A scientometric analysis of drone-based structural health monitoring and new technologies

IF 4.6 Q2 MATERIALS SCIENCE, BIOMATERIALS
T. Fayyad, Su Taylor, Kun Feng, Felix Kin Peng Hui
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Abstract

Critical global challenges, such as climate change and the insufficient availability of resources, mean that it is a pivotal time to make cities more intelligent, efficient, and sustainable in a drive towards a net-zero carbon future. This requires intelligent, interactive, and responsive structural health monitoring (SHM) to assure the longevity and safety of ageing infrastructure. Drones have the potential to revolutionise SHM. Drone-based SHM (as a potential fly-by technique) involves equipping drones with various sensors, or using inbuilt sensors, to capture data and images of structures from different angles and perspectives. The data is then processed and analysed to facilitate accurate assessment of the structure’s health and early diagnosis of damage. Although the use of fly-by is relatively new, the speedy advances in various technologies that could be integrated with it, such as computer vision with artificial intelligence, deep learning, and links to digital twins, put these systems on the verge of a potential breakthrough. This paper provides an overview of fly-by SHM technique using both scientometric and qualitative systematic literature review processes, in order to provide a distinct understanding of the state of the art of research. As an original contribution, our research identified four main clusters of research within the field of fly-by SHM: (1) the application of UAV-enabled vision-based monitoring; (2) the integration of drones, advanced sensor technologies, and artificial intelligence; (3) drone-based SHM integrating modal analysis, energy harvesting, and deep learning; and (4) automation and robotics in drone-based SHM. The paper highlights the integration of new technologies such as artificial intelligence, machine learning, and sensors with the fly-by technique for SHM, identifies the gaps in current fly-by SHM research, and suggests new directions for research.
基于无人机的结构健康监测和新技术的科学计量分析
气候变化和资源供应不足等严峻的全球性挑战意味着,现在是使城市更加智能、高效和可持续发展的关键时刻,以推动实现净零碳未来。这就需要智能、互动和反应灵敏的结构健康监测(SHM),以确保老化基础设施的使用寿命和安全性。无人机具有彻底改变 SHM 的潜力。基于无人机的 SHM(作为一种潜在的飞越技术)包括在无人机上安装各种传感器,或使用内置传感器,从不同角度和视角捕捉结构的数据和图像。然后对数据进行处理和分析,以便准确评估结构的健康状况和早期诊断损坏情况。尽管飞越式测量的使用相对较新,但可与之集成的各种技术(如计算机视觉与人工智能、深度学习以及与数字双胞胎的链接)的快速发展,使这些系统濒临潜在突破的边缘。本文采用科学计量学和定性系统文献综述的方法,对飞越式 SHM 技术进行了概述,以提供对研究现状的独特理解。作为一项原创性贡献,我们的研究确定了飞越式 SHM 领域的四个主要研究集群:(1) 基于视觉的无人机监控应用;(2) 无人机、先进传感器技术和人工智能的集成;(3) 集成了模态分析、能量采集和深度学习的无人机 SHM;以及 (4) 无人机 SHM 中的自动化和机器人技术。论文重点介绍了人工智能、机器学习和传感器等新技术与飞越式 SHM 技术的整合,指出了当前飞越式 SHM 研究中的不足,并提出了新的研究方向。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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来源期刊
ACS Applied Bio Materials
ACS Applied Bio Materials Chemistry-Chemistry (all)
CiteScore
9.40
自引率
2.10%
发文量
464
期刊介绍: ACS Applied Bio Materials is an interdisciplinary journal publishing original research covering all aspects of biomaterials and biointerfaces including and beyond the traditional biosensing, biomedical and therapeutic applications. The journal is devoted to reports of new and original experimental and theoretical research of an applied nature that integrates knowledge in the areas of materials, engineering, physics, bioscience, and chemistry into important bio applications. The journal is specifically interested in work that addresses the relationship between structure and function and assesses the stability and degradation of materials under relevant environmental and biological conditions.
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