{"status":"ok","message-type":"work","message-version":"1.0.0","message":{"indexed":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T14:17:30Z","timestamp":1774621050657,"version":"3.50.1"},"reference-count":85,"publisher":"MDPI AG","issue":"7","license":[{"start":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T00:00:00Z","timestamp":1774569600000},"content-version":"vor","delay-in-days":0,"URL":"https:\/\/creativecommons.org\/licenses\/by\/4.0\/"}],"funder":[{"name":"EU Erasmus+","award":["101083272"],"award-info":[{"award-number":["101083272"]}]}],"content-domain":{"domain":[],"crossmark-restriction":false},"short-container-title":["Applied Sciences"],"abstract":"<jats:p>Objective: The objective of this study was to map and critically synthesize empirical evidence on ML\/AI applications across surface mining unit operations, and to characterize models, validation practices, and evidence gaps. Eligibility criteria: Our eligibility criteria comprised peer-reviewed studies (2020\u20132025) applying ML\/AI to surface mining activities, training\/validating models on empirical datasets, and reporting quantitative performance metrics. Information sources: Scopus, ScienceDirect, Dimensions, and Web of Science were our information sources, last searched December 2025 and supplemented by website and citation snowballing. Risk of bias: Risk of bias was assessed using an adapted domain-based approach based on PROBAST, used to interpret findings without excluding studies. Synthesis method: Our research employed a narrative synthesis (no meta-analysis due to heterogeneity in datasets, algorithms, contexts, and metrics), grouped by application domain. Results: From 5317 records, 57 studies were included, concentrated in blasting (43), followed by load and haul (6), post-dismantling management (4), extraction (2), and overall exploitation (2). Studies predominantly reported statistical metrics (e.g., R2, RMSE, and MAE), with limited operational performance indicators; validation was frequently site-specific. Dataset sizes were not reported consistently across studies. Limitations: This study\u2019s limitations were database coverage, restricted timeframe, and incomplete reporting (e.g., software\/tooling). Conclusions: ML\/AI shows strong potential, especially in blasting, but scalable deployment is constrained by site specificity, inconsistent reporting, and heterogeneous validation; standardized reporting and operational indicators are priorities. Registration: The systematic review protocol was registered in OSF with DOI 10.17605\/OSF.IO\/5UMKB. Funding: EU Erasmus+ STRIM project (1010832727).<\/jats:p>","DOI":"10.3390\/app16073246","type":"journal-article","created":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T12:45:53Z","timestamp":1774615553000},"page":"3246","update-policy":"https:\/\/doi.org\/10.3390\/mdpi_crossmark_policy","source":"Crossref","is-referenced-by-count":0,"title":["Machine Learning in Surface Mining\u2014A Systematic Review"],"prefix":"10.3390","volume":"16","author":[{"given":"Vasco Belo","family":"Reis","sequence":"first","affiliation":[{"name":"Associated Laboratory for Energy, Transports and Aeronautics (LAETA)\u2014PROA, Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-8524-5503","authenticated-orcid":false,"given":"Jo\u00e3o Santos","family":"Baptista","sequence":"additional","affiliation":[{"name":"Associated Laboratory for Energy, Transports and Aeronautics (LAETA)\u2014PROA, Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal"}]},{"ORCID":"https:\/\/orcid.org\/0000-0002-5856-5317","authenticated-orcid":false,"given":"Joana","family":"Duarte","sequence":"additional","affiliation":[{"name":"Associated Laboratory for Energy, Transports and Aeronautics (LAETA)\u2014PROA, Faculty of Engineering, University of Porto, 4200-465 Porto, Portugal"}]}],"member":"1968","published-online":{"date-parts":[[2026,3,27]]},"reference":[{"key":"ref_1","doi-asserted-by":"crossref","first-page":"e70006","DOI":"10.1002\/tjo3.70006","article-title":"Raw Material Supply Risks: Examining Extraction and Geopolitical Conflict","volume":"64","author":"Bell","year":"2025","journal-title":"Transp. J."