Prediction of the cross-sectional capacity of cold-formed CHS using numerical modelling and machine learning

M. Rabi, F.P.V. Ferreira, I. Abarkan, V. Limbachiya, R. Shamass

Research output: Contribution to journal › Article › peer-review

30 Citations (SciVal)

Access to Document

@article{d7baa8c1b317464b8b46829cc8c50be2,

title = "Prediction of the cross-sectional capacity of cold-formed CHS using numerical modelling and machine learning",

author = "M. Rabi and F.P.V. Ferreira and I. Abarkan and V. Limbachiya and R. Shamass",

year = "2023",

doi = "10.1016/j.rineng.2023.100902",

language = "English",

journal = "Results in Engineering",

issn = "2590-1230",

publisher = "Elsevier",

}

TY - JOUR

T1 - Prediction of the cross-sectional capacity of cold-formed CHS using numerical modelling and machine learning

AU - Rabi, M.

AU - Ferreira, F.P.V.

AU - Abarkan, I.

AU - Limbachiya, V.

AU - Shamass, R.

PY - 2023

Y1 - 2023

UR - http://www.scopus.com/inward/record.url?eid=2-s2.0-85147254664&partnerID=MN8TOARS

U2 - 10.1016/j.rineng.2023.100902

DO - 10.1016/j.rineng.2023.100902

M3 - Article

SN - 2590-1230

JO - Results in Engineering

JF - Results in Engineering

ER -