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Effect of polymer fibers on pore pressure development and explosive spalling of ultra-high performance concrete at elevated temperature

Zhang Dong, Chen Baochun, Wu Xiangguo, Weng Yiwei, Li Ye

Archives of Civil and Mechanical Engineering

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2022

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Vol. 22, no. 4

art. no. e187, 2022

EN

This paper investigated pore pressure development of ultra-high performance concrete (UHPC) included various polymer fibers, i.e., linear low-density polyethylene (LLDPE), ultra-high molecular weight polyethylene (UHMWPE), polypropylene (PP), polyester (PET), and polyamide (PA) fibers. Temperature and pore pressure were measured simultaneously at different depths of UHPC specimens subjected to one-dimensional heating. It was found that the PP and PA fibers prevented spalling of UHPC by enhancing moisture migration, which resulted in the development of pore pressure in the deeper region of the specimens. The moisture migration in UHPC with LLDPE fibers caused spalling of a layer of concrete in a deep region of specimen. UHMWPE fibers did not affect pore pressure development and spalling resistance of UHPC significantly, while with PET fibers, the pore pressure of UHPC raised sharply due to inadequate moisture migration, leading to spalling of a whole layer. Instead of melting polymer fibers and empty channels left, microcracks created by the fibers were responsible for releasing vapor pressure and spalling prevention. Fibers with high thermal expansion between 100 and 200 °C are recommended for spalling prevention of UHPC.

2

A machine learning approach to predict explosive spalling of heated concrete

Liu Jin-Cheng, Zhang Zhigang

Archives of Civil and Mechanical Engineering

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2020

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Vol. 20, no. 4

535--559

EN

Explosive spalling is an unfavorable phenomenon observed in concrete when exposed to heating load. It is a great potential threat to safety of concrete structures subjected to accidental thermal loads. Therefore, assessing explosive spalling risk of concrete is important for fire safety design of concrete structures. This paper proposed a popular machine learning approach, i.e., artificial neural network (ANN), to assess explosive spalling risk of concrete. Besides, the decision tree method was also used to execute the same mission for a comparison purpose. Twenty-eight groups of heating tests were conducted to validate the proposed ANN model. The ANN model behaved well in assessing explosive spalling of concrete, with a prediction accuracy of 82.1%. This study shows that ANN is a promising method for adequate classification of concrete as material resistant or not resistant to thermal explosive spalling.

3

Uszkodzenia pożarowe betonu konstrukcyjnego

Chudyba K.

Inżynieria i Budownictwo

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2010

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R. 66, nr 1

30-33

PL

Omówiono rodzaje uszkodzeń betonu konstrukcyjnego spowodowanych oddziaływaniami wysokiej temperatury oraz czynniki warunkujące ich wystąpienie. Szczególną uwagę zwrócono na zjawisko odprysków eksplozyjnych. Podano informacje o efektywnych metodach eliminowania tych odprysków. Przedstawiono konsekwencje uszkodzeń pożarowych prowadzących do możliwych mechanizmów zniszczenia elementów z betonu.

EN

The paper presents different types of structural concrete damages due to fire and major factors conditioning their occurance. Special attention was paid to explosive spalling phenomenon with summing up the effective methods for its elimination. Consequences of fire damages as the possible initiation of typical failure modes for concrete elements were also discussed.