In order to meet the demand for segment floating prediction in shield construction and the problem of insufficient training data for deep learning models, a set of shield segment floating prediction model was proposed by combining the tunneling mechanism of the shield machine with the process of segment floating.The numerical simulation software was used to simulate the process of segment floating of the shield structure, and using the large amount of numerical simulation data and the engineering field data for the deep learning training, so as to realize the data enhancement of the segment floating prediction model. The prediction model consists of the tube sheet floating process. The prediction model consists of a segment floating prediction model and two auxiliary models, which consider the interaction of active control and passive response parameters. Finally, a typical case study was carried out based on the shield section of the Beijing East 6th Ring Road Rehabilitation Project, and the results show that the prediction accuracy of the model is controlled within 4 mm, which meets the project requirements. The grouting parameters of the shield tail have the greatest influence on the model performance, followed by the digging parameters, and the shield attitude parameters have the smallest influence. Moreover, the training data of the segment floating based on the numerical simulation data can improve the prediction accuracy of the prediction model by 30%, which proves the effectiveness of the data enhancement method. The effectiveness of the data enhancement method is demonstrated. The data enhancement method based on numerical simulation data proposed in the article provides a new idea for the training and optimization of similar deep learning models.

FDS(flocculation-dehydration-solidification) coupling process has been proven to significantly enhance the efficiency of resource conversion in engineering waste soil. However, the material fate within the process and the advantages of recycling press-filter filtrate remain to be further investigated. FDS experiments were conducted to analyze the material fate of each component in the flocculant-solidifying agent during the FDS process. Based on the findings, the potential benefits of recycling press-filter filtrate were explored. The results reveal that approximately 18% to 35.57% of Na⁺ and 0.1% to 0.56% of Si elements are detected in the press-filter filtrate, whereas Ca, Mg, and Al elements primarily remained in the filter cake, with proportions close to or equal to 100%. The recycling of highly alkaline press-filter filtrate into the process is found to not only improve the dissociation efficiency of mud and sand but also serve as a “pretreatment” for subsequent FDS stages. Waste soil particles are observed to adsorb residual materials from the filtrate, enabling dynamic adjustments in material dosage according to material transformation patterns and filter cake performance requirements. This approach ensures that materials lost in the filtrate are continuously recycled and utilized, maintaining a dynamic circular process.

In order to guarantee the safety of UAV operation in low-altitude airspace and promote the rapid development of low-altitude economy, a detection method and resolution strategy for multi-UAV flight conflicts are constructed. Firstly, based on ADS-B(automatic dependent surveillance-broadcast) flight data, an improved FR-IMMCKF(fuzzy reasoning interactive multiple model cubature Kalman filter) algorithm was used to predict the UAV trajectory, and secondly, based on the relative motion status between UAVs, a preliminary screening of the conflict aircraft was carried out, and based on the velocity obstacle method, the vertical detection part was added so as to support the three-dimensional range of conflict detection, and then, the conflict coefficient was introduced as the weight in the flight conflict network, and the conflict status was proposed as the conflict status. Then, the conflict coefficients were introduced as the weights in the flight conflict network, and the conflict state SSM(space model) was proposed to visualize the resolution intervals, and finally, the resolution strategies of height adjustment, heading adjustment and speed adjustment were set up, and the optional resolution intervals of heading and speed were introduced. A low-altitude airspace five UAV flight conflict scenario was constructed for simulation and validation, and the results show that the proposed method is able to give a conflict resolution order and provide a feasible resolution strategy in a complex flight situation.

The catenary system, which is regarded as a critical component of the high-speed rail traction power supply system, is deemed essential for the normal operation of high-speed trains. It has been demonstrated by previous earthquake disasters that the catenary system is susceptible to varying degrees of damage under seismic effects. The seismic research progress of the catenary system was systematically reviewed from four aspects: the dynamics modeling and inherent dynamic characteristics of the catenary system, the seismic damage characteristics and common types of failures, the seismic response of the catenary system and its influencing factors, and an overview of the current state of earthquake resistance research, which includes a comparative analysis of the seismic design standards and regulations for catenary systems in different countries and regions. By summarizing the relevant research, prospects for future research directions are provided.

In order to explore the influence of environmental lighting on visual fatigue during safety sign identification, 12 mixed lighting environments were designed with illuminance and color temperature as environmental variables. Eye movement data were tested under different conditions by eye-tracking technology and a two-factor ANOVA was conducted.Combined with the test results of reaction speed and comfort, the changes of visual fatigue under different lighting conditions were analyzed.The experimental results show that the change of illumination has a significant effect on visual fatigue, subjects are more prone to visual fatigue in low illumination environment; The change of color temperature has little effect on visual fatigue, but in different mixed lighting environments, the change of color temperature will affect the visual comfort of the subjects, and the fatigue state will change accordingly.The suitable color temperature range for the lighting conditions in the working environment is 2 100~3 500 K, and the illuminance range is 550~900 lx. It is concluded that companies should pay attention to improving the lighting conditions during production, avoid reducing the recognition efficiency of safety signs by workers due to visual fatigue, and ensure the safety and health of workers.

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.

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.

The decomposition of submerged plants such as Potamogeton crispus releases a large amount of nutrients into the water body, which has a negative impact on aquatic ecosystems. To investigate the slowing effect of filter feeding benthic animal, the Hyriopsis cumingii, on the deterioration of water quality after the decomposition of submerged plants, a 45 day experimental chamber simulation experiment was conducted from May to July 2023 using different specifications and densities of clams and seagrass combinations to monitor changes in water quality indicators and phytoplankton community structure. It was found that filtration through the Hyriopsis cumingii can reduce the total nitrogen (TN), total phosphorus (TP), chemical oxygen demand (COD), and chlorophyll a (CHLA) in the water to a certain extent. To investigate the density effect and size effect of its Hyriopsis cumingii, low, medium, and high-density triangular sail clams were released. The experimental results show that different sizes and densities of Hyriopsis cumingii can significantly control the biomass of phytoplankton, and Hyriopsis cumingii have a significant impact on the community structure of phytoplankton. Among them, releasing small-sized (shell length 4 cm) low-density (1 g/L) Hyriopsis cumingii has the best effect on improving water quality and controlling phytoplankton biomass.

Although the multi-task convolutional neural networks (MTCNN) face detection algorithm has achieved good results in some face recognition tasks, the accuracy of face detection needs to be improved in the face of some complex small-scale and multi-person face detection tasks. An improved MTCNN algorithm was proposed. Firstly, the intersection over union (IoU) threshold parameter was fine-tuned when creating the data set to classify face samples more accurately. Secondly, replacing the max pooling layer of the network with convolutional layers can improve network performance. Finally, the squeeze-excitation(SE) attention mechanism was introduced into the O-Net network to improve the feature expression ability of the network. The test results show that compared with the original MTCNN algorithm, the detection accuracy of the P-Net network and R-Net network of the improved algorithm has increased by 1%, and the detection accuracy of the O-Net network has increased by 0.5%. Moreover, the improved algorithm performs better in the actual face detection task.

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.