Technical Papers
Sep 20, 2021

Hybrid Framework for Forecasting Circular Excavation Collapse: Combining Physics-Based and Data-Driven Modeling

Publication: Journal of Geotechnical and Geoenvironmental Engineering
Volume 147, Issue 12

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.

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Data Availability Statement

Some or all data, models, or code that support the findings of this study are available from the corresponding author upon reasonable request.

Acknowledgments

This project was funded by the Royal Academy of Engineering under the Research Fellowship scheme. The author gratefully acknowledges the support and input from Ward and Burke Construction Ltd.

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Go to Journal of Geotechnical and Geoenvironmental Engineering
Journal of Geotechnical and Geoenvironmental Engineering
Volume 147Issue 12December 2021

History

Received: Sep 6, 2019
Accepted: Jul 23, 2021
Published online: Sep 20, 2021
Published in print: Dec 1, 2021
Discussion open until: Feb 20, 2022

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Royal Academy of Engineering Research Fellow, Dept. of Engineering Science, Univ. of Oxford, Oxford OX1 3PJ, UK. ORCID: https://orcid.org/0000-0002-1462-1401. Email: [email protected]

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