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Rizqi Prastowo
Institut Teknologi Nasional Yogyakarta
Aerospace Engineering Agriculture, Biological Sciences & Forestry Arts Humanities Automotive Engineering Chemical Engineering, Chemistry & Bioengineering Chemistry Civil Engineering, Building, Construction & Architecture Computer Science & IT Control & Systems Engineering Decision Sciences, Operations Research & Management Earth & Planetary Sciences Economics, Econometrics & Finance Education Electrical & Electronics Engineering Energy Engineering Environmental Science Industrial & Manufacturing Engineering Languange, Linguistic, Communication & Media Materials Science & Nanotechnology Mechanical Engineering Medicine & Pharmacology Physics Public Health Social Sciences Transportation Other

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Journal : Indonesian Mining Journal

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PENILAIAN DAN PREDIKSI JARINGAN SYARAF TIRUAN TERHADAP KECEPATAN PARTIKEL YANG DIINDUKSI PELEDAKAN - STUDI KASUS PENAMBANGAN BATUGAMPING Prastowo, Rizqi; Hendro Purnomo; Firhad Firmansyah; Ipmawan, Vico Luthfi
Indonesian Mining Journal Vol 27 No 1 (2024): Indonesian Mining Journal, April 2024
Publisher : Balai Besar Pengujian Mineral dan Batubara tekMIRA

Show Abstract | Download Original | Original Source | Check in Google Scholar | DOI: 10.30556/imj.Vol27.No1.2024.1531

Abstract

In recent decades, generation of ground vibrations results from blasting activities in mining sector has been identified as a significant cause of extensive harm to nearby structures, vegetation, and individuals. Hence, it is imperative to closely monitor and accurately forecast the uncertain levels of vibration, and implement the appropriate steps to mitigate their potentially harmful impact. The objective of this study was to establish a correlation between the peak particle velocity and the various parameters that influence it. This study employed the deployment of the artificial neural network approach to assess and forecast the uncertain ground vibrations. In this study, a multilayer perception neural network with three layers and a feed-forward back-propagation architecture was employed. The network consisted of five input parameters, namely the distance from the blast face, maximum charge per delay, spacing, burden, and depth hole. The output of interest was the peak particle velocity. The neural network was trained using the Levenberg–Marquardt algorithm, and the training dataset comprised 29 experimental records and blast event data obtained from the limestone mine in Indonesia. In order to assess the effectiveness and the precision of the artificial neural network model that was created, a total of four conventional predictor models were utilized. These models were proposed by reputable sources such as the US Bureau of Mines, Ambraseys–Hendron, Langefors–Kihlstrom, and the Bureau of Indian Standards. The results collected from the demonstrate study show that the artificial neural network model suggested in this research has the ability to provide more precise estimations of ground vibrations in comparison to existing conventional prediction models. The artificial neural network model yielded a coefficient of determination (R2) of 0.9332 and a root mean square error (RMSE) of 0.4763.
Co-Authors A.A Inung Arie Adnyano, A.A Inung Arie Abdul Latif Ashadi Agustinus Isjudarto Ahmad Said Akhmad Zamroni Al Hussein Flowers Rizqi Aldri, Riaferdo Amir Machmud Amir Machmud, Amir Andika Eko Prasetiyo Andika Eko Prasetiyo, Andika Eko Andyono B. Santoso Angger Bagus Prasetiyo Anggi Hermawan Anita Susiana Arimurti, Dyah Arum Bagus Gilang Pratama, Bagus Gilang Bahy, Muhammad Nauval Brahme, Nameeta Cengiz, Korhan Dandi Pranomo Eko Wahyu Hidayat, Eko Wahyu Fahrul Nurfajri Mokoagow Fajar Yulianto Prabowo Farid, Fajri Filipus Alfriyadi Junaidi firhad firmansyah Hasan Said Tortop Hendro Purnomo Herman, Sofiana Hidayatullah Shidiq hidayatullah sidiq Hikmahtiar, Syouma Hurien Helmi Ichwan Noor Ardiyat Ikah N. P. Permanasari Ilham Mopio Iman Pradana A. Assagaf Iqbal, Mochamad Iwan Tri Riyadi Yanto, Iwan Tri Riyadi Jemssy Ronald Rohi Jermsittiparsert, Kittisak Junior, Prince Robert Kapugu, Excellentia Riane Kittisak Jermsittiparsert Manmeet Kaur Melfa Utari Muhamad Syazali Muhammad Hafizh Hibullah Murkute, Yogesh Novandri Kusuma Wardana Oggi Heical Ardian Oggi Heicqal Ardian Oky Sugarbo Ozyurt, Basak Panse, Vishal R. Paramitha Tedja Trisnaning Puspasari, Fitri Radhitya, Berwyn Dzaky Rahmad Junaidi, Rahmad Randy Galaxy Rita Desiasni Rizal Maulana Rizal Maulana, Rizal Rofiqul Umam Sabrina Putri Puspitasari Sely Novita Sari Setiawan, Nanda Juli Setyo Pambudi Setyo Pambudi Shilvyanora Aprilia Rande, Shilvyanora Syamsul Huda Syamsul Huda Tarumasely, Nofry Hence Tedy Kurniawan Topac, Tuna Tortop, Hasan Said Tri Nugroho Suwarno Urip Nurwijayanto Prabowo Urip Nurwijayanto Prabowo Urip Nurwijayanto Prabowo, Urip Nurwijayanto Veronica Diana Anis Anggorowati Vico Luthfi Ipmawan Yonathan Ito