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An optimized system of GMDH-ANFIS predictive model by ICA for estimating pile bearing capacity

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Abstract

The pile bearing capacity is considered as the most essential factor in designing deep foundations. Direct determination of this parameter in site is costly and difficult. Hence, this study presents a new technique of intelligence system based on the adaptive neuro-fuzzy inference system (ANFIS)-group method of data handling (GMDH) optimized by the imperialism competitive algorithm (ICA), ANFIS-GMDH-ICA for forecasting pile bearing capacity. In this advanced structure, the ICA role is to optimize the membership functions obtained by ANFIS-GMDH technique for receiving a higher accuracy level and lower error. To develop this model, the results of 257 high strain dynamic load tests (performed by authors) were considered and used in the analysis. For comparison purposes, ANFIS and GMDH models were selected and built for pile bearing capacity estimation. In terms of model accuracy, the obtained results showed that the newly developed model (i.e., ANFIS-GMDH-ICA) receives more accurate predicted values of pile bearing capacity compared to those obtained by ANFIS and GMDH predictive models. The proposed ANFIS-GMDH-ICA can be utilized as an advanced, applicable and powerful technique in issues related to foundation engineering and its design.

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Acknowledgements

The authors would like to acknowledge the role of technicians in performing the PDA tests. Since, the presented paper is part of a comprehensive research project entitled “Investigation on the Application of Artificial Intelligence in Civil Engineering” in Lorestan University, the corresponding author also would like to appreciate the supports of Lorestan University in this regard.

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Authors and Affiliations

  1. Department of Urban Planning, Engineering Networks and Systems, Institute of Architecture and Construction, South Ural State University, 76, Lenin Prospect, Chelyabinsk, 454080, Russia

    Danial Jahed Armaghani

  2. Department of Civil Engineering, Faculty of Engineering, Shahid Bahonar University of Kerman, Pajoohesh Sq., Imam Khomeni Highway, P.O. Box 76169133, Kerman, Iran

    Hooman Harandizadeh

  3. Faculty of Engineering, Lorestan University, Khorramabad, 6815144316, Iran

    Ehsan Momeni

  4. Department of Civil Engineering, Sekolah Tinggi Teknologi Pekanbaru, Pekanbaru, Indonesia

    Harnedi Maizir

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

    Jian Zhou

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  1. Danial Jahed Armaghani
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Correspondence to Danial Jahed Armaghani or Ehsan Momeni.

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Armaghani, D.J., Harandizadeh, H., Momeni, E. et al. An optimized system of GMDH-ANFIS predictive model by ICA for estimating pile bearing capacity. Artif Intell Rev 55, 2313–2350 (2022). https://doi.org/10.1007/s10462-021-10065-5

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