Behavior of rubberized high strength reinforced concrete columns incorporating polypropylene fibers under eccentric loadings DOI
Emad Omar Ali Azzam,

Mahmoud Ahmed Ashour,

Mahmoud Elsayed

et al.

Structural Concrete, Journal Year: 2025, Volume and Issue: unknown

Published: April 22, 2025

Abstract This study investigates the behavior of rubberized high‐strength reinforced concrete (RHSRC) columns, including polypropylene (PP) fibers, under eccentric loads. Five distinct mixtures were formulated, wherein natural fine aggregate (NFA) was substituted with crumb rubber (CR) at varying replacement volumes 0%, 10%, and 20% for initial three mixtures, 10% in conjunction PP fibers subsequent two mixtures. Fifteen columns fabricated (three from each mixture) subjected to monotonic loading eccentricity ratios e / t = 0.25, 0.5, 0.75. The results demonstrated that incorporation CR adversely impacts load‐bearing capacity stiffness examined columns. mitigated improved these effects. Consequently, a blend may be suitable option diverse structural applications. Finally, analytical assessments conducted utilizing established nominal axial force‐bending moment (N–M) diagrams test specimens analytically determine column capacity. findings validate devised N–M precisely forecast failure loads

Language: Английский

Axial capacity of rubberized RC short columns comprising glass powder as a partial replacement of cement DOI
Mahmoud Elsayed, Ahmed D. Almutairi, Mostafa A. Hussein

et al.

Structures, Journal Year: 2024, Volume and Issue: 64, P. 106612 - 106612

Published: May 23, 2024

Language: Английский

Citations

10
Enhancing flexural performance of rubberized concrete beams through incorporation of rice husk ash as cement replacement DOI
Norelyza Hussein, Mohamed Abou Elmaaty, Mansour Alturki

et al.

Engineering Structures, Journal Year: 2025, Volume and Issue: 330, P. 119958 - 119958

Published: Feb. 24, 2025

Language: Английский

Citations

1
Shear behavior of reinforced concrete beams comprising a combination of crumb rubber and rice husk ash DOI

Mahmoud.A.M. Hassanean,

Sara.A.M. Hussein,

Mahmoud Elsayed

et al.

Engineering Structures, Journal Year: 2024, Volume and Issue: 319, P. 118862 - 118862

Published: Sept. 1, 2024

Language: Английский

Citations

7
Prediction Models for the Hybrid Effect of Nano Materials on Radiation Shielding Properties of Concrete Exposed to Elevated Temperatures DOI Creative Commons
Mohammed K. Alkharisi, Hany A. Dahish, Osama Youssf

et al.

Case Studies in Construction Materials, Journal Year: 2024, Volume and Issue: 21, P. e03750 - e03750

Published: Sept. 12, 2024

Language: Английский

Citations

7
Effect of recycled materials as supplementary cementitious materials on the shear strength of UHPFRC beams without shear reinforcement DOI

Ahmed O. Osman,

Mahmoud Elsayed,

Alaa A. El‐Sayed

et al.

Construction and Building Materials, Journal Year: 2024, Volume and Issue: 452, P. 138935 - 138935

Published: Nov. 1, 2024

Language: Английский

Citations

4
The Application of Response Surface Methodology and Machine Learning for Predicting the Compressive Strength of Recycled Aggregate Concrete Containing Polypropylene Fibers and Supplementary Cementitious Materials DOI Open Access
Mohammed K. Alkharisi, Hany A. Dahish

Sustainability, Journal Year: 2025, Volume and Issue: 17(7), P. 2913 - 2913

Published: March 25, 2025

The construction industry’s development trend has resulted in a large volume of demolished concrete. Improving the efficiency proper use this waste as recycled aggregate (RA) concrete is promising solution. In study, we utilized response surface methodology (RSM) and three machine learning (ML) techniques—the M5P algorithm, random forest (RF) extreme gradient boosting (XGB)—to optimize predict compressive strength (CS) RA containing fly ash (FA), silica fume (SF), polypropylene fiber (PPF). To build models, results regarding 529 data points were used dataset with varying numbers input parameters (out total ten). CS quadratic model under RSM exhibited acceptable prediction accuracy. best was found 100% consisting coarse aggregate, 1.13% PPF by concrete, 7.90% FA, 5.30% SF partial replacements binders weight. XGB superior performance high accuracy, higher R² lower values errors, depicted MAE, RMSE, MAPE, when compared to other developed models. Furthermore, SHAP analysis showed that had positive impact on predicting CS, but curing age superplasticizer dose highest

Language: Английский

Citations

0
Flexural behavior of reinforced high strength concrete slabs containing glass powder, flay ash, and rice husk ash as cement substitute DOI
Mahmoud Elsayed, M. Saad,

Mahmoud.A.M. Hassanean

et al.

Construction and Building Materials, Journal Year: 2025, Volume and Issue: 477, P. 141393 - 141393

Published: April 19, 2025

Language: Английский

Citations

0
Behavior of rubberized high strength reinforced concrete columns incorporating polypropylene fibers under eccentric loadings DOI
Emad Omar Ali Azzam,

Mahmoud Ahmed Ashour,

Mahmoud Elsayed

et al.

Structural Concrete, Journal Year: 2025, Volume and Issue: unknown

Published: April 22, 2025

Abstract This study investigates the behavior of rubberized high‐strength reinforced concrete (RHSRC) columns, including polypropylene (PP) fibers, under eccentric loads. Five distinct mixtures were formulated, wherein natural fine aggregate (NFA) was substituted with crumb rubber (CR) at varying replacement volumes 0%, 10%, and 20% for initial three mixtures, 10% in conjunction PP fibers subsequent two mixtures. Fifteen columns fabricated (three from each mixture) subjected to monotonic loading eccentricity ratios e / t = 0.25, 0.5, 0.75. The results demonstrated that incorporation CR adversely impacts load‐bearing capacity stiffness examined columns. mitigated improved these effects. Consequently, a blend may be suitable option diverse structural applications. Finally, analytical assessments conducted utilizing established nominal axial force‐bending moment (N–M) diagrams test specimens analytically determine column capacity. findings validate devised N–M precisely forecast failure loads

Language: Английский

Citations

0