###### Abstract  
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 3\(\sigma\) method. The results show that the proposed method has the highest gross errors identification rate, the strongest generalization ability and the best prediction effect. m 2022  
utilized 3D geometric measurements extracted from photogrammetric point clouds to assess the performance of highway bridges in static load testing. The credibility of the main application of measurement results is determined by the credibility of the input data [9]. When obtaining raw data for concrete dam safety monitoring, there are often a certain number of gross errors generated from reading errors, calculation errors, the sudden failure of the detection instrument and other factors. The existence of gross errors seriously affects the accuracy of the dam observation value sequences, so effective measures must be taken to achieve a real and reliable dam safety prediction result. At present, the widely used gross errors eliminating methods include the process line method, statistical test method and mathematical model method [10; 11]. However, these traditional methods have some problems such as less robustness and less comprehensive consideration. Therefore, [PERSON] et al. [12] proposed a method for three-dimensional subsurface stratification to modify the cross-validation based on the outlier detection method. [PERSON], F [13] utilized fast Fourier transform to highlight the areas with high frequency change to identify outlier regions. [PERSON] [14] applied several statistical test methods to detect and remove outliers in the Dobczyc dam over the period 2012-2016. [PERSON] et al. [15] proposed a method to identify the outliers of dam monitoring data with an abnormal index matrix updated with real-time data based on integrated learning; [PERSON] et al. [16] established a theoretical method for dam outliers monitoring based on multivariate panel data and K-mean clustering; [PERSON] et al. [17] utilized image processing techniques and cuckoo search algorithms to process outliers in dam monitoring data. Meanwhile, scholars introduced the robust estimation theory into the model of gross errors identification, so as to enhance the robustness and generalization ability of the model. [PERSON] et al. [18] proposed an improved algorithm of the 3\(\sigma\) method based on MCD robust estimation to identify the residual outliers in the dam detection data; [PERSON] et al. [19] established an early warning model combining robust statistics and confidence intervals to identify detection outliers; [PERSON] et al.