Abstract
In this article, a new method for establishing creep predictive model using gene expression programming (GEP) is proposed. The three-point bending tests under constant load are carried out to determine time-dependent creep curves of fiber reinforced polymer materials with different lay-up styles, a modeling program is developed to predict creep behavior of composite materials. The creep of fiber is much smaller than that of resin matrix, various fiber layups play a role in constraining the deformation of resin, resulting in differences in creep performance of composites. The mathematical model satisfies the variation law that creep strain monotonically increases with time and tends to be stable. Based on 0 ~ 1000 h experimental data, the creep model is established by GEP, and then utilized to predict creep ranging from 1000 to 2000 h, the predicted values are in good agreement with experimental values. The fitting efficiency and prediction accuracy of GEP model are demonstrated by R2, RMSE, MAE and RRSE metrics. Moreover, taking R2 as a statistical metric, the validity of developed model is verified by comparison with Burgers model, Findley model and HKK model. Creep factor calculated by GEP model is lower than standard specified value, and the relative errors δ of creep deflection are very low, all within about 10%, indicating that GEP model can accurately predict the long-term creep performance of composites.
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The data used to support the findings of this study are available from the corresponding author upon reasonable request.
References
Demircan, G., Kisa, M., Ozen, M., Aktas, B.: Surface-modified alumina nanoparticles-filled aramid fiber-reinforced epoxy nanocomposites: preparation and mechanical properties. Iran. Polym. J. 29, 253–264 (2020). https://doi.org/10.1007/s13726-020-00790-z
Demircan, G., Kisa, M., Ozen, M., Acikgoz, A.: Quasi-static penetration behavior of glass-fiber-reinforced epoxy nanocomposites. Mech. Compos. Mater. 57(4), 503–516 (2021). https://doi.org/10.1007/s11029-021-09973-y
Ozen, M., Demircan, G., Kisa, M., Acikgoz, A., Ceyhan, G., Isıker, Y.: Thermal properties of surface-modified nano-Al2O3/Kevlar fiber/ epoxy composites. Mater. Chem. Phys. 278, 125689 (2022). https://doi.org/10.1016/j.matchemphys.2021.125689
Berardi, V.P., Perrella, M., Feo, L., Cricrì, G.: Creep behavior of GFRP laminates and their phases: experimental investigation and analytical modeling. Compos. Part B-Eng. 122, 136–144 (2017). https://doi.org/10.1016/j.compositesb.2017.04.015
Jia, Y., Peng, K., Gong, X., Zhang, Z.: Creep and recovery of polypropylene/carbon nanotube composites. Int. J. Plasticity. 27(8), 1239–1251 (2011). https://doi.org/10.1016/j.ijplas.2011.02.004
Rafiee, R., Mazhari, B.: Simulation of the long-term hydrostatic tests on glass fiber reinforced plastic pipes. Compos. Struct. 136, 56–63 (2016). https://doi.org/10.1016/j.compstruct.2015.09.058
Katouzian, M., Vlase, S., Scutaru, M.L.: Finite element method-based simulation creep behavior of viscoelastic carbon-fiber composite. Polymers 13(7), 1017 (2021). https://doi.org/10.3390/polym13071017
Asyraf, M.R.M., Ishak, M.R., Sapuan, S.M., Yidris, N.: Comparison of static and long-term creep behaviors between balau wood and glass fiber reinforced polymer composite for cross-arm application. Fiber. Polym. 22(3), 793–803 (2021). https://doi.org/10.1007/s12221-021-0512-1
