The classical earth pressure theory only calculates the ultimate earth pressure in translational mode of the retaining wall. In order to get closer to the real engineering situation, it is necessary to develop the theory of non-ultimate earth pressure of the cohesive fill behind the wall under arbitrary load action and arbitrary displacement mode. The concept of equivalent uniform load was adopted to convert any type of load acting on the earth filling surface into uniform load. Based on the classical earth pressure theory, the rigid retaining wall was regarded as a combination of an ideal rigid plastic body and a series of springs, and the soil stiffness coefficient of the clay was obtained, which improved the Coulomb earth pressure theory and derives the general calculation method of the non-ultimate passive earth pressure of the soil in any displacement mode that can consider the arbitrary load.The research results indicate that the distribution and magnitude of passive soil pressure, the position of the resultant force point are closely related to the displacement mode of the retaining wall, the magnitude of cohesive force, and the load acting on the fill surface. When the equivalent uniform load and cohesive force are both 0, this method degenerates into Coulomb soil pressure theory.The model validation results show that the theoretical calculated values are in good agreement with the experimental measured values, proving that this calculation method has certain theoretical significance for calculating the passive soil pressure of cohesive soil under non limit states, and also has practical value for engineering practice.

SPH (smooth particle hydrodynamics) and MoorDyn numerical model were used to investigate systematically the dynamic behavior of moored floating bodies on stepped seabed. Based on the comparison with the experimental data, the influence of wave height and step height on the motion response and mooring force of moored box floating body on stepped seabed was discussed in detail. The results show that the motion response of the two floating bodies increases with the increase of wave height. The mooring power of the other three cables also show an increasing trend except for L3. As the height of the ladder continues to rise, the two floating bodies above and below the seabed ladder appear different change rules. In addition, it is found that the relationship between L2 mooring and L0 and L1 is about 3 times, and the relationship between L2 mooring and L3 is about 2 times.

To achieve rapid and reliable prediction of asphalt mixture performance, a method for predicting asphalt mixture performance by optimizing the back propagation (BP) neural network with a genetic algorithm (GA) from the perspective of material composition design was proposed. Initially, a grey relational analysis method was employed to reduce the dimensionality of multidimensional input variables, identifying the core influencing factors of asphalt mixture performance. Subsequently, integrating the GA, a GA-BP neural network prediction model was constructed with the core influencing factors as the input layer and asphalt mixture performance as the output layer. The model underwent training, validation analysis, and prediction generalization application. A comparison with the training effectiveness and prediction accuracy of the BP neural network was conducted to verify the accuracy of the GA-BP neural network model. The research results indicate that the grey relational degrees of eight performance characteristics, including air void, asphalt-aggregate ratio, nominal maximum aggregate size, 4.75 mm passing rate, asphalt type, softening point, penetration, and ductility, are all greater than 0.6, signifying their significant impact on asphalt mixture performance. Compared to the BP neural network model, the GA-BP neural network model reduces the root mean square error (RMSE) by 16% to 31%, decreases the mean absolute error (MAE) by 15% to 24%, and improves the R² value by 0.01 to 0.27, indicating that it has better learning and fitting capabilities. The prediction accuracy for dynamic modulus, dynamic stability, residual stability, splitting tensile strength ratio, and ultimate bending strain of the asphalt mixture is respectively enhanced by 35.26%, 47.78%, 23.13%, 31.92%, and 35.75%, revealing the superior generalization application capability of the GA-BP neural network model. The research findings provide essential references for the rapid prediction of asphalt mixture performance and guidance in the design of asphalt mixture material composition.

