Steel and Composite Structures

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Accurate theoretical modeling and code prediction of the punching shear failure capacity of reinforced concrete slabs

  • Rajai Z. Al-Rousan (Department of Civil Engineering, Faculty of Engineering, Jordan University of Science and Technology) ;
  • Bara'a R. Alnemrawi (Department of Civil Engineering, Faculty of Engineering, Jordan University of Science and Technology)
  • Received : 2024.04.07
  • Accepted : 2024.08.03
  • Published : 2024.08.25
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Abstract

A flat slab is a structural system where columns directly support it without the presence of beam elements. However, despite its wide advantages, this structural system undergoes a major deficiency where stresses are concentrated around the column perimeter, resulting in the progressive collapse of the entire structure as a result of losing the shear transfer mechanisms at the cracked interface. Predicting the punching shear capacity of RC flat slabs is a challenging problem where the factors contributing to the overall slab strength vary broadly in their significance and effect extent. This study proposed a new expression for predicting the slab's capacity in punching shear using a nonuniform concrete tensile stress distribution assumption to capture, as well as possible, the induced strain effect within a thick RC flat slab. Therefore, the overall punching shear capacity is composed of three parts: concrete, aggregate interlock, and dowel action contributions. The factor of the shear span-to-depth ratio (a_v/d) was introduced in the concrete contribution in addition to the aggregate interlock part using the maximum aggregate size. Other significant factors were considered, including the concrete type, concrete grade, size factor, and the flexural reinforcement dowel action. The efficiency of the proposed model was examined using 86 points of published experimental data from 19 studies and compared with five code standards (ACI318, EC2, MC2010, CSA A23.3, and JSCE). The obtained results revealed the efficiency and accuracy of the model prediction, where a covariance value of 4.95% was found, compared to (13.67, 14.05, 15.83, 19.67, and 20.45) % for the (ACI318, CSA A23.3, MC2010, EC2, and JSCE), respectively.

Keywords

Acknowledgement

The authors acknowledge the technical support provided by the Jordan University of Science and Technology (JUST).

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