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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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Journal : Journal of the Civil Engineering Forum

 1 
3D Back Analysis of Karyamekar Landslide, West Java, Indonesia: Effects of Tension Crack and Rainfall on Peak and Residual Soil Shear Strength Aisya Galuh Laksita; Faris, Fikri; Ahmad Rifa’i
Journal of the Civil Engineering Forum Vol. 10 No. 1 (January 2024)
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.7837

Abstract

A landslide was experienced in Karyamekar Village, Cilawu District, Garut Regency, West Java, on 12 February 2021 at approximately 300 m length with a depth of 20 m, leading to a steep slope. Therefore, this study aimed to use 3D back analysis to determine soil shear strength to be subsequently applied in analyzing the possibility of further landslide with due consideration for tension crack and rainfall effect. It was also used to understand the influence of these factors on slope stability. Filled tension crack and rainfall effects were modeled using Finite Element Method (FEM) while Limit Equilibrium Method (LEM) was applied for back analysis. The results of back analysis showed that peak shear strength value of φ was 31.18° at a cohesion of 8.01 kPa while the residual shear strength value of φ was 10.35° with 2.31 kPa. The φpeak value was found to be close to the estimated 32°, but there was a significant difference in the φresidual which was approximated to be 30°. This discrepancy could be attributed to several factors such as the accuracy of rainfall data and geometry as well as the absence of some soil samples during the investigation. The cohesion values for peak and residual soil shear strength were considered acceptable because of the smaller values compared to the typical cohesion of SM (Silty Sand) which was set at 20°. Moreover, slope stability analyses conducted using only the effect of tension crack produced a safety factor of 0.996 while those with only the effect of rainfall had 1.172. The results showed that water pressure in tension crack had a more significant influence on slope stability compared to rain. However, it was important to state that the variation in the significance of each factor was based on the assumptions made during the analysis.
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