Efficient framework for ultimate strength prediction and production-based CO2 emission optimization of CFST columns using categorical boosting algorithm and moth …
This study develops an efficient framework for predicting the ultimate strength and optimizing
production-based carbon dioxide (CO 2) emission of concrete-filled steel tube (CFST) …
production-based carbon dioxide (CO 2) emission of concrete-filled steel tube (CFST) …
Finite element and generalized regression neural network modelling of multiple cracks growth under the influence of multiple crack parameters
MIP Hidayat, AD Pramata, PP Airlangga - Multidiscipline Modeling in …, 2023 - emerald.com
Purpose This study presents finite element (FE) and generalized regression neural network
(GRNN) approaches for modeling multiple crack growth problems and predicting crack …
(GRNN) approaches for modeling multiple crack growth problems and predicting crack …
Interpretable domain knowledge enhanced machine learning framework on axial capacity prediction of circular CFST columns
D Wang, Z Ren, G Kondo, P Li - arXiv preprint arXiv:2402.04405, 2024 - arxiv.org
This study introduces a novel machine learning framework, integrating domain knowledge,
to accurately predict the bearing capacity of CFSTs, bridging the gap between traditional …
to accurately predict the bearing capacity of CFSTs, bridging the gap between traditional …
Residual strength index prediction of circular concrete-filled steel tubular columns through advanced machine learning methods
A Narang, R Kumar, A Dhiman - Asian Journal of Civil Engineering, 2024 - Springer
Concrete-filled steel tube (CFST), which has a long-standing relationship for their
exceptional mechanical performance and cost-effectiveness, is frequently utilized as the …
exceptional mechanical performance and cost-effectiveness, is frequently utilized as the …
[HTML][HTML] Application of Machine Learning approaches in Cancer prediction
DL Rivera, RA Narvaez - Canadian Journal of Nursing Informatics, 2023 - cjni.net
Background: Cancer has long been a major concern in the healthcare industry. Scientists'
countless studies to find resolutions, conduct trials, correct errors have required enormous …
countless studies to find resolutions, conduct trials, correct errors have required enormous …