Prediction of Shear-Wave Velocity from SPT and Soil Index Properties: Comparison Between NSPT and (N1)60 Using Classical Baselines and Machine Learning Under Grouped Validation

Shear-wave velocity (Vs) estimation from the Standard Penetration Test (SPT) can support preliminary site characterization when direct geophysical data are limited, but empirical correlations require validation schemes that reflect transferability between sites. This study evaluates Vs prediction using an interval-paired dataset derived from geotechnical investigations of school foundations in Piura, Peru. Its novelty lies in comparing the raw SPT blow count (NSPT) and the overburden- and energy-corrected SPT blow count ((N1)60) on the same strict common sample, using grouped cross-validation by school, thereby emphasizing transferability across sites rather than only internal fit. Five predictive scenarios were tested, from penetration-only formulations to geotechnically enriched specifications. The lowest grouped out-of-fold error among the evaluated models was obtained by a generalized power baseline using (N1)60 and the integral geotechnical predictor set, yielding root mean square error (RMSE) = 80.48 m/s, mean absolute error (MAE) = 60.15 m/s, and coefficient of determination (R2) = 0.338. This moderate R2 indicates limited standalone predictive capacity under transfer to unseen schools; therefore, the model is interpreted as a preliminary transfer-oriented correlation rather than as a substitute for direct Vs measurements or as an independent design equation. In the complementary full-data analysis, the strongest descriptive fit was obtained with Hist Gradient Boosting, whereas the strongest explicit equation corresponded to the log-semi baseline. Overall, the findings show that externally validated transferability, descriptive full-data fit, and equation-based interpretability represent different analytical roles in Vs-SPT modeling.