Adhesively bonded composite-steel structures are widely used in civil engineering due to their excellent mechanical properties, particularly for strengthening and repairing damaged steel structures. This study investigates the adhesive damage behavior of Araldite 2015 used to join cracked S235JR steel structures. Finite element analysis (FEM) and machine learning (ML) techniques were employed to predict adhesive damage. Two types of composites, graphite and boron, were used, and the adhesive was aged in deionized water over a period of 7.5 months. Damage was evaluated at four intervals: before immersion, and after 1, 3, and 7.5 months, under different applied loads of F = 100 MPa, F = 200 MPa, and F = 300 MPa, at a constant temperature of 25°C. The damage ratio (Dr) was calculated using SolidWorks based on damage zone theory. Three regression models, linear regression, polynomial regression, and support vector regression (SVR) were employed to predict adhesive damage. The results demonstrate that the adhesive maintained its integrity under prolonged immersion and high loads, even after 7.5 months of exposure and at a load of 300 MPa. Among the ML models, SVR provided the most accurate predictions, achieving a determination coefficient of R2 = 0.999 and outperforming other models across all evaluation metrics (MAE, MSE, RMSE).
KonurOMatthewsFL. Effect of the properties of the constituents on the fatigue performance of composites: a review. Composites1989; 20: 317. DOI: 10.1016/0010-4361.2889.2990657-5.
2.
ZhaoXLZhangL. State-of-the-art review on FRP strengthened steel structures. Eng Struct2007; 29: 1808–1823. DOI: 10.1016/j.engstruct.2006.10.006.
3.
TengJGYuTFernandoD. Strengthening of steel structures with fiberreinforced polymer composites. J Constr Steel Res2012; 78: 131–143. DOI: 10.1016/j,jcsr.2012.06.011.
4.
RamjiMSrilakshmiRBhanu PrakashM. Towards optimization of patch shape on the performance of bonded composite repair using FEM. Compos B Eng2013; 45: 710.
5.
OrsatelliJ-BParoissienELachaudF, et al.Bonded flush repairs for aerospace composite structures: a review on modelling strategies and application to repairs optimization, reliability and durability. Compos Struct2023; 304(Part 2): 116338.
6.
CampilhoRde MouraMPintoA, et al.Modelling the tensile fracture behaviour of CFRP scarf repairs. Composites B2009; 40(2): 149.
7.
CampilhoRDe MouraMDominguesJ. Stress and failure analyses of scarf repaired CFRP laminates using a cohesive damage model. J Adhes Sci Technol2007; 21(9): 855.
8.
SunLTieYHouY, et al.Prediction of failure behavior of adhesively bonded CFRP scarf joints using a cohesive zone model. Eng Fract Mech2020; 228: 106897.
9.
YooJ-STruongV-HParkM-Y, et al.Parametric study on static and fatigue strength recovery of scarf-patch-repaired composite laminates. Compos Struct2016; 140: 417.
10.
BendemraHCompstonPCrothersPJ. Optimisation study of tapered scarf and stepped-lap joints in composite repair patches. Compos Struct2015; 130: 1–8.
11.
RamjiMSrilakshmiRBhanu PrakashM. Towards optimization of patch shape on the performance of bonded composite repair using FEM. Compos B Eng2013; 45(1): 710–720.
12.
RachidMSerierBBachirBouiadjraB, et al.Numerical analysis of the patch shape effects on the performances of bonded composite repair in aircraft structures. Compos B Eng2012; 43(2): 391–397.
13.
BudheSBaneaMDde BarrosS, et al.An updated review of adhesively bonded joints in composite materials. Int J Adhesion Adhes2017; 72: 30–42.
14.
ErgunETopcuMTasgetirenS. An experimental study on the fatigue of cracked aluminum plate with composite patches under varied temperatures. J Compos Mater2011; 45(3): 341–355.
15.
