Fatigue & Fracture of Engineering Materials & Structures · 2010 · 39 citations · 27 references
EngineeringCivil EngineeringMaterials ScienceBuilding MaterialsComputational Materials ScienceMachine LearningCrack AssessmentFailure TheoriesFracture CharacterizationFracture MechanicsFracture PropagationNeural NetworksConcrete Fracture MechanicsCrack Growth ModelingCrack Propagation AnalysisCrack QuantificationDesign CodesFracture Mechanics SimulationFracture Propagation AnalysisStress Intensity Factor
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 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.
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