Highway construction in China has witnessed remarkable achievements, with rapid growth in the national road network and continuous breakthroughs in the key technologies. This review aims to further enhance the influential level of pavement engineering in China, as well to promote its sustainable and high-quality development. The review systematically summarizes the current status, cutting-edge issues, and future development in pavement engineering. Specifically, it covers seven research topics:highway resilience evaluation and recovery, long-life pavement structures and materials, highway energy self-sufficiency, low environmental impact technologies, the genome of pavement materials and high-throughput computations, highway digitalization and intelligence, and highway intelligent inspection and high-performance maintenance. Focusing on the fields of green, resilience, intelligence, longevity, and traffic-energy interaction, the review identifies 20 critical research topics, including factors leading to highway disasters and their mechanisms evaluation and recovery of highway resilience, key technologies for enhancing highway resilience, full-scale tests for long-life pavement structures, technologies for extending the longevity of highway structures and functions, energy harvesting technologies, energy self-sufficient highways designs, environmental impact testing methodologies and evaluations; innovative materials for low-impact pavements, warm mix asphalt recycling technology, genomic studies on pavement materials, multiscale computation for pavement materials, research on the genome of pavement materials and high-throughput computations, digital modeling technologies, digital twin simulation technologies, data-driven technologies for highway maintenance operations, ground-penetrating radar detection technologies; lightweight detection of pavement performance, strategies for detecting and recovering pavement skid resistance, and high-performance preventive maintenance technologies. The review can provide guidance for the pavement engineering development in China, offering valuable reference for the researchers and practitioners in this field.

With the promotion and application of electric vehicles, the scale of vehicles has exploded. Simultaneously, a series of problems have emerged on the product side, operation side, and service side of electric vehicles:the effectiveness of product quality evaluation is low, the operation safety monitoring is inaccurate, and the service management is incomplete. These problems affect the safe and reliable operation of electric vehicles, restrict the development of the electric vehicle industry, and have become a significant issue for the industry. In the era of automobile digitalization, where big data serves as the core, advancing the development of key technologies in electric vehicle management and services is crucial. This not only ensures the safe, reliable, and efficient application of electric vehicles but also provides essential support and assurance in alleviating consumer safety concerns, ultimately fostering the industry's leapfrog development. To provide a comprehensive overview of technology and innovation in the field, this study extensively discusses the research status of key technologies in electric vehicle management and services. It delves into three aspects:electric vehicle quality assessment and traceability technology, safe operation control and charging service technology, and behavior analysis, identification, and carbon accounting technology. The focus centers on the technical system of 'operation big data +' in the electric vehicle 'quality evaluation-safety monitoring-service management.' Finally, summarizes the existing problems in product quality supervision, operation safety monitoring, and public service management of key technologies of new energy vehicle management and service. Outlook the "quality monitoring-operation control-service management" technology of new energy vehicles with big data analysis and mining as the core. This review can provide a reference for the further improvement of electric vehicle management and service-related technologies and also provide a reference for the future development direction of technology in this field.

Human-machine collaborative decision-making in intelligent vehicles refers to the process of information sharing, interaction, and collaborative decision-making between the driver and driving automation system. Therefore, it is necessary for enhancing the safety, comfort, and traffic efficiency of intelligent vehicles. Currently, studies on human-machine collaborative decision-making technology are fragmented, and relevant review studies are lacking. Therefore, this study primarily reviewed the current research status and future development trends of human-machine collaborative decision-making technology in intelligent vehicles. First, based on a review of the current research status in human-machine collaborative driving, human-machine collaborative decision-making methods are classified based on driving authority and human-machine interaction. The former includes three decision-making modes:human-centric, human-machine driving authority switching, and machine-centric. The latter comprises two decision-making forms:direct and indirect interactions between humans and machines. Furthermore, based on the hierarchical differences in human-machine collaborative decision-making within intelligent driving systems, human-machine collaborative decision-making technology is divided into two key domains:human-machine collaborative behavior planning and human-machine collaborative motion planning. The former encompasses technologies such as driving-authority arbitration, driving-intent coordination, and human-in-the-loop learning. The latter focuses on two categories of methods:path planning and trajectory planning within human-machine collaboration. Finally, a summary and outlook on the development trends of intelligent vehicle human-machine collaborative decision-making technology are presented. The development of intelligent vehicle human-machine collaborative decision-making technology is not limited to the decision-making level but also relies on common technological progress upstream and downstream, and technologies such as multimodal human-machine interface (HMI) and deep reinforcement learning will become crucial driving forces for its further development. In the future, human-machine collaborative decision-making technology will rely on new decision-making intention transmission technologies and large language models to take it to the next level.

