Symbolic machine learning improved MCFT model for punching shear resistance of FRP-reinforced concrete slabs
Created by W.Langdon from
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- @Article{LIANG:2023:jobe,
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author = "Shixue Liang and Yuanxie Shen and Xiangling Gao and
Yiqing Cai and Zhengyu Fei",
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title = "Symbolic machine learning improved {MCFT} model for
punching shear resistance of {FRP-reinforced} concrete
slabs",
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journal = "Journal of Building Engineering",
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volume = "69",
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pages = "106257",
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year = "2023",
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ISSN = "2352-7102",
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DOI = "doi:10.1016/j.jobe.2023.106257",
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URL = "https://www.sciencedirect.com/science/article/pii/S2352710223004369",
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keywords = "genetic algorithms, genetic programming,
FRP-Reinforced concrete slab, Punching shear
resistance, Modified compression field theory, Machine
learning",
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abstract = "Fiber reinforced polymer (FRP)-reinforced concrete
slabs, an extension of reinforced concrete (RC) slabs
leveraged for resisting environment corrosion, are
susceptible to punching shear failure due to the lower
elasticity modulus of FRP reinforcement. To estimate
the punching shear resistance accurately, there are two
types of models (e.g., white box and black-box models)
proposed based on theoretical derivations and machine
learning methods. However, these two types of models
are considered as independent of each other. In this
study, a hybrid model (e.g., grey-box model) derived
from modified compression field theory (MCFT) is
proposed by this paper, in which the performance is
improved by a machine-learning-aided approach (genetic
programming). In order to exploit the performance of
machine learning, a database containing 154
experimental data is established and used for fitting
the correction equations. Iterating the population
containing 300 tree-based individuals in 300 times, a
correction equation with simple format is obtained,
which performs well in performance improvement of the
basic model derived from MCFT. Herein, the influential
factors involved in the correction equation comply with
the sorting in order of the importance quantified by
extreme gradient boosting (XGBoost) and shapley
additive explanation (SHAP). Combining the correction
equation with the basic model derived from MCFT, a
symbolic regression MCFT (SR-MCFT) model is
established, which performs better prediction
performance than other five empirical models",
- }
Genetic Programming entries for
Shixue Liang
Yuanxie Shen
Xiangling Gao
Yiqing Cai
Zhengyu Fei
Citations