Genetic programming based symbolic regression for shear capacity prediction of SFRC beams
Created by W.Langdon from
gp-bibliography.bib Revision:1.7964
- @Article{BENCHAABENE:2021:CBM,
-
author = "Wassim {Ben Chaabene} and Moncef L. Nehdi",
-
title = "Genetic programming based symbolic regression for
shear capacity prediction of {SFRC} beams",
-
journal = "Construction and Building Materials",
-
volume = "280",
-
pages = "122523",
-
year = "2021",
-
ISSN = "0950-0618",
-
DOI = "doi:10.1016/j.conbuildmat.2021.122523",
-
URL = "https://www.sciencedirect.com/science/article/pii/S095006182100283X",
-
keywords = "genetic algorithms, genetic programming, Steel fiber,
Concrete, Beam, Shear strength, Symbolic regression,
Generative adversarial network, Synthetic data",
-
abstract = "The complexity of shear transfer mechanisms in steel
fiber-reinforced concrete (SFRC) has motivated
researchers to develop diverse empirical and
soft-computing models for predicting the shear capacity
of SFRC beams. Yet, such existing methods have been
developed based on limited experimental databases,
which makes their generalization capability uncertain.
To account for the limited experimental data available,
this study pioneers a novel approach based on tabular
generative adversarial networks (TGAN) to generate 2000
synthetic data examples. A {"}train on synthetic - test
on real{"} philosophy was adopted. Accordingly, the
entire 2000 synthetic data were used for training a
genetic programming-based symbolic regression (GP-SR)
model to develop a shear strength equation for SFRC
beams without stirrups. The model accuracy was then
tested on the entire set of 309 real experimental data
examples, which thus far are unknown to the model.
Results show that the novel GP-SR model achieved
superior predictive accuracy, outperforming eleven
existing equations. Sensitivity analysis revealed that
the shear-span-to-depth ratio was the most influential
parameter in the proposed equation. The present study
provides an enhanced predictive model for the shear
capacity of SFRC beams, which should motivate further
research to effectively train evolutionary algorithms
using synthetic data when acquiring large and
comprehensive experimental datasets is not feasible",
- }
Genetic Programming entries for
Wassim Ben Chaabene
Moncef L Nehdi
Citations