AIstructure-Copilot-v0.1.7功能更新：实现多标准层的PKPM/YJK自动建模

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内容提要

此前版本可以完成剪力墙和梁构件的PKPM/YJK建模（AIstructure-Copilot-v0.1.5：自动生成YJK/PKPM建模文件），新版本增加楼板建模功能，同时可进行多标准层参数设置以及楼层组装，实现基于剪力墙-梁构件布置图生成全楼的剪力墙结构模型。

欢迎大家试用！

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AIstructure-Copilot更新

1.1 多标准层参数设置

在原有参数设置的面板中，增加了多标准层参数的设置选项，可以分别对不同标准层设置剪力墙厚、剪力墙混凝土等级、梁高、梁混凝土等级、板厚、板混凝土等级参数。其中墙厚默认值为“-1”，即采用程序推荐墙厚参数。若进行修改，则采用用户自定义墙厚。

更多操作请详见使用说明手册。需注意，目前不同标准层仍采用相同的剪力墙和梁布置。

1.2 楼层组装

楼层组装功能则是在标准层参数的基础上进行定义，目前楼层组装功能参考PKPM/YJK的楼层组装方式，可设置自然层层高以及对应的标准层层号。

1.3 PKPM/YJK标准层楼板自动生成

完成剪力墙-梁构件的布置设计后，将从CAD导出剪力墙-梁布置设计和构件截面尺寸，随后将基于剪力墙和梁的布置设计，布置楼板并写入PKPM/YJK的SQLite数据库文件中。可以看到目前的每个标准层都有对应的楼板布置，以及对应的默认楼面荷载参数。

1.4 PKPM/YJK整楼模型

可以看到，采用了AIstructure-Copilot中楼层组装参数设置后，生成的PKPM/YJK模型便可以自动生成对应的楼层组装参数，并且形成正确的整楼模型。

1.5 剪力墙智能设计模块其他重要改进

解除建筑平面尺寸小于51m×25m的限制，当平面尺寸大于51m×25m时，基于GNN的剪力墙设计功能可以正常执行，而基于GAN和扩散模型的智能设计功能无法使用，我们将继续改进。

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典型案例

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结语

我们进一步完善了从建筑CAD设计图纸到结构计算模型的自动化过程，主要增加了设置多个标准层参数并进行楼层组装的功能、自动生成楼板功能，从而进一步提升智能设计的便捷程度。

彩蛋！欢迎大家试用建筑前处理中的建筑空间分割功能，有望提升后期梁布置的合理性。

后续，我们还将不断完善相关产品功能。欢迎大家持续关注我们的工作，多多支持！

温馨提示：为更好使用AI设计工具，请仔细阅读使用说明书。

联系方式

QQ群，AI-structure-交流群：741840451

廖文杰：[email protected]

ai-structure.com联系方式

QQ群，AI-structure-交流群：741840451

廖文杰：[email protected]

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1. Liao WJ, Lu XZ, Huang YL, Zheng Z, Lin YQ, Automated structural design of shear wall residential buildings using generative adversarial networks, Automation in Construction, 2021, 132, 103931. DOI: 10.1016/j.autcon.2021.103931.
   

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5. Fei YF, Liao WJ, Zhang S, Yin PF, Han B, Zhao PJ, Chen XY, Lu XZ, Integrated schematic design method for shear wall structures: a practical application of generative adversarial networks, Buildings, 2022, 12(9): 1295. DOI: 10.3390/buildings1209129.

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