Bibliometric AI for predicting soil liquefaction

What is it about?

This study focuses on the use of artificial intelligence (AI) techniques for predicting soil liquefaction, a geotechnical phenomenon with significant implications for infrastructure and human safety. The researchers conducted a bibliometric analysis of 258 relevant articles published between 1994 and 2023 to understand the trends in AI applications for soil liquefaction prediction. They analyzed publication trends, authorship patterns, affiliated institutions, publication venues, and citation patterns. Additionally, the study employed the MULTIMOORA method, a Multi-Criteria Decision Making (MCDM) technique, to further evaluate the journals that contributed to the academic knowledge inventory regarding AI techniques in soil liquefaction. This approach helped in understanding the most influential journals in this area. The research highlights the interdisciplinary nature of this field, which combines geotechnical engineering, computer science, and machine learning. It also reveals a steady rise in publications on AI in liquefaction, with notable increases in 2011 and 2019. The Soil Dynamics and Earthquake Engineering journal was identified as 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.

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Photo by Jens Aber on Unsplash

Photo by Jens Aber on Unsplash

Why is it important?

This research is important for several reasons: Infrastructure and Human Safety: Soil liquefaction can lead to significant damage to infrastructure, such as buildings, bridges, and roads, and pose risks to human safety. Predicting and mitigating soil liquefaction is crucial for minimizing these risks. Advanced Techniques: The study focuses on the use of advanced artificial intelligence techniques for predicting soil liquefaction, highlighting the potential of these methods to improve prediction accuracy and effectiveness compared to traditional methods. Decision-Making Support: The use of the MULTIMOORA method for evaluating the most influential journals in the field provides valuable insights for researchers, engineers, and decision-makers involved in soil liquefaction prediction and mitigation. Interdisciplinary Collaboration: The research highlights the interdisciplinary nature of soil liquefaction prediction, emphasizing the collaboration between geotechnical engineering, computer science, and machine learning fields. This interdisciplinary approach can lead to more comprehensive and effective solutions. Future Research Directions: The study identifies trends in research on AI applications for soil liquefaction prediction, providing guidance for future research directions and highlighting areas where further investigation is needed.

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