Investigation of optimized machine learning models with PSO for forecasting the shear capacity of steel fiber-reinforced SCC beams with/out stirrups-Reference-Cited by-同舟云学术

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Investigation of optimized machine learning models with PSO for forecasting the shear capacity of steel fiber-reinforced SCC beams with/out stirrups

Published:2024-04 Issue: Volume:83 Page:108455
ISSN:2352-7102
Container-title:Journal of Building Engineering
language:en
Short-container-title:Journal of Building Engineering

Author:

Ergen Faruk^ORCID,Katlav Metin^ORCID

Publisher

Elsevier BV

Reference80 articles.

1. Effects of stirrup, steel fiber, and beam size on shear behavior of high-strength concrete beams;Yoo;Cem. Concr. Compos.,2018

2. Shear strength prediction of reinforced concrete beams using machine learning;Sandeep;Structures,2023

3. Effect of steel fibers on the flexural behavior of RC beams with very low reinforcement ratios;Yoo;Construct. Build. Mater.,2018

4. Flexural performance of V-shaped RC folded plates: the role of plate thickness and fiber hybridization;Katlav;Construct. Build. Mater.,2023

5. Shear behavior model for steel fiber-reinforced concrete members without transverse reinforcements;Kim;Composites, Part B,2012

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2. An investigation using resampling techniques and explainable machine learning to minimize fire losses in residential buildings;Journal of Building Engineering;2024-10

3. Electrical resistivity of eco-friendly hybrid fiber-reinforced SCC: Effect of ground granulated blast furnace slag and copper slag content as well as hooked-end fiber length;Construction and Building Materials;2024-08

4. AI-driven design for the compressive strength of ultra-high performance geopolymer concrete (UHPGC): From explainable ensemble models to the graphical user interface;Materials Today Communications;2024-08

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