In mining and civil engineering, the efficiency of a rock blast is measured not just by its ability to move material, but by how well it utilizes explosive energy. Studies indicate that while 20% to 30% of explosive energy is effectively used for rock fragmentation, the remaining 70% to 80% is often “wasted” in the form of environmental impacts and operational inefficiencies [1]. Identifying the indicators of poor performance and utilizing post-blast analysis is critical for moving from a “trial and error” approach to a data-driven optimization model [2].
Common indicators of poor blast performance
When a blast design fails to match the geological conditions, several visible and measurable “red flags” emerge. These indicators usually signal energy loss or poor confinement.
Inefficient fragmentation
Fragmentation is the most significant indicator of blast success. Poor performance is characterized by:
- Oversize boulders: large blocks that require secondary blasting or mechanical breaking, which increases costs and causes bottlenecks at the crusher [3].
- Excessive fines: too much dust and small particles indicate over-blasting or localized energy concentration, leading to wasted explosives and environmental hazards.
- Poor uniformity: a wide variation in rock sizes suggests an inconsistent drill pattern or uneven explosive distribution.
Excessive ground vibration and airblast
While some vibration is inevitable, excessive levels (measured as Peak Particle Velocity or PPV) indicate that energy is traveling through the ground rather than breaking rock. Similarly, a loud airblast (concussion wave) often results from premature stemming ejection, where high-pressure gases vent directly into the atmosphere instead of working on the rock mass [4].
Flyrock and backbreak
- Flyrock: the uncontrolled ejection of rock fragments beyond the blast zone. It is a major safety hazard often caused by insufficient burden (the distance between the borehole and the free face) or poor stemming.
- Backbreak: damage to the rock behind the last row of blast holes. This destabilizes the new bench face, making the next round of drilling more difficult and dangerous.
How post-blast analysis improves future designs
Post-blast analysis transforms the outcome of one blast into the blueprint for the next. By quantifying what went wrong, engineers can adjust controllable parameters to improve efficiency.
Digital fragmentation analysis
Modern post-blast analysis heavily utilizes 3D photogrammetry and Unmanned Aerial Vehicles (UAVs) [5]. Tools like WipFrag or FragMetriX analyze digital images of the muckpile to calculate the exact size distribution [6]. If analysis shows a high percentage of oversize material, engineers can respond in the next design by increasing the powder factor (the amount of explosive per cubic meter) or reducing the spacing between holes.
Vibration and airblast monitoring
Using seismographs placed at strategic distances, engineers collect PPV and frequency data. This data is used to develop a site-specific regression analysis. By understanding how the local geology transmits energy, designers can calculate the “maximum charge per delay” to ensure vibrations stay below regulatory limits while still achieving fragmentation [7].
The feedback loop
The most effective way to improve future designs is through a closed-loop system:
- Bench profiling: 3D laser scanning of the rock face before the blast to identify weak zones.
- Execution: precise drilling and charging based on the 3D model.
- Measurement: post-blast fragmentation and vibration recording.
- Refinement: adjusting the delay timing (the millisecond intervals between hole detonations) to prevent wave super-imposition, which reduces vibration and improves the “throw” of the muckpile for easier loading.
Conclusion
Poor blast performance is rarely the result of a single factor; it is typically an imbalance between explosive energy and rock resistance. By systematically monitoring fragmentation, flyrock, and vibration, operations can reduce their “cost per ton” and minimize their environmental footprint.
References
[1] O. Saubi, R. S. J. Jr, R. S. Suglo, and O. Matsebe, “Simultaneous Prediction and Optimisation of Rock Fragmentation and Ground Vibration Using an ANN-RF Ensemble in Open-Pit Blasting,” Jul. 07, 2025, Preprints: 2025070565. doi: 10.20944/preprints202507.0565.v1.
[2] “Digital Twins for Blast Optimization in Mining | Anvil Labs.” Accessed: Jan. 27, 2026. [Online]. Available: https://anvil.so/post/digital-twins-for-blast-optimization-in-mining
[3] M. Hashim and M. Khider, “Improving Blast Design for Optimum Rock Breakage and Sustainable Operations,” vol. 11, Dec. 2017.
[4] M. Mpofu, S. Ngobese, B. Maphalala, D. Roberts, and S. Khan, “The influence of stemming practice on ground vibration and air blast,” Journal of the Southern African Institute of Mining and Metallurgy, vol. 121, no. 1, pp. 1–10, Jan. 2021, doi: 10.17159/2411-9717/1204/2021.
[5] “Digital Twins for Blast Optimization in Mining | Anvil Labs.” Accessed: Jan. 27, 2026. [Online]. Available: https://anvil.so/post/digital-twins-for-blast-optimization-in-mining
[6] “Blast Optimization in Open-Pit Mining: How BlastMetriX and FragMetriX Drive Efficiency – 3GSM.” Accessed: Jan. 27, 2026. [Online]. Available: https://3gsm.at/learning/blast-optimization-in-open-pit-mining-how-blastmetrix-and-fragmetrix-drive-efficiency/
[7] F. Leon et al., “Mathematical Modelling and Optimization Methods in Geomechanically Informed Blast Design: A Systematic Literature Review,” Mathematics, vol. 13, no. 15, p. 2456, Jan. 2025, doi: 10.3390/math13152456.
