| Vol. - No. | Vol.12 - No.3 |
|---|---|
| Date | Oct., 2023 |
| Title |
Generative Artificial Intelligence for Structural Design of Tall Buildings |
| Author | Wenjie Liao, Xinzheng Lu+, and Yifan Fei |
| Institutions |
Key Laboratory of Civil Engineering Safety and Durability of China Education Ministry, Department of Civil Engineering, Tsinghua University, Beijing 100084, China |
| Abstract |
The implementation of artificial intelligence (AI) design for tall building structures is an essential solution for addressing critical challenges in the current structural design industry. Generative AI technology is a crucial technical aid because it can acquire knowledge of design principles from multiple sources, such as architectural and structural design data, empirical knowledge, and mechanical principles. This paper presents a set of AI design techniques for building structures based on two types of generative AI: generative adversarial networks and graph neural networks. Specifically, these techniques effectively master the design of vertical and horizontal component layouts as well as the cross-sectional size of components in reinforced concrete shear walls and frame structures of tall buildings. Consequently, these approaches enable the development of high-quality and high-efficiency AI designs for building structures. |
| Keyword |
Tall building structures, Generative artificial intelligence (AI), Generative adversarial network (GAN), Graph neural network (GNN) |
| PP. | PP.203~208 |
| Paper File |
 View |