Fracture in random quasibrittle media: II. Analytical model based on extremes of the averaging process

journal article in Web of Science

English

The paper delivers an analytical model for prediction of the peak force in concrete specimens loaded in bending (both notched and unnotched). The model is capable of predicting peak force statistics by computing the extreme values of sliding averages of random strength fields. The local strength of the specimen is modeled by a stationary isotropic random field with Gaussian distribution and a given autocorrelation function. The averaging operation represents the progressive loss in material integrity and the associated stress redistribution that takes place prior to reaching the peak load. Once the (linear) averaging process is performed analytically, the resulting random field of averaged strength is assumed to represent a series of representative volume elements (RVEs) and the global strength is found by solving for the minimum of such an effective strength field. All these operations can be written analytically and there are only four model parameters: the three dimensions of the averaging volume (RVE) and the length of the final weakest-link chain. The model is verified using detailed numerical computations of notched and unnotched concrete beams simulated by mesoscale discrete simulations of concrete fracture performed with probabilistic distributions of model parameters. These are presented in the companion paper Part I (Elias; and Vorechovsky, 2020). The numerical model used for verification represents material randomness both by assigning random locations to the largest aggregates and by simulating random fluctuations of material parameters via a homogeneous random field.

Random strength field; Mesoscale; Concrete; Local averaging; RVE; Weibull theory; Fracture process zone; Weakest-link model

VOŘECHOVSKÝ, M.; ELIÁŠ, J.

1. 8. 2020

Elsevier

0013-7944

Engineering Fracture Mechanics

235

1

United Kingdom of Great Britain and Northern Ireland

107155-1

107155-20

20

https://www.sciencedirect.com/science/article/pii/S0013794420307384