},{"key":"ref_2","doi-asserted-by":"crossref","first-page":"9","DOI":"10.1016\/j.proeng.2012.09.440","article-title":"Surface Mining Technology: Progress and Prospects","volume":"46","author":"Ramani","year":"2012","journal-title":"Procedia Eng."},{"key":"ref_3","doi-asserted-by":"crossref","first-page":"545","DOI":"10.1007\/s13563-022-00334-2","article-title":"Decarbonizing the Automotive Sector: A Primary Raw Material Perspective on Targets and Timescales","volume":"36","author":"Petavratzi","year":"2023","journal-title":"Min. Econ."},{"key":"ref_4","doi-asserted-by":"crossref","first-page":"142621","DOI":"10.1016\/j.jclepro.2024.142621","article-title":"Green Supply Chain for Steel Raw Materials under Price and Demand Uncertainty","volume":"462","author":"Cheng","year":"2024","journal-title":"J. Clean. Prod."},{"key":"ref_5","doi-asserted-by":"crossref","first-page":"102841","DOI":"10.1016\/j.gloenvcha.2024.102841","article-title":"Assessing the Social and Environmental Impacts of Critical Mineral Supply Chains for the Energy Transition in Europe","volume":"86","author":"Berthet","year":"2024","journal-title":"Glob. Environ. Change"},{"key":"ref_6","doi-asserted-by":"crossref","unstructured":"Valentini, L. (Mater. Today Proc., 2023). Sustainable Sourcing of Raw Materials for the Built Environment, Mater. Today Proc., in press.","DOI":"10.1016\/j.matpr.2023.07.308"},{"key":"ref_7","doi-asserted-by":"crossref","first-page":"633","DOI":"10.1007\/s11625-022-01234-8","article-title":"An Extractive Bioeconomy? Phosphate Mining, Fertilizer Commodity Chains, and Alternative Technologies","volume":"18","author":"Anlauf","year":"2023","journal-title":"Sustain. Sci."},{"key":"ref_8","doi-asserted-by":"crossref","first-page":"103222","DOI":"10.1016\/j.resourpol.2022.103222","article-title":"Moving towards Deep Underground Mineral Resources: Drivers, Challenges and Potential Solutions","volume":"80","author":"Ghorbani","year":"2023","journal-title":"Resour. Policy"},{"key":"ref_9","doi-asserted-by":"crossref","unstructured":"Nikkhah, A., Vakylabad, A.B., Hassanzadeh, A., Niedoba, T., and Surowiak, A. (2022). An Evaluation on the Impact of Ore Fragmented by Blasting on Mining Performance. Minerals, 12.","DOI":"10.3390\/min12020258"},{"key":"ref_10","doi-asserted-by":"crossref","unstructured":"Rakhmangulov, A., Burmistrov, K., and Osintsev, N. (2022). Selection of Open-Pit Mining and Technical System\u2019s Sustainable Development Strategies Based on MCDM. Sustainability, 14.","DOI":"10.3390\/su14138003"},{"key":"ref_11","doi-asserted-by":"crossref","first-page":"423","DOI":"10.1007\/s10951-024-00808-x","article-title":"Short-Term Underground Mine Planning with Uncertain Activity Durations Using Constraint Programming","volume":"27","author":"Aalian","year":"2024","journal-title":"J Sched"},{"key":"ref_12","doi-asserted-by":"crossref","first-page":"01034","DOI":"10.1051\/e3sconf\/20184101034","article-title":"Geo-Environmental Problems of Open PitMining: Classification and Solutions","volume":"41","author":"Hellmer","year":"2018","journal-title":"E3S Web Conf."