Abstract
Risk assessment of dam’s running status is an important part of dam management. A data-driven method based on monitored displacement data has been applied in risk assessment, owing to its easy operation and real-time analysis. However, previous data-driven methods considered displacement data series at each monitoring point as an independent variable and assessed the running status of each monitoring point separately, without considering the correlation between displacement of different monitoring points. In addition, previous studies assessed the dam’s running status qualitatively, without quantifying the risk probability. To solve the above two issues, a displacement-data driven method based on a multivariate copula function is proposed in this paper. Multivariate copula functions can construct a joint distribution which reveals the relevance structure of random variables. We assumed that the risk probability of each dam section is independent and took monitoring points at one dam section as examples. Starting from the risk assessment of single monitoring points, we calculated the residual between the monitored displacement data and the modelled data estimated by the statistical model, and built a risk ratio function based on the residual. Then, using the multivariate copula function, we obtained a combined risk ratio of multi-monitoring points which took the correlation between each monitoring point into account. Finally, a case study was provided. The proposed method not only quantitatively assessed the probability of the real-time dam risk but also considered the correlation between the displacement data of different monitoring points.
Highlights
Risk assessment of a dam’s running status is of great importance for dam safety management [1]
With the development of soft computing techniques, data-driven methods based on displacement data have been applied to dam risk assessment, as the displacement data can reflect a dam’s structural behaviour and it can be obtained by the scores of monitoring instruments buried in the dam body [5,6]
The present study proposed a data-driven method based on multivariate copulas for the risk assessment of a dam’s safety, with the objective of assessing the dam risk quantitatively and to consider the correlation of displacement between each monitoring point
Summary
Risk assessment of a dam’s running status is of great importance for dam safety management [1]. A dam’s risk was assessed based on the physical mechanism of its structural behaviours using numerical simulations or theoretical analysis [2,3,4]. With the development of soft computing techniques, data-driven methods based on displacement data have been applied to dam risk assessment, as the displacement data can reflect a dam’s structural behaviour and it can be obtained by the scores of monitoring instruments buried in the dam body [5,6]. The principle of data-driven methods is an analysis of the absolute residual between monitored and modelled displacement data [7,8,9]. The value of monitored data deviating from modelled data is an indicator of a potentially unusual running status. In practical engineering, the displacements of adjacent monitoring points are highly interrelated and they interact with each other, and any parts of the dam jointly afford the common external load such as hydrostatic pressure and temperature [12,13]
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