Response Surface Model Optimization Algorithm for Structural Assessment Based on Vibration Frequency Article Swipe
Xiaoguang Wang
,
Miao Ming
,
Bin Zhao
,
Wenhao Zhang
,
Shuai Song
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1155/vib/5446251
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1155/vib/5446251
Finite element model modification based on response surface method is an effective engineering method that can significantly improve the accuracy of finite element model and structural evaluation. In order to solve the shortcomings of the response surface method based finite element model correction, which exists that the polynomial order selection rule is unknown and the correction result is easily affected by the quality of the optimization target, based on the relationship between the polynomial order and the response surface accuracy and computation amount, a robust estimation algorithm was introduced to optimize the response surface solution process, which improves the reliability of the response surface based model correction. Taking an actual project of a cable‐stayed bridge as an example, the response surface‐based finite element model modification method was adopted to compare the response surface accuracy and computation amount of three kinds of response surface models, namely, second‐mode polynomials, third‐mode polynomials and fourth‐mode polynomials. The optimization results of the robust estimation method were compared with the traditional solution method under the conditions of large fluctuation and small fluctuation of optimization target between finite element index. The results show that with the increase of the polynomial order of the response surface model, the unknown covariates to be solved in the response surface model increase sharply, which increases the computational cost, but the effect of improving the accuracy of the response surface model is limited. When the target frequency fluctuates greatly, compared with the least‐squares method, robust estimation will not transfer the error of the correction result of the order with the large fluctuation of the index difference to the other orders, which ensures the stability of the correction results.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1155/vib/5446251
- OA Status
- gold
- Cited By
- 4
- References
- 34
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408532319
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https://openalex.org/W4408532319Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1155/vib/5446251Digital Object Identifier
- Title
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Response Surface Model Optimization Algorithm for Structural Assessment Based on Vibration FrequencyWork title
- Type
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articleOpenAlex work type
- Language
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enPrimary language
- Publication year
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2025Year of publication
- Publication date
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2025-01-01Full publication date if available
- Authors
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Xiaoguang Wang, Miao Ming, Bin Zhao, Wenhao Zhang, Shuai SongList of authors in order
- Landing page
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https://doi.org/10.1155/vib/5446251Publisher landing page
- Open access
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YesWhether a free full text is available
- OA status
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goldOpen access status per OpenAlex
- OA URL
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https://doi.org/10.1155/vib/5446251Direct OA link when available
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Vibration, Surface (topology), Algorithm, Computer science, Structural engineering, Engineering, Acoustics, Mathematics, Physics, GeometryTop concepts (fields/topics) attached by OpenAlex
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4Total citation count in OpenAlex
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2025: 4Per-year citation counts (last 5 years)
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34Number of works referenced by this work
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.method, | 241 |
| abstract_inverted_index.models, | 143 |
| abstract_inverted_index.namely, | 144 |
| abstract_inverted_index.orders, | 267 |
| abstract_inverted_index.project | 110 |
| abstract_inverted_index.quality | 62 |
| abstract_inverted_index.results | 154, 184 |
| abstract_inverted_index.surface | 7, 36, 78, 93, 103, 132, 142, 197, 208, 227 |
| abstract_inverted_index.target, | 66 |
| abstract_inverted_index.unknown | 52, 200 |
| abstract_inverted_index.accuracy | 19, 79, 133, 223 |
| abstract_inverted_index.affected | 59 |
| abstract_inverted_index.compared | 161, 237 |
| abstract_inverted_index.example, | 117 |
| abstract_inverted_index.greatly, | 236 |
| abstract_inverted_index.improves | 97 |
| abstract_inverted_index.increase | 189, 210 |
| abstract_inverted_index.limited. | 230 |
| abstract_inverted_index.optimize | 90 |
| abstract_inverted_index.process, | 95 |
| abstract_inverted_index.response | 6, 35, 77, 92, 102, 119, 131, 141, 196, 207, 226 |
| abstract_inverted_index.results. | 275 |
| abstract_inverted_index.sharply, | 211 |
| abstract_inverted_index.solution | 94, 165 |
| abstract_inverted_index.transfer | 246 |
| abstract_inverted_index.algorithm | 86 |
| abstract_inverted_index.effective | 11 |
| abstract_inverted_index.frequency | 234 |
| abstract_inverted_index.improving | 221 |
| abstract_inverted_index.increases | 213 |
| abstract_inverted_index.selection | 49 |
| abstract_inverted_index.stability | 271 |
| abstract_inverted_index.conditions | 169 |
| abstract_inverted_index.correction | 55, 251, 274 |
| abstract_inverted_index.covariates | 201 |
| abstract_inverted_index.difference | 263 |
| abstract_inverted_index.estimation | 85, 158, 243 |
| abstract_inverted_index.fluctuates | 235 |
| abstract_inverted_index.introduced | 88 |
| abstract_inverted_index.polynomial | 47, 73, 192 |
| abstract_inverted_index.structural | 25 |
| abstract_inverted_index.computation | 81, 135 |
| abstract_inverted_index.correction, | 42 |
| abstract_inverted_index.correction. | 106 |
| abstract_inverted_index.engineering | 12 |
| abstract_inverted_index.evaluation. | 26 |
| abstract_inverted_index.fluctuation | 172, 175, 259 |
| abstract_inverted_index.polynomials | 148 |
| abstract_inverted_index.reliability | 99 |
| abstract_inverted_index.traditional | 164 |
| abstract_inverted_index.modification | 3, 124 |
| abstract_inverted_index.optimization | 65, 153, 177 |
| abstract_inverted_index.polynomials, | 146 |
| abstract_inverted_index.polynomials. | 151 |
| abstract_inverted_index.relationship | 70 |
| abstract_inverted_index.shortcomings | 32 |
| abstract_inverted_index.third‐mode | 147 |
| abstract_inverted_index.computational | 215 |
| abstract_inverted_index.fourth‐mode | 150 |
| abstract_inverted_index.second‐mode | 145 |
| abstract_inverted_index.significantly | 16 |
| abstract_inverted_index.cable‐stayed | 113 |
| abstract_inverted_index.least‐squares | 240 |
| abstract_inverted_index.surface‐based | 120 |
| cited_by_percentile_year.max | 98 |
| cited_by_percentile_year.min | 97 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 5 |
| citation_normalized_percentile.value | 0.95078674 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |