Hybrid Framework for Forecasting Circular Excavation Collapse: Combining Physics-Based and Data-Driven Modeling
Brian Sheil
Abstract
The use of supporting fluids to stabilize excavations is a common technique adopted in the construction industry. Rapid detection of incipient collapse for deep excavations and timely decision making are crucial to ensure safety during construction. This paper explores a hybrid framework for forecasting the collapse of fluid-supported circular excavations by combining physics-based and data-driven modeling. Finite-element limit analysis is first used to develop a numerical database of stability numbers for both unsupported and fluid-supported circular excavations. The parameters considered in the modeling include excavation geometry, soil strength profile, and support fluid properties. A data-driven algorithm is used to learn the numerical results to develop a fast surrogate amenable for integration within real-time monitoring systems. By way of example, the proposed forecasting strategy is retrospectively applied to a recent field monitoring case history where the observational method is used to update the input parameters of the data-driven surrogate.