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

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 predictionwith AI techniques, followed by the Bulletin of Engineering Geology and the Environment and Environmental Earth Sciences journals.