SadekKBennounaMSAourB, et al. Numerical investigation of the adhesive damage used for the repair of A5083 H11 aluminum structures by composites patches. In International Journal of Engineering Research in Africa. Trans Tech Publications, Ltd., 2019, 44, pp. 22–31. https://doi.org/10.4028/www.scientific.net/jera.44.22
16.
SadekKAourBBouiadjraBB, et al.Analysis of crack propagation by bonded composite for different patch shapes repairs in marine structures: a numerical analysis. Int J Eng Res Afr2018; 35: 175–184.
17.
JiangXKolsteinHBijlaardF, et al.Effects of hygrothermal aging on glass-fibre reinforced polymer laminates and adhesive of FRP composite bridge: moisture diffusion characteristics. Compos Part A-Appl Sci Manuf2014; 57(1): 49–58.
18.
EftekhariMFatemiA. Tensile behavior of thermoplastic composites including temperature, moisture, and hygrothermal effects. Polym Test2016; 51: 151.
19.
XinHLiuYMosallamA, et al.Moisture diffusion and hygrothermal aging of pultruded glass fiber reinforced polymer laminates in bridge application. Compos Part B-Eng2016; 100: 197–207.
20.
HanXJinYZhangW, et al.Characterisation of moisture diffusion and strength degradation in an epoxy-based structural adhesive considering a postcuring process. J Adhes Sci Technol2018; 32: 1643. DOI: 10.1080/01694243.2018.1436876.
21.
HanXPickeringEBoA, et al.Characterisation on the hygrothermal degradation in the mechanical property of structural adhesive: a novel meso-scale approach. Compos B Eng2020; 182: 107609. DOI: 10.1016/j.compositesb.2019.107609.
22.
BordesMDaviesPCognardJ-Y, et al.Prediction of long term strength of adhesively bonded steel/epoxy joints in sea water. Int J Adhesion Adhes2009; 29: 595–608. DOI: 10.1016/j.ijadhadh.2009.02.013.
23.
AdamsRDCowapJWFarquharsonG, et al.The relative merits of the Boeing wedge test and the double cantilever beam test for assessing the durability of adhesively bonded joints, with particular reference to the use of fracture mechanics. Int J Adhesion Adhes2009; 29: 609.
24.
SugimanSCrocombeAD. The static and fatigue responses of aged metal laminate doublers joints under tension loading. J Adhes Sci Technol2016; 30(3): 313.
25.
TiryakiSAydınA. An artificial neural network model for predicting compression strength of heat treated woods and comparison with a multiple linear regression model. Construct Build Mater2014; 62: 102–108.
26.
WeiHZhaoSRongQ, et al.Predicting the effective thermal conductivities of composite materials and porous media by machine learning methods. Int J Heat Mass Tran2018; 127(218): 908–916.
27.
PathanMVPonnusamiSAPathanJ, et al.Predictions of the mechanical properties of unidirectional fibre composites by supervised machine learning. Sci Rep2019; 9(1): 13964.
28.
SangSXuCWangZ, et al.Accurate prediction of topology of composite plates via machine learning and propagation of elastic waves. Compos Commun2023; 37: 101465.
29.
FurtadoCPereiraLFTavaresRP, et al.A methodology to generate design allowables of composite laminates using machine learning. Int J Solid Struct2021; 233: 111095.
30.
AbbasiMCiardielloRGoglioL. Backface strain as an index to detect damage initiation in composite single-lap bonded joints: effects of adhesive type and joint dimensions. Int J Adhesion Adhes2024; 134: 103791, ISSN 0143-7496. DOI: 10.1016/j.ijadhadh.2024.103791.
31.
OshimaSKobayashiS. Experimental and numerical investigation of mesoscopic damage accumulation in adhesively bonded composite scarf joints. Compos Appl Sci Manuf2024; 179: 108036, ISSN 1359-835X. DOI: 10.1016/j.compositesa.2024.108036.
32.
BenyahiaFFari BouananiMAlbedahA, et al.Effect of water absorption on the adhesive damage in bonded composite repair of aircraft structures. Mater Des2014; 57(2014): 435–441, ISSN 0261-3069. DOI: 10.1016/j.matdes.2013.12.081.
