期刊
ENGINEERING STRUCTURES
卷 231, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.engstruct.2020.111747
关键词
Transfer length; Genetic programming; Pretensioned concrete; Generative adversarial network; Artificial neural network; Random forest
资金
- Basic Science Research Program through the National Research Foundation of Korea (NRF) - Ministry of Education [2018R1A4A1025953]
This study derived a practical equation using genetic programming to predict the transfer length of prestressing strands, which exhibited a higher level of accuracy compared to other existing equations.
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 x 10(4) 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.
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