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| Azevedo, Nuno Monteiro |
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Alabduljabbar, Hisham
in Cooperation with on an Cooperation-Score of 37%
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Publications (6/6 displayed)
- 2024Indirect prediction of graphene nanoplatelets-reinforced cementitious composites compressive strength by using machine learning approachescitations
- 2024Estimating Compressive Strength of Concrete Containing Rice Husk Ash Using Interpretable Machine Learning-based Models
- 2021Strength and acid resistance of ceramic-based self-compacting alkali-activated concretecitations
- 2021Sugarcane bagasse ash-based engineered geopolymer mortar incorporating propylene fiberscitations
- 2020A comparative study on performance evaluation of hybrid GNPs/CNTs in conventional and self-compacting mortarcitations
- 2020New Prediction Model for the Ultimate Axial Capacity of Concrete-Filled Steel Tubescitations
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article
New Prediction Model for the Ultimate Axial Capacity of Concrete-Filled Steel Tubes
Abstract
The complication linked with the prediction of the ultimate capacity of concrete-filled steel tubes (CFST) short circular columns reveals a need for conducting an in-depth structural behavioral analyses of this member subjected to axial-load only. The distinguishing feature of gene expression programming (GEP) has been utilized for establishing a prediction model for the axial behavior of long CFST. The proposed equation correlates the ultimate axial capacity of long circular CFST with depth, thickness, yield strength of steel, the compressive strength of concrete and the length of the CFST, without need for conducting any expensive and laborious experiments. A comprehensive CFST short circular column under an axial load was obtained from extensive literature to build the proposed models, and subsequently implemented for verification purposes. This model consists of extensive database literature and is comprised of 227 data samples. External validations were carried out using several statistical criteria recommended by researchers. The developed GEP model demonstrated superior performance to the available design methods for AS5100.6, EC4, AISC, BS, DBJ and AIJ design codes. The proposed design equations can be reliably used for pre-design purposes—or may be used as a fast check for deterministic solutions.