DataVXApplied Composite MaterialsPaper
journal article·2023-06

Predictive Model for Creep Behavior of Composite Materials Using Gene Expression Programming

Hua Tan, Sirong Zhu, Shilin Yan, Pin Wen

Applied Composite Materials· Volume 30· Issue 3
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Composites

OpenAlex topics: Innovative concrete reinforcement materials · Smart Materials for Construction · Innovations in Concrete and Construction Materials

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References (48)

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1. 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-z10.1007/s13726-020-00790-z ↗
2. 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-y10.1007/s11029-021-09973-y ↗
3. 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.2021.12568910.1016/j.matchemphys.2021.125689 ↗
4. 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.2017.04.01510.1016/j.compositesb.2017.04.015 ↗
5. 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.2011.02.00410.1016/j.ijplas.2011.02.004 ↗
6. 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.2015.09.05810.1016/j.compstruct.2015.09.058 ↗
7. 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/polym1307101710.3390/polym13071017 ↗
8. 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/s12221-021-0512-110.1007/s12221-021-0512-1 ↗
9. 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/polym1304062010.3390/polym13040620 ↗
10. 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.2017.08.09810.1016/j.compstruct.2017.08.098 ↗
11. 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.2018.01.03010.1016/j.compositesa.2018.01.030 ↗
12. 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.2018.03.08310.1016/j.compstruct.2018.03.083 ↗
13. 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.2018.10.03510.1016/j.compositesb.2018.10.035 ↗
14. 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/s10443-006-9017-810.1007/s10443-006-9017-8 ↗
15. 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/s00170-016-9409-310.1007/s00170-016-9409-3 ↗
16. 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.2005.7010.1109/SYNASC.2005.70 ↗
17. 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.530-531.62510.4028/www.scientific.net/amm.530-531.625 ↗
18. 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.2006.1144182210.1080/1206212X.2006.11441822 ↗
19. 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.2015.12.00510.1016/j.compstruct.2015.12.005 ↗
20. 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.2021.02.00610.1016/j.asej.2021.02.006 ↗
21. 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.2020.26.6.49710.12989/cac.2020.26.6.497 ↗
22. 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.2019.1079810.3846/jcem.2019.10798 ↗
23. 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-x10.1617/s11527-021-01783-x ↗
24. 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.2021.06.04510.1016/j.istruc.2021.06.045 ↗
25. 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.2019.04.05610.1016/j.engstruct.2019.04.056 ↗
26. 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.e0043210.1016/j.cscm.2020.e00432 ↗
27. 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.2021.11278110.1016/j.engstruct.2021.112781 ↗
28. 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.2020.10122510.1016/j.jobe.2020.101225 ↗
29. 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.2020.11156310.1016/j.engstruct.2020.111563 ↗
30. 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-04-2020-001310.1108/JSFE-04-2020-0013 ↗
31. 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.2017.07.00210.1016/j.istruc.2017.07.002 ↗
32. 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.2021.03.08110.1016/j.istruc.2021.03.081 ↗
33. 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.2019.12132210.1016/j.jhazmat.2019.121322 ↗
34. 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.2017.01.12010.1016/j.conbuildmat.2017.01.120 ↗
35. 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.2016.10.11410.1016/j.conbuildmat.2016.10.114 ↗
36. 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.2019.10990810.1016/j.engstruct.2019.109908 ↗
37. 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.2018.09.04810.1016/j.ijmecsci.2018.09.048 ↗
38. 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.1341510.1111/ffe.13415 ↗
39. 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/BF0017535510.1007/BF00175355 ↗
40. 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/010202710.48550/arXiv.cs/0102027 ↗
41. 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.2015.242441010.1109/TEVC.2015.2424410 ↗
42. 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.2021.02.02010.1016/j.apm.2021.02.020 ↗
43. 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/s00366-021-01398-410.1007/s00366-021-01398-4 ↗
44. 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.2020.12043710.1016/j.conbuildmat.2020.120437 ↗
45. 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.2015.08.01110.1016/j.polymertesting.2015.08.011 ↗
46. 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.1943-5614.000097110.1061/(ASCE)CC.1943-5614.0000971 ↗
47. 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-y10.1617/s11527-019-1409-y ↗
48. 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.2022.11134010.1016/j.matdes.2022.111340 ↗