Predicting the Ultimate Axial Capacity of Uniaxially Loaded CFST Columns Using Multiphysics Artificial Intelligence
Created by W.Langdon from
gp-bibliography.bib Revision:1.8010
- @Article{Khan:2022:Materials,
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author = "Sangeen Khan and Mohsin Ali Khan and Adeel Zafar and
Muhammad Faisal Javed and Fahid Aslam and
Muhammad Ali Musarat and Nikolai Ivanovich Vatin",
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title = "Predicting the Ultimate Axial Capacity of Uniaxially
Loaded {CFST} Columns Using Multiphysics Artificial
Intelligence",
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journal = "Materials",
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year = "2022",
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volume = "15",
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number = "1",
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article-number = "39",
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keywords = "genetic algorithms, genetic programming, Gene
Expression Programming, concrete filled steel tube,
artificial neural network, multi-physics model, Random
Forest Regression, Adaptive Neuro-Fuzzy Inference
System, gene expression programming, bearing capacity
of columns",
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ISSN = "1996-1944",
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URL = "https://www.mdpi.com/1996-1944/15/1/39",
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URL = "https://www.mdpi.com/1996-1944/15/1/39.pdf",
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pubmedid = "35009186",
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DOI = "doi:10.3390/ma15010039",
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size = "27 pages",
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abstract = "The object of this research is concrete-filled steel
tubes (CFST). The article aimed to develop a prediction
Multiphysics model for the circular CFST column by
using the Artificial Neural Network (ANN), the Adaptive
Neuro-Fuzzy Inference System (ANFIS) and the Gene
Expression Program (GEP). The database for this study
contains 1667 data points in which 702 are short CFST
columns and 965 are long CFST columns. The input
parameters are the geometric dimensions of the
structural elements of the column and the mechanical
properties of materials. The target parameters are the
bearing capacity of columns, which determines their
life cycle. A Multiphysics model was developed, and
various statistical checks were applied using the three
artificial intelligence techniques mentioned above.
Parametric and sensitivity analyses were also performed
on both short and long GEP models. The overall
performance of the GEP model was better than the ANN
and ANFIS models, and the prediction values of the GEP
model were near actual values. The PI of the predicted
Nst by GEP, ANN and ANFIS for training are 0.0416,
0.1423, and 0.1016, respectively, and for Nlg these
values are 0.1169, 0.2990 and 0.1542, respectively.
Corresponding OF values are 0.2300, 0.1200, and 0.090
for Nst, and 0.1000, 0.2700, and 0.1500 for Nlg. The
superiority of the GEP method to the other techniques
can be seen from the fact that the GEP technique
provides suitable connections based on practical
experimental work and does not rely on prior solutions.
It is concluded that the GEP model can be used to
predict the bearing capacity of circular CFST columns
to avoid any laborious and time-consuming experimental
work. It is also recommended that further research
should be performed on the data to develop a prediction
equation using other techniques such as Random Forest
Regression and Multi Expression Program.",
-
notes = "Department of Structural Engineering, Military College
of Engineering (MCE), National University of Science
and Technology (NUST), Islamabad 44000, Pakistan",
- }
Genetic Programming entries for
Sangeen Khan
Mohsin Ali Khan
Adeel Zafar
Muhammad Faisal Javed
Fahid Aslam
Muhammad Ali Musarat
Nikolai Ivanovich Vatin
Citations