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A systematic review and meta-analysis of artificial neural network application in geotechnical engineering: theory and applications

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Abstract

Artificial neural network (ANN) aimed to simulate the behavior of the nervous system as well as the human brain. Neural network models are mathematical computing systems inspired by the biological neural network in which try to constitute animal brains. ANNs recently extended, presented, and applied by many research scholars in the area of geotechnical engineering. After a comprehensive review of the published studies, there is a shortage of classification of study and research regarding systematic literature review about these approaches. A review of the literature reveals that artificial neural networks is well established in modeling retaining walls deflection, excavation, soil behavior, earth retaining structures, site characterization, pile bearing capacity (both skin friction and end-bearing) prediction, settlement of structures, liquefaction assessment, slope stability, landslide susceptibility mapping, and classification of soils. Therefore, the present study aimed to provide a systematic review of methodologies and applications with recent ANN developments in the subject of geotechnical engineering. Regarding this, a major database of the web of science has been selected. Furthermore, meta-analysis and systematic method which called PRISMA has been used. In this regard, the selected papers were classified according to the technique and method used, the year of publication, the authors, journals and conference names, research objectives, results and findings, and lastly solution and modeling. The outcome of the presented review will contribute to the knowledge of civil and/or geotechnical designers/practitioners in managing information in order to solve most types of geotechnical engineering problems. The methods discussed here help the geotechnical practitioner to be familiar with the limitations and strengths of ANN compared with alternative conventional mathematical modeling methods.

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Authors and Affiliations

  1. Department for Management of Science and Technology Development, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Hossein Moayedi

  2. Faculty of Civil Engineering, Ton Duc Thang University, Ho Chi Minh City, Vietnam

    Hossein Moayedi

  3. Department of Civil and Environmental Engineering, Shiraz University, Shiraz, Iran

    Mansour Mosallanezhad

  4. Department of Geotechnics and Transportation, School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia

    Ahmad Safuan A. Rashid

  5. Centre of Tropical Geoengineering (Geotropik), School of Civil Engineering, Faculty of Engineering, Universiti Teknologi Malaysia, Johor Bahru, Malaysia

    Ahmad Safuan A. Rashid

  6. Faculty of Civil Engineering and Environment, Universiti Tun Hussein Onn Malaysia, Parit Raja, 86400, Batu Pahat, Johor Darul Takzim, Malaysia

    Wan Amizah Wan Jusoh

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

    Mohammed Abdullahi Muazu

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Moayedi, H., Mosallanezhad, M., Rashid, A.S.A. et al. A systematic review and meta-analysis of artificial neural network application in geotechnical engineering: theory and applications. Neural Comput & Applic 32, 495–518 (2020). https://doi.org/10.1007/s00521-019-04109-9

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