 kPa for the nonlinear models, 5.64 kPa for ridge regression and 6.09 to 6.10 kPa for the ln-MVN baselines (paired differences 0.4 to 0.9 kPa, 95% intervals excluding zero). Differences among the nonlinear models are small; only the ANN ensemble and random forest clearly outperform the mixed-effects baseline, and adding depth improves no model. Split-conformal intervals reach 0.88 to 0.91 coverage at the 90% level, but coverage varies strongly between sites. Effective vertical stress dominates out-of-site permutation importance. Monotonicity constraints did not change accuracy; an offset estimated from five local measurements lowered the remaining error by 0.1 to 0.4 kPa. The work applies existing methods to an existing database under deployment-consistent validation; it has no external validation set.