0
引言
AIstructure-Copilot经过了一年多的迭代更新,听取了广大用户的意见,不断完善,V0.3.0版本的进步主要体现在以下三个方面:
(1)增加了英文版,可随时实现中英文的切换,满足不同用户的需求;
(2)增加自动识别图层功能,不需要用户再手动逐一选取图层,增加操作便捷性;
(3)通过墙梁联合优化,进一步提升设计效果,特别是对称性上有明显改进。
AIstructure-Copilot V0.3.0版本将给大家带来更好的使用体验,AI在一些细节的处理上更接近有经验的工程师了,欢迎大家试用。
1
软件增加英文版
AIstructure-Copilot软件开发了英文版本,用户可在菜单栏完成中英文版本的切换。
(a)中文版切换英文版
(b)英文版切换中文版
2
增加自动识别图层功能
在参数设置的“图层信息”选项卡中,增加“自动获取图层名”功能(如图1所示),软件通过对图纸中所有图元信息和图层信息进行遍历和特征分析,自动完成图层的模糊匹配,增加操作的便利性。
参见操作演示视频,用户点击“自动获取图层名”按键后,软件将要求用户选取需要提取的建筑平面图,用户框选建筑平面图后,可点击鼠标右键结束选取,将由软件自动获取建筑图中轴网、建筑墙、门窗、阳台构件、建筑空间文字的图层名称,并绘制出来,如图2所示。
图1 增加“自动获取图层名”功能
图2 自动提取结果
如果图纸建模比较规范,则自动识别结果一般没有问题。
如果图纸质量不高,还可以手动对漏缺进行修正。可点击对应位置的“获取图层名”按钮(图3),进行补充选取;如发现有多选中的图层,可点击“取消选择框中的图层名”按钮,再点选多选中的图层,即可取消选取,具体操作可参照视频。
图3 点击对应位置的“获取图层名”按钮,补充选取
图4 点击“取消选择框中的图层名”按钮,取消选取
3
墙梁联合优化,进一步提升设计效果
在V0.2.9版本中,我们提出了“墙-梁联合布置算法”,使得设计质量得到有效提升(详见:AIstructure-Copilot-V0.2.9 梁布置设计算法改进),V0.3.0通过继续完善墙梁联合优化算法,进一步提升了设计效果,例如在对称性上有明显改进。
工程师在人工设计的时候,往往会很好的体现对称性,为了使AI生成的结果更符合工程师的设计习惯,我们针对房间布置空间属性进行了进一步的算法分析,使得AI生成的结果更好的符合对称性,进而提升设计效果。
案例一:优化前左右两侧客餐厅的结构布置不对称,优化后可以很好的实现对称性,设计效果进一步提升。
(a)优化前的设计
(b)优化后的设计
(注:红色为剪力墙,蓝色为梁)
案例二:优化前剪力墙的布置不符合对称原则,优化后满足对称要求,设计效果进一步提升。
(a)优化前的设计
(b)优化后的设计
(注:红色为剪力墙,蓝色为梁)
案例三:优化前结构方案左右不对称,优化后对称性明显提升,设计质量改善。
(a)优化前的设计
(b)优化后的设计
(注:红色为剪力墙,蓝色为梁)
案例四:优化前左右边户户型的卫生间结构布置不对称,优化后对称性显著提升,设计质量改善。
(a)优化前的设计
(b)优化后的设计
(注:红色为剪力墙,蓝色为梁)
经过以上优化后,整个设计效果的对称性会有较为明显的提升,使得整体结构更为合理。
4
结语
AIstructure-Copilot V0.3.0版本增加了英文版,在前处理上增加图层自动识别功能,使得操作更便捷。在设计算法中,结合空间布置情况对墙梁进行联合优化,使整体设计结果更接近工程师,设计更合理,欢迎大家试用。
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(https://ai-structure.com)。
相关论文
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.
Lu XZ, Liao WJ, Zhang Y, Huang YL, Intelligent structural design of shear wall residence using physics-enhanced generative adversarial networks, Earthquake Engineering & Structural Dynamics, 2022, 51(7): 1657-1676. DOI: 10.1002/eqe.3632.
Zhao PJ, Liao WJ, Xue HJ, Lu XZ, Intelligent design method for beam and slab of shear wall structure based on deep learning, Journal of Building Engineering, 2022, 57: 104838. DOI: 10.1016/j.jobe.2022.104838.
