摘要
建筑结构的智能化方案设计是智能建造的重要内容。既有研究提出了基于深度神经网络的剪力墙结构生成式设计方法框架、智能设计算法、设计性能评价方法等,完成了从数据驱动到物理增强的智能化设计方法的发展,但目前尚未有研究针对不同设计条件下数据驱动和物理增强方法的设计能力进行详细对比,且基于计算机视觉与基于力学性能的评价方法尚未有明确的关系,难以有效保证计算机视觉评价方法的合理性。基于深度生成式算法对比和算例分析,开展数据驱动和物理增强数据驱动方法的详细对比,并进一步验证基于计算机视觉评价与基于力学分析评价方法的正相关性。结果表明:数据驱动的方法易受到数据质量与数量的约束,而物理增强数据驱动的方法设计性能更加稳定,基本摆脱数据质量和数量的约束;基于计算机视觉综合评价指标SCV的合理性阈值为0.5,对应力学性能差异约为10%。
Intelligent structural design in the scheme phase is an essential component of intelligent construction.Existing studies have proposed the deep neural network-based framework of intelligent generative structural design,intelligent design algorithms,and design performance evaluation methods for shear wall structures,which have developed intelligent structural design methods from data-driven to physics-enhanced data-driven.However,little detailed design performance comparison of data-driven and physics-enhanced methods under different design conditions is conducted.Furthermore,the relationship between the computer vision-based and mechanical analysis-based evaluation methods are still unclear,resulting in difficulties in effectively guaranteeing the rationality of the computer vision-based evaluation methods.Hence,in this study,the comparative analysis of data-driven and physics-enhanced intelligent design methods is conducted by algorithm comparison and case studies;and the consistent relationship between computer vision-based and mechanical analysis-based evaluation methods is validated.The comparison results reveal that data-driven methods are more prone to be limited by the quality and quantity of training data.In contrast,the physics-enhanced data-driven design method is more robust under different design conditions and is little affected by the data-caused limitation.Moreover,the rationality threshold of the computer vision-based evaluation index(SCV)is 0.5,corresponding to a difference in the mechanical performance of approximately 10%.
作者
廖文杰
陆新征
黄羽立
赵鹏举
费一凡
郑哲
LIAO Wenjie;LU Xinzheng;HUANG Yuli;ZHAO Pengju;FEI Yifan;ZHENG Zhe(Key Laboratory of Civil Engineering Safety and Durability of China Education Ministry,Department of Civil Engineering,Tsinghua University,Beijing 400045,P.R.China)
出处
《土木与环境工程学报（中英文）》
CSCD
北大核心
2024年第1期82-92,共11页
Journal of Civil and Environmental Engineering
基金
国家重点研发计划(2019YFE0112800)
腾讯基金会(科学探索奖)
清华大学“水木学者”计划项目(2022SM005)。
关键词
智能化结构设计
生成对抗网络
数据驱动
物理增强
设计评价
intelligent structural design
generative adversarial networks
data-driven
physics-enhanced
design evaluation
