Multi-perspective analysis on rainfall-induced spatial response of soil suction in a vegetated soil
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- @Article{CHENG:2022:jrmge,
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author = "Zhiliang Cheng and Wanhuan Zhou and Chen Tian",
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title = "Multi-perspective analysis on rainfall-induced spatial
response of soil suction in a vegetated soil",
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journal = "Journal of Rock Mechanics and Geotechnical
Engineering",
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volume = "14",
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number = "4",
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pages = "1280--1291",
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year = "2022",
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ISSN = "1674-7755",
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DOI = "doi:10.1016/j.jrmge.2022.02.009",
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URL = "https://www.sciencedirect.com/science/article/pii/S1674775522000622",
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keywords = "genetic algorithms, genetic programming, Global
sensitivity analysis (GSA), Multi-gene genetic
programming (MGGP), Soil suction response, Spatial
variation of suction response, Uncertainty assessment",
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abstract = "In this study, an intelligent monitoring platform is
established for continuous quantification of soil,
vegetation, and atmosphere parameters (e.g. soil
suction, rainfall, tree canopy, air temperature, and
wind speed) to provide an efficient dataset for
modeling suction response through machine learning. Two
characteristic parameters representing suction response
during wetting processes, i.e. response time and mean
reduction rate of suction, are formulated through
multi-gene genetic programming (MGGP) using eight
selected influential parameters including depth,
initial soil suction, vegetation- and
atmosphere-related parameters. An error standard-based
performance evaluation indicated that MGGP has
appreciable potential for model development when
working with even fewer than 100 data. Global
sensitivity analysis revealed the importance of tree
canopy and mean wind speed to estimation of response
time and indicated that initial soil suction and
rainfall amount have an important effect on the
estimated suction reduction rate during a wetting
process. Uncertainty assessment indicated that the two
MGGP models describing suction response after rainfall
are reliable and robust under uncertain conditions.
In-depth analysis of spatial variations in suction
response validated the robustness of two obtained MGGP
models in prediction of suction variation
characteristics under natural conditions",
- }
Genetic Programming entries for
Zhi-Liang Cheng
Wan-Huan (Hanna) Zhou
Chen Tian
Citations