In the modern era of mining and civil engineering, rock blasting remains a foundational process for mineral extraction and infrastructure development. Despite the proliferation of sophisticated software, high-speed cameras, and digital detonators, a common adage persists within the industry: blasting is still more art than science. While scientific principles of thermodynamics and rock mechanics provide the framework, the inherent unpredictability of geological formations and the limitations of current modeling techniques necessitate a level of “artistic” intuition and site-specific fine-tuning (Bhagat et al., 2021; Zvarivadza et al., 2025).
The scientific framework and its limitations
The “science” of blasting is rooted in empirical models designed to predict fragmentation, ground vibration, and fly-rock. Models such as the Kuz-Ram distribution have long been the industry standard for estimating mean fragment size based on explosive energy and rock mass properties (Saadoun et al., 2022). However, recent research highlights that these models often produce “idealistic” results that fail to account for the extreme heterogeneity of real-world rock masses. For instance, the presence of joints, dips, and unpredictable discontinuities significantly alters the transmission of shock waves and gas pressure, often leading to results that deviate sharply from theoretical predictions (Bedri et al., 2023).
Furthermore, the efficiency of explosive energy remains surprisingly low. It is estimated that only about 20% of the energy released during a blast is effectively used for rock fragmentation, while the remaining 80% is lost to environmental “waste” such as noise, fly-rock, and ground vibrations (Zhang et al., 2024). This gap between theoretical energy potential and practical outcome underscores the limits of a purely scientific approach.
The “art” of site-specific adaptation
The “art” of blasting refers to the nuanced adjustments made by experienced engineers to account for conditions that sensors and models cannot fully capture. In complex environments like deep-level hard rock mines, achieving an optimal “destress blast” is frequently described as more of an art than a science because the design must be continuously updated to reflect actual, prevailing ground conditions (Zvarivadza et al., 2025).
Variables such as “rock response time” (Tmin)—the critical interval between detonation and actual displacement—are fundamental to success but remain difficult to calculate with universal precision. While increased delay times generally improve fragmentation, the specific “sweet spot” for a particular site often requires experimental emphasis and historical knowledge rather than a standard formula (Zhang et al., 2024).
The role of artificial intelligence
As the industry moves toward 2026, the bridge between art and science is increasingly being built by Artificial Intelligence (AI). Traditional empirical formulas are being supplemented by machine learning techniques, such as Random Forest and XGBoost, which can handle non-linear relationships between variables more effectively than classical mechanics (Sui et al., 2025). Studies have shown that AI models can achieve a high coefficient of determination (R² > 0.94) in predicting fragmentation, suggesting that what was once considered “intuition” is being quantified through multidimensional data (“Intelligence Prediction of Some Selected Environmental Issues of Blasting,” 2020; Sui et al., 2025)
Conclusion
Blasting has undoubtedly evolved into a high-tech discipline, yet it remains an “art” because the laboratory of the earth is never a controlled environment. The unpredictability of geological discontinuities and the low efficiency of energy transfer mean that scientific models provide a baseline, but human experience and site-specific adaptation provide the result. As engineering continues to integrate AI and real-time monitoring, the “art” may become more data-driven, but the fundamental need for human judgment in the face of geological uncertainty remains indispensable.
References
Bedri, K., Ould Hamou, M., Filali, M., Hadji, R., & Taib, H. (2023). Optimizing the blast fragmentation quality of discontinuous rock mass: Case study of Jebel Bouzegza Open-Cast Mine, North Algeria. Mining of Mineral Deposits, 17, 35–44. https://doi.org/10.33271/mining17.04.035
Bhagat, N. K., Rana, A., Mishra, A. K., Singh, M. M., Singh, A., & Singh, P. K. (2021). Prediction of fly-rock during boulder blasting on infrastructure slopes using CART technique. Geomatics, Natural Hazards and Risk, 12(1), 1715–1740. https://doi.org/10.1080/19475705.2021.1944917
Intelligence Prediction of Some Selected Environmental Issues of Blasting: A Review. (2020). The Open Construction and Building Technology Journal, 14, 298–308. https://doi.org/10.2174/1874836802014010298
Saadoun, A., Fredj, M., Boukarm, R., & Hadji, R. (2022). Fragmentation analysis using digital image processing and empirical model (KuzRam): A comparative study. Journal of Mining Institute, 257, 822–832. https://doi.org/10.31897/PMI.2022.84
Sui, Y., Zhou, Z., Zhao, R., Yang, Z., & Zou, Y. (2025). Open-Pit Bench Blasting Fragmentation Prediction Based on Stacking Integrated Strategy. Applied Sciences, 15(3). https://doi.org/10.3390/app15031254
Zhang, X., Li, Z., Wei, Z., & Gao, W. (2024). Experimental and Numerical Study on the Effect of Three-Hole Simultaneous Blasting Technology on Open-Pit Mine Bench Blasting. Applied Sciences, 14(5). https://doi.org/10.3390/app14052169
Zvarivadza, T., Yi, C., Dineva, S., Onifade, M., Khandelwal, M., & Genc, B. (2025). Reflections on destress blasting for deep level hardrock mining: Key considerations for successful application of the techniques. Journal of the Southern African Institute of Mining and Metallurgy, 125, 317–338. https://doi.org/10.17159/2411-9717/3686/2025
