Influenced by environment and human factors, the observed data of dam deformation consist of real deformation value and observation error (noise). The conventional GM(1,1) model based on nondenoised observation data is not very effective. In order to improve the prediction effect of conventional GM(1,1) model, wavelet threshold denoising method is used to eliminate the noise in the original data and improve the smoothness of the data sequence. Then, based on the conventional GM(1,1) model, the metabolic GM(1,1) model is established by eliminating the oldest information and adding the newest information. The application results show that the wavelet threshold denoising can obviously remove the noise from the original data. The predicted vertical displacement of the metabolic GM(1,1) model based on the denoised data has little difference with the measured value, and the predicted precision is obviously higher than that of the conventional GM (1,1) model. Therefore, the metabolic GM(1,1) model based on wavelet denoising can be used for prediction and early warning of dam deformation.
Seepage behavior assessment is an important part of the safety operation assessment of earth-rock dams, because of insufficient intelligent analysis of monitoring information, abnormal phenomena or measured values are often ignored or improperly processed. To improve the intelligent performance of the monitoring system, this article has established an assessment framework covering project quality, maintenance status, monitoring data analysis, and on-site inspection based on the relevant norms of seepage safety assessment of earth-rock dams and the expert survey scoring method, and the Leaky Noisy-OR Gate extended model were used to determine the probability of events, and the dynamic and static Bayesian networks used to assess the possibility of seepage failure of earth-rock dams and diagnose the most likely cause of failure. The function of static and dynamic Bayesian networks to assess the seepage behavior of earth-rock dams, abnormal measured values, and causes of anomalies can make up for the limitations of reservoir management personnel and monitoring system in seepage failure experience and seepage knowledge of earth-rock dams and enable better handling of abnormal phenomena and monitoring information, making the monitoring system more intelligent.
Abstract. Based on the analysis of deformation characteristics of arch dams of LiJiaxia hydropower station, Dong Jian hydropower station and Feng Le reservoir, the deformation laws are acquired, and the further understandings of deformation safety monitoring index under different conditions are provided to monitor important parts instead of all the sites, which avoid the waste of monitoring resource and improve the early-warning efficiency.
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