Estimation of factor of safety of rooted slope using an evolutionary approach
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- @Article{Garg:2014:EE,
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author = "Akhil Garg and Ankit Garg and K. Tai and S. Sreedeep",
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title = "Estimation of factor of safety of rooted slope using
an evolutionary approach",
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journal = "Ecological Engineering",
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year = "2014",
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volume = "64",
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pages = "314--324",
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month = mar,
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keywords = "genetic algorithms, genetic programming, FOS
prediction, Evolutionary, GPTIPS, LS-SVM, Multi-gene
genetic programming",
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ISSN = "0925-8574",
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URL = "http://www.sciencedirect.com/science/article/pii/S0925857413005478",
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DOI = "doi:10.1016/j.ecoleng.2013.12.047",
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size = "11 pages",
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abstract = "Use of roots as one of slope stabilization technique
via mechanical reinforcement has received considerable
attention in the past few decades. Several mathematical
models have been developed to estimate the additional
cohesion due to roots, which is useful for the
calculation of factor of safety (FOS) of the rooted
slopes using finite element method (FEM) or finite
difference method. It is well understood from the
literature that the root properties such as root area
ratio (RAR) and root depth affects the mobilized
tensile stress per unit area of soil consequently
affecting the FOS of the rooted slope. In addition, a
fracture phenomenon also influences the FOS of the
rooted slope and should also be considered. In the
present work, a new evolutionary approach, namely,
multi-gene genetic programming (MGGP) is presented,
and, applied to formulate the mathematical relationship
between FOS and input variables such as slope angles,
root depth and RAR of the rooted slope. The performance
of MGGP is compared to those of artificial neural
network and support vector regression. Based on the
evaluation of the performance of the models, the
proposed MGGP model outperformed the two other models
and is proved able to capture the characteristics of
the FEM model by unveiling important parameters and
hidden non-linear relationships.",
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notes = "School of Mechanical and Aerospace Engineering,Nanyang
Technological University, 50 Nanyang Ave,Singapore
639798",
- }
Genetic Programming entries for
Akhil Garg
Ankit Garg
Kang Tai
Sekharan Sreedeep
Citations