33.
Fari BouananiMBenyahiaFAlbedahA, et al.Analysis of the adhesive failure in bonded composite repair of aircraft structures using modified damage zone theory. Mater Des2013; 50(2013): 433–439, ISSN 0261-3069. DOI: 10.1016/j.matdes.2013.03.017.
34.
BerrahouMDemmoucheN. Study of the effects of adhesive damage and plasticity on the repair of cracked aluminum structures. J Fail Anal Prev2023; 23: 1597–1607. DOI: 10.1007/s11668-023-01706-8.
35.
BanCSLeeYHChoiJH, et al.Strength prediction of adhesive joints using the modified damage zone theory. Compos Struct2008; 86: 96–100.
36.
WangCHRoseLRF. A crack bridging model for bonded plates subject to tension and bending. Int J Solid Struct1985; 36: 1985–2014.
37.
SugimanSSetyawanPDSalmanS, et al.Experimental and numerical investigation of the residual strength of steel-composites bonded joints: effect of media and aging condition. Compos B Eng2019; 173: 106977, ISSN13598368. DOI: 10.1016/j.compositesb.2019.106977.
38.
SugimanSCrocombeADAschroftIA. Experimental and numerical investigation of the static response of environmentally aged adhesively bonded joints. Int J Adhesion Adhes2013; 40: 224.
39.
MubasharAAshcroftIACritchlowGW, et al.Moisture absorption–desorption effects in adhesive joints. Int J Adhesion Adhes2009; 29: 751.
40.
SangSXuCFanJ, et al.Accurate prediction of microstructure of composites using machine learning. Adv Theory Simul2023; 6(2): 2200674.
41.
SheppardAKellyDTongL. A damage zone model for the failure analysis of adhesively bonded joints. Int J Adhesion Adhes1998; 18: 385–400.
42.
XuDLiuPFChenZP, et al.Achieving robust damage mode identification of adhesive composite joints for wind turbine blade using acoustic emission and machine learning. Compos Struct2020; 236: 111840, ISSN02638223. DOI: 10.1016/j.compstruct.2019.111840.
43.
SilvaGCBeberVCPitzDB. Machine learning and finite element analysis: an integrated approach for fatigue lifetime prediction of adhesively bonded joints. Fatig Fract Eng Mater Struct2021; 44: 3334–3348.
44.
RajakarunakaranSALourduARMuthusamyS, et al.Prediction of strength and analysis in self-compacting concrete using machine learning based regression techniques. Adv Eng Software2022; 173: 103267, ISSN 0965-9978. DOI: 10.1016/j.advengsoft.2022.103267.
45.
LepretreEChataignerSDiengL, et al.Numerical and experimental investigations of hot driven riveting process on old metal structures. Eng Struct2016; 127: 583–593.
AbdelouahedEBenzaamaHMokhtariM, et al.Pipeline repair by composite patch under temperature and pressure loading. Frat Ed Integrità Strutt2019; 13: 690–697.
48.
AdamsRDCowapJWFarquharsonG, et al.The relative merits of the Boeing wedge test and the double cantilever beam test for assessing the durability of adhesively bonded joints, with particular reference to the use of fracture mechanics. Int J Adhesion Adhes2009; 29: 609.
49.
KatnamKBCrocombeADSugimanH, et al.Static and fatigue failures of adhesively bonded laminate joints in moist environments. Int J Damage Mech2011; 20: 1217.
50.
SugimanSSulardjakaS. Water absorption and desorption behaviour and their effect on the tensile properties of FM 73M adhesive film. IJTech2016; 7(3): 438.
51.
YeYJiaXHanX, et al.Analytical investigation of hygrothermal effects on failure mechanisms of adhesively bonded corrugated sandwich structures under three-point bending. Int J Adhesion Adhes2024; 128: 103561, ISSN 0143-7496.
