Predicting Rock Brittleness Using a Robust Evolutionary Programming Paradigm and Regression-Based Feature Selection Model
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- @Article{jamei:2022:AS,
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author = "Mehdi Jamei and Ahmed Salih Mohammed and
Iman Ahmadianfar and Mohanad Muayad Sabri Sabri and
Masoud Karbasi and Mahdi Hasanipanah",
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title = "Predicting Rock Brittleness Using a Robust
Evolutionary Programming Paradigm and Regression-Based
Feature Selection Model",
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journal = "Applied Sciences",
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year = "2022",
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volume = "12",
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number = "14",
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pages = "Article No. 7101",
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keywords = "genetic algorithms, genetic programming",
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ISSN = "2076-3417",
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URL = "https://www.mdpi.com/2076-3417/12/14/7101",
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DOI = "doi:10.3390/app12147101",
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abstract = "Brittleness plays an important role in assessing the
stability of the surrounding rock mass in deep
underground projects. To this end, the present study
deals with developing a robust evolutionary programming
paradigm known as linear genetic programming (LGP) for
estimating the brittleness index (BI). In addition, the
bootstrap aggregate (Bagged) regression tree (BRT) and
two efficient lazy machine learning approaches, namely
local weighted linear regression (LWLR) and KStar
approach, were examined to validate the LGP model. To
the best of our knowledge, this is the first attempt to
estimate the BI through the LGP model. A tunneling
project in Pahang state, Malaysia, was investigated,
and the requirement datasets were measured to construct
the proposed models. According to the results from the
testing phase, the LGP model yielded the best
statistical indicators (R = 0.9529, RMSE = 0.4838, and
IA = 0.9744) for modelling BI, followed by LWLR (R =
0.9490, RMSE = 0.6607, and IA = 0.9400), BRT (R =
0.9433, RMSE = 0.6875, and IA = 0.9324), and KStar (R =
0.9310, RMSE = 0.7933, and IA = 0.9095), respectively.
In addition, the sensitivity analysis demonstrated that
the dry density factor demonstrated the most effective
prediction of BI.",
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notes = "also known as \cite{app12147101}",
- }
Genetic Programming entries for
Mehdi Jamei
Ahmed Salih Mohammed
Iman Ahmadianfar
Mohanad Muayad Sabri Sabri
Masoud Karbasi
Mahdi Hasanipanah
Citations