Prediction of shear strength and behavior of RC beams strengthened with externally bonded FRP sheets using machine learning techniques
Source abstract
Abstract
Abstract 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, fib 14 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.
Source: openaire · Quality: candidate unverified · Source record ↗
Research context
OpenAlex topics: Structural Behavior of Reinforced Concrete · Concrete Corrosion and Durability · Structural Load-Bearing Analysis
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (50)
Show source-provided references
1. Confinement model for FRP confined normal- and high-strength concrete circular columns10.1016/j.conbuildmat.2014.06.036 ↗
2. Strength enhancement modeling of concrete cylinders confined with CFRP composites using artificial neural networks10.1016/j.compositesb.2012.05.044 ↗
3. Behavior of reinforced concrete beams strengthened with externally bonded hybrid fiber reinforced polymer systems10.1016/j.matdes.2013.07.087 ↗
4. Effects of ratio of CFRP plate length to shear span and end anchorage on flexural behavior of SCC RC beams10.1061/(ASCE)CC.1943-5614.0000221 ↗
5. CFRP mechanical anchorage for externally strengthened RC beams under flexure10.1016/j.phpro.2014.07.002 ↗
6. Bond behavior of CFRP cured laminates: experimental and numerical investigation10.1115/1.4003565 ↗
7. Process of debonding in RC beams shear-strengthened with FRP U-strips or side strips10.1016/j.ijsolstr.2012.02.007 ↗
8. Experimental and analytical investigations on debonding of hybrid FRPs for flexural strengthening of RC beams10.1016/j.compositesb.2012.06.022 ↗
9. Shear strengthening of reinforced concrete beams using externally-bonded aluminum alloy plates: an experimental study10.1016/j.conbuildmat.2016.10.071 ↗
10. Finite element analysis of strengthened RC beams in shear with aluminum plates10.1016/j.compstruc.2014.10.009 ↗
11. Flexural behavior of reinforced concrete beams strengthened with externally bonded aluminum alloy plates10.1016/j.engstruct.2017.05.067 ↗
12. Behavior of RC beams externally strengthened with mortar bonded steel mesh
13. Flexural behavior of reinforced concrete beams externally strengthened with Hardwire steel-fiber sheets10.1016/j.conbuildmat.2018.03.225 ↗
14. Experimental investigations of repair of pre-damaged steel-concrete composite beams using CFRP laminates and mechanical anchors10.1016/j.tws.2016.12.024 ↗
15. Performance of externally strengthened RC beams with side-bonded CFRP sheets10.1016/j.compstruct.2019.01.045 ↗
16. Modeling of nonlinear cyclic response of shear-deficient RC T-beams strengthened with side bonded CFRP fabric strips10.12989/cac.2011.8.2.193 ↗
17. Shear strength model for FRP-strengthened RC beams with adverse FRP-steel interaction10.1061/(ASCE)CC.1943-5614.0000313 ↗
18. Unified analytical approach for determining shear capacity of RC beams strengthened with FRP10.1016/j.engstruct.2010.12.005 ↗
19. Modeling and simulation of shear resistance of R/C beams using artificial neural network10.1016/j.jfranklin.2005.12.005 ↗
20. Prediction of minimum factor of safety against slope failure in clayey soils using artificial neural network10.1007/s12665-014-3800-x ↗
21. Modeling and simulation of low-cycle fatigue life of steel reinforcing bars using artificial neural network10.1016/j.jfranklin.2010.04.005 ↗
22. Artificial neural networks and intelligent finite elements in non-linear structural mechanics10.1016/j.tws.2018.06.035 ↗
23. Crack width in concrete using artificial neural networks10.1016/j.engstruct.2013.03.020 ↗
24. Modeling of ultimate load for R.C. beams strengthened with Carbon FRP using artificial neural networks10.33899/rengj.2010.34813 ↗
25. An artificial neural networks model for the prediction of the compressive strength of FRP-confined concrete circular columns10.1016/j.engstruct.2017.02.047 ↗
26. Prediction of FRP-confined compressive strength of concrete using artificial neural networks10.1016/j.compstruct.2010.04.008 ↗
27. Neural network modeling of strength enhancement for CFRP confined concrete cylinders10.1016/j.buildenv.2007.01.036 ↗
28. Prediction of debonding strength for masonry elements retrofitted with FRP composites using neuro fuzzy and neural network approaches10.1016/j.compositesb.2014.11.023 ↗
29. Feature selection approaches for predictive modelling of groundwater nitrate pollution: an evaluation of filters, embedded and wrapper methods10.1016/j.scitotenv.2017.12.152 ↗
30. Prediction of unconfined compressive strength of geopolymer stabilized clayey soil using artificial neural network10.1016/j.compgeo.2015.05.021 ↗
31. Prediction of compressive strength of ultra-high performance concrete using SFS and ANN
32. Predicting the shear capacity of FRP in shear strengthened RC beams using ANN and NID
33. Advanced supervised learning in multi-layer perceptrons—from backpropagation to adaptive learning algorithms10.1016/0920-5489(94)90017-5 ↗
34. An artificial neural network approach to spatial habitat modelling with interspecific interaction10.1016/S0304-3800(98)00149-5 ↗
35. Illuminating the ‘black box’: a randomization approach for understanding variable contributions in artificial neural networks10.1016/S0304-3800(02)00064-9 ↗
36. Feature selection in machine learning: a new perspective10.1016/j.neucom.2017.11.077 ↗
37. An approach for estimating the capacity of RC beams strengthened in shear with FRP reinforcements using artificial neural networks10.1016/j.conbuildmat.2011.12.008 ↗
38. Shear resistance prediction of concrete beams reinforced by FRP bars using artificial neural networks10.1016/j.measurement.2018.05.051 ↗
39. Artificial neural networks for the prediction of shear capacity of steel plate strengthened RC beams10.1016/j.conbuildmat.2004.03.002 ↗
40. Artificial intelligence techniques for prediction of the capacity of RC beams strengthened in shear with external FRP reinforcement10.1016/j.compstruct.2009.10.027 ↗
41. Prediction of the ultimate strength of reinforced concrete beams FRP-strengthened in shear using neural networks10.1016/j.compositesb.2010.03.003 ↗
42. W. Li, X. Ren, C. Hu, “Artificial Neural Network Model for FRP Shear Contribution of RC Beams Strengthened with Externally Bonded FRP Composites BT - 4th Annual International Conference on Material Engineering and Application (ICMEA 2017).”.
43. A proposed model to estimate shear contribution of FRP in strengthened RC beams in terms of adaptive neuro-fuzzy inference system10.1016/j.compstruct.2017.03.028 ↗
44. ACI Committee 318, Building Code Requirements for Structural Concrete (ACI 318-14). Farmington Hills, Michigan (USA): American Concrete Institute, 2019.
45. ACI Committee 440, Guide for the Design and Construction of Externally Bonded FRP Systems for Strengthening Concrete Structures. Farmington Hills, Michigan (USA): American Concrete Institute, 2017.
46. F. bulletin 14, Externally bonded FRP reinforcement for RC structures. Lausanne, Switzerland: International Federation for Structural Concrete (fib), 2001.
47. CNR-DT200, Guide for the design and construction of externally bonded FRP systems for strengthening existing structures. Rome, Italy: Italian National Research Council, 2004.
48. Effect of flexural CFRP sheets on shear resistance of reinforced concrete beams10.1016/j.compstruct.2014.12.010 ↗
49. Effect of transverse steel on the performance of RC T-beams strengthened in shear zone with GFRP sheet10.1016/j.conbuildmat.2012.11.098 ↗
50. Experimental and numerical investigations on the performance evaluation of shear deficient and GFRP strengthened reinforced concrete beams10.1016/j.conbuildmat.2017.01.089 ↗