1-6 of 6
Keywords: machine learning
Close
Sort by
Journal Articles
Publisher: ASME
Article Type: Research-Article
ASME J. Risk Uncertainty Part B. September 2023, 9(3): 031106.
Paper No: RISK-23-1034
Published Online: August 4, 2023
... focus on the direct hardening of the transmission towers to accomplish an increase in resiliency. This machine learning-based study fills this need by analyzing three direct hardening scenarios and determining the effectiveness of these changes in the context of H-Maria. A methodology for estimating...
Journal Articles
Publisher: ASME
Article Type: Research-Article
ASME J. Risk Uncertainty Part B. March 2023, 9(1): 011204.
Paper No: RISK-21-1099
Published Online: August 8, 2022
... 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...
Journal Articles
Publisher: ASME
Article Type: Research-Article
ASME J. Risk Uncertainty Part B. December 2022, 8(4): 041104.
Paper No: RISK-21-1048
Published Online: June 2, 2022
... 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...
Journal Articles
Publisher: ASME
Article Type: Research-Article
ASME J. Risk Uncertainty Part B. March 2022, 8(1): 011112.
Paper No: RISK-21-1006
Published Online: January 6, 2022
... to points A, B, and C of the Pareto fronts. The results show that the optimum solution using the proposed process optimization framework gives better part quality than randomly selected process parameter settings. In this paper, a data-driven machine learning model is proposed for multi-objective...
Journal Articles
Publisher: ASME
Article Type: Research-Article
ASME J. Risk Uncertainty Part B. March 2022, 8(1): 011102.
Paper No: RISK-21-1003
Published Online: August 2, 2021
... 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...
Journal Articles
Publisher: ASME
Article Type: Research-Article
ASME J. Risk Uncertainty Part B. March 2021, 7(1): 011002.
Paper No: RISK-20-1026
Published Online: January 22, 2021
... 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...