A novel sparse learning method with feature selection for optimizing sensors and predicting structural vibration responses Article Swipe
Minzhao Zhang
,
Junliang Ding
,
Bin Li
·
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-2830735/v1
YOU?
·
· 2023
· Open Access
·
· DOI: https://doi.org/10.21203/rs.3.rs-2830735/v1
Monitoring the vibration responses of structures accurately and efficiently is the key point of structural health monitoring (SHM). The monitoring of structural vibration responses depends on the sensor system. Therefore, discovering critical sensor positions is a challenging but beneficial task for SHM. Unfortunately, the predominant approaches only focus on predicting the vibration responses and lack the capability to identify meaningful sensor positions, which inevitably restricts their predictive capabilities. Furthermore, the strong linear correlation between sensors results in instability when selecting sensor locations, which leads to the difficulty of sensor optimization. To bridge this gap, we propose a novel sparse learning method with feature selection (SLMFS) to identify meaningful and interpretable sensor positions. In this method, to facilitate interpretation and stability, we use innovative sparsity-inducing penalties to select the important sensors at both individual and group levels. In addition, we introduce independently regularization term (IR) for stable and consistent feature selection. We also present an efficient iterative optimization algorithm to address the SLMFS, which is guaranteed to converge to the global optimum. Experiments of finite element model are used to validate the effectiveness and accuracy of SLMFS. The results show that the SLMFS could not only predict vibration responses effectively but also identify more accurate and relevant sensor positions that satisfy industrial requirements. Therefore, our learning method could provide a novel and useful methodology for SHM.
Related Topics
Concepts
Structural health monitoring
Computer science
Feature (linguistics)
Bridge (graph theory)
Regularization (linguistics)
Feature selection
Artificial intelligence
Machine learning
Vibration
Stability (learning theory)
Selection (genetic algorithm)
Pattern recognition (psychology)
Data mining
Engineering
Philosophy
Quantum mechanics
Internal medicine
Physics
Medicine
Structural engineering
Linguistics
Metadata
- Type
- preprint
- Language
- en
- Landing Page
- https://doi.org/10.21203/rs.3.rs-2830735/v1
- OA Status
- green
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4366827865
All OpenAlex metadata
Raw OpenAlex JSON
- OpenAlex ID
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https://openalex.org/W4366827865Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.21203/rs.3.rs-2830735/v1Digital Object Identifier
- Title
-
A novel sparse learning method with feature selection for optimizing sensors and predicting structural vibration responsesWork title
- Type
-
preprintOpenAlex work type
- Language
-
enPrimary language
- Publication year
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2023Year of publication
- Publication date
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2023-04-24Full publication date if available
- Authors
-
Minzhao Zhang, Junliang Ding, Bin LiList of authors in order
- Landing page
-
https://doi.org/10.21203/rs.3.rs-2830735/v1Publisher landing page
- Open access
-
YesWhether a free full text is available
- OA status
-
greenOpen access status per OpenAlex
- OA URL
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https://doi.org/10.21203/rs.3.rs-2830735/v1Direct OA link when available
- Concepts
-
Structural health monitoring, Computer science, Feature (linguistics), Bridge (graph theory), Regularization (linguistics), Feature selection, Artificial intelligence, Machine learning, Vibration, Stability (learning theory), Selection (genetic algorithm), Pattern recognition (psychology), Data mining, Engineering, Philosophy, Quantum mechanics, Internal medicine, Physics, Medicine, Structural engineering, LinguisticsTop concepts (fields/topics) attached by OpenAlex
- Cited by
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0Total citation count in OpenAlex
- Related works (count)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.global | 170 |
| abstract_inverted_index.health | 16 |
