Efficient boosting-based algorithms for shear strength prediction of squat RC walls
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{FARZINPOUR:2023:cscm,
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author = "Alireza Farzinpour and
Esmaeil {Mohammadi Dehcheshmeh} and Vahid Broujerdian and Samira {Nasr Esfahani} and
Amir H. Gandomi",
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title = "Efficient boosting-based algorithms for shear strength
prediction of squat {RC} walls",
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journal = "Case Studies in Construction Materials",
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volume = "18",
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pages = "e01928",
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year = "2023",
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ISSN = "2214-5095",
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DOI = "doi:10.1016/j.cscm.2023.e01928",
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URL = "https://www.sciencedirect.com/science/article/pii/S2214509523001079",
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keywords = "genetic algorithms, genetic programming, Squat RC
wall, Genetic algorithm (GA), Hyperparameter
optimization, Boosting methods, Principal component
analysis (PCA), Machine learning",
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abstract = "Reinforced concrete shear walls have been considered
as an effective structural system due to their optimal
cost and great behavior in resisting lateral loads. For
the slender type of these walls, failure modes are
mainly related to flexure, while for the squat type
with height-to-length ratios less than two, shear is
the dominant factor. Thus, accurate estimation of shear
strength for squat shear walls is necessary for design
applications and can also be complex due to the various
effective parameters. In order to address this issue,
first a comprehensive dataset with 558 samples of squat
shear walls is conducted, and three hybrid models
consisting of genetic algorithms and boosting-based
ensemble learning methods, i.e., XGBoost, CatBoost, and
LightGBM, are used for estimation of shear strength.
The results showed high prediction accuracy, with a
coefficient of determination of at least 98.6percent
for all three models. Genetic algorithm has been proven
to be an effective method for tuning boosting-based
algorithms compared to manual testing. In addition, the
results of the algorithms are compared to their default
hyperparameters and other conventional regression
Models. Also, multicollinearity and principal component
analysis (PCA) were studied. Furthermore, the
performance of three tuned models is compared with that
of a mechanics-based semi-empirical model and other
genetic programming (GP)-based models. Finally,
parametric and sensitivity analyses were performed, to
demonstrate the ability of the models to identify the
most critical parameters with significant influence on
shear strength",
- }
Genetic Programming entries for
Alireza Farzinpour
Esmaeil Mohammadi Dehcheshmeh
Vahid Broujerdian
Samira Nasr Esfahani
A H Gandomi
Citations