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

arXiv: https://arxiv.org/abs/2401.00700 · HTML: https://mlsanity.com/paper/2401.00700/

## Key facts

- Cited by 5 papers in the map (data through 14 Oct 2026).
- Builds on 1 of its 3 references that are in the map.
- First posted on arXiv 1 Jan 2024.

## Builds on (most cited first, 1 of 1)

- [Wasserstein GAN](https://mlsanity.com/paper/1701.07875/) (2017)

## Built on by (most cited first, 5 of 5)

- [An attempt to generate new bridge types from latent space of PixelCNN](https://mlsanity.com/paper/2401.05964/) (2024)
- [An attempt to generate new bridge types from latent space of generative flow](https://mlsanity.com/paper/2401.10299/) (2024)
- [An attempt to generate new bridge types from latent space of denoising diffusion Implicit model](https://mlsanity.com/paper/2402.07129/) (2024)
- [An attempt to generate new bridge types from latent space of energy-based model](https://mlsanity.com/paper/2401.17657/) (2024)
- [Aided design of bridge aesthetics based on Stable Diffusion fine-tuning](https://mlsanity.com/paper/2409.15812/) (2024)

## Abstract (arXiv)

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.