To investigate the synergistic enhancement of vehicle ride comfort and lateral stability through mechatronic ISD （Inerter-Spring-Damper） suspensions， and to explore the impact of switching external electrical networks on mechatronic inerter performance， this study establishes a three-degree-of-freedom vehicle dynamics model considering steering maneuvers. The NSGA-II is employed to optimize the external electrical networks of the mechatronic ISD suspension under various driving conditions. Results demonstrate that the mechatronic ISD suspension without an external electrical network yields marginal performance improvements. In contrast， the configuration that switches external electrical networks based on driving conditions achieves significant reductions of 12.7% and 25.9% in the root mean square values of body acceleration and body roll angle， respectively， markedly enhancing both ride comfort and lateral stability.

The hydraulic excavator track link was selected as the research object. A comprehensive methodology for predicting track link fatigue life， based on integrated dynamics-FEA-fatigue criterion analysis， was proposed. Initially， a dynamic model of the hydraulic excavator track link was constructed and validated by contrasting key parameter data obtained from experiments and simulations. Subsequently， the extracted dynamic loads，including track tension and contact forces derived from the dynamic model，were imported into finite element analysis software. Stress and strain solutions were numerically computed at all nodal points within the track link mesh. Finally， addressing the characteristic multi-mode， alternating， asymmetric loading of the track link， structural life prediction was implemented based on a multiaxial fatigue criterion. The results demonstrate that the mean error between critical parameter data from the established simulation model and experimental data was maintained within 10%. Fatigue life predictions were obtained for the track link under both straight-line travel and steering conditions of the hydraulic excavator. The predicted life exceeded 10⁷ cycles under straight-line travel， while a life of 3.59×10⁶ cycles was predicted for the steering condition.

Aiming at the problem that the traditional user equilibrium assignment model is difficult to describe the phenomenon of congestion space queuing and spillover， in order to describe the macroscopic and overall traffic operation state of the road network， portray the impact of congestion spatial queuing and spillback phenomena on the traffic assignment of road network， and provide reference ideas for traffic planning， demand management and congestion mitigation， considering the general road network with both smooth and congested state， a novel traffic user equilibrium assignment model with congestion space queuing and spillover caused by the link capacity and storage capacity constraints was constructed. The equivalence of model with user equilibrium principle and the uniqueness of solution were proved. Moreover， mechanisms such as demand conversion， congestion backtracking， congestion area identification and demand loading of different traffic conditions were introduced， the calculation method of demand flow transfer coefficient was proposed， and an improved FW algorithm was constructed to solve the model. Finally， the effectiveness of the model and the algorithm was compared and analyzed by numerical examples. The error accuracy of the calculation results of the simple numerical example is e=\mathrm{1.044}\text{?}\mathrm{5}\times {\mathrm{10}}^{-\mathrm{5}}， and the routes with loaded traffic volume have equal travel times， which conforms to the user equilibrium principle. The results show that the model is an integration and expansion of the traditional smooth and fully congested user equilibrium assignment model， which can effectively calculate the equilibrium assignment results and the final equilibrium steady states of the road network under different traffic conditions， and describe the phenomenon of congestion space queuing and spillover. The assignment results can reflect the macroscopic and global traffic operation characteristics when the road network reaches the equilibrium state， identify bottleneck locations and congestion queuing areas， and provide basis for road network structure optimization， traffic guidance， demand management and congestion relief.

To study the mechanism of hybrid fiber enhancement of asphalt toughening， calcium sulfate whisker and basalt fiber hybrid fiber reinforced asphalt materials were prepared. The temperature scanning test of dynamic shear rheometer， multiple stress creep recovery test， and scanning electron microscope test and other tests were conducted to analyze the the effects of hybrid fibers on asphalt rheological properties， microstructure， etc. The results show that both calcium sulfate whisker and basalt fiber can significantly improve the high-temperature stability of asphalt binder， and the effect of calcium sulfate whisker is especially prominent； when the content of calcium sulfate whisker is 5% and the content of basalt fiber is 1.0%， the failure temperature of hybrid fiber-modified asphalt is improved by about 19.58% compared with that of matrix asphalt. However， calcium sulfate whisker is detrimental to the flexibility performance at low temperatures， and the addition of basalt fiber can effectively alleviate the excessive hardening problem caused by calcium sulfate whisker. In addition， the temperature sensitivity of asphalt increased and then decreased with the increase of basalt fiber proportion， and reached the optimal equilibrium point when the basalt fiber doping was 1%. Scanning electron microscope analysis confirmed that calcium sulfate whisker had good dispersion and compatibility in asphalt， and that basalt fiber mainly played the role of toughening in the system.

Aiming at the mechanism of traffic conflicts in urban expressway weaving areas under different weather conditions， a traffic conflict identification algorithm based on vehicle bounding box information was proposed. The peak over threshold extreme value theory was employed to determine severe conflict thresholds under distinct weather conditions， and machine learning models combined with interpretability analysis methods were utilized to systematically investigate the impact of weather on traffic conflict characteristics. By leveraging high-precision trajectory data， the limitations of traditional conflict metric extraction methods， such as insufficient accuracy， subjectivity in threshold selection， and the lack of exploration into weather-related influences were addressed. The results demonstrate that the severe conflict thresholds for sunny and rainy conditions are 1.75 s and 1.85 s， respectively， with the proportion of severe conflicts in rainy conditions （39.1%） being significantly higher than in sunny conditions （9.7%）. Through interpretability analysis， the following findings are revealed： ①Weather conditions significantly modulate the influencing factors of traffic conflicts； ②Under sunny conditions， spatial vehicle positions （particularly whether the conflicted vehicle is located in a ramp or weaving lane） dominate the severity of conflicts； ③In rainy conditions， the intensity of influencing factors increases markedly， and traffic flow characteristics emerge as the predominant factors. These findings provide a theoretical foundation for differentiated management and safety prevention and control in weaving areas.

A multi-modal emergency evacuation strategy combining emergency buses and emergency cabs is proposed for the problem that existing studies mostly relied only on emergency shuttle buses for passenger flow evacuation under metro operation disruption scenarios， with little consideration given to coordinated evacuation via multi?mode travel. First， a multi-modal emergency access model is constructed to minimize passenger travel delays and reduce emergency evacuation costs. Then， a genetic algorithm is designed to solve the problem. Finally， the two-way operational disruptions in the morning peak interval of metro line 1 in Fuzhou City are used as a case study to verify the effectiveness and practicality of the model and algorithm. The results show that compared with the single bridging bus strategy， the multi-modal evacuation strategy achieves a reduction of 9.2% and 22.4% in the total cost and passenger travel time cost， respectively.

To promote the low-carbon transformation of urban transportation， the carbon emission reduction benefits of carpooling were returned to passengers in the form of price discounts， so as to incentivize the ride-sharing behavior in ride?hailing services. Based on prospect theory， passenger carpooling willingness under the incentive strategy was modeled. A carpooling matching model incorporating incentive mechanisms was developed， with two objective functions： maximizing the platform's total profit and maximizing passengers' generalized travel cost savings. The model accounts for multi-passenger carpooling， platform profitability， and time window constraints were solved using an optimization approach. A case study was conducted using real-world operational data from ride-hailing services in Xiamen City. From the perspectives of both passengers and the platform， the impact of the incentive strategy on passengers' generalized travel cost， platform profitability， and carbon emission reduction are analyzed. The results indicate that the incentive strategy has a positive significance in promoting passengers to shift from non-carpooling to carpooling. A platform-optimized strategy maximizes the carbon emission reduction but may lead to excessive detours for passengers. A fare discount rate of approximately 0.40 is found to be optimal， effectively promoting multi-passenger ride-sharing while balancing the interests of both passengers and the platform.

