An evolutionary approach to formulate the compressive strength of roller compacted concrete pavement
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- @Article{Ashrafian:2020:Measurement,
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author = "Ali Ashrafian and Amir H. Gandomi and
Mohammad Rezaie-Balf and Mohammad Emadi",
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title = "An evolutionary approach to formulate the compressive
strength of roller compacted concrete pavement",
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journal = "Measurement",
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year = "2020",
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volume = "152",
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pages = "107309",
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month = feb,
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keywords = "genetic algorithms, genetic programming, Gene
expression programming, Evolutionary approach, Roller
compacted concrete pavement, Compressive strength,
Prediction",
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ISSN = "0263-2241",
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URL = "http://www.sciencedirect.com/science/article/pii/S026322411931173X",
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DOI = "doi:10.1016/j.measurement.2019.107309",
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abstract = "The construction and maintenance of roads pavement was
a critical problem in the last years. Therefore, the
use of roller-compacted concrete pavement (RCCP) in
road problems is widespread. The compressive strength
(fc) is the key characteristic of the RCCP caused to
significant impact on the cost of production. In this
study, an evolutionary-based algorithm named gene
expression programming (GEP) is implemented to propose
novel predictive formulas for the fc of RCCP. The fc is
formulated based on important factor used in mixture
proportion in three different combinations of
dimensional form (coarse aggregate, fine aggregate,
cement, pulverized fly ash, water, and binder),
non-dimensional form (water to cement ratio, water to
binder ratio, coarse to fine aggregate ratio and
pulverized fly ash to binder ratio) and percentage form
of input variables. A comprehensive and reliable
database incorporating 235 experimental cases collected
from several studies. Furthermore, mean absolute error
(MAE), root mean square error (RMSE), correlation
coefficient (r), average absolute error (AAE),
performance index (PI), and objective function (OBJ) as
the internal standard statistical measures and external
validation evaluated proposed GEP-based models.
Uncertainty and parametric studies were carried out to
verify the results. Moreover, sensitivity analysis to
determine the importance of each predictor on fc of
RCCP revealed that fine aggregate content and water to
binder ratio is the most useful predictor in
dimensional, non-dimensional and percentage forms,
respectively. The proposed equation-based models are
found to be simple, robustness and straightforward to
use, and provide consequently new formulations for fc
of RCCP.",
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notes = "also known as \cite{ASHRAFIAN2020107309}",
- }
Genetic Programming entries for
Ali Ashrafian
A H Gandomi
Mohammad Rezaie-Balf
Mohammad Emadi
Citations