Affected by hydrodynamic excitation and other factors, the opening-closing operation of hydraulic gates exhibits multi-field coupling effects and complex nonlinear dynamic characteristics, leading to difficulties in identifying equipment safety states. Test data of gate operation demonstrate that artificial neural network algorithms can identify hydrodynamic excitation disease features and accurately predict its development trends. To address this, BP and GA-BP neural networks were employed to construct identification and prediction models for hydrodynamic excitation disease. These models were applied to identify and forecast the effective values of reel vibration, with model performance evaluated using metrics including Relative Error (R_RE), Mean Absolute Percentage Error (M_MAPE), and Root Mean Square Error (R_RMSE). Compared to the BP model, the results indicate that the GA-BP model achieves reductions of 20.77% in R_RE, 4.74% in M_MAPE, and 6.27% in R_RMSE, demonstrating superior fitting to measured samples and enhanced stability with extended prediction durations, thus providing critical technical support for engineering risk mitigation and hazard prevention.

To address the issue of reduced detection accuracy under complex working conditions due to the fixed threshold of the isolation forest algorithm, an anomaly detection method for ship lock miter gate monitoring data based on singular spectrum analysis (SSA) and an improved isolation forest (KMIF) is proposed. The SSA is employed to decompose and reconstruct the monitoring data, and separate the trend and noise components. The isolation forest algorithm is improved by incorporating K-Means++ clustering to dynamically set anomaly thresholds for different monitoring datasets. The noise component is then fed into the improved isolation forest algorithm for training and anomaly detection. Taking the stress and vibration data from multiple measuring points of the lower lock miter gate in Jiangsu ship gate project as an example for validation, the results show that the proposed SSA-KMIF method performs excellently in terms of false positive rate, precision, recall ratio, and accuracy. It demonstrates high accuracy and flexibility, which provides a reliable technical support for health monitoring of ship lock miter gates.

To improve the efficiency and accuracy of fault diagnosis for hydroelectric units, combination of multifractal detrended fluctuation analysis algorithm and probabilistic neural network was used to establish a vibration signal feature extraction and recognition model. The binary gravity search algorithm was used to optimize its parameters. The results show that the classification accuracy of the feature extraction and recognition classification model can be improved to 99% and reduce the signal processing time to about 1.3 seconds after optimizing by the binary gravity search algorithm. The proposed vibration signal feature extraction and recognition model for hydroelectric units can significantly distinguish between the normal working state and the fault working state of hydroelectric units, achieving the purpose of using vibration signal features to diagnose faults in hydroelectric units.

Predicting risks in later diversion stages is essential for the scientific evaluation of initial impoundment schemes for hydropower stations. Considering the gradual development process from dam overtopping to the failure of the diversion system, taking the later diversion systems of the upstream existing and downstream under-construction power stations as the research objects, a later diversion risk model for cascade power stations based on level sets is established, and the risk is expressed in the form of interval numbers. Based on the prospect theory, the initial impoundment scheme of the cascade power stations is evaluated by taking risk loss, relative storage reservoir power generation benefits and storage duration as the indicators. Finally, taking two adjacent under-construction power stations in the upper reaches of the Jinsha River as an example, the results show that when the level sets change from 0 to 1, the equivalent recurrence interval corresponding to the later diversion risk interval of the original impoundment scheme meets the flood design standard with a high safety margin. The staged storage time can be advanced by 40 days, which provides a reference for the safety and economy of advanced impoundment.

Aiming at the complex karst environment of the lower reservoir of a pumped storage power station, a three-dimensional finite element model for seepage analysis was established to simulate the main buildings and karst passageways in the reservoir area. "The depth of seepage control curtain at the base of the dam is 0.5 times of the pre-dam head, the depth of seepage control curtain on both sides of the dam is 3 m below the 3 Lu line, the length of seepage control curtain on the right side of the dam is 200 m, and a single-row curtain is set up" is used as the preliminary seepage control scheme. The seepage field, infiltration slope and infiltration volume of the reservoir area were calculated. The preliminary seepage control scheme met the specification requirements. But the seepage rate and infiltration slope were close to the critical value. The four indexes of curtain depth on both sides of the bank, curtain depth at the base of the dam, length of curtain on the right bank, and double-row curtain were changed to optimize seepage control scheme. The impact of the changes of the indexes on the infiltration volume of the reservoir area was investigated so that the optimization of the seepage control scheme was put forward. The analysis results show that on the basis of the preliminary scheme, the double-row curtain is set up at the dam base, the infiltration flow at the dam base is reduced by 267.3 m³/d, and the effect of seepage control in the reservoir area is remarkable.

