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Rock brittleness prediction through two optimization algorithms namely particle swarm optimization and imperialism competitive algorithm

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Abstract

Brittleness index (BI) is a significant rock parameter when dealing with projects performed in rocks. The main goal of this research work is to propose the novel practical models to predict the BI through particle swarm optimization (PSO) and imperialism competitive algorithm (ICA). For this aim, two forms of equations, i.e., linear and power are considered and the weights of these equations are optimized by PSO and ICA. In the other words, four predictive models, namely ICA linear, ICA power, PSO linear, and PSO power models are developed to predict BI in this study. In the modeling of the predictive models, 79 datasets are used, so that Schmidt hammer rebound number, wave velocity, density, and Point Load Index (Is50) are selected as the independent (input) parameters and the BI values are considered as the dependent (output) parameter. Then, the performances of the proposed predicting models are checked using two error indices, namely coefficient correlation (R2) and root mean squared error (RMSE). The results showed that the PSO power model has superior fitting specification for the prediction of the BI compared to the other prediction models and is quite practical for use. As a result, linear and power models of PSO received higher performance prediction compared to ICA. PSO power (with R2 train = 0.937, R2 test = 0.959, RMSE train = 0.377 and RMES test = 0.289) showed the most powerful technique to predict BI of the granite samples.

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Acknowledgements

The authors would like to express their sincere appreciation to Dr. Armaghani for his cooperation in developing the idea of this research.

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Authors and Affiliations

  1. Human-Centered Computing Research Lab, School of Computing, Universiti Utara Malaysia, 06010, Kedah, Malaysia

    Azham Hussain

  2. School of Electronics, Vignan Foundation for Science, Technology and Research, Guntur, India

    A. Surendar

  3. PG and Research Department of Computer Science, Don Bosco College, Yelagiri Hills, India

    A. Clementking

  4. Department of Computer Science, Annai Vailankanni Arts and Science College (Affiliated to Bharathidhasan University), Thanjavur, Tamilnadu, 613 007, India

    Sujith Kanagarajan

  5. Tyumen Industrial University, Tyumen’, Russia

    Lubov K. Ilyashenko

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  1. Azham Hussain
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Hussain, A., Surendar, A., Clementking, A. et al. Rock brittleness prediction through two optimization algorithms namely particle swarm optimization and imperialism competitive algorithm. Engineering with Computers 35, 1027–1035 (2019). https://doi.org/10.1007/s00366-018-0648-9

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