Journal Article

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

Fei, Yifan, Chen, Xingyu, Lu, Xinzheng, Zhang, Shen, Zhao, Pengju, Liao, Wenjie, Yin, Pengfei, Han, Bo

2022

Overview

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.

• Publisher: MDPI AG
• Subject: Algorithms / Architectural elements / Architecture / Automation / CAD / Cloud computing / Computer aided design / Construction / Design techniques / Efficiency / Engineering drawings / Engineers / Generative adversarial networks / intelligent structural design / Literature reviews / Machine learning / Methods / Neural networks / parametric modeling / Parametric statistics / Reinforced concrete / reinforced concrete shear wall structures / schematic design / Shear walls / Software services / Structural analysis / Structural design / Structural engineering