FEM- and ANN-based design of CFRP-strengthened RC walls under close-in explosions
Reinforced concrete (RC) structures are vulnerable to explosion loading, especially under close-in detonations. Recently, externally bonded carbon fiber reinforced polymer (CFRP) sheets have been used as strengthening layers to improve the blast resistance of structural components. Due to limited pu...
| Main Authors: | , , , , |
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| Other Authors: | |
| Format: | Journal Article |
| Language: | English |
| Published: |
2024
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| Subjects: | |
| Online Access: | https://hdl.handle.net/10356/177967 |
| _version_ | 1811683318435938304 |
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| author | Tu, Huan Yu, Qing Jun Tan, Kang Hai Fung, Tat Ching Riedel, Werner |
| author2 | School of Civil and Environmental Engineering |
| author_facet | School of Civil and Environmental Engineering Tu, Huan Yu, Qing Jun Tan, Kang Hai Fung, Tat Ching Riedel, Werner |
| author_sort | Tu, Huan |
| collection | NTU |
| description | Reinforced concrete (RC) structures are vulnerable to explosion loading, especially under close-in detonations. Recently, externally bonded carbon fiber reinforced polymer (CFRP) sheets have been used as strengthening layers to improve the blast resistance of structural components. Due to limited published research studies, there are very few tools or models capable of predicting blast-induced damage on RC walls strengthened with FRP subjected to blast effect. In this paper, an Artificial Neural Network (ANN) based model is developed for damage predictions. The ANN model is trained by a database of numerical simulations, which was previously established and validated by field blast tests. Good agreement among experiments, existing design diagrams, numerical simulations, and ANN predictions show that the model is capable of quantitively predicting local damage of structures with strengthening layers on the rear face. For fast engineering applications, design diagrams are developed as a quick assessment tool to evaluate the extent of damage on CRFP-strengthened RC walls caused by close-in explosions. |
| first_indexed | 2024-10-01T04:10:50Z |
| format | Journal Article |
| id | ntu-10356/177967 |
| institution | Nanyang Technological University |
| language | English |
| last_indexed | 2024-10-01T04:10:50Z |
| publishDate | 2024 |
| record_format | dspace |
| spelling | ntu-10356/1779672024-06-03T08:00:01Z FEM- and ANN-based design of CFRP-strengthened RC walls under close-in explosions Tu, Huan Yu, Qing Jun Tan, Kang Hai Fung, Tat Ching Riedel, Werner School of Civil and Environmental Engineering Engineering RC structures Blast loading Reinforced concrete (RC) structures are vulnerable to explosion loading, especially under close-in detonations. Recently, externally bonded carbon fiber reinforced polymer (CFRP) sheets have been used as strengthening layers to improve the blast resistance of structural components. Due to limited published research studies, there are very few tools or models capable of predicting blast-induced damage on RC walls strengthened with FRP subjected to blast effect. In this paper, an Artificial Neural Network (ANN) based model is developed for damage predictions. The ANN model is trained by a database of numerical simulations, which was previously established and validated by field blast tests. Good agreement among experiments, existing design diagrams, numerical simulations, and ANN predictions show that the model is capable of quantitively predicting local damage of structures with strengthening layers on the rear face. For fast engineering applications, design diagrams are developed as a quick assessment tool to evaluate the extent of damage on CRFP-strengthened RC walls caused by close-in explosions. Defence Science and Technology Agency (DSTA) The authors are grateful to the Defence Science & Technology Agency (DSTA) for supporting the work and kindly providing the blast test results. 2024-06-03T08:00:01Z 2024-06-03T08:00:01Z 2024 Journal Article Tu, H., Yu, Q. J., Tan, K. H., Fung, T. C. & Riedel, W. (2024). FEM- and ANN-based design of CFRP-strengthened RC walls under close-in explosions. Structures, 61, 105930-. https://dx.doi.org/10.1016/j.istruc.2024.105930 2352-0124 https://hdl.handle.net/10356/177967 10.1016/j.istruc.2024.105930 2-s2.0-85184515857 61 105930 en Structures © 2024 Published by Elsevier Ltd on behalf of Institution of Structural Engineers. All rights reserved. |
| spellingShingle | Engineering RC structures Blast loading Tu, Huan Yu, Qing Jun Tan, Kang Hai Fung, Tat Ching Riedel, Werner FEM- and ANN-based design of CFRP-strengthened RC walls under close-in explosions |
| title | FEM- and ANN-based design of CFRP-strengthened RC walls under close-in explosions |
| title_full | FEM- and ANN-based design of CFRP-strengthened RC walls under close-in explosions |
| title_fullStr | FEM- and ANN-based design of CFRP-strengthened RC walls under close-in explosions |
| title_full_unstemmed | FEM- and ANN-based design of CFRP-strengthened RC walls under close-in explosions |
| title_short | FEM- and ANN-based design of CFRP-strengthened RC walls under close-in explosions |
| title_sort | fem and ann based design of cfrp strengthened rc walls under close in explosions |
| topic | Engineering RC structures Blast loading |
| url | https://hdl.handle.net/10356/177967 |
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