Optimization of blasting design in open pit limestone mines with the aim of reducing ground vibration using robust techniques

Abstract

Blasting operations create significant problems to residential and other structures located in the close proximity of the mines. Blast vibration is one of the most crucial nuisances of blasting, which should be accurately estimated to minimize its effect. In this paper, an attempt has been made to apply various models to predict ground vibrations due to mine blasting. To fulfill this aim, 112 blast operations were precisely measured and collected in one the limestone mines of Iran. These blast operation data were utilized to construct the artificial neural network (ANN) model to predict the peak particle velocity (PPV). The input parameters used in this study were burden, spacing, maximum charge per delay, distance from blast face to monitoring point and rock quality designation and output parameter was the PPV. The conventional empirical predictors and multivariate regression analysis were also performed on the same data sets to study the PPV. Accordingly, it was observed that the ANN model is more accurate as compared to the other employed predictors. Moreover, it was also revealed that the most influential parameters on the ground vibration are distance from the blast and maximum charge per delay, whereas the least effective parameters are burden, spacing and rock quality designation (RQD). Finally, in order to minimize PPV, the developed ANN model was used as an objective function for imperialist competitive algorithm (ICA). Eventually, it was found that the ICA algorithm is able to decrease PPV up to 59% by considering burden of 2.9m, spacing of 4.4m and charge per delay of 627Kg.

Parameter Optimization and Fragmentation Prediction of Fan-Shaped Deep Hole Blasting in Sanxin Gold and Copper Mine

“…For example, Akande and Lawal examine optimization of blasting parameters for economic production of granite aggregates in Ratcon and NSCE quarries located at Ibadan using regression models [4]. Rezaeineshat et al used robust techniques to design the blasting parameters in open-pit mine with the aim to reducing ground vibration [5]. Inanloo Arabi Shad and Ahangari proposed an empirical relation to calculate the proper burden in blast design of open-pit mines and promoted this method to other mines [6].…”

Predição de vibrações induzidas por desmontes de rochas por explosivos usando redes neurais artificiais

“…Os rápidos avanços na tecnologia computacional fizeram com que sistemas inteligentes se tornassem ferramentas promissoras na estimativa dos níveis de VPP. Nesse contexto, técnicas de soft computing tais como redes neurais artificiais (Bayat et al, 2020;Rezaeineshat et al, 2020), algoritmos genéticos (Ataei & Sereshki, 2017;Dehghani & Beiromvand, 2019), máquina de vetor suporte (Longjun et al, 2011;Li et al, 2012), random forest (Longjun et al, 2011;Zhou et al, 2020) e adaptive neuro-fuzzy inference system (Iphar et al, 2008;Armaghani et al, 2015) têm sido amplamente aplicadas para a predição de vibrações sísmicas induzidas por desmonte de rochas.…”

Digital Processing of Seismic Data from Open-Pit Mining Blasts

“…The process of ANN modeling for PPV prediction using five input parameters, namely, load, interval, maximum charge per deceleration stage, distance from the face to the observation point, and rock properties (burden, spacing, maximum charge per delay, distance from blast face to monitoring point and rock quality designation) is described in [24]. The results are based on the datasets of 112 values collected from limestone mines in Iran.…”