期刊
JOURNAL OF GEOTECHNICAL AND GEOENVIRONMENTAL ENGINEERING
卷 147, 期 12, 页码 -出版社
ASCE-AMER SOC CIVIL ENGINEERS
DOI: 10.1061/(ASCE)GT.1943-5606.0002683
关键词
Data-driven; Artificial neural networks; Excavation; Stability; Undrained; Numerical modelling; Limit analysis
资金
- Royal Academy of Engineering
- Ward and Burke Construction Ltd.
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 used to develop a numerical database of stability numbers, considering excavation geometry, soil strength profile, and support fluid properties. The proposed forecasting strategy is retrospectively applied to a recent field monitoring case history using observational method to update the data-driven surrogate model's input parameters.
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.
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