},{"key":"ref_13","doi-asserted-by":"crossref","first-page":"103978","DOI":"10.1016\/j.resourpol.2023.103978","article-title":"Challenges and Applications of Digital Technology in the Mineral Industry","volume":"85","author":"Onifade","year":"2023","journal-title":"Resour. Policy"},{"key":"ref_14","doi-asserted-by":"crossref","unstructured":"Arezes, P.M., Melo, R.B., Carneiro, P., Castelo Branco, J., Colim, A., Costa, N., Costa, S., Duarte, J., Guedes, J.C., and Perestrelo, G. (2024). Digital Twin Applications in the Extractive Industry\u2014A Short Review. Occupational and Environmental Safety and Health V, Springer Nature.","DOI":"10.1007\/978-3-031-38277-2"},{"key":"ref_15","doi-asserted-by":"crossref","unstructured":"Noshi, C.I., and Schubert, J.J. (2018, January 7). The Role of Machine Learning in Drilling Operations; A Review|Request PDF. Proceedings of the SPE\/AAPG Eastern Regional Meeting, Pittsburgh, PA, USA.","DOI":"10.2118\/191823-18ERM-MS"},{"key":"ref_16","doi-asserted-by":"crossref","unstructured":"Baek, J., and Choi, Y. (2020). Deep Neural Network for Predicting Ore Production by Truck-Haulage Systems in Open-Pit Mines. Appl. Sci., 10.","DOI":"10.3390\/app10051657"},{"key":"ref_17","doi-asserted-by":"crossref","first-page":"21868","DOI":"10.1038\/s41598-025-99026-4","article-title":"Machine Learning Based Prediction of Geotechnical Parameters Affecting Slope Stability in Open-Pit Iron Ore Mines in High Precipitation Zone","volume":"15","author":"Gladious","year":"2025","journal-title":"Sci. Rep."},{"key":"ref_18","doi-asserted-by":"crossref","first-page":"223","DOI":"10.1007\/s42452-024-05888-0","article-title":"Enhancing Rock Fragmentation Assessment in Mine Blasting through Machine Learning Algorithms: A Practical Approach","volume":"6","author":"Gebretsadik","year":"2024","journal-title":"Discov. Appl. Sci."},{"key":"ref_19","doi-asserted-by":"crossref","first-page":"2051","DOI":"10.18311\/jmmf\/2025\/49143","article-title":"Ensemble Machine Learning Models for Blast-Induced Air Noise: A Review of Transformative Innovations in Minerals","volume":"73","author":"Bonagiri","year":"2025","journal-title":"J. Mines Met. Fuels"},{"key":"ref_20","doi-asserted-by":"crossref","first-page":"100583","DOI":"10.1016\/j.dajour.2025.100583","article-title":"An Optimization-Based Approach to Fleet Reliability and Allocation in Open-Pit Mining","volume":"15","author":"Senses","year":"2025","journal-title":"Decis. Anal. J."},{"key":"ref_21","doi-asserted-by":"crossref","first-page":"133","DOI":"10.1007\/s12517-023-11237-z","article-title":"Review of Machine Learning Application in Mine Blasting","volume":"16","author":"Elwahab","year":"2023","journal-title":"Arab. J. Geosci."},{"key":"ref_22","doi-asserted-by":"crossref","unstructured":"Jung, D., and Choi, Y. (2021). Systematic Review of Machine Learning Applications in Mining: Exploration, Exploitation, and Reclamation. Minerals, 11.","DOI":"10.3390\/min11020148"},{"key":"ref_23","doi-asserted-by":"crossref","unstructured":"Arthur, C.K., Bhatawdekar, R.M., Temeng, V.A., Agyei, G., and Ziggah, Y.Y. (2024). Application of Artificial Intelligence in Predicting Blast-Induced Ground Vibration. Applications of Artificial Intelligence in Mining and Geotechnical Engineering, Elsevier.","DOI":"10.1016\/B978-0-443-18764-3.00016-3"},{"key":"ref_24","doi-asserted-by":"crossref","first-page":"n71","DOI":"10.1136\/bmj.n71","article-title":"The PRISMA 2020 Statement: An Updated Guideline for Reporting Systematic Reviews","volume":"372","author":"Page","year":"2021","journal-title":"BMJ"},{"key":"ref_25","doi-asserted-by":"crossref","first-page":"51","DOI":"10.7326\/M18-1376","article-title":"PROBAST: A Tool to Assess the Risk of Bias and Applicability of Prediction Model Studies","volume":"170","author":"Wolff","year":"2019","journal-title":"Ann. Intern. Med."