Macro and mesoscopic mechanical behavior of concrete with actual aggregate segmented by hybrid Transformers and convolutional neural networks
The precise establishment of mesoscale models for concrete is crucial for understanding its mechanical behavior through numerical simulations. This study presents a deep learning-based numerical framework aimed at investigating both macro- and mesoscopic mechanical behavior of concrete, focusing on...
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| Format: | Article |
| Language: | English |
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Elsevier
2025-07-01
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| Series: | Case Studies in Construction Materials |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2214509525002128 |
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| author | Dong Wang Junxing Zheng Jichen Zhong Lin Gao Shuling Huang Jiajia Zheng |
| author_facet | Dong Wang Junxing Zheng Jichen Zhong Lin Gao Shuling Huang Jiajia Zheng |
| author_sort | Dong Wang |
| collection | DOAJ |
| description | The precise establishment of mesoscale models for concrete is crucial for understanding its mechanical behavior through numerical simulations. This study presents a deep learning-based numerical framework aimed at investigating both macro- and mesoscopic mechanical behavior of concrete, focusing on the actual aggregate morphology and distribution in 2D cross-sections. A dual encoder concrete aggregate segmentation network (DECAS-Net) based on CNN and Transformer is developed to segment aggregates and construct a refined mesoscale model compatible with the discrete element method (DEM). A multi-branch parallel dilated convolutional feature fusion module is designed to effectively integrate multi-scale features from the different encoders. Uniaxial compression tests are conducted to simulate the effects of actual aggregate shape and distribution on the macro- and mesoscopic mechanical behavior of concrete. Comparative analyses are performed utilizing models with circular aggregates and randomly distributed irregular aggregates, providing further insights into the influence of actual aggregate morphology and distribution on concrete mechanical behavior. The results demonstrate that the proposed DECAS-Net method provides accurate foundational data for establishing refined 2D mesoscale models, and highlights the significant impact of aggregate morphology and distribution on the mechanical behavior of concrete. |
| format | Article |
| id | doaj-art-57205f9f05ce4a1c96e204e050ee256c |
| institution | OA Journals |
| issn | 2214-5095 |
| language | English |
| publishDate | 2025-07-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Case Studies in Construction Materials |
| spelling | doaj-art-57205f9f05ce4a1c96e204e050ee256c2025-08-20T02:01:15ZengElsevierCase Studies in Construction Materials2214-50952025-07-0122e0441410.1016/j.cscm.2025.e04414Macro and mesoscopic mechanical behavior of concrete with actual aggregate segmented by hybrid Transformers and convolutional neural networksDong Wang0Junxing Zheng1Jichen Zhong2Lin Gao3Shuling Huang4Jiajia Zheng5School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaSchool of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China; Corresponding authors.School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, China; Corresponding authors.School of Civil and Hydraulic Engineering, Huazhong University of Science and Technology, Wuhan 430074, ChinaKey Laboratory of Geotechnical Mechanics and Engineering of the Ministry of Water Resources, Changjiang River Scientific Research Institute, Wuhan 430010, ChinaChina Road and Bridge Engineering Co., Ltd., Beijing 100011, ChinaThe precise establishment of mesoscale models for concrete is crucial for understanding its mechanical behavior through numerical simulations. This study presents a deep learning-based numerical framework aimed at investigating both macro- and mesoscopic mechanical behavior of concrete, focusing on the actual aggregate morphology and distribution in 2D cross-sections. A dual encoder concrete aggregate segmentation network (DECAS-Net) based on CNN and Transformer is developed to segment aggregates and construct a refined mesoscale model compatible with the discrete element method (DEM). A multi-branch parallel dilated convolutional feature fusion module is designed to effectively integrate multi-scale features from the different encoders. Uniaxial compression tests are conducted to simulate the effects of actual aggregate shape and distribution on the macro- and mesoscopic mechanical behavior of concrete. Comparative analyses are performed utilizing models with circular aggregates and randomly distributed irregular aggregates, providing further insights into the influence of actual aggregate morphology and distribution on concrete mechanical behavior. The results demonstrate that the proposed DECAS-Net method provides accurate foundational data for establishing refined 2D mesoscale models, and highlights the significant impact of aggregate morphology and distribution on the mechanical behavior of concrete.http://www.sciencedirect.com/science/article/pii/S2214509525002128Concrete macro and mesoscopic mechanical behaviorActual aggregate morphology and distributionDeep learningAggregate segmentationDiscrete element method |
| spellingShingle | Dong Wang Junxing Zheng Jichen Zhong Lin Gao Shuling Huang Jiajia Zheng Macro and mesoscopic mechanical behavior of concrete with actual aggregate segmented by hybrid Transformers and convolutional neural networks Case Studies in Construction Materials Concrete macro and mesoscopic mechanical behavior Actual aggregate morphology and distribution Deep learning Aggregate segmentation Discrete element method |
| title | Macro and mesoscopic mechanical behavior of concrete with actual aggregate segmented by hybrid Transformers and convolutional neural networks |
| title_full | Macro and mesoscopic mechanical behavior of concrete with actual aggregate segmented by hybrid Transformers and convolutional neural networks |
| title_fullStr | Macro and mesoscopic mechanical behavior of concrete with actual aggregate segmented by hybrid Transformers and convolutional neural networks |
| title_full_unstemmed | Macro and mesoscopic mechanical behavior of concrete with actual aggregate segmented by hybrid Transformers and convolutional neural networks |
| title_short | Macro and mesoscopic mechanical behavior of concrete with actual aggregate segmented by hybrid Transformers and convolutional neural networks |
| title_sort | macro and mesoscopic mechanical behavior of concrete with actual aggregate segmented by hybrid transformers and convolutional neural networks |
| topic | Concrete macro and mesoscopic mechanical behavior Actual aggregate morphology and distribution Deep learning Aggregate segmentation Discrete element method |
| url | http://www.sciencedirect.com/science/article/pii/S2214509525002128 |
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