Liao WJ, Huang YL, Zheng Z, Lu XZ, Intelligent generative structural design method for shear-wall building based on “fused-text-image-to-image” generative adversarial networks, Expert Systems with Applications, 2022, 118530, DOI: 10.1016/j.eswa.2022.118530.
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.
Fei YF, Liao WJ, Huang YL, Lu XZ, Knowledge-enhanced generative adversarial networks for schematic design of framed tube structures, Automation in Construction, 2022, 144: 104619. DOI: 10.1016/j.autcon.2022.104619.
Zhao PJ, Liao WJ, Huang YL, Lu XZ, Intelligent design of shear wall layout based on attention-enhanced generative adversarial network, Engineering Structures, 2023, 274, 115170. DOI: 10.1016/j.engstruct.2022.115170.
Zhao PJ, Liao WJ, Huang YL, Lu XZ, Intelligent beam layout design for frame structure based on graph neural networks, Journal of Building Engineering, 2023, 63, Part A: 105499. DOI: 10.1016/j.jobe.2022.105499.
Zhao PJ, Liao WJ, Huang YL, Lu XZ, Intelligent design of shear wall layout based on graph neural networks, Advanced Engineering Informatics, 2023, 55, 101886, DOI: 10.1016/j.aei.2023.101886
Liao WJ, Wang XY, Fei YF, Huang YL, Xie LL, Lu XZ*, Base-isolation design of shear wall structures using physics-rule-co-guided self-supervised generative adversarial networks, Earthquake Engineering & Structural Dynamics, 2023, DOI:10.1002/eqe.3862.
Feng YT, Fei YF, Lin YQ, Liao WJ, Lu XZ, Intelligent generative design for shear wall cross-sectional size using rule-embedded generative adversarial network, Journal of Structural Engineering-ASCE, 2023, 149(11). 04023161. DOI:10.1061/JSENDH.STENG-12206.
Fei YF, Liao WJ, Lu XZ*, Guan H*, Knowledge-enhanced graph neural networks for construction material quantity estimation of reinforced concrete buildings, Computer-Aided Civil and Infrastructure Engineering, 2023, DOI: 10.1111/mice.13094.
Zhao PJ, Fei YF, Huang YL, Feng YT, Liao WJ, Lu XZ*, Design-condition-informed shear wall layout design based on graph neural networks, Advanced Engineering Informatics, 2023, 58: 102190. DOI: 10.1016/j.aei.2023.102190.
Fei YF, Liao WJ, Lu XZ*, Taciroglu E, Guan H, Semi-supervised learning method incorporating structural optimization for shear-wall structure design using small and long-tailed datasets, Journal of Building Engineering, 2023, DOI:10.1016/j.jobe.2023.107873
Liao WJ, Lu XZ*, Fei YF, Gu Y, Huang YL, Generative AI design for building structures, Automation in Construction, 2024, 157: 105187. DOI: 10.1016/j.autcon.2023.105187
Zhao PJ, Liao WJ, Huang YL, Lu XZ*, Beam layout design of shear wall structures based on graph neural networks, Automation in Construction, 2024, 158: 105223. DOI: 10.1016/j.autcon.2023.105223
Qin SZ, Liao WJ*, Huang SN, Hu KG, Tan Z, Gao Y, Lu XZ, AIstructure-Copilot: assistant for generative AI-driven intelligent design of building structures, Smart Construction, 2024, DOI: 10.55092/sc20240001
Gu Y, Huang YL, Liao WJ, Lu XZ*, Intelligent design of shear wall layout based on diffusion models, Computer-Aided Civil and Infrastructure Engineering, 2024, DOI: 10.1111/mice.13236
Fei YF, Liao WJ, Zhao PJ, Lu XZ*, Guan H, Hybrid surrogate model combining physics and data for seismic drift estimation of shear-wall structures, Earthquake Engineering & Structural Dynamics, 2024, DOI: 10.1002/eqe.4151
Han J, Lu XZ, Gu Y, Cai Q, Xue HJ, Liao WJ, Optimized data representation and understanding method for the intelligent design of shear wall structures, Engineering Structures, 2024, 315: 118500. DOI: 10.1016/j.engstruct.2024.118500
Qin SZ, Guan H, Liao WJ, Gu Y, Zheng Z, Xue HJ, Lu XZ*, Intelligent design and optimization system for shear wall structures based on large language models and generative artificial intelligence, Journal of Building Engineering, 2024, 95: 109996. DOI: 10.1016/j.jobe.2024.109996
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