},{"key":"ref_26","doi-asserted-by":"crossref","unstructured":"Ling, H., Gao, T., Gong, T., Wu, J., and Zou, L. (2023). Hydraulic Rock Drill Fault Classification Using X\u2212Vectors. Mathematics, 11.","DOI":"10.3390\/math11071724"},{"key":"ref_27","doi-asserted-by":"crossref","first-page":"32","DOI":"10.1016\/j.dt.2025.06.019","article-title":"Accurate Prediction of Blast-Induced Ground Vibration Intensity Using Optimized Machine Learning Models","volume":"52","author":"Chen","year":"2025","journal-title":"Def. Technol."},{"key":"ref_28","doi-asserted-by":"crossref","unstructured":"Mame, M., Huang, S., Li, C., and Zhou, J. (2025). Application of Extra-Trees Regression and Tree-Structured Parzen Estimators Optimization Algorithm to Predict Blast-Induced Mean Fragmentation Size in Open-Pit Mines. Appl. Sci., 15.","DOI":"10.3390\/app15158363"},{"key":"ref_29","doi-asserted-by":"crossref","first-page":"94907287","DOI":"10.26599\/NR.2025.94907287","article-title":"A Self-Powered Triboelectric Nano-Sensor Enabled Digital Twin for Self-Sustained Machine Monitoring in Smart Mine","volume":"18","author":"Jiang","year":"2025","journal-title":"Nano Res."},{"key":"ref_30","doi-asserted-by":"crossref","unstructured":"Feng, Z., Liu, G., Wang, L., Gu, Q., and Chen, L. (2023). Research on the Multiobjective and Efficient Ore-Blending Scheduling of Open-Pit Mines Based on Multiagent Deep Reinforcement Learning. Sustainability, 15.","DOI":"10.3390\/su15065279"},{"key":"ref_31","doi-asserted-by":"crossref","first-page":"6221","DOI":"10.1007\/s13762-022-03999-y","article-title":"A Study on Environmental Issues of Blasting Using Advanced Support Vector Machine Algorithms","volume":"19","author":"Chen","year":"2022","journal-title":"Int. J. Environ. Sci. Technol."},{"key":"ref_32","doi-asserted-by":"crossref","first-page":"2647","DOI":"10.1007\/s11053-021-09826-4","article-title":"Optimal ELM-Harris Hawks Optimization and ELM-Grasshopper Optimization Models to Forecast Peak Particle Velocity Resulting from Mine Blasting","volume":"30","author":"Yu","year":"2021","journal-title":"Nat. Resour. Res."},{"key":"ref_33","doi-asserted-by":"crossref","unstructured":"Xie, C., Nguyen, H., Xuan Nam, X.-N., Choi, Y., Zhou, J., and Nguyen-Trang, T. (2021). Predicting Rock Size Distribution in Mine Blasting Using Various Novel Soft Computing Models Based on Meta-Heuristics and Machine Learning Algorithms. Geosci. Front., 12.","DOI":"10.1016\/j.gsf.2020.11.005"},{"key":"ref_34","doi-asserted-by":"crossref","first-page":"1889","DOI":"10.1007\/s11053-020-09773-6","article-title":"A Combination of Expert-Based System and Advanced Decision-Tree Algorithms to Predict Air-Overpressure Resulting from Quarry Blasting","volume":"30","author":"He","year":"2021","journal-title":"Nat. Resour. Res."},{"key":"ref_35","doi-asserted-by":"crossref","unstructured":"Zhang, H., Zhou, J., Armaghani, D., Tahir, M., Pham, B., and Huynh, V. (2020). A Combination of Feature Selection and Random Forest Techniques to Solve a Problem Related to Blast-Induced Ground Vibration. Appl. Sci., 10.","DOI":"10.3390\/app10030869"},{"key":"ref_36","doi-asserted-by":"crossref","first-page":"106390","DOI":"10.1016\/j.soildyn.2020.106390","article-title":"Prediction of Ground Vibration Induced by Blasting Operations through the Use of the Bayesian Network and Random Forest Models","volume":"139","author":"Zhou","year":"2020","journal-title":"SOIL Dyn. Earthq. Eng."},{"key":"ref_37","doi-asserted-by":"crossref","unstructured":"Luan, B., Zhou, W., Jiskani, I.M., and Wang, Z. (2023). An Improved Machine Learning Approach for Optimizing Dust Concentration Estimation in Open-Pit Mines. Int. J. Environ. Res. Public Health, 20.","DOI":"10.3390\/ijerph20021353"},{"key":"ref_38","doi-asserted-by":"crossref","first-page":"12523","DOI":"10.1007\/s00603-025-04730-2","article-title":"Optimizing Flyrock Forecasting in Open-Pit Blasting Using Hybrid Machine Learning Models","volume":"58","author":"Zhang","year":"2025","journal-title":"Rock Mech. Rock Eng."