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Design and implementation of a new tuned hybrid intelligent model to predict the uniaxial compressive strength of the rock using SFS-ANFIS

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Abstract

This study proposes a novel design to systematically optimize the parameters for the adaptive neuro-fuzzy inference system (ANFIS) model using stochastic fractal search (SFS) algorithm. To affirm the efficiency of the proposed SFS-ANFIS model, the predicting results were compared with ANFIS and three hybrid methodologies based on ANFIS combined with genetic algorithm (GA), differential evolution (DE), and particle swarm optimization (PSO). Accurate prediction of uniaxial compressive strength (UCS) is of great significance for all geotechnical projects such as tunnels and dams. Hence, this study proposes the use of SFS-ANFIS, GA-ANFIS, DE-ANFIS, PSO-ANFIS, and ANFIS models to predict UCS. In this regard, the fresh water tunnel of Pahang–Selangor located in Malaysia was considered and the requirement data samples were collected. Different statistical metrics such as coefficient of determination (R2) and mean absolute error were used to evaluate the models. Referring to the efficiency results of SFS-ANFIS, it can be found that the SFS-ANFIS (with the R2 of 0.981) has higher ability than PSO-ANFIS, DE-ANFIS, GA-ANFIS, and ANFIS models in predicting the UCS.

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Acknowledgements

This research was supported by the National Natural Science Foundation of China (No. 41672305).

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Authors and Affiliations

  1. School of Architecture and Civil Engineering, Xi’an University of Science and Technology, Xi’an, 710054, China

    Hongjun Jing

  2. College of Computer Science, Tabari University of Babol, Babol, Iran

    Hima Nikafshan Rad

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

    Mahdi Hasanipanah

  4. Modeling Evolutionary Algorithms Simulation and Artificial Intelligence, Faculty of Electrical & Electronics Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Danial Jahed Armaghani

  5. Computer Science Department, College of Computer and Information Sciences, Al Imam Mohammad Ibn Saud Islamic University (IMSIU), Riyadh, Saudi Arabia

    Sultan Noman Qasem

  6. Computer Science Department, Faculty of Applied Science, Taiz University, Taiz, Yemen

    Sultan Noman Qasem

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  1. Hongjun Jing
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Correspondence to Mahdi Hasanipanah.

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Jing, H., Nikafshan Rad, H., Hasanipanah, M. et al. Design and implementation of a new tuned hybrid intelligent model to predict the uniaxial compressive strength of the rock using SFS-ANFIS. Engineering with Computers 37, 2717–2734 (2021). https://doi.org/10.1007/s00366-020-00977-1

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  • DOI: https://doi.org/10.1007/s00366-020-00977-1

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