A Solution Surface in a Nine-Dimensional Space to Optimise the Effects of Ground Vibration Through Artificial Intelligence During Blasting in an Open-Pit Mine

In this study, we model a solution surface with each point having nine components using artificial intelligence (AI) in optimising the effects of ground vibration during blasting operations in an open-pit diamond mine. This model has eight input parameters that can be adjusted by blasting engineers to arrive at a desired output value of ground vibration. It is built using the best performing artificial neural network architecture that best fits the blasting data from 100 blasting events provided by the Debswana diamond mine. Other AI algorithms used to compare the model performance were k-nearest neighbour, support vector machine, and random forest – together with more traditional statistical approach, i.e., multivariate and regression analysis. The inputs parameters were burden, spacing, stemming length, hole depth, hole diameter, distance from the blast face to the monitoring point, maximum charge per delay, and powder factor. The optimised model allows variations in the inputs values, given constraints, such that the output ground vibration will be within the minimum acceptable value. Through unconstrained optimisation, the minimum value of ground vibration is around 0.1 mm/s that is within the range caused by a passing vehicle.