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学者姓名：项程
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Abstract ：
The advent of deep learning provides a promising opportunity to improve the efficiency of topology optimization. However, existing methods make it difficult to achieve a balance between efficiency, accuracy, and generalization ability. To tackle this challenge, we propose a novel method based on a two -stage network framework. In the network, the partial convolution block and shifted windows attention mechanism are integrated to improve the model performance. In the first stage, a convolutional neural network -based model trained with a novel -designed loss function is employed to achieve real-time prediction of suboptimal structures. In the second stage, transfer learning is introduced to inherit the output of the first stage. Subsequently, the second stage optimizes the suboptimal structures to get the final optimal structures in a physical information -driven way. On the 2000 dataset, the two -stage method achieves an average compliance error of -1.45%, and 95.5% of the optimal structures perform better than that obtained by the traditional method and strictly meet volume constraints while eliminating structural disconnections. Finally, the proposed method is applied to a real -world engineering application for the first time, and the design of bridge pylons is given as an example. The results show that the proposed method is a promising exploration of topology optimization based on deep learning.
Keyword ：
Bridge pylon design Bridge pylon design Convolutional neural network Convolutional neural network Deep learning Deep learning Physical information Physical information Self-attention mechanism Self-attention mechanism Topology optimization Topology optimization
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| GB/T 7714 | Wang, Dalei , Ning, Yun , Xiang, Cheng et al. A two-stage network framework for topology optimization incorporating deep learning and physical information [J]. | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE , 2024 , 133 . |
| MLA | Wang, Dalei et al. "A two-stage network framework for topology optimization incorporating deep learning and physical information" . | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE 133 (2024) . |
| APA | Wang, Dalei , Ning, Yun , Xiang, Cheng , Chen, Airong . A two-stage network framework for topology optimization incorporating deep learning and physical information . | ENGINEERING APPLICATIONS OF ARTIFICIAL INTELLIGENCE , 2024 , 133 . |
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Abstract ：
Bridges are critical components of transportation systems and are susceptible to various natural and man-made disasters throughout their lifecycle. With the rapid development of the transportation industry, the frequency of vehicle-induced disasters has been steadily increasing. These incidents not only result in structural damage to bridges but also have the potential to cause traffic interruptions, weaken social service functions, and impose significant economic losses. In recent years, research on resilience has become a new focus in civil engineering disaster prevention and mitigation. This study proposes a concept of generalized bridge resilience and presents an evaluation framework for cable-stayed bridges under disasters. The framework includes a resilience evaluation indicator system from multiple dimensions, including safety, society, environment, and economy, which facilitates the dynamic and comprehensive control of bridge resilience throughout its entire lifecycle with the ultimate goals of enhancing structural safety and economic efficiency while promoting the development of environmentally friendly structural ecosystems. Furthermore, considering the influence of recovery speed, the study evaluates various repair strategies through resilience assessment, revealing the applicable environments and conditions for different repair strategies. This methodology offers significant implications for enhancing the safety, efficiency, and environmental sustainability of infrastructure systems, providing valuable guidance for future research in this field.
Keyword ：
disaster disaster evaluation framework evaluation framework functionality functionality resilience resilience
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| GB/T 7714 | Yanjie Liu , Cheng Xiang . A Comprehensive Framework for Evaluating Bridge Resilience: Safety, Social, Environmental, and Economic Perspectives [J]. | Sustainability , 2024 , 16 (3) . |
| MLA | Yanjie Liu et al. "A Comprehensive Framework for Evaluating Bridge Resilience: Safety, Social, Environmental, and Economic Perspectives" . | Sustainability 16 . 3 (2024) . |
| APA | Yanjie Liu , Cheng Xiang . A Comprehensive Framework for Evaluating Bridge Resilience: Safety, Social, Environmental, and Economic Perspectives . | Sustainability , 2024 , 16 (3) . |
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