Universiti Teknologi Malaysia Institutional Repository

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

Mohamad, E. T. and Li, D. and Murlidhar, B. R. and Armaghani, D. J. and Kassim, K. A. and Komoo, I. (2019) The effects of ABC, ICA, and PSO optimization techniques on prediction of ripping production. Engineering with Computers, 36 (4). pp. 1355-1370. ISSN 0177-0667

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Official URL: http://dx.doi.org/10.1007/s00366-019-00770-9


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.

Item Type:Article
Uncontrolled Keywords:neuro-bee, neuro-imperialism, neuro-swarm
Subjects:T Technology > TA Engineering (General). Civil engineering (General)
Divisions:Civil Engineering
ID Code:90708
Deposited By: Narimah Nawil
Deposited On:30 Apr 2021 07:28
Last Modified:30 Apr 2021 07:28

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