Temperature Compensation in Vibration-Based Structural Health Monitoring Using Neural Network Regression Article Swipe
Soroosh Kamali
,
Alessandro Marzani
,
Luca Sciullo
,
Marco Di Felice
,
Giuseppe Augugliaro
,
Canio Mennuti
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/icsrs59833.2023.10381287
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.1109/icsrs59833.2023.10381287
Vibration-based structural health monitoring (SHM) systems continuously estimate modal parameters to detect structural anomalies. The modal data corresponding to a healthy state are stored in a database during a training period, forming a baseline for comparison. However, variations in modal frequencies due to environmental and operational factors can lead to larger false positive rates and decrease the sensitivity of system to small damages, reducing the probability of damage detection. To mitigate these challenges, temperature compensation techniques are commonly employed to reduce variations in recorded modal data. In this paper, we propose a temperature compensation technique using neural network regression models. Unlike commonly used multivariate linear regression (MVLR), neural networks can capture the nonlinear relationship between temperature and modal frequencies effectively. The results of the numerical simulation in the present work demonstrate the superiority of the neural network-based compensation over the MVLR approach.
Related Topics
Concepts
Modal
Compensation (psychology)
Artificial neural network
Structural health monitoring
Vibration
Computer science
Regression
Sensitivity (control systems)
Nonlinear system
Multivariate statistics
Data mining
Artificial intelligence
Machine learning
Engineering
Statistics
Structural engineering
Mathematics
Acoustics
Electronic engineering
Materials science
Polymer chemistry
Psychology
Quantum mechanics
Physics
Psychoanalysis
Metadata
- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1109/icsrs59833.2023.10381287
- OA Status
- green
- Cited By
- 2
- References
- 14
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4390678314
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4390678314Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1109/icsrs59833.2023.10381287Digital Object Identifier
- Title
-
Temperature Compensation in Vibration-Based Structural Health Monitoring Using Neural Network RegressionWork title
- Type
-
articleOpenAlex work type
- Language
-
enPrimary language
- Publication year
-
2023Year of publication
- Publication date
-
2023-11-22Full publication date if available
- Authors
-
Soroosh Kamali, Alessandro Marzani, Luca Sciullo, Marco Di Felice, Giuseppe Augugliaro, Canio MennutiList of authors in order
- Landing page
-
https://doi.org/10.1109/icsrs59833.2023.10381287Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
-
https://hdl.handle.net/11585/953402Direct OA link when available
- Concepts
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Modal, Compensation (psychology), Artificial neural network, Structural health monitoring, Vibration, Computer science, Regression, Sensitivity (control systems), Nonlinear system, Multivariate statistics, Data mining, Artificial intelligence, Machine learning, Engineering, Statistics, Structural engineering, Mathematics, Acoustics, Electronic engineering, Materials science, Polymer chemistry, Psychology, Quantum mechanics, Physics, PsychoanalysisTop concepts (fields/topics) attached by OpenAlex
- Cited by
-
2Total citation count in OpenAlex
- Citations by year (recent)
-
2025: 1, 2024: 1Per-year citation counts (last 5 years)
- References (count)
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14Number of works referenced by this work
- Related works (count)
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10Other works algorithmically related by OpenAlex
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