HYBRID BOND STRENGTH MODEL OF CFRP-LIGHTWEIGHT CONCRETE COMPOSITE USING NEURAL NETWORK-GENETIC ALGORITHM
Different retrofitting techniques are commonly used to sustain the design life of heavy damage and deteriorated concrete structures, whilst epoxy-bonded carbon fiber reinforced polymer (CFRP) has emerged as a widely known retrofitting method. Consequently, a sound understanding of the bond strength between structural lightweight concrete (LWC) and CFRP based on influential factors is essential in safety and economic requirements. In this study, a hybrid bond strength model using the artificial neural network (ANN) and genetic algorithm (GA) was developed to further understand the bond of a CFRP strengthened LWC structure. ANN was able to establish under satisfactory performance the relationship between the maximum bond load and the following influential parameters: width of CFRP (bfrp), total CFRP bond length (Lfrp), CFRP thickness (tf), and CFRP angle of orientation (qfrp). Furthermore, GA was able to derive the optimal configuration of the influential parameters resulted in high bond performance. Moreover, the optimization results also validated the sensitivity of each parameter on the interfacial bond behavior between LWC and CFRP.