In order to investigate the mechanism of risk persistence effects between overtaking vehicles and different interacting vehicles， two UAVs （Unmanned Aerial Vehicles） were used to collect video data of overtaking behaviors on a mountainous highway. The two-dimensional time to collision （TTC） calculation method was improved to assess overtaking risk. And an accelerated failure time （AFT） model was developed based on the survival analysis theory， considering the differences in overtaking styles. The findings indicate that the average risk exposure duration was considerably extended （5.19 s） when the overtaking vehicle interacted with the overtaken vehicles， in contrast to interactions with oncoming vehicles in the opposite direction. Speeding driving style increased the risk exposure duration by 15% compared to conservative driving style. When the overtaken vehicle was a truck， the risk exposure duration increased by 19% and 42% compared to motorcycles and minibuses， respectively. In addition to the overtaking vehicle's speed， the lateral position difference between the two vehicles and the oncoming vehicle's speed had the greatest average effect on the risk exposure duration for the different overtaking scenarios.

This study selected four different types of asphalt mixtures（AC-25， AC-13， AM-13， OGFC-13） and conducted nanoindentation testing to investigate the distribution characteristics of asphalt binder films within the mixtures. The test results indicated that asphalt binder films are widely present in asphalt mixtures， with an elastic modulus ranging from 13 to 17 GPa and a thickness between 2-7 μm. These films form a structural layer that closely adheres to the surface of aggregate particles. The asphalt binder film exhibits a load-bearing capacity intermediate between that of aggregate particles and asphalt， but with poor resistance to deformation. It tends to undergo plastic deformation and cannot fully recover after unloading. Although nanoscale pores are commonly present within the binder film， its micromechanical properties are primarily determined by other factors. This paper proposes a Delamination Model for the asphalt binder film， which reveals the failure mechanism whereby， under loading-unloading cycles， the mechanical responses of the binder film， aggregates， and asphalt are not synchronized， leading to the detachment of the binder film from the aggregate surface.

To study the improvement effect of different seismic reinforcement methods on the seismic performance of service beam bridges， the seismic performance of piers and bearings of bridges during frequent ground motion， rare ground motion and very rare ground motion is evaluated based on the vulnerability analysis method of incremental dynamic analysis（IDA）. Three seismic reinforcement methods have been designed for engineering comparison， including wrapping fiber reinforced polymer（FRP）materials around piers， installing metal dampers， and installing cable restrainer. The finite element dynamic analysis model of the whole bridge was established with the help of OpenSees platform. Considering the uncertainty of ground motion and traveling wave effects， 130 ground motions were selected for IDA to obtain IDA curve clusters of uniform excitation and non-uniform excitation ground motion. Based on the capacity-demand ratio model， the curvature and displacement are used as damage measures to define the seismic damage states of piers and bearings respectively， and the seismic vulnerability curves are plotted to conduct a vulnerability comparison analysis of the seismic performance of bridges under different seismic strengthening methods. The research results show that： wrapping FRP materials has a significant effect on improving the seismic performance of bridge piers. After adopting the FRP reinforcement scheme， the probability of pier being slightly damaged under very rare ground motion can be reduced by 53.73%； The installation of metal dampers can effectively control the degree of bearing damage. Under major earthquake， the probability of the bearing exceeding a slight damage state after shock absorption is only 13.57%， and the bearing is likely to be in intact state； The installation of cable restrainer has little benefit in improving the seismic performance of bridges； Considering the traveling wave effect will reduce the damage exceeding probability of the pier， Compared with the damage exceeding probability of the pier under uniform excitation ground motion， before and after adopting FPR seismic reinforcement measures， the reduction amplitude of damage exceeding probability under non-uniform excitation ground motion of the pier is 19.88% and 22.07%， respectively. The traveling wave effect has little effect on the damage exceeding probability of the bearing.

