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AI设计就是根据输入的数据进行学习，然后模仿生成新的设计。所以输入的数据质量越好，AI模仿生成的新设计质量就越高。优秀的学习数据就像给马儿喂了精饲料，马儿的表现就更好。
自2023年11月，基于Diffusion Model驱动的建筑结构智能设计算法发布以来，AIstructure-Copilot就可以实现三驾马车并驾齐驱，为用户提供生成对抗网络（GAN）、图神经网络（GNN）、以及扩散模型（Diffusion Model）的三套独立算法，实现更加多样性的智能设计。 在4月19日发布的文案中，我们提到了软件设计效果有所提升，主要源于我们对三驾马车的动力系统进行了全方位的提升，通过优化算法提升了数据质量。包括：(4) 采用经验规则编码高效评价合理性（覃思中, 等, 工程力学, 2023）。从优化方法和性能评估等方面，多管齐下，大力出奇迹！实现了数据质量的有效提升。在本版本AIstructure改进完成后，我们进行了多次的测试，设计效果有比较明显的提升，特别是一些细节的处理更完善，欢迎大家继续试用。2.1 对局部细节处理能力的提升
（1）封闭管井的处理 如图a所示的更新前剪力墙的布置结果，蓝图框中的局部我们进行放大，图（b）可见局部的封闭管井AI布置了剪力墙，不符合工程实际，本次更新后，AI可以自动识别管井位置，并在布置结构方案的时候合理的避开（如图c）。 （a）剪力墙布置结果（更新前） （b）更新前局部设计效果 （2）建筑外轮廓角部细节处理 本次更新后，扩散模型可以更好的关注到建筑外轮廓角部的细节处理，布置的剪力墙也更加符合工程实际需求，如下图蓝色图框中所示。 （a）更新前设计效果 （3）避免不合理布置 如下图蓝色图框中所示，本次更新后，避免了局部剪力墙布置过于琐碎和“一字墙”等问题。 （a）更新前设计效果 2.2 取消了GAN算法对于平面尺寸的限制要求 本次更新后，对超出51m×25m的平面图纸GAN也可以很好的完成设计。 （a）原始建筑图（平面尺寸51.7m×18.1m ） （b）更新前超出平面尺寸GAN无法完成设计 我们通过优化算法提升数据质量，给三驾马车提供更加精细的饲料，从而对设计效果进行了全方位的提升，更符合工程师的经验习惯，可以辅助工程师更好的开展智能设计，欢迎大家试用。参考文献： Fei YF, Liao WJ, Lu XZ, et al. Semi-supervised learning method incorporating structural optimization for shear-wall structure design using small and long-tailed datasets[J]. Journal of Building Engineering, 2023, 79: 107873. 陆新征,韩进,韩博,等.基于规则学习与编码的剪力墙智能设计优化[J].东南大学学报(自然科学版),2023,53(06):1199-1208. Fei YF, Liao WJ, Lu XZ, et al. Knowledge‐enhanced graph neural networks for construction material quantity estimation of reinforced concrete buildings[J]. Computer‐Aided Civil and Infrastructure Engineering, 2024, 39(4): 518-538. 覃思中,廖文杰,林元庆,等. An efficient assessment method for intelligent design results of shear wall structure based on mechanical performance, material consumption, and empirical rules[J].工程力学,2023,40(12):148-159.
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。 --End-- QQ群，AI-structure-交流群：741840451 黄盛楠：huangshengnan@mail.tsinghua.edu.cn 廖文杰：liaowj17@tsinghua.org.cn 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
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