Deformation monitoring is the main program in the area of dam safety. Because statistical model is simple and intuitive, it is widely used in dam safety monitoring. However, in dam's displacement statistic model, there is a high degree of linear relationship between influence factors. Due to the influence of multicollinearity, models calculated with traditional methods are not accurate and stable. Besides, because of dam integrity, each part of dam is interrelated and interactive. Currently, single point or multipoints displacement monitoring models cannot accurately reflect the actual dam running state. In this paper, the theory of panel data is introduced to dam deformation analysis. Panel data contain time series data and cross section data, which is able to solve serious multicollinearity problem of traditional regression method. Moreover, all measuring points are classified into several groups according to their similar deformation law. Based on the random-coefficient model of panel data, potential relationship between different measuring points is built. Take 1 hydropower station, for example, to examine that random-coefficient model is able to improve the modeling situation that estimators are not significant and simultaneously provide a stable model, which explores a new approach for the research of dam displacement monitoring. [4] used statistical regression method to research dam prototype observation data. He set deformation measured value as explained variable and set appropriate explanation variables at the same time. The unbiased efficient estimator of each explanation variable was obtained through the traditional least squares method (ordinary least squares, OLS).After that, influence factors of deformation-measured values were analyzed quantitatively. On the basis of Chen JY's study,
Summary In order to discover anomalies of dam structure behaviors and evaluate the operation status timely, it is quite demanding to analyze the dam safety monitoring data that has been collected from the instruments. However, outliers in original monitoring data may affect the accuracy of dam performance assessment, which need to be detected before analyzing monitoring data. Model‐based methods have been applied in outlier detection as a kind of common method for a long time, but they generally rely heavily on model accuracy and easily lead to misjudgment of outliers once the data structure is complex. Considering the monitoring data of dam effect variables (e.g., deformation, cracking, or seepage) tend to show strong continuity, complex periodic and trending changes with the environment, valid monitoring data can reflect the variation trend by forming a data process line. Therefore, data that deviate from the process line can be detected as outliers. In this paper, an automatic process line identification method for dam safety monitoring data outlier detection is proposed. First, after drawing a scatter plot of the dam monitoring data, a binary image of the scatter plot is inputted into the computer program. Afterwards, the binary image would be processed by Gaussian blur and image binarization techniques, and then the continuous points could be identified. After constant adjustment of the vertical ordinate range and introducing Cuckoo Search (CS) algorithm, the optimal process line identification and outlier detection were finally completed. The case studies demonstrate the proposed method can enhance the efficiency of outlier detection.
We use fuzzy finite element method (FEM) to analyze the impact of cold wave on face slab cracking of a concrete-faced rockfill dam (CFRD). The static response of dam and the temperature field of face slab are calculated using deterministic FEM since some observed and test data can be obtained. Some parameters of Goodman contact element between face slabs and cushion material are selected as fuzzy variables, and the fuzzy FEM is used to calculate fuzzy stress of face slab. The fuzzy FEM is implemented using vertex method based on the extension principle. Through the analysis of two selected calculation cases of cold wave, it is shown that the calculated cracking direction and cracking zone caused by thermal stress are similar to those of the observed cracks. This proves that the cold wave that caused swift air temperature drop is an important reason for the cracking of face slab. According to these analysis results, some cracking prevention measures are then proposed.
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