In a complex traffic environment where autonomous and human-driven vehicles coexist, reducing the influence of complex interactions between two vehicle types, which have drastically different driving behaviors on vehicle driving safety, ride comfort, and traffic efficiency is a key issue that needs to be addressed in the field of autonomous driving decision-making and control. Accordingly, this study proposed a non-cooperative game interaction framework between human-driven vehicles (HV) and autonomous vehicles (AV) in a mixed driving environment. First, a longitudinal game strategy for human-driven vehicles was established, considering the driver's longitudinal control characteristics of linearly decreasing vehicle acceleration, differentiated coordination degree, and different characteristics of time delay. Second, a longitudinal game strategy for autonomous vehicles was designed, considering the safety constraints of autonomous vehicles and surrounding vehicles, as well as the comfort and traffic efficiency objectives constraining the autonomous vehicles during the lane-changing process. Then, the interactions between human-driven vehicles and autonomous vehicles in different mixed-driving environments were solved based on the Stackelberg game theory to obtain the optimal lane-changing gaps and longitudinal speed trajectories of autonomous vehicles. The model predictive control (MPC) method was used to generate safe lateral lane-changing trajectories for autonomous vehicles. Finally, multiple sets of mixed driving conditions were designed according to the differences in the coordination degree and response delay time of human-driven vehicles. The test results showed that autonomous vehicles could quickly and accurately identify the coordination degree of human-driven vehicles, select the optimal lane-changing gap, and cooperate with surrounding autonomous vehicles to merge into the target gap. During the lane-changing process, the autonomous vehicles always maintained a safe distance from the surrounding vehicles, and both the longitudinal and lateral accelerations of the lane-changing vehicle did not exceed 1.25 m·s^-2 at a speed of 20 m·s^-1. Finally, the safety and comfort performance were guaranteed, verifying the effectiveness of the non-cooperative game interaction framework proposed in this study.

To develop a merging control algorithm for intelligent connected vehicles (ICVs) on freeway acceleration lanes interacting with human-driven vehicles (HDVs) on the mainline, we propose a merging control model (DQN-RF). This model integrates the deep Q-network (DQN) algorithm and the random forest (RF) algorithm. First, a roadside data acquisition platform was established to collect the naturalistic merging behavior data of HDVs at a typical merging zone with an acceleration lane on the G70 freeway in China. Second, a human-like merging decision model using RF was built using historical merging environmental contextual data and the merging urgency of the merging vehicle on the acceleration lane as input. We constructed a simulated merging scenario featuring an acceleration lane on the freeway using the simulation of urban mobility (SUMO) platform. Utilizing the Python language, we developed a testing script environment for the deep reinforcement learning algorithm. Additionally, we introduced a longitudinal acceleration control algorithm based on DQN. Finally, the DQN-RF merging control model, which embedded the RF merging decision algorithm into the DQN longitudinal acceleration control algorithm, was established to embrace merging decision control and longitudinal acceleration control in a comprehensive framework. The default lane-changing control algorithm in SUMO, known as "LC2013," was also combined with the proposed DQN algorithm to serve as a baseline model. The simulation results show that, with the same acceleration action value space[-1, 2] m·s^-2, compared to the DQN-LC2013 model, the DQN-RF model receives a higher total reward value. The average accelerations of the ICV for the DQN-RF and DQN-LC2013 models are 0.55 and 0.09 m·s^-2, respectively. Furthermore, the average speeds are 21.4 and 19.7 m·s^-1, respectively. There are no stop-and-wait phenomena observed when the DQN-RF model is adopted, while there are seven stop-and-wait events in 100 turns of simulation when the DQN-LC2013 model is adopted. The proposed DQN-RF merging control model can realize human-like merging decisions and improve the merging efficiency and success rate of the ICV. The DQN-RF model can be used for merging decision control and longitudinal acceleration control of the ICVs on the freeway acceleration lane.

