Development of imperialist competitive algorithm in predicting the particle size distribution after mine blasting

被引:0
|
作者
Khosro Sayevand
Hossein Arab
Saeid Bagheri Golzar
机构
[1] Malayer University,Faculty of Mathematical Sciences
[2] Islamic Azad University,Young Researchers and Elite Club, Qom Branch
来源
关键词
Blasting; Rock fragmentation; Imperialist competitive algorithm; ANN;
D O I
暂无
中图分类号
学科分类号
摘要
Proper rock fragmentation is one of the most important aims in surface mines as well as tunneling projects. The main purpose of the current study is to forecast rock fragmentation through imperialist competitive algorithm (ICA). Shur river dam region, in Iran, was considered and 80 sets of data, including D80, as a standard for evaluating the fragmentation, maximum charge per delay, spacing, burden, powder factor, stemming and rock mass rating were prepared. For comparison aims, artificial neural network was also developed and the predicted values by ICA model was then compared to ANN results. In the other words, two forms of ICA models, i.e., ICA-linear and ICA-power models as well as ANN were employed for predicting the D80. To compare the performance capacity of the ICA and ANN models, several statistical evaluation criteria, such as variance account for (VAF), R-square (R2), root mean square error (RMSE) were computed. Finally, it was demonstrated that the ICA-power model with the R2 of 0.947, VAF of 93.96% and RMSE of 1.23 was more suitable and acceptable model for predicting the D80 than the ICA-linear model with the R2 of 0.943, VAF of 93.49% and RMSE of 1.28 and the ANN model with the R2 of 0.897, VAF of 88.78% and RMSE of 1.68 and had the capacity to generalize.
引用
收藏
页码:329 / 338
页数:9
相关论文
共 50 条
  • [1] Development of imperialist competitive algorithm in predicting the particle size distribution after mine blasting
    Sayevand, Khosro
    Arab, Hossein
    Golzar, Saeid Bagheri
    [J]. ENGINEERING WITH COMPUTERS, 2018, 34 (02) : 329 - 338
  • [2] Predicting the ground vibration induced by mine blasting using imperialist competitive algorithm
    Behzadafshar, Katayoun
    Mohebbi, Fahimeh
    Soltani Tehrani, Meharn
    Hasanipanah, Mahdi
    Tabrizi, Omid
    [J]. ENGINEERING COMPUTATIONS, 2018, 35 (04) : 1774 - 1787
  • [3] Assessing the suitability of imperialist competitive algorithm for the predicting aims: an engineering case
    Mao Wu
    Qingxiang Cai
    Tao Shang
    [J]. Engineering with Computers, 2019, 35 : 627 - 636
  • [4] Assessing the suitability of imperialist competitive algorithm for the predicting aims: an engineering case
    Wu, Mao
    Cai, Qingxiang
    Shang, Tao
    [J]. ENGINEERING WITH COMPUTERS, 2019, 35 (02) : 627 - 636
  • [5] A New Method for Clustering Based on Development of Imperialist Competitive Algorithm
    Zadeh, Mohammad Reza Dehghani
    Fathian, Mohammad
    Gholamian, Mohammad Reza
    [J]. CHINA COMMUNICATIONS, 2014, 11 (12) : 54 - 61
  • [6] Development of a Compound Optimization Approach Based on Imperialist Competitive Algorithm
    Wang, Qimei
    Yang, Zhihong
    Wang, Yong
    [J]. PROCEEDINGS OF THE 2015 INTERNATIONAL CONFERENCE ON APPLIED MECHANICS, MECHATRONICS AND INTELLIGENT SYSTEMS (AMMIS2015), 2016, : 611 - 617
  • [7] Investigating the Applicability of the Imperialist Competitive Algorithm in the Problem of Allocating Trucks to an Open Pit Mine
    Dabbagh, Ali
    Bagherpour, Raheb
    [J]. RUDARSKO-GEOLOSKO-NAFTNI ZBORNIK, 2019, 34 (02): : 34 - 41
  • [8] Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm
    Hajihassani, Mohsen
    Armaghani, Danial Jahed
    Marto, Aminaton
    Mohamad, Edy Tonnizam
    [J]. BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT, 2015, 74 (03) : 873 - 886
  • [9] Hybrid of imperialist competitive algorithm and particle swarm optimization for parameter extraction of photovoltaic cells
    Wang, Hongli
    Song, Shanfei
    Li, Peng
    Zhang, Wenjun
    Lei, Dongge
    Wu, Fei
    [J]. AIP Advances, 2024, 14 (10)
  • [10] Application of cuckoo search algorithm to estimate peak particle velocity in mine blasting
    Nazanin Fouladgar
    Mahdi Hasanipanah
    Hassan Bakhshandeh Amnieh
    [J]. Engineering with Computers, 2017, 33 : 181 - 189