Integrated Schematic Design Method for Shear Wall Structures: A Practical Application of Generative Adversarial Networks

Abstract

The intelligent design method based on generative adversarial networks (GANs) represents an emerging structural design paradigm where design rules are not artificially defined but are directly learned from existing design data. GAN-based methods have exhibited promising potential compared to conventional methods in the schematic design phase of reinforced concrete (RC) shear wall structures. However, for the following reasons, it is challenging to apply GAN-based approaches in the industry and to integrate them into the structural design process. (1) The data form of GAN-based methods is heterogeneous from that of the widely used computer-aided design (CAD) methods, and (2) GAN-based methods have high requirements on the hardware and software environment of the user’s computer. As a result, this study proposes an integrated schematic design method for RC shear wall structures, providing a workable GAN application strategy. Specifically, (1) a preprocessing method of architectural CAD drawings is proposed to connect the GAN with the upstream architectural design; (2) a user-friendly cloud design platform is built to reduce the requirements of the user’s local computer environment; and (3) a heterogeneous data transformation method and a parametric modeling procedure are proposed to automatically establish a structural analysis model based on GAN’s design, facilitating downstream detailed design tasks. The proposed method makes it possible for the entire schematic design phase of RC shear wall structures to be intelligent and automated. A case study reveals that the proposed method has a heterogeneous data transformation accuracy of 97.3% and is capable of generating shear wall layout designs similar to the designs of a competent engineer, with 225 times higher efficiency.

Base‐isolation design of shear wall structures using physics‐rule‐co‐guided self‐supervised generative adversarial networks

“…Therefore, intelligent and automated design is needed to improve design efficiency and give valuable design references for engineers. [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28] The preliminary but critical seismic isolation scheme design process can significantly affect subsequent optimal designs. In scheme design, precise structural analysis is not as important as the determination of isolation bearing close to the ideal design.…”

“…With the rise of deep learning, intelligent structural design methods that can learn from existing design data are rapidly evolving, thereby opening up new possibilities. [13][14][15][16][17][18][19][20][21][22][23][24][25][26][27][28] Liao et al, 14,15 Lu et al, 16 Fei et al, 17 , 18 Zhao et al, 19,20 Pizarro et al, 24 and Fu et al 25 undertook comprehensive research to develop a generative adversarial network (GAN)-based intelligent design approaches for shear walls, beams, and frame-core tube structures; Chang et al 13 and Zhao et al 21 developed graph neural network (GNN)-based structural design methods; Hayashi et al, 26 Zhu et al, 27 and Jeong et al 28 used reinforcement learning for structural design. Among those methods, GAN is one of the most effective and extensively utilized technologies for generating structural designs, due to its powerful generation ability.…”

“…While, the prevailing artificial design approach is time‐consuming and labor‐cost, and unbeneficial for the advancement of seismic isolation. Therefore, intelligent and automated design is needed to improve design efficiency and give valuable design references for engineers 13–28 …”

“…With the rise of deep learning, intelligent structural design methods that can learn from existing design data are rapidly evolving, thereby opening up new possibilities 13–28 . Liao et al., 14,15 Lu et al., 16 Fei et al., 17 , 18 Zhao et al., 19,20 Pizarro et al., 24 and Fu et al 25 .…”

“…With the rise of deep learning, intelligent structural design methods that can learn from existing design data are rapidly evolving, thereby opening up new possibilities 13–28 . Liao et al., 14,15 Lu et al., 16 Fei et al., 17 , 18 Zhao et al., 19,20 Pizarro et al., 24 and Fu et al 25 . undertook comprehensive research to develop a generative adversarial network (GAN)‐based intelligent design approaches for shear walls, beams, and frame‐core tube structures; Chang et al 13 .…”