Technical Papers
Jan 24, 2023

Transverse Shear Capacity Predictions of GFRP Bars Subjected to Accelerated Aging Using Artificial Neural Networks

Publication: Journal of Materials in Civil Engineering
Volume 35, Issue 4

Abstract

This paper presents the results of three different types of glass fiber-reinforced polymer (GFRP) bars subjected to different harsh environmental conditions. Three types of pultruded GFRP bars, namely, Bar A (13.04 mm dia.), Bar B1 (13.02 mm dia.), Bar B2 (10.19 mm dia.), and Bar C (13.22 mm dia.) were subjected to accelerated aging. GFRP specimens were exposed to four conditionings (alkaline, alkaline with salt, acidic, and water), two temperature regimes (20°C and 60°C), and three time regimes (3, 6, and 12 months). The mechanical strength retention of the bars investigated based on the transverse shear strength (TSS) test, conforming to ASTM D7617M-11 revealed a decline in the shear strength characteristics of specimens proportionally with the exposure time. In general, Bar A, Bar B1, and Bar B2 performed well, but Bar C performed the worst since Bar C exhibited the least mechanical strength retention at elevated temperatures. Scanning electron microscopy (SEM) of the cross section of the conditioned specimens revealed the nature of the evolution of deterioration and the state of glass fibers, polymeric resin matrix, and fiber-matrix interface, validating the decrease in transverse shear capacity of the bars. SEM micrographs showed fiber damage, with debonding occurring at the fiber-matrix interface and cracking of the polymeric resin. Leaching out of glass fibers into the matrix of varying degrees upon conditioning was observed through the X-ray mapping of silicon. Furthermore, strength prediction models developed using multiple linear regression and artificial neural network (ANN) techniques were compared. Coefficients of correlation (R2) of 0.94 and 0.76 and root mean square errors (RMSE) of 7.43 and 12.00 were obtained with models developed using ANN and multiple linear regression, respectively, showing that ANN can be used as a robust tool for GFRP shear strength prediction.

Practical Applications

With glass fiber-reinforced polymer (GFRP) bars possessing properties such as corrosion-resistance, high strength, and being light-weight, it is emerging to be a promising alternative to conventional steel rebars, which are often plagued by durability issues such as corrosion and consequent financial losses caused by its repair and maintenance. The present study makes an effort to contribute to the understanding of the durability performance of GFRP bars exposed to some aggressive environments such as alkaline, acidic, and moisture at room and high temperatures. The effect of these conditions was compared based on variations in shear strength and microstructural characteristics such as damage to the fibers, the polymeric matrix, and the fiber-matrix interface to build confidence in the application of nonmetallic materials in construction.

Get full access to this article

View all available purchase options and get full access to this article.

Data Availability Statement

Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.

Acknowledgments

The authors would like to thank King Fahd University of Petroleum and Minerals (KFUPM) for availing the necessary lab facilities. The authors also thank Eng. Abdulrahman Hijji, Assistant Scientific Researcher, Civil and Environmental Engineering Department, CDBE, KFUPM, and Eng. Rizwan Ali, Research Engineer III, IRC-CBM, Research Institute, KFUPM, for their help in the experimental work.

References

ACI (American Concrete Institute). 2004. Guide test methods for fiber-reinforced polymers (FRPs) for reinforcing or strengthening concrete structures. ACI 440.3R-04. Farmington Hills, MI: ACI.
ACI (American Concrete Institute). 2015. Guide for the design and construction of structural concrete reinforced with FRP bars. ACI 440.1R-15. Farmington Hills, MI: ACI.
Al-Khafaji, A. F., R. T. Haluza, V. Benzecry, J. J. Myers, C. E. Bakis, and A. Nanni. 2021.“Durability assessment of 15- to 20-year-old GFRP bars extracted from bridges in the US. II: GFRP bar assessment.” J. Compos. Constr. 25 (2): 04021008. https://doi.org/10.1061/(ASCE)CC.1943-5614.0001112.
Almusallam, T. H., Y. A. Al-Salloum, S. H. Alsayed, S. El-Gamal, and M. Aqel. 2013. “Tensile properties degradation of glass fiber-reinforced polymer bars embedded in concrete under severe laboratory and field environmental conditions.” J. Compos. Mater. 47 (4): 393–407. https://doi.org/10.1177/0021998312440473.
Alsayed, S., Y. Al-Salloum, T. Almusallam, S. El-Gamal, and M. Aqel. 2012. “Performance of glass fiber reinforced polymer bars under elevated temperatures.” Composites, Part B 43 (5): 2265–2271. https://doi.org/10.1016/j.compositesb.2012.01.034.
Al-Zahrani, M. M., S. U. Al-Dulaijan, A. Sharif, and M. Maslehuddin. 2002. “Durability performance of glass fiber reinforced plastic reinforcement in harsh environments.” In Vol. 3 of Proc., 6th Saudi Engineering Conf., 307. Dhahran, Saudi Arabia: King Fahd Univ. of Petroleum and Minerals.
ASTM. 2016a. Standard test method for apparent horizontal shear strength of pultruded reinforced plastic rods by the short-beam method. ASTM D4475-02. West Conshohocken, PA: ASTM.
ASTM. 2016b. Standard test method for tensile properties of fiber reinforced polymer matrix composite bars. ASTM D7205/D7205M-06. West Conshohocken, PA: ASTM.
ASTM. 2017. Transverse shear strength of fiber-reinforced polymer matrix composite bars. ASTM D7617-D7617M-11 (Reapproved 2017). West Conshohocken, PA: ASTM.
ASTM. 2018. Test method for water absorption of plastics. ASTM D570-98 (2018). West Conshohocken, PA: ASTM.
ASTM. 2019. Standard test method for alkali resistance of fiber reinforced polymer (FRP) matrix composite bars used in concrete construction. ASTM D7705/D7705M-12. West Conshohocken, PA: ASTM.
Baiz, A. A., H. Ahmadi, F. Shariatmadari, and M. A. K. Torshizi. 2020. “A Gaussian process regression model to predict energy contents of corn for poultry.” Poult. Sci. 99 (11): 5838–5843. https://doi.org/10.1016/j.psj.2020.07.044.
Bazli, M., X.-L. Zhao, Y. Bai, R. K. S. Raman, S. Al-Saadi, and A. Haque. 2020. “Durability of pultruded GFRP tubes subjected to seawater sea sand concrete and seawater environments.” Constr. Build. Mater. 245 (Jun): 118399. https://doi.org/10.1016/j.conbuildmat.2020.118399.
Behnood, A., and E. M. Golafshani. 2018. “Predicting the compressive strength of silica fume concrete using hybrid artificial neural network with multi-objective grey wolves.” J. Cleaner Prod. 202 (Nov): 54–64. https://doi.org/10.1016/j.jclepro.2018.08.065.
Benmokrane, B., A. Manalo, J.-C. Bouhet, K. Mohamed, and M. Robert. 2017. “Effects of diameter on the durability of glass fiber–reinforced polymer bars conditioned in alkaline solution.” J. Compos. Constr. 21 (5): 04017040. https://doi.org/10.1061/(ASCE)CC.1943-5614.0000814.
Bootle, J., F. Burzesi, and L. Fiorini. 2001. “Design guidelines.” In ASM handbook, Vol. 21: Composites. Material Park, OH: ASM International.
Chen, Y., J. F. Davalos, I. Ray, and H.-Y. Kim. 2007. “Accelerated aging tests for evaluations of durability performance of FRP reinforcing bars for concrete structures.” Compos. Struct. 78 (1): 101–111. https://doi.org/10.1016/j.compstruct.2005.08.015.
CSA (Canadian Standards Association). 2019. Specification for fibre-reinforced polymers. CSA S807:19. Toronto, ON, Canada: CSA.
El-Hassan, H., T. El-Maaddawy, A. Al-Sallamin, and A. Al-Saidy. 2017. “Performance evaluation and microstructural characterization of GFRP bars in seawater-contaminated concrete.” Constr. Build. Mater. 147 (Aug): 66–78. https://doi.org/10.1016/j.conbuildmat.2017.04.135.
Farooq, M., and N. Banthia. 2018. “An innovative FRP fibre for concrete reinforcement: Production of fibre, micromechanics, and durability.” Constr. Build. Mater. 172 (May): 406–421. https://doi.org/10.1016/j.conbuildmat.2018.03.198.
Feng, D.-C., Z.-T. Liu, X.-D. Wang, Y. Chen, J.-Q. Chang, D.-F. Wei, and Z.-M. Jiang. 2020. “Machine learning-based compressive strength prediction for concrete: An adaptive boosting approach.” Constr. Build. Mater. 230 (Jan): 117000. https://doi.org/10.1016/j.conbuildmat.2019.117000.
FIB (Fédération Internationale du Béton). 2007. FRP reinforcement in RC structures. Lausanne, Switzerland: FIB.
Foster, S. K., and L. A. Bisby. 2008. “Fire survivability of externally bonded FRP strengthening systems.” J. Compos. Constr. 12 (5): 553–561. https://doi.org/10.1061/(ASCE)1090-0268(2008)12:5(553).
Genikomsou, A. S., G. P. Balomenos, P. Arczewska, and M. A. Polak. 2018. “Transverse shear testing of GFRP bars with reduced cross sections.” J. Compos. Constr. 22 (5): 1–9. https://doi.org/10.1061/(ASCE)CC.1943-5614.0000880.
Gooranorimi, O., and A. Nanni. 2017. “GFRP reinforcement in concrete after 15 years of service.” J. Compos. Constr. 21 (5): 04017024. https://doi.org/10.1061/(ASCE)CC.1943-5614.0000806.
Gooranorimi, O., W. Suaris, E. Dauer, and A. Nanni. 2017. “Microstructural investigation of glass fiber reinforced polymer bars.” Composites, Part B. 110 (Feb): 388–395. https://doi.org/10.1016/j.compositesb.2016.11.029.
Hajiloo, H., M. F. Green, and J. Gales. 2018. “Mechanical properties of GFRP reinforcing bars at high temperatures.” Constr. Build. Mater. 162 (Feb): 142–154. https://doi.org/10.1016/j.conbuildmat.2017.12.025.
Imam, A., B. A. Salami, and T. A. Oyehan. 2021. “Predicting the compressive strength of a quaternary blend concrete using Bayesian regularized neural network.” J. Struct. Integrity Maint. 6 (4): 237–246. https://doi.org/10.1080/24705314.2021.1892572.
James, G., D. Witten, T. Hastie, and R. Tibshirani. 2000. An introduction to statistical learning. New York: Springer.
Kamal, A. S. M., and M. Boulfiza. 2011. “Durability of GFRP rebars in simulated concrete solutions under accelerated aging conditions.” J. Compos. Constr. 15 (4): 473–481. https://doi.org/10.1061/(ASCE)CC.1943-5614.0000168.
Kayri, M. 2016. “Predictive abilities of bayesian regularization and levenberg–Marquardt algorithms in artificial neural networks: a comparative empirical study on social data.” Math. Comput. Appl. 21 (2): 20. https://doi.org/10.3390/mca21020020.
Khademi, F., M. Akbari, S. Mohammadmehdi, and M. Nikoo. 2017. “Multiple linear regression, artificial neural network, and fuzzy logic prediction of 28 days compressive strength of concrete.” Front. Struct. Civ. Eng. 11 (1): 90–99. https://doi.org/10.1007/s11709-016-0363-9.
Khashman, A., and P. Akpinar. 2017. “Non-destructive prediction of concrete compressive strength using neural networks.” Procedia Comput. Sci. 108 (Jan): 2358–2362. https://doi.org/10.1016/j.procs.2017.05.039.
Kim, H. Y., Y. H. Park, Y. J. You, and C. K. Moon. 2008. “Short-term durability test for GFRP rods under various environmental conditions.” Compos. Struct. 83 (1): 37–47. https://doi.org/10.1016/j.compstruct.2007.03.005.
MATLAB Documentation. 2021. “What is a neural network?” MATLAB & Simulink. Accessed March 11, 2021. https://www.mathworks.com/discovery/neural-network.html#why-they-matter.
Moon, D. Y., Y. C. Ou, and H. Roh. 2017. “Interlaminar shear capacity of thermally damaged GFRP bars under alkaline concrete environment.” Constr. Build. Mater. 152 (Oct): 105–114. https://doi.org/10.1016/j.conbuildmat.2017.06.158.
Mukherjee, A., and S. J. Arwikar. 2005. “Performance of glass fiber-reinforced polymer reinforcing bars in tropical environments—Part II: Microstructural tests.” ACI Struct. J. 102 (6): 816. https://doi.org/10.14359/14789.
Naderpour, H., A. Hossein Rafiean, and P. Fakharian. 2018. “Compressive strength prediction of environmentally friendly concrete using artificial neural networks.” J. Build. Eng. 16 (Mar): 213–219. https://doi.org/10.1016/j.jobe.2018.01.007.
Nanni, A., A. De Luca, and H. Zadeh. 2014. Reinforced concrete with FRP bars. Boca Raton, FL: CRC Press.
Nkurunziza, G., A. Debaiky, P. Cousin, and B. Benmokrane. 2005. “Durability of GFRP bars: A critical review of the literature.” Prog. Struct. Eng. Mater. 7 (4): 194–209. https://doi.org/10.1002/pse.205.
Onofrei, M. 2005. Durability of GFRP reinforced concrete from field demonstration structures: Final report. Winnipeg, MB, Canada: Univ. of Manitoba.
Park, Y. S., and S. Lek. 2016. “Artificial neural networks: Multilayer perceptron for ecological modeling.” Dev. Environ. Modell. 28 (Jan): 123–140. https://doi.org/10.1016/B978-0-444-63623-2.00007-4.
Pinder, J. P. 2016. Introduction to business analytics using simulation. Cambridge, MA: Academic Press.
Priya, A., and S. Garg. 2020. “A comparison of prediction capabilities of Bayesian regularization and Levenberg–Marquardt training algorithms for cryptocurrencies.” In Vol. 159 of Smart Intelligent Computing and Applications, 657–664. Singapore: Springer. https://doi.org/10.1007/978-981-13-9282-5_62.
Rajisha, K. R., B. Deepa, L. A. Pothan, and S. Thomas. 2011. “Thermomechanical and spectroscopic characterization of natural fibre composites.” In Interface engineering of natural fibre composites for maximum performance, 241–274. Cambridge, UK: Woodhead Publishing. https://doi.org/10.1533/9780857092281.2.241.
Ramanathan, S., V. Benzecry, P. Suraneni, and A. Nanni. 2021. “Condition assessment of concrete and glass fiber reinforced polymer (GFRP) rebar after 18 years of service life.” Case Stud. Constr. Mater. 14 (Jun): e00494. https://doi.org/10.1016/j.cscm.2021.e00494.
Robert, M., and B. Benmokrane. 2010. “Behavior of GFRP reinforcing bars subjected to extreme temperatures.” J. Compos. Constr. 14 (4): 353–360. https://doi.org/10.1061/(ASCE)CC.1943-5614.0000092.
Robert, M., and B. Benmokrane. 2013. “Combined effects of saline solution and moist concrete on long-term durability of GFRP reinforcing bars.” Constr. Build. Mater. 38 (Jan): 274–284. https://doi.org/10.1016/j.conbuildmat.2012.08.021.
Robert, M., P. Cousin, and B. Benmokrane. 2009. “Durability of GFRP reinforcing bars embedded in moist concrete.” J. Compos. Constr. 13 (2): 66–73. https://doi.org/10.1061/(ASCE)1090-0268(2009)13:2(66).
Robert, M., P. Wang, P. Cousin, and B. Benmokrane. 2010. “Temperature as an accelerating factor for long-term durability testing of FRPs: Should there be any limitations?” J. Compos. Constr. 14 (4): 361–367. https://doi.org/10.1061/(ASCE)CC.1943-5614.0000102.
Sadiq, R., M. J. Rodriguez, and H. R. Mian. 2019. “Empirical models to predict disinfection by-products (DBPs) in drinking water: An updated review.” In Encyclopedia of environmental health, 324–338. Amsterdam, Netherlands: Elsevier. https://doi.org/10.1016/B978-0-12-409548-9.11193-5.
Sawpan, M. A. 2016. “Effects of alkaline conditioning and temperature on the properties of glass fiber polymer composite rebar.” Polym. Compos. 37 (11): 3181–3190. https://doi.org/10.1002/pc.23516.
Sawpan, M. A. 2019. “Shear properties and durability of GFRP reinforcement bar aged in seawater.” Polym. Test. 75 (May): 312–320. https://doi.org/10.1016/j.polymertesting.2019.02.033.
Sen, R., G. Mullins, and T. Salem. 2002. “Durability of E-glass/vinylester reinforcement in alkaline solution.” ACI Struct. J. 99 (3): 369–375. https://doi.org/10.14359/11921.
Shokry, A., and A. Espuña. 2018. “The ordinary Kriging in multivariate dynamic modelling and multistep-ahead prediction.” In Computer aided chemical engineering, 265–270. Amsterdam, Netherlands: Elsevier. https://doi.org/10.1016/B978-0-444-64235-6.50047-4.
Tannous, F. E., and H. Saadatmanesh. 1999. “Durability of ar glass fiber reinforced plastic bars.” J. Compos. Constr. 3 (1): 12–19. https://doi.org/10.1061/(ASCE)1090-0268(1999)3:1(12).
Wang, Z., X.-L. Zhao, G. Xian, G. Wu, R. K. Singh Raman, and S. Al-Saadi. 2017. “Durability study on interlaminar shear behaviour of basalt-, glass- and carbon-fibre reinforced polymer (B/G/CFRP) bars in seawater sea sand concrete environment.” Constr. Build. Mater. 156 (Dec): 985–1004. https://doi.org/10.1016/j.conbuildmat.2017.09.045.
Yeh, I.-C. 1998. “Modeling of strength of high-performance concrete using artificial neural networks.” Cem. Concr. Res. 28 (12): 1797–1808. https://doi.org/10.1016/S0008-8846(98)00165-3.
Yilmaz, V. T., and F. P. Glasser. 1991. “Reaction of alkali-resistant glass fibers with cement. Part I: Review, assessment, and microscopy.” Glass Technol.: Eur. J. Glass Sci. Technol., Part A 32 (3): 91–98.
Zhou, J., X. Chen, and S. Chen. 2011. “Durability and service life prediction of GFRP bars embedded in concrete under acid environment.” Nucl. Eng. Des. 241 (10): 4095–4102. https://doi.org/10.1016/j.nucengdes.2011.08.038.

Information & Authors

Information

Published In

Go to Journal of Materials in Civil Engineering
Journal of Materials in Civil Engineering
Volume 35Issue 4April 2023

History

Received: Feb 8, 2022
Accepted: Jul 22, 2022
Published online: Jan 24, 2023
Published in print: Apr 1, 2023
Discussion open until: Jun 24, 2023

Permissions

Request permissions for this article.

Authors

Affiliations

Ph.D. Student, Civil and Environmental Engineering Dept., College of Design and Built Environment (CDBE), King Fahd Univ. of Petroleum and Minerals, Dhahran, Eastern Province 31261, Saudi Arabia. ORCID: https://orcid.org/0000-0001-5423-1021. Email: [email protected]
Associate Professor, Civil and Environmental Engineering Dept., College of Design and Built Environment (CDBE), King Fahd Univ. of Petroleum and Minerals, Dhahran, Eastern Province 31261, Saudi Arabia (corresponding author). ORCID: https://orcid.org/0000-0002-1047-2170. Email: [email protected]

Metrics & Citations

Metrics

Citations

Download citation

If you have the appropriate software installed, you can download article citation data to the citation manager of your choice. Simply select your manager software from the list below and click Download.

View Options

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Get Access

Access content

Please select your options to get access

Log in/Register Log in via your institution (Shibboleth)
ASCE Members: Please log in to see member pricing

Purchase

Save for later Information on ASCE Library Cards
ASCE Library Cards let you download journal articles, proceedings papers, and available book chapters across the entire ASCE Library platform. ASCE Library Cards remain active for 24 months or until all downloads are used. Note: This content will be debited as one download at time of checkout.

Terms of Use: ASCE Library Cards are for individual, personal use only. Reselling, republishing, or forwarding the materials to libraries or reading rooms is prohibited.
ASCE Library Card (5 downloads)
$105.00
Add to cart
ASCE Library Card (20 downloads)
$280.00
Add to cart
Buy Single Article
$35.00
Add to cart

Media

Figures

Other

Tables

Share

Share

Copy the content Link

Share with email

Email a colleague

Share