A dam is a super‐structure widely used in water conservancy engineering fields, and its long‐term safety is a focus of social concern. Deformation is a crucial evaluation index and comprehensive reflection of the structural state of dams, and thus there are many research papers on dam deformation data analysis. However, the accuracy of deformation data is the premise of dam safety monitoring analysis, and original deformation data may have some outliers caused by manual errors or instruments aging after long‐time running. These abnormal data have a negative impact on the evaluation of dam structural safety. In this study, an analytical method for detecting outliers of dam deformation data was established based on multivariable panel data and K‐means clustering theory. First, we arranged the original spatiotemporal monitoring data into the multivariable panel data format. Second, the correlation coefficients between the deformation signals of different measuring points were studied based on K‐means clustering theory. Third, the outlier detection rules were established through the changes of the correlation coefficients. Finally, the proposed model was applied to the Jinping‐I Arch Dam in China which is the highest dam in the world, and results indicate that the detection method has high accuracy detection ability, which is valuable in dam safety monitoring applications.

The safety operation of dams after extreme load is an important frontier research topic in the field of dam engineering. The dam health monitoring provides a reliable data basis for a safety evaluation after extreme loads. This study proposes a novel data-driven fusion model for a dam safety evaluation after extreme load based on monitoring data derived by sensors. First, the relationship between dam environmental quantity and effect quantity is deeply excavated based on bidirectional long short-term memory (BiLSTM) network, which is a deeply improved LSTM model. Aiming at the parameter optimization problem of BiLSTM model, sparrow search algorithm (SSA), which is an advanced optimization algorithm, is integrated. Second, conducting the constructed SSA-BiLSTM model to estimate the change law of dam effect quantity after the extreme load. Finally, the Mann–Whitney U-test theory is introduced to establish the evaluation criterion of the dam safety state. Project case shows that the multiple quantitative prediction accuracy evaluation indicators of the proposed method are significantly superior to the comparison method, with mean absolute percentage error (MAPE) and mean absolute error (MAE) values decreasing by 30.5% and 27.8%, respectively, on average. The proposed model can accurately diagnose the dam safety state after the extreme load compared with on-site inspection results of the engineering department, which provides a new method for dam safety evaluation.