Open Access

ARTICLE

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

by Xueya Wang¹, Yiming Zhang^2,3,*, Qi Liu⁴, Huanran Wang¹

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

* Corresponding Author: Yiming Zhang. Email:

Computers, Materials & Continua 2024, 79(2), 2907-2922. https://doi.org/10.32604/cmc.2024.050736

Received 15 February 2024; Accepted 08 April 2024; Issue published 15 May 2024

Abstract

Concrete subjected to fire loads is susceptible to explosive spalling, which can lead to the exposure of reinforcing steel bars to the fire, substantially jeopardizing the structural safety and stability. The spalling of fire-loaded concrete is closely related to the evolution of pore pressure and temperature. Conventional analytical methods involve the resolution of complex, strongly coupled multifield equations, necessitating significant computational efforts. To rapidly and accurately obtain the distributions of pore-pressure and temperature, the Pix2Pix model is adopted in this work, which is celebrated for its capabilities in image generation. The open-source dataset used herein features RGB images we generated using a sophisticated coupled model, while the grayscale images encapsulate the 15 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-loaded concrete has been meticulously designed and trained. The applicability and reliability of the Pix2Pix model in concrete parameter prediction are verified by comparing its outcomes with those derived from the strong coupling THC model. Notably, for the practical engineering applications, our findings indicate that utilizing monochrome images as the initial target for analysis yields more dependable results. This work not only offers valuable insights for civil engineers specializing in concrete structures but also establishes a robust methodological approach for researchers seeking to create similar predictive models.

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Keywords

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