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

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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.

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Authors and Affiliations

  1. Centre of Tropical Geoengineering (GEOTROPIK), School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia

    Edy Tonnizam Mohamad, Bhatawdekar Ramesh Murlidhar & Khairul Anuar Kassim

  2. School of Resources and Safety Engineering, Central South University, Changsha, 410083, China

    Diyuan Li

  3. Institute of Research and Development, Duy Tan University, Da Nang, 550000, Vietnam

    Danial Jahed Armaghani

  4. Institute for Environment and Development (LESTARI), Universiti Kebangsaan Malaysia, 43600, Bangi, Selangor, Malaysia

    Ibrahim Komoo

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  1. Edy Tonnizam Mohamad
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Mohamad, E.T., Li, D., Murlidhar, B.R. et al. The effects of ABC, ICA, and PSO optimization techniques on prediction of ripping production. Engineering with Computers 36, 1355–1370 (2020). https://doi.org/10.1007/s00366-019-00770-9

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