Intelligent Generative Design for Shear Wall Cross-Sectional Size Using Rule-Embedded Generative Adversarial Network

Abstract

Deep learning–driven intelligent generative design for building structures provides novel insights into intelligent construction. In a structural scheme design, the cross-sectional design of the shear wall components is critical. However, the current manual method is time-consuming and labor-intensive, and a statistical regression–based design is insufficiently accurate. Satisfying the requirements of a complex shear wall design in the real world is difficult for both methods. Generative adversarial networks (GANs) can extract implicit design laws by learning from design data and conduct end-to-end design effectively and rapidly. Although GANs have been adopted for intelligent structural design, some design rules established by competent engineers are difficult to capture. Hence, this study developed and subsequently adopted a rule-embedded GAN called StructGAN-Rule to address the demand for a rapid and accurate cross-sectional design of shear wall components. Specifically, a representation method that integrates design images and multiple design conditions was first established, which was followed by the construction of the training data set. Subsequently, based on the design rules, a differentiable tensor operator was built as a rule evaluator, which was embedded in the GAN to guide and constrain the training process. Finally, following the training of StructGAN-Rule, intelligent generative cross-sectional design based on the developed postprocessing method was effectively completed. Case studies on typical shear wall structures demonstrated that the StructGAN-Rule design satisfied the rule constraints well and was highly consistent with the design of engineers (approximately 1% difference). Moreover, the design efficiency was improved 6–10 times compared with that of the latter.