Structural health monitoring (SHM) is crucial in ensuring the safety and integrity of civil infrastructure. The development and implementation of advanced sensing technologies, machine learning algorithms, and data analytics have revolutionized the field of SHM, making it more predictive, efficient, and effective than ever before.

We are pleased to announce a special issue on New Trends in Structural Health Monitoring aimed at promoting research, collaboration, and awareness in this critical field. We welcome original research articles, review papers, and case studies that address the following topics:

  • Advanced sensing technologies for SHM
  • Data analytics and machine learning algorithms for SHM
  • SHM of buildings, bridges, dams, and other infrastructures
  • Reliability and risk analysis of SHM systems
  • SHM for extreme events such as earthquakes and floods
  • Wireless sensor networks for SHM

The submission deadline is 30 August 2024 and papers may be submitted immediately or at any point until 30 August 2024. Papers will be published in the first available issue after the acceptance as a dedicated “Special Section”. The Journal is free for both readers and authors, without any APC.

Please Note:

  • Papers must be prepared following the Authors Guidelines and Manuscript Template.
  • During the submission, the corresponding author is responsible for filling out the requested information, including all the co-authors’ affiliations, and email addresses.
  • Papers must be submitted using the journal website and select the correct special issue (New Trends in Structural Health Monitoring).

Published Papers:

  • Introduction and application of a drive-by damage detection methodology for bridges using variational mode decomposition. https://doi.org/10.3221/IGF-ESIS.70.02

    Authors: Shahrooz Khalkhali Shandiz, Hamed Khezrzadeh, Saeed Eftekhar Azam

  • Enhancing Generalizability of a Machine Learning Model for Infrared Thermographic Defect Detection by Using 3D Numerical Modeling. https://doi.org/10.3221/IGF-ESIS.70.10

     Authors: Vladimir Vavilov, Arsenii Chulkov, Alexey Moskovchenko