Uncertainty‐aware fuzzy knowledge embedding method for generalized structural performance prediction Article Swipe
Xiangyu Wang
,
X. Ma
,
Shi‐Zhi Chen
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1111/mice.13457
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.1111/mice.13457
Structural performance prediction for structures and their components is a critical issue for ensuring the safety of civil engineering structures. Thus, enhancing the reliability of the prediction models with better generalization capability and quantifying the uncertainties of their predictions is significant. However, existing mechanism‐driven and data‐driven prediction models for reliable engineering applications incorporate complicated modifications on models and are sensitive to the precision of relevant prior knowledge. Focusing on these issues, a novel and concise data‐driven approach, named “R2CU” (stands for transforming regression to classification with uncertainty‐aware), is proposed to introduce the relative fuzzy prior knowledge into the data‐driven prediction models. To enhance generalization capacity, the conventional regression task is transformed into a classification task based on the fuzzy prior knowledge and the experimental data. Then the aleatoric and epistemic uncertainty of the prediction is estimated to provide the confidence interval, which reflects the prediction's trustworthiness. A validation case study based on shear capacity prediction of reinforced concrete (RC) deep beams is carried out. The result proved that the model's generalization capability for extrapolating has been effectively enhanced with the proposed approach (the prediction precision was raised 80%). Meanwhile, the uncertainties within the model's prediction are rationally estimated, which made the proposed approach a practical alternative for structural performance prediction.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.1111/mice.13457
- https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/mice.13457
- OA Status
- bronze
- Cited By
- 7
- References
- 70
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408461379
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https://openalex.org/W4408461379Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.1111/mice.13457Digital Object Identifier
- Title
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Uncertainty‐aware fuzzy knowledge embedding method for generalized structural performance predictionWork 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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2025-03-14Full publication date if available
- Authors
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Xiangyu Wang, X. Ma, Shi‐Zhi ChenList of authors in order
- Landing page
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https://doi.org/10.1111/mice.13457Publisher landing page
- PDF URL
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/mice.13457Direct link to full text PDF
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YesWhether a free full text is available
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bronzeOpen access status per OpenAlex
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https://onlinelibrary.wiley.com/doi/pdfdirect/10.1111/mice.13457Direct OA link when available
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Embedding, Computer science, Fuzzy logic, Artificial intelligence, Machine learning, Data miningTop concepts (fields/topics) attached by OpenAlex
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7Total citation count in OpenAlex
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2025: 7Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.sensitive | 60 |
| abstract_inverted_index.Meanwhile, | 189 |
| abstract_inverted_index.Structural | 1 |
| abstract_inverted_index.capability | 32, 172 |
| abstract_inverted_index.components | 8 |
| abstract_inverted_index.confidence | 140 |
| abstract_inverted_index.estimated, | 198 |
| abstract_inverted_index.knowledge. | 67 |
| abstract_inverted_index.prediction | 3, 27, 47, 100, 134, 155, 184, 195 |
| abstract_inverted_index.rationally | 197 |
| abstract_inverted_index.regression | 83, 108 |
| abstract_inverted_index.reinforced | 157 |
| abstract_inverted_index.structural | 208 |
| abstract_inverted_index.structures | 5 |
| abstract_inverted_index.validation | 148 |
| abstract_inverted_index.“R2CU” | 79 |
| abstract_inverted_index.alternative | 206 |
| abstract_inverted_index.complicated | 54 |
| abstract_inverted_index.effectively | 177 |
| abstract_inverted_index.engineering | 19, 51 |
| abstract_inverted_index.incorporate | 53 |
| abstract_inverted_index.performance | 2, 209 |
| abstract_inverted_index.prediction. | 210 |
| abstract_inverted_index.predictions | 39 |
| abstract_inverted_index.quantifying | 34 |
| abstract_inverted_index.reliability | 24 |
| abstract_inverted_index.structures. | 20 |
| abstract_inverted_index.transformed | 111 |
| abstract_inverted_index.uncertainty | 131 |
| abstract_inverted_index.applications | 52 |
| abstract_inverted_index.conventional | 107 |
| abstract_inverted_index.experimental | 124 |
| abstract_inverted_index.prediction's | 145 |
| abstract_inverted_index.significant. | 41 |
| abstract_inverted_index.transforming | 82 |
| abstract_inverted_index.data‐driven | 46, 76, 99 |
| abstract_inverted_index.extrapolating | 174 |
| abstract_inverted_index.modifications | 55 |
| abstract_inverted_index.uncertainties | 36, 191 |
| abstract_inverted_index.classification | 85, 114 |
| abstract_inverted_index.generalization | 31, 104, 171 |
| abstract_inverted_index.trustworthiness. | 146 |
| abstract_inverted_index.mechanism‐driven | 44 |
| abstract_inverted_index.uncertainty‐aware), | 87 |
| cited_by_percentile_year.max | 99 |
| cited_by_percentile_year.min | 98 |
| countries_distinct_count | 1 |
| institutions_distinct_count | 3 |
| citation_normalized_percentile.value | 0.98156862 |
| citation_normalized_percentile.is_in_top_1_percent | True |
| citation_normalized_percentile.is_in_top_10_percent | True |