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Developing a hybrid adoptive neuro-fuzzy inference system in predicting safety of factors of slopes subjected to surface eco-protection techniques

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Abstract

This study predicts the investigation of surface eco-protection techniques for cohesive soil slopes along the selected Guthrie Corridor Expressway stretch by way of analyzing a new set of probabilistic models using a hybrid technique of artificial neural network and fuzzy inference system namely adaptive neuro-fuzzy inference system (ANFIS). Soil erosion and mass movement which induce landslides have become one of the disasters faced in Selangor, Malaysia causing enormous loss affecting human lives, destruction of property and the environment. Establishing and maintaining slope stability using mechanical structures are costly. Hence, biotechnical slope protection offers an alternative which is not only cost effective but also aesthetically pleasing. A parametric study was carried out to discover the relationship between various eco-protection techniques, i.e., application of grasses, shrubs and trees with different soil properties as well as slope angles. Then the data have been used to develop a new hybrid ANFIS technique for prediction of factor of safety (FOS) of slopes. Four inputs were considered in relation to the different vegetation types, i.e., slope angle (θ), unit weight (γ), effective cohesion (c′), effective friction angle (ø′). Then, many hybrid ANFIS models were constructed, trained and tested using various parametric studies. Eventually, a hybrid ANFIS model with a high performance prediction and a low system error was developed and introduced for solving problem of slope stability.

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Acknowledgements

The study presented herein was made possible by the University of Malaya Research Grant (UMRG), RP005E − 13SUS. The authors would also like to acknowledge the support from PROLINTAS Expressway Sdn. Bhd., Malaysia, for the permission to use their Guthrie Corridor Expressway (GCE) slopes as experimental sites.

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Authors and Affiliations

  1. Department of Civil Engineering, Faculty of Engineering, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Puteri Azura Sari & Meldi Suhatril

  2. Institute of Biological Sciences, Faculty of Science, University of Malaya, 50603, Kuala Lumpur, Malaysia

    Normaniza Osman

  3. Civil Engineering Department, University of Hafr Al-Batin, Al-Jamiah, Hafr Al-Batin, Eastern Province, 39524, Kingdom of Saudi Arabia

    M. A. Mu’azu

  4. Faculty of Civil Engineering, University of Tabriz, Tabriz, Iran

    Javad Katebi

  5. DLSIIS, Universidad Politecnica de Madrid, Madrid, Spain

    Ali Abavisani

  6. Centre for Biomedical Technology, Universidad Politécnica de Madrid, Madrid, Spain

    Ali Abavisani

  7. Department of Computer Engineering, Neyshabur Branch, Islamic Azad University, Neyshabur, Iran

    Naser Ghaffari

  8. Department of Civil Engineering, Qeshm International Branch, Islamic Azad University, Qeshm, Iran

    Esmaeil Sadeghi Chahnasir

  9. Research Center, Sulaimani Polytechnic University, Sulaimani, 46001, Kurdistan Region, Iraq

    Karzan Wakil

  10. Universidad UTE, Facultad de Arquitectura y Urbanismo, Calle Rumipamba s/n y Bourgeois, Quito, Ecuador

    Majid Khorami

  11. Faculty of Engineering, University of Kragujevac, Sestre Janic 6, Kragujevac, 34000, Serbia

    Dalibor Petkovic

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  1. Puteri Azura Sari
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  2. Meldi Suhatril
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  3. Normaniza Osman
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  4. M. A. Mu’azu
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  6. Ali Abavisani
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  8. Esmaeil Sadeghi Chahnasir
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  9. Karzan Wakil
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  11. Dalibor Petkovic
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Correspondence to Meldi Suhatril.

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Sari, P.A., Suhatril, M., Osman, N. et al. Developing a hybrid adoptive neuro-fuzzy inference system in predicting safety of factors of slopes subjected to surface eco-protection techniques. Engineering with Computers 36, 1347–1354 (2020). https://doi.org/10.1007/s00366-019-00768-3

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