A Combined Safety Monitoring Model for High Concrete Dams

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 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.

AIoT Monitoring Technology for Optimal Fill Dam Installation and Operation

“…In recent years, various techniques, such as advanced photogrammetry, digital twin, GNSS, deep learning algorithms, and numerical methods [7][8][9][10], have been reported. Lee [11] constructed a 3D model for the Malpasset Dam located in southern France by introducing short-range photogrammetry and observed the dam failure geometry using the model.…”

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“…Zhang et al [30] integrated multiple learners and proposed an anomaly diagnosis method using an anomaly index matrix updated with real-time data. Gu et al [31] used an improved IGG method and an extreme learning machine to identify gross errors in deformation monitoring data. Li et al [32] proposed an outlier identification method based on a BP neural network.…”