Asyraf, M.R.M., Ishak, M.R., Sapuan, S.M., Yidris, N.: Utilization of bracing arms as additional reinforcement in pultruded glass fiber-reinforced polymer composite cross-arms: creep experimental and numerical analyses. Polymers 13(4), 620 (2021). https://doi.org/10.3390/polym13040620
Harries, K.A., Guo, Q., Cardoso, D.: Creep and creep buckling of pultruded glass-reinforced polymer members. Compos. Struct. 181, 315–324 (2017). https://doi.org/10.1016/j.compstruct.2017.08.098
Ghosh, S.K., Rajesh, P., Srikavya, B., Rathore, D.K., Prusty, R.K., Ray, B.C.: Creep behavior prediction of multi-layer graphene embedded glass fiber/epoxy composites using time-temperature superposition principle. Compos. Part A-Appl. S. 107, 507–518 (2018). https://doi.org/10.1016/j.compositesa.2018.01.030
Yang, Z., Wang, H., Ma, X., Shang, F., Ma, Y., Shao, Z., Hou, D.: Flexural creep tests and long-term mechanical behavior of fiber-reinforced polymeric composite tubes. Compos. Struct. 193, 154–164 (2018). https://doi.org/10.1016/j.compstruct.2018.03.083
Yu, L., Ma, Y.: Loading rate and temperature dependence of flexural behavior in injection-molded glass fiber reinforced polypropylene composites. Compos. Part B-Eng. 161, 285–299 (2019). https://doi.org/10.1016/j.compositesb.2018.10.035
Alwis, K.G.N.C., Burgoyne, C.J.: Time-Temperature superposition to determine the stress-rupture of aramid fibres. Appl. Compos. Mater. 13(4), 249–264 (2006). https://doi.org/10.1007/s10443-006-9017-8
Li, K., Yan, S.L., Pan, W.F., Zhao, G.: Warpage optimization of fiber-reinforced composite injection molding by combining back propagation neural network and genetic algorithm. Int. J. Adv. Manuf. Technol. 90, 963–970 (2017). https://doi.org/10.1007/s00170-016-9409-3
Bautu, E., Bautu, A., Luchian, H.: Symbolic regression on noisy data with genetic and gene expression programming. International Symposium on Symbolic & Numeric Algorithms for Scientific Computing. IEEE Computer Society. 321–324 (2005). https://doi.org/10.1109/SYNASC.2005.70
Zhang, H.R., Hao, J., Lv, Y.G., Zhao, L.: Symbolic regression on noisy data with stepwise genetic programming algorithm. Appl. Mech. Mater. 530(531), 625–628 (2014). https://doi.org/10.4028/www.scientific.net/amm.530-531.625
Yuan, C., Tang, C., Wen, Y., Zuo, J., Peng, J., Hu, J.: Convergency of genetic regression in data mining based on gene expression programming and optimized solution. Int. J. Comput. Appl. 28(4), 359–366 (2006). https://doi.org/10.1080/1206212X.2006.11441822
Kalfat, R., Nazari, A., Al-Mahaidi, R., Sanjayan, J.: Genetic programming in the simulation of FRP-to-concrete patch-anchored joints. Compos. Struct. 138, 305–312 (2016). https://doi.org/10.1016/j.compstruct.2015.12.005
Ebid, A.M., Deifalla, A.: Prediction of shear strength of FRP reinforced beams with and without stirrups using GP technique. Ain. Shams. Eng. J. 12(3), 2493–2510 (2021). https://doi.org/10.1016/j.asej.2021.02.006
Murad, Y., Tarawneh, B., Ashteyat, A.: Prediction model for concrete carbonation depth using gene expression programming. Comput. Concrete. 26(6), 497–504 (2020). https://doi.org/10.12989/cac.2020.26.6.497
Murad, Y., Ashteyat, A., Hunaifat, R.: Predictive model to the bond strength of FRP-to concrete under direct pullout using gene expression programming. J. Civ. Eng. Manage. 25(8), 773–784 (2019). https://doi.org/10.3846/jcem.2019.10798
