Probabilistic Digital Twins for Geotechnical Design and Construction

This research article presents a probabilistic digital twin (PDT) framework specifically designed for geotechnical design and construction. The document outlines the limitations of traditional digital twins, which rely on deterministic models that fail to account for the inherent uncertainties in geotechnical projects. The proposed PDT framework integrates various sources of uncertainty, including aleatoric, data, model, and prediction uncertainties, and employs Bayesian methods for model updating. An illustrative case study involving a highway foundation construction project demonstrates the effectiveness of the PDT framework in improving decision-making and project outcomes. The article discusses the challenges faced in the adoption of digital twin technologies in the construction industry and emphasizes the need for tailored solutions that address the unique characteristics of geotechnical engineering. The structured approach of the PDT framework aims to lower barriers to practical implementation, making probabilistic methods more accessible for engineering practitioners. The paper is organized into sections that review existing approaches, detail the mathematical formulation, and analyze the benefits and challenges of implementing the PDT framework.