When applying reliability analysis to the monitoring of structural health, it is very important that gross errors–which affect prediction accuracy–are included within the monitoring information. An approach using gross errors identification and a dam safety monitoring model for deformation monitoring data of concrete dams is proposed in this paper. It can solve the problems of strong nonlinearity and the difficulty of identifying and eliminating gross errors in deformation monitoring data in concrete dams. This new method combines the advantages of an incremental extreme learning machine (I-ELM) method to seek an optimal network structure, the Least Median Squares (LMS) method with strong robustness to multiple failure points, the robust estimation IGG method with the good robustness to outliers (gross errors) and extreme learning machine (ELM) method with high prediction efficiency and handling of nonlinear problems. The proposed method can eliminate gross errors and be utilized to predict the behavior of concrete dams. The deformation monitoring data of an existing 305 m-high concrete arch dam is acquired by combining remote sensing technology with other monitoring methods. The LMS-IGG-ELM method is utilized to eliminate outliers from the dam monitoring sequence and is compared with the processing result from a DBSCAN clustering algorithm, Romanovsky criterion and the 3σ method. The results show that the proposed method has the highest gross errors identification rate, the strongest generalization ability and the best prediction effect.

Core wall rockfill dams are susceptible to cracking at the dam’s crest, as well as collapse and settlement of the rockfill during storage and operation periods, particularly due to rapid fluctuations in the water level in pumped storage power stations. Most studies on the impact of fluctuations in the reservoir’s water level on dam deformation have considered fluctuations of less than 5 m/d, while pumped storage power stations experience much larger fluctuations. Additionally, the seepage and stress fields within the dam’s rock and soil interact and influence each other. Few studies have used the coupling theory of seepage and stress to analyze seepage and deformation in core wall rockfill dams. To address these issues, a finite element model using seepage–stress coupling theory was utilized to investigate the variations in the phreatic line, earth pressure, and deformation of a core wall rockfill dam due to rapid fluctuations in the reservoir’s water level. Additionally, the results of the finite element simulation were compared with and analyzed alongside safety monitoring data. The results indicated that, upon a sudden decrease in the reservoir’s water level, there was a lag in the decline of the phreatic line in Rockfill I, which created a large hydraulic gradient, resulting in a reverse seepage field on the dam’s slope surface and generating a drag force directed upstream. Consequently, a significant concentration of stress occurred on one-third of the upstream slope surface of the dam and the seepage curtain, and the increase in horizontal displacement was substantially greater than the increase in settlement from one-third of the rockfill’s height to the dam’s foundation. The deformation was more sensitive to the lowest water level of the reservoir rather than to the fastest rate of decline. Sudden rises in the reservoir’s water level result in decreased horizontal displacements and settlement of the dam. Amid rapid fluctuations of the reservoir’s water level, changes in the vertical earth pressure were more pronounced at the bottom of the core wall than in its midsection. Compared with the core wall, variations in the vertical earth pressure in the upstream and downstream filter layers were minor at similar elevations. A peak horizontal displacement of 6.5 mm was noted at one-third the height of Rockfill I, with the greatest increase in settlement of 3.5 mm at the dam’s crest. To ensure a project’s safety, it is crucial to control the elevation of the lowest point during a sudden drop in the reservoir’s level and to carefully monitor for cracks or voids within approximately one-third of the dam’s height in Rockfill I and at the dam crest. This study’s results provide a scientific basis for assessing core wall rockfill dams’ health and securing long-term safety at pumped storage power facilities.

Real and effective monitoring data are crucial in assessing the structural safety of dams. Gross errors, resulting from manual mismeasurement, instrument failure, or other factors, can significantly impact the evaluation process. It is imperative to eliminate such anomalous data. However, existing methods for detecting gross errors in concrete dam deformation often focus on analyzing a single monitoring effect quantity. This can lead to sudden jumps in values of effect quantity caused by changes in environmental variables being mistakenly identified as gross error. Therefore, a method based on Fuzzy C-Means clustering algorithm (FCM) partitioning and density clustering algorithm (Ordering Points To Identify the Clustering Structure, OPTICS) combined with Local Outlier Factor (LOF) algorithm for gross error identification is proposed. Firstly, the FCM algorithm is used to achieve the division of measurement point areas. Then, the OPTICS and LOF algorithms are jointly utilized to determine the gross errors. Finally, the real gross errors are identified by comparing the time of occurrence of the gross errors at measurement points in the same area. Through the case study, the results indicate that the method can effectively identify spurious, gross errors in the monitoring effect quantity caused by environmental mutations. The accuracy of gross error detection is significantly improved, and the rate of misjudgment of gross errors is reduced.