Several non-linear models in estimating air-overpressure resulting from mine blasting

Hasanipanah, Mahdi; Armaghani, Danial Jahed; Khamesi, Hossein; Amnieh, Hassan Bakhshandeh; Ghoraba, Saber

doi:10.1007/s00366-015-0425-y

Cited by 96 publications

(10 citation statements)

References 50 publications

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“…Before modeling by PSO and GWO, dataset was normalized by following equation (Armaghani, Mohamad, Hajihassani, Yagiz, & Motaghedi, ; Hasanipanah, Armaghani, Khamesi, Amnieh, & Ghoraba, ):

R_{n} = \frac{R - R_{\min}}{R_{\max} - R_{\min}}

where, R min , R max , R , and R n are the minimum, maximum, actual, and normalized amount of the determined parameters, respectively . Good fitness of the models was defined using ( R 2 ), (MSE), and (MAE) using following equations, respectively (Kaveh et al, ):

R^{2} = 1 - \frac{\sum_{i = 1}^{N} {(S_{normalk} - T_{normalk})}^{2}}{\sum_{i = 1}^{N} {(S_{normalk} - T_{normalm})}^{2}}

MSE = \frac{1}{N} \sum_{i = 1}^{N} {(S_{normalk} - T_{normalk})}^{2}

MAE = \frac{1}{N} \sum_{i = 1}^{N} (||, S_{k} - T_{k})

…”

Section: Methodsmentioning

confidence: 99%

Using PSO and GWO techniques for prediction some drying properties of tarragon (Artemisia dracunculus L.)

Karami

Kaveh

Mirzaee‐Ghaleh

et al. 2018

J Food Process Engineering

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In this article, the effects of air temperature and velocity on drying characteristics of tarragon (Artemisia dracunculus L.) were investigated. The experiments were done at four temperatures of 40, 50, 60, and 70 °C and three air velocities of 1, 1.5, and 2 m/s. According to the results, the drying term of tarragon reduced significantly with increasing drying air temperature. The values of effective moisture diffusivity (Deff) were ranged between 1.34 × 10−10 and 2.74 × 10−10 m2/s. Also, by increasing drying air temperature the values of specific energy consumption (SEC) were decreased. The values of SEC were between 20.50 and 66.90 MJ/kg. Also Deff and SEC values were modeled by particle swarm optimizer (PSO) and gray wolf optimizer (GWO) algorithms. Drying air velocity and air temperature were considered as input parameters for the models. Based on three statistical parameters includes R2, MSE, and MAE for predicting Deff and SEC, GWO performance was better than the PSO. Practical applications The purpose of drying crops is decreasing of moisture content for providing secure storage and significant influence on the quality of dried crops. In this research, the effective moisture diffusivity (Deff) and specific energy consumption (SEC) values were modeled by PSO and GWO algorithms. The procedure can be applied in postharvest processing of medicinal plants.

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“…Before modeling by PSO and GWO, dataset was normalized by following equation (Armaghani, Mohamad, Hajihassani, Yagiz, & Motaghedi, ; Hasanipanah, Armaghani, Khamesi, Amnieh, & Ghoraba, ):

R_{n} = \frac{R - R_{\min}}{R_{\max} - R_{\min}}

R^{2} = 1 - \frac{\sum_{i = 1}^{N} {(S_{normalk} - T_{normalk})}^{2}}{\sum_{i = 1}^{N} {(S_{normalk} - T_{normalm})}^{2}}

MSE = \frac{1}{N} \sum_{i = 1}^{N} {(S_{normalk} - T_{normalk})}^{2}

MAE = \frac{1}{N} \sum_{i = 1}^{N} (||, S_{k} - T_{k})

…”

Section: Methodsmentioning

confidence: 99%

Using PSO and GWO techniques for prediction some drying properties of tarragon (Artemisia dracunculus L.)

Karami

Kaveh

Mirzaee‐Ghaleh

et al. 2018

J Food Process Engineering

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“…The common environmental issues of blasting are flyrock, air overpressure, back-break and ground vibration [7][8][9][10][11]. Flyrock can cause the most important effects of damages among them according to several scholars [12].…”

Section: Introductionmentioning

confidence: 99%

Three hybrid intelligent models in estimating flyrock distance resulting from blasting

Koopialipoor

Fallah

Armaghani

et al. 2018

Engineering with Computers

149

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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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“…They are explosion-induced large shock waves that are refracted horizontally by density variations in the atmosphere. The atmospheric pressure waves of airblasts consist of a high audible frequency and subaudible low-frequency sound [50,127]. Airblasts can impact structures close to the blast zone by rattling windows and the roofing materials.…”

Section: Airblastmentioning

confidence: 99%

Advances in Blast-Induced Impact Prediction—A Review of Machine Learning Applications

2021

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Rock fragmentation in mining and construction industries is widely achieved using drilling and blasting technique. The technique remains the most effective and efficient means of breaking down rock mass into smaller pieces. However, apart from its intended purpose of rock breakage, throw, and heave, blasting operations generate adverse impacts, such as ground vibration, airblast, flyrock, fumes, and noise, that have significant operational and environmental implications on mining activities. Consequently, blast impact studies are conducted to determine an optimum blast design that can maximize the desirable impacts and minimize the undesirable ones. To achieve this objective, several blast impact estimation empirical models have been developed. However, despite being the industry benchmark, empirical model results are based on a limited number of factors affecting the outcomes of a blast. As a result, modern-day researchers are employing machine learning (ML) techniques for blast impact prediction. The ML approach can incorporate several factors affecting the outcomes of a blast, and therefore, it is preferred over empirical and other statistical methods. This paper reviews the various blast impacts and their prediction models with a focus on empirical and machine learning methods. The details of the prediction methods for various blast impacts—including their applications, advantages, and limitations—are discussed. The literature reveals that the machine learning methods are better predictors compared to the empirical models. However, we observed that presently these ML models are mainly applied in academic research.

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Several non-linear models in estimating air-overpressure resulting from mine blasting

Cited by 96 publications

References 50 publications

Using PSO and GWO techniques for prediction some drying properties of tarragon (Artemisia dracunculus L.)

Using PSO and GWO techniques for prediction some drying properties of tarragon (Artemisia dracunculus L.)

Three hybrid intelligent models in estimating flyrock distance resulting from blasting

Advances in Blast-Induced Impact Prediction—A Review of Machine Learning Applications

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