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Five Machine Learning Models Predicting the Global Shear Capaicty of Composite Cellular Beams with Hillow-Core Units

Ferreira, F. P. V., Jeong, S. H., Mansouri, E. , Shamass, R., Tsavdaridis, K. ORCID: 0000-0001-8349-3979, Martins, C. H. & De Nardin, S. Five Machine Learning Models Predicting the Global Shear Capaicty of Composite Cellular Beams with Hillow-Core Units.


The calculation of the global shear capacity of steel-concrete composite downstand cellular beams with precast hollow-core units is important as it affects the span to depth ratios and the amount of material used, hence affects the embodied CO2 calculation when designers are deciding on the floor grids. This paper presents a reliable tool that can be used by designers to alter and optimise grip options during the preliminary design stages, without the need to run onerous calculations. The global shear capacity prediction formula is developed using five machine learning models. First, a finite element model database is developed. The influence of the opening diameter, web opening spacing, tee-section height, concrete topping thickness, the interaction degree, and the number of shear studs above the web opening are investigated. Reliability analysis is conducted to assess the design method and propose new partial safety factors. The Catboost Regressor algorithm presented beĴer accuracy compared to the other algorithms. An equation to predict the shear capacity of composite cellular beams with hollow-core units is proposed by Gene Expression Programming. In general, the partial safety factor for resistance, according to the reliability analysis, varied between 1.25 and 1.26.

Publication Type: Other (Preprint)
Additional Information: This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Publisher Keywords: Machine learning; Composite floors; Hollow-core units; Shear capacity; Reliability analysis
Subjects: T Technology > TA Engineering (General). Civil engineering (General)
Departments: School of Science & Technology
School of Science & Technology > Engineering
SWORD Depositor:
[thumbnail of preprints202405.1316.v1.pdf]
Text - Submitted Version
Available under License Creative Commons: Attribution International Public License 4.0.

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