Efficient computational techniques for predicting the California bearing ratio of soil in soaked conditions
Created by W.Langdon from
gp-bibliography.bib Revision:1.7954
- @Article{BARDHAN:2021:EG,
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author = "Abidhan Bardhan and Candan Gokceoglu and
Avijit Burman and Pijush Samui and Panagiotis G. Asteris",
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title = "Efficient computational techniques for predicting the
California bearing ratio of soil in soaked conditions",
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journal = "Engineering Geology",
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volume = "291",
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pages = "106239",
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year = "2021",
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ISSN = "0013-7952",
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DOI = "doi:10.1016/j.enggeo.2021.106239",
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URL = "https://www.sciencedirect.com/science/article/pii/S0013795221002507",
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keywords = "genetic algorithms, genetic programming, Soaked CBR,
Machine learning, MARS, GPR, Sub-grade design",
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abstract = "California bearing ratio (CBR) is one of the important
parameters that is used to express the strength of the
pavement subgrade of railways, roadways, and airport
runways. CBR is usually determined in the laboratory in
soaked conditions, which is an exhaustive and
time-consuming process. Therefore, to sidestep the
operation of conducting actual laboratory tests, this
study presents the development of four efficient soft
computing techniques, namely multivariate adaptive
regression splines with piecewise linear models
(MARS-L), multivariate adaptive regression splines with
piecewise cubic models (MARS-C), Gaussian process
regression, and genetic programming. For this purpose,
a wide range of experimental results of soaked CBR was
collected from an ongoing railway project of Indian
Railways. Three explicit expressions are proposed to
estimate the CBR of soils in soaked conditions.
Separate laboratory experiments were performed to
evaluate the generalization capabilities of the
developed models. Furthermore, simulated datasets were
used to validate the feasibility of the best-performing
model. Experimental results reveal that the proposed
MARS-L model attained the most accurate prediction (R2
= 0.9686 and RMSE = 0.0359 against separate laboratory
experiments) in predicting the soaked CBR at all
stages. Based on the accuracies attained, the proposed
MARS-L model is very potential to be an alternate
solution to estimate the CBR value in different phases
of civil engineering projects",
- }
Genetic Programming entries for
Abidhan Bardhan
Candan Gokceoglu
Avijit Burman
Pijush Samui
Panagiotis G Asteris
Citations