Predicting Fatigue Crack Growth via Path Slicing and Re-Weighting

Predicting potential risks associated with the fatigue of key structural components is crucial in engineering design. However, fatigue often involves entangled complexities of material microstructures and service conditions, making diagnosis and prognosis of fatigue damage challenging. We report a statistical learning framework to predict the growth of fatigue cracks and the life-to-failure of the components under loading conditions with uncertainties. Digital libraries of fatigue crack patterns and the remaining life are constructed by high-fidelity physical simulations. Dimensionality reduction and neural network architectures are then used to learn the history dependence and nonlinearity of fatigue crack growth. Path-slicing and re-weighting techniques are introduced to handle the statistical noises and rare events. The predicted fatigue crack patterns are self-updated and self-corrected by the evolving crack patterns. The end-to-end approach is validated by representative examples with fatigue cracks in plates, which showcase the digital-twin scenario in real-time structural health monitoring and fatigue life prediction for maintenance management decision-making

Big Data Analytics Using Neural networks

Machine learning is a branch of artificial intelligence in which the system is made to learn from data which can be used to make predictions, real world simulations, pattern recognitions and classifications of the input data. Among the various machine learning approaches in the sub-field of data classification, neural-network methods have been found to be an useful alternatives to the statistical techniques. An artificial neural network is a mathematical model, inspired by biological neural networks, are used for modeling complex relationships between inputs and outputs or to find patterns in data. The goal of the project is to construct a system capable of analyzing and predicting the output for the evaluation dataset provided by the IBM Watson: The Great Mind Challenge organized by IBM Research and InnoCentive INSTINCT (Investigating Novel Statistical Techniques to Identify Neurophysiological Correlates of Trustworthiness) : The IARPA Trustworthiness Challenge organized by the office of The Director Of National Intelligence. The objective of this paper is to understand the machine learning using neural networks. At the end of the paper, the comparison between different learning strategies have been shown which are used to increase the accuracy of the predictions. From the trained neural network up to a satisfactory level, we will be able to classify any generalized input, process often termed as generalization capability of the learning system