The dynamic modulus of asphalt mixture is an important parameter in the design of asphalt pavement. Extracting material characteristics, dynamic modulus, and phase angle information from a large amount of asphalt concrete datasets using integrated methods is of great significance for optimizing the performance of asphalt pavement. The extreme gradient boost (XGBoost) model aggregated a series of decision tree models through weighted summation to construct a powerful prediction model, while optimizing the loss function to minimize prediction errors. In order to further improve the accuracy of dynamic modulus and phase angle prediction, heuristic algorithms were used to optimize the model. Initially, the basic model was initialized based on samples and the gradient of the loss function of the training data was calculated. Subsequently, XGBoost utilized gradient details to construct a decision tree model, optimized leaf node weights, and updated the model’s predictions through weighted summation. During this process, heuristic algorithms are used to optimize the optimal parameters of the entire XGBoost model. The experimental results show that the improved XGBoost model outperforms the original model in all performance evaluation indicators, improving the accuracy of predicting the dynamic modulus and phase angle of asphalt mixtures.

In ultra-long-distance rock micro-shield pipe jacking, pipe wall friction is a key factor in determining the size of the jacking force and the arrangement of the intermediate jacking station, and the selection of a good bentonite mud is a key factor in controlling the pipe wall friction. In order to give full play to the polymer bentonite mud in the pipe jacking construction process of lubrication and support, the Chongqing Guanjingkou water conservancy hub project was used as the basis, through orthogonal tests and indoor straight shear test method on the basis of different types of bentonite mud ratio optimization, based on the performance parameters of the slurry, as well as the friction reduction mechanism to derive the optimal type of mud and its ratio. The results show these as follow. Sodium bentonite mud is the optimal type of mud, the ratio of bentonite to water is 14%, guar gum is 0.20%, soda ash is 0.40%, CMC(carboxymethyl cellulose) is 0.40%, PAM(polyacrylamide) is 0.10%, and its friction reduction effect is obvious, it can make the friction coefficient between concrete and gray rock reduced by 35%, which makes the friction between the pipe and the rock greatly reduced in the process of pipe jacking in the rock.

In order to reasonably and efficiently carry out the risk assessment of road tunnel construction collapse, the risk assessment model of road tunnel construction collapse was studied by rough set (RS), grid search method(GS) and support vector classification (SVC). Firstly, the index system of highway tunnel construction collapse risk evaluation was constructed by integrating advanced geological prediction. At the same time, the information of 100 tunnel collapse cases was collected and the index data was discretized. Secondly, attribute reduction was conducted based on the condition information entropy of rough set to obtain the reduced core index set. Then, grid search method was used to find the optimal parameters of the support vector classification training set, the risk assessment model of highway tunnel construction collapse based on rough set-grid search-support vector classification (RS-GS-SVC) was established. Finally, the model was used to predict the test samples. The results show that under the condition of the same learning sample, compared with rough set-genetic algorithm-support vector classification (RS-GA-SVC) model and Rough set-particle swarm optimization-support vector classification (RS-PSO-SVC) model, RS-GS-SVC model has higher classification accuracy; Under the same proportion of training set and test set, the prediction accuracy of RS-GS-SVC model is higher than that of GS-SVC model, with the accuracy rates of 93.33% and 90% respectively, and the operation time of RS-GS-SVC model is shorter. It can clearly be seen that the model complexity is effectively reduced and the classification accuracy is improved through the reduction of rough set conditional information entropy attributes.

Land use and traffic safety is a hot topic of mutual concern in the fields of urban geography and transportation. However, existing research on the impact of land use on pedestrian traffic accidents often incorporates a unified framework of built environment and mainly adopts measures such as land use mix or the proportion of land use types, lacking detailed analysis on land use types, thus making it difficult to translate their findings into actionable design strategies. Taking Yuzhong District, Chongqing City as an example, the land use types were finely characterized based on point of interest (POI) data, and the extreme gradient boosting (XGBoost) model was applied to explore the nonlinear relationships between land use types, pedestrian, road conditions, road environment, and the severity of pedestrian traffic accidents. The study finds these as follows. ①Land use types play an important role in the severity of pedestrian traffic accidents, with hospitals, residential areas, and educational land being the most influential. The presence of hospitals, residential areas, and educational land within a 300-meter buffer zone around accident sites reduces the severity of pedestrian traffic accidents. ②Road sections with curved and sloped road alignments are high-risk areas for severe pedestrian traffic accidents; entrances and exits of road sections, narrow road sections, and intersections have a mitigating effect on the severity of pedestrian traffic accidents. The findings provide policy insights for refined land use planning and governance to reduce the severity of pedestrian traffic accidents.

