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Prediction of rock interlocking by developing two hybrid models based on GA and fuzzy system

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Abstract

Rock shear strength parameters (interlocking and internal friction angel) are considered as significant factors in the designing stage of various geotechnical structures such as tunnels and foundations. Direct determination of these parameters in laboratory is time-consuming and expensive. Additionally, preparation of good quality of core samples is sometimes difficult. The objective of this paper is introducing and evaluating two hybrid artificial neural network (ANN)-based models by considering genetic algorithm (GA) and fuzzy inference system for prediction of interlocking of shale rock samples. Therefore, hybrid GA-ANN and adoptive neuro-fuzzy inference system (ANFIS) were developed and to show the capability of the hybrid models, the predicted results were compared to those of a pre-developed ANN model. In development of these models, the results of rock index tests, i.e., point load index, dry density, p-wave velocity, Brazilian tensile strength and Schmidt hammer were taken into account as the input parameters, whereas the interlocking of the shale samples was set as the output. The results obtained in this study confirmed the high reliability of the developed hybrid models, however, ANFIS predictive model receives slightly higher performance prediction compared to GA-ANN technique. The obtained results of the developed models were (0.865, 0.852), (0.933, 0.929) and (0.957, 0.965) for ANN, GA-ANN and ANFIS models, respectively, based on coefficient of determination (R2). ANFIS can be introduced as an innovative model to the field of rock mechanics.

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Authors and Affiliations

  1. Geotropik-Centre of Tropical Geoengineering, Faculty of Civil Engineering, Universiti Teknologi Malaysia, 81310, Skudai, Johor, Malaysia

    Bhatawdekar Ramesh Murlidhar & Edy Tonnizam Mohamad

  2. Department of Management Sciences, COMSATS University Islamabad, Vehari Campus, Islamabad, Pakistan

    Munir Ahmed

  3. College of Computing and Informatics, Saudi Electronic University Riyadh, Riyadh, Saudi Arabia

    Dinesh Mavaluru

  4. UCP Business School, University of Central Punjab, Lahore, Pakistan

    Ahmed Faisal Siddiqi

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  1. Bhatawdekar Ramesh Murlidhar
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  5. Edy Tonnizam Mohamad
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Murlidhar, B.R., Ahmed, M., Mavaluru, D. et al. Prediction of rock interlocking by developing two hybrid models based on GA and fuzzy system. Engineering with Computers 35, 1419–1430 (2019). https://doi.org/10.1007/s00366-018-0672-9

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