Fracture in random quasibrittle media: I. Discrete mesoscale simulations of load capacity and fracture process zone. (August 2020)

• Record Type: Journal Article
• Authors: Eliáš, Jan; Vořechovský, Miroslav
• Is Part Of: Engineering fracture mechanics. Volume 235(2020)
• Publication Date: 2020-08
• Subjects: Discrete model -- Mesoscale -- Concrete -- Probability -- Random field -- Fracture -- Fracture process zone; Fracture mechanics -- Periodicals; Rupture, Mécanique de la -- Périodiques; Fracture mechanics; Periodicals; 620.112605
• DOI: 10.1016/j.engfracmech.2020.107160
• Languages: English
• ISSNs: 0013-7944

Abstract

Highlights: Mesoscale discrete model of concrete enhanced by spatially random material parameters. Notched and unnotched three point bending and uniaxial tension are analyzed. Fracture process zone statistically evaluated for various random field parameters. Nontrivial dependence of peak loads on correlation length and variance is reported. Local averaging and weakest-link are identified as the underlying mechanisms. Abstract: Numerical simulations of concrete fracture performed with a probabilistic mesoscale discrete model are presented. The model represents a substantial part of material randomness by assigning random locations to the largest aggregates. The remaining part of randomness is introduced by causing material parameters to fluctuate randomly via a homogeneous random field. An extensive numerical study performed with the model considers prisms loaded in uniaxial tension with both fixed and rotating platens, and also beams with and without a notch loaded in three point bending. The results show the nontrivial effect of (i) autocorrelation length and (ii) variance of the random field on the fracture behavior of the model. Statistics of the peak load are presented as well as the size and shape of the fracture process zone at the moment when the maximum load is attained. Local averaging within the fracture process zone and weakest-link are identified as underlying mechanisms explaining the reported results. The companion paper, Part II (Vořechovský and Eliáš, 2020), introduces an analytical model capable of predicting the distribution of the peak load obtained with the probabilistic discrete model via the simple estimation of extremes of a random field obtained as moving average of local strength.