Multivariate modeling of soil suction response to various rainfall by multi-gene genetic programing

• Published in: Acta geotechnica Vol. 16; no. 11; pp. 3601 - 3616
• Main Authors: Cheng, Zhi-Liang, Yang, Shuaidong, Zhao, Lin-Shuang, Tian, Chen, Zhou, Wan-Huan
• Format: Journal Article
• Language: English
• Published: Berlin/Heidelberg Springer Berlin Heidelberg 01.11.2021; Springer Nature B.V
• Subjects: Atmospheric models; Complex Fluids and Microfluidics; Computer applications; Engineering; Environmental conditions; Foundations; Genetic algorithms; Geoengineering; Geotechnical Engineering & Applied Earth Sciences; Hydraulics; Learning algorithms; Machine learning; Mathematical models; Monitoring; Multivariate analysis; Parameter sensitivity; Parameters; Parametric statistics; Plant cover; Rain; Rainfall; Research Paper; Sensitivity analysis; Soft and Granular Matter; Soil; Soil analysis; Soil Science & Conservation; Soil suction; Soils; Solid Mechanics; Multi-gene genetic programming; Global sensitivity analysis; Temporal rainfall pattern; Parametric study; Soil suction response

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....