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Cited by 46Structural Health Monitoring TechniquesInfrastructure Maintenance and MonitoringBIM and Construction Integration

The DOI matches its Crossref record; 2 of the 2 references it cites were found in a registry or an index.

Visualization of structural health monitoring information using Internet-of-Things integrated with building information modeling

Open access
Sakr, Micheal, Sadhu, Ayan·2023·Journal of Infrastructure Intelligence and Resilience
DOI 10.1016/j.iintel.2023.100053
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Abstract

Structural Health Monitoring (SHM) has become a paramount necessity in civil engineering for improving the operational performance of aging infrastructure. Recent monitoring techniques have utilized emerging technologies in Industry 4.0, such as the Internet of Things, Big Data analytics, cloud computing, and cybersecurity, to automate SHM methodologies. However, they have found challenges in linking these technologies and developing an autonomous, well-established digital framework for applications of SHM. Visualizing processed SHM data in a real-time digital interface generates multiple obstacles, such as witnessing delays in data transfer and resorting to offline tools for manual data processing. This paper, therefore, explores the integration of Building Information Modeling (BIM) and the Internet of Things (IoT) through an Arduino micro-processing unit for tracking and visualizing the data from the time and frequency domains. Strategies for enabling data monitoring and processing are developed while continuously acquiring structural responses. The query of data is established in a web-based database instead of storing the data in offline resources that await manual intervention. The proposed real-time SHM methodology is validated experimentally using two practical applications: a dynamically moving vehicle over a simply-supported bridge prototype and a randomly excited three-story model with real-time visualization of both time- and frequency-domain information under undamaged and damaged conditions. The proposed research develops an early-phase Digital Twin (DT) to present static and real-time dynamic data in a rich-fed BIM database.

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Citations

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Research works printed but not linked: 41

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Metadata

Authors
Sakr, M., & Sadhu, A.
Affiliations
Western University (CA)
Corresponding author
Sadhu, Ayan
ORCID
Venue
Journal of Infrastructure Intelligence and Resilience
Publisher
Elsevier BV
Published
2023-09
Volume
2
Issue
3
Pages
100053
ISSN
2772-9915 (ISSN-L)
DOI
10.1016/j.iintel.2023.100053
OpenAlex
W4385830732
Cited by
46
References
2
Citations by year
2026: 6 · 2025: 15 · 2024: 23 · 2023: 2
FWCI
5.39
Type
journal article
Language
en
Open access
Licence
Locations
Journal of Infrastructure Intelligence and Resilience (publishedVersion, cc-by-nc-nd)
Funders
Natural Sciences and Engineering Research Council of Canada
Topics
  • Physical Sciences › Engineering › Civil and Structural Engineering › Structural Health Monitoring Techniques
  • Physical Sciences › Engineering › Civil and Structural Engineering › Infrastructure Maintenance and Monitoring
  • Physical Sciences › Engineering › Building and Construction › BIM and Construction Integration
Keywords
structural health monitoring, building information modeling, Internet of Things, digital twin, real-time data visualization, Arduino, online databases, simply supported bridges, vehicle-induced vibration, frequency domain analysis, time-domain analysis, structural damage detection, aging infrastructure, Industry 4.0

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