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.

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.

With the construction and operation of integrated clean energy bases, there is an urgent need for multi-energy joint dispatch. On the basis of cascade hydropower joint scheduling, this article embeds the risk of channel electricity curtailment as a penalty constraint, integrates the working experience of scheduling personnels with rolling ideas, and explores a method for formulating multi-time scale cascade hydro-photovoltaic complementary joint scheduling rules based on actual operation. The potential risks of power abandonment is identified in advance and control measures are proposed. By selecting the benefits of hydroelectric power generation and energy storage, as well as photovoltaic power generation, a complementary function of hydro-photovoltaic joint system is established to evaluate the scheduling rules. The proposed method has been applied to the hydro-photovoltaic complementary system of Xiaowan and Manwan on the Lancang River. The results show that the economic benefits of the hydro-photovoltaic joint system is significantly increased without significantly affecting the hydropower regulations. The idea has the feasibility of promoting the joint operation of the integrated hydro -photovoltaic energy storage and clean energy watershed base in future.

The number of long-distance, high drop, pressurized, and self-flowing water pipeline projects is increasing in the northwest region. Most of the pipelines show undulating shapes, and the hydraulic transition process of the entire pipeline system becomes very complex during operation and regulation. When the water hammer protection setting is unreasonable, it will lead to pipe explosion, seriously threatening the safety of people and property. In order to ensure the safe operation of the entire system, the characteristic line method and the HAMMER V8i water hammer analysis software were used to analyze the hydraulic transition process of a long and high drop inverted siphon in a water transmission project. By setting isolation and maintenance valves, submerged energy dissipation valves, and exhaust valves along the pipeline, and setting regulating valves at the end of the pipeline, the positive pressure of the pipeline system is effectively controlled during normal operation and valve closure. By simulating the hydraulic transition process of the pipeline system under different flow rates after pipe explosion, the installation of water hammer protection equipment minimizes the harm caused by pipe explosion. The flow rate of the pipeline system after complete pipe explosion is not continuous. The action time and operation rules of the water hammer protection equipment for long-distance and high drop inverted siphon lines play a crucial role in the safety of the entire system. The research results can provide reference for the similar projects.

Addressing the issues of single model algorithm, low accuracy, and poor generalization in existing shield tunneling speed prediction methods, this study proposes a shield tunneling speed prediction approach to improve prediction accuracy based on Variational Mode Decomposition (VMD), Dung Beetle Optimizer (DBO), and Stacking ensemble learning. Firstly, to obtain more effective data, VMD is applied to decompose and reconstruct the original data to obtain denoised construction parameter data for subsequent model prediction. Secondly, based on the ensemble learning strategy, Support Vector Regression (SVR), Random Forest (RF), and Extreme Gradient Boosting (XGBoost) models are selected as base learners, while Gaussian Process Regression (GPR) is chosen as the meta-learner to construct a Stacking ensemble learning prediction model with higher prediction accuracy and stronger generalization ability. Thirdly, to further enhance prediction accuracy, DBO is employed to optimize the hyperparameters of the ensemble learning model. Finally, this prediction method is applied to the shield tunneling construction of a water diversion tunnel project in Henan Province and compared with other prediction methods. Compared to other single models (SVR, RF, XGBoost), the results indicate that the proposed method achieves higher prediction accuracy, with average accuracy improvements of 7.76%, 6.70%, and 4.97%, respectively, providing a new approach for shield tunneling speed prediction.

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.

The tailwater level of hydropower station is a critical parameter for calculating the unit's output. When influenced by the downstream reservoir's backwater effect, discrepancies often arise between the designed tailwater curve and the actual observed values, leading to increased errors in the output-flow calculations. Utilizing the latest historical observation data, this study explores the relationship between the tailwater level of BHT Hydropower Station, its discharge, and the water level of the downstream XLD Reservoir. A Bayesian optimized long short-term memory (BO-LSTM) prediction model is developed based on multi-scenario analysis. The applied effect is analyzed under conditions of peak load and flood discharge. The results indicate that when the water level of XLD exceeds 585 meters, the tailwater level of BHT Hydropower Station is significantly influenced. Compared to the nonlinear curve fitting method, the BO-LSTM model based multi-scenario analysis demonstrates a substantial improvement in accuracy, with an average absolute error (M_MAE) reduced by 68.1%. The BO-LSTM model more accurately captures the fluctuations and changes in water levels under various operating conditions. The research results have important significant for refined operation of hydropower stations.

