Safety monitoring of hydraulic structures is a critical task in the field of hydraulic engineering construction. This study developed a method for preprocessing and classifying monitoring data for the identification of gross errors in hydraulic structures. By utilizing linear regression and wavelet analysis techniques, it effectively differentiated various waveform characteristics in data sets, such as Sinusoidal Wave Cyclical, Triangular Wave Cyclical, Seasonal Cyclical, and Weakly Cyclical growth types. In the experiments for gross error identification, the 3σ algorithm, K-medoids algorithm, and Isolation Forest algorithm were applied to test the data. The results showed that the K-medoids algorithm excelled in processing Sinusoidal Wave Cyclical Data Sets; the 3σ algorithm adapted better to Triangular Wave Cyclical Data Sets; the Isolation Forest algorithm performed well in handling data sets with significant anomalies or atypical fluctuations and excelled in scenarios with strong seasonality and large data fluctuations; and for complex Weakly Cyclical Growth Data Sets, all three algorithms were less effective, indicating the potential need for more advanced analysis methods or a combination of multiple techniques. Testing on actual engineering data further confirmed the importance of using specific gross error identification techniques for special data types after data set pre-classification, providing a more effective technical solution for the safety monitoring of hydraulic structures.

Rock-filled concrete (RFC) is an advanced construction material that integrates high-performance self-compacting concrete (HSCC) with large rocks exceeding 300 mm, providing advantages such as reduced hydration heat and increased construction processes. The elastic modulus of RFC is a critical parameter that directly influences its structural performance, making it vital for modern construction applications that require strength and stiffness. However, there is a scientific gap in understanding the effects of rock size, shape, arrangement, and volumetric ratio on this parameter. This study investigates these factors using mesoscale finite element models (FEMs) with spherical and polyhedral rocks. The results reveal that polyhedral rocks increase the elastic modulus compared to spherical rocks, enhancing RFC’s load-bearing capacity. Additionally, a 5% increase in the elastic modulus was observed when the rockfill ratio was increased from 50% to 60%, demonstrating a direct correlation between rock volume and mechanical performance. Furthermore, the elastic modulus rises significantly in the early stages of placement, followed by a gradual increase over time. Optimal rock sizes and a balanced mix of rock shapes allow for improved concrete flow and mechanical properties, making RFC a highly efficient material for construction. These findings offer valuable insights for designers and engineers looking to optimize RFC for structural applications.

Through the numerical simulations of the hydrodynamic model, the water storage and discharge are found to be limited to envelope lines and the discharge/water depth process lines during water rising and falling showed a grid-shaped distribution. Furthermore, if a catchment is regarded as a semi-open water storage system, there is a nonlinear relationship between the inside average water depth and the outlet water depth, namely the water storage ratio curve, which resembles the shape of a “plume”. In the case of an open channel without considering spatial variability, the water storage ratio curve is limited to three values (i.e., the upper, the steady, and the lower limit), which are found to be independent of meteorological (rainfall intensity), vegetation (Manning’s coefficient), and terrain (slope gradient) conditions. Meteorological, vegetation, and terrain conditions only affect the size of the “plume” without changing its shape. Rainfall, especially weak rain (rainfall intensity is less than 5.0 mm h^-1) significantly affects the fluctuations of water storage ratio, which can be divided into three modes, that is Mode Ⅰ during rainfall beginning stage, Mode Ⅱ during rainfall duration stage, and Mode Ⅲ during rainfall end stage. Results indicate that the determination of the nonlinear relationship of the water storage ratio curve under different geographical scenarios will provide new ideas for simulation and early warning of floods.