The digital twin approach has gained recognition as a promising solution to the challenges faced by the Architecture, Engineering, Construction, Operations, and Management (AECOM) industries. However, its broader application across AECOM sectors remains limited. One significant obstacle is that traditional digital twins rely on deterministic models, which require deterministic input parameters. This limits their accuracy, as they do not account for the substantial uncertainties inherent in AECOM projects. These uncertainties are particularly pronounced in geotechnical design and construction. To address this challenge, we propose a Probabilistic Digital Twin (PDT) framework that extends traditional digital twin methodologies by incorporating uncertainties, and is tailored to the requirements of geotechnical design and construction. The PDT framework provides a structured approach to integrating all sources of uncertainty, including aleatoric, data, model, and prediction uncertainties, and propagates them throughout the entire modeling process. To ensure that site-specific conditions are accurately reflected as additional information is obtained, the PDT leverages Bayesian methods for model updating. The effectiveness of the probabilistic digital twin framework is showcased through an application to a highway foundation construction project, demonstrating its potential to improve decision-making and project outcomes in the face of significant uncertainties.

翻译：数字孪生方法已被公认为应对建筑、工程、施工、运营与管理（AECOM）行业所面临挑战的一种前景广阔的解决方案。然而，其在AECOM各领域的广泛应用仍受到限制。一个主要障碍在于传统数字孪生依赖确定性模型，这要求输入参数必须是确定性的。由于未能考虑AECOM项目中固有的显著不确定性，其准确性受到制约。这些不确定性在岩土工程设计与施工中尤为突出。为应对这一挑战，我们提出了一种概率数字孪生（PDT）框架。该框架通过纳入不确定性因素扩展了传统数字孪生方法，并针对岩土工程设计与施工的需求进行了专门设计。PDT框架提供了一种结构化方法，用于整合所有不确定性来源（包括偶然不确定性、数据不确定性、模型不确定性和预测不确定性），并在整个建模过程中传播这些不确定性。为确保在获取额外信息时能准确反映现场特定条件，PDT利用贝叶斯方法进行模型更新。通过在一个高速公路基础施工项目中的应用，展示了该概率数字孪生框架的有效性，证明了其在面对重大不确定性时改善决策与项目成果的潜力。