基于多源数据融合的土体空间参数不确定性缩减OA北大核心CSTPCD

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.