土木与环境工程学报（中英文）2024，Vol.46Issue(1)：82-92,11.DOI:10.11835/j.issn.2096-6717.2022.078
剪力墙结构智能化生成式设计方法:从数据驱动到物理增强
Intelligent generative structural design methods for shear wall buildings:From data-driven to physics-enhanced
摘要
Abstract
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％.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Key words
intelligent structural design/generative adversarial networks/data-driven/physics-enhanced/design evaluation分类
土木建筑引用本文复制引用
廖文杰,陆新征,黄羽立,赵鹏举,费一凡,郑哲..剪力墙结构智能化生成式设计方法:从数据驱动到物理增强[J].土木与环境工程学报（中英文）,2024,46(1):82-92,11.基金项目
国家重点研发计划(2019YFE0112800) （2019YFE0112800）
腾讯基金会(科学探索奖) （科学探索奖）
清华大学"水木学者"计划项目(2022SM005)National Key R & D Program of China(No.2019YFE0112800) （2022SM005）
Tencent Foundation(XPLORER PRIZE) （XPLORER PRIZE）
Shuimu Tsinghua Scholar Program(2022SM005) （2022SM005）
