Smart Structures and Systems

  • 제29권2호
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  • Pages.361-374
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  • 2022
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  • 1738-1584(pISSN)
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  • 1738-1991(eISSN)
테크노프레스 (Techno-Press)
권호 표지
DOI QR코드

DOI QR Code

Capturing research trends in structural health monitoring using bibliometric analysis

  • Yeom, Jaesun (School of Business Administration, Ulsan National Institute of Science and Technology (UNIST)) ;
  • Jeong, Seunghoo (Advanced Railroad Civil Engineering Division, Korea Railroad Research Institute) ;
  • Woo, Han-Gyun (School of Business Administration, Ulsan National Institute of Science and Technology (UNIST)) ;
  • Sim, Sung-Han (School of Civil, Architectural Engineering, and Landscape Architecture, Sungkyunkwan University)
  • 투고 : 2021.06.19
  • 심사 : 2021.11.12
  • 발행 : 2022.02.25
⟨ 이전 논문 다음 논문 ⟩

초록

As civil infrastructure has continued to age worldwide, its structural integrity has been threatened owing to material deteriorations and continual loadings from the external environment. Structural Health Monitoring (SHM) has emerged as a cost-efficient method for ensuring structural safety and durability. As SHM research has gradually addressed an increasing number of structure-related problems, it has become difficult to understand the changing research topic trends. Although previous review papers have analyzed research trends on specific SHM topics, these studies have faced challenges in providing (1) consistent insights regarding macroscopic SHM research trends, (2) empirical evidence for research topic changes in overall SHM fields, and (3) methodological validations for the insights. To overcome these challenges, this study proposes a framework tailored to capturing the trends of research topics in SHM through a bibliometric and network analysis. The framework is applied to track SHM research topics over 15 years by identifying both quantitative and relational changes in the author keywords provided from representative SHM journals. The results of this study confirm that overall SHM research has become diversified and multi-disciplinary. Especially, the rapidly growing research topics are tightly related to applying machine learning and computer vision techniques to solve SHM-related issues. In addition, the research topic network indicates that damage detection and vibration control have been both steadily and actively studied in SHM research.

키워드

과제정보

This research was supported by the Basic Science Research Program through the National Research Foundation of Korea (NRF) funded by the Ministry of Education (NRF-2020R1A2C2014797) and an R&D Program (PK2203B1) of the Korea Railroad Research Institute, Korea.

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