Prediction of long-term prestress loss for prestressed concrete cylinder structures using machine learning

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@Article{ZHANG:2023:engstruct,

• author = "Hang Zhang and Quan-Quan Guo and Li-Yan Xu",
• title = "Prediction of long-term prestress loss for prestressed concrete cylinder structures using machine learning",
• journal = "Engineering Structures",
• volume = "279",
• pages = "115577",
• year = "2023",
• ISSN = "0141-0296",
• DOI = "doi:10.1016/j.engstruct.2022.115577",
• URL = "https://www.sciencedirect.com/science/article/pii/S0141029622016534",
• keywords = "genetic algorithms, genetic programming, Prestressed concrete cylinder structure, Long-term prestress loss, Machine learning, Shrinkage, Creep, Prediction model",
• abstract = "The long-term prestress loss caused by shrinkage and creep of concrete and stress relaxation of prestressed tendons has significant effects on the sealability and safety of prestressed concrete cylinder structures such as nuclear reactor containments and liquified natural gas (LNG) tanks. By using machine learning (ML) techniques, this study aims to establish an intelligent approach for the long-term prestress loss prediction of concrete cylinder structures. Firstly, based on the Infrastructure Technology Institute of Northwestern University (NU-ITI) database of concrete shrinkage and creep performance, the explicit expressions are presented for concrete shrinkage and creep function using genetic programming (GP); Moreover, the concrete constitutive model is incorporated into a general finite-element software package based on the ABAQUS UMAT platform. Then finite element analysis (FEA) models are established and calibrated based on the existing long-term prestress loss tests of prestressed concrete beams. In addition to the experimental results in the literature, the numerical results of the FEA model are also used to form the database of the long-term prestress losses for concrete cylinder structures. Finally, three prediction models of long-term prestress loss are proposed by using the artificial neural network (ANN), one-dimensional convolutional neural network (1D CNN) and genetic programming (GP). Compared with the measured results of nuclear containments in practical engineering, the ML based prediction models are demonstrated to be accurate and efficient in evaluating the long-term prestress loss for prestressed concrete cylinder structures",

}

Genetic Programming entries for Hang Zhang Quan-Quan Guo Li-Yan Xu

Citations