Cemented gravel dam construction technique combines the advantages of earth-rock dams and concrete dams, and has broad application prospects. Taking Shaping first-level Hydropower Station as an example, production tests were carried out on C₁₈₀6, C₁₈₀10 and C₁₈₀20 cemented sand and gravel, and core drilling tests were carried out on their compressive strength, splitting tensile strength, permeability and SEM scanning tests at the age of 180 days. The results show that under scientific ratio and reasonable construction technology, the compressive strengths of C₁₈₀6, C₁₈₀10 and C₁₈₀20 have reached 7.1 MPa, 14.8 MPa and 29.4 MPa, respectively. Among them, the impermeability grade of C₁₈₀10 with the largest amount of project consumption is W8. The performance of cemented sand and gravel can achieve the expected results, which verifies the feasibility of the construction plan. Thus, it provides technical support for subsequent construction, and also provides reference examples for subsequent hydropower station construction.

The maximum water head endured by the floor of the high-pressure branch pipe at the Zhongdong Pumped Storage Power Station in Huizhou, Guangdong, is approximately 800 m during operation. The stability of the surrounding rock under this high internal water pressure is critical to the station's safe operation. To address this, in-situ stress and high-pressure water injection tests were conducted. Combined with three-dimensional in-situ stress field inversion, the stress field distribution, permeability characteristics, and hydraulic fracturing resistance of the high-pressure branch pipe area were analyzed, and the layout of bifurcated pipe was optimized. The results indicate that the maximum principal stress in the high-pressure branch pipe section ranges from 15.0 to 16.6 MPa, and the minimum principal stress ranges from 8.4 to 9.7 MPa. The rock permeability ranges from 0.01 to 0.19 Lu, indicating very low to low permeability. The initial high-pressure branch pipe location meets the stability requirements against uplift and seepage. However, within a 7 m range of the branch pipe opening, the class Ⅲ rock mass segment is affected by faults and does not meet the engineering requirements for hydraulic fracturing resistance. Based on a comprehensive analysis of the surrounding rock conditions, uplift resistance, hydraulic fracturing resistance, and seepage resistance, the initial high-pressure branch pipe location was shifted 10 m toward the powerhouse, which meets the stability requirements for uplift, hydraulic fracturing resistance, and seepage resistance.

The Longyangxia and Liujiaxia Reservoirs in the upper reaches of the Yellow River have annual regulation capacity and undertake comprehensive utilization tasks such as flood control, water supply and irrigation, and power generation in the Yellow River Basin. The coordination and consistency of multiple objectives need to be achieved by constructing a multi-objective optimized dispatching system for cascade reservoirs. A multi-objective scheduling model has been established for the Longyangxia-Liujiashan cascaded reservoirs, with the goals of maximizing the peak shaving rate, total power generation, and average sediment flushing ratio. The model is solved using the NSGA-Ⅲ algorithm, and an analysis is conducted regarding the competitive relationships among the objectives of flood control, power generation, and sediment flushing. The established multi-objective optimization scheduling scheme is further evaluated through a developed indicator system, and the TOPSIS method is applied to optimize the set of scheduling solutions. The results show that there is a significant competitive relationship between the objectives of power generation and flood control; No significant competition exists between the objectives of sediment discharge and flood control, and there is some competition between the objectives of sediment discharge and power generation. Through a comparison of the optimal scheme and the actual scheduling data, it can be seen that the benefits of flood control, power generation, and sediment discharge in the optimal scheme increased by 20.89%, 16.02%, and 3.61%, respectively, compared to the actual scheduling.