| abstract_inverted_index.linear | 72 |
| abstract_inverted_index.method | 101, 216 |
| abstract_inverted_index.select | 127 |
| abstract_inverted_index.sensor | 28, 33, 61, 81, 89, 111, 207 |
| abstract_inverted_index.sparse | 99 |
| abstract_inverted_index.stable | 146 |
| abstract_inverted_index.strong | 71 |
| abstract_inverted_index.useful | 222 |
| abstract_inverted_index.(SLMFS) | 105 |
| abstract_inverted_index.address | 160 |
| abstract_inverted_index.between | 74 |
| abstract_inverted_index.depends | 25 |
| abstract_inverted_index.element | 175 |
| abstract_inverted_index.feature | 103, 149 |
| abstract_inverted_index.levels. | 136 |
| abstract_inverted_index.method, | 115 |
| abstract_inverted_index.predict | 196 |
| abstract_inverted_index.present | 153 |
| abstract_inverted_index.propose | 96 |
| abstract_inverted_index.provide | 218 |
| abstract_inverted_index.results | 76, 188 |
| abstract_inverted_index.satisfy | 210 |
| abstract_inverted_index.sensors | 75, 130 |
| abstract_inverted_index.system. | 29 |
| abstract_inverted_index.Abstract | 0 |
| abstract_inverted_index.accuracy | 184 |
| abstract_inverted_index.accurate | 204 |
| abstract_inverted_index.converge | 167 |
| abstract_inverted_index.critical | 32 |
| abstract_inverted_index.identify | 59, 107, 202 |
| abstract_inverted_index.learning | 100, 215 |
| abstract_inverted_index.optimum. | 171 |
| abstract_inverted_index.relevant | 206 |
| abstract_inverted_index.validate | 180 |
| abstract_inverted_index.addition, | 138 |
| abstract_inverted_index.algorithm | 158 |
| abstract_inverted_index.efficient | 155 |
| abstract_inverted_index.important | 129 |
| abstract_inverted_index.introduce | 140 |
| abstract_inverted_index.iterative | 156 |
| abstract_inverted_index.penalties | 125 |
| abstract_inverted_index.positions | 34, 208 |
| abstract_inverted_index.responses | 4, 24, 53, 198 |
| abstract_inverted_index.restricts | 65 |
| abstract_inverted_index.selecting | 80 |
| abstract_inverted_index.selection | 104 |
| abstract_inverted_index.vibration | 3, 23, 52, 197 |
| abstract_inverted_index.Monitoring | 1 |
| abstract_inverted_index.Therefore, | 30, 213 |
| abstract_inverted_index.accurately | 7 |
| abstract_inverted_index.approaches | 46 |
| abstract_inverted_index.beneficial | 39 |
| abstract_inverted_index.capability | 57 |
| abstract_inverted_index.consistent | 148 |
| abstract_inverted_index.difficulty | 87 |
| abstract_inverted_index.facilitate | 117 |
| abstract_inverted_index.guaranteed | 165 |
| abstract_inverted_index.individual | 133 |
| abstract_inverted_index.industrial | 211 |
| abstract_inverted_index.inevitably | 64 |
| abstract_inverted_index.innovative | 123 |
| abstract_inverted_index.locations, | 82 |
| abstract_inverted_index.meaningful | 60, 108 |
| abstract_inverted_index.monitoring | 17, 20 |
| abstract_inverted_index.positions, | 62 |
| abstract_inverted_index.positions. | 112 |
| abstract_inverted_index.predicting | 50 |
| abstract_inverted_index.predictive | 67 |
| abstract_inverted_index.selection. | 150 |
| abstract_inverted_index.stability, | 120 |
| abstract_inverted_index.structural | 15, 22 |
| abstract_inverted_index.structures | 6 |
| abstract_inverted_index.Experiments | 172 |
| abstract_inverted_index.challenging | 37 |
| abstract_inverted_index.correlation | 73 |
| abstract_inverted_index.discovering | 31 |
| abstract_inverted_index.effectively | 199 |
| abstract_inverted_index.efficiently | 9 |
| abstract_inverted_index.instability | 78 |
| abstract_inverted_index.methodology | 223 |
| abstract_inverted_index.predominant | 45 |
| abstract_inverted_index.Furthermore, | 69 |
| abstract_inverted_index.optimization | 157 |
| abstract_inverted_index.capabilities. | 68 |
| abstract_inverted_index.effectiveness | 182 |
| abstract_inverted_index.independently | 141 |
| abstract_inverted_index.interpretable | 110 |
| abstract_inverted_index.optimization. | 90 |
| abstract_inverted_index.requirements. | 212 |
| abstract_inverted_index.Unfortunately, | 43 |
| abstract_inverted_index.interpretation | 118 |
| abstract_inverted_index.regularization | 142 |
| abstract_inverted_index.sparsity-inducing | 124 |
| cited_by_percentile_year | |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.04944003 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | False |