Probabilistic digital twins for geotechnical design and construction

Abstract 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 some AECOM sectors remains limited. A significant obstacle is that traditional DTs rely on deterministic models, which require deterministic input parameters. This limits their accuracy, as they do not account for the substantial uncertainties that are 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 DT 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 PDT framework is showcased through an application to a highway foundation construction project, demonstrating its potential to integrate existing probabilistic methods to improve decision-making and project outcomes in the face of significant uncertainties. By embedding these methods within the PDT framework, we lower the barriers to practical implementation, making probabilistic approaches more accessible and applicable in real-world engineering workflows.