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Journal : Journal of Geoscience, Engineering, Environment, and Technology

Water and Reinforced Effects on Slope: Case Study on District Koto Panjang, Riau, Indonesia Nugroho, Soewignjo Agus; Yusa, Muhamad; Sujatmoko, Bambang; Ongko, Andarsin
Journal of Geoscience, Engineering, Environment, and Technology Vol. 9 No. 2 (2024): JGEET Vol 09 No 02 : June (2024)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/jgeet.2024.9.2.12604

Abstract

This paper discussed a study case related to slope stability and its analysis. The observation and also research object was a hill side on border area between West Sumatera-Riau, Indonesia. This border area consists of numerous slopes with heterogeneous soil characteristics. This location is also susceptible for having landslides, especially on rainy season. The schemes of this research consisted of collecting samples, laboratory tests, finite element method analysis, and slope`s reinforcement planning with anchors or geosynthetic plates. The soil samples were tested on their actual condition and liquid limit condition. This purposed to predict the failures on slope. Afterwards, some reinforcement plannings need to be done. The results of this researach have shown that on existing condition, the safety factor was 1.262. If the soil reach its liquid limit, the safety factor decreased to 0.568. After the reinforcement planning was done, the safety factor went up to 1.120 and the slope stability could be maintained.
Sensitivity Analysis Based on Physical Properties to Permeability Coefficient of Cohesive Soil Using Artificial Neural Network Fatnanta, Ferry; Suprayogi, Imam; Nugroho, Soewignjo Agus; Satibi, Syawal; Saputra, Riola
Journal of Geoscience, Engineering, Environment, and Technology Vol. 9 No. 1 (2024): JGEET Vol 09 No 01 : March (2024)
Publisher : UIR PRESS

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.25299/jgeet.2024.9.1.13536

Abstract

Permeability is the ability of a soil to allow liquids to pass through. Of course the soil has a physical characteristic that can be known by laboratory testing. This study aims to determine the physical properties that most affect the coefficient of cohesive soil permeability using the Artificial Neural Network (ANN) tool, the results obtained will later be matched with actual conditions according to the context of engineering geology. The research method begins with an influence or sensitivity analysis using ANN which will produce a correlation coefficient (R). Then, these results will be compared with the influence analysis based on the value of the coefficient of determination (R2). After that, accuracy and error tests will be carried out using the Mean Absolute Percentage Error (MAPE), the highest accuracy values is categorized as the most influential physical property of the 7 physical property parameters, namely liquid limit, plastic limit, plasticity index, %sand, %fines, %silt, and %clay. Based on the result of the analysis, %fines is the parameter that most influences permeability and is able to make very strong predictions with an R value using an ANN of 0.9941875, an R2 value of 0.6336, an accuracy of 99.6962%, and a MAPE of 0.3038%. These results are compared with the existing empirical equations with an accuracy of 96.4393% and MAPE of 3.5607%. It can be concluded that ANN is more effective and optimal in making predictions. In this case, in the context of engineering geology, the more %fines, the smaller the permeability coefficient of the soil.