To investigate the deformation mechanisms of subgrade in cold regions under hydro-thermal coupling effects， this study focuses on the TQ-Yichun section （62.44 km） of the G1111 He-ha Expressway. A two-dimensional coupled hydro-thermal migration model was developed to analyze the interactions among temperature fields， moisture fields， and deformation fields during freeze-thaw cycles. Based on the theory of phase change heat transfer and non-isothermal moisture migration， Abaqus software was used for simulation and analysis， and nine integrated monitoring points were deployed along the roadway cross-section at a spacing of 1.25 m to quantify the deformation characteristics of the surface layer of the roadway in the freezing and thawing stages. Key findings include： The maximum frost heave displacement at the subgrade surface during freezing periods reached 8.48 mm， with cross-sectional deformation exhibiting a parabolic distribution. The thaw settlement rate in spring （March-May， 0.12 mm/day） was significantly higher than the frost heave rate during freezing periods （0.07 mm/day）， with frost heave rates between November-January surpassing those of January-February by 18.7%. Vertical deformation extremes showed a negative correlation with geothermal gradients. By introducing dynamic ice-water phase-change correction coefficients， the study improves simulation accuracy and proposes a predictive model for subgrade deformation. These results provide critical insights for optimizing frost-resistant designs in cold-region road engineering， particularly in determining interfacial heat transfer coefficients and insulation layer thickness. The research establishes a theoretical foundation for mitigating subgrade deformation and controlling freeze-thaw-induced pavement deterioration in permafrost environments.

In order to improve the comprehensive utilization rate of tuff powder， the alkali-activated ternary solid waste cementitious material was prepared by replacing part of slag and fly ash with tuff powder. Firstly， the compressive and flexural strength of cementitious material system with different contents of tuff powder， fly ash and slag under single activator modulus were tested. It was found that the compressive strength increased with the increase of slag content， and decreased with the increase of tuff powder and fly ash content. In the case of a certain amount of tuff powder， with the increase of fly ash content and the decrease of slag content， the difference between 28 d and 3 d flexural strength gradually decreases. When the proportion of tuff powder， fly ash and slag is 1∶1∶3， the compressive strength of the ternary solid waste cementitious system is the highest. Secondly， the mechanical properties of the ratio under different activator modulus and alkali equivalent were tested. The results show that when the activator modulus is low， the alkalinity is too strong， resulting in too fast polymerization reaction， insufficient hydration reaction and low compressive strength. With the increase of the activator modulus， the compressive strength increases， but the excessive modulus of the activator will reduce the OH^―content in the activator， and the polymerization reaction will be weakened， resulting in a decrease in compressive strength. With the increase of alkali equivalent， the compressive strength and flexural strength increase. After reaching the optimal alkali equivalent， the compressive strength is basically unchanged， and the flexural strength begins to decrease. SEM， EDS， XRD， NMR and other microscopic tests were used to explain the reason and mechanism of the decrease of flexural strength of partially alkali-activated ternary solid waste cementitious system with age. It was found that when the alkalinity was too high， the colloidal skeleton structure formed by the rapid reaction of OH^―and solid waste provided early strength. However， with the increase of age， excessive OH^―entered the interior of the colloidal skeleton and reacted with Ca²⁺to form Ca（OH）₂. The high concentration of NaOH solution reacted with CO₂ in the air to form carbonate ions， which further generated CaCO₃， causing volume expansion， forming internal stress and microcracks， resulting in a decrease in flexural strength at 28 d.