},{"key":"ref_39","doi-asserted-by":"crossref","first-page":"145621","DOI":"10.1016\/j.jclepro.2025.145621","article-title":"Machine Learning-Driven Multi-Objective Optimization for Sustainable, Cost-Effective, and Low-Emission Gold Mining","volume":"511","author":"Qiu","year":"2025","journal-title":"J. Clean. Prod."},{"key":"ref_40","doi-asserted-by":"crossref","first-page":"278","DOI":"10.1007\/s12145-025-01817-w","article-title":"Feasibility of a Hybrid AHA-GPR Model for Predicting Blasting Fragmention in Surface Mines","volume":"18","author":"Yu","year":"2025","journal-title":"Earth Sci. Inform."},{"key":"ref_41","doi-asserted-by":"crossref","first-page":"413","DOI":"10.1007\/s10489-025-06308-5","article-title":"E-GCDT: Advanced Reinforcement Learning with GAN-Enhanced Data for Continuous Excavation System","volume":"55","author":"Zhao","year":"2025","journal-title":"Appl. Intell."},{"key":"ref_42","doi-asserted-by":"crossref","first-page":"173","DOI":"10.1007\/s00366-019-00816-y","article-title":"Deep Neural Network and Whale Optimization Algorithm to Assess Flyrock Induced by Blasting","volume":"37","author":"Guo","year":"2021","journal-title":"Eng. Comput."},{"key":"ref_43","doi-asserted-by":"crossref","first-page":"012157","DOI":"10.1088\/1742-6596\/1631\/1\/012157","article-title":"An Anti-Collision Early Warning System for Mine Trucks Based on RBF Network and WIFI","volume":"1631","author":"Xiao","year":"2020","journal-title":"J. Phys. Conf. Ser."},{"key":"ref_44","first-page":"109","article-title":"Prediction of Blast-Induced Flyrock by Using Neural-Imperialist Competitive Method (Case Study: Sungun Copper Mine)","volume":"39","author":"Hanifehnia","year":"2024","journal-title":"Rud. Geol. Naft. Zb."},{"key":"ref_45","first-page":"69","article-title":"Application of Machine Learning Techniques to Predict Haul Truck Fuel Consumption in Open-Pit Mines","volume":"13","author":"Alamdari","year":"2022","journal-title":"J. Min. Environ."},{"key":"ref_46","doi-asserted-by":"crossref","first-page":"6591","DOI":"10.1038\/s41598-023-33796-7","article-title":"Prediction of Ground Vibration Due to Mine Blasting in a Surface Lead\u2013Zinc Mine Using Machine Learning Ensemble Techniques","volume":"13","author":"Hosseini","year":"2023","journal-title":"Sci. Rep."},{"key":"ref_47","doi-asserted-by":"crossref","first-page":"100217","DOI":"10.1016\/j.rockmb.2025.100217","article-title":"Prediction of Blast-Induced Ground Vibration in Dolomitic Marble Quarry Using Z-Number Information and Fuzzy Cognitive Map Based Neural Network Models","volume":"4","author":"Hosseini","year":"2025","journal-title":"Rock Mech. Bull."},{"key":"ref_48","doi-asserted-by":"crossref","first-page":"629","DOI":"10.1007\/s11053-025-10546-2","article-title":"Intelligent Prediction of Flyrock Hazards in Surface Mining Using Optimized Gradient Boosting Models","volume":"35","author":"Rouhani","year":"2025","journal-title":"Nat. Resour. Res."},{"key":"ref_49","doi-asserted-by":"crossref","first-page":"8661","DOI":"10.1007\/s13762-022-04096-w","article-title":"Ensemble Machine Learning Models for Prediction of Flyrock Due to Quarry Blasting","volume":"19","author":"Barkhordari","year":"2022","journal-title":"Int. J. Environ. Sci. Technol."},{"key":"ref_50","doi-asserted-by":"crossref","first-page":"122469","DOI":"10.1016\/j.eswa.2023.122469","article-title":"Green Policy for Managing Blasting Induced Dust Dispersion in Open-Pit Mines Using Probability-Based Deep Learning Algorithm","volume":"240","author":"Hosseini","year":"2024","journal-title":"Expert Syst. Appl."