Trajectory planning remains one of the key challenges in the large-scale application of autonomous driving technology. For instance, in autonomous driving, lane-changing trajectory planning algorithms are typically built as an optimization process targeting the cost function. However, manually adjusting the feature weights in the cost function to suit diverse traffic scenarios is a highly challenging task. To address this issue, our study proposed a new lane-changing trajectory planning method based on the heterogeneous edge-enhanced spatial-temporal graph attention network (HEST-GAT). Initially, we employed inverse reinforcement learning techniques to extract feature weight vectors of the cost function from a multitude of expert lane-changing demonstrations, thereby constructing an expert-level lane-changing demonstration dataset. Subsequently, traffic scenarios were modeled as heterogeneous directed graphs, where the locations of traffic participants were defined as node attributes, their relative positions as edge attributes, and the types of connections between them as edge types. These attributes and types were combined to form the edge feature representation. To capture the spatial and temporal information within traffic scenes, we utilized the HEST-GAT network for feature extraction, calculating the feature weights of the cost function for each scenario. We then constructed a cost function that integrates trajectory features and feature weights, generating the final lane-changing trajectory plan through an optimization process. To validate the practicality of our proposed method, multiple rounds of lane-changing trajectory planning tests and assessments were conducted on real driving datasets. The results demonstrate that, in comparison to spatial-temporal graph convolutional network methods, lane-changing trajectory planning based on HEST-GAT significantly reduces errors when emulating expert demonstration trajectories. Specifically, errors in longitudinal comfort, longitudinal efficiency, lateral comfort, and safety are reduced by 5.5%, 5.4%, 1.4%, and 6.0%, respectively. These outcomes prove that our method can generate lane-changing trajectories highly consistent with human driving behavior, exhibiting superior scene adaptability.

To address the problems of tracking accuracy degradation and stability deterioration when operating intelligent vehicles under changing driving conditions, a multi-objective control strategy based on reinforcement learning variable parameter model predictive control (MPC) algorithm was proposed in this study. The proposed method effectively realizes the parameter adaptive tuning of intelligent vehicle path tracking control system. The proposed linear time-varying MPC controller was designed based on a vehicle dynamics model to obtain the optimal front-wheel steering angle and additional yaw moment. Based on the Actor-Critic reinforcement learning architecture, the Deep Deterministic Policy Gradient (DDPG) and Twin Delayed Deep Deterministic Policy Gradient (TD3) agents were designed for control parameter tuning. The gain function was constructed with tracking accuracy and system stability as the goal, and two typical simulation scenarios of docking road and variable curvature road were constructed for the algorithm performance verification. For the docking road scenario, the prediction horizon and weight matrix of the controller were adjusted in time according to the changes in the road adhesion coefficient. Whereas for the variable curvature road scenario, the prediction horizon and weight matrix of the controller were adjusted in time according to the changes in the road curvature. Through joint simulation analyses conducted using MATLAB/SimuLink, CarSim, and Python, the reinforcement learning-tuned MPC was compared with fixed parameter MPC and Fuzzy-tuned MPC models. The results showed that the reinforcement learning methods yielded the best performance regarding the path tracking accuracy of intelligent vehicles under different road conditions, while guaranteeing the vehicle safety as much as possible. Under the docking road condition, compared with the fixed parameter MPC and Fuzzy-tuned MPC models, the average lateral deviation of the vehicle was reduced by 99.8% and 97.6%, respectively, when using the reinforcement learning-tuned MPC, and the average front-wheel angle change rate was reduced by 99.7% and 77.0%, respectively. Moreover, under the variable curvature road condition, the average lateral deviation was decreased by 79.6% and 90.8%, respectively, and the average front-wheel angle change rate decreased by 40.6% and 2.6%, respectively, compared with those obtained when using the fixed parameter MPC and Fuzzy-tuned MPC models.

