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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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.
Perspectives
Research Direction: The study identifies a growing interest in using artificial intelligence (AI) techniques for predicting soil liquefaction. This trend suggests that future research in this area is likely to focus on further improving the accuracy and efficiency of AI models for soil liquefaction prediction. Practical Applications: The study highlights the potential practical applications of AI techniques in predicting soil liquefaction. By leveraging AI models, engineers and decision-makers can better assess the risks associated with soil liquefaction and implement more effective mitigation measures. Interdisciplinary Collaboration: The study underscores the interdisciplinary nature of research on AI applications for soil liquefaction prediction, highlighting the importance of collaboration between geotechnical engineering, computer science, and machine learning fields. This collaboration can lead to the development of more robust and accurate prediction models. Decision-Making Support: The use of the MULTIMOORA method for evaluating the most influential journals in the field provides decision-makers with valuable insights into the current state of research on AI applications for soil liquefaction prediction. This information can help decision-makers stay informed about the latest developments in the field and make informed decisions regarding soil liquefaction mitigation strategies. Future Opportunities: The study opens up opportunities for future research in AI applications for soil liquefaction prediction. Researchers can build on the findings of this study to further improve the accuracy and efficiency of AI models for soil liquefaction prediction and explore new ways to leverage AI techniques for geotechnical engineering applications.
Dr. Caner Erden
Sakarya University of Applied Sciences
Read the Original
This page is a summary of: Bibliometric analysis of artificial intelligence techniques for predicting soil liquefaction: insights and MCDM evaluation, Natural Hazards, May 2024, Springer Science + Business Media,
DOI: 10.1007/s11069-024-06630-0.
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