Document Type : Original Research Paper
Authors
- Zaenal Zaenal 1
- Noor Fauzi Isniarno 1
- Delina Mutiara 1
- Sofie Nur’aini 2
- Hasyim Fadhilah 2
- Elfida Moralista 2
- Andrieanto Nurrochman 2
1 Department of Mining Engineering, Faculty of Engineering, Universitas Islam Bandung, Bandung, Indonesia
2 Department of Mining Engineering, Faculty of Engineering, Universitas Islam Bandung, Bandung Indonesia
10.22044/jme.2025.16675.3271
Abstract
Blasting is a fundamental open-pit mining operation necessary for rock breakage, but it also generates significant environmental noise pollution. Excessive noise from blasting not only endangers health but also poses problems to compliance with regulations, particularly in regions where acoustic standards differ, such as Indonesia's use of both dBL and dBA standards. This research addresses the need for reliable and context-dependent predictive models for blasting noise, aiming to compare analytical and empirical formulas with machine learning techniques in dBA prediction. Measurements were conducted at 30 blasts at an open-pit coal mine in Indonesia, South Sumatra, using homogeneous acoustic sensors. The measured data points for frequency, dBL, and dBA were matched to calculated data using equations. Random Forest (RF) and Artificial Neural Network (ANN) predictive models using measured frequency and dBL as predictive variables were also derived. Results show that used Finn-derived equation has poor predictive accuracy, with errors exceeding 80%. Among the analytical and empirical models, Equation 3 performed the best, with an average error of 9%, while a site-spesific regression model based on measurements had an improved error rate of 5%. Machine learning models outperformed all models, with the RF model exhibiting an average error of 2% and demonstrating higher stability and consistency. The ANN model also did well, but with more variation and some overestimations.
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Main Subjects
[1]. Chi, M.-b., Li, Q.-s., Cao, Z.-g., Fang, J., Wu, B.-y., Zhang, Y., Wei, S.-r., Liu, X.-q., Yang, Y.-m. (2022). Evaluation of water resources carrying capacity in ecologically fragile mining areas under the influence of underground reservoirs in coal mines. Journal of Cleaner Production, 379, 134449
[2]. Lee, C.-W., Kim, J., & Kang, G.-C. (2018). Full-Scale Tests for Assessing Blasting-Induced Vibration and Noise. Shock and Vibration, 2018(1), 9354349
[3]. Karagianni, A., Lazos, I., & Chatzipetros, A. (2018). Remote Sensing Techniques in Disaster Management: Amynteon Mine Landslides, Greece. Int. Arch. Photogramm. Remote Sens. Spatial Inf. Sci., XLII-3/W4, 269-276
[4]. Hilson, G. (2019). Why is there a large-scale mining ‘bias’ in sub-Saharan Africa?. Land Use Policy, 81, 852-861.
[5]. Tiwari, S. K., Kumaraswamidhas, L. A., & Garg, N. (2023). Assessment of noise pollution and associated subjective health complaints in Jharia Coalfield, India: A structural equation model analysis. Noise Mapping, 10(1)
[6]. Mocek, P. (2020). Noise in the Mining Work Environment - Causes, Effects and Threats. IOP Conference Series: Earth and Environmental Science, 609(1), 012075
[7]. Baffoe, P. E., Duker, A. A., & Senkyire-Kwarteng, E. V. (2022). Assessment of health impacts of noise pollution in the Tarkwa Mining Community of Ghana using noise mapping techniques. Global Health Journal, 6(1), 19-29
[8]. Waye, K. P. (2011). Effects of Low Frequency Noise and Vibrations: Environmental and Occupational Perspectives. Encyclopedia of Environmental Health, 240 -253.