The narrow passageways and limited visibility in large underground parking lots often lead to vehicle collisions at blind intersections, posing significant dangers. In order analyze approach behavior at these intersections, a driving simulation experiment was designed. The experiment involved constructing a 3D model of the underground parking lot and integrating it with a connected vehicle warning information system. Four experimental scenarios were devised, considering variations in technical features (with or without warning information) and events (with or without vehicle conflicts at blind intersections). Using micro-driving behavior data from 31 participants, key metrics such as speed, acceleration, and braking position were selected to analyze approach behavior from the warning point to the blind intersection. Subsequently, correlation analysis was conducted, followed by the application of the k-means method to cluster driver types and examine the effects of warning information on the approach behavior of different drivers. Finally, the utility of the system was evaluated. The results indicate the following. ①When drivers approached blind intersections without warning information, the process typically involved three stages: speed maintenance, speed increase, and emergency braking. In contrast, when warning information was provided, speed decreased more uniformly and was 34.08% higher than without warning information. ②The warning information system reduced the average speed by 9.94 km/h compared to scenarios without warnings, and advanced the braking position by 4.49 meters, thereby effectively enhancing the safety of drivers passing through blind intersections in parking lots. ③The warning information system had discernible effects on conservative and general drivers, suggesting the need for additional training for radical drivers to help them understand the positive role of the warning system in improving driving efficiency and promoting safe driving practices. ④The warning information system significantly improved overall driver safety, with the greatest impact observed among conservative drivers, followed by ordinary and aggressive drivers. These research findings provide support for the application of connected vehicle warning information systems in parking lots and contribute to the enhancement of parking lot safety.

A comprehensive joint optimization solution was proposed to address the issue of traditional UAV(unmanned aerial vehicle)-assisted wireless sensor network data collection schemes, where only UAV energy consumption was optimized, while wireless sensor energy consumption is neglected. Firstly, clustering analysis was performed using the K-means algorithm and communication threshold between UAVs and wireless sensors to achieve effective clustering of wireless sensors. Secondly, a multi-objective optimization model was constructed to collaboratively optimize sensor energy consumption and UAV hovering energy consumption. The optimal UAV hovering position and wireless sensor transmission power were determined using a multi-objective particle swarm optimization algorithm. Finally, based on the optimal hovering positions of UAVs in each cluster, an ant colony algorithm was applied to compute the optimal flight path of UAVs, minimizing UAV’s flight energy consumption and thus minimizing the overall energy consumption of the entire data collection system. Simulation results indicate that the proposed solution achieves significant improvements in system energy consumption compared to traditional methods. Specifically, when the clustering radius is 120 meters, sensor energy consumption is reduced by 16.2%, and UAV energy consumption is reduced by 24.9%.

In order to analyze the effect of fan rotor blade leading edge erosion on the aerodynamic characteristics of the compression system for different internal and external culverting conditions, multilevel cascade calculations were used to investigate the changes in the aerodynamic characteristics of the compression system of a certain type of engine with large culverting ratio. The results show that the total pressure ratio, isentropic efficiency and channel flow rate of the engine compression system after leading edge erosion relatively decrease by 0.18%, 0.879% and 0.972%, respectively, under the peak efficiency point working condition. For fan rotor blades, leading edge erosion leads to a decrease in the slope of the surface static pressure coefficient curve in the axial 0~24% interval of the surface static pressure coefficient curve at the height of 95% of the blades under near-surge point conditions in the outer culvert channel, with a decrease in static pressure, and an increase in the slope in the axial 24%~40% interval, which reduces the loading of the blade, reduces the pressurizing capacity, and moves the surge wave forward.In addition, leading edge erosion increases the angle of attack of the overall fan blade at the near-surge point of the outer culvert by about 2°, resulting in changes in blade aerodynamic efficiency and stall margin, which deviates from the original blade design.