Prediction of the Pore-Pressure Built-Up and Temperature of Fire-Loaded Concrete with Pix2Pix  

作　　者：Xueya Wang Yiming Zhang Qi Liu Huanran Wang 

机构地区：[1]Mechanics and Materials Science Research Center,Ningbo University,Ningbo,315211,China [2]Jinyun Institute,Zhejiang Sci-Tech University,Lishui,321400,China [3]School of Civil Engineering and Architecture,Zhejiang Sci-Tech University,Hangzhou,310018,China [4]School of Computer Science,Nanjing University of Information Science and Technology,Nanjing,210044,China

出　　处：《Computers, Materials & Continua》2024年第5期2907-2922,共16页计算机、材料和连续体（英文）

基　　金：the National Natural Science Foundation of China(NSFC)(52178324).

摘　　要：Concrete subjected to fire loads is susceptible to explosive spalling, which can lead to the exposure of reinforcingsteel bars to the fire, substantially jeopardizing the structural safety and stability. The spalling of fire-loaded concreteis closely related to the evolution of pore pressure and temperature. Conventional analytical methods involve theresolution of complex, strongly coupled multifield equations, necessitating significant computational efforts. Torapidly and accurately obtain the distributions of pore-pressure and temperature, the Pix2Pix model is adoptedin this work, which is celebrated for its capabilities in image generation. The open-source dataset used hereinfeatures RGB images we generated using a sophisticated coupled model, while the grayscale images encapsulate the15 principal variables influencing spalling. After conducting a series of tests with different layers configurations,activation functions and loss functions, the Pix2Pix model suitable for assessing the spalling risk of fire-loadedconcrete has been meticulously designed and trained. The applicability and reliability of the Pix2Pix model inconcrete parameter prediction are verified by comparing its outcomes with those derived fromthe strong couplingTHC model. Notably, for the practical engineering applications, our findings indicate that utilizing monochromeimages as the initial target for analysis yields more dependable results. This work not only offers valuable insightsfor civil engineers specializing in concrete structures but also establishes a robust methodological approach forresearchers seeking to create similar predictive models.

关 键 词：Fire loaded concrete spalling risk pore pressure generative adversarial network(GAN) Pix2Pix 

分 类 号：TU528[建筑科学—建筑技术科学]