Abstract Classification and stratification (or zonation) of subsurface soils are important tasks in geotechnical site investigation. Due to the limit of time, budget, or access to subsurface soils, subsurface soil information obtained from investigation points (e.g., boreholes, cone penetration tests (CPTs)) in a specific site is often limited (e.g., a few boreholes or CPT soundings), resulting in great challenge in interpretation of the site investigation data obtained and significant uncertainty in the inferred subsurface soil classification and stratification. A novel probabilistic method is developed in this paper for properly accounting for the uncertainty associated with CPT-based subsurface soil classification and stratification. The method properly classifies and stratifies subsurface soils in a 2D vertical cross-section from limited CPT soundings. A limited number of 1D CPT sounding data is firstly interpolated to produce a 2D vertical cross-section, and the associated interpolation uncertainty is modelled explicitly using random field theory. Probabilistic soil classification model is also developed to account for the uncertainty associated with the empirical soil behavior type classification model. Then, the interpolation uncertainty and soil classification model uncertainty are considered simultaneously in a Monte Carlo simulation framework. Both simulated and real data examples are used to illustrate the proposed method. The results indicate that the proposed method well predicts subsurface soil classification and stratification in a 2D vertical cross-section from limited CPT soundings, and properly quantifies the associated uncertainty. In addition, sensitivity studies on interpolation uncertainty and soil classification model uncertainty are performed.

中文翻译：

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使用随机场和蒙特卡罗模拟的有限锥入度试验在垂直截面中进行概率土壤分类和分层

摘要 地下土壤的分类和分层（或分区）是岩土工程现场调查的重要任务。由于时间、预算或访问地下土壤的限制，从特定地点的调查点（例如钻孔、锥入度测试 (CPT)）获得的地下土壤信息通常是有限的（例如，几个钻孔或 CPT 测深） )，导致对获得的现场调查数据的解释和推断的地下土壤分类和分层的重大不确定性带来了巨大挑战。本文开发了一种新的概率方法，用于正确解释与基于 CPT 的地下土壤分类和分层相关的不确定性。该方法根据有限的 CPT 测深对二维垂直截面中的地下土壤进行了适当的分类和分层。首先对有限数量的 1D CPT 探测数据进行插值以产生 2D 垂直横截面，然后使用随机场理论对相关的插值不确定性进行明确建模。还开发了概率土壤分类模型以解决与经验土壤行为类型分类模型相关的不确定性。然后，在蒙特卡罗模拟框架中同时考虑了插值不确定性和土壤分类模型的不确定性。模拟和真实数据示例都用于说明所提出的方法。结果表明，所提出的方法从有限的 CPT 探测中很好地预测了 2D 垂直截面中的地下土壤分类和分层，并正确量化了相关的不确定性。此外，还进行了对插值不确定性和土壤分类模型不确定性的敏感性研究。