[9]. David E. Siskind, V. J. S., Mark S. Stagg, John W. Kopp (1980). Structure Response and Damage Produced by Airblast From Surface Mining. Report of Investigations 8485, United States Department of The Interior, Bureau of Mines
[10]. ONECRC (1997). A study on examining criteria on cause and effect relationship for noise damage and calculation of damage cost. South Korea: Office of National Environmental Conflict Resolution Commission
[11]. Yang Hyung-sik, K. N.-s. Blasting design considering noise and vibration regulatory law, KSRM annual conference of Conference, 7
[12]. IOERSNU (2000). Influence evaluation of blasting-induced vibration and noise at rock excavation part of 9th construction area of Daegu-Pohang Expressway. Institute of Engineering Research at Seoul National University
[13]. Dumakor-Dupey, N. K., Arya, S., & Jha, A. (2021). Advances in Blast-Induced Impact Prediction—A Review of Machine Learning Applications. Minerals, 11(6), 601
[14]. Victor Amoako Temeng, Y. Y. Z., Clement Kweku Arthur (2021). Blast-Induced Noise Level Prediction Model Based on Brain Inspired Emotional Neural Network. Journal of Sustainable Mining, 20(1), 12
[15]. Jahed Armaghani, D., Hajihassani, M., Sohaei, H., Tonnizam Mohamad, E., Marto, A., Motaghedi, H., et al. (2015). Neuro-fuzzy technique to predict air-overpressure induced by blasting. Arabian Journal of Geosciences, 8(12), 10937-10950
[16]. Jahed Armaghani, D., Hasanipanah, M., & Tonnizam Mohamad, E. (2016). A combination of the ICA-ANN model to predict air-overpressure resulting from blasting. Engineering with Computers, 32(1), 155-171
[17]. Jahed Armaghani, D., Hasanipanah, M., Mahdiyar, A., Abd Majid, M. Z., Bakhshandeh Amnieh, H., & Tahir, M. M. D. (2018). Airblast prediction through a hybrid genetic algorithm-ANN model. Neural Computing and Applications, 29(9), 619-629
[18]. Jahed Armaghani, D., Hajihassani, M., Marto, A., Shirani Faradonbeh, R., & Mohamad, E. T. (2015). Prediction of blast-induced air overpressure: a hybrid AI-based predictive model. Environmental Monitoring and Assessment, 187(11), 666
[19]. Bui, X.-N., Nguyen, H., Le, H.-A., Bui, H.-B., & Do, N.-H. (2020). Prediction of Blast-induced Air Over-pressure in Open-Pit Mine: Assessment of Different Artificial Intelligence Techniques. Natural Resources Research, 29(2), 571-591
[20]. Wen, P.-J., & Huang, C. (2020). Noise Prediction Using Machine Learning with Measurements Analysis. Applied Sciences, 10(18), 6619
[21]. Yang, Y., Hou, K., Sun, H., Guo, L., & Zhe, Y. (2025). Random Forest-Based Stability Prediction Modeling of Closed Wall for Goaf. Applied Sciences, 15(5), 2300
[22]. Chi, X., Yue, Y., Jin, Z., Zhang, P., & Sun, X. (2025). PSO-SVM Machine Learning for Blasting Vibration Velocity Prediction in Open Pit Mines. Preprints, 2025031679
[23]. Kazemi, M. M. K., Nabavi, Z., & Khandelwal, M. (2023). Prediction of blast-induced air overpressure using a hybrid machine learning model and gene expression programming (GEP): A case study from an iron ore mine. AIMS Geosciences, 9(2), 357-381.
[24]. Peterson, R. A., McGrath, M., & Cavanaugh, J. E. (2024). Can a Transparent Machine Learning Algorithm Predict Better than Its Black Box Counterparts? A Benchmarking Study Using 110 Data Sets. Entropy, 26(9), 746
[25]. Pranjal Atrey, M. P. B., Wu, M., Dutta, S. (2025). Demystifying the Accuracy-Interpretability Trade-Off: A Case Study of Inferring Ratings from Reviews. DAI Workshop, AAAI-2025
[26]. Finn Jacobsen, P. M. J. (2013). Fundamentals of General Linear Acoustics. John Wiley & Sons Ltd, United Kingdom.
[27]. Gafoer, S., & Purbo-Hadiwidjoyo, M. M. (1986). The geology of Southern Sumatra and its bearing on the occurrence of mineral deposits. Bulletin Geological Research and Development Centre, (12), 15-30.
[28]. M. C. Daly, B. G. D. H., D. G. Smith. Tertiary Plate Tectonics and Basin Evolution in Indonesia. Proceedings of Indonesian Petroleum Association, 16th Annual Convention, Indonesia, 399-428.
[29]. A. Pulunggono, A. H. S., Christine G. Kosuma. Pre-Tertiary and Tertiary Fault Systems as a Framework of the South Sumatra Basin; A Study of SAR-Maps. Proceedings Indonesian Petroleum Association, 21st Annual Convention, Jakarta, 339-360
[30]. P. Adiwidjaja, G. L. D. Pre-Tertiary Paleotopography and Related Sedimentation in South Sumatra. Proceedings of Indonesian Petroleum Association, 2nd Annual Convention, of Conference, 89-103
[31]. Sosrowidjojo, I. B., & Saghafi, A. (2009). Development of the first coal seam gas exploration program in Indonesia: Reservoir properties of the Muaraenim Formation, south Sumatra. International Journal of Coal Geology, 79(4), 145-156
[32]. Darman, H., & Sidi, F. H. (2000). An outline of the geology of Indonesia. Indonesian Association of Geologists, Jakarta Selatan, 11
