大规模现代化展览场馆对基础不均匀沉降较为敏感, 持力层压缩模量的空间分布特征对于控制地基变形至关重要. 常规工程勘察钻孔仅提供少量精确的压缩模量土工试验值, 但原位测试可提供大量随机的静力触探值, 为了融合室内试验和原位测试的数据, 提出压缩模量的贝叶斯空间插值方法. 核心研究内容包括: 根据岩土工程勘察的数据精度, 将测试数据分为硬数据和软数据; 使用空间随机函数描述压缩模量的空间变异性; 利用最大熵理论分析软数据的不确定性, 基于贝叶斯理论, 建立随机场插值方法, 对未知点压缩模量的后验分布进行估计. 为了验证该方法的有效性, 将贝叶斯空间插值方法应用于上海国家会展中心浅部持力层 (粉质黏土层) 的压缩模量空间变异性分析. 与普通克里金插值方法比较, 贝叶斯方法能融合多源勘察数据进行空间插值, 精度更高.

Large-scale modern exhibition venues are more sensitive to uneven foundation settlements, where the spatial distribution of the compressive modulus of the bearing layer is essential in controlling foundation deformations. Conventional engineering survey boreholes provide only a small number of precise compressive modulus geotechnical test values, whereas in-situ testing can provide numerous random cone penetration values. To integrate the data of indoor and in-situ tests, a Bayesian spatial interpolation method of compression modulus is proposed in this study. Our research was conducted as follows. Based on the data accuracy of geotechnical engineering investigation, test data were divided into hard and soft data. A spatial random function was then used to describe the spatial variability of the compression modulus. Next, maximum entropy theory was applied to analyze the uncertainty of the soft data. Based on Bayesian theory, a random field interpolation method was then established to estimate the posterior distribution of the compression modulus of unknown points. Finally, to verify the effectiveness of the proposed method, a Bayesian spatial interpolation method was applied to the spatial variability analysis of the compressive modulus of silty clay in the shallow bearing layer ②$_1$ of Shanghai National Convention and Exhibition Center. Compared with the ordinary Kriging interpolation method, the proposed Bayesian method can integrate multi-source survey data for spatial interpolation with greater accuracy.