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