Development of a new methodology for estimating the amount of PPV in surface mines based on prediction and probabilistic models (GEP-MC)

ABSTRACT

Peak particle velocity (PPV) is one of the most-used parameters for assessment of ground vibration resulting from blasting and associated damage to nearby areas. To prevent and control the damages might be caused by PPV, its amount should be predicted and controlled before conducting blasting operation. This paper analyzes results of this environmental issue of blasting using a combination of predictive and probabilistic models (prediction and simulation phases). To get the right patterns, a new intelligent model (regression tree-based), known as gene expression programming (GEP), is applied and developed. Considering various conditions of data, GEP model was able to propose a predictive model that consists of mathematical relations between input and output parameters. The design of this model was carried out with different conditions, and the most optimal model with the lowest error was selected based on several performance indices. Using the selected model of GEP model, the Monte Carlo simulation technique was implemented to examine the possible risks and control them. The results showed that there is a need to have a better controlling of conditions of explosive operations before blasting operations. Using the developed models, the blast-safety-area can be obtained/determined and all workers and equipment are in safe side during blasting operations.

KEYWORDS:

• Ground vibration
• gene expression programming
• Mont Carlo simulation
• blast-safety-area

Disclosure statement

The authors declare that they have no conflict of interest.

Additional information

Funding

This research is partially supported by the National Natural Science Foundation Project of China [Grant no. 41807259], the Natural Science Foundation of Hunan Province [2018JJ3693], the Innovation-Driven Project of Central South University [2020CX040] and the Shenghua Lieying Program of Central South University (Principle Investigator: Dr. Jian Zhou).