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All Journal Buletin Sumber Daya Geologi Journal of Engineering and Technological Sciences
Ridho Kresna Wattimena, Ridho Kresna
Institut Teknologi Bandung
Aerospace Engineering Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Civil Engineering, Building, Construction & Architecture Education Electrical & Electronics Engineering Energy Engineering Environmental Science

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KRITERIA KERUSAKAN AKIBAT PELEDAKAN PADA PEMBUATAN TEROWONGAN CIURUG, TAMBANG EMAS PONGKOR, KABUPATEN BOGOR, PROVINSI JAWA BARAT Suwandhi, Awang; Rosana, Mega Fatimah; Sudradjat, Adjat; Wattimena, Ridho Kresna
Buletin Sumber Daya Geologi Vol 12 No 2 (2017): Buletin Sumber Daya Geologi
Publisher : Pusat Sumber Daya Mineral Batubara dan Panas Bumi

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.47599/bsdg.v12i2.36

Abstract

Untuk mencapai urat (vein) bijih di lokasi Central pada Level 500 mdpl, Ciurug, dibuat terowongan baru yang dinamakan RU4C-L500 dengan menerapkan teknik peledakan. Terowongan tersebut dirancang berukuran lebar 4 m, tinggi 4 m dan panjang 100 m sampai ke cross-cut menuju urat bijih. Penelitian dilakukan dengan tujuan untuk mendapatkan kriteria kerusakan terowongan yang diakibatkan oleh induksi getaran peledakan yang berpotensi menghasilkan overbreak, sehingga ukuran terowongan menjadi lebih lebar dari rencana semula. Pendekatan Blast Damage Index (BDI) digunakan sebagai metode untuk memperoleh tingkat kerusakan terowongan tersebut. Terowongan menerobos batuan andesit vulkanik yang berdasarkan hasil pengujian memiliki kecepatan rambat gelombang seismik 4157 m/det, densitas 2,37 g/cc dan kuat tarik 8,10 MPa. Batuan tersebut tergolong batuan berkekuatan sedang hingga keras dengan nilai Rock Mass Rating (RMR) antara 58,3 sampai 69,5. Data getaran peledakan diukur dengan menggunakan geofon pada jarak yang aman dari titik ledakan dan menghasilkan Peak Vector Sum (PVS) berkisar antara 2 mm/s sampai 120 mm/s. Hasil pengolahan data getaran menunjukkan, bahwa nilai BDI sebesar 2 yang mengidentifikasi adanya ambrukan terowongan karena induksi getaran peledakan terjadi pada jarak kurang dari 3 m dari titik ledakan. Besar getaran peledakan yang mengambrukan batuan andesit vulkanik tersebut mencapai PVS 1170 mm/s. Pada jarak antara 3,0 m sampai 10 m tidak terjadi kerusakan terowongan yang parah dan dapat direhabilitasi. Kondisi tersebut diidentifikasi oleh nilai BDI 0,5 sampai 1. Kemudian pada jarak di atas 10 m induksi getaran peledakan tidak lagi berpengaruh terhadap kerusakan atau kestabilan dinding terowongan. 
Classifying Coal Mine Pillar Stability Areas with Multiclass SVM on Ensemble Learning Models Hertono, Gatot Fatwanto; Wattimena, Ridho Kresna; Mendrofa, Gabriella Aileen; Handari, Bevina Desjwiandra
Journal of Engineering and Technological Sciences Vol. 56 No. 1 (2024)
Publisher : Directorate for Research and Community Services, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/j.eng.technol.sci.2024.56.1.8

Abstract

Pillars are key structural components in coal mining. The safety requirements of underground coal mines are non-negotiable. Accurately classifying the areas of pillar stability helps ensure safety in coal mines. This study aimed to classify new pillar stability categories and their stability areas. The multiclass support vector machine (SVM) method was implemented with two types of kernel functions (polynomial and radial basis function (RBF) kernels) on pillar stability data with four new categories: failed or intact, either with or without an appropriate safety factor. This classification uses three basic ensemble learning models: Artificial Neural Network-Backpropagation Rectified Linear Unit, Artificial Neural Network-Backpropagation Exponential Linear Unit, and Artificial Neural Network-Backpropagation Gaussian Error Linear Unit. The results with four data proportions and ten experiments had an average accuracy and standard deviation of 92.98% and 0.56%-1.64% respectively. The accuracies of the multiclass SVM method using the polynomial kernel and the RBF kernel with Bayesian parameter optimization to classify the areas of pillar stability were 91% and 92%, respectively. The multiclass SVM method with the RBF kernel captured 96.6% of potentially dangerous pillars. The visualization of classification areas showed that areas with intact pillars may also have failed pillars.
Classifying Rockburst Events and Intensity in Underground Mines using Grey Wolf Optimization–Support Vector Machine and Extreme Gradient Boosting Nugraha, Adhitya Dwi; Wattimena, Ridho Kresna; Handari, Bevina Desjwiandra; Hertono, Gatot
Journal of Engineering and Technological Sciences Vol. 57 No. 6 (2025): December 2025 (In Progress)
Publisher : Directorate for Research and Community Services, Institut Teknologi Bandung

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.5614/j.eng.technol.sci.2025.57.6.6

Abstract

Rockbursts are destructive accidents that often occur in underground mines. With the advancement of technology, machine learning has emerged as an alternative solution that can be used for rockburst mitigation. In this research, we classify rockburst events and their intensities in underground mines using two machine learning models: grey wolf optimization–support vector machine (GWO–SVM) and extreme gradient boosting (XGBoost). Rockburst events are classified into two categories: Existent and None. Meanwhile, the intensities are classified into three categories: weak, moderate, and strong. The implementation used 476 cases of rockbursts with six variables: maximum tangential stress, uniaxial compressive strength, uniaxial tensile strength, stress coefficient, rock brittleness coefficient, and elastic strain index. Both models can better predict the “Existent” rockburst class with a “Weak” intensity compared with the other intensity classes. The performances of the models are evaluated using different proportions of training data, ranging from 50% to 90%. Both models have the same performance for rockburst event classification with 97.53% accuracy, 0.9444 precision, 0.9846 recall, and 0.9628 F1-score. Meanwhile, for intensity classification, XGBoost outperforms GWO-SVM with its 88.24% accuracy, 0.8413 precision, 0.9137 recall, and 0.8651 F1-score.
Co-Authors Adjat Sudradjat Bevina D Handari Hertono, Gatot Hertono, Gatot Fatwanto Mega Fatimah Rosana Mendrofa, Gabriella Aileen Nugraha, Adhitya Dwi Suwandhi, Awang