Abstract This paper proposes a new design approach based on an iterative machine learning algorithm to speed up the topological design exploration of compression-only shell structures with planar faces, considering both structural performance and construction constraints. In this paper, we show that building neural networks allows one to train a surrogate model to accelerate the structural performance assessment of various possible structural forms without going through a significantly slower process of geometric form-finding. The geometric form-finding methods of 3D graphic statics are used as the primary structural design tool to generate a single-layer, compression-only shell with planar faces. Subdividing the force diagram and its polyhedral cells using various rules results in a variety of topologically different compression-only structures with different load-bearing capacities for the same boundary conditions. The solution space for all possible compression-only forms for a given boundary condition is vast, which makes iterating through all forms to find the ideal solutions practically impossible. After training with an iterative active sampling method, the surrogate model can evaluate the input data, including the subdivision rules, and predict the value of the structural performance and the construction constraints of the planar faces within milliseconds. As a result, one can then evaluate the nonlinear relations among all the subdivision rules and the chosen structural performance measures, and then, visualize the entire solution space. Consequently, multiple solutions with customized thresholds of the evaluation criteria are found that show the strength of this method of form-finding in generating design solutions. Besides, considering the total training time of the neural network model, the proposed framework is still faster than a traditional optimization method, such as the genetic algorithm that can find only the optimum values. This process will result in interactive sampling methods in which the machine learning models assist the designer in choosing and controlling different design strategies by providing real-time feedback on the effects of the selected parameters on the design outputs.

中文翻译：

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结构设计和施工中的机器学习辅助评估

摘要 本文提出了一种基于迭代机器学习算法的新设计方法，以加速具有平面的仅受压壳结构的拓扑设计探索，同时考虑结构性能和构造约束。在本文中，我们展示了构建神经网络允许人们训练代理模型以加速对各种可能结构形式的结构性能评估，而无需经过显着减慢的几何找形过程。3D 图形静力学的几何找形方法被用作主要的结构设计工具，以生成具有平面的单层、仅压缩壳。使用各种规则细分力图及其多面体单元会导致在相同边界条件下具有不同承载能力的各种拓扑不同的仅压缩结构。对于给定的边界条件，所有可能的仅压缩形式的解空间很大，这使得迭代所有形式以找到理想的解决方案几乎是不可能的。通过迭代主动采样方法训练后，代理模型可以评估输入数据，包括细分规则，并在毫秒内预测平面的结构性能和构造约束的值。结果，然后可以评估所有细分规则和所选结构性能度量之间的非线性关系，然后，可视化整个解空间。因此，找到了多个具有自定义评估标准阈值的解决方案，这些解决方案显示了这种找形方法在生成设计解决方案方面的优势。此外，考虑到神经网络模型的总训练时间，所提出的框架仍然比传统的优化方法更快，例如只能找到最佳值的遗传算法。该过程将产生交互式采样方法，其中机器学习模型通过提供有关所选参数对设计输出的影响的实时反馈，帮助设计人员选择和控制不同的设计策略。找到了多个具有自定义评估标准阈值的解决方案，这些解决方案显示了这种找形方法在生成设计解决方案方面的优势。此外，考虑到神经网络模型的总训练时间，所提出的框架仍然比传统的优化方法更快，例如只能找到最佳值的遗传算法。该过程将产生交互式采样方法，其中机器学习模型通过提供有关所选参数对设计输出的影响的实时反馈，帮助设计人员选择和控制不同的设计策略。找到了多个具有自定义评估标准阈值的解决方案，这些解决方案显示了这种找形方法在生成设计解决方案方面的优势。此外，考虑到神经网络模型的总训练时间，所提出的框架仍然比传统的优化方法更快，例如只能找到最佳值的遗传算法。该过程将产生交互式采样方法，其中机器学习模型通过提供有关所选参数对设计输出的影响的实时反馈，帮助设计人员选择和控制不同的设计策略。比如只能找到最优值的遗传算法。该过程将产生交互式采样方法，其中机器学习模型通过提供有关所选参数对设计输出的影响的实时反馈，帮助设计人员选择和控制不同的设计策略。比如只能找到最优值的遗传算法。该过程将产生交互式采样方法，其中机器学习模型通过提供有关所选参数对设计输出的影响的实时反馈，帮助设计人员选择和控制不同的设计策略。