Seepage analysis is the key research content of dam safety and stability, and it is of great significance for dam disaster risk control by constructing a high-precision prediction model of seepage quantity for earth-rock dam. In order to further improve the seepage prediction capability of earth-rock dam, a prediction model combining long short-term memory neural(LSTM) networks, convolutional neural(CNN) networks, and attention mechanism (Attention) was proposed. Firstly, CNN was used to mine the deep features of the data, then the time series features of the seepage flow monitoring data was extracted through LSTM, and finally the attention mechanism to the pooling layer and the fully connected layer was added to determine the importance of different time features and assign weights. Through the application analysis of engineering examples, compared with CNN, LSTM and CNN-LSTM models, the CNN-LSTM-Attention model has better prediction effect, and its coefficient of determination R² is as high as more than 0.98, and it can capture the spatial characteristics and temporal dependence of seepage data at the same time, which shows strong reliability and stability in the prediction of seepage flow of earth-rock dam.

In order to solve the defects of poor water stability and easy disintegration of red clay, industrial solid waste [fly ash (FA), phosphogypsum] combined with cement (C) was used to improve red clay. The mechanical properties, water stability and micro-mechanisms of the industrial solid waste-cement amended red clay were investigated through indoor tests. The results show that the strength of the improved soil shows a trend of increasing and then decreasing with the increase of the ratio (R) of phosphogypsum replacing fly ash.When the cement doping is 7% and R=7%, the maximum dry density of improved soil increases by 2.6%, the 7-day unconfined compressive strength (UCS) increases by 11%, and the 28-day UCS increases by 57%, which meets the bearing standard of subgrade for road use. There is no obvious change in the water-filled specimen after the 7-day maintenance, the resistance to disintegration is enhanced, and the water stability coefficient of the specimen reaches 92.9% in the 28-day maintenance. The water stability coefficient of the specimen reaches 92.9%, and the water stability coefficient increases 1.61 times. The microscopic analysis shows that the replacement of fly ash by phosphogypsum promoted the generation of new hydration products of ettringite and calcium-silicate-hydrate (C-S-H), which transforms the soil body from fragmented granular to a denser gel network structure, enhances the bonding between the red clay particles, and fills up the pore space at the same time. The results verifies the feasibility of industrial solid waste-cement-amended red clay as roadbed fill, provides a solid theoretical foundation and basis for engineering practice, and broadened the reuse of industrial solid waste.

The alignment monitoring of steel arch bridges constitutes an essential component of bridge health monitoring systems. Three-dimensional laser scanning technology was utilized, and the traditional density-based spatial clustering of applications with noise(DBSCAN) algorithm was improved by integrating the random sample consensus(RANSAC) algorithm to extract the alignment of steel arch bridge ribs. Three-dimensional laser point cloud data, characterized by its comprehensiveness and detailed representation, is capable of fully presenting the structural shape and deformation information of the bridge. The RANSAC-integrated improved DBSCAN algorithm, constrained by the structural features of the steel arch bridge, effectively achieves the removal of discrete points as well as point clouds from the bridge deck, cross bracing, lateral connections, and web members. Point clouds extracted using the RANSAC-integrated improved DBSCAN algorithm are fitted to identify key points, and a comparison is made with results obtained manually. The extraction errors for the key points of the arch ribs are all within the millimeter range, with the maximum error being 9.2 mm and the minimum error being 0.1 mm. This extraction method is demonstrated to more accurately and effectively accomplish the alignment extraction of steel arch bridges, achieving millimeter-level precision in alignment extraction. It significantly reduces labor and time costs, provides better robustness for the complex structures of steel arch bridges, and adapts well to practical production demands.

The cathode flow channel of proton exchange membrane fuel cell (PEMFC) serves as the site of oxidant reduction, and the interaction of the flow channel configuration and operating parameters is one of the keys to enhance the performance of the cell. A three-dimensional proton exchange membrane cell model with cathode sidewall shrinkage runner was established to meet the design requirements of PEMFC sidewall shrinkage runner regarding the operating parameters. The changing rules of electrochemical performance, temperature distribution on the membrane surface and water content distribution were investigated under different temperatures, pressures and cathode stoichiometric ratios. It is shown that under the constant operating parameters, the current density curves of the ridge centerline and the flow channel centerline are impulsively fluctuated, and the temperature curves of the membrane surface and the water content curves of the membrane surface are regularly fluctuated. The current density, temperature and water content at the ridge centerline are obviously higher than those at the flow channel centerline. Under the change of operating parameters, when the pressure is increased from 0.1 MPa to 0.3 MPa, the current density is increased from 0.860 A/cm² to 1.500 A/cm², with an increase of 74.4%. When the temperature is increased from 50 ℃ to 80 ℃, the current density is increased from 0.822 A/cm² to 0.856 A/cm², with an increase of 4.1%. And when the cathode stoichiometry ratio is increased from 10 to 90, the current density is increased from 1.502 A/cm² to 1.568 A/cm², with an increase of 4.4%. Furthermore, a PEMFC output performance evaluation method based on the combined assignment method and the improved radar diagram method has been established. The cathode sidewall-retracted proton exchange membrane fuel cell is shown to exhibit excellent output performance under the operating parameters of 0.25~0.3 MPa, 70~80 ℃ and the stoichiometric ratio in the range of 70~90.

