In stone column composite foundations, the Priebe parameter composite method is commonly used to calculate the equivalent shear strength. Most of the existing studies are based on low stresses in conventional embankments, but few studies have been carried out on the applicability of this method under high stresses. This study focuses on the foundation treatment project for a clay core rock fill dam in Rwanda. The stress-strain behavior, micro-failure mechanism and equivalent shear strength of composite specimens with different area replacement ratio were investigated by simulating large-scale triaxle tests with discrete element numerical methods and compared with the widely used parametric composite method. The results show that the stress-strain characteristics of stone column composite foundations at high stresses differ from those at low stresses, and show a strain hardening trend. Composite specimens show a significant non-linear trend in strength at high stresses. The shear strength parameters calculated by the parameter composite method overestimate the strength of composite foundations, and it is recommended that they be discounted in engineering applications.

In order to study the ion flow field problem near the ground of UHV DC transmission lines, the BPA method is applied to solve the ion flow field of bipolar DC transmission lines. The synthetic electric field is solved combined with the nominal electric field calculated by the successive image method. The ion flow field and synthetic electric field problems of ±800 kV DC transmission lines are calculated using the BPA method, and the effects of erection height, interelectrode distance, and sub-conductor radius on the synthetic electric field of UHV DC transmission lines are analyzed. In addition, the power-line method for solving the synthetic electric field strength is compared. The results show that the BPA method is accurate and effective in solving the ion flow field, and the calculation efficiency is greatly improved. Increasing the height of the wire erection and increasing the radius of the split sub-conductor can improve the electromagnetic environment near the ground, while reducing the interelectrode distance can reduce the synthetic electric field intensity near the ground, but the effect is not obvious.

For the task of condition evaluation of peaking power equipment, the monitoring indicators are defined as a set of time series, among which the complex coupling relationships need to be considered. Besides, requirements on processing time are introduced by the real-time systems. A new framework is proposed to address these problems via the technology of parallel feature extraction as well as data fusion. The time series are statistically analyzed in parallel where multiple hypothesis testing is used to select the important features. Using a defined hierarchical graph convolutional network, the related information is integrated for the final condition evaluation task. Compared with the existing models, experiments indicate that the proposed method with stronger transferability and shorter processing time has a much higher predication accuracy.

In order to study the effect of arrangement of non-submerged rigid vegetation on surface flow hydraulic characteristics, this paper established a hydraulic model based on the actual situation. The average vegetation was evenly arranged as the control group, the vegetation was arranged in a strip pattern and the distance between the strips increased in multiples of 1.333, 1.667, 2.000, 2.333, 2.667, while the distance within the strips decreased by 0.833, 0.667, 0.500, 0.333, 0.167 as the experimental group. The numerical simulation based on Fluent verified the effects of changes in the arrangement of vegetation with the 5% coverage (210 plants) on the water flow and hydraulic characteristics. The results show that the vegetation coverage was the same and the vegetation arrangement tended to be uniform. Under the influence of water depth, the flow rate decreased, and the Reynolds stress in the lower half of the water depth increased. The surface flow rate on the non-submerged vegetation decreased, and the turbulent flow increased. The water energy is consumed to prevent water and reduce the erosion of water surface. This study provides reference for flood control and disaster reduction.

Considering the characteristics of inversion problems of concrete face dam, including high dimensionality, complex calculation and excessive calculation time, the orthogonal experimental design was used to construct the learning sample composed of the combination of permeability coefficient and the water head of pressure measuring point. The nonlinear mapping relationship between the water head at monitoring points and permeability coefficient was established by general regression neural network (GRNN), and the particle swarm optimization (PSO) algorithm was used to search for the smoothing factor σ suitable for the specific project to improve the generalization and convergence speed of the model. The PSO-GRNN model for the inversion of the permeability coefficient of concrete face dam was established, and was applied on an engineering example. The results show that the value of permeability coefficient obtained by inversion based on the model is reasonable, and the maximum relative error between the calculated value of water head at monitoring points obtained by seepage analysis and the measured value is 3.64%, and the accuracy meets the needs of engineering.

