Neural network-based performance prediction of marine UHPC with coarse aggregates Article Swipe
Yunhao Luan
,
Denise J. Cai
,
Biao Wang
,
Changqing Luo
,
Anni Wang
,
Chao Wang
,
Dezeng Kong
,
Chaohui Xu
,
Shipei Huang
·
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fmats.2025.1550991
YOU?
·
· 2025
· Open Access
·
· DOI: https://doi.org/10.3389/fmats.2025.1550991
In order to improve bearing capacity and service life of marine structure using marine UHPC with coarse aggregate (UHPC-CA), it is necessary to reasonably predict the performance of UHPC-CA. The performance of UHPC-CA was predicted in this paper based on five prediction models: multiple linear regression, multiple nonlinear regression, traditional neural network (T-BP), principal component approach neural network (PCA-BP), and improved neural network based on genetic algorithm (GA-BP). Seven influencing factors were taken as input, such as coarse aggregate type, coarse aggregate content, steel fiber type, steel fiber content, water-binder ratio, rubber particle sand replacement rate and curing system. Mechanical and long-term performance of UHPC-CA were taken as outputs. The results show that artificial neural network can be applied to predict performance of UHPC-CA with multi-parameter input and multi-index output. In terms of the prediction accuracy of mechanical properties and long-term performance of UHPC-CA, the order is GA-BP > PCA-BP > T-BP > multiple nonlinear regression > multiple linear regression. The GA-BP neural network has the highest goodness of fit for the prediction of mechanical properties and long-term performance of UHPC-CA, which is 93.87%, 37.34%, 5.13% and 3.21% averagely higher than that of multiple linear regression, multiple nonlinear regression, T-BP and PCA-BP, respectively. Furthermore, GA-BP neural network has the lowest error index for each performance prediction. MAE, MSE and RMSE are 18.13%, 77.26% and 52.31% lower than PCA-BP on average.
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- Type
- article
- Language
- en
- Landing Page
- https://doi.org/10.3389/fmats.2025.1550991
- https://www.frontiersin.org/journals/materials/articles/10.3389/fmats.2025.1550991/pdf
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- gold
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- 1
- References
- 43
- Related Works
- 10
- OpenAlex ID
- https://openalex.org/W4408040642
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https://openalex.org/W4408040642Canonical identifier for this work in OpenAlex
- DOI
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https://doi.org/10.3389/fmats.2025.1550991Digital Object Identifier
- Title
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Neural network-based performance prediction of marine UHPC with coarse aggregatesWork 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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2025-02-28Full publication date if available
- Authors
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Yunhao Luan, Denise J. Cai, Biao Wang, Changqing Luo, Anni Wang, Chao Wang, Dezeng Kong, Chaohui Xu, Shipei HuangList of authors in order
- Landing page
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https://doi.org/10.3389/fmats.2025.1550991Publisher landing page
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https://www.frontiersin.org/journals/materials/articles/10.3389/fmats.2025.1550991/pdfDirect link to full text PDF
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YesWhether a free full text is available
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goldOpen access status per OpenAlex
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https://www.frontiersin.org/journals/materials/articles/10.3389/fmats.2025.1550991/pdfDirect OA link when available
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Artificial neural network, Materials science, Computer science, Geology, Artificial intelligenceTop concepts (fields/topics) attached by OpenAlex
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1Total citation count in OpenAlex
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2025: 1Per-year citation counts (last 5 years)
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10Other works algorithmically related by OpenAlex
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| abstract_inverted_index.output. | 129 |
| abstract_inverted_index.predict | 24, 120 |
| abstract_inverted_index.results | 110 |
| abstract_inverted_index.service | 7 |
| abstract_inverted_index.system. | 98 |
| abstract_inverted_index.> | 148, 150, 152, 156 |
| abstract_inverted_index.(GA-BP). | 67 |
| abstract_inverted_index.UHPC-CA, | 143, 180 |
| abstract_inverted_index.UHPC-CA. | 28 |
| abstract_inverted_index.accuracy | 135 |
| abstract_inverted_index.approach | 55 |
| abstract_inverted_index.average. | 229 |
| abstract_inverted_index.capacity | 5 |
| abstract_inverted_index.content, | 82, 88 |
| abstract_inverted_index.goodness | 167 |
| abstract_inverted_index.improved | 60 |
| abstract_inverted_index.multiple | 43, 46, 153, 157, 193, 196 |
| abstract_inverted_index.outputs. | 108 |
| abstract_inverted_index.particle | 92 |
| abstract_inverted_index.(PCA-BP), | 58 |
| abstract_inverted_index.aggregate | 17, 78, 81 |
| abstract_inverted_index.algorithm | 66 |
| abstract_inverted_index.averagely | 188 |
| abstract_inverted_index.component | 54 |
| abstract_inverted_index.long-term | 101, 140, 177 |
| abstract_inverted_index.necessary | 21 |
| abstract_inverted_index.nonlinear | 47, 154, 197 |
| abstract_inverted_index.predicted | 34 |
| abstract_inverted_index.principal | 53 |
| abstract_inverted_index.structure | 11 |
| abstract_inverted_index.(UHPC-CA), | 18 |
| abstract_inverted_index.Mechanical | 99 |
| abstract_inverted_index.artificial | 113 |
| abstract_inverted_index.mechanical | 137, 174 |
| abstract_inverted_index.prediction | 41, 134, 172 |
| abstract_inverted_index.properties | 138, 175 |
| abstract_inverted_index.reasonably | 23 |
| abstract_inverted_index.regression | 155 |
| abstract_inverted_index.influencing | 69 |
| abstract_inverted_index.multi-index | 128 |
| abstract_inverted_index.performance | 26, 30, 102, 121, 141, 178, 214 |
| abstract_inverted_index.prediction. | 215 |
| abstract_inverted_index.regression, | 45, 48, 195, 198 |
| abstract_inverted_index.regression. | 159 |
| abstract_inverted_index.replacement | 94 |
| abstract_inverted_index.traditional | 49 |
| abstract_inverted_index.Furthermore, | 203 |
| abstract_inverted_index.water-binder | 89 |
| abstract_inverted_index.respectively. | 202 |
| abstract_inverted_index.multi-parameter | 125 |
| cited_by_percentile_year.max | 95 |
| cited_by_percentile_year.min | 91 |
| countries_distinct_count | 0 |
| institutions_distinct_count | 9 |
| citation_normalized_percentile.value | 0.76969582 |
| citation_normalized_percentile.is_in_top_1_percent | False |
| citation_normalized_percentile.is_in_top_10_percent | True |