A Neural Network-Based Structural Parameter Assessment Method for Prefabricated Concrete Pavement
Due to their construction efficiency, prefabricated concrete pavements are becoming a good choice for airport construction or refreshing. However, as a new type of pavement structure, their structural analysis theory and actual structural performance have not been determined. Therefore, a new method...
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MDPI AG
2025-03-01
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| Series: | Buildings |
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| Online Access: | https://www.mdpi.com/2075-5309/15/6/843 |
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| author | Yongsheng Tang Yunzhen Lin Tao Yu |
| author_facet | Yongsheng Tang Yunzhen Lin Tao Yu |
| author_sort | Yongsheng Tang |
| collection | DOAJ |
| description | Due to their construction efficiency, prefabricated concrete pavements are becoming a good choice for airport construction or refreshing. However, as a new type of pavement structure, their structural analysis theory and actual structural performance have not been determined. Therefore, a new method based on a neural network is applied to implement a long-term structural assessment, with the input being monitored strain data; it is named the jellyfish search algorithm-optimized BP neural network (JS-BP) model. Considering the structural characteristics, three key parameters are selected as the key parameters to implement the assessment, namely, the bending and tensile modulus, reaction modulus at top of the subgrade, and seam equivalent modulus. To implement the method, the databases are established first with the simulation results from some finite element models of prefabricated concrete pavement. Then, the proposed JS-BP neural network model is trained and checked with the established database. The simulation results verify an excellent accuracy of the proposed method as the difference between the predicted value and the true value is smaller than 1%. Moreover, the aircraft loads show some influence on the prediction results, in which the prediction error is about 5% for most cases, while it is up to 15% for assessing the top surface reaction modulus of the subgrade. Compared with the proposed JS-BP model, the accuracy of the traditional BP model is not so high, as the largest error can be up to 25%. Lastly, the proposed method is verified with some experiments using laboratory models. From the test results it is indicated that the prediction accuracy of the proposed method for the three parameters is still good enough, as the prediction error is within 5%. |
| format | Article |
| id | doaj-art-c418e286e8894f6fb0f0f46431b06392 |
| institution | DOAJ |
| issn | 2075-5309 |
| language | English |
| publishDate | 2025-03-01 |
| publisher | MDPI AG |
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| series | Buildings |
| spelling | doaj-art-c418e286e8894f6fb0f0f46431b063922025-08-20T02:42:35ZengMDPI AGBuildings2075-53092025-03-0115684310.3390/buildings15060843A Neural Network-Based Structural Parameter Assessment Method for Prefabricated Concrete PavementYongsheng Tang0Yunzhen Lin1Tao Yu2College of Civil and Transportation Engineering, Hohai University, Nanjing 210098, ChinaCollege of Civil and Transportation Engineering, Hohai University, Nanjing 210098, ChinaCollege of Civil and Transportation Engineering, Hohai University, Nanjing 210098, ChinaDue to their construction efficiency, prefabricated concrete pavements are becoming a good choice for airport construction or refreshing. However, as a new type of pavement structure, their structural analysis theory and actual structural performance have not been determined. Therefore, a new method based on a neural network is applied to implement a long-term structural assessment, with the input being monitored strain data; it is named the jellyfish search algorithm-optimized BP neural network (JS-BP) model. Considering the structural characteristics, three key parameters are selected as the key parameters to implement the assessment, namely, the bending and tensile modulus, reaction modulus at top of the subgrade, and seam equivalent modulus. To implement the method, the databases are established first with the simulation results from some finite element models of prefabricated concrete pavement. Then, the proposed JS-BP neural network model is trained and checked with the established database. The simulation results verify an excellent accuracy of the proposed method as the difference between the predicted value and the true value is smaller than 1%. Moreover, the aircraft loads show some influence on the prediction results, in which the prediction error is about 5% for most cases, while it is up to 15% for assessing the top surface reaction modulus of the subgrade. Compared with the proposed JS-BP model, the accuracy of the traditional BP model is not so high, as the largest error can be up to 25%. Lastly, the proposed method is verified with some experiments using laboratory models. From the test results it is indicated that the prediction accuracy of the proposed method for the three parameters is still good enough, as the prediction error is within 5%.https://www.mdpi.com/2075-5309/15/6/843prefabricated concrete pavementstructural parameter inversionjellyfish search algorithmBP neural network |
| spellingShingle | Yongsheng Tang Yunzhen Lin Tao Yu A Neural Network-Based Structural Parameter Assessment Method for Prefabricated Concrete Pavement Buildings prefabricated concrete pavement structural parameter inversion jellyfish search algorithm BP neural network |
| title | A Neural Network-Based Structural Parameter Assessment Method for Prefabricated Concrete Pavement |
| title_full | A Neural Network-Based Structural Parameter Assessment Method for Prefabricated Concrete Pavement |
| title_fullStr | A Neural Network-Based Structural Parameter Assessment Method for Prefabricated Concrete Pavement |
| title_full_unstemmed | A Neural Network-Based Structural Parameter Assessment Method for Prefabricated Concrete Pavement |
| title_short | A Neural Network-Based Structural Parameter Assessment Method for Prefabricated Concrete Pavement |
| title_sort | neural network based structural parameter assessment method for prefabricated concrete pavement |
| topic | prefabricated concrete pavement structural parameter inversion jellyfish search algorithm BP neural network |
| url | https://www.mdpi.com/2075-5309/15/6/843 |
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