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Advances in Structural Health Monitoring in Civil Engineering: 2nd Edition

A special issue of Applied Sciences (ISSN 2076-3417). This special issue belongs to the section "Civil Engineering".

Deadline for manuscript submissions: 20 September 2026 | Viewed by 870

Editor


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Guest Editor
Department of Civil Engineering, College of Science and Technology, Nihon University, Tokyo 101-8308, Japan
Interests: non-destructive techniques; tomography; system identification
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The degradation of infrastructure is one of the most severe problems in the field of civil engineering. Structural health monitoring plays an important role in maintaining the safety and serviceability of degrading infrastructures. Techniques related to structural health monitoring have been actively studied in recent years. Although many methods have been proposed for structural health monitoring in these studies, there remains a wide range of possibilities.

This Special Issue aims to collect papers related to advanced studies in the field of structural health monitoring. The methodology is not limited only to the techniques that are extended from conventional ones. Papers proposing new and original ideas are very welcome.

Prof. Dr. Yoshikazu Kobayashi
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 250 words) can be sent to the Editorial Office for assessment.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-anonymized peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Applied Sciences is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2400 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • non-destructive testing
  • structural health monitoring
  • system identification
  • tomography
  • elastic wave
  • X-ray
  • unmanned system
  • artificial intelligence

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Published Papers (1 paper)

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Review

21 pages, 1585 KB  
Review
Digital Twin Technology for Structural Lifecycle Management and Health Monitoring
by Alaa Elsisi, John Cabage and Elsayed Salem
Appl. Sci. 2026, 16(13), 6524; https://doi.org/10.3390/app16136524 - 30 Jun 2026
Cited by 1 | Viewed by 536
Abstract
Digital twin (DT) technology is reshaping structural engineering by linking physical assets to dynamic and data-driven virtual counterparts. DTs enable monitoring, predictive analytics, and autonomous decisions across design, construction, operation, and maintenance. Additionally, DTs are updated with real-time streams continuously. This study focuses [...] Read more.
Digital twin (DT) technology is reshaping structural engineering by linking physical assets to dynamic and data-driven virtual counterparts. DTs enable monitoring, predictive analytics, and autonomous decisions across design, construction, operation, and maintenance. Additionally, DTs are updated with real-time streams continuously. This study focuses on the applications of DTs and the intersection between the Internet of Things (IoT), Building Information Modeling (BIM), and artificial intelligence (AI). Applications include structural health monitoring (SHM) and predictive maintenance for bridges and buildings, in addition to construction safety optimization and stewardship of architectural heritage. The paper also examines barriers to adoption, including data interoperability, cybersecurity, upfront cost, and workforce readiness, and discusses standardization needs. In addition, it highlights educational impacts and pathways for small and medium enterprises (SMEs) to adopt scalable DT solutions. By consolidating recent advances, the review shows how DTs can deliver more resilient, efficient, sustainable, and intelligent infrastructure and outlines the research priorities to overcome remaining gaps and fully realize their potential. Full article
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