Predicting carbonation coefficient using Artificial neural networks and genetic programming
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- @Article{LONDHE:2021:JBE,
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author = "S. N. Londhe and P. S. Kulkarni and P. R. Dixit and
A. Silva and R. Neves and J. {de Brito}",
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title = "Predicting carbonation coefficient using Artificial
neural networks and genetic programming",
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journal = "Journal of Building Engineering",
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volume = "39",
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pages = "102258",
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year = "2021",
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ISSN = "2352-7102",
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DOI = "doi:10.1016/j.jobe.2021.102258",
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URL = "https://www.sciencedirect.com/science/article/pii/S2352710221001145",
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keywords = "genetic algorithms, genetic programming, Concrete
carbonation, Durability, Artificial neural networks
(ANNs), Genetic programming (GP)",
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abstract = "Concrete carbonation is considered an important
problem in both the Civil Engineering and Materials
Science fields. Over time, the properties of concrete
change because of the interaction between the material
and the environment and, consequently, its durability
is affected. Conventionally, concrete carbonation depth
at a given time under steady-state conditions can
reasonably be estimated using Fick's second law of
diffusion. This study addresses the statistical
modelling of the concrete carbonation phenomenon, using
a large number of results (827 specimens or samples,
i.e. 827 is the number of data concerning the
measurement of the carbonation coefficient in concrete
test specimens), collected in the literature.
Artificial Neural Networks (ANNs) and Genetic
Programming (GP) were the Soft Computing techniques
used to predict the carbonation coefficient, as a
function of a set of conditioning factors. These models
allow the estimation of the carbonation coefficient
and, accordingly, carbonation as a function of the
variables considered statistically significant in
explaining this phenomenon. The results obtained
through Artificial Neural Networks and Genetic
Programming were compared with those obtained through
Multiple Linear Regression (MLR) (which has been
previously used to model the carbonation coefficient of
concrete). The results reveal that ANNs and GP models
present a better performance when compared with MLR,
being able to deal with the nonlinear influence of
relative humidity on concrete carbonation, which was
the main limitation of MLR in modelling the carbonation
coefficient in previous study. ANNs are commonly seen
as a black box; in this study, an attempt is made to
address this issue through Knowledge Extraction (KE)
from trained weights and biases. KE helps to understand
the influence of each input on the output and the
influences identified by the KE technique are in
accordance with general knowledge",
- }
Genetic Programming entries for
S N Londhe
Preeti S Kulkarni
Pradnya R Dixit
Arlindo Ferreira da Silva
R Neves
Jorge Manuel Calico Lopes de Brito
Citations