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Artificial neural network‐based analysis of effective crack model in concrete fracture
Author(s) -
INCE R.
Publication year - 2010
Publication title -
fatigue and fracture of engineering materials and structures
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.887
H-Index - 84
eISSN - 1460-2695
pISSN - 8756-758X
DOI - 10.1111/j.1460-2695.2010.01469.x
Subject(s) - artificial neural network , fracture (geology) , structural engineering , stress intensity factor , test data , fracture mechanics , computer science , materials science , engineering , artificial intelligence , composite material , programming language
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 a e and the critical stress intensity factor . Nevertheless, ECM requires a closed‐loop testing system and the calculation of a e 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.