從變分自編碼器隱空間中生成新橋型的嘗試
首發時間：2023-11-06
摘要：嘗試利用生成式人工智能技術生成新橋型。采用3dsMax動畫軟件渲染構件寬度變化的橋梁立面灰度圖片、接著OpenCV模塊對圖片進行適量的幾何變換（旋轉、水平縮放、豎向縮放），獲得三跨梁式橋、拱式橋、斜拉橋、懸索橋圖像數據集?；赑ython編程語言、TensorFlow及Keras深度學習平臺框架，構建和訓練變分自編碼器，得到便于向量運算的低維橋型隱空間，實踐發現從隱空間中采樣能夠生成新的組合橋型。變分自編碼器能夠在人類原創橋型的基礎上，將兩種橋型合為一體，組合創造。生成式人工智能技術能夠協助橋梁設計師進行橋型創新、可以作為副駕駛。
關鍵詞： 生成式人工智能 橋型創新 變分自編碼器 隱空間 深度學習
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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.
Keywords： variational autoencoder latent space bridge-type innovation generative artificial intelligence deep learning
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從變分自編碼器隱空間中生成新橋型的嘗試
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