This study investigates a full-scale prototype tank from actual engineering practice， employing the ANSYS platform to develop a finite element model for a systematic analysis of the tank's dynamic response under three-component seismic excitation， the influence of non-uniform settlement of the annular foundation on structural behavior， and the sloshing wave height variation induced by long-period horizontal ground motions. The results indicate that under triaxial seismic excitation， the tank exhibits pronounced acceleration responses at both the base and the roof， with a horizontal acceleration amplification factor of approximately 5. The horizontal deformation and stress in the tank wall first increase and then decrease along the height， with peak values occurring in the lower-middle region of the tank. Under foundation settlement， the maximum vertical displacement at the tank base occurs at the center of the annular settlement zone， while the base edge exhibits a lifting-like warping deformation. As the settlement extent increases， the lowest shell course progressively buckles inward， and localized stresses at the tank base reach the yield strength of the steel， leading to plastic deformation. When weld stiffness and connection details are considered， the junction between the tank wall and base is susceptible to local buckling or toe-cracking damage modes. Furthermore， even ground motions with relatively low peak ground acceleration but long predominant periods can induce significant liquid sloshing， resulting in substantial wave height responses—this phenomenon provides a plausible explanation for the structural jamming and damage observed at a Sinopec oil depot in Xi'an during the 2008 Wenchuan earthquake， which was attributed to intense liquid sloshing. The results of this study offer theoretical support and practical engineering references for seismic performance assessment and optimized design of large-scale floating-roof storage tanks.

A multimodal semantic segmentation model was proposed for indoor scene perception tasks. Existing multimodal fusion methods mainly focus on a single modality， such as RGB or depth image attention， while lacking effective utilization of attention in the joint feature space after modality fusion. Additionally， many networks suffer from a significant drop in accuracy when the number of parameters is reduced， due to their large parameter sizes. The RGB and depth features are extracted using ConvNeXt and MiT， respectively. A Cross-modal Feature Correction Module （CMFCM） is introduced to enhance spatial and channel features， facilitating interaction and correction between modalities. Additionally， a Cross-modal Attention Fusion Module （CMAFM） is designed to apply attention in the joint feature space， enabling effective multi-scale feature fusion and maximizing the utilization of segmentation information from both modalities. Depthwise separable convolution is also employed to significantly reduce computational costs. Finally， experiments on the NYU-Depth V2 dataset show that， compared with CMX， Dformer， and SA-Gate models， the proposed method improves segmentation accuracy by 1% while reducing the number of parameters by 15.1%， significantly lowering computational complexity.

Aiming at the problems of low coverage， high path redundancy and slow convergence speed of traditional DQN in UAV coverage path planning， an improved DQN model based on multi-mechanism fusion was proposed. Firstly， the workspace of the UAV was modeled as a two-dimensional grid map. It was preprocessed and the reward function was designed. Secondly， based on the traditional DQN， three mechanisms of Double DQN， Dueling Network and Priority Experience Replay were gradually integrated， and a multi-scale convolutional neural network was built using techniques such as one-hot encoding to construct an improved DQN model. Finally， a visual simulation experiment was designed， and an ablation experiment was performed using evaluation indicators such as grid coverage. The experimental results show that the improved DQN model performs well in the coverage path planning task， and its performance is better than that of traditional DQN and other models. Compared with traditional DQN， its grid coverage rate is increased by 8.28%. Also， the grid repetition rate， path length， number of collisions with obstacles， and number of times exceeding boundaries are reduced by 28.44%， 45.14%， 77.31%， and 90.14%. The network convergence speed is significantly accelerated as well.

To address the heterogeneity problems in sensor ontology interaction within Internet of Things （IoT） systems， this paper proposes a sensor ontology matching method based on a Genuine-Pseudo Triplet Neural Network（GP-TNN）. Firstly， core entities are extracted by analyzing the in-degree and out-degree of entities to enhance the quality of training samples. Secondly， a deterministic hybrid similarity measurement method is designed to generate high-confidence anchor matching pairs， thereby reducing the dependence on reference alignments during training. Finally， by leveraging the representation capabilities of Siamese and pseudo-Siamese networks for homogeneous and heterogeneous annotation attributes， the GP-TNN is constructed to enhance the extraction of deep semantic features. Evaluation experiments are conducted on the Benchmark dataset from the Ontology Alignment Evaluation Initiative（OAEI） and three real-world sensor ontologies. Experimental results show that the proposed method can more effectively improve the quality of matching results than the comparative methods， and provides reliable theoretical support for the sensor ontology matching task in the Internet of Things.

