水资源与水工程学报2024，Vol.35Issue(3)：183-191,9.DOI:10.11705/j.issn.1672-643X.2024.03.21
基于多源数据融合的土体空间参数不确定性缩减
Spatial uncertainty reduction of soil parameters based on multi-source data fusion
摘要
Abstract
The spatial variability of soil parameters leads to uncertainties in structural response.Ignoring these uncertainties or using biased parameters may result in engineering safety issues and even cause engi-neering disasters.Field measurement data can improve the estimation of structural responses in geotechni-cal engineering,such as foundation pit excavation.Investigation data obtained from direct measurement methods like the standard penetration test(SPT)are directly related to soil structural parameters.Based on these data,conditional simulation constrained by the Kriging method can improve the estimation of the spatial distribution of parameters.Additionally,when monitoring data are related to soil structural per-formance or responses,such as displacement,inverse analysis using the ensemble Kalman filter(EnKF)can also reduce the uncertainty of soil parameters.This study combines both direct and indirect methods through multi-source data fusion to analyze soil excavation.The results indicate that combining these two methods can significantly reduce the uncertainty of soil parameters,thereby decreasing the uncertainty in structural responses.关键词
空间变异性/多源数据融合/反演分析/集合卡尔曼滤波器(EnKF)/Kriging法/不确定性缩减/随机场Key words
spatial variability/multi-source data fusion/inverse analysis/EnKF/Kriging/uncertainty reduction/random field分类
建筑与水利引用本文复制引用
贾唯龙,常鹏飞,李亚军,钱铖,郭祺,李瑞杰,傅中志,张彬..基于多源数据融合的土体空间参数不确定性缩减[J].水资源与水工程学报,2024,35(3):183-191,9.基金项目
资源环境与灾害监测山西省重点实验室开放课题(2023-S01) （2023-S01）
中央高校基本科研业务费项目(2652019321) （2652019321）
