Volume 4, Issue 4 (12-2022)
sjfst 2022, 4(4): 1-12 |
Back to browse issues page
Download citation:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
BibTeX | RIS | EndNote | Medlars | ProCite | Reference Manager | RefWorks
Send citation to:
Adimi A, Khaloo A R. Behavior of Concrete Slabs Reinforced with FRP Bars, Design and Analysis. sjfst 2022; 4 (4) :1-12
URL: http://sjfst.srpub.org/article-6-169-en.html
URL: http://sjfst.srpub.org/article-6-169-en.html
Professor, Department of Civil Engineering, Sharif University of Technology, Tehran, Iran.
Abstract: (689 Views)
In this study, in order to ensure the accuracy of numerical simulations, the numerical 3D simulation of a one-way concrete slab reinforced with tensile fiber-reinforced polymer (FRP) rebars was performed using the finite element Abaqus software. Moreover, the effects of the type of FRP rebars including carbon fiber-reinforced polymer (CFRP) or glass fiber reinforced polymer (GFRP), as well as their amount (depending on the number and diameter of FRP rebars), were evaluated on the vertical load-bearing capacity of the middle of concrete slabs. Moreover, with the 3D numerical simulation of the slab reinforced with tensile and compressive FRP rebars, the effect of using them simultaneously (compared to the use of only FRP rebars) was investigated on the vertical load-bearing capacity at the middle of the slab with respect to the compressive strength of the slab’s concrete. Eventually, the slab was numerically simulated reinforced with tensile steel and FRP rebars to study the effect of combining them, compared to the use of only FRP rebars, on the load-bearing capacity at the middle of the slab. The obtained results revealed that the vertical load-bearing capacity of the slab reinforced with tensile CFRP rebars was much higher than that of the one reinforced with tensile GFRP rebars. Moreover, the vertical load-bearing capacity of the concrete slab reinforced with tensile FRP rebars increased with the number and diameter of the tensile FRP rebars. On the other hand, the reduction in the compressive strength of the one-way concrete slab decreased the load-bearing capacity of the concrete slab reinforced with FRP rebars. The simultaneous use of tensile and compressive FRP rebars had no significant effect on increasing the load-bearing capacity of the concrete slabs, especially those with high compressive strengths. Meanwhile, the combination of the tensile steel and FRP rebars increased the vertical load-bearing capacity of the slab.
Keywords: Reinforced concrete slab, FRP rebars, The vertical load-bearing capacity of the slab, Finite element method, Abaqus software
Type of Study: Research |
Subject:
Civil and Structural Engineering
Received: 2022/09/15 | Revised: 2022/11/17 | Accepted: 2022/12/5 | Published: 2022/12/25
Received: 2022/09/15 | Revised: 2022/11/17 | Accepted: 2022/12/5 | Published: 2022/12/25
References
1. Neves RLP, et al. Post-harvesting silvicultural treatments in canopy logging gaps: medium-term responses of commercial tree species under tending and enrichment planting. Forest Ecol Manag. 2019; 451: 117521. [DOI:10.1016/j.foreco.2019.117521]
2. Jiang C, et al. Failure mode-based calculation method for bending bearing capacities of normal cross-sections of corroded reinforced concrete beams. Eng Struct. 2022; 258: 114113. [DOI:10.1016/j.engstruct.2022.114113]
3. Ma H, et al. Failure mechanism and design method of reticulated shells considering joint damage accumulation effect under earthquake load. in Structures. Elsevier. 2022. [DOI:10.1016/j.istruc.2022.03.069]
4. Christensen RM. Mechanisms and measures for the ductility of materials failure. Proceedings of the Royal Society A. 2020; 476(2239): 20190719. [DOI:10.1098/rspa.2019.0719]
