Effect of Wall Thickness on Thermal Behaviors of RC Walls Under Fire Conditions View Full Text


Ontology type: schema:ScholarlyArticle      Open Access: True


Article Info

DATE

2016-09

AUTHORS

Jiyeon Kang, Hyunah Yoon, Woosuk Kim, Venkatesh Kodur, Yeongsoo Shin, Heesun Kim

ABSTRACT

The objective of this paper is to investigate the effect of thickness and moisture on temperature distributions of reinforced concrete walls under fire conditions. Toward this goal, the first three wall specimens having different thicknesses are heated for 2 h according to ISO standard heating curve and the temperature distribution through the wall thickness is measured. Since the thermal behavior of the tested walls is influenced by thickness, as well as moisture content, three additional walls are prepared and preheated to reduce moisture content and then tested under fire exposure. The experimental results clearly show the temperatures measured close to the fire exposed surface of the thickest wall with 250 mm thickness is the highest in the temperatures measured at the same location of the thinner wall with 150 mm thickness because of the moisture clog that is formed inside the wall with 250 mm of thickness. This prevents heat being transferred to the opposite side of the heated surface. This is also confirmed by the thermal behavior of the preheated walls, showing that the temperature is well distributed in the preheated walls as compared to that in non-preheated walls. Finite element models including moisture clog zone are generated to simulate fire tests with consideration of moisture clog effect. The temperature distributions of the models predicted from the transient heat analyses are compared with experimental results and show good agreements. In addition, parametric studies are performed with various moisture contents in order to investigate effect of moisture contents on the thermal behaviors of the concrete walls. More... »

PAGES

19-31

References to SciGraph publications

Identifiers

URI

http://scigraph.springernature.com/pub.10.1007/s40069-016-0164-5

DOI

http://dx.doi.org/10.1007/s40069-016-0164-5

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https://app.dimensions.ai/details/publication/pub.1021260699


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