Monitoring of long-term static deformation data of Fei-Tsui arch dam using artificial neural network-based approaches

The objective of this paper is to develop methods for extracting trends from long-term static deformation data of a dam and try to set an early warning threshold level on the basis of the results of analyses. The static deformation of a dam is mainly influenced by the water pressure (or water level) of the dam and the temperature distribution of the dam body. The relationship among the static deformation, the water level, and the temperature distribution of the dam body is complex and unknown; therefore, it can be approximated by static neural networks. Although the static deformation almost has no change during a very short time, it changes with time for long-term continuous observation. Therefore, long-term static deformation can be approximated dynamically using dynamic neural networks. Moreover, static deformation data is rich, but information is poor. Linear and nonlinear principal component analyses are particularly well suited to deal with this kind of problem. With these reasons, different approaches are applied to extract features of the long-term daily based static deformations of the Fei-Tsui arch dam (Taiwan). The methods include the static neural network, the dynamic neural network, principal component analysis, and nonlinear principal component analysis. Discussion of these methods is made. By using these methods, the residual deformation between the estimated and the recorded data are generated, and through statistical analysis, the threshold level of the static deformation of a dam can be determined on the basis of the normality assumption of the residual deformation. Copyright (C) 2011 John Wiley & Sons, Ltd.