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Deflection statistical monitoring model identification of the concrete gravity dam based on uncertainty analysis

Deflection statistical monitoring model identification of the concrete gravity dam based on... Dam monitoring model is a contentious and complicated topic in dam safety monitoring research. The single measuring point model and the multidimensional and multi‐measuring point models are the most common dam monitoring models. The single measuring point model has higher accuracy and is widely used, but the rate of utilization of all the monitoring data is limited, making it difficult to reflect the full characteristics of the dam. However, the construction and solution of multidimensional and multipoint models, as well as the analysis of their results, are relatively complicated, and this is still a topic that needs to be explored and researched on a regular basis. The deflection model is a special form of multipoint model, which is rarely studied at present. Therefore, this paper takes concrete gravity dam as the research object, constructs the general form of its deflection statistical monitoring model, and proposes the corresponding identification method based on the uncertainty analysis. The numerical simulation results show that the optimal order of the deflection statistical monitoring model is mainly affected by the upstream water and temperature, showing a strong seasonal pattern. The deflection caused by the upstream water is nearly linear, while that caused by temperature is almost parabolic. The nonlinear degree of deflection will be enhanced when the deformation caused by the upstream water and temperature are superimposed in the same direction. Big measuring errors will “drown” the nonlinear characteristics of the dam's deflection, while a few measuring points will “exaggerate” the nonlinear characteristics. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Structural Control and Health Monitoring Wiley

Deflection statistical monitoring model identification of the concrete gravity dam based on uncertainty analysis

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References (41)

Publisher
Wiley
Copyright
© 2022 John Wiley & Sons, Ltd.
ISSN
1545-2255
eISSN
1545-2263
DOI
10.1002/stc.3026
Publisher site
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Abstract

Dam monitoring model is a contentious and complicated topic in dam safety monitoring research. The single measuring point model and the multidimensional and multi‐measuring point models are the most common dam monitoring models. The single measuring point model has higher accuracy and is widely used, but the rate of utilization of all the monitoring data is limited, making it difficult to reflect the full characteristics of the dam. However, the construction and solution of multidimensional and multipoint models, as well as the analysis of their results, are relatively complicated, and this is still a topic that needs to be explored and researched on a regular basis. The deflection model is a special form of multipoint model, which is rarely studied at present. Therefore, this paper takes concrete gravity dam as the research object, constructs the general form of its deflection statistical monitoring model, and proposes the corresponding identification method based on the uncertainty analysis. The numerical simulation results show that the optimal order of the deflection statistical monitoring model is mainly affected by the upstream water and temperature, showing a strong seasonal pattern. The deflection caused by the upstream water is nearly linear, while that caused by temperature is almost parabolic. The nonlinear degree of deflection will be enhanced when the deformation caused by the upstream water and temperature are superimposed in the same direction. Big measuring errors will “drown” the nonlinear characteristics of the dam's deflection, while a few measuring points will “exaggerate” the nonlinear characteristics.

Journal

Structural Control and Health Monitoring – Wiley

Published: Oct 1, 2022

Keywords: concrete gravity dam; deflection statistical monitoring model; identification; polynomial regression; uncertainty analysis

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