The effects of ABC, ICA, and PSO optimization techniques on prediction of ripping production View Full Text


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Article Info

DATE

2019-05-10

AUTHORS

Edy Tonnizam Mohamad, Diyuan Li, Bhatawdekar Ramesh Murlidhar, Danial Jahed Armaghani, Khairul Anuar Kassim, Ibrahim Komoo

ABSTRACT

As blasting has some environmental constraints, ripping has got more prevalent as a method of breaking ground in both civil and mining engineering applications. A rippability model of a higher applicability is, therefore, needed to appropriately predict the ripping production (Q) prior to carrying out such tests. For the purpose of predicting the results of ripping production that were attained in three sites located in Johor state, Malaysia, the present study applied three hybrid intelligent techniques, i.e., neuro-bee, neuro-imperialism, and neuro-swarm, to predict Q. In fact, the effects of artificial bee colony (ABC), imperialism competitive algorithm (ICA), and particle swarm optimization (PSO) on weights and biases of neural networks were examined in the current research to receive better prediction/evaluation of ripping production. To do this, totally, 74 ripping tests were taken into consideration in the investigated regions and their influential parameters were assessed. Many parametric studies on ABC, ICA, and PSO parameters were conducted, and then, a comparison was done on the obtained results of the predictive hybrid models using several performance indices. As confirmed by the comparative results, the neuro-bee model proposed in this study estimated Q with a higher accuracy than the other hybrid models. The root-mean-square error (RMSE) values of 0.060, 0.076, and 0.094 were obtained for testing the data sets of neuro-bee, neuro-imperialism, and neuro-swarm techniques, respectively. This clearly shows that the newly developed hybrid model was superior to its rival in terms of predicting the ripping production. Furthermore, results of sensitivity analysis showed that weathering zone is the most influential factor on ripping production as compared to other inputs. More... »

PAGES

1355-1370

References to SciGraph publications

  • 2016-06-17. Application of PSO to develop a powerful equation for prediction of flyrock due to blasting in NEURAL COMPUTING AND APPLICATIONS
  • 2019-01-30. Effects of a proper feature selection on prediction and optimization of drilling rate using intelligent techniques in ENGINEERING WITH COMPUTERS
  • 2018-06-22. A combination of artificial bee colony and neural network for approximating the safety factor of retaining walls in ENGINEERING WITH COMPUTERS
  • 2015-10-04. Prediction of blast-induced air overpressure: a hybrid AI-based predictive model in ENVIRONMENTAL MONITORING AND ASSESSMENT
  • 2016-12-21. Rock strength estimation: a PSO-based BP approach in NEURAL COMPUTING AND APPLICATIONS
  • 2017-05-10. Ripping Production Prediction in Different Weathering Zones According to Field Data in GEOTECHNICAL AND GEOLOGICAL ENGINEERING
  • 2013-11-27. Blasting-induced flyrock and ground vibration prediction through an expert artificial neural network based on particle swarm optimization in ARABIAN JOURNAL OF GEOSCIENCES
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  • 2014-09-04. Ground vibration prediction in quarry blasting through an artificial neural network optimized by imperialist competitive algorithm in BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
  • 2016-07-11. Prediction of the durability of limestone aggregates using computational techniques in NEURAL COMPUTING AND APPLICATIONS
  • 2019-01-09. The use of new intelligent techniques in designing retaining walls in ENGINEERING WITH COMPUTERS
  • 2016-09-14. Airblast prediction through a hybrid genetic algorithm-ANN model in NEURAL COMPUTING AND APPLICATIONS
  • 2016-04-09. RETRACTED ARTICLE: Potential of soft computing approach for evaluating the factors affecting the capacity of steel–concrete composite beam in JOURNAL OF INTELLIGENT MANUFACTURING
  • 2019-02-27. Developing a new intelligent technique to predict overbreak in tunnels using an artificial bee colony-based ANN in ENVIRONMENTAL EARTH SCIENCES
  • 2016-04-11. Prediction of ground vibration due to quarry blasting based on gene expression programming: a new model for peak particle velocity prediction in INTERNATIONAL JOURNAL OF ENVIRONMENTAL SCIENCE AND TECHNOLOGY
  • 2009-08-14. Excavatability assessment of rock masses using the Geological Strength Index (GSI) in BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
  • 2015-03-17. Blast-induced air and ground vibration prediction: a particle swarm optimization-based artificial neural network approach in ENVIRONMENTAL EARTH SCIENCES
  • 2017-09-04. Applications of Particle Swarm Optimization in Geotechnical Engineering: A Comprehensive Review in GEOTECHNICAL AND GEOLOGICAL ENGINEERING
  • 2019-03-08. A Monte Carlo simulation approach for effective assessment of flyrock based on intelligent system of neural network in ENGINEERING WITH COMPUTERS
  • 2016-03-28. Feasibility of PSO-ANN model for predicting surface settlement caused by tunneling in ENGINEERING WITH COMPUTERS
  • 2018-05-28. Applying various hybrid intelligent systems to evaluate and predict slope stability under static and dynamic conditions in SOFT COMPUTING
  • 2017-06-05. An expert system based on hybrid ICA-ANN technique to estimate macerals contents of Indian coals in ENVIRONMENTAL EARTH SCIENCES
  • 2016-11-29. Intelligent modelling of sandstone deformation behaviour using fuzzy logic and neural network systems in BULLETIN OF ENGINEERING GEOLOGY AND THE ENVIRONMENT
  • 2015-12-14. Prediction of the strength and elasticity modulus of granite through an expert artificial neural network in ARABIAN JOURNAL OF GEOSCIENCES
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    30 schema:description As blasting has some environmental constraints, ripping has got more prevalent as a method of breaking ground in both civil and mining engineering applications. A rippability model of a higher applicability is, therefore, needed to appropriately predict the ripping production (Q) prior to carrying out such tests. For the purpose of predicting the results of ripping production that were attained in three sites located in Johor state, Malaysia, the present study applied three hybrid intelligent techniques, i.e., neuro-bee, neuro-imperialism, and neuro-swarm, to predict Q. In fact, the effects of artificial bee colony (ABC), imperialism competitive algorithm (ICA), and particle swarm optimization (PSO) on weights and biases of neural networks were examined in the current research to receive better prediction/evaluation of ripping production. To do this, totally, 74 ripping tests were taken into consideration in the investigated regions and their influential parameters were assessed. Many parametric studies on ABC, ICA, and PSO parameters were conducted, and then, a comparison was done on the obtained results of the predictive hybrid models using several performance indices. As confirmed by the comparative results, the neuro-bee model proposed in this study estimated Q with a higher accuracy than the other hybrid models. The root-mean-square error (RMSE) values of 0.060, 0.076, and 0.094 were obtained for testing the data sets of neuro-bee, neuro-imperialism, and neuro-swarm techniques, respectively. This clearly shows that the newly developed hybrid model was superior to its rival in terms of predicting the ripping production. Furthermore, results of sensitivity analysis showed that weathering zone is the most influential factor on ripping production as compared to other inputs.
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