Cargando…
Prediction of creep failure time using machine learning
A subcritical load on a disordered material can induce creep damage. The creep rate in this case exhibits three temporal regimes viz. an initial decelerating regime followed by a steady-state regime and a stage of accelerating creep that ultimately leads to catastrophic breakdown. Due to the statist...
Autores principales: | Biswas, Soumyajyoti, Fernandez Castellanos, David, Zaiser, Michael |
---|---|
Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
Nature Publishing Group UK
2020
|
Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC7547726/ https://www.ncbi.nlm.nih.gov/pubmed/33037259 http://dx.doi.org/10.1038/s41598-020-72969-6 |
Ejemplares similares
-
Machine learning predictions of COVID-19 second wave end-times in Indian states
por: Kondapalli, Anvesh Reddy, et al.
Publicado: (2021) -
Creeping Bentgrass Yield Prediction With Machine Learning Models
por: Zhou, Qiyu, et al.
Publicado: (2021) -
Corrigendum: Creeping Bentgrass Yield Prediction With Machine Learning Models
por: Zhou, Qiyu, et al.
Publicado: (2022) -
Comparative Study of Machine Learning Approaches for Predicting Creep Behavior of Polyurethane Elastomer
por: Yang, Chunhao, et al.
Publicado: (2021) -
Nonlinear deformation and localized failure of bacterial streamers in creeping flows
por: Biswas, Ishita, et al.
Publicado: (2016)