Prediction of airblast-overpressure induced by blasting using a hybrid artificial neural network and particle swarm optimization

Blasting is an inseparable part of the rock fragmentation process in hard rock mining. As an adverse and undesirable effect of blasting on surrounding areas, airblast-overpressure (AOp) is constantly considered by blast designers. AOp may impact the human and structures in adjacent to blasting area. Consequently, many attempts have been made to establish empirical correlations to predict and subsequently control the AOp. However, current correlations only investigate a few influential parameters, whereas there are many parameters in producing AOp. As a powerful function approximations, artificial neural networks (ANNs) can be utilized to simulate AOp. This paper presents a new approach based on hybrid ANN and particle swarm optimization (PSO) algorithm to predict AOp in quarry blasting. For this purpose, AOp and influential parameters were recorded from 62 blast operations in four granite quarry sites in Malaysia. Several models were trained and tested using collected data to determine the optimum model in which each model involved nine inputs, including the most influential parameters on AOp. In addition, two series of site factors were obtained using the power regression analyses. Findings show that presented PSO-based ANN model performs well in predicting the AOp. Hence, to compare the prediction performance of the PSO-based ANN model, the AOp was predicted using the current and proposed formulas. The training correlation coefficient equals to 0.94 suggests that the PSO-based ANN model outperforms the other predictive models.     

Operational and geological parameters in the assessing blast induced airblast-overpressure in quarries

Airblast disturbances, like ground vibrations, are undesirable by-products of blasting. Airblast damage and annoyance are directly related to factors such as blast design, weather and terrain conditions, and human response. But, unlike blast induced ground vibrations, airblast impacts the structures through the roof, walls and windows leading to human annoyance. Because any structure rumbling and rattling, including blast induced vibrations can fuel the fear of people. The experiences on airblast-overpressure (AOp) measurements show that it is sometime impracticable to set a maximum AOp limit, with or without allowed appropriate percentile of exceedances, because of the significance and unpredictability of variable geological conditions and the restrictive character of this kind of limitations in blasting practice. We therefore utilized the method of modified scaled distances (SDs) based on empirical equations considering such factors as blasting parameters and geological parameters of rock mass for bench blasting in quarries. The aim of this application was to get an environmentally friendly and technically practicable results by using site specific SDs instead of conservative SD values. This method was realized in a quarry in Istanbul with numerous experimental shots. In these experiments, the levels of airblast-overpressures were investigated as a function of variable conditions. The results show that the performances of these estimations are governed by the site-specific character of these empirical relations.