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A.C. Pires, A. Antunes Dos Santos, G.F.M.D. Santos, "Knowledge Structure of Structural Health Monitoring Methods Applied to Railways: A Review Using CiteSpace From 2015-2023", in J. Pombo, (Editor), "Proceedings of the Sixth International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 7, Paper 7.3, 2024, doi:10.4203/ccc.7.7.3

Keywords: structural health monitoring, Web of Science, CiteSpace, railway, bibliometrics, review.

Abstract

Structural health monitoring has gained popularity in recent years with the technological advancement of sensor technology and data transmission via cloud computing. In the field of railway systems, structural health monitoring is becoming increasingly dynamic and interdisciplinary. This complexity makes it challenging for researchers to determine the current trends, identify research gaps, and understand key concepts. This paper presents a fast and systematic approach to conducting a bibliometric analysis of structural health monitoring methods applied to railways, aiming to give readers an overall understanding of the field. Utilizing data spanning from 2015 to 2023 from the Web of Science, this study identifies key publications, researchers, and institutions on the subject. Moreover, CiteSpace was used to provide intuitive visuals that reveal partnerships between institutions and emerging areas of research through clustering algorithms. The analysis indicates that structural health monitoring in railway applications will increasingly embrace interdisciplinarity, with an emphasis on data-driven methods such as deep learning and big data analytics. Although this application is specific, the step-by-step process aims to assist researchers in identifying promising areas and facilitating the literature review process.

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Front Page Browse CCC CCP CSETS CTR IJRT Other Authors Search Purchase Guide FAQ Contact us	Civil-Comp Conferences ISSN 2753-3239 CCC: 7 PROCEEDINGS OF THE SIXTH INTERNATIONAL CONFERENCE ON RAILWAY TECHNOLOGY: RESEARCH, DEVELOPMENT AND MAINTENANCE Edited by: J. Pombo Paper 7.3 Knowledge Structure of Structural Health Monitoring Methods Applied to Railways: A Review Using CiteSpace From 2015-2023 A.C. Pires¹, A. Antunes Dos Santos¹ and G.F.M.D. Santos² ¹School of Mechanical Engineering, State University of Campinas, Brazil ²Department of Mechanical Engineering, Federal University of Espirito Santo, Brazil doi:10.4203/ccc.7.7.3 Full Bibliographic Reference for this paper A.C. Pires, A. Antunes Dos Santos, G.F.M.D. Santos, "Knowledge Structure of Structural Health Monitoring Methods Applied to Railways: A Review Using CiteSpace From 2015-2023", in J. Pombo, (Editor), "Proceedings of the Sixth International Conference on Railway Technology: Research, Development and Maintenance", Civil-Comp Press, Edinburgh, UK, Online volume: CCC 7, Paper 7.3, 2024, doi:10.4203/ccc.7.7.3 Keywords: structural health monitoring, Web of Science, CiteSpace, railway, bibliometrics, review. Abstract Structural health monitoring has gained popularity in recent years with the technological advancement of sensor technology and data transmission via cloud computing. In the field of railway systems, structural health monitoring is becoming increasingly dynamic and interdisciplinary. This complexity makes it challenging for researchers to determine the current trends, identify research gaps, and understand key concepts. This paper presents a fast and systematic approach to conducting a bibliometric analysis of structural health monitoring methods applied to railways, aiming to give readers an overall understanding of the field. Utilizing data spanning from 2015 to 2023 from the Web of Science, this study identifies key publications, researchers, and institutions on the subject. Moreover, CiteSpace was used to provide intuitive visuals that reveal partnerships between institutions and emerging areas of research through clustering algorithms. The analysis indicates that structural health monitoring in railway applications will increasingly embrace interdisciplinarity, with an emphasis on data-driven methods such as deep learning and big data analytics. Although this application is specific, the step-by-step process aims to assist researchers in identifying promising areas and facilitating the literature review process. download the full-text of this paper (PDF, 14 pages, 918 Kb) go to the previous paper go to the next paper return to the table of contents return to the volume description
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