Performance assessment of gaussian process regression to predict the bond strength of FRP sheets to concrete
Email:
anhnt@utt.edu.vn
Từ khóa:
Bond strength; FRP-to-concrete; Gaussian process regression.
Tóm tắt
A Gaussian process regression (GPR) model for predicting the bond strength of FRP-to-concrete is proposed in this study. Published single-lap shear test specimens are used to predict the bond strength of externally bonded FRP systems adhered to concrete prisms. A database of 150 experimental results collected from published works is used for the training and testing phases of the proposed GPR model, containing 6 input parameters (width of concrete prism, concrete compressive strength, FRP thickness, FRP width, FRP length, and FRP modulus of elasticity). The output parameter of the prediction problem is bond strength. Three statistical indicators, namely the coefficient of determination, root mean square error (RMSE), and mean absolute error (MAE) are used to evaluate the performance of the proposed GPR model over 500 simulations. The results of this study indicate that the GPR provides an efficient alternative method for predicting the bond strength of FRP-to-concrete when compared to experimental results.Tài liệu tham khảo
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[34] H. T. Ren, Study on basic theories and long time behavior of concrete structures strengthened by fiber reinforced polymers, Dalian University of technology, 2003.
[2] J. G. Teng, J. F. Chen, S. T. Smith, L. Lam, FRP: strengthened RC structures, 2002.
[3] J. G. Teng et al., Behaviour and strength of FRP-strengthened RC structures: a state-of-the-art review, Proceedings of the Institution of Civil Engineers-Structures and Buildings, 156 (2003) 51–62. https://doi.org/10.1680/stbu.2003.156.1.51
[4] Y. Murad, A. Ashteyat, R. Hunaifat, Predictive model to the bond strength of FRP-to-concrete under direct pullout using Gene expression programming, Journal of Civil Engineering and Management, 25 (2019) 773–784. https://doi.org/10.3846/jcem.2019.10798
[5] S. M. Hamze-Ziabari, A. Yasavoli, Predicting Bond Strength between FRP Plates and Concrete Sub-strate: Applications of GMDH and MNLR Approaches, Journal of Advanced Concrete Technology, 15 (2017) 644–661. https://doi.org/10.3151/jact.15.644
[6] Z. S. Wu, H. Yoshizawa, Analytical/experimental study on composite behavior in strengthening structures with bonded carbon fiber sheets, Journal of Reinforced Plastics and Composites, 18 (1999) 1131–1155. https://doi.org/10.1177/073168449901801207
[7] J. Yao, J. G. Teng, J. F. Chen, Experimental study on FRP-to-concrete bonded joints, Composites Part B: Engineering, 36 (2005) 99–113. https://doi.org/10.1016/j.compositesb.2004.06.001
[8] M. J. Chajes, W. W. Finch, T. A. Thomson, Bond and force transfer of composite-material plates bonded to concrete, Structural Journal, 93 (1996) 209–217. https://doi.org/10.14359/1491
[9] S. K. Sharma et al., Plate-concrete interfacial bond strength of FRP and metallic plated concrete specimens, Composites Part B: Engineering, 37 (2006) 54-63. https://doi.org/10.1016/j.compositesb.2005.05.011
[10] H. YUAN, Z. WU, H. YOSHIZAWA, Theoretical solutions on interfacial stress transfer of externally bonded steel/composite laminates, Doboku Gakkai Ronbunshu, 2001 (2001) 27-39. https://doi.org/10.2208/jscej.2001.675_27
[11] Z. Wu, H. Yuan, H. Niu, Stress transfer and fracture propagation in different kinds of adhesive joints, Journal of Engineering Mechanics, 128 (2002) 562-573. https://doi.org/10.1061/(ASCE)07339399(2002)128:5(562)
[12] H. Yuan et al., Full-range behavior of FRP-to-concrete bonded joints, Engineering Structures, 26 (2004) 553-565. https://doi.org/10.1016/j.engstruct.2003.11.006
[13] J. F. Chen, Z. J. Yang, G. D. Holt, FRP or steel plate-to-concrete bonded joints: effect of test methods on experimental bond strength, Steel and Composite Structures, 1 (2001) 231-244. https://doi.org/10.12989/scs.2001.1.2.231
[14] H. Niu, Z. Wu, Interfacial debonding mechanism influenced by flexural cracks in FRP-strengthened beams, Journal of Structural Engineering, 47 (2001) 1277–1288.
[15] J. F. Chen, J. G. Teng, Anchorage strength models for FRP and steel plates bonded to concrete, Journal of Structural Engineering, 127 (2001) 784-791. http://hdl.handle.net/10397/29977
[16] O. Chaallal, M. -J. Nollet, D. Perraton, Strengthening of reinforced concrete beams with externally bonded fiber-reinforced-plastic plates: design guidelines for shear and flexure, Canadian Journal of Civil Engineering, 25 (1998) 692–704. https://doi.org/10.1139/l98-008
[17] T. A. Nguyen, H. B. Ly, Development of ANN-based models to predict the bond strength of GFRP bars and concrete beams, Transport and Communications Science Journal, 71 (2020) 814-827. https://doi.org/10.47869/tcsj.71.7.7
[18] H. Q. Nguyen et al., Optimization of Artificial Intelligence System by Evolutionary Algorithm for Prediction of Axial Capacity of Rectangular Concrete Filled Steel Tubes under Compression, Materials, 13 (2020) 1205. https://doi.org/10.3390/ma13051205
[19] T. A. Nguyen, H. B. Ly, Prediction of critical elastic buckling load of cellular h-section beams using support vector machine, Transport and Communications Science Journal, 71 (2020) 500-513. https://doi.org/10.25073/tcsj.71.5.4
[20] 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. https://doi.org/ 10.25073/tcsj.71.2.10
[21] D.V. Dao et al., Investigation and Optimization of the C-ANN Structure in Predicting the Compressive Strength of Foamed Concrete, Materials, 13 (2020) 1072. https://doi.org/10.3390/ma13051072
[22] J. Hu, J. Wang, Short-term wind speed prediction using empirical wavelet transform and Gaussian process regression, Energy, 93 (2015) 1456-1466. https://doi.org/10.1016/j.energy.2015.10.041
[23] L. Zhou, J. Chen, Z. Song, Recursive Gaussian process regression model for adaptive quality monitoring in batch processes, Mathematical Problems in Engineering, 2015 (2015) 9. https://doi.org/10.1155/2015/761280
[24] S. Banerjee et al., Gaussian predictive process models for large spatial data sets, Journal of the Royal Statistical Society: Series B (Statistical Methodology). 70 (2008) 825-848. https://doi.org/10.1111/j.1467-9868.2008.00663
[25] C. E. Rasmussen, Gaussian processes in machine learning, in: Summer School on Machine Learning, Springer, (2003), pp. 63-71. http://www.tuebingen.mpg.de/∼carl
[26] I. Murray, R. P. Adams, Slice sampling covariance hyperparameters of latent Gaussian models, Advances in neural information processing systems, (2010), pp. 1732–1740.
[27] S. Sundararajan, S. S. Keerthi, Predictive approaches for choosing hyperparameters in Gaussian processes, Neural Computation. 13 (2001) 1103–1118. https://doi.org/10.1162/08997660151134343
[28] C. Xu et al., Intelligent analysis model of landslide displacement time series based on coupling PSO-GPR, Rock and Soil Mechanics, 32 (2011) 1669–1675.
[29] K. Liu, B. Liu, C. Xu, Intelligent analysis model of slope nonlinear displacement time series based on genetic-Gaussian process regression algorithm of combined kernel function, Chinese Journal of Rock Mechanics and Engineering, 10 (2009) 2128-2134. http://www.rockmech.org/EN/Y2009/V28/I10/2128
[30] K. Takeo et al., Bond characteristics of CFRP sheets in the CFRP bonding technique, Proceedings of Japan Concrete Institute, 19 (1997) 1599–1604.
[31] S. -K. Woo, Y. Lee, Experimental study on interfacial behavior of CFRP-bonded concrete, KSCE Journal of Civil Engineering, 14 (2010) 385–393. https://doi.org/10.18770/KEPCO.2015.01.01.127
[32] H. Toutanji, P. Saxena, L. Zhao, T. Ooi, Prediction of interfacial bond failure of FRP–concrete surface, Journal of Composites for Construction, 11 (2007) 427-436. http://worldcat.org/issn/10900268
[33] H. D. Zhao, Y. Zhang, M. Zhao, Research on the bond performance between CFRP plate and concrete, in Proc., 1st Conf. on FRP Concrete Structures of China, (2000), pp. 247-253.
[34] H. T. Ren, Study on basic theories and long time behavior of concrete structures strengthened by fiber reinforced polymers, Dalian University of technology, 2003.
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Kiểu trích dẫn
Thuy Anh, N., & Hai Bang, L. (1622048400). Performance assessment of gaussian process regression to predict the bond strength of FRP sheets to concrete. Tạp Chí Khoa Học Giao Thông Vận Tải, 72(4), 411-422. https://doi.org/10.47869/tcsj.72.4.2
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