Numerical analysis of plain and fiber reinforced concrete structures during cyclic loading: Influence of frictional sliding and crack roughness

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Abstract
Concrete is a quasi‐brittle material, characterized by a non‐negligible, finite‐sized fracture process zone (FPZ) in which various toughening mechanisms play a significant role on crack development and propagation. Concrete is often reinforced with fibers to improve the serviceability and longevity of concrete structures by controlling the maximal crack widths and providing residual carrying capacity to initiated cracks. In quasi‐brittle materials such as concrete, toughening mechanisms such as crack deflection due to the presence of large heterogeneities, contact shielding, i.e., wedging and crack closure induced by debris and rough crack faces, crack bridging by tough aggregates, unbroken ligaments and fibers (in fiber‐reinforced concretes), are mostly governing damage evolution under monotonic and especially under cyclic loadings [1]. All these items contribute to a complex non‐linear response in terms of load‐displacement curves observed in experimental investigations. Many investigations have been performed on plain concrete, but not so many for high‐performance fiber‐reinforced concrete [2] which is the main subject of investigation within the DFG Priority Program 2020. In order to better understand the influence of these toughening mechanisms on the formation of hysteresis during cyclic loading‐unloading of plain and steel fiber‐reinforced high‐performance concrete, we model these mechanisms and their evolving influence on damage development using a discrete crack approach, and take into account the imperfect closure of cracks and friction between crack faces that come in contact during loading/unloading of the specimen. Within the scope of this contribution, we present the formulation of the model and illustrate its performance through selected numerical experiments under monotonic and cyclic loading of plain and fiber‐reinforced high‐performance concrete specimens. Model predictions are compared with laboratory measurements.

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