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Prediction of Dam Foundation Displacement due to Excavation Unloading Based on Digital Twin: Case Study of Baihetan Hydropower Project.

Authors :
Shi, Anchi
Lyu, Changhao
Fan, Xuewen
Hu, Mingtao
Wang, Huanling
Xu, Weiya
Source :
Journal of Engineering Mechanics; Jun2024, Vol. 150 Issue 6, p1-14, 14p
Publication Year :
2024

Abstract

When excavating the rock foundation of a hydropower station, it will be affected by the phenomenon of unloading and relaxation, which may increase the risk of stability of the dam foundation engineering system. The dam foundation of Baihetan Hydropower is a columnar jointed rock mass (CJRM), which presents strong brittleness and anisotropy compared to traditional dam foundation rocks. Therefore, this type of rock mass is prone to disturbance to the dam body, structure, etc. during excavation, so it is necessary to accurately evaluate the impact of dam foundation excavation. Establishing a rock mass creep models serve as an effective tool for evaluating such stability but often suffer from significant parameter uncertainty. Digital twin technology, a virtual model, is capable of real-time learning from actual monitoring data obtained from the physical entity to enhance the performance of the built-in mechanistic model. In this study, the researchers employ the classical Burgers constitutive equation as the theoretical framework and integrate it with an ensemble smoother with multiple data assimilation (ESMDA) method based on Bayesian principles, along with displacement monitoring data from the Baihetan Dam foundation, to construct a digital twin model. Within this framework, the researchers analyze the uncertainty of rheological parameters at various measurement points in the Baihetan Dam foundation. Subsequently, the most suitable rheological parameters are selected and incorporated into the constitutive model to obtain displacement estimates, which are then compared with on-site monitoring data. The results demonstrate that the proposed method effectively performs probabilistic parameter estimation and model prediction for rheological mechanics. This research integrates data-driven methods with mechanical principles, offering a reliable approach for assessing the uncertainty of unloading rheological parameters and displacement prediction in dam foundations, thereby providing essential support for the evaluation of excavation projects in the CJRM of the Baihetan Dam foundation. [ABSTRACT FROM AUTHOR]

Details

Language :
English
ISSN :
07339399
Volume :
150
Issue :
6
Database :
Complementary Index
Journal :
Journal of Engineering Mechanics
Publication Type :
Academic Journal
Accession number :
176654413
Full Text :
https://doi.org/10.1061/JENMDT.EMENG-7542