},{"key":"ref_51","doi-asserted-by":"crossref","first-page":"103087","DOI":"10.1016\/j.resourpol.2022.103087","article-title":"Mine-to-Crusher Policy: Planning of Mine Blasting Patterns for Environmentally Friendly and Optimum Fragmentation Using Monte Carlo Simulation-Based Multi-Objective Grey Wolf Optimization Approach","volume":"79","author":"Hosseini","year":"2022","journal-title":"Resour. Policy"},{"key":"ref_52","doi-asserted-by":"crossref","first-page":"409","DOI":"10.1007\/s41939-025-01008-6","article-title":"PCA-Integrated Machine Learning Framework for Predicting Rock Fragmentation in Blasting Operations","volume":"8","author":"Dukuly","year":"2025","journal-title":"Multiscale Multidiscip. Model. Exp. Des."},{"key":"ref_53","first-page":"961","article-title":"Analysis of Concentration of Ambient Particulate Matter in the Surrounding Area of an Opencast Coal Mine Using Machine Learning Techniques","volume":"15","author":"Podicheti","year":"2024","journal-title":"J. Min. Environ."},{"key":"ref_54","doi-asserted-by":"crossref","unstructured":"Jha, S., Agrawal, H., and Rai, P. (2025). AI-Powered Prediction for Estimating Specific Fuel Consumption in Heavy-Duty Dumpers in Coal Mines. J. Inst. Eng. (India) Ser. D.","DOI":"10.1007\/s40033-025-00933-7"},{"key":"ref_55","doi-asserted-by":"crossref","first-page":"13","DOI":"10.1007\/s44291-025-00054-1","article-title":"Smart Driving Assistance System for Mining Operations in Foggy Environments","volume":"2","author":"Chaulya","year":"2025","journal-title":"Discov. Electron."},{"key":"ref_56","doi-asserted-by":"crossref","first-page":"106046","DOI":"10.1016\/j.rineng.2025.106046","article-title":"An Explainable AI-Based Framework for Predicting and Optimizing Blast-Induced Ground Vibrations in Surface Mining","volume":"27","author":"Ala","year":"2025","journal-title":"Results Eng."},{"key":"ref_57","doi-asserted-by":"crossref","unstructured":"Chandrahas, N.S., Choudhary, B.S., Teja, M.V., Venkataramayya, M.S., and Prasad, N.S.R.K. (2022). XG Boost Algorithm to Simultaneous Prediction of Rock Fragmentation and Induced Ground Vibration Using Unique Blast Data. Appl. Sci., 12.","DOI":"10.3390\/app12105269"},{"key":"ref_58","doi-asserted-by":"crossref","first-page":"3221","DOI":"10.1007\/s00366-020-00997-x","article-title":"A Novel Approach for Forecasting of Ground Vibrations Resulting from Blasting: Modified Particle Swarm Optimization Coupled Extreme Learning Machine","volume":"37","author":"Armaghani","year":"2021","journal-title":"Eng. Comput."},{"key":"ref_59","first-page":"79","article-title":"Machine Learning Algorithms for Data Enrichment: A Promising Solution for Enhancing Accuracy in Predicting Blast-Induced Ground Vibration in Open-Pit Mines","volume":"1","author":"Nguyen","year":"2023","journal-title":"Inz. Miner."},{"key":"ref_60","doi-asserted-by":"crossref","first-page":"234","DOI":"10.1080\/17480930.2025.2552707","article-title":"Improving PPV Prediction in Open-Pit Blasting through Cubist-Based Feature Enrichment and Machine Learning Models","volume":"40","author":"Nguyen","year":"2025","journal-title":"Int. J. Min. Reclam. Environ."},{"key":"ref_61","doi-asserted-by":"crossref","unstructured":"Fissha, Y., Ikeda, H., Toriya, H., Adachi, T., and Kawamura, Y. (2023). Application of Bayesian Neural Network (BNN) for the Prediction of Blast-Induced Ground Vibration. Appl. Sci., 13.","DOI":"10.3390\/app13053128"},{"key":"ref_62","doi-asserted-by":"crossref","first-page":"267","DOI":"10.4236\/ojapps.2024.142019","article-title":"A Comparative Study of Two Tree-Based Models for Predicting Flyrock Velocity at Open Pit Bench Mining","volume":"14","author":"Ezatullah","year":"2024","journal-title":"Open J. Appl. Sci."