5. Fallah-Valukolaee S, Hashemi S, Nematzadeh M. Effect of steel fiber on flexural performance of bilayer concrete beams with steel and GFRP rebars: Experiments and predictions. Elsevier. Struct. 2022. [DOI:10.1016/j.istruc.2022.03.007]
6. Fu B, et al. Concrete reinforced with macro fibres recycled from waste GFRP. Construct Build Mater. 2021; 310: 125063. [DOI:10.1016/j.conbuildmat.2021.125063]
7. Reichenbach S, et al. A review on embedded fibre-reinforced polymer reinforcement in structural concrete in Europe. Construct Build Mater. 2021; 307: 124946. [DOI:10.1016/j.conbuildmat.2021.124946]
8. Shen Y, Sun J, Liang S. Interpretable machine learning models for punching shear strength estimation of FRP reinforced concrete slabs. Crystal. 2022; 12(2): 259. [DOI:10.3390/cryst12020259]
9. Law R, et al. Ecological information from spatial patterns of plants: insights from point process theory. J Ecol. 2009; 97(4): 616-628. [DOI:10.1111/j.1365-2745.2009.01510.x]
10. Geetha N, Bridjesh P. Overview of machine learning and its adaptability in mechanical engineering. Materials Today: Proceedings, 2020. [DOI:10.1016/j.matpr.2020.09.611] [PMID] [PMCID]
11. Zhou Y, et al. Improved finite difference analysis of dynamic responses of concrete members reinforced with FRP bars under explosion. Compos Struct. 2019; 230: 111518. [DOI:10.1016/j.compstruct.2019.111518]
12. Nigro E, et al. Guidelines for flexural resistance of FRP reinforced concrete slabs and beams in fire. Compos B Eng. 2014; 58: 103-112. [DOI:10.1016/j.compositesb.2013.10.007]
13. Shahnewaz M, et al. Optimized shear design equation for slender concrete beams reinforced with FRP bars and stirrups using genetic algorithm and reliability analysis. Eng Struct. 2016; 107: 151-165. [DOI:10.1016/j.engstruct.2015.10.049]
14. Chen SZ, et al. Development of data-driven prediction model for CFRP-steel bond strength by implementing ensemble learning algorithms. Construct Build Mater. 2021; 303: 124470. [DOI:10.1016/j.conbuildmat.2021.124470]
15. Mangalathu S, et al. Machine-learning interpretability techniques for seismic performance assessment of infrastructure systems. Eng Struct. 2022; 250: 112883. [DOI:10.1016/j.engstruct.2021.112883]
16. Nguyen HD, Truong GT, Shin M. Development of extreme gradient boosting model for prediction of punching shear resistance of r/c interior slabs. Eng Struct. 2021; 235: 112067. [DOI:10.1016/j.engstruct.2021.112067]
17. Mangalathu S, et al. Explainable machine learning models for punching shear strength estimation of flat slabs without transverse reinforcement. J Build Eng. 2021; 39: 102300. [DOI:10.1016/j.jobe.2021.102300]
18. Rahman A, et al. A machine learning framework for predicting the shear strength of carbon nanotube-polymer interfaces based on molecular dynamics simulation data. Compos Sci Tech. 2021; 207: 108627. [DOI:10.1016/j.compscitech.2020.108627]
19. Ilawe NV, Zimmerman JA, Wong BM. Breaking badly: DFT-D2 gives sizeable errors for tensile strengths in palladium-hydride solids. J Chem Theory Comput. 2015; 11(11): 5426-5435. [DOI:10.1021/acs.jctc.5b00653] [PMID]
20. Michaluk CR, et al. Flexural behavior of one-way concrete slabs reinforced by fiber reinforced plastic reinforcements. Struct J. 1998; 95(3): 353-365. [DOI:10.14359/552]
21. Abaqus Users Manual V. 6.10-1. Dassault Systemes Simulia Corp. Providence, RI, 2011.
22. Sheil D, Burslem DF, Alder D. The interpretation and misinterpretation of mortality rate measures. J Ecol. 1995; 331-333. [DOI:10.2307/2261571]
23. Rocha FR, et al. Multicommutation in flow analysis: concepts, applications and trends. Anal Chim Act. 2002; 468(1): 119-131. [DOI:10.1016/S0003-2670(02)00628-1]
24. Park R, Paulay T. Reinforced Concrete Structures, John Wiley & Sons. NY, USA, 1975. [DOI:10.1002/9780470172834]
| Rights and permissions | |
 |
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License. |