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Cited by 95Structural Load-Bearing AnalysisRecycled Aggregate Concrete PerformanceStructural Behavior of Reinforced Concrete

The DOI matches its Crossref record; 25 of the 26 references it cites were found in a registry or an index.

Prediction model on compressive strength of recycled aggregate concrete filled steel tube columns

Nour, Abdikarim Idiris, Güneyisi, Esra Mete·2019·Composites Part B Engineering

Abstract

Abstract The design of composite members consisting of a tubular column and in-filled conventional concrete has been available and well-defined in the provisions. However, for steel tube members filled with recycled aggregate concrete, there aren't any other special requirements in the current provisions. In this study, the experimental test results are used to model and evaluate the ultimate strength of axially loaded recycled aggregate concrete filled steel tube columns. The test specimens has a length to diameter ratio ranging from 2.0 to 3.5, indicating that they are the stub column and not failing in overall buckling. The proposed model was derived based the technique of gene expression programming. The database utilized contains a total of 97 test results. The predictor variable employed to generate the model are outer diameter, thickness and yield strength of steel tube, length of the column, recycled coarse aggregate replacement ratio, and concrete compressive strength. The experimental data and associated analytical model are examined by four widely used design codes of ACI, Eurocode 4, AIJ, and DL/T. A sensitivity statistical analysis is further conducted to assess the contributions of each parameter affecting the axial capacity. The outcome indicates that the developed model gives reasonable predictions of the axial capacity of recycled aggregate concrete filled circular steel tube stub columns compared to the existing expressions.

Overview

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Registry

Crossref · 10.1016/j.compositesb.2019.106938

Cites

25 of 25 references verified0 citation errors · 0 not checkable

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Citations

Cites

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Research works printed but not linked: 1

  1. Punching, 2001, Of structural concrete slabs, Fib Bulletin, 12

Cited by

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Metadata

Authors
Nour, A. I., & Güneyisi, E. M.
Affiliations
  • Amoud University (SO)
  • Gaziantep University (TR)
Corresponding author
Nour, Abdikarim Idiris
Venue
Composites Part B Engineering
Publisher
Elsevier BV
Published
2019-09
Volume
173
Pages
106938
ISSN
1359-8368 (ISSN-L)
OpenAlex
W2947325973
Cited by
93
References
26
Citations by year
2026: 8 · 2025: 9 · 2024: 10 · 2023: 20 · 2022: 20 · 2021: 9 · 2020: 16 · 2019: 1
FWCI
8.60
Type
journal article
Language
en
Open access
closed
Licence
  • www.elsevier.com/tdm/userlicense/1.0/ (tdm)
  • www.elsevier.com/legal/tdmrep-license (tdm)
Locations
Composites Part B Engineering (publishedVersion)
Topics
  • Physical Sciences › Engineering › Civil and Structural Engineering › Structural Load-Bearing Analysis
  • Physical Sciences › Engineering › Building and Construction › Recycled Aggregate Concrete Performance
  • Physical Sciences › Engineering › Building and Construction › Structural Behavior of Reinforced Concrete
Keywords
recycled aggregate concrete, concrete-filled steel tube columns, stub columns, axial compressive strength, gene expression programming, strength prediction model, recycled coarse aggregate replacement ratio, design code comparison, sensitivity analysis, circular steel tubes

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CoChat. (2026). Citation check: Prediction model on compressive strength of recycled aggregate concrete filled steel tube columns. https://app.cochat.ai/papers/10.1016/j.compositesb.2019.106938
The paper
Nour, A. I., & Güneyisi, E. M. (2019). Prediction model on compressive strength of recycled aggregate concrete filled steel tube columns. Composites Part B Engineering. https://doi.org/10.1016/j.compositesb.2019.106938

Data sources

Bibliographic data: Semantic Scholar, OpenAlex, Crossref.

Contains information from the Semantic Scholar Academic Graph Datasets, made available under the ODC Attribution License (ODC-BY 1.0).

Semantic Scholar’s data is described in: Kinney, R., et al. (2023). The Semantic Scholar Open Data Platform. arXiv:2301.10140.