Accelerated Fatigue Reliability Analysis of Stiffened Sections Using Deep Learning

Fatigue is one of the main failure mechanisms in structures subjected to fluctuating loads such as bridges and ships. If inadequately designed for such loads, fatigue can be detrimental to the safety of the structure. When fatigue cracks reach a certain size, sudden fracture failure or yielding of the reduced section can occur. Accordingly, quantifying the critical crack size is essential for determining the reliability of fatigue critical structures under growing cracks. Failure Assessment Diagrams (FADs) can be used to determine the critical crack size or whether the state of the crack is acceptable or not at a particular instant in time. Due to the presence of uncertainties in loads, material properties and crack growth behavior, probabilistic analysis is essential to understand the fatigue performance of the structure over its service life. A time dependent reliability profile for the structure can be established to help schedule maintenance and repair activities. However, probabilistic analysis of crack growth under complex geometrical and loading conditions can be very expensive computationally. Deep learning is a useful tool that is used in this study to curtail this lengthy process by establishing multi-variate non-linear approximations for complex fatigue crack growth profiles. This study proposes a framework for establishing the fatigue reliability profiles of stiffened panels under uncertainty. Monte Carlo simulation is used to draw samples from relevant probabilistic parameters and establish the time dependent reliability profile of the structure under propagating cracks. Deep learning is adopted to improve the computational efficiency of the probabilistic analysis in establishing the probabilistic crack growth profiles. The proposed framework is illustrated on a bridge with stiffened tub girders subjected to fatigue loading.Civil Engineerin

Comparative study of neural network-based models for fatigue crack growth predictions of small cracks

The behavior of small cracks (less than 1 mm in length) have been shown to be quite different than large cracks for a variety of materials. In the past two decades, the large-crack test procedure (load shedding) has been shown to cause a load-history effect in the low-rate regime, generating elevated thresholds, and slower rates than steady-state behavior, which caused a large part of these differences. The literature has shown that small-crack data is more appropriate for damage tolerance and fatigue analyses. The objective of this work was to validate the development of artificial neural network (ANN) methods in fatigue crack growth predictions of small cracks. Two ANNs were developed: extreme learning machine (ELM) and radial basis function network (RBFN) to predict fatigue crack growth of small cracks for various materials. A wide range in stress ratio R and stress levels were considered for selected materials. The two ANNs were compared with each other in terms of mean squared error achieved and performance. The ELM method showed a superior interpolation and extrapolation ability compared to the RBFN method