Development of ANN-based models to predict the bond strength of GFRP bars and concrete beams
Email:
anhnt@utt.edu.vn
Từ khóa:
bond strength, GFRP bars, Artificial Intelligence (AI), Artificial Neural Network (ANN)
Tóm tắt
The use of glass fiber-reinforced polymer (GFRP) has gained increasing attention over the past decades, aiming at replacing traditional steel rebar in concrete structures, especially in corrosion or magnetic conditions. Understanding the working mechanism between the reinforcements and concrete is crucial in many practical applications, in which the corresponding bond strength is considered as a critical element. In this study, a database including 159 experimental beam results gathered from the available literature was used for the development of an artificial neural network (ANN) model in an effort to predict the bond strength between GFRP bars and concrete. Two ANN models using BFGS quasi-Newton backpropagation and conjugate gradient backpropagation with Polak-Ribiére algorithms were constructed and evaluated in terms of bond strength prediction accuracy. The considered database consisted of five input parameters, including the bar diameter, concrete compressive strength, minimum cover to bar diameter ratio, bar development length to bar diameter ratio, the ratio of the area of transverse reinforcement to the product of transverse reinforcement spacing, the number of developed bars and bar diameter. The evaluation of the models was conducted and compared using well-known statistical measurements, namely the correlation coefficient (R), root mean square error (RMSE), and absolute mean error (MAE). The results demonstrated that both ANN models could accurately predict the bond strength between GFRP bars and concrete, paving the way for engineers to possess a useful alternative design solution for reinforced concrete structuresTài liệu tham khảo
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[27]. F. Yan, Z. Lin, X. Wang, F. Azarmi, K. Sobolev, Evaluation and prediction of bond strength of GFRP-bar reinforced concrete using artificial neural network optimized with genetic algorithm, Compos. Struct., 161 (2017) 441-452. https://doi.org/10.1016/j.compstruct.2016.11.068
[28]. F. Yan, Z. Lin, New strategy for anchorage reliability assessment of GFRP bars to concrete using hybrid artificial neural network with genetic algorithm, Compos. Part B Eng., 92 (2016) 420-433. https://doi.org/10.1016/j.compositesb.2016.02.008
[2]. J. Rovira, A. Almerich, J. Molines, P. Martin, Develpoment and applications of glass fiber bars as a reinforced in concrete structures, 2011, pp. 1–6. https://www.researchgate.net/publication/289730640_Develpoment_and_applications_ of_glass_fiber_bars_as_a_reinforced_in_concrete_structures
[3]. K. M. A. Hossain, Bond Strength of GFRP Bars Embedded in Engineered Cementitious Composite using RILEM Beam Testing, International Journal of Concrete Structures and Materials, 12 (2018). https://doi.org/10.1186/s40069-018-0240-0
[4]. F. Yan, Z. Lin, M. Yang, Bond mechanism and bond strength of GFRP bars to concrete: A review, Compos. Part B Eng., 98 (2016) 56–69. https://doi.org/10.1016/j.compositesb.2016.04.068
[5]. B. T. B. Benmokrane, 0. Chaallal, Bond Strength and Load Distribution of Composite GFRP Reinforcing Bars in Concrete, ACI Mater. J., 93 (1996) 254-259. https://doi.org/10.14359/9810
[6]. B. Tighiouart, B. Benmokrane, D. Gao, Investigation of bond in concrete member with fibre reinforced polymer (FRP) bars, Constr. Build. Mater., 12 (1998) 453–462. https://doi.org/10.1016/S0950-0618(98)00027-0
[7]. H. Mazaheripour, J. Barros, J. Sena-Cruz, M. Pepe, E. Martinelli, Experimental study on bond performance of GFRP bars in self-compacting steel fiber reinforced concrete, Compos. Struct., 95 (2013) 202–212. https://doi.org/10.1016/j.compstruct.2012.07.009
[8]. L. J. Malvar, Bond Stress-Slip Characteristics of FRP Rebars, (No. NFESC-TR-2013-SHR), Nav. Facil. Eng. Serv. Cent. Port Hueneme, CA, USA, 1994.
[9]. V. Eligehausen, R., Popov, E.P., Bertero, Local Bond Stress-Slip Relationships of Deformed Bars under Generalized Excitations, Rep. No. 83/23; Earthq. Eng. Serv. Center, Univ. Calif. Berkeley, CA, USA, 1983.
[10]. R. Cosenza, E., Manfredi, G., Realfonzo, Analytical modelling of bond between frp reinforcing bars and concrete, Non-Metallic Reinf. Concr. Struct. Proc. Second Int. RILEM Symp. RC Press London, England, 29 (1995) 164-171. https://www.researchgate.net/publication/262259677_Analytical_modelling_of_bond_ between_FRP_reinforcing_bars_and_concrete
[11]. M. Pepe, H. Mazaheripour, J. Barros, J. Sena-Cruz, E. Martinelli, Numerical calibration of bond law for GFRP bars embedded in steel fibre-reinforced self-compacting concrete, Compos. Part B Eng., 50 (2013) 403-412. https://doi.org/10.1016/j.compositesb.2013.03.006
[12]. W. J. Long, K. H. Khayat, G. Lemieux, S. D. Hwang, F. Xing, Pull-out strength and bond behavior of prestressing strands in prestressed self-consolidating concrete, Materials (Basel)., 7 (2014) 6930–6946. https://doi.org/10.3390/ma7106930
[13]. ACI Committee 440.1R-06, Guide for the design and construction of concrete reinforced with FRP bars, Am. Concr. Inst., 2006, pp. 44.
[14]. CAN, Design and Construction of Building Components with Fibre-Reinforced Polymers (CAN/CSA S806-02), Csa S806-02, no. Reaffirmed, 2009, pp. 177.
[15]. A. Machida, T. Uomoto, Recommendation for design and construction of concrete structures using continuous fiber Recommendation for Design and Constr Continuous Fiber Rei Alsuhiko Machida Saitama University Takcto Uomoto University of Tokyo Summary This paper describes the outli, 1999.
[16]. R. Masmoudi, A. Masmoudi, M. Ben Ouezdou, A. Daoud, Long-term bond performance of GFRP bars in concrete under temperature ranging from 20°C to 80°C, Constr. Build. Mater., 25 (2011) 486-493. https://doi.org/10.1016/j.conbuildmat.2009.12.040
[17]. H.B. Ly, T.A. Nguyen, Artificial neural network based modeling of the axial capacity of rectangular concrete filled steel tubes, Transport and Communications Science Journal, 71 (2020) 154-166. (in Vietnamese) https://doi.org/10.25073/tcsj.71.2.10
[18]. H. Q. Nguyen, H. B. Ly, V. Q. Tran, T. A. Nguyen, T. T. Le, B. T. Pham, Optimization of artificial intelligence system by evolutionary algorithm for prediction of axial capacity of rectangular concrete filled steel tubes under compression, Materials (Basel)., 13 (2020). https://doi.org/10.3390/ma13051205
[19]. H.-B. Ly et al., Prediction and Sensitivity Analysis of Bubble Dissolution Time in 3D Selective Laser Sintering Using Ensemble Decision Trees, Materials (Basel)., 12 (2019). https://doi.org/10.3390/ma12091544
[20]. G. F. Dahou Z, Sbartai ZM, Castel A, Artificial neural network model for steel-concrete bond prediction, Eng. Struct., 31(2009) 1724–1733. https://doi.org/10.1016/j.engstruct.2009.02.010
[21]. E. M. Golafshani, A. Rahai, M. H. Sebt, H. Akbarpour, Prediction of bond strength of spliced steel bars in concrete using artificial neural network and fuzzy logic, Constr. Build. Mater., 36 (2012) 411-418. https://doi.org/10.1016/j.conbuildmat.2012.04.046
[22]. S. Haykin, “Neural Networks – A Comprehensive Foundation,” 2nd Ed. Prentice Hall, 2000.
[23]. R. S. E. Dennis, Numerical Methods for Unconstrained Optimization and Nonlinear Equations, Englewood Cliffs, NJ PrenticeHall, 1983.
[24]. E. M. Golafshani, A. Rahai, M. H. Sebt, Artificial neural network and genetic programming for predicting the bond strength of GFRP bars in concrete, Mater. Struct., 48 (2015) 1581-1602. https://doi.org/10.1617/s11527-014-0256-0
[25]. G. Cybenko, Approximation by superpositions of a sigmoidal function, Math. Control. Signals Syst., 2 (1989) 303-314. https://doi.org/10.1007/BF02551274
[26]. Bounds, Lloyd, Mathew, Waddell, A multilayer perceptron network for the diagnosis of low back pain, in IEEE 1988 International Conference on Neural Networks, 2 (1988) 481-489. https://doi.org/10.1109/ICNN.1988.23963
[27]. F. Yan, Z. Lin, X. Wang, F. Azarmi, K. Sobolev, Evaluation and prediction of bond strength of GFRP-bar reinforced concrete using artificial neural network optimized with genetic algorithm, Compos. Struct., 161 (2017) 441-452. https://doi.org/10.1016/j.compstruct.2016.11.068
[28]. F. Yan, Z. Lin, New strategy for anchorage reliability assessment of GFRP bars to concrete using hybrid artificial neural network with genetic algorithm, Compos. Part B Eng., 92 (2016) 420-433. https://doi.org/10.1016/j.compositesb.2016.02.008
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Nhận bài
05/05/2020
Nhận bài sửa
10/09/2020
Chấp nhận đăng
22/09/2020
Xuất bản
30/09/2020
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Kiểu trích dẫn
Thuy Anh, N., & Hai Bang, L. (8800). Development of ANN-based models to predict the bond strength of GFRP bars and concrete beams. Tạp Chí Khoa Học Giao Thông Vận Tải, 71(7), 814-827. https://doi.org/10.47869/tcsj.71.7.7
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