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从变分自编码器隐空间中生成新桥型的尝试
首发时间：2023-11-06
摘要：尝试利用生成式人工智能技术生成新桥型。采用3dsmax动画软件渲染构件宽度变化的桥梁立面灰度图片、接着opencv模块对图片进行适量的几何变换（旋转、水平缩放、竖向缩放），获得三跨梁式桥、拱式桥、斜拉桥、悬索桥图像数据集。基于python编程语言、tensorflow及keras深度学习平台框架，构建和训练变分自编码器，得到便于向量运算的低维桥型隐空间，实践发现从隐空间中采样能够生成新的组合桥型。变分自编码器能够在人类原创桥型的基础上，将两种桥型合为一体，组合创造。生成式人工智能技术能够协助桥梁设计师进行桥型创新、可以作为副驾驶。
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an attempt to generate new bridge types from latent space of variational autoencoder
abstract：try to generate new bridge types using generative artificial intelligence technology. the grayscale pictures of the bridge fa?ade with the change of component width was rendered by 3dsmax animation software, and then the opencv module performed an appropriate amount of geometric transformation (rotation, horizontal scale, vertical scaling) to obtain the image dataset of three-span beam bridge, arch bridge, cable-stayed bridge and suspension bridge. based on python programming language, tensorflow and keras deep learning platform framework, variational autoencoder was constructed and trained, and low-dimensional bridge-type latent space that is convenient for vector operations was obtained. variational autoencoder can combine two bridge types into one on the basis of the original human bridge type. generative artificial intelligence technology can assist bridge designers in bridge-type innovation, and can be used as copilot.
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从变分自编码器隐空间中生成新桥型的尝试
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