1引言 今年5月发布了AIstructure-Copilot-v0.2.6版本，AIstructure整体的设计效果有比较明显的提升（详见：AIstructure-Copilot-v0.2.6：给马儿换上精饲料，AIstructure设计效果持续改善），但涉及到梁布置的一些细节还不是很让我们满意，因此，AIstructure-Copilot-v0.2.9版本给大家带来了新算法：“墙-梁联合布置算法”，使得AI在一些细节的处理上更接近有经验的工程师，欢迎大家试用。典型问题1过长梁的优化处理 此前版本的设计：AI布置的梁可能会出现一些跨度较大梁，其对应的梁高较高，影响建筑内部使用空间，设计合理性有待提升。 （a）此前版本的设计AIstructure-Copilot-v0.2.9版本：算法结合梁布置与跨度情况，自动找寻中间合理的位置优化剪力墙布置，从而让梁能有更好更合理的支座，提升受力整体性、降低梁高需求。 （b）v0.2.9版本的设计（注：红色为剪力墙，蓝色为梁）典型问题2冗余次梁的优化处理 此前版本的设计：AI生成的方案，可能会在一个较小的范围内存在多个次梁，从而不利于施工以及建筑美观需求。 （a）此前版本的设计AIstructure-Copilot-v0.2.9版本：算法 会结合空间布置情况，自动优化一些不必要的次梁，从而使整个结构布置更加合理。 （b）v0.2.9版本的设计典型问题3不共线梁的优化处理 在建筑方案中，有部分建筑空间的轮廓存在微小的错位，即两个空间的分割并非完全在一条直线上，使得梁构件的受力不连续。此前版本的设计：AI生成的方案会出现梁不共线，在比较近的距离里会有两个次梁搭接在主梁上，这种结构布置方案在受力上不是很合理。 （a）此前版本的设计AIstructure-Copilot-v0.2.9版本：针对这种情况进行了优化处理，算法 会针对受力不连续的梁布置，增加部分剪力墙的布置，从而让梁的搭接更为合理。 （b）v0.2.9版本的设计3前述系列优化的综合设计效果提升 采用新算法后，整个方案的设计效果会有较为明显的提升，如下图所示，此前版本的设计，箭头位置的梁非常长，且存在部分在很小范围内长轴梁搭接到短轴梁的情况，v0.2.9版本，通过在部分位置增设剪力墙的方式，极大的改善了梁的受力模式，也去掉了部分冗余的次梁，使得整体结构更为合理。 （a）此前版本的设计 （b）v0.2.9版本的设计4结语AIstructure-Copilot V0.2.9版本改进了梁布置算法，给出了更合理的结构布置方案，设计结果更加贴近结构工程师，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)。--End-- ai-structure.com联系方式 商务问题请联系：黄盛楠(huangshengnan@mail.tsinghua.edu.cn)技术问题请联系：廖文杰(liaowj17@tsinghua.org.cn)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.2023.101886Liao 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.2023.107873Liao 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.2023.105187Zhao 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.2023.105223Qin 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/sc20240001Gu 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.13236Fei 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.4151Han 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.2024.118500Qin 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来源：陆新征课题组
