... and supervised machine learning and deep learning has profoundly accelerated the probability of failure (PoF) assessment and analysis. K-means clustering and Gaussian mixture models show direct relevance between the corrosion depth and corrosion rate, while the overlapping PoF value is scattered in three...

... to this remote condition monitoring and diagnostic computational framework quantify the sensitivity of the structural dynamics to different degradation scenarios. This methodology leverages benchmarked computational structural mechanics models and machine learning methods to enhance the interpretability...

.... There are several machine learning techniques to build prediction models using data. Commonly used approaches include decision tree, K-nearest neighbor, support vector machine, linear discriminant analysis, quadratic discriminant analysis, ANN, Polynomial chaos expansion, radial basis function, Gaussian process...

... distance has more influence on variation in neck size than that of surface diffusivity. The machine learning algorithm Gaussian process regression was used to create the surrogate model of the QOI. Bayesian optimization method was used to find optimal values of the input parameters. For equal-sized...

... the randomness of fatigue damage classification in composite materials using machine learning (ML) algorithms?” To answer this question, piezo-electric signals for carbon fiber reinforced polymer (CFRP) test specimens were taken from NASA Ames prognostics data repository. A framework based on a comparative...