In underground construction works, uncertainties and insufficient information about the underground environment lead to inaccurate predictions of soil-structure interactions. Supported excavations are often over-designed, which underscores a significant potential for cost optimization. However, the uncertainties exist, and the traditional design process does not allow for leaner designs at the start of the project. The emergence of advanced analysis tools enables the development of an Observational Method based approach for a decision-making process in which data can be best utilized to deliver real value, confidence, and control.An automated back analysis approach based on Bayesian inference is developed in this paper and validated with a synthetic case study. Probabilistic modeling and Markov Chain Monte Carlo simulation are used to deliver estimates of soil parameters for a given a geotechnical model, update the prediction of future excavation stages, and fully quantify uncertainties from the constructed model and measurements. Sensitivity analysis is used for model selection to achieve modeling robustness. The impact of prior engineering knowledge about the soil properties on the precision of the predictions is also examined. This approach significantly improves the efficiency of back analysis in current practice and provides a tool for data-driven decision making of design optimization during construction.

在地下建筑工程中，地下环境的不确定性和信息不足导致对土-结构相互作用的预测不准确。支持的挖掘通常是过度设计的，这突显了成本优化的巨大潜力。然而，存在不确定性，传统的设计过程不允许在项目开始时进行更精简的设计。高级分析工具的出现使得基于观察方法的决策方法得以开发，在该方法中可以最好地利用数据来提供真实的价值、置信度和控制。基于贝叶斯推理的自动反向分析方法开发于本文并通过综合案例研究进行了验证。概率建模和马尔可夫链蒙特卡罗模拟用于为给定的岩土工程模型提供土壤参数的估计值，更新对未来开挖阶段的预测，并从构建的模型和测量中完全量化不确定性。敏感性分析用于模型选择，以实现建模鲁棒性。还检查了有关土壤特性的先验工程知识对预测精度的影响。这种方法显着提高了当前实践中的反向分析效率，并为施工期间设计优化的数据驱动决策提供了工具。敏感性分析用于模型选择，以实现建模鲁棒性。还检查了有关土壤特性的先验工程知识对预测精度的影响。这种方法显着提高了当前实践中的反向分析效率，并为施工期间设计优化的数据驱动决策提供了工具。敏感性分析用于模型选择，以实现建模鲁棒性。还检查了有关土壤特性的先验工程知识对预测精度的影响。这种方法显着提高了当前实践中的反向分析效率，并为施工期间设计优化的数据驱动决策提供了工具。