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Prediction of residual tensile strength of glass fiber reinforced polymer bars in harsh alkaline concrete environment using fuzzy metaheuristic models

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Journal of Ocean Engineering and Science 2023年第5期8卷 546-558页

作者： Mudassir Iqbal Khalid Elbaz Daxu Zhang Lili Hu Fazal E.Jalal State Key Laboratory of Ocean Engineering Shanghai Key Laboratory for Digital Maintenance of Buildings and InfrastructureSchool of Naval ArchitectureOcean&Civil EngineeringShanghai Jiao Tong UniversityShanghai 200240China Department of Civil Engineering University of Engineering and Technology PeshawarPakistan MOE Key Laboratory of Intelligent Manufacturing Technology Department of Civil and Environmental EngineeringCollege of EngineeringShantou UniversityShantouGuangdong 515063China Department of Civil Engineering Higher Future Institute of Engineering and TechnologyMansouraEgypt

The long-term durability of glass fiber reinforced polymer(gfrp)bars in harsh alkaline environments is of great importance in engineering,which is reflected by the environmental reduction factor in vari-ous structural *** calculation of this factor requires robust models to predict the residual tensile strength of gfrp ***,three robust metaheuristic algorithms,namely particle swarm optimiza-tion(PSO),genetic algorithm(GA),and support vector machine(SVM),were deployed in this study for achieving the best hyperparameters in the adaptive neuro-fuzzy inference system(ANFIS)in order to obtain more accurate prediction *** optimized models were developed to predict the tensile strength retention(TSR)of degraded gfrp rebars in typical alkaline environments(e.g.,seawater sea sand concrete(SWSSC)environment in this study).The study also proposed more reliable model to predict the TSR of gfrp bars exposed to alkaline environmental conditions under accelerating laboratory aging.A to-tal number of 715 experimental laboratory samples were collected in a form of extensive database to be trained.K-fold cross-validation was used to assess the reliability of the developed models by dividing the dataset into five equal *** order to analyze the efficiency of the metaheuristic algorithms,multiple statistical tests were *** was concluded that the ANFIS-SVM-based model is robust and accu-rate in predicting the TSR of conditioned gfrp *** the meantime,the ANFIS-PSO model also yielded reasonable results concerning the prediction of the tensile strength of gfrp bars in alkaline concrete *** sensitivity analysis revealed gfrp bar size,volume fraction of fibers,and pH of solution were the most influential parameters of TSR.

关键词： gfrp seawater sea sand concrete Durability Metaheuristic Anfis-pso,anfis-ga ANFIS-SVM

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