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/10.20944/preprints202409.1639.v1
YOU?
·
· 2024
· Open Access
·
· DOI: https://doi.org/10.20944/preprints202409.1639.v1
Conventional approaches in Structural Health Monitoring (SHM) tend to be complex, destructive, and time-intensive. Additionally, they often require a large number of sensors to thoroughly assess structural integrity. In this study, we present a novel, non-destructive SHM framework based on machine learning (ML) for the accurate detection and localization of structural cracks. This approach leverages a minimal number of strain gauge sensors linked via Bluetooth communication. The framework is validated through empirical data collected from 3D carbon fiber-reinforced composites, including three distinct specimens, ranging from crack-free samples to specimens with up to ten cracks of varying lengths and depths. Strain data from five sensors were analyzed using a combination of Shewhart charts, Grubbs Test (GT), and a hierarchical clustering algorithm, specifically designed to evaluate and classify fractures. Our ML-based framework offers a streamlined and efficient alternative to traditional laboratory procedures, delivering precise crack detection with significant potential for applications in the composites industry.
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Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.20944/preprints202409.1639.v1
- OA Status
- green
- Cited By
- 1
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4402718339
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4402718339Canonical identifier for this work in OpenAlex
- DOI
-
https://doi.org/10.20944/preprints202409.1639.v1Digital Object Identifier
- Title
-
Machine Learning-Based Structural Health Monitoring Technique for Crack Detection and Localisation Using Bluetooth Strain Gauge Sensor NetworkWork title
- Type
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preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2024Year of publication
- Publication date
-
2024-09-21Full publication date if available
- Authors
-
Tahereh Shah Mansouri, Gennady Lubarsky, Dewar Finlay, James McLaughlinList of authors in order
- Landing page
-
https://doi.org/10.20944/preprints202409.1639.v1Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://doi.org/10.20944/preprints202409.1639.v1Direct OA link when available
- Concepts
-
Strain gauge, Bluetooth, Strain (injury), Structural health monitoring, Gauge (firearms), Computer science, Wireless sensor network, Artificial intelligence, Real-time computing, Engineering, Materials science, Structural engineering, Computer network, Wireless, Biology, Telecommunications, Anatomy, MetallurgyTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2024: 1Per-year citation counts (last 5 years)
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.clustering | 118 |
| abstract_inverted_index.composites | 151 |
| abstract_inverted_index.crack-free | 85 |
| abstract_inverted_index.delivering | 140 |
| abstract_inverted_index.fractures. | 126 |
| abstract_inverted_index.integrity. | 27 |
| abstract_inverted_index.laboratory | 138 |
| abstract_inverted_index.specimens, | 82 |
| abstract_inverted_index.structural | 26, 50 |
| abstract_inverted_index.thoroughly | 24 |
| abstract_inverted_index.alternative | 135 |
| abstract_inverted_index.combination | 108 |
| abstract_inverted_index.composites, | 78 |
| abstract_inverted_index.procedures, | 139 |
| abstract_inverted_index.significant | 145 |
| abstract_inverted_index.streamlined | 132 |
| abstract_inverted_index.traditional | 137 |
| abstract_inverted_index.Conventional | 0 |
| abstract_inverted_index.applications | 148 |
| abstract_inverted_index.destructive, | 11 |
| abstract_inverted_index.hierarchical | 117 |
| abstract_inverted_index.localization | 48 |
| abstract_inverted_index.specifically | 120 |
| abstract_inverted_index.Additionally, | 14 |
| abstract_inverted_index.communication. | 65 |
| abstract_inverted_index.non-destructive | 35 |
| abstract_inverted_index.time-intensive. | 13 |
| abstract_inverted_index.fiber-reinforced | 77 |
| cited_by_percentile_year.max | 94 |
| cited_by_percentile_year.min | 90 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 4 |
| citation_normalized_percentile.value | 0.598187 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |