Fiber reinforced polymer (FRP) waste is becoming an environmental concern due to the widespread use and non-biodegradable nature of FRP composites. Cutting FRP waste into discrete reinforce-ments (referred to as “needles” hereafter) as coarse aggregate in concrete has been suggested as a possible solution to FRP waste recycling. It has previously been observed in small specimens that FRP needles increase the tensile strength and energy absorption capacity of concrete. This paper presents an experimental investiga-tion into the effect of GFRP needles as coarse aggregate partial replacement in concrete on shear behavior of full-scale reinforced concrete (RC) beams. A total of 10 RC beams without steel stirrups in the critical zone were tested under four-point bending. The volume replacement ratio of the coarse aggregate and the surface type of GFRP needles were chosen as the test parameters. GFRP needles, with either smooth or helically wrapped surfaces, were added to the concrete mix to replace 5% or 10% of coarse aggregate by volume, respectively. All test beams failed in shear in a brittle manner with the ductility being slightly enhanced by the partial replace-ment of coarse aggregate using GFRP needles. An enhancement of 8%-10% in the load carrying capacity was observed in beams with helically wrapped needles, while beams with smooth needles showed a reduction in the load carrying capacity.
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Prediction of shear strength and behavior of RC beams strengthened with externally bonded FRP sheets using machine learning techniques
e. This paper presents the use Machine Learning (ML) techniques to study the behavior of shear-deficient reinforced concrete (RC) beams strengthened in shear with side-bonded and U-wrapped fiber-reinforced polymers (FRP) laminates. An extensive database consisting of 120 tested specimen and 15 parameters was collected. The resilient back-propagating neural network (RBPNN) was used as a regression tool and the recursive feature elimination (RFE) algorithm and neural interpretation diagram (NID) were employed within the validated RBPNN to identify the parameters that greatly influence the prediction of FRP shear capacity. The results indicated that the RBPNN with the selected parameters was capable of predicting the FRP shear capacity more accurately (r^2 = 0.885; RMSE = 8.1 kN) than that of the RBPNN with the original 15 parameters (r^2 = 0.668; RMSE = 16.6 kN). The model also outperformed previously established standard predictions of ACI 440.R-17, fib14 and CNRDT200. A comprehensive parametric study was conducted and it concluded that the implementation of RBPNN with RFE and NID, separately, is a viable tool for assessing the strength and behavior of FRP in shear strengthened beams.
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- Award ID(s):
- 1633608
- NSF-PAR ID:
- 10341139
- Date Published:
- Journal Name:
- Composite structures
- Volume:
- 234
- Issue:
- 111698
- ISSN:
- 0263-8223
- Format(s):
- Medium: X
- Sponsoring Org:
- National Science Foundation
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- Free Publicly Accessible Full Text
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Accepted Manuscript1.0
- Journal Article:
- https://doi.org/10.1016/j.compstruct.2019.111698
