Prediction of Ground Vibration due to Blasting Activities of Coal Open Pit Mine Near Village Residents Using Multivariate Logarithmic Regression

Authors

  • Alloysius Vendhi Prasmoro Institut Teknologi Sepuluh Nopember Indonesia https://orcid.org/0009-0004-0269-7136
  • Nurhadi Siswanto Institut Teknologi Sepuluh Nopember Indonesia
  • Budi Santosa Institut Teknologi Sepuluh Nopember Indonesia
  • Giri Waluyo Nugraha PT RML Indonesia
  • Mohd Shukor Salleh Universiti Teknikal Malaysia Melaka Malaysia

DOI:

https://doi.org/10.26877/asset.v8i3.3616

Keywords:

ground vibrations, blasting, peak particle velocity, multivariate logarithmic regression

Abstract

Blasting activities are among the most important in mining and can have a negative impact on the environment and surrounding communities, causing disruption and even damaging nearby buildings and infrastructure. If the community protests and demonstrates, mining operations may be shut down, which would be very detrimental to the company. Studies are needed on effective planning to reduce the negative impacts. Ground vibrations measured by Peak Particle Velocity (PPV) are subject to thresholds set by each region's standards; in this research area, the maximum threshold is 3 mm/s to avoid damage to nearby buildings or settlements. The important variables are the explosive charge per delay and the distance, along with other blasting geometry variables such as spacing, burden, stemming, powder factor, and number of blast holes. Several previous researchers have used methods to predict PPV, with detonation parameters as the independent variables. This research uses general empirical methods and multivariate logarithmic regression (MLR). Prediction using MLR is better than general empirical methods; with R2 = 0.925, RMSE = 0.247, MAE = 0.548, MAPE = 0.218, and VAF = 96.66%, indicating near-perfect prediction. The MLR model produces a reference maximum explosive charge per delay of 59.09 kg.

Author Biographies

  • Alloysius Vendhi Prasmoro, Institut Teknologi Sepuluh Nopember

    Department of Industrial and Systems Engineering, Institut Teknologi Sepuluh Nopember, Jl. Keputih, Sukolilo, Surabaya 60111, East Java, Indonesia

    Department of Industrial Engineering, Universitas Bhayangkara Jakarta Raya, Jl.Harsono RM No.67 Ragunan Pasar Minggu, Jakarta 12140, Indonesia

  • Nurhadi Siswanto, Institut Teknologi Sepuluh Nopember
    Department of Industrial and Systems Engineering, Institut Teknologi Sepuluh Nopember, Jl. Keputih, Sukolilo, Surabaya 60111, East Java, Indonesia
  • Budi Santosa, Institut Teknologi Sepuluh Nopember
    Department of Industrial and Systems Engineering, Institut Teknologi Sepuluh Nopember, Jl. Keputih, Sukolilo, Surabaya 60111, East Java, Indonesia
  • Giri Waluyo Nugraha, PT RML

    PT RML, Harapan Indah, Bekasi 17131, West Java, Indonesia.

  • Mohd Shukor Salleh , Universiti Teknikal Malaysia Melaka
    Fakulti Teknologi dan Kejuruteraan Industri dan Pembuatan, Universiti Teknikal Malaysia Melaka, Hang Tuah Jaya, 76100 Durian Tunggal Melaka, Malaysia

References

[1] Hossain Khan MF, Hossain MJ, Ahmed MT, Monir MU, Rahman MA, Sweety TS, et al. Ground vibration effect evaluation due to blasting operations. Heliyon [Internet]. 2025;11(2):e41759. Available from: https://doi.org/10.1016/j.heliyon.2025.e41759

[2] Kumar A, Sharma SK, Kishore N. Prediction of blast-induced ground vibration using multi-variate regression analysis in an opencast mine. J Mines, Met Fuels. 2021;69(7):216–24.

