IJ
IJCRM
International Journal of Contemporary Research in Multidisciplinary
ISSN: 2583-7397
Open Access • Peer Reviewed
Impact Factor: 5.67

International Journal of Contemporary Research In Multidisciplinary, 2026;5(2):43-47

Flyrock Prediction and Control Techniques in Surface Mine Blasting: A Review

Author Name: Sumeet Kishore;   Sachin Kumar;   Chandan Kumar Tiwartha;   Sabir Ansari;   Satish Kumar;   Chandradeep Kumar;   Vidya Sagar;   Anshu Kumar;  

1. Assistant Professor, Department of Mining Engineering, Jharkhand Rai University, Ranchi, India

2. Students, Department of Mining Engineering, Jharkhand Rai University, Ranchi, India

3. Students, Department of Mining Engineering, Jharkhand Rai University, Ranchi, India

4. Students, Department of Mining Engineering, Jharkhand Rai University, Ranchi, India

5. Students, Department of Mining Engineering, Jharkhand Rai University, Ranchi, India

6. Students, Department of Mining Engineering, Jharkhand Rai University, Ranchi, India

7. Students, Department of Mining Engineering, Jharkhand Rai University, Ranchi, India

8. Students, Department of Mining Engineering, Jharkhand Rai University, Ranchi, India

Abstract

Flyrock remains one of the most critical hazards in surface mining and quarry operations, accounting for 20% to 40% of all blasting-related fatalities and severe injuries globally. Occurring when explosive detonation energy propels rock fragments beyond the designated blast safety clearance zone, flyrock is primarily driven by unconfined high-pressure gas expansion, sub-optimal blast geometry, inappropriate delay timing, and structural geological anomalies (such as mud seams, voids, and joint sets).

This review provides a comprehensive technical synthesis of flyrock generation mechanics, explicitly categorizing its primary operational modes: face burst, cratering, and rifling (stemming ejection). It systematically evaluates classical empirical formulations (e.g., Lundborg and Richards & Moore scaled depth of burial models) alongside their inherent limitations in non-homogeneous rock masses. Furthermore, this study highlights recent advancements in predictive modelling using soft computing and artificial intelligence (AI), including Deep Neural Networks (DNN) integrated with metaheuristic optimization algorithms (such as GSA, JAYA, and MVO), which consistently yield superior predictive accuracy (R2 > 0.95).

Finally, actionable operational control measures are detailed across the blast execution lifecycle—emphasizing 3D laser face profiling, angular aggregate stemming selection, electronic timing sequence control, and physical containment techniques. By bridging fundamental mechanics with advanced AI prediction and targeted engineering controls, this paper offers a unified framework to optimize blast safety clearance design and eliminate flyrock risk in surface extraction operations.

Keywords

Surface mining; Flyrock; Blasting safety; Artificial intelligence; Machine learning; Blast design; Electronic detonators