Correlation between fracture roughness and material strength parameters in SFRCs using 2D image analysis

Recently, relationship between fracture surface roughness and material's strength has received too much attention in literature. In this paper, 2D image is used as a novel and economic tool to characterize the fracture surface roughness of steel fiber reinforced concrete (SFRC). The main purpose of this study was to investigate the effect of the steel fiber content, V-f, and the concrete design mix on the surface roughness. The direct tensile tests are carried out on dog-bone shape of SFRC's specimens with a constant size. The fracture surface of the specimens is photographed then fracture trajectory as a XY list of pixel numbers are extracted using image analysis (IA) techniques. The fractal dimensions are calculated based on the Box-counting algorithm via a computational code. Finally, to validate the results, they are applied into formulations of "Discrete fractal fracture mechanics" by Wnuk and Yavari published in 2008 [11]. Accordingly, an estimation for ratio of quantum fracture step to maximum void's size is given to describe the relationship between design strength f(d) and the fracture roughness. Also, steel fiber effect on formation of surface trajectory is discussed according to image analysis results. Correlation matrix of all significant parameters in this study is presented. It is concluded that 2D image analysis is a very economic and accessible method for surface roughness characterization. (C) 2017 Elsevier Ltd. All rights reserved.