Scenario-based virtual testing is a necessary approach to developing intelligent vehicles with high safety and reliability. Automatic scenario generation technology is valuable for constructing the test scenario library for intelligent vehicles. Therefore, a scenario generation method based on neighbor object region representation (NORR) and conditional variational autoencoder (CVAE) was developed for dynamic test scenarios with multivehicle to rapidly generate complex test scenarios and control the types of generated scenarios. First, the NORR method was developed to describe the highway scene situation, and the key information of vehicle objects in the test scenario was converted into grayscale images with uniform sizes. Next, the HighD dataset of naturalistic vehicle trajectories was used to extract many scene fragments, and the real-scene library was constructed after data normalization processing. Based on this, the CVAE-based generative model was trained with the number of vehicle objects in the scene as the conditional parameter, which could generate a dynamic test scenario containing eight vehicle trajectories. By calculating the matching error, coverage degree, and unreasonableness of the generated sample set, the performances of the generative model were analyzed in terms of sample authenticity, diversity, and rationality. The verification results show that ① compared with the random trajectory sampling method and generative adversarial network-based model, the quality of the scenario samples generated using the variational autoencoder model is the best. The average matching error of the generated samples is lower than 5.22, the coverage degree is up to 57.2%, and the proportion of unreasonable samples only accounts for 1.7%. ② The proposed NORR method helps improve the scenario generation effect of generative models. ③ The CVAE model can establish the correlation between the conditional input and the generated results. By adjusting the conditional parameter, the number of vehicle objects in the generated scene can be varied.

To address the problems related to the limited perception ability of single sensors and complex late-fusion processing of multi sensors in intelligent vehicle road target detection tasks, this study proposes a multi-modal perception fusion method based on Transformer Cross Attention. First, by utilizing the advantage of cross-attention, which can effectively fuse multimodal information, an end-to-end fusion perception network was constructed to receive the output of visual and point cloud detection networks and perform post-fusion processing. Second, the 3D target detection of the point cloud detection network was subjected to high-recall processing, which was used as an input to the network, along with the target detection output by the visual detector. Finally, the fusion of 2D target information with 3D information was achieved through the network, and the correction of the 3D target detection information was output, yielding more accurate post-fusion detection information. The validation metrics on the KITTI public dataset showed that after introducing 2D detection information through the fusion method proposed in this study, compared with the four benchmark methods, PointPillars, PointRCNN, PV-RCNN, and CenterPoint, the comprehensive average improvements for the three categories of vehicles, cyclists, and pedestrians were 7.07%, 2.82%, 2.46%, and 1.60%, respectively. Compared with rule-based post-fusion methods, the fusion network proposed in this study obtained an average improvement of 1.88% and 4.90% in detecting medium- and highly-difficult samples for pedestrians and cyclists, respectively, indicating that the proposed method has a stronger adaptability and generalization ability. Finally, a real vehicle test platform was constructed, and algorithm validation was performed. A visual qualitative analysis was conducted on selected real vehicle test scenarios, and the detection method and network model proposed in this study were validated under actual road scenarios.

