Scientometric analysis on seismic stability analysis of soil retaining structures using conventional methods and machine learning techniques
Abstract
Seismic analysis of structures is an indubitable prerequisite for the understanding and visualization of the necessary responses to earthquake episodes. The inferred curative knowledge on seismic performances can drastically minimize the consequential impact of disruptive tremors. There are no previous works globally to identify the research hotspots in geotechnical engineering focusing on seismic responses of soil retaining structures and the advancement of machine learning techniques. This analysis addresses the gap in existing literature by implementing a scientometric and content analysis of the identified research hotspot, employing text mining and literature network mapping. The machine learning techniques were perceived as pertinent to systematically assess the seismic analysis. The current analysis explores both the existing state and future trends, as well as co-occurrence of keywords in the field of study. The findings of the study revealed the accelerated growth in the artificial intelligent (AI) research in seismic analysis. The scientometric analysis provides discernment of the linkages of co-authors, countries, keywords, and research areas, beyond the limits of manual analysis. This AI-powered techniques have identified the textual pattern and trends of unstructured data in diverse research areas and the research hotspots to be explored in future.
Document Type
Conference Proceeding
Date of Publication
1-1-2026
Volume
723 LNCE
Publication Title
Lecture Notes in Civil Engineering
Publisher
Springer
School
School of Engineering
Copyright
subscription content
First Page
223
Last Page
236
Comments
Muthukumar, S., Sathyan, D., & Shukla, S. K. (2025). Scientometric analysis on seismic stability analysis of soil retaining structures using conventional methods and machine learning techniques. Lecture Notes in Civil Engineering, 723, 223–236. https://doi.org/10.1007/978-981-95-0241-7_18