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All Journal Inersia : Jurnal Teknik Sipil dan Arsitektur Jurnal Teknik Sipil dan Perencanaan Semesta Teknika Civil Engineering Journal Borneo Engineering: Jurnal Teknik Sipil Journal of Applied Geology Journal of the Civil Engineering Forum Jurnal Teknik Sipil
Fikri Faris, Fikri
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Civil Engineering, Building, Construction & Architecture Earth & Planetary Sciences Energy Engineering Environmental Science Transportation

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Enhancing Soil Liquefaction Prediction: Overcoming Data Challenges in SPT-Based Machine Learning with Imputation Technique Fadliansyah, Fandi; Faris, Fikri; Wilopo, Wahyu; Ardiansyah
Journal of the Civil Engineering Forum Vol. 12 No. 1 (January 2026)
Publisher : Department of Civil and Environmental Engineering, Faculty of Engineering, Universitas Gadjah Mada

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.22146/jcef.21347

Abstract

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.
Co-Authors Ahmad Rifai Aisya Galuh Laksita Ardiansyah Danang Kurniawan, Danang Fachry Abda El Rahman Fadliansyah, Fandi Fathani, Faisal Hary Christady Hardiyatmo, Hary Christady Kurniadhi, Rizki Minmahddun, Anafi Nasril, Moh Purba, Byon Rezy Pradana Ramayanti, Anggie Ridwan, Banata Wahid Rifa’I, Ahmad Santie, Putri Anjary Widya Subiyantoro, Andy Suryana, Eka Priangga Hari Teuku Faisal Fathani Trisnawati, Tiara R. Wahyu Wilopo Yoga Setiawan, Yoga Yuniawan, Ragil A. Yus, Bella Esfadiary