Genetic programming approach and data generation for transfer lengths in pretensioned concrete members

Abstract

This study aims to derive a practical equation that can predict the transfer length of prestressing strands with the use of genetic programming. Towards this end, a total of 260 transfer length test results were collected from previous studies, and a feature selection procedure was applied to the collected database to extract the key features influencing the transfer length. Based on the five most important features, a practical equation was derived using a genetic programming approach, and the rationality of the proposed equation was verified by comparing it with design codes, existing models, and machine learning models (random forest and artificial neural network). In addition, 1.0 × 104 fake transfer length data that follow the probability distribution of the real data were generated using a generative adversarial network, based on which the prediction performances were visualized and compared in detail. The results showed that the proposed equation exhibited a higher level of accuracy than other existing equations.