An attempt to generate new bridge types from latent space of generative adversarial network

2024/01/01 by Hongjun Zhang, Zhang, Hongjun
Computer Science · Earth and Planetary Sciences · #3D Surveying and Cultural Heritage #Artificial Intelligence (cs.AI) #Computer Vision and Pattern Recognition (cs.CV) #FOS: Computer and information sciences #Image Processing and 3D Reconstruction #Machine Learning (cs.LG)

paper · pdf · doi:10.48550/arxiv.2401.00700

openalex publication_date 2024/01/01 · openalex created_date 2024/01/03 · openalex updated_date 2026/07/28

Abstract

Try to generate new bridge types using generative artificial intelligence technology. Symmetric structured image dataset of three-span beam bridge, arch bridge, cable-stayed bridge and suspension bridge are used . Based on Python programming language, TensorFlow and Keras deep learning platform framework , as well as Wasserstein loss function and Lipschitz constraints, generative adversarial network is constructed and trained. From the obtained low dimensional bridge-type latent space sampling, new bridge types with asymmetric structures can be generated. Generative adversarial network can create new bridge types by organically combining different structural components on the basis of human original bridge types. It has a certain degree of human original ability. Generative artificial intelligence technology can open up imagination space and inspire humanity.

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