In order to solve the problem that pairwise noisy images and clean images are difficult to obtain in the process of denoising， this paper proposes a multi-modular self-supervised image denoising network based on feed-forward convolutional neural network（MMS-DNet）. The algorithm uses the improved DnCNN architecture. By adding a multivariate input module that combines original image down-sampling and global noise level estimation， multiple input sub-images are generated while compensating for the information loss in the generation process. The introduction of redundant information in the image reconstruction process is avoided by adding a sub-pixel convolution module. Experimental results show that the network outperforms existing self-supervised learning algorithms on the BSD68 and Set12 datasets of intensity images， and also achieves good results on CBSD68 and Kodak24 datasets of color images.

Aiming at the problem of missed detection and false detection caused by occlusion and small-scale targets in dense pedestrian detection， the paper proposes a dense pedestrian detection algorithm DSS R-CNN （Dual-branch Single-layer Sparse R-CNN） based on dual-branch multi-scale attention and single-layer output. Firstly， a Dual-Branch Multi-scale Attention Block （DBMAB） is designed and embedded into the CloFormer backbone network to enhance the feature extraction ability， so that the model can pay more attention to important information， so as to improve the detection accuracy. Secondly， aiming at the problem that the multi-layer output of feature pyramid network has limited contribution to Sparse R-CNN， a Single-layer Output Neck （SLON） based on Feature Modulation Block （FMB） is proposed. It improves the inference speed while maintaining the detection accuracy. Finally， Focal-EIoU loss function is used to further optimize the model training. Experimental results on the CrowdHuman dataset show that the proposed algorithm achieves 90.4% AP， 96.2% Recall and 44.1% MR-2， and the detection speed remains at 46 f/s， which is better than the existing pedestrian detection algorithms in complex scenes.

As a clean and renewable energy source， wind energy has been extensively promoted globally. However， the issue of wind turbine blade icing has severely impacted the safe operation and efficiency of the equipment. Existing blade icing detection algorithms are prone to being affected by data imbalance or noise， leading to reduced accuracy. Consequently， there is an urgent need for a precise and efficient ice detection algorithm to mitigate operational risks. In this paper， we propose an ice detection algorithm for wind turbine blades based on a dual-channel temporal feature fusion network. First， we preprocess Supervisory Control and Data Acquisition （SCADA） data and apply feature engineering to SCADA data. Secondly， a detection model combining Long Short-Term Memory Network （LSTM） and attention mechanism is proposed. LSTM is used to fully learn the time dependence of fan state， and the attention mechanism is used to dynamically adjust the weights of different time steps. Finally， a blade ice detection algorithm based on dual channel temporal feature fusion network is proposed. Dynamic trends of features over time can be captured. Multi-head attention mechanism is introduced to enhance feature interaction. The experimental results show that： compared with other algorithms， the detection accuracy of the proposed algorithm is improved by 10%-15%. It has good performance in the ice detection of fan blades.

In order to solve the problem that the adhesive force between the vehicle tire and the ground tends to be saturated under high speed or low adhesion road surface， this paper puts forward a method to establish the driving feasible region considering the action of the active rear wing and control the stability of the vehicle by the active rear wing. Firstly， based on the analysis of the aerodynamic performance of the active rear wing， the aerodynamic lift and aerodynamic drag brought by the active rear wing are analyzed and decomposed into the vehicle model. Secondly， based on the vehicle model with active rear wing， the driving feasible region of active rear wing is established with the tire friction circle as the constraint. Finally， a layered controller was designed based on the active rear wing， and a joint simulation platform of CarSim and Simulink was built. physical simulation experiments were carried out under different working conditions. The results show that the active rear wing can improve the distribution of vertical loads on vehicle tires， expand the feasible range of vehicle driving， and while accurately completing trajectory tracking， enhance the stability and safety of the vehicle.