Enhancing Soil Liquefaction Prediction: Overcoming Data Challenges in SPT-Based Machine Learning with Imputation Technique
DOI:
https://doi.org/10.22146/jcef.21347Keywords:
Machine learning, Missing value imputation, Soil liquefaction, Earthquake, Standard penetration testAbstract
In addition to the adverse effects of earthquakes, the loss of soil-bearing capacity during liquefaction can exacerbate damage to buildings. Liquefaction phenomena involve many parameters, making it more complex to evaluate. Machine learning has been studied to deal with liquefaction complexity in recent decades. However, incomplete liquefaction data can result in missing information, complicating model development across various datasets. Therefore, this study aims to assess the capability of machine learning models to predict liquefaction by implementing the missing value imputation technique. Seismicity, soil properties, and soil condition parameters were utilized to develop models. Random Forest (RF), k-Nearest Neighbor (k-NN), and eXtreme Gradient Boosting (XGBoost) were trained by applying feature selection and parameter optimization based on standard penetration test (SPT) data. The confusion matrix was used to assess the performance of the model based on the performance matrix of Overall Accuracy (OA), Precision (Prec), Recall (Rec), F1-Score (F1), and Area Under the Curve (AUC). In addition, the preprocessing stage included data normalization and outlier treatment to enhance the reliability of model predictions, ensuring consistent learning behavior across different variable scales. The results show that the RF achieved the highest performance (OA = 90.71%), which is comparable to findings from other previous studies. The AUC results indicate that the models deliver excellent classification performance. These findings suggest that the integration of imputation and preprocessing techniques can significantly improve data-driven approaches in geotechnical earthquake engineering. In conclusion, the missing imputation is quite effective in the predictive model. Finally, this study offers a new perspective on developing machine learning models using a more user-friendly software and applying imputation techniques to handle missing data.
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