Punching shear strength under static and dynamic loads

Abstract

Modern domestic calculation methods and developed countries for determining the bearing capacity of monolithic reinforced concrete slabs for punching do not fully take into account all factors of design solutions and operating conditions. The available design provisions are made for the static operation of structures and there are no recommendations for taking into account the features of the dynamic impact on the overlap and the nature of the work of the node interfaces. The accepted empirical assumptions of the calculation, based on numerous experimental data, do not take into account the features of the stress-strain state of the coupling of the overlap with the column during destruction according to the punching scheme. This is due to the lack of computational models in which all the acting internal forces ensuring the resistance of the interface to penetration would be considered comprehensively. The complexity of the problem is due to the fact that the sections of the nodal interface are in an inhomogeneous stressed state. The stress-strain state of plates for punching under dynamic load is currently little studied. This article proposes a method for determining the bearing capacity of a symmetrical nodal coupling of a column with an overlap for punching under static and short-term dynamic loading. The proposed design model of the punching strength is based on the following prerequisites: the resistance to punching of a monolithic reinforced floor consists of the shear resistance along the surface of the reduced punching pyramid formed by the height of the compressed concrete zone; the strength of the concrete shear resistance increases due to volumetric compressive forces on the surface of the reduced punching pyramid; the angle of inclination of the faces of the punching pyramid depends on the loading speed. The obtained theoretical dependences are applicable under static and dynamic loading and are in satisfactory agreement with experimental data.