Prediction of the mechanical properties of structural recycled concrete using multivariable regression and genetic programming
Created by W.Langdon from
gp-bibliography.bib Revision:1.8098
- @Article{GonzalezTaboada:2016:CBM,
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author = "Iris Gonzalez-Taboada and Belen Gonzalez-Fonteboa and
Fernando Martinez-Abella and Juan Luis Perez-Ordonez",
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title = "Prediction of the mechanical properties of structural
recycled concrete using multivariable regression and
genetic programming",
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journal = "Construction and Building Materials",
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year = "2016",
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volume = "106",
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pages = "480--499",
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month = "1 " # mar,
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keywords = "genetic algorithms, genetic programming, Structural
recycled concrete, Database, Mixing procedure,
Mechanical properties, Multivariable regression",
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ISSN = "0950-0618",
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URL = "http://www.sciencedirect.com/science/article/pii/S0950061815308072",
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DOI = "doi:10.1016/j.conbuildmat.2015.12.136",
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abstract = "This study focuses on the prediction of some of the
most important properties of structural recycled
concrete (compressive strength, modulus of elasticity
and splitting tensile strength) taking into account,
not only the recycled percentage and the quality of the
recycled aggregates used, but also the production
method. For said purpose, a database has been developed
with 1831 mixes obtained from 81 papers. Firstly, in
this manner, these properties have been compared with
those of conventional concrete. Then, the need to adapt
the prediction code expressions (adjusted for
conventional concretes) was analysed to take into
account the use of recycled concrete, developing, if
finally necessary, the correction coefficients which
allow engineers to predict the recycled properties with
the same approximation degree as in conventional
concretes. These correction coefficients have been
adjusted using multivariable regression, and have been
analysed using different statistical indexes. Lastly,
specific expressions used to predict these properties
in structural recycled concretes have been optimized.
Two different tools have been used to develop these
expressions: multivariable regression and genetic
programming. The proposed expressions have been
analysed using statistical parameters which have been
compared with those obtained using the expressions
proposed by other authors. In this regard, and finally,
the best prediction expressions for the modulus of
elasticity and the splitting tensile strength of
structural recycled concretes have been proposed.",
- }
Genetic Programming entries for
Iris Gonzalez-Taboada
Belen Gonzalez-Fonteboa
Fernando Martinez Abella
Juan Luis Perez
Citations