Automatic regression methods for formulation of elastic modulus of recycled aggregate concrete
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- @Article{GOLAFSHANI:2018:ASC,
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author = "Emadaldin Mohammadi Golafshani and Ali Behnood",
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title = "Automatic regression methods for formulation of
elastic modulus of recycled aggregate concrete",
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journal = "Applied Soft Computing",
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volume = "64",
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pages = "377--400",
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year = "2018",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "1568-4946",
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DOI = "doi:10.1016/j.asoc.2017.12.030",
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URL = "http://www.sciencedirect.com/science/article/pii/S156849461730755X",
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abstract = "The use of recycled concrete aggregate to produce new
concrete can assist the sustainability in construction
industry. However, the mechanical properties of this
type of aggregate should be precisely investigated
before its using in different applications. The elastic
modulus of concrete is one of the most important design
parameters in many construction applications. Because
of various mix designs, the existing formulas for the
elastic modulus of concrete cannot be used for recycled
aggregate concrete (RAC). In recent years, there have
been a few attempts for predicting the elastic modulus
of RAC, especially, with various types of artificial
intelligence (AI) methods: In this paper, three
automatic regression methods, namely, genetic
programming (GP), artificial bee colony programming
(ABCP) and biogeography-based programming (BBP) were
used for estimating the elastic modulus of RAC.
Performances of the different automatic regression
models were compared with each other. Moreover, the
sensitivity analysis was performed to assess the trend
of the elastic modulus as a function of effective input
parameters used for developing the different automatic
regression models. Overall, the results show that GP,
ABCP, and BBP can be used as reliable algorithms for
prediction of the elastic modulus of RAC. In addition,
the water absorption of the mixed coarse aggregate and
the ratio of the fine aggregate to the total aggregate
were found as two of the most effective parameters
affecting the elastic modulus of RAC",
- }
Genetic Programming entries for
Emadaldin Mohammadi Golafshani
Ali Behnood
Citations