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Physics-guided deep learning for generative design of large-diameter tunnels under existing metro lines
Automation in Construction ( IF 9.6 ) Pub Date : 2024-12-17 , DOI: 10.1016/j.autcon.2024.105901
Limao Zhang, Jiaqi Wang, Zhuang Xia, Xieqing Song
Automation in Construction ( IF 9.6 ) Pub Date : 2024-12-17 , DOI: 10.1016/j.autcon.2024.105901
Limao Zhang, Jiaqi Wang, Zhuang Xia, Xieqing Song
The overlapping construction of large-diameter tunnels is inevitable, but the construction control faces great challenges due to the complexity of underground environments. A generative design method for large-diameter tunnels under existing metro lines based on physic-guided deep learning is proposed, aiming at optimizing tunnel layouts from a physical perspective to ensure effective construction control. A topology-optimized model dataset considering soil uncertainties is fed into a physics-guided Wasserstein generative adversarial network (PGWGAN) for training, producing numerous physically consistent schemes. The optimal scheme is selected using the multiple-attribute decision-making (MADM) method under multi-working conditions. A case study on large-diameter tunnel construction demonstrates the method's feasibility, showing that it meets the safety requirements across various conditions and achieves significant improvement. This paper contributes a physics-guided generative design method for large-diameter tunnel overlapping construction. It accounts for multiple working conditions and includes an evaluation module that integrates parametric finite element analysis (FEA) with multi-attribute evaluation.
中文翻译:
物理引导深度学习,用于现有地铁线路下大直径隧道的创成式设计
大直径隧道的重叠施工是不可避免的,但由于地下环境的复杂性,施工控制面临巨大挑战。提出了一种基于物理引导深度学习的既有地铁线路下大直径隧道生成式设计方法,旨在从物理角度优化隧道布局,确保施工的有效控制。将考虑土壤不确定性的拓扑优化模型数据集输入物理引导的 Wasserstein 生成对抗网络 (PGWGAN) 进行训练,生成大量物理一致的方案。在多工况下,采用多属性决策 (MADM) 方法选择最优方案。大直径隧道施工的案例研究证明了该方法的可行性,表明它满足了各种条件下的安全要求,并取得了显著的改进。本文为大直径隧道重叠施工提供了一种物理引导的生成式设计方法。它考虑了多种工况,并包括一个将参数有限元分析 (FEA) 与多属性评估集成的评估模块。
更新日期:2024-12-17
中文翻译:
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物理引导深度学习,用于现有地铁线路下大直径隧道的创成式设计
大直径隧道的重叠施工是不可避免的,但由于地下环境的复杂性,施工控制面临巨大挑战。提出了一种基于物理引导深度学习的既有地铁线路下大直径隧道生成式设计方法,旨在从物理角度优化隧道布局,确保施工的有效控制。将考虑土壤不确定性的拓扑优化模型数据集输入物理引导的 Wasserstein 生成对抗网络 (PGWGAN) 进行训练,生成大量物理一致的方案。在多工况下,采用多属性决策 (MADM) 方法选择最优方案。大直径隧道施工的案例研究证明了该方法的可行性,表明它满足了各种条件下的安全要求,并取得了显著的改进。本文为大直径隧道重叠施工提供了一种物理引导的生成式设计方法。它考虑了多种工况,并包括一个将参数有限元分析 (FEA) 与多属性评估集成的评估模块。