Implementation of probabilistic digital twins in the monitoring of geotechnical infrastructures
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Abstract
The increasing complexity of geotechnical infrastructures and their exposure to dynamic and variable conditions has motivated the implementation of probabilistic digital twins as a tool for advanced monitoring. This study adopts a systematic literature review methodology, examining recent academic articles that address the development and application of these models in geotechnical contexts. Methodological advances such as the integration of Bayesian inference, stochastic simulations and machine learning were analyzed, which allow representing and updating models in real time, incorporating the uncertainty inherent to ground behavior. Likewise, applications in dams, slopes and tunnels were documented, showing how these systems improve failure prediction and optimize decision making. However, technical and economic challenges related to instrumentation, geological variability, model validation and implementation costs remain. The study concludes that, despite these limitations, probabilistic digital twins represent a significant evolution in structural management, with high potential for adoption in modern civil engineering.
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