Combination of neural network and ant colony optimization algorithms for prediction and optimization of flyrock and back-break induced by blasting

Saghatforoush, Amir; Monjezi, Masoud; Faradonbeh, Roohollah Shirani; Armaghani, Danial Jahed

doi:10.1007/s00366-015-0415-0

Cited by 138 publications

(20 citation statements)

References 52 publications

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“…The previous computational techniques developed to predict flyrock distance comprising of artificial neural network (ANN), fuzzy inference system (FIS), Monte Carlo simulation, multiple regression analyses, support vector machine (SVM), rock engineering systems (RES), and adaptive neuro-fuzzy inference system (ANFIS) models [14,[21][22][23][24]. According to the above-mentioned methods, providing new ways to predict this phenomenon is necessary.…”

Section: Introductionmentioning

confidence: 99%

Three hybrid intelligent models in estimating flyrock distance resulting from blasting

Koopialipoor

Fallah

Armaghani

et al. 2018

Engineering with Computers

155

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Flyrock is an adverse effect produced by blasting in open-pit mines and tunnelling projects. So, it seems that the precise estimation of flyrock is essential in minimizing environmental effects induced by blasting. In this study, an attempt has been made to evaluate/predict flyrock induced by blasting through applying three hybrid intelligent systems, namely imperialist competitive algorithm (ICA)-artificial neural network (ANN), genetic algorithm (GA)-ANN and particle swarm optimization (PSO)-ANN. In fact, ICA, PSO and GA were used to adjust weights and biases of ANN model. To achieve the aim of this study, a database composed of 262 datasets with six model inputs including burden to spacing ratio, blast-hole diameter, powder factor, stemming length, the maximum charge per delay, and blast-hole depth and one output (flyrock distance) was established. Several parametric investigations were conducted to determine the most effective factors of GA, ICA and PSO algorithms. Then, at the end of modelling process of each hybrid model, eight models were constructed and their results were checked considering two performance indices, i.e., root mean square error (RMSE) and coefficient of determination (R 2 ). The obtained results showed that although all predictive models are able to approximate flyrock, PSO-ANN predictive model can perform better compared to others. Based on R 2 , values of (0.943, 0.958 and 0.930) and (0.958, 0.959 and 0.932) were found for training and testing of ICA-ANN, PSO-ANN and GA-ANN predictive models, respectively. In addition, RMSE values of (0.052, 0.045 and 0.057) and (0.045, 0.044 and 0.058) were achieved for training and testing of ICA-ANN, PSO-ANN and GA-ANN predictive models, respectively. These results show higher efficiency of the PSO-ANN model in predicting flyrock distance resulting from blasting. Moreover, sensitivity analysis shows that hole diameter is more effective than others.

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Section: Introductionmentioning

confidence: 99%

Three hybrid intelligent models in estimating flyrock distance resulting from blasting

Koopialipoor

Fallah

Armaghani

et al. 2018

Engineering with Computers

155

View full text Add to dashboard Cite

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“…Numerous other researchers have investigated the development of nonlinear and simple mathematical models according to biological neuron [27,28]. These studies allow for the production of a big number of structures (e.g., topologies) and network learning algorithms [29][30][31][32][33][34][35]. With using a randomly selected testing database, ANN-based models run the dataset in a training network and can also analyze the predicted result (i.e., less than 30% of the whole datasets) [36][37][38].…”

Section: Artificial Neural Networkmentioning

confidence: 99%

Development of Two Novel Hybrid Prediction Models Estimating Ultimate Bearing Capacity of the Shallow Circular Footing

et al. 2019

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In the present work, we employed artificial neural network (ANN) that is optimized with two hybrid models, namely imperialist competition algorithm (ICA) as well as particle swarm optimization (PSO) in the case of the problem of bearing capacity of shallow circular footing systems. Many types of research have shown that ANNs are valuable techniques for estimating the bearing capacity of the soils. However, most ANN training models have some drawbacks. This study aimed to focus on the application of two well-known hybrid ICA–ANN and PSO–ANN models to the estimation of bearing capacity of the circular footing lied in layered soils. In order to provide the training and testing datasets for the predictive network models, extensive finite element (FE) modelling (a database includes 2810 training datasets and 703 testing datasets) are performed on 16 soil layer sets (weaker soil rested on stronger soil and vice versa). Note that all the independent variables of ICA and PSO algorithms are optimized utilizing a trial and error method. The input includes upper layer thickness/foundation width (h/B) ratio, footing width (B), top and bottom soil layer properties (e.g., six of the most critical soil characteristics), vertical settlement of circular footing (s), where the output was taken ultimate bearing capacity of the circular footing (Fult). Based on coefficient of determination (R2) and Root Mean Square Error (RMSE), amounts of (0.979, 0.076) and (0.984, 0.066) predicted for training dataset and amounts of (0.978, 0.075) and (0.983, 0.066) indicated in the case of the testing dataset of proposed PSO–ANN and ICA–ANN models of prediction network, respectively. It demonstrates a higher reliability of the presented PSO–ANN model for predicting ultimate bearing capacity of circular footing located on double sandy layer soils.

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“…Raina and Murthy developed an empirical model for predicting the flyrock in open pit mines using the surface response analysis. Saghatforoush et al combined the neural network and ant colony optimization algorithms for predicting the flyrock and back-break induced by blasting [16].…”

Section: Introductionmentioning

confidence: 99%

Prediction of blast-induced flyrock using differential evolution algorithm

Dehghani

Shafaghi

2016

Engineering with Computers

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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.

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Combination of neural network and ant colony optimization algorithms for prediction and optimization of flyrock and back-break induced by blasting

Abstract: changeable parameters of the ACO algorithm, blasting pattern parameters were optimized to minimize results of flyrock and back-break. Eventually, implementing ACO algorithm, reductions of 61 and 58 % were observed in flyrock and back-break results, respectively.

Cited by 138 publications

References 52 publications

Three hybrid intelligent models in estimating flyrock distance resulting from blasting

Three hybrid intelligent models in estimating flyrock distance resulting from blasting

Development of Two Novel Hybrid Prediction Models Estimating Ultimate Bearing Capacity of the Shallow Circular Footing

Prediction of blast-induced flyrock using differential evolution algorithm

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