Machine Learning-Based Structural Health Monitoring Technique for Crack Detection and Localisation Using Bluetooth Strain Gauge Sensor Network Article Swipe
Tahereh Shah Mansouri
,
Gennady Lubarsky
,
Dewar Finlay
,
James McLaughlin
·
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/jsan13060079
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.3390/jsan13060079
Within the domain of Structural Health Monitoring (SHM), conventional approaches generally are complicated, destructive, and time-consuming. It also necessitates an extensive array of sensors to effectively evaluate and monitor the structural integrity. In this research work, we present a novel, non-destructive SHM framework based on machine learning (ML) for the accurate detection and localisation of structural cracks. This approach leverages a minimal number of strain gauge sensors linked via Bluetooth Low Energy (BLE) communication. The framework is validated through empirical data collected from 3D carbon fibre-reinforced composites, including three distinct specimens, ranging from crack-free samples to specimens with up to ten cracks of varying lengths and depths. The methodology integrates an analytical examination of the Shewhart chart, Grubbs’ test (GT), and hierarchical clustering (HC) algorithm, tailored towards the metrics of fracture measurement and classification. Our novel ML framework allows one to replace exhausting laboratory procedures with a modern and quick mechanism for the material, with unprecedented properties that could provide potential applications in the composites industry.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3390/jsan13060079
- OA Status
- gold
- Cited By
- 3
- References
- 49
- Related Works
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- OpenAlex ID
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All OpenAlex metadata
Raw OpenAlex JSON
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https://openalex.org/W4404691528Canonical identifier for this work in OpenAlex
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https://doi.org/10.3390/jsan13060079Digital Object Identifier
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Machine Learning-Based Structural Health Monitoring Technique for Crack Detection and Localisation Using Bluetooth Strain Gauge Sensor NetworkWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2024Year of publication
- Publication date
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2024-11-23Full publication date if available
- Authors
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Tahereh Shah Mansouri, Gennady Lubarsky, Dewar Finlay, James McLaughlinList of authors in order
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https://doi.org/10.3390/jsan13060079Publisher landing page
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://doi.org/10.3390/jsan13060079Direct OA link when available
- Concepts
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Structural health monitoring, Computer science, Bluetooth, Strain gauge, Wireless sensor network, Ranging, Cluster analysis, Materials science, Wireless, Artificial intelligence, Composite material, Telecommunications, Computer networkTop concepts (fields/topics) attached by OpenAlex
- Cited by
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3Total citation count in OpenAlex
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2025: 3Per-year citation counts (last 5 years)
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49Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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