Iqbal, M., Zhao, Q., Zhang, D., Jalal, F.E., Jamal, A.: Evaluation of tensile strength degradation of GFRP rebars in harsh alkaline conditions using non-linear genetic-based models. Mater. Struct. 54(5), 190 (2021). https://doi.org/10.1617/s11527-021-01783-x
Murad, Y., Tarawneh, A., Arar, F., Al-Zu’bi, A., Al-Ghwairi, A., Al-Jaafreh, A., Tarawneh, M.: Flexural strength prediction for concrete beams reinforced with FRP bars using gene expression programming. Struct. 33, 3163–3172 (2021). https://doi.org/10.1016/j.istruc.2021.06.045
Güneyisi, E.M., Nour, A.I.: Axial compression capacity of circular CFST columns transversely strengthened by FRP. Eng. Struct. 191, 417–431 (2019). https://doi.org/10.1016/j.engstruct.2019.04.056
Murad, Y., Hunifat, R., AL-Bodour, W.: Interior reinforced concrete beam-to-column joints subjected to cyclic loading: shear strength prediction using gene expression programming. Case. Stud. Constr. Mat. 13, e00432 (2020). https://doi.org/10.1016/j.cscm.2020.e00432
Murad, Y.: Predictive model for bidirectional shear strength of reinforced concrete columns subjected to biaxial cyclic loading. Eng. Struct. 244, 112781 (2021). https://doi.org/10.1016/j.engstruct.2021.112781
Murad, Y.: Joint shear strength models for exterior RC beam-column connections exposed to biaxial and uniaxial cyclic loading. J. Build. Eng. 30, 101225 (2020). https://doi.org/10.1016/j.jobe.2020.101225
Mansouri, I., Güneyisi, E.M., Mosalam, K.M.: Improved shear strength model for exterior reinforced concrete beam-column joints using gene expression programming. Eng. Struct. 228, 111563 (2021). https://doi.org/10.1016/j.engstruct.2020.111563
Hassani, M., Safi, M., Ardakani, R.R., Daryan, A.S.: Predicting fire resistance of SRC columns through gene expression programming. J. Struct. Fire. Eng. 12(2), 125–140 (2020). https://doi.org/10.1108/JSFE-04-2020-0013
Beheshti Aval, S.B., Ketabdari, H., Asil Gharebaghi, S.: Estimating shear strength of short rectangular reinforced concrete columns using nonlinear regression and gene expression programming. Struct. 12, 13–23 (2017). https://doi.org/10.1016/j.istruc.2017.07.002
Tarawneh, A., Almasabha, G., Alawadi, R., Tarawneh, M.: Innovative and reliable model for shear strength of steel fibers reinforced concrete beams. Struct. 32, 1015–1025 (2021). https://doi.org/10.1016/j.istruc.2021.03.081
Iqbal, M.F., Liu, Q.F., Azim, I., Zhu, X., Yang, J., Javed, M.F., Rauf, M.: Prediction of mechanical properties of green concrete incorporating waste foundry sand based on gene expression programming. J. Hazard. Mater. 384, 121322 (2020). https://doi.org/10.1016/j.jhazmat.2019.121322
Jafari, S., Mahini, S.S.: Lightweight concrete design using gene expression programing. Constr. Build. Mater. 139, 93–100 (2017). https://doi.org/10.1016/j.conbuildmat.2017.01.120
Gholampour, A., Gandomi, A.H., Ozbakkaloglu, T.: New formulations for mechanical properties of recycled aggregate concrete using gene expression programming. Constr. Build. Mater. 130, 122–145 (2017). https://doi.org/10.1016/j.conbuildmat.2016.10.114
Bouziadi, F., Boulekbache, B., Haddi, A., Hamrat, M., Djelal, C.: Finite element modeling of creep behavior of FRP-externally strengthened reinforced concrete beams. Eng. Struct. 204, 109908 (2020). https://doi.org/10.1016/j.engstruct.2019.109908
Anand, A., Banerjee, P., Sahoo, D., Rathore, D.K., Prusty, R.K., Ray, B.C.: Effects of temperature and load on the creep performance of CNT reinforced laminated glass fiber/epoxy composites. Int. J. Mech. Sci. 150, 539–547 (2019). https://doi.org/10.1016/j.ijmecsci.2018.09.048
