Soft computing techniques for the prediction of concrete compressive strength using Non-Destructive tests
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- @Article{ASTERIS:2021:CBM,
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author = "Panagiotis G. Asteris and Athanasia D. Skentou and
Abidhan Bardhan and Pijush Samui and
Paulo B. Lourenco",
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title = "Soft computing techniques for the prediction of
concrete compressive strength using Non-Destructive
tests",
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journal = "Construction and Building Materials",
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volume = "303",
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pages = "124450",
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year = "2021",
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ISSN = "0950-0618",
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DOI = "doi:10.1016/j.conbuildmat.2021.124450",
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URL = "https://www.sciencedirect.com/science/article/pii/S0950061821022078",
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keywords = "genetic algorithms, genetic programming, Artificial
neural networks, Compressive strength of Concrete,
Non-destructive testing methods, Soft computing,
Artificial Intelligence",
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abstract = "This study presents a comparative assessment of
conventional soft computing techniques in estimating
the compressive strength (CS) of concrete using two
non-destructive tests, namely ultrasonic pulse velocity
and rebound hammer test. In specific, six conventional
soft computing models namely back-propagation neural
network (BPNN), relevance vector machine, minimax
probability machine regression, genetic programming,
Gaussian process regression, and multivariate adaptive
regression spline, were used. To construct and validate
these models, a total of 629 datasets were collected
from the literature. Experimental results show that the
BPNN attained the most accurate prediction of concrete
CS based on both ultrasonic pulse velocity and rebound
number values. The results of the employed MARS and
BPNN models are significantly better than those
obtained in earlier studies. Thus, these two models are
very potential to assist engineers in the design phase
of civil engineering projects to estimate the concrete
CS with a greater accuracy level",
- }
Genetic Programming entries for
Panagiotis G Asteris
Athanasia D Skentou
Abidhan Bardhan
Pijush Samui
Paulo B Lourenco
Citations