Abstract Soil settlement is one of the most common and critical issues in geologic and geotechnical engineering. Due to various sources of uncertainties, it is hard to predict soil settlement accurately. An inverse analysis using the information provided by field measurements is desirable for prediction with higher confidence. In this study, an inverse framework based on ensemble Kalman filtering (EnKF) is proposed to evaluate the soil settlement with quantified uncertainty. The theoretical and practical effectiveness of this scheme is demonstrated through synthetic and realistic tests to predict soil settlement of embankment roads. Inferred results including quantified uncertainties are obtained based on Bayesian theory, which makes a distinction between this method and conventional settlement prediction methods. The results of two synthetic tests show the parameters inferred by the EnKF converge to true values, which verify the satisfactory performance of the proposed scheme. A realistic application of Saga airport road is investigated, and the simulated settlement results are consistent with the field measurements. Moreover, Sobol method is adopted to study the sensitivity of model parameters, and detailed parameter studies are conducted to estimate the influence of ensemble size, the value range of prior distribution and observation error.

中文翻译：

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使用集成卡尔曼滤波预测具有量化不确定性的土壤沉降

摘要 土壤沉降是地质岩土工程中最常见、最关键的问题之一。由于各种不确定性来源，很难准确预测土壤沉降。使用现场测量提供的信息进行逆向分析对于具有更高置信度的预测是可取的。在这项研究中，提出了一个基于集合卡尔曼滤波 (EnKF) 的逆框架来评估具有量化不确定性的土壤沉降。该方案的理论和实践有效性通过综合和现实测试来预测路堤道路的土壤沉降。基于贝叶斯理论获得包括量化不确定性的推断结果，这使得该方法与常规沉降预测方法有区别。两次综合测试的结果表明，EnKF 推断的参数收敛于真实值，验证了所提出方案的令人满意的性能。研究了佐贺机场道路的实际应用，模拟沉降结果与现场测量结果一致。此外，采用Sobol方法研究模型参数的敏感性，并通过详细的参数研究来估计集合大小、先验分布的取值范围和观测误差的影响。