Genetic programming approach and data generation for transfer lengths in pretensioned concrete members
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- @Article{JEONG:2021:ES,
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author = "Hoseong Jeong and Sun-Jin Han and Seung-Ho Choi and
Jae-Hyun Kim and Kang Su Kim",
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title = "Genetic programming approach and data generation for
transfer lengths in pretensioned concrete members",
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journal = "Engineering Structures",
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volume = "231",
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pages = "111747",
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year = "2021",
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ISSN = "0141-0296",
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DOI = "doi:10.1016/j.engstruct.2020.111747",
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URL = "https://www.sciencedirect.com/science/article/pii/S0141029620343480",
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keywords = "genetic algorithms, genetic programming, Transfer
length, Pretensioned concrete, Generative adversarial
network, Artificial neural network, Random forest",
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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 times 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",
- }
Genetic Programming entries for
Hoseong Jeong
Sun-Jin Han
Seung-Ho Choi
Jae-Hyun Kim
Kang Su Kim
Citations