Artificial neural network approach to predict the fracture parameters of the size effect model for concrete. Issue 11 (27th April 2015)

• Record Type: Journal Article
• Authors: Yan, Y.; Ren, Q.; Xia, N.; Shen, L.; Gu, J.; Wang, Q.; Palin‐Luc, T.
• Is Part Of: Fatigue & fracture of engineering materials & structures. Volume 38:Issue 11(2015:Nov.)
• Page Start: 1347
• Page End: 1358
• Publication Date: 2015-04-27
• Subjects: Materials -- Fatigue -- Periodicals; Fracture mechanics -- Periodicals; 620.1123
• DOI: 10.1111/ffe.12309
• Languages: English
• ISSNs: 8756-758X

Abstract

<abstract abstract-type="main"> <title>Abstract</title> <p>The fracture parameters (the fracture energy and the effective length of the fracture process zone for an infinitely large specimen) in the size effect model of concrete exhibit a large scatter as measured in most of the experimental studies. This phenomenon is ubiquitous and has presented a great challenge to characterize the structural failure over the last decade. In order to remove the perplexing issue, this paper develops two models to predict the two fracture parameters using the artificial neural network (ANN) methodology. The proposed models are verified by using 77 experimental data collected from the literature. The results demonstrate that the two ANN‐based size effect model models (ANN‐I and ANN‐II) are viable for predicting the fracture parameters and yield more accurate results than those obtained from the conventional regression formulations. Additionally, a parametric study is employed to evaluate the impact of each independent material parameter on the fracture parameters.</p> </abstract>