AI-Based Self-Learning System in Distributed Structural Health Monitoring and Control Article Swipe
Kai Yan
,
Xin Lin
,
Wenfeng Ma
,
Yuxiao Zhang
·
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1007/s11063-021-10571-1
YOU?
·
· 2021
· Open Access
·
· DOI: https://doi.org/10.1007/s11063-021-10571-1
Artificial intelligence is predicted to play a big part in self-learning, industrial automation that will negotiate the bandwidth of structural health and control systems. The industrial structural health and control system based on discrete sensors possesses insufficient spatial coverage of sensing information, while the distributed condition monitoring has been mainly studied at the sensor level, relatively few studies have been conducted at the artificial intelligence level. This paper presents an innovative method for distributed structural health and control systems based on artificial intelligence. The structural condition was divided into regional and local features, the feature extraction and characterization are performed separately. Structural abnormality recognition and risk factor calculation method were proposed by considering the response values and the distribution patterns of both the regional and the local structural behaviours. The test results show that the method can effectively identify the full-scale and local damage of the structure, respectively. Subsequently, structural safety assessment method for long-span structures at kilometres level in view of fully length strain distributions measured by distributed fiber optic sensors were developed. A series of load tests on the long-span structure were carried out. Finite element (FE) model was developed using finite element code, ABAQUS, and an extensive parametric study was conduct to explore the effect of load cases on the structural responses. The differences in the structural response results among load test, structural safety assessment and FE simulation were investigated. It is shown that AI-based self-learning system could offer suitable speed in deployment, reliability in solution and flexibility to adjust in distributed structural health monitoring and control.
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Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1007/s11063-021-10571-1
- https://link.springer.com/content/pdf/10.1007/s11063-021-10571-1.pdf
- OA Status
- hybrid
- Cited By
- 1
- References
- 30
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W3194430432
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Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W3194430432Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1007/s11063-021-10571-1Digital Object Identifier
- Title
-
AI-Based Self-Learning System in Distributed Structural Health Monitoring and ControlWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2021Year of publication
- Publication date
-
2021-08-25Full publication date if available
- Authors
-
Kai Yan, Xin Lin, Wenfeng Ma, Yuxiao ZhangList of authors in order
- Landing page
-
https://doi.org/10.1007/s11063-021-10571-1Publisher landing page
- PDF URL
-
https://link.springer.com/content/pdf/10.1007/s11063-021-10571-1.pdfDirect link to full text PDF
- Open access
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YesWhether a free full text is available
- OA status
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hybridOpen access status per OpenAlex
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https://link.springer.com/content/pdf/10.1007/s11063-021-10571-1.pdfDirect OA link when available
- Concepts
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Structural health monitoring, Computer science, Parametric statistics, Finite element method, Structural system, Artificial intelligence, Reliability engineering, Structural engineering, Engineering, Mathematics, StatisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
1Total citation count in OpenAlex
- Citations by year (recent)
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2025: 1Per-year citation counts (last 5 years)
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30Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.code, | 196 |
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| abstract_inverted_index.fully | 163 |
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| abstract_inverted_index.studies | 58 |
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| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.control. | 260 |
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| abstract_inverted_index.discrete | 34 |
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| abstract_inverted_index.extensive | 200 |
| abstract_inverted_index.features, | 93 |
| abstract_inverted_index.long-span | 155, 182 |
| abstract_inverted_index.negotiate | 16 |
| abstract_inverted_index.performed | 100 |
| abstract_inverted_index.possesses | 36 |
| abstract_inverted_index.predicted | 4 |
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| abstract_inverted_index.artificial | 64, 82 |
| abstract_inverted_index.assessment | 152, 228 |
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| abstract_inverted_index.developed. | 174 |
| abstract_inverted_index.extraction | 96 |
| abstract_inverted_index.full-scale | 141 |
| abstract_inverted_index.industrial | 12, 26 |
| abstract_inverted_index.innovative | 71 |
| abstract_inverted_index.kilometres | 158 |
| abstract_inverted_index.monitoring | 47, 258 |
| abstract_inverted_index.parametric | 201 |
| abstract_inverted_index.relatively | 56 |
| abstract_inverted_index.responses. | 215 |
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| abstract_inverted_index.structures | 156 |
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| abstract_inverted_index.behaviours. | 129 |
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| abstract_inverted_index.reliability | 247 |
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| abstract_inverted_index.investigated. | 233 |
| abstract_inverted_index.respectively. | 148 |
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| cited_by_percentile_year.min | 91 |
| corresponding_author_ids | https://openalex.org/A5101735650 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 4 |
| corresponding_institution_ids | https://openalex.org/I44445938 |
| citation_normalized_percentile.value | 0.12837632 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |