An improved random forest model to predict bond strength of FRP-to-concrete
Abstract
Fiber-reinforced polymer (FRP) is an excellent building material for strengthening concrete structures, but it is difficult to accurately evaluate the bond strength of FRP-to-concrete due to the influence of various parameters. In this study, a novel hybrid model which combines particle swarm optimization (PSO) with random forest (RF) was proposed to predict the bond strength of FRP-to-concrete. The PSO algorithm was used to optimize the hyperparameters of the RF model. A total of 749 specimens collected from the literature were used to develop the proposed PSO-RF model. Each sample contains 11 parameters required for the model. These 11 parameters are (1) the compressive strength of concrete, (2) the tensile strength of concrete, (3) the width of concrete specimen, (4) the maximum aggregate size of concrete, (5) the tensile strength of FRP, (6) the thickness of FRP, (7) the elastic modulus of FRP, (8) the tensile strength of adhesive, (9) the bond length of FRP, (10) the bond width of FRP, and (11) the bond strength of FRP-to-concrete. The proposed PSO-RF model was compared with other machine learning models as well as ten empirical equations. Six statistical indices, namely root mean squared error (RMSE), mean absolute error (MAE), coefficient of determination (R2), Nash-Sutcliffe efficiency coefficient (NSE), Willmott’s Index of Agreement (WIA), and Legates-McCabe’s Index (LM) were used to evaluate the prediction performance of the abovementioned models. The results show that the RMSE, MAE, R2, NSE, WIA and LM values of the PSO-RF model are 1.529 kN, 0.942 kN, 0.986, 0.984, 0.996 and 0.892, respectively, for the training datasets and 2.672 kN, 1.967 kN, 0.963, 0.961, 0.989 and 0.761, respectively, for the test datasets. It can be concluded that the proposed PSO-RF model has the best comprehensive performance in predicting the bond strength of FRP-to-concrete. In addition, the sensitivity analysis of the PSO-RF model was also conducted in this study.
Keyword : fiber-reinforced polymer, multivariate adaptive regression splines, wavelet neural network, particle swarm optimization, random forest
How to Cite
Tao, L., & Xue, X. (2024). An improved random forest model to predict bond strength of FRP-to-concrete. Journal of Civil Engineering and Management, 30(6), 520–535. https://doi.org/10.3846/jcem.2024.21636
This work is licensed under a Creative Commons Attribution 4.0 International License.
References
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Barris, C., Correia, L., & Sena-Cruz, J. (2018). Experimental study on the bond behaviour of a transversely compressed mechanical anchorage system for externally bonded reinforcement. Composite Structures, 200, 217–228. https://doi.org/10.1016/j.compstruct.2018.05.084
Bakis, C. E., Uppuluri, V. S., Nanni, A., & Boothby, T. E. (1998). Analysis of bonding mechanisms of smooth and lugged FRP rods embedded in concrete. Composites Science and Technology, 58(8), 1307–1319. https://doi.org/10.1016/S0266-3538(98)00016-5
Breiman, L. (2001). Random forests. Machine Learning, 45, 5–32. https://doi.org/10.1023/A:1010933404324
Cascardi, A., Micelli, F., & Aiello, M. A. (2017). An artificial neural networks model for the prediction of the compressive strength of FRP-confined concrete circular columns. Engineering Structures, 140, 199–208. https://doi.org/10.1016/j.engstruct.2017.02.047
Chróścielewski, J., Ferenc, T., Mikulski, T., Miśkiewicz, M., & Pyrzowski, Ł. (2019). Numerical modelling and experimental validation of full-scale segment to support design of novel GFRP footbridge. Composite Structures, 213, 299–307. https://doi.org/10.1016/j.compstruct.2019.01.089
Chen, J. F., & Teng, J. (2001). Anchorage strength models for FRP and steel plates bonded to concrete. Journal of Structural Engineering, 127(7), 784–791. https://doi.org/10.1061/(ASCE)0733-9445(2001)127:7(784)
Chen, S. Z., Zhang, S. Y., Han, W. S., & Wu, G. (2021). Ensemble learning based approach for FRP-concrete bond strength prediction. Construction and Building Materials, 302, Article 124230. https://doi.org/10.1016/j.conbuildmat.2021.124230
Correial, L., Barris, C., Franca, P., & Sena-Curz, J. (2019). Effect of temperature on bond behavior of externally bonded FRP laminates with mechanical end anchorage. Journal of Composites for Construction, 23(5), Article 04019036. https://doi.org/10.1061/(ASCE)CC.1943-5614.0000961
Czaderski, C., Soudki, K., & Motavalli, M. (2010). Front and side view image correlation measurements on FRP to concrete pull-off bond tests. Journal of Composites for Construction, 14(4), 451–463. https://doi.org/10.1061/(ASCE)CC.1943-5614.0000106
Dai, J., Ueda, T., & Sato, Y. (2005). Development of the nonlinear bond stress–slip model of fiber reinforced plastics sheet–concrete interfaces with a simple method. Journal of Composites for Construction, 9(1), 52–62. https://doi.org/10.1061/(ASCE)1090-0268(2005)9:1(52)
Dai, Y., Khandelwal, M., Qiu, Y., Zhou, J., Monjezi, M., & Yang, P. X. (2022). A hybrid metaheuristic approach using random forest and particle swarm optimization to study and evaluate backbreak in open-pit blasting. Neural Computing and Applications, 34, 6273–6288. https://doi.org/10.1007/s00521-021-06776-z
Daneshvar, D., & Behnood, A. (2020). Estimation of the dynamic modulus of asphalt concretes using random forests algorithm. International Journal of Pavement Engineering, 23(2), 250–260. https://doi.org/10.1080/10298436.2020.1741587
Daud, R. A., Cunningham, L. S., & Wang, Y. C. (2015). Static and fatigue behaviour of the bond interface between concrete and externally bonded CFRP in single shear. Engineering Structures, 97, 54–67. https://doi.org/10.1016/j.engstruct.2015.03.068
Daud, R. A., Cunningham, L. S., Wang, & Y. C. (2017). New model for post-fatigue behaviour of CFRP to concrete bond interface in single shear. Composite Structures, 163, 63–76. https://doi.org/10.1016/j.compstruct.2016.12.029
Friedman, J. H. (1991). Multivariate adaptive regression splines. Annals of Statistics, 19(1), 1–67. https://doi.org/10.1214/aos/1176347963
Ferreira, C. (2001). Gene expression programming: a new adaptive algorithm for solving problems. Complex and Intelligent Systems, 13, 87–129. https://doi.org/10.48550/arXiv.cs/0102027
Garzon-Roca, J., Sena-Cruz, J. M., Fernandes, P., & Xavier, J. (2015). Effect of wet-dry cycles on the bond behaviour of concrete elements strengthened with NSM CFRP laminate strips. Composite Structures, 132, 331–340. https://doi.org/10.1016/j.compstruct.2015.05.053
Ghorbani, M., Mostofinejad, D., & Hosseini, A. (2017). Experimental investigation into bond behavior of FRP-to-concrete under mixed-mode I/II loading. Construction and Building Materials, 132, 303–312. https://doi.org/10.1016/j.conbuildmat.2016.11.057
Haddad, R. H., Al-Rousan, R., Ghanma, L., & Nimri, Z. (2015). Modifying CFRP-concrete bond characteristics from pull-out testing. Magazine of Concrete Ressearch, 67(13), 707–717. https://doi.org/10.1680/macr.14.00271
Hadigheh, S., Gravina, R., & Setunge, S. (2015). Identification of the interfacial fracture mechanism in the FRP laminated substrates using a modified single lap shear test set-up. Engineering Fracture Mechanics, 134, 317–329. https://doi.org/10.1016/j.engfracmech.2014.12.001
Hiroyuki, Y., & Wu, Z. S. (1997). Analysis of debonding fracture properties of CFS strengthened RC member subject to tension. In Proceedings of 3rd International Symposium on the Non-Metallic (FRP) Reinforcement for Concrete Structures, The Japan Concrete Institute, Japan.
Irshidat, M. R., & Al-Saleh, M. H. (2016). Effect of using carbon nanotube modified epoxy on bond-slip behavior between concrete and FRP sheets. Construction and Building Materials, 105, 511–518. https://doi.org/10.1016/j.conbuildmat.2015.12.183
Ivakhnenko, A. G. (1971). Polynomial theory of complex systems. IEEE Transactions on Systems, Man, and Cybernetics, 4, 364–378. https://doi.org/10.1109/TSMC.1971.4308320
Jahangir, H., & Eidgahee, D. R. (2021). A new and robust hybrid artificial bee colony algorithm – ANN model for FRP-concrete bond strength evaluation. Composite Structures, 257, Article 113160. https://doi.org/10.1016/j.compstruct.2020.113160
Japan Concrete Institute. (2003). Technical report of Technical Committee on retrofit technology.
Jekabsons, G. (2011). ARESLab: Adaptive regression splines toolbox for Matlab/Octave. http://www.cs.rtu.lv/jekabsons/
Khalifa, A., Gold, W. J., Nanni, A., & Abdel Aziz, M. I. (1998). Contribution of externally bonded FRP to shear capacity of RC flexural members. Journal of Composites for Construction, 2(4), 195–202. https://doi.org/10.1061/(ASCE)1090-0268(1998)2:4(195)
Legates, D. R., & McCabe, G. J. (1999). Evaluating the use of “goodness-of-fit” measures in hydrologic and hydroclimatic model validation. Water Resources Research, 35(1), 233–241. https://doi.org/10.1029/1998WR900018
Luat, N. V., Shin, J., & Lee, K. (2021). Ultimate axial capacity prediction of CCFST columns using hybrid intelligence models-a new approach. Steel and Composite Structures, 40(3), 461–479. https://doi.org/10.12989/scs.2021.40.3.461
Maeda, T., Asano, Y., Sato, Y., Ueda, T., & Kakuta, Y. (1999). A study on bond mechanism of carbon fiber sheet. In Proceedings of the 3rd International Symposium on Non-Metallic (FRP) Reinforcement for Concrete Structures (pp. 279–286), Sapporo, Japan.
Mirzania, E., Vishwakarma, D. K., Bui, Q. A. T., Band, S. S., & Dehghani, R. (2023). A novel hybrid AIG-SVR model for estimating daily reference evapotranspiration. Arabian Journal of Geosciences, 16, Article 301. https://doi.org/10.1007/s12517-023-11387-0
Murad, Y., Ashteyat, A., & Hunaifat, R. (2019). Predictive model to the bond strength of FRP-to-concrete under direct pullout using gene expression programming. Journal of Civil Engineering and Management, 25(8), 773–784. https://doi.org/10.3846/jcem.2019.10798
Nash, J. E., & Sutcliffe, J. V. (1970). River flow forecasting through conceptual models part I ̶ A discussion of principles. Journal of Hydrology, 10, 282–290. https://doi.org/10.1016/0022-1694(70)90255-6
Naser, M. Z., Kodur, V., Thai, H. T., Hawileh, R., Abdalla, J., & Degtyarev, V. V. (2021). StructuresNet and FireNet: Benchmarking datasets and machine learning algorithms in structural and fire engineering domains. Journal of Building Engineering, 44, Article 102977. https://doi.org/10.1016/j.jobe.2021.102977
Nerilli, F., & Vairo, G. (2018). Experimental investigation on the debonding failure mode of basalt-based FRP sheets from concrete. Composites Part B: Engineering, 153, 205–216. https://doi.org/10.1016/j.compositesb.2018.07.002
Neubauer, U., & Rostásy, F. S. (1997). Design aspects of concrete structures strengthened with externally bonded CFRP plates. In Proceedings of the 7th International Conference on Structural Faults and Repairs (Vol. 2, pp. 109–118). ECS Publications.
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