[3] Nateghi R. Prediction of ground vibration level induced by blasting at different rock units. Int J Rock Mech Min Sci [Internet]. 2011;48(6):899–908. Available from: http://dx.doi.org/10.1016/j.ijrmms.2011.04.014

[4] Khandelwal M, Singh TN. Prediction of blast-induced ground vibration using artificial neural network. Int J Rock Mech Min Sci [Internet]. 2009;46(7):1214–22. Available from: https://doi.org/10.1016/j.ijrmms.2009.03.004

[5] Ravikumar A, Vardhan H, Sarma KVS. Prediction of blast-induced ground vibrations in limestone mine using Multiple Linear Regression (MLR) analysis. J Sustain Min [Internet]. 2025;24(3). Available from: https://doi.org/10.46873/2300-3960.1460

[6] Yu C, Ma Y, Li H, Wang C, Wang H, Meng L. Spatial response and prediction model for blasting-induced vibration in a deep double-line tunnel. Int J Min Sci Technol [Internet]. 2025;36(1):169–86. Available from: https://doi.org/10.1016/j.ijmst.2025.11.009

[7] Bulushi IR, Deressar GW, Choudhary BS. Prediction and control of ground vibrations due to blasting activities in aggregate mines. J Sustain Min [Internet]. 2025;24(2). Available from: https://doi.org/10.46873/2300-3960.1452

[8] Lawal AI, Kwon S, Hammed OS, Idris MA. Blast-induced ground vibration prediction in granite quarries: An application of gene expression programming, ANFIS, and sine cosine algorithm optimized ANN. Int J Min Sci Technol [Internet]. 2021;31(2):265–77. Available from: https://doi.org/10.1016/j.ijmst.2021.01.007

[9] Cardu M, Coragliotto D, Oreste P. Analysis of predictor equations for determining the blast-induced vibration in rock blasting. Int J Min Sci Technol [Internet]. 2019;29(6):905–15. Available from: https://doi.org/10.1016/j.ijmst.2019.02.009

[10. Tran QH, Nguyen H, Bui XN. Novel Soft Computing Model for Predicting Blast-Induced Ground Vibration in Open-Pit Mines Based on the Bagging and Sibling of Extra Trees Models. C - Comput Model Eng Sci. 2023;134(3):2227–46. DOI: 10.32604/cmes.2022.021893

[11] Himanshu VK, Roy MP, Mishra AK, Paswan RK, Panda D, Singh PK. Multivariate statistical [analysis approach for prediction of blast-induced ground vibration. Arab J Geosci. 2018;11(16). https://doi.org/10.1007/s12517-018-3796-8

[12] Komadja GC, Rana A, Glodji LA, Anye V, Jadaun G, Onwualu PA, et al. Assessing Ground Vibration Caused by Rock Blasting in Surface Mines Using Machine-Learning Approaches: A Comparison of CART, SVR and MARS. Sustain. 2022;14(17). https://doi.org/10.3390/su141711060

[13] Nguyen H, Choi Y, Bui XN, Nguyen-Thoi T. Predicting blast-induced ground vibration in open-pit mines using vibration sensors and support vector regression-based optimization algorithms. Sensors (Switzerland). 2020;20(1). doi: https://doi.org/10.3390/s20010132

[14] Rafiee-Dehkharghani R, Esmaeili K, Najari M. Prediction of Construction-Induced Ground Vibrations Using Field Measurements and Bidirectional Gated Recurrent Unit Neural Network. Vibration. 2025;8(4):1–28. https://doi.org/10.3390/vibration8040070

[15] Sulaiman N, Adnan M, Mat Isa NA, Lazi M, Rafidah M, Yaacob H, et al. Evaluation of Ground Borne Vibration With Respect To Pile Driving. Malaysian J Civ Eng. 2024;36(1):33–9. https://doi.org/10.11113/mjce.v36.21649

[16] Temeng VA, Ziggah YY, Arthur CK. Blast-induced noise level prediction model based on brain inspired emotional neural network. J Sustain Min. 2021;20(1). https://doi.org/10.46873/2300-3960.1043

[17] Agrawal H, Mishra AK. Modified scaled distance regression analysis approach for prediction of blast-induced ground vibration in multi-hole blasting. J Rock Mech Geotech Eng [Internet]. 2019;11(1):202–7. Available from: https://doi.org/10.1016/j.jrmge.2018.07.004

[18] Fuławka K, Stolecki L, Mertuszka P, Szumny M, Anderko A. Predictive model of seismic vibrations’ peak value induced by multi-face blasting. J Sustain Min. 2023;22(3):248–56. https://doi.org/10.46873/2300-3960.1390

[19] Odeyemi OY, Taiwo BO, Alaba OC. Influence of explosive maximum instantaneous charge on blasting environmental impact. J Sustain Min. 2023;22(4):344–57. https://doi.org/10.46873/2300-3960.1398

[20] Pradatama D, Pradasara C, Nurdiansyah S. Linier Superposition Analysis on Managing Blasting Ground Vibration in Coal Mining. Indones Min Prof J. 2019;1(1):22–8. https://doi.org/10.36986/impj.v1i1.8