The outlet area of river diversion tunnel in mountainous areas is characterized by large discharge per unit width, high flow velocity, deep riverbed overburden and low anti-scour flow velocity. The river bed at the outlet of the tunnel is severely scoured, and it is difficult to arrange large energy dissipation measures, so it is proposed to arrange small flip bucket at the outlet of the tunnel. On the basis of the physical model test to observe the flow pattern and river bed scouring in the outlet area of the tunnel, the numerical simulation method is used to calculate the flow field in the outlet area of the diversion tunnel, and the internal mechanism of the improvement of river bed scouring is studied. The results show that after adding a small flip bucket at the outlet of the diversion tunnel can increase the lateral diffusion of the high-speed flow out of the tunnel, reduce the unit width discharge, increase the local energy dissipation rate, and thus reduce the velocity of the river bank. The proportion of water body coming out of the tunnel with high flow velocity is obviously reduced, which makes the high-speed water flow unable to directly rush the landslide mass on the right bank, and reduces the flow velocity within the landslide mass. The width of water flow out of the tunnel increases, and the direction of water flow out of the tunnel becomes inclined upward, which weakens the impact on the landslide on the right bank, and significantly weakens the scouring of the outlet area of the diversion tunnel and the downstream river channel. This study can provide reference for engineering practice.

The mechanical properties of hydraulic concrete are an important influencing factor on the stability of hydraulic engineering facilities. A fine damage intrinsic structure model considering the effect of sulfate corrosion was established using a combination of mechanical experiments and theoretical analysis. The results show that the effect of sulfate corrosion increases the damage degree of concrete after damage. With the increase of corrosion time, the compressive strength and elastic modulus show an exponential decay law, and the damage strain tends to increase. From the experimental results, the shape parameter m and scale parameter ε₀ are calculated. Besides, the mesoscopic damage constitutive model considering the effect of sulfate corrosion is solved. The reasonableness of the theoretical model for predicting the stress-strain relationship was verified by experimental data. The constitutive model proposed in this study can provide scientific reference for the accurate prediction of mechanical behavior of hydraulic concrete.

To address the problem of excess pressure in the water supply network system, this paper proposed to add pressure reducing valves with certain opening in the system. With the objective of minimizing the square sum of the difference between the operating water pressure at each node and the minimum service head at the node, the pressure reduction valve optimized pressure leakage control model was constructed by combining the hydraulic balance constraint of the pipe network and the node pressure constraint. The model was solved by using genetic algorithm. Taking the main water supply network of southeast coastal towns as an example, the results show that the proposed model and method are effective and feasible, and it has better optimal effectiveness.

For the value of the surrounding rock parameters of the underground construction, a hybrid network approach combining backtracking search optimization algorithm (BSA) and BP neural network (BSA-BP) was proposed for the inversion study of the tunnel surrounding rock parameters. By establishing a tunnel finite element excavation model, the inversion parameters were used to calculate the displacement of the monitoring section and compare with the measured values in the field. Finally, the stability of the surrounding rock was analyzed and predicted. Compared with the GA-BP neural network, the results show that the BP neural network optimized by BSA algorithm has faster inversion speed and computational efficiency. The relative errors between the calculated displacement values and the field measured values obtained by using BSA-BP neural network inversion parameters are within 5%, indicating that the model has high inversion accuracy and is reasonable and feasible. The research results provide a new method for the inversion of underground engineering parameters.

In order to realize the timing matching and disposing of the main alarm information of the hydropower centralized control platform, a timing matching and disposing model was constructed based on Python data visualization. The model transmitted the fault information of hydropower station equipment to the receiver of hydropower centralized control platform by using communication server, and the fault alarm sequence was generated. After matching the main fault alarm information according to the time sequence matching rules in the domain expert matching rule base, state function and state change function were used to analyze the time sequence characteristics. Then the mapping table was used to describe the timing characteristics of the main alarm information. After analyzing the timing characteristics of the main alarm information, the timing matching and disposal of the main alarm information in the hydropower centralized control platform were realized. Experimental results show that the model has good visualization ability of main alarm information, and the coverage rate of main alarm information crawled by the model is up to 98.99%. The model has higher Pearson correlation coefficient and better ability to deal with the main alarm information.