},{"key":"ref_63","doi-asserted-by":"crossref","unstructured":"Gaopale, K., Sasaoka, T., Hamanaka, A., and Shimada, H. (2025). Integrated Capuchin Search Algorithm-Optimized Multilayer Perceptron for Robust and Precise Prediction of Blast-Induced Airblast in a Blasting Mining Operation. Geosciences, 15.","DOI":"10.3390\/geosciences15080306"},{"key":"ref_64","doi-asserted-by":"crossref","unstructured":"Komadja, G., Rana, A., Glodji, L., Anye, V., Jadaun, G., Onwualu, P., and Sawmliana, C. (2022). Assessing Ground Vibration Caused by Rock Blasting in Surface Mines Using Machine-Learning Approaches: A Comparison of CART, SVR and MARS. Sustainability, 14.","DOI":"10.3390\/su141711060"},{"key":"ref_65","doi-asserted-by":"crossref","first-page":"132","DOI":"10.70425\/rml.202502.18","article-title":"Artificial Intelligence Based Smart Blasting Using ICA Optimized Neural Network for Oversize Prediction in a Small Scale Dolomite Quarry in Nigeria","volume":"2","author":"Taiwo","year":"2025","journal-title":"Rock Mech. Lett."},{"key":"ref_66","doi-asserted-by":"crossref","first-page":"346","DOI":"10.1016\/j.gsme.2024.08.006","article-title":"Machine Learning Based Prediction of Flyrock Distance in Rock Blasting: A Safe and Sustainable Mining Approach","volume":"1","author":"Taiwo","year":"2024","journal-title":"Green Smart Min. Eng."},{"key":"ref_67","doi-asserted-by":"crossref","first-page":"508","DOI":"10.1007\/s12665-023-11194-6","article-title":"Simulation of Blast-Induced Ground Vibrations Using a Machine Learning-Assisted Mechanical Framework","volume":"82","author":"Yardimci","year":"2023","journal-title":"Environ. EARTH Sci."},{"key":"ref_68","first-page":"75","article-title":"Fostering Sustainable Mining Practices in Rock Blasting: Assessment of Blast Toe Volume Prediction Using Comparative Analysis of Hybrid Ensemble Machine Learning Techniques","volume":"1","author":"Kahraman","year":"2024","journal-title":"J. Saf. Sustain."},{"key":"ref_69","first-page":"3483","article-title":"A New Synergetic Model of Neighbourhood Component Analysis and Artificial Intelligence Method for Blast-Induced Noise Prediction","volume":"9","author":"Ziggah","year":"2023","journal-title":"Model. Earth Syst. Environ."},{"key":"ref_70","doi-asserted-by":"crossref","unstructured":"Arthur, C.K., Bhatawdekar, R.M., Mohamad, E.T., Sabri, M.M.S., Bohra, M., Khandelwal, M., and Kwon, S. (2022). Prediction of Blast-Induced Ground Vibration at a Limestone Quarry: An Artificial Intelligence Approach. Appl. Sci., 12.","DOI":"10.3390\/app12189189"},{"key":"ref_71","doi-asserted-by":"crossref","first-page":"233","DOI":"10.3390\/mining2020013","article-title":"Rock Fragmentation Prediction Using an Artificial Neural Network and Support Vector Regression Hybrid Approach","volume":"2","author":"Amoako","year":"2022","journal-title":"Mining"},{"key":"ref_72","doi-asserted-by":"crossref","first-page":"1141","DOI":"10.1007\/s11053-020-09766-5","article-title":"Estimating Ore Production in Open-Pit Mines Using Various Machine Learning Algorithms Based on a Truck-Haulage System and Support of Internet of Things","volume":"30","author":"Choi","year":"2021","journal-title":"Nat. Resour. Res."},{"key":"ref_73","doi-asserted-by":"crossref","first-page":"200","DOI":"10.1007\/s10064-025-04216-z","article-title":"An Empirical-Driven Machine Learning (EDML) Approach to Predict PPV Caused by Quarry Blasting","volume":"84","author":"Asteris","year":"2025","journal-title":"Bull. Eng. Geol. Environ."},{"key":"ref_74","first-page":"1","article-title":"MineNetCD: A Benchmark for Global Mining Change Detection on Remote Sensing Imagery","volume":"62","author":"Yu","year":"2024","journal-title":"IEEE Trans. Geosci. Remote Sens."},{"key":"ref_75","doi-asserted-by":"crossref","first-page":"31826","DOI":"10.1109\/ACCESS.2021.3059018","article-title":"Development of an Optimized Regression Model to Predict Blast-Driven Ground Vibrations","volume":"9","author":"Moustafa","year":"2021","journal-title":"IEEE Access"},{"key":"ref_76","doi-asserted-by":"crossref","first-page":"1380","DOI":"10.1016\/j.jrmge.2021.07.013","article-title":"Prediction of Blasting Mean Fragment Size Using Support Vector Regression Combined with Five Optimization Algorithms","volume":"13","author":"Li","year":"2021","journal-title":"J. Rock Mech. Geotech. Eng."