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Peran Gradasi dan Mineralogi dalam Menentukan Kekuatan dan Kompresibilitas Material Disposal Tambang Nikel: Kajian Literatur Nugraha, Arief Pambudi
Venus: Jurnal Publikasi Rumpun Ilmu Teknik  Vol. 3 No. 6 (2025): Venus: Jurnal Publikasi Rumpun Ilmu Teknik
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/venus.v3i6.1209

Abstract

Mine disposal materials such as tailings, overburden, and waste rocks are critical components in mining operations that require comprehensive understanding of their geotechnical properties to ensure stability and safety of storage facilities. This literature review aims to analyze the role of particle gradation and mineralogical composition in determining shear strength and compressibility of mine disposal materials, with particular focus on nickel mining. A sistematic literature review method was employed by analyzing 30 scientific publications from 2019-2025 obtained from various academic databases. The review findings indicate that particle size distribution (gradation) has significant influence on shear strength and compressibility, where materials with coarser gradation and higher coefficient of uniformity (Cu) exhibit greater shear strength and lower compressibility. Mineralogy, particularly clay mineral content, increases cohesion and microporosity but also increases compressibility under loose conditions. Studies on nickel mine waste demonstrate that ferronickel slag possesses favorable drainage characteristics suitable for rockfill material, while tailings require strict gradation control. In conclusion, comprehensive characterization integrating gradation parameters (Cu, Cc, D50) with mineralogical analysis (XRD, XRF) is essential for predicting mechanical behavior of mine disposal materials and designing safe storage facilities.
Studi Literatur Akurasi Metode Slope Mass Rating untuk Evaluasi Kestabilan Lereng Tambang Batubara di Indonesia Nugraha, Arief Pambudi
Globe: Publikasi Ilmu Teknik, Teknologi Kebumian, Ilmu Perkapalan Vol. 3 No. 4 (2025): November : Publikasi Ilmu Teknik, Teknologi Kebumian, Ilmu Perkapalan
Publisher : Asosiasi Riset Ilmu Teknik Indonesia

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.61132/globe.v3i4.1189

Abstract

This literature study evaluates the accuracy of the Slope Mass Rating (SMR) method for coal mine slope stability in Indonesia through a systematic descriptive synthesis of 25 empirical studies from 2020 to 2025. The objectives of the study were to identify the level of SMR prediction accuracy, factors affecting the method's performance, and modifications required for local Indonesian conditions. The research method involved a systematic search with inclusion criteria for empirical studies reporting SMR and/or Safety Factor (SF) values ​​for coal mines and associated slopes in Indonesia. Quantitative analysis showed a range of reported SMR values ​​between 41 and 96 with a median of 72, while SF values ​​ranged from 1.137 to 4.09 for normal operational conditions. The synthesis results indicated that SMR provides a consistent stability classification for initial slope design and failure mode identification (planar, wedge, toppling), with historical validation showing a correlation of up to 91.23% between SMR-based hazard zoning and actual field events in some cases. Key limitations include dependence on discontinuity data quality, sensitivity to groundwater conditions and tropical weathering, and variation in the interpretation of adjustment factors F1-F4. Modifications such as NAAF23 and integration with numerical modeling have been shown to improve prediction reliability. It is recommended that coal mining practitioners combine SMR with kinematic analysis and limit equilibrium modeling as standard operating procedures, and develop adjustment factors specific to Indonesian geological conditions. Further research should focus on standardizing parameter reporting and cross-site quantitative validation to enable more robust statistical meta-analyses.