[21] Sun Y, Wang X, Zhang C, Zuo M. Multiple Regression: Methodology and Applications. Highlights Sci Eng Technol. 2023;49:542–8. https://doi.org/10.54097/hset.v49i.8611

[22] Jamaluddin, Wagreich M, Schöpfer K, Sachsenhofer RF, Maria, Rahmawati D. Hydrocarbon potential and depositional environment of the Middle Miocene Balikpapan Formation, lower Kutai Basin, Indonesia: Sedimentology, calcareous nannofossil, organic geochemistry, and organic petrography integrated approach. Int J Coal Geol. 2024;293(July). https://doi.org/10.1016/j.coal.2024.104591

[23] Permana AK, Sendjadja YA, Panggabean H, Fauzely L. Depositional Environment and Source Rocks Potential of the Miocene Organic Rich Sediments , Balikpapan Formation , East Kutai Sub Basin , Kalimantan Lingkungan Pengendapan dan Potensi Batuan Induk Sedimen Kaya Bahan Organik Berumur Miosen , Formasi Balikp. J Geol dan Sumberd Miner. 2020;19(3):171–86. https://doi.org/10.33332/jgsm.geologi.v19i3.407

[24] Liu Z, Du X, Zhu Z, Li X. Wave Velocity in Sandstone and Mudstone under High Temperature and Overpressure in Yinggehai Basin. Energies. 2022;15(7). https://doi.org/10.3390/en15072615

[25] Wara SSM, Adziima AF, Nasrudin M, Pratama AR. Evaluasi Kinerja Uji Normalitas pada Ragam Distribusi dan Ukuran Sampel. J Difer. 2025;7(2):172–83. https://doi.org/10.35508/jd.v7i2.24042

[26] Junaedi N, Bayuaji R, Susilo AJ. Development of A Parametric Cost Estimation Model for Landfill Construction Projects. Adv Sustain Sci Eng Technol. 2025;7(4):02504030. https://doi.org/10.26877/asset.v7i4.2424

[27] Oktaviani R, Respati L. Studi Pengaruh Geometri Peledakan Berdasarkan Analisis Regresi Linear Berganda Terhadap Nilai Airblast di PT . X Study of the Effect of Blast Geometry Based on Multiple Linear Regression. 2025;(November):131–8. http://journal.itny.ac.id/index.php/ReTII

[28] Hosseini S, Khatti J, Taiwo BO, Fissha Y, Grover KS, Ikeda H, et al. Assessment of the ground vibration during blasting in mining projects using different computational approaches. Sci Rep [Internet]. 2023;13(1):1–29. Available from: https://doi.org/10.1038/s41598-023-46064-5

[29] Ritonga M, Nasution AP, Muti’ah R. Determinants of MSME Sustainability: A Regression-Based Study in Labuhanbatu. Adv Sustain Sci Eng Technol. 2025;7(4):1–11. https://doi.org/10.26877/asset.v7i4.2180

[30] Schober P, Schwarte LA. Correlation coefficients: Appropriate use and interpretation. Anesth Analg. 2018;126(5):1763–8. https://doi.org/10.1213/ANE.0000000000002864

[31] Achmad Fauzan, Kusman Sadik, Anang Kurnia. Evaluating Ordinal Multivariate Models under Multicollinearity via Pairwise Likelihood: A Simulation Perspective. Adv Sustain Sci Eng Technol. 2025;7(4):02504024. https://doi.org/10.26877/asset.v7i4.2282

[32] Labambe MR, Ardiansyah R, Pratama SA, Wirdayanti. Predicting Waste Production Trends in Palu City Using Linear Regression Analysis. Adv Sustain Sci Eng Technol. 2024;6(3):1–7. https://doi.org/10.26877/asset.v6i3.523

[33] Fissha Y, Khatti J, Ikeda H, Grover KS, Owada N, Toriya H, et al. Predicting ground vibration during rock blasting using relevance vector machine improved with dual kernels and metaheuristic algorithms [Internet]. Vol. 14, Scientific Reports. Nature Publishing Group UK; 2024. 1–31 p. Available from: https://doi.org/10.1038/s41598-024-70939-w

[34] Badan Standarisasi Nasional S. Sni 7571:2023. Baku Tingkat Getaran Peledakan pada Kegiatan Tambang Terbuka (Standard Blasting Vibration Levels in Open Mining Activities). 2023;

Downloads

Published

2026-07-10

Issue

Section

Articles