},{"key":"ref_77","doi-asserted-by":"crossref","first-page":"104839","DOI":"10.1016\/j.ijrmms.2021.104839","article-title":"A Deep Learning Approach for Rock Fragmentation Analysis","volume":"145","author":"Bamford","year":"2021","journal-title":"Int. J. Rock Mech. Min. Sci."},{"key":"ref_78","doi-asserted-by":"crossref","first-page":"148","DOI":"10.1504\/IJMME.2025.146863","article-title":"Machine Learning Tool to Minimise and Predict Airblast during Blasting and to Optimize the Design of Blasting Operations","volume":"16","author":"Saubi","year":"2025","journal-title":"Int. J. Min. Miner. Eng."},{"key":"ref_79","doi-asserted-by":"crossref","first-page":"679","DOI":"10.1007\/s00603-024-04166-0","article-title":"Adaptive Weighted Multi-Kernel Learning for Blast-Induced Flyrock Distance Prediction","volume":"58","author":"Zhang","year":"2025","journal-title":"Rock Mech. Rock Eng."},{"key":"ref_80","doi-asserted-by":"crossref","unstructured":"Liu, Z., Zhang, R., Ma, J., Zhang, W., and Li, L. (2023). Analysis and Prediction of the Meteorological Characteristics of Dust Concentrations in Open-Pit Mines. Sustainability, 15.","DOI":"10.3390\/su15064837"},{"key":"ref_81","doi-asserted-by":"crossref","first-page":"265","DOI":"10.1016\/j.ijmst.2021.01.007","article-title":"Blast-Induced Ground Vibration Prediction in Granite Quarries: An Application of Gene Expression Programming, ANFIS, and Sine Cosine Algorithm Optimized ANN","volume":"31","author":"Lawal","year":"2021","journal-title":"Int. J. Min. Sci. Technol."},{"key":"ref_82","doi-asserted-by":"crossref","unstructured":"Saldana, M., Gallegos, S., Arias, D., Salazar, I., Castillo, J., Salinas-Rodr\u00edguez, E., Navarra, A., Toro, N., and Cisternas, L.A. (2024). Applications of Kuz\u2013Ram Models in Mine-to-Mill Integration and Optimization\u2014A Review. Minerals, 14.","DOI":"10.3390\/min14111162"},{"key":"ref_83","doi-asserted-by":"crossref","first-page":"106024","DOI":"10.1016\/j.cie.2019.106024","article-title":"A Systematic Literature Review of Machine Learning Methods Applied to Predictive Maintenance","volume":"137","author":"Carvalho","year":"2019","journal-title":"Comput. Ind. Eng."},{"key":"ref_84","doi-asserted-by":"crossref","first-page":"103676","DOI":"10.1016\/j.rineng.2024.103676","article-title":"Review of Machine Learning Applications in Additive Manufacturing","volume":"25","author":"Inayathullah","year":"2025","journal-title":"Results Eng."},{"key":"ref_85","doi-asserted-by":"crossref","unstructured":"Nemala, P., Chen, B., and Cui, H. (2025). A Privacy Preserving Attribute-Based Access Control Model for the Tokenization of Mineral Resources via Blockchain. Appl. Sci., 15.","DOI":"10.3390\/app15158290"}],"container-title":["Applied Sciences"],"original-title":[],"language":"en","link":[{"URL":"https:\/\/www.mdpi.com\/2076-3417\/16\/7\/3246\/pdf","content-type":"unspecified","content-version":"vor","intended-application":"similarity-checking"}],"deposited":{"date-parts":[[2026,3,27]],"date-time":"2026-03-27T13:15:05Z","timestamp":1774617305000},"score":1,"resource":{"primary":{"URL":"https:\/\/www.mdpi.com\/2076-3417\/16\/7\/3246"}},"subtitle":[],"short-title":[],"issued":{"date-parts":[[2026,3,27]]},"references-count":85,"journal-issue":{"issue":"7","published-online":{"date-parts":[[2026,4]]}},"alternative-id":["app16073246"],"URL":"https:\/\/doi.org\/10.3390\/app16073246","relation":{},"ISSN":["2076-3417"],"issn-type":[{"value":"2076-3417","type":"electronic"}],"subject":[],"published":{"date-parts":[[2026,3,27]]}}}