## 5 Conclusions  
This study integrates Bayesian GLM and the XGBoost algorithm, coupled with SHAP and partial dependence analysis, to robustly predict Vs. Six key features were identified as the most influential factors for Vs prediction: cone tip resistance and its coefficient of variation, sleeve friction, soil behavior type index, depth, and overburden stress. These features formed the foundation for the final development of the XGBoost model. For the Bayesian GLM, cone tip resistance and its coefficient of variation, sleeve friction, depth, and overburden stress were identified as key predictor variables to fit the model on the same preprocessed datasets as the XGBoost model.  
The XGBoost model demonstrated strong predictive performance, achieving high scores across various performance metrics and maintaining low prediction errors. SHAP analysis enhanced the model's interpretability by quantifying the contribution of each feature to the Vs predictions. Overburden stress was identified as the most influential factor, followed by cone tip resistance and sleeve friction. The validation results on the SCPT dataset revealed a strong agreement between measured and predicted Vs values for both models, with the Bayesian GLM offering credible intervals representing prediction uncertainty.  
The integration of Bayesian GLM and explainable ML algorithms signifies a robust framework for predicting Vs while leveraging the strengths of both approaches. Explainable ML model excel at identifying critical input geotechnical parameters and explaining their contributions to the prediction. This capability not only enhances the interpretation of the underlying factors influencing Vs but also delivers high predictive performance. On the other hand, Bayesian GLM offers complementary advantages by explicitly capturing the uncertainties associated with geotechnical variability, measurement errors, and other sources of uncertainty. This approach enhances confidence in Vs predictions and their applicability to seismic response assessments.  
Figure 11: Predictive performance of Bayesian GLM and XGBoost models against validation dataset.  
To further enhance the applicability of this work, developing a user-friendly computational platform for Vs prediction is recommended. Such a platform could integrate both Bayesian GLM and ML approaches, enabling geotechnical engineers to easily interpret predictions and uncertainties. Additionally, site-specific seismic response analyses using Vs profiles obtained from the two approaches should be conducted to further validate their performance in practical engineering scenarios.  
Conceptualization, A.T.C. and R.R.; methodology, A.T.C.; software, A.T.C.; validation, A.T.C. formal analysis, A.T.C.; writing--original draft preparation, A.T.C.; writing--review and editing, R.R.; visualization, A.T.C.; supervision, R.R. All authors have read and agreed to the published version of the manuscript.  
This research was funded by Szechenyi Istvan University.  
Not applicable.  
The date utilized for this study can be downloaded from the following link: [[https://www.tugraz.at/en/institutes/ibg/research/computational-geotechnics-group/database/](https://www.tugraz.at/en/institutes/ibg/research/computational-geotechnics-group/database/)]([https://www.tugraz.at/en/institutes/ibg/research/computational-geotechnics-group/database/](https://www.tugraz.at/en/institutes/ibg/research/computational-geotechnics-group/database/)) (accessed on 12 May 2023).  
The authors declare no conflicts of interest.