Prediction of blast-induced flyrock using differential evolution algorithm

Abstract

loading and hauling. Fragments can be thrown beyond the desired pile limit, which is normal but is considered adverse effect of blasting and is called flyrock. Fragments propelled far beyond predefined safety limit are considered security breach and are called wild flyrock [1].Due to high potential to cause damage to machinery and nearby structures and to cause injuries, even fatal, to personnel, flyrock is the most dangerous adverse effect of blasting and this phenomenon is responsible for 28.3 % of the damages and injuries in surface mining activities [2].To prevent flyrock problem, various parameters such as the physico-mechanical properties of rock mass, explosives specifications and geometrical aspects of the blasting pattern should be considered while designing a blasting pattern. For example, flyrock may occur in situations where there is insufficient stemming, too small a burden because of overbreak from the preceding or proceeding blast, planes of weaknesses in rock which reduce resistance to blasting, and finally existence of loose rock fragment on top of the bench [3]. Explosives with high energy can increase the probability of the flyrock. Most of the conventional formula used the TNT strength instead of the explosive strength.To convert this factor, usually, the ballistic mortar test was used (Fig. 1).Since this phenomenon is very dangerous, many researchers trying to establish the flyrock prediction models. Lundborg presented an equation for estimating the flyrock based on the hole diameter and specific charge as follows [4]:where L is the maximum rock projection (m), D is the hole diameter (in) and q is the specific charge (kg/m 3 ). Also, Lundborg et al. established another empirical model based on hole diameter as follows [5]:Abstract Flyrock is an undesirable phenomenon in blasting operations. Due to high potential to cause damage to machinery and nearby structures and to cause injuries, even fatal, to personnel, flyrock is the most dangerous adverse effect of blasting. For controlling and decreasing the effect of this phenomenon, it is necessary to predict it. Because of multiplicity of effective parameters and complexity of interactions among these parameters, empirical methods may not be fully appropriate for flyrock estimation. The scope of this study is to predict flyrock induced by blasting through a novel approach based on the combination of differential evaluation algorithm (DE) and dimensional analysis algorithm (DA). For this purpose, the parameters of 300 blasting operations were accurately recorded and flyrock distances were measured for each operation. In the next stage, two new empirical predictors were developed to predict flyrock distance. The results clearly showed the superiority of the proposed DE-DA model in comparison with the empirical approaches.

Prioritizing and Analyzing the Role of Climate and Urban Parameters in the Confirmed Cases of COVID-19 Based on Artificial Intelligence Applications

“…Then, after initialization, the search space is expanded by the mutation. The V g i is the mutant solution vector of X g i which is calculated based on Equation 4 [88].…”

“…At first, the control parameters of DE algorithm are determined to find the optimum weights and biases of ANN model that can converge faster and accurately. For this purpose, similar to PSO model, the crossover probability coefficient was selected as 0.2, and other parameters were determined by trial and error method from previous studies and experts' opinions [87,88]. In addition, the datasets for modeling were randomly divided into several subsets, including 70% for training and the rest for validation (15%) and testing (15%) [ According to Figure 6, it is evident that after the sixth iteration with 0.00133, the best cost was reached, and the model achieves a worthy convergence, and it was fixed to the end of the iteration.…”

A critical analysis of recent research into the prediction of flyrock and related issues resulting from surface blasting activities

“…3. Dehghani and Shafaghi (2017) attempted to address the inadequate predictive capability of existing empirical models by using a combination of differential evaluation (DE) and dimensional analysis (DA) algorithms. DA is defined as an engineering method that is used to create equations that will satisfy the analysis of complex multivariable systems.…”

“…The methodology involved collecting data from 300 blasts and measuring and recording both the input parameters and the resulting flyrock. The input parameters considered were the blast-hole diameter (D) and length (L), number of blast-holes (NB), spacing (S), burden (B), ANFO charge mass (Q), stemming length (St), powder factor (PF) and specific drilling (SD) (Dehghani and Shafaghi, 2017). Data was collected in the same manner as in the studies discussed previously.…”

Implementation of an optimized binary classification by GMDH‐type neural network algorithm for predicting the blast produced ground vibration

“…In the next step, to analyse data and provide a prediction model, a combination of differential evaluation algorithm (DE) and dimensional analysis algorithm (DA) was used. Their developed model had a more suitable performance in predicting the blast‐produced flyrock phenomenon compared to other experimental methods (Dehghani & Shafaghi, 2017). In another study by Fouladgar et al, a series of conventional experimental relations and a new metaheuristic algorithm called the cuckoo search algorithm, were used to estimate the peak particle velocity in the mine blasting.…”