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Applying two optimization techniques in evaluating tensile strength of granitic samples

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Abstract

The Brazilian tensile strength (BTS) test is the most common method to evaluate the tensile strength in mining and civil engineering projects. This paper aims to employ the genetic algorithm (GA) and particle swarm optimization (PSO) for predicting the BTS. For this work, linear and power equations were considered and their weights were optimized By GA and PSO. To achieve the objective of this research, a database including 80 sets of data were prepared so that dry density (DD), Schmidt hammer (Rn), and point load (IS50) parameters were used as the independent parameters. After modeling, the accuracy of PSO linear, PSO power, GA linear and GA power models were assessed using coefficient correlation (R2) and root mean square error (RMSE). According to the obtained results, it was found that both GA and PSO optimization algorithms proposed in this research predicted the BTS values satisfactorily; however, PSO power model with the R2 of 0.963 demonstrated a better generalization capability and it can be used for similar problems in the future. Also, the values of R2 for the PSO linear, GA Linear and GA power models were 0.958, 0.948 and 0.962, respectively.

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Acknowledgements

The work is executed with the support of the Grant of Ministry of education of Russia no. 5.3606.2017/PCH. Also, the authors would like to express their sincere appreciation to Dr. Hasanipanah for his cooperation in developing the idea of this research.

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Authors and Affiliations

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

    A. Surendar

  2. Belgorod State National Research University, 85 Pobedy St., 308015, Belgorod, Russia

    Oleg R. Kuzichkin

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

    Sujith Kanagarajan

  4. Department of Construction Management, Islamic Azad University of Ahar, Ahar, Iran

    Mir Heydar Hashemi

  5. Department of Civil Engineering, Faculty of Engineering, Azarbaijan Shahid Madani University, Tabriz, Iran

    Mir Heydar Hashemi

  6. Facultad de Arquitectura y Urbanismo, Universidad Tecnológica Equinoccial, Calle Rumipamba s/n y Bourgeois, Quito, Ecuador

    Majid Khorami

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  1. A. Surendar
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  2. Oleg R. Kuzichkin
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  3. Sujith Kanagarajan
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  4. Mir Heydar Hashemi
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Correspondence to Oleg R. Kuzichkin.

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Surendar, A., Kuzichkin, O.R., Kanagarajan, S. et al. Applying two optimization techniques in evaluating tensile strength of granitic samples. Engineering with Computers 35, 985–992 (2019). https://doi.org/10.1007/s00366-018-0645-z

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