Modeling of transfer length of prestressing strands using genetic programming and neuro-fuzzy

In this study, the efficiency of neuro-fuzzy inference system (ANFIS) and genetic expression programming (GEP) in predicting the transfer length of prestressing strands in prestressed concrete beams was investigated. Many models suggested for the transfer length of prestressing strands usually consider one or two parameters and do not provide consistent accurate prediction. The alternative approaches such as GEP and ANFIS have been recently used to model spatially complex systems. The transfer length data from various researches have been collected to use in training and testing ANFIS and GEP models. Six basic parameters affecting the transfer length of strands were selected as input parameters. These parameters are ratio of strand cross-sectional area to concrete area, surface condition of strands, diameter of strands, percentage of debonded strands, effective prestress and concrete strength at the time of measurement. Results showed that the ANFIS and GEP models are capable of accurately predicting the transfer lengths used in the training and testing phase of the study. The GEP model results better prediction compared to ANFIS model.