Berardi, V.P., Perrella, M., Armentani, E., Cricrì, G.: Experimental investigation and numerical modeling of creep response of glass fiber reinforced polymer composites. Fatigue. Fract. Eng. M. 44(4), 1085–1095 (2021). https://doi.org/10.1111/ffe.13415
Koza, J.R.: Genetic programming as a means for programming computers by natural selection. Stat. Comput. 4(2), 87–112 (1994). https://doi.org/10.1007/BF00175355
Ferreira, C.: Gene expression programming: a new adaptive algorithm for solving problems. Complex. Syst. 13(2), 87–129 (2001). https://doi.org/10.48550/arXiv.cs/0102027
Zhong, J.H., Ong, Y.S., Cai, W.T.: Self-learning gene expression programming. Ieee. T. Evolut. Comput. 20(1), 65–80 (2016). https://doi.org/10.1109/TEVC.2015.2424410
Gao, Y.F., Yin, D.S.: A full-stage creep model for rocks based on the variable-order fractional calculus. Appl. Math. Model. 95(1), 435–446 (2021). https://doi.org/10.1016/j.apm.2021.02.020
Milad, A., Hussein, S.H., Khekan, A.R., Rashid, M., Al-Msari, H., Tran, T.H.: Development of ensemble machine learning approaches for designing fiber-reinforced polymer composite strain prediction model. Eng. Comput. 38, 3625–3637 (2022). https://doi.org/10.1007/s00366-021-01398-4
Sokairge, H., Elgabbas, F., Rashad, A., Elshafie, H.: Long-term creep behavior of basalt fiber reinforced polymer bars. Constr. Build. Mater. 260, 120437 (2020). https://doi.org/10.1016/j.conbuildmat.2020.120437
Liang, N., Zhu, S.R., Chen, J.Z., Fang, X.: Long-term behavior of GFRP pipes: optimizing the distribution of failure points during testing. Polym. Test. 48, 7–11 (2015). https://doi.org/10.1016/j.polymertesting.2015.08.011
Benmokrane, B., Brown, Vicki L., Mohamed, K., Nanni, A., Rossini, M., Shield, C.: Creep-rupture limit for GFRP bars subjected to sustained loads. J. Compos. Constr. 23(6), 06019001 (2019). https://doi.org/10.1061/(ASCE)CC.1943-5614.0000971
Farooq, M., Banthia, N.: FRP fibre-cementitious matrix interfacial bond under time-dependent loading. Mater. Struct. 52, 109 (2019). https://doi.org/10.1617/s11527-019-1409-y
Li, M., Zhang, H., Li, S., Zhu, W., Ke, Y.: Machine learning and materials informatics approaches for predicting transverse mechanical properties of unidirectional CFRP composites with microvoids. Mater. Design. 224, 111340 (2022). https://doi.org/10.1016/j.matdes.2022.111340
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This work was financially supported by the National Natural Science Foundation of China (No. 11902232).
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Hua Tan: Conceptualization; Formal analysis; Investigation; Methodology; Software; Visualization; Writing-original draft; Writing-review & editing. Sirong Zhu: Conceptualization; Supervision; Resources; Validation. Shilin Yan: Data curation; Methodology; Supervision; Project administration. Pin Wen: Validation; Funding acquisition. All authors have read and agreed to the submitted version of the manuscript.
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Tan, H., Zhu, S., Yan, S. et al. Predictive Model for Creep Behavior of Composite Materials Using Gene Expression Programming. Appl Compos Mater 30, 1003–1030 (2023). https://doi.org/10.1007/s10443-023-10109-9
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DOI: https://doi.org/10.1007/s10443-023-10109-9