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Multivariate modeling of soil suction response to various rainfall by multi-gene genetic programing
Cheng, Zhi Liang1; Yang, Shuaidong2; Zhao, Lin Shuang1; Tian, Chen1; Zhou, Wan Huan1,3
2021-05-12
Source PublicationActa Geotechnica
ISSN1861-1125
Volume16Issue:11Pages:3601-3616
Abstract

In this study, field monitoring testing and machine learning are used to analyze vegetated soil’s response to various rainfall events under natural environmental conditions. Parameters that reflect the soil, vegetation, and atmosphere of three monitoring points at different distances from a tree (0.5 m, 1.5 m, and 3.0 m) at a constant depth (0.2 m) are quantified and used for multivariate model development. A machine learning method, multi-gene genetic programming (MGGP), is used to formulate the relationships between two indices representing vegetated soil response and six selected influential parameters. Analysis indicates that for a complicated system, the MGGP method is suitable for establishing an efficient computational model under conditions of limited data. Global sensitivity analysis and parametric study are conducted, based on the obtained multivariate models, to reveal the effect of each influential parameter, indicating that rainfall pattern has much the same impact on variations in soil suction as rainfall amount and intensity and tree canopy do. An advanced rainfall pattern can trigger a more rapid response of vegetated soil than intermediate and delayed rainfall patterns can. Rainfall pattern’s effect on the descent rate of soil suction is nonlinear.

KeywordGlobal Sensitivity Analysis Multi-gene Genetic Programming Parametric Study Soil Suction Response Temporal Rainfall Pattern
DOI10.1007/s11440-021-01211-y
URLView the original
Indexed BySCIE
Language英語English
WOS Research AreaEngineering
WOS SubjectEngineering, Geological
WOS IDWOS:000650125500001
PublisherSPRINGER HEIDELBERGTIERGARTENSTRASSE 17, D-69121 HEIDELBERG, GERMANY
Scopus ID2-s2.0-85105891998
Fulltext Access
FWCI0.78115124
Citation statistics
Document TypeJournal article
CollectionTHE STATE KEY LABORATORY OF INTERNET OF THINGS FOR SMART CITY (UNIVERSITY OF MACAU)
Faculty of Science and Technology
DEPARTMENT OF CIVIL AND ENVIRONMENTAL ENGINEERING
Corresponding AuthorZhou, Wan Huan
Affiliation1.State Key Laboratory of Internet of Things for Smart City and Department of Civil and Environmental Engineering, University of Macau, Macao
2.Pearl River Water Resources Research Institute, Guangzhou, 80 Tianshou Rd., China
3.Zhuhai UM Science and Technology Research Institute, Zhuhai, China
First Author AffilicationUniversity of Macau
Corresponding Author AffilicationUniversity of Macau
Recommended Citation
GB/T 7714
Cheng, Zhi Liang,Yang, Shuaidong,Zhao, Lin Shuang,et al. Multivariate modeling of soil suction response to various rainfall by multi-gene genetic programing[J]. Acta Geotechnica,2021,16(11):3601-3616.
APA Cheng, Zhi Liang,Yang, Shuaidong,Zhao, Lin Shuang,Tian, Chen,&Zhou, Wan Huan.(2021).Multivariate modeling of soil suction response to various rainfall by multi-gene genetic programing.Acta Geotechnica,16(11),3601-3616.
MLA Cheng, Zhi Liang,et al."Multivariate modeling of soil suction response to various rainfall by multi-gene genetic programing".Acta Geotechnica 16.11(2021):3601-3616.
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