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OACSTPCD

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％.