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Multi-perspective analysis on rainfall-induced spatial response of soil suction in a vegetated soil

Journal of Rock Mechanics and Geotechnical Engineering 2022年第4期14卷 1280-1291页

作者： Zhiliang Cheng Wanhuan Zhou Chen Tian State Key Laboratory of Internet of Things for Smart City Department of Civil and Environmental EngineeringUniversity of MacaoMacaoChina

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 windspeed) to provide an efficient dataset for modeling suction response through machine learning. Twocharacteristic parameters representing suction response during wetting processes, i.e. response time andmean reduction rate of suction, are formulated through multi-gene genetic programming (MGGP) usingeight selected influential parameters including depth, initial soil suction, vegetation- and atmosphererelated parameters. An error standardebased performance evaluation indicated that MGGP has appreciable potential for model development when working with even fewer than 100 data. Global sensitivityanalysis revealed the importance of tree canopy and mean wind speed to estimation of response timeand indicated that initial soil suction and rainfall amount have an important effect on the estimatedsuction reduction rate during a wetting process. Uncertainty assessment indicated that the two MGGPmodels describing suction response after rainfall are reliable and robust under uncertain conditions. Indepth analysis of spatial variations in suction response validated the robustness of two obtained MGGPmodels in prediction of suction variation characteristics under natural conditions.

关键词： Global sensitivity analysis(GSA) Multi-gene genetic programming(MGGP) Soil suction response spatial variation of suction response Uncertainty assessment

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