To improve the environmental perception ability of intelligent driving systems, multimodal sensors have been integrated with the artificial intelligence technology to solve the problem of single-modal sensors, such as poor recognition and vulnerability to interference in environmental perception. However, problems of feature matching between cross-modal sensors, such as inconsistent feature representations, sensing errors, and delay errors, persist. To address these problems, this study proposes a view-cone distance metric-based method, constructs a target-matching matrix, and uses the Hungarian algorithm for inter-frame association matching. Based on the intersection over union (IOU) and Euclidean distance metrics, the multiple object tracking accuracy (MOTA) of the proposed method improved to 81.22% and 58.62% in the cyclist and pedestrian categories, respectively. An adaptive weight-adjustment technique was also introduced to optimize the Kalman filter algorithm so that low-complexity and efficient cross-modal sensor fusion target detection and tracking could be achieved. Compared with the individual camera and light detection and ranging (LIDAR) predictions, the root mean square error of the fusion method reaches 0.3490, denoting a reduction by 30.37% and 30.53% compared with the camera and LIDAR methods, respectively, and confirming the accuracy of the proposed adaptive weighting Kalman filter fusion tracking method. The multi-target tracking experimental test conducted on the KITTI dataset achieved an accuracy of 88.25%, comparable to the performance of current mainstream methods. The test results under multiple weather conditions also demonstrated excellent performance, with target detection accuracies of 96.40%, 75.51%, and 91.87% for vehicles, pedestrians, and cyclists, respectively. Compared to a single sensor, the fusion method attained superior detection results under multiple road conditions, improved the reliability and robustness of the system, and laid a solid foundation for the further development of driverless technology.

Distributed-drive intelligent electric vehicles can yield improved maneuverability when driving on bisectional slopes by independently distributing the driving torque of each wheel. However, they require a high torque output at each wheel and posing challenges in maintaining vehicle lateral stability. To address these issues, a dual-mode coupling drive system that can achieve a centralized and distributed coupling drive function was developed to jointly control the coupling drive anti-slip control and active steering systems to improve driving stability on bisectional slopes. First, a vehicle model was established. Subsequently, the dynamic mechanism of using the dual-mode coupling drive to improve vehicle maneuverability on bisectional slopes was analyzed. Second, a driving stability control system based on the coordination of the coupling drive anti-slip and active steering systems was designed. The designed system consists of an upper-level anti-slip controller that achieves optimal slip rate control, active steering feedforward controller that reduces control overshoot and counteracts differential torque, and active steering feedback controller based on T-S fuzzy model predictive control designed to mitigate speed disturbances. Finally, an offline simulation and real vehicle test verification were conducted to assess the driving stability control effect. The research results indicated that on a 10% gradient bisectional slope, the coupling drive significantly enhanced the vehicle's dynamic performance by 41.32% compared to the performance achieved with a distributed drive system. Compared with the control method without feedforward coordination, the proposed coordinated control reduced the lateral displacement error by 68% and shortened the adjustment time by 10.81%. The proposed control method not only significantly improves the power delivery and maneuverability characteristics of the vehicle but also improves its directional stability.

To achieve accurate evaluation and diagnosis of battery temperature inconsistency by fusing vehicle running status, this study designed and conducted the naturalistic driving experiment of electric vehicles (EVs), and the long-term and high-frequency vehicle running data were used to explore the association characteristics between battery temperature consistency and driving behavior from the perspective of microscopic operation segments. Based on the driver's driving behavior of pressing/releasing the pedal, the vehicular running process was divided into four kinds of segments, namely, segments A, B, C, and D. For the four types of segments, the correlation between driving behavior parameters and the variation coefficient of probe temperature (VCPT) was obtained by calculating the maximum information coefficient (MIC), then the importance and influence mechanism on VCPT of driving behavior parameters were analyzed using random forest model, and the quantitative impact on VCPT of driving behavior parameters was calculated by data grouping and statistics. The results show that, the driving behavior parameters are weakly correlated to battery temperature consistency, and their impacts on temperature consistency