Constructing a high-precision dam settlement prediction model is of great significance for ensuring the safety and risk control of dam during the construction period. Taking dam height, rainfall and aging as the influencing factors of dam settlement deformation during construction period, the long-term and short-term memory neural network LSTM algorithm is introduced, and the attention mechanism is embedded. Thus, a prediction model suitable for dam settlement of concrete face rockfill dam during construction period is proposed. The engineering application shows that the attention-LSTM model makes up for the defect that the LSTM cannot dynamically adjust the weight coefficient at the network layer, improves the computational efficiency and accuracy of the model, and has better nonlinear data processing ability, which can more accurately reflect the change trend of monitoring data in the time dimension during the construction period. The relevant experience can be used as a reference for similar projects.

To investigate the damage evolution and failure mechanism of fiber-reinforced concrete under action of earthquake, a series of monotonic and reciprocating axial compression tests were conducted on steel fiber reinforced concrete to analyze the influence of fiber addition on the stress-strain behavior of concrete. The focus was on exploring the relationship between the mechanical behavior degradation and internal damage accumulation of concrete under cyclic axial compression, including plastic strain, stiffness degradation, and energy dissipation. The results indicate that the monotonic loading curve is close to the envelope of the reciprocating axial compression curve. The addition of steel fibers significantly improves the post peak ductility of concrete and increases residual stress. Due to the crack resistance and toughening effect of steel fibers, the failure mode of concrete after adding fibers has evolved from brittle failure of a single vertical main crack to ductile failure mode of multi crack cracking. Steel fibers can effectively improve the seismic mechanical behavior of concrete. After adding fibers, the degradation of elastic stiffness and plastic strain development of concrete are effectively controlled, and the energy dissipation performance is enhanced.

Unsteady friction plays a very complex role in the water hammer phenomenon within pipeline systems. For hydropower station with super-long headrace tunnel (SLHT), it is crucial to consider the effects of unsteady friction adequately. This paper conducts a study on the nonlinear transient response characteristics of hydropower station with SLHT based on unsteady friction. Firstly, a system model of hydropower station with SLHT is established using the method of characteristics, incorporating unsteady friction based on instantaneous acceleration. Then, simulations of this model are performed under load and frequency disturbances. Finally, the stability of the model under frequency disturbances is evaluated using the Bode plot method, and the impact of unsteady friction parameters on system stability is investigated in conjunction with the energy equation. The results show that the unsteady friction in the pipeline primarily affects the rapid response phase of the water turbine in the hydropower station with SLHT. When the impact coefficients of local acceleration (k_t) and convective acceleration (k_l) increase, the system stability deteriorates. Moreover, changes in k_t and k_l make the system more sensitive to high-frequency disturbances.

Conventional dam displacement monitoring methods are often associated with large errors and low efficiency. Manual monitoring methods cannot provide continuous real-time monitoring, while automated monitoring methods, such as total station robot and GNSS, are affected by weather and have limited accuracy for vertical displacement. To overcome these shortcomings, this study proposes a new intelligent monitoring method for dam displacement based on machine vision. The method utilizes the internet of things and intelligent disaster recognition algorithm to convert picture data into deformation data, enabling ultra-high precision non-contact real-time measurement of the dam. The monitoring system was tested at Lianghui Reservoir, the results demonstrate that the operation of monitoring system is stable, and the horizontal and vertical monitoring accuracy are both 1.5 mm. The proposed method has the potential to be widely applied in other water conservancy projects for surface displacement monitoring.

Studying the propagation of meteorological drought to hydrological drought is essential for meteorological information-based drought management and hydrological drought warning systems. Taking the Ganjiang River source basin as an example, this paper uses the basin's monthly meteorological and hydrological data for the past 60 years to study the propagation pattern of meteorological drought to hydrological drought, and explores the differences in drought propagation among sub-basins. The results show that the propagation of meteorological drought to hydrological drought has various ways, with a one-to-one correspondence; The drought events increase hazards after propagation from meteorology to hydrology; The Logistic model can better reflect the response relationship of hydrological drought characteristics to precipitation shortage conditions; The large reservoirs have a significant effect on the drought resistance of the basin.