Uncertainties influence every phase of a geotechnical system's life cycle, from the initial planning and design ("cradle") through testing, long-term monitoring, and eventual decommissioning ("grave"). This session focuses on the intersectional challenges and opportunities in model prediction, monitoring, and uncertainty management within subsurface processes. Our goal is to foster a comprehensive understanding and effective handling of these uncertainties to enhance the reliability and safety of subsurface engineering solutions throughout their entire life-cycle.
- Model prediction: Reliable predictions are fundamental to the successful design, operation, and long-term performance of engineered systems in the subsurface. However, uncertainties inherent in simulations must be carefully explored, understood, and managed. We invite contributions that aim to improve the reliability of geotechnical behavior predictions and digital twins, encompassing aspects such as model complexity, system couplings, hydrogeological uncertainties, and the impacts of climate change. We also welcome insights into how observational data can be utilized to refine predictions and support system design and long-term monitoring.
- Monitoring: Effective monitoring is crucial for enhancing our understanding of the interaction between an engineering solution and natural phenomena in the subsurface to reduce uncertainties throughout the structure's life-cycle. We explore the latest advancements in geophysical and multi-physical surveys that provide comprehensive data, improve prediction accuracy, and offer early warning capabilities while aiming to showcase methods for preliminary investigations and ongoing monitoring, with a focus on integrating model predictions with real-world observations. Contributions highlighting approaches for minimizing uncertainty "from cradle to grave" are particularly encouraged.
- Uncertainty Management: Managing uncertainty is essential for building reliable decision-making processes. Selected topics will discuss how modeling and monitoring can be unified into a comprehensive workflow using various uncertainty and data management tools. We invite contributions exploring integrated, "cradle to grave" approaches to improve transparency, reliability, and trust in decision-making. Discussions on techniques, conceptual principles, and methodologies for transparent data collection (open-data, open-source software) and processing workflows are highly welcome.
- Model prediction: Reliable predictions are fundamental to the successful design, operation, and long-term performance of engineered systems in the subsurface. However, uncertainties inherent in simulations must be carefully explored, understood, and managed. We invite contributions that aim to improve the reliability of geotechnical behavior predictions and digital twins, encompassing aspects such as model complexity, system couplings, hydrogeological uncertainties, and the impacts of climate change. We also welcome insights into how observational data can be utilized to refine predictions and support system design and long-term monitoring.
- Monitoring: Effective monitoring is crucial for enhancing our understanding of the interaction between an engineering solution and natural phenomena in the subsurface to reduce uncertainties throughout the structure's life-cycle. We explore the latest advancements in geophysical and multi-physical surveys that provide comprehensive data, improve prediction accuracy, and offer early warning capabilities while aiming to showcase methods for preliminary investigations and ongoing monitoring, with a focus on integrating model predictions with real-world observations. Contributions highlighting approaches for minimizing uncertainty "from cradle to grave" are particularly encouraged.
- Uncertainty Management: Managing uncertainty is essential for building reliable decision-making processes. Selected topics will discuss how modeling and monitoring can be unified into a comprehensive workflow using various uncertainty and data management tools. We invite contributions exploring integrated, "cradle to grave" approaches to improve transparency, reliability, and trust in decision-making. Discussions on techniques, conceptual principles, and methodologies for transparent data collection (open-data, open-source software) and processing workflows are highly welcome.