To accurately calculate the effective range of vibration rods in reinforced concrete, this study proposes a new prediction model for the effective radius of vibration rods based on the rheological theory of fresh concrete. Under the assumptions that the vibrated concrete exhibits pseudoplastic fluid characteristics and the flow in the steel mesh is equivalent to seepage through porous media, an expression for the effective radius of the vibration rod in reinforced concrete is derived. The empirical parameters in the expression are then empirically fitted in conjunction with experimental results. By comparing the model calculations with experimental values, it shows that, except for a few cases with low vibration intensity of the rod, the overall prediction accuracy of the effective radius prediction model is higher. Considering that the vibration intensity of the rods used in concrete pouring at construction sites is generally high, the model can effectively guide on-site compaction operations and quantitatively evaluate the impact of steel bar layout schemes on the vibration range of the rod.

In order to further improve the control performance of water turbine, a control strategy based on the adaptive chaotic particle swarm optimization variable domain fuzzy PID (CAS-PSO-VFPID) was proposed for the turbine regulation system. Firstly, a model of nonlinear hydraulic turbine regulation system was established, and a variable domain fuzzy controller was constructed according to the system model. Then, the adaptive chaotic particle swarm optimization (CAS-PSO) was used to optimize and design the variable domain fuzzy controller, and the CAS-PSO-VFPID controller was obtained. Finally, the applicability of the nonlinear turbine regulation system model was verified by simulation. The multiple control strategies under different working conditions were compared and simulated with Whale Algorithm Optimization Variation Domain Fuzzy PID (WOA-VFPID), Standard Particle Swarm Optimization PID (PSO-PID), Standard Particle Swarm Optimization Optimization Variable Domain Fuzzy PID (PSO-VFPID). The simulation results show that the system convergence speed and optimization ability of the CAS-PSO-VFPID control strategy are fast, which can effectively improve the response speed and accuracy of the hydraulic turbine regulation system, and make the system have better dynamic stability.

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.

The long-term prediction of concrete dam deformation is an important requirement for maintaining its structural integrity during actual operation. To improve the accuracy of long-term deformation prediction of concrete, a long-term dam deformation prediction model based on multi-layer perceptron (MLP) and ecoder-decoder (Ecoder-Decoder) architecture, MLP-Ecoder-Decoder (MED), was constructed. This model captured the long-term dependence of dam deformation and environmental loads through a deep auto-correlation (Deep-Auto-Correlation) mechanism, and used time series decomposition and deep auto-correlation mechanism for multi-step deformation prediction. The model was used to predict the deformation of a 250 m height arch dam in Qinghai Province under complex environmental conditions. The results show that the MED model effectively improves the prediction accuracy and has a strong advantage in extracting long-term time features.

It is the key issues of reasonably and accurately predicting the thrust and torque of tunnel boring machines (TBM) to realize the intelligent control of TBMs. This paper proposes a two-stage prediction method of knowledge-data-driven spatio-temporal stacked convolutional network (KD-NTS-GAT). Firstly, based on expert knowledge and the NTS-NOTEARS method, a new information fusion technique is proposed. The discrete expert experience and the continuous NTS-NOTEARS indicators is mapped and smoothly fused through clustering. The causal relationships among the key operating parameters of the TBM is quantitatively extracted to improve the authenticity of the causal relationships significantly. Then, causality is further combined as a prior knowledge with stacked convolutional network deep learning model for predicting thrust and torque of TBM. Taking the bid Ⅳ of Xinjiang Water Conveyance Tunnel Project as an example, a comparative analysis of the KD-NTS-GAT method and the pure data-driven method shows that the KD-NTS-GAT has better prediction capability on thrust and torque. The conclusions can provide a reference for the intelligent control of TBM construction.

Aqueducts are common water conveyance structures in water diversion projects, and accurate prediction of aqueduct deformation is crucial for ensuring the stable operation of water conservancy projects. For this purpose, taking the Liaohe Aqueduct in the South-to-North Water Diversion Project as an example, five different linear additive models, namely elastic net regression, multiple linear regression, stepwise regression, ridge regression and LASSO regression, were established based on the long-term deformation monitoring data of the aqueduct. The prediction results of the aqueduct's deformation behavior by the five different linear additive models were compared. The results indicate that as the prediction time increases, the prediction accuracy of different linear additive models gradually decreases. The LASSO model selects the optimal regularization parameter through cross-validation, achieving variable selection simplification and minimizing model complexity. Additionally, it is verified that the training length affects the prediction performance of multiple linear regression and stepwise regression. The findings of this study provide valuable references for selecting prediction model of aqueduct deformation.

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.

Perforated structures are commonly found in engineering applications, where stress concentration around holes significantly affects structural load-bearing capacity and safety. Therefore, accurate mechanical analysis of perforated structures is essential. However, the traditional finite element analysis methods face several challenges, including complex mesh generation, high computational resource requirements, and poor convergence when dealing with perforated structures. To overcome these challenges, a finite element analysis method based on the superposition principle is proposed. This approach replaces the original model with a simplified equivalent model featuring a less complex mesh for simulation. The stress field of sub-models is subsequently employed to correct the stress distribution around holes in the equivalent model. Comparative results indicate that the discrepancy between the equivalent model and the original model is within 5%. The equivalent model achieves a 25.2% reduction in mesh elements and a 75.1% reduction in simulation time, substantially improving simulation efficiency.

In order to solve the problem that the response ability of a large mixed-flow hydropower unit is affected by the change of operating water head, a method based on the adaptive feedforward function is proposed for the application of the turbine's primary frequency regulation function. The 3D curve function library of turbine's water head, guide vane opening and power is introduced into the primary frequency regulation feedforward control of the turbine. According to the real-time water head and power and the position of guide vane, the power change corresponding to the frequency change is calculated through the 3D database to directly affect the output of the controller. Compared with the conventional control mode of primary frequency regulation, it is verified by field practice. The experimental results show that the feedforward function can not only satisfy the consistency of the next frequency regulation performance of different water heads, but also effectively avoid setting and switching multi-group PID control parameters. Thus, it provides a new way to realize the function of primary frequency regulation of hydraulic turbine units.

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.