The previous structural seismic vulnerability analysis is generally based on the characteristics of the structure itself, it is rare to combine with the differences of engineering sites in the study area, the location differences of different engineering sites within the city are ignored. Taking Chengdu City as the research area and the three-story reinforced concrete frame structure as an example, an analysis method for the seismic vulnerability of reinforced concrete frame structures based on peak ground acceleration (PGA) and the maximum inter-story displacement angle θ_max of the structure was proposed. For three-story reinforced concrete frame structures, this method conducts dynamic time-history analysis using the interlayer shear model to obtain θ_max under each seismic response. Then, logarithmic linear fitting is performed on θ_max and its corresponding ground motion to obtain the relationship between the two. For the Chengdu area, this method takes the historical ground motion data of Chengdu as the data basis and combines PGA calculation formula to obtain the PGA of each engineering site location in Chengdu. Furthermore, taking the maximum inter-layer displacement angle as the structural damage index and PGA as the ground motion intensity index, the highest structural failure probabilities of the structure under four different performance levels of full operation, basic operation, life safety and near collapse were studied, which were 94.1%, 89.1%, 74.7% and 40.8% respectively. Moreover, the overall changing trend of the structural failure probability at each performance level of the structure decreases from the west to the east. Therefore, the seismic construction requirements for structures in the western region can be appropriately strengthened, and those for structures in the eastern region can be appropriately relaxed, so as to save economic costs. The proposed method has certain application value in reducing the losses caused by earthquakes and provides a certain theoretical basis for the seismic design of building structures.

Traditional robot localization and navigation methods in complex building environments are characterized by low accuracy, heavy reliance on sensors, and an inability to effectively address dynamic obstacles, making it challenging to achieve satisfactory results in practical applications. To address these issues, building information modeling (BIM) technology was introduced. BIM, with its geometric and semantic information, was utilized to assist robot localization and navigation in complex environments. More accurate environmental perception and optimal path planning were provided to robots, reducing the risk of collisions with environmental components and improving the accuracy and efficiency of task execution. The current status of BIM technology in robot localization, mapping, and path planning was compared, the advantages and challenges of its application in architectural environments were analyzed, and future prospects for its application in intelligent buildings and robotic intelligence were explored.

As the trend toward more-electric and all-electric aircraft accelerates, multi-electric engines have become a key technology. A coaxial high-torque permanent magnet synchronous motor based on a new type of rotary cylinder disc engine was designed to achieve the integration of the engine and the motor. Firstly, based on the relationship between the engine performance parameters and the drive shaft, the coaxial structure and motor dimensions were determined. Secondly, the inhibitory effect of the number and size of the flat wire winding layers on copper loss was analyzed through the finite element soft analysis. Meanwhile, the motor topology was optimized by using the rotor segmented inclined pole, auxiliary slot and Taguchi algorithm to reduce the cog-slot torque, rated torque ripple, stator iron loss and air-gap magnetic flux density distortion rate of the motor, significantly improving the electromagnetic performance. Finally, the various working conditions of the motor were simulated, the driving and power generation efficiencies were calculated, and it is verified that the motor does not demagnetize under the condition of large current. Results indicate the motor delivers 200 N·m of torque at a rated speed of 6 000 r/min, with a peak torque of 400 N·m and a maximum power output of 250 kW in high-speed generation mode, meeting all design specifications.

Forest fires pose a significant threat to human lives and property. Accurate prediction of forest fire risk is crucial for disaster mitigation and prevention. Influenced by factors such as terrain, meteorology, vegetation cover, and human activities, the causes of forest fires exhibit regional differences. This study uses historical forest fire events in Muli County, Sichuan Province as the response variable, with terrain, meteorological data, vegetation cover, and human activity data as explanatory variables. Leveraging CatBoost's strengths in handling high-dimensional sparse data and classification problems, a high-precision forest fire prediction model was constructed based on CatBoost. The experimental results indicate that, compared to random forest (RF), extreme gradient boosting(XGBoost), and gradient boosting decision trees(GBDT) models, the CatBoost model achieves higher modeling accuracy and significantly improves forest fire prediction accuracy, with a prediction accuracy rate of 91.36% and an area under curve(AUC) value of 0.970. Predictions made using this model can provide valuable references for the early prevention of forest fires in Muli County.

To mitigate the impact of highway accidents on traffic capacity and driving safety, a coordinated control strategy was proposed involving both service areas and toll stations. Firstly, the proposed coordinated control strategy was described in detail. Secondly, to simulate traffic flow more accurately under highway accident scenarios, the cellular automata model was enhanced by introducing different random deceleration probabilities, acceleration/deceleration rates, and lane-changing conditions for different vehicle types. Finally, the effectiveness of the proposed control strategy was validated through simulation. The results indicate that, compared to scenarios without control measures, implementing service area control can reduce average vehicle delay, fuel consumption, and cumulative carbon emissions by 62.90%, 69.50%, and 69.50% respectively. Moreover, using the coordinated control strategy of service areas and toll stations can further reduce these metrics by 55.76%, 59.58%, and 59.58% respectively. Precise control measures can significantly reduce the impact of accidents.

The weak fault characteristics and the presence of numerous harmonic signals in distribution networks with renewable energy sources reduce the effectiveness of traditional fault diagnosis methods. A fault diagnosis method based on an improved graph neural network was proposed. Wavelet transform was applied to extract the detail coefficients of current and voltage before and after faults. Weighted projection correlation analysis was performed to calculate the correlation between electrical quantities. Highly correlated quantities were selected as inputs to construct the fault diagnosis model using a graph neural network. Fault simulation models for different voltage levels were developed in MATLAB/Simulink. The results indicate that fault signals are effectively enhanced, and faults are accurately located and classified in distribution networks with renewable energy sources at different voltage levels. Good diagnostic performance is maintained in the presence of data loss and noise, demonstrating strong robustness and generalization.