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Bibliometric analysis of artificial intelligence techniques for predicting soil liquefaction: insights and MCDM evaluation

Author

Listed:
  • Abdullah Hulusi Kökçam

    (Sakarya University)

  • Caner Erden

    (Sakarya University of Applied Sciences
    Sakarya University of Applied Sciences)

  • Alparslan Serhat Demir

    (Sakarya University)

  • Talas Fikret Kurnaz

    (Technical Sciences Vocational School, Mersin University)

Abstract

The geotechnical phenomenon of soil liquefaction has serious implications for infrastructure and human safety, making it crucial to develop effective prediction and mitigation strategies as urbanization and infrastructure development expand. Recently, there has been significant interest in the potential of artificial intelligence (AI) techniques to address complex geotechnical issues, such as soil liquefaction. This study provides a bibliometric analysis of research literature on AI applications in predicting soil liquefaction. By systematically searching the Web of Science database, we identified 258 relevant articles published between 1994 and 2023 and applied bibliometric indicators to analyze publication trends, authorship patterns, affiliated institutions, publication venues, and citation patterns. This study presents a novel approach to evaluating the results obtained from bibliometric analysis. The MULTIMOORA method, a Multi-Criteria Decision Making (MCDM) technique, was employed to analyze further the journals that contributed to creating an academic knowledge inventory regarding AI techniques in soil liquefaction. This study demonstrates the utility of MCDM techniques as aggregators of bibliometric analysis results and their ability to facilitate decision-making. The interdisciplinary nature of this field, combining geotechnical engineering, computer science, and machine learning, is highlighted. The study also reveals a steady rise in publications on AI in liquefaction, with a notable increase in 2011 and 2019. The Soil Dynamics and Earthquake Engineering journal is shown to be particularly significant in studies on soil liquefaction prediction with AI techniques, followed by the Bulletin of Engineering Geology and the Environment and Environmental Earth Sciences journals.

Suggested Citation

  • Abdullah Hulusi Kökçam & Caner Erden & Alparslan Serhat Demir & Talas Fikret Kurnaz, 2024. "Bibliometric analysis of artificial intelligence techniques for predicting soil liquefaction: insights and MCDM evaluation," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 120(12), pages 11153-11181, September.
    • Handle: RePEc:spr:nathaz:v:120:y:2024:i:12:d:10.1007_s11069-024-06630-0
    DOI: 10.1007/s11069-024-06630-0
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    References listed on IDEAS

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    1. Aria, Massimo & Cuccurullo, Corrado, 2017. "bibliometrix: An R-tool for comprehensive science mapping analysis," Journal of Informetrics, Elsevier, vol. 11(4), pages 959-975.
    2. Hsin-Ning Su & Pei-Chun Lee, 2010. "Mapping knowledge structure by keyword co-occurrence: a first look at journal papers in Technology Foresight," Scientometrics, Springer;Akadémiai Kiadó, vol. 85(1), pages 65-79, October.
    3. Yong-gang Zhang & Junbo Qiu & Yan Zhang & Yongyao Wei, 2021. "The adoption of ELM to the prediction of soil liquefaction based on CPT," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 107(1), pages 539-549, May.
    4. Xinhua Xue & Xingguo Yang, 2013. "Application of the adaptive neuro-fuzzy inference system for prediction of soil liquefaction," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 67(2), pages 901-917, June.
    5. Sufyan Ghani & Sunita Kumari, 2022. "Liquefaction behavior of Indo-Gangetic region using novel metaheuristic optimization algorithms coupled with artificial neural network," Natural Hazards: Journal of the International Society for the Prevention and Mitigation of Natural Hazards, Springer;International Society for the Prevention and Mitigation of Natural Hazards, vol. 111(3), pages 2995-3029, April.
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