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.

The strong ground motions (GMs) recorded by strong motion networks are significant to increase the accuracy of seismic damage assessment. However, the influence of sensor density on seismic damage assessment remains unclarified. Therefore, a workflow is proposed in this study to quantitatively analyze the influence of sensor density on seismic damage assessment. The scenario-based earthquake simulation method is first used to provide the time history of GM at each location as the ground truth of the analysis. Subsequently, a GM prediction method, namely the interpolation method, is adopted to predict GMs at locations without sensors using measuring data from the limited sensors. Finally, the building scale and region scale seismic damage under different sensor densities are compared to quantitatively analyze the influence of sensor density on seismic damage assessment. A detailed case study for Zeytinburnu District, Istanbul, Turkey, is performed to demonstrate the proposed methods. The findings of this study can provide an important reference for seismic damage assessment and the deployment of strong motion networks.