Artificial neural network-based analysis of effective crack model in concrete fracture

• Published in: Fatigue & fracture of engineering materials & structures Vol. 33; no. 9; pp. 595 - 606
• Main Author: INCE, R.
• Format: Journal Article
• Language: English
• Published: Oxford, UK Blackwell Publishing Ltd 01.09.2010; Blackwell; Wiley Subscription Services, Inc
• Subjects: Applied sciences; artificial neural networks; Cements; Concrete; cracks; Design engineering; effective crack model; Electrochemical machining; Exact sciences and technology; Fatigue failure; Fracture mechanics; Fractures; Learning theory; Mathematical models; Mechanical properties and methods of testing. Rheology. Fracture mechanics. Tribology; Metals. Metallurgy; Neural networks; Nonlinear systems; concrete; artificial neural networks; Fracture mechanics; Rupture; Mechanical properties; cracks; effective crack model; Neural network; Application; Artificial intelligence; Modeling; Reinforced concrete
• ISSN: 8756-758X; 1460-2695
• DOI: 10.1111/j.1460-2695.2010.01469.x

ABSTRACT Many non‐linear fracture models have been proposed by design codes and investigators to determine fracture parameters of cement‐based materials. To characterise failure of concrete structures, the effective crack model (ECM) needs two fracture parameters: the effective crack length ae and the critical stress intensity factor . Nevertheless, ECM requires a closed‐loop testing system and the calculation of ae needs considerable computational effort. For this reason, ECM is simulated with an artificial neural network (ANN) in this study. The main benefit of using an ANN approach is that the network is built directly on experimental data by using the self‐organizing capabilities of the ANN. The presented fracture model was developed by utilising 464 noisy test data taken from the literature, which were obtained via different test methods in different laboratories. The results of an ANN‐based ECM look viable and very promising.