On the strength prediction of adhesively bonded FRP-steel joints using cohesive zone modelling - Heshmati, Mohsen; Haghani, Reza; Al-Emrani, Mohammad; André, Alann (Theoretical and applied fracture mechanics, 2018)
•Cohesive zone modelling is used to predict the strength of FRP-steel joints with various failure modes.•Shape of cohesive law is found to have negligible effect on strength of joints with long overlaps.•The strength of joints with varying overlap length are correlated with the length of damage process zone.•Application of directly measured cohesive laws to FRP/steel double-lap shear joint.•The performance of strengthened steel beams with interfacial failure is accurately predicted. The variety of failure modes that are likely to occur in fibre-reinforced polymer (FRP)/steel joints used in the construction industry adds to the complexity associated with the design of these joints. This variation in possible failure modes is mainly attributed to the lack of a controlled application environment and to rather insufficient quality assurance protocols and procedures. The use of energy-based methods such as, cohesive zone modelling (CZM), can be a solution to circumvent such complexities. This paper investigates a number of issues related to CZM analyses of FRP/steel adhesive joints using various test configurations and a comprehensive numerical study. Parameters such as the effect of shape and type of cohesive law, crack path location, length of damage process zone, variations of adhesive and FRP properties, and different failure modes including cohesive, interfacial debonding and FRP failure on the strength of joints are investigated. The results show that the behaviour of the tested joints is accurately predicted provided that the variation of failure modes are taken into account. Moreover, it is shown that the damage process zone in adhesive layer is directly proportional to the shape of cohesive laws. This feature can be employed in the design phase to ensure sufficient overlap length and to account for important in-service parameters such as temperature and moisture.