[33]. Gafoer, S., Cobrie, T., & Purnomo, J. (1996). The Geology of The Lahat Quadrangle, Sumatera. Report Geological Research and Development Centre, Bandung
[34]. Bishop, M. G. (2000). South Sumatra Basin Province, Indonesia; the Lahat/Talang Akar-Cenozoic total petroleum system. Open-File Report 99-50-S, U. S. Department of the Interior, U. S. Geological Survey
[35]. Pavol Liptai, M. B., Katarína Lukáčová (2015). Influence of Atmospheric Conditions on Sound Propagation - Mathematical Modeling. Óbuda University e-Bulletin, 5(1)
[36]. D. Keith Wilson, C. L. P., Vladimir E. Ostashev (2015). Sound Propagation in the Atmospheric Boundary Layer. Acoustics Today, 11(2)
[37]. Raina Avtar, K., Wankhede, P., & Singh, P. K. Controlled blasting for safe excavation of a portion of irrigation canal in close vicinity of a village, Proceedings of the conference on Recent Advances in Rock Engineering (RARE 2016), 2016/11, 82-84
[38]. Fernández, P. R., Rodríguez, R., & Bascompta, M. (2022). Holistic Approach to Define the Blast Design in Quarrying. Minerals, 12(2), 191
[39]. Azizi, A., & Moomivand, H. (2021). A New Approach to Represent Impact of Discontinuity Spacing and Rock Mass Description on the Median Fragment Size of Blasted Rocks Using Image Analysis of Rock Mass. Rock Mechanics and Rock Engineering, 54(4), 2013-2038
[40]. Shahrin, M. I., Abdullah, R. A., Jeon, S., Sa’ari, R., Rahim, A., Ghani, J. A. A., & Siti N. Jusoh (2022). Effect of Bench Height to Burden Ratio on Rock Fragmentation Induced by Blasting, ISRM Regional Symposium - 12th Asian Rock Mechanics Symposium
[41]. Moomivand, H., Soltanalinejad, S., & Karwansara, A. M. (2025). Assessment of the optimized stemming length considering rock fragmentation and escape of explosive gases using actual large-scale results. Results in Engineering, 25, 103575
[42]. Uysal, Ö., Arpaz, E., & Berber, M. (2007). Studies on the effect of burden width on blast-induced vibration in open-pit mines. Environ. Geol., 53(3), 643-650
[43]. Filice, A., Mynarz, M., & Zinno, R. (2022). Experimental and Empirical Study for Prediction of Blast Loads. Applied Sciences, 12(5), 2691
[44]. Faramarzi, ., Ebrahimi Farsangi, M. A., & Mansouri, H. (2014). Simultaneous investigation of blast induced ground vibration and airblast effects on safety level of structures and human in surface blasting. International Journal of Mining Science and Technology, 24(5), 663-669
[45]. Richards, A. B. (2008). Prediction and Control of Air overpressure from Blasting in Hongkong. Hongkong Geotechnical Engineering Office, Civil Engineering and Development Department, 33
[46]. Kuzu, C. (2008). The importance of site-specific characters in prediction models for blast-induced ground vibrations. Soil Dynamics and Earthquake Engineering, 28(5), 405-414
[47]. Richards, A. B. (2008). Prediction and Control of Air Overpressure from Blasting in Hong Kong. Hongkong Geotechnical Engineering Office, 46.
[48]. Foster, R., & Teague, P. (2004). Prediction Results and Validation of Long Range Noise Propagation from Blast Events. The Annual Conference of the Australian Acoustical Society, Gold Coast, Australia, 51-56
[49]. Kazemi, M. M. K., Nabavi, Z., & Khandelwal, M. (2023). Prediction of blast-induced air overpressure using a hybrid machine learning model and gene expression programming (GEP): A case study from an iron ore mine. AIMS Geosciences, 9(2)
[50]. Guo, G., Cui, X., & Du, B. (2021). Random-Forest Machine Learning Approach for High-Speed Railway Track Slab Deformation Identification Using Track-Side Vibration Monitoring. Applied Sciences, 11(11), 4756
[51]. Yao, J., & Fang, L. (2021). Building Vibration Prediction Induced by Moving Train with Random Forest. Journal of Advanced Transportation, 2021(1), 6642071
[52]. Cao, Y., Li, B., Xiang, Q., & Zhang, Y. (2023). Experimental Analysis and Machine Learning of Ground Vibrations Caused by an Elevated High-Speed Railway Based on Random Forest and Bayesian Optimization. Sustainability, 15(17), 12772
[53]. Yan, Y., Guo, J., Bao, S., & Fei, H. (2024). Prediction of peak particle velocity using hybrid random forest approach. Scientific Reports, 14(1), 30793
[54]. Yu, Z., Shi, X., Zhou, J., Chen, X., & Qiu, X. (2020). Effective Assessment of Blast-Induced Ground Vibration Using an Optimized Random Forest Model Based on a Harris Hawks Optimization Algorithm. Applied Sciences, 10(4), 1403
[55]. Sawmliana, C., Roy, P. P., Singh, R. K., & Singh, T. N. (2007). Blast induced air overpressure and its prediction using artificial neural network. Mining Technology, 116(2), 41-48
[56]. Qiu, L., Zhu, Y., Xu, C., Ren, G., Hu, Y., & Liu, X. (2025). Physics-informed neural network optimized by particle swarm algorithm for accurate prediction of blast-induced peak particle velocity. Intelligent Geoengineering, 2(3), 126-140
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