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

• URL: http://arxiv.org/abs/2401.00700v1
• Date: Mon, 1 Jan 2024 08:46:29 GMT
• Title: An attempt to generate new bridge types from latent space of generative adversarial network
• Authors: Hongjun Zhang
• Abstract summary: Symmetric structured image dataset of three-span beam bridge, arch bridge, cable-stayed bridge and suspension bridge are used. Based on Python programming language, and Keras deep learning platform framework, generative adversarial network is constructed and trained.
• Score: 2.05750372679553
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• 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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