The knowledge of the explosive/ rock mass interaction process is important in predicting blast performance. It includes understanding rock properties, energy usage, and attenuation in the rock mass. The paper proposes a method to quantify blast-induced rock mass damage from the field measurements and apply it to modeling blast fragmentation.
Given the limited efficiency of blast-induced ground vibrations prediction empirical models, due to the complex geological system from a Peruvian mine. An artificial neural network was built in order to get better results. More accurate and precise predictions allowed the generation of blasting domains for future designs and decision making.
Mina El toro has been making substantial changes in its operation to improve its social impact and productivity. Because of the use of gasified emulsions and electronic initiation system, it was possible to mitigate vibrations and carry out massive blasting, increasing its production by 4.2% and minimizing complaints from communities.
The incident of Ammonium Nitrate explosion in Beirut on August 4, 2020, became a wake-up call to Ammonium Nitrate stakeholders for mining and construction regarding the deadly risks associated with explosives management. This is a very expensive a lesson learned for all parties involved in the storage of explosives.