During the operation of a concrete dam

various uncertainties－such as sudden events

natural disasters

and changes in human management－are possible to impose an impact on it

potentially deviating its structure deformation from the conventional patterns. An accurate identification of such changes is crucial for raising the level of concrete dam warning and forecasting. This paper presents an intelligent method for identifying dam deformation under uncertainties. First

we use a spatial clustering method to categorize measurement points that are located in different regions of the concrete dam structure but share certain similarity. Then

a fuzzy clustering (Gath-Geva) algorithm is used to segment a multivariate time series into different phases

allowing its data points to belong to multiple periods based on the membership degree

to measure the homogeneity of segments and detect changes in its hidden structure. Last

we use a fuzzy decision algorithm based on the cluster compatibility criteria to determine the number of segments required

and adopts the principal component analysis (PCA) to identify the number of principal components

further improving the accuracy of the Gath-Geva algorithm. This intelligent method has been applied in a case study of a concrete arch dam structure to identify the changes hidden in the time series of its displacement measurements. Comparison of its results with those of single-period data shows that it is effective in extracting sudden anomalous changes during the operational phase of the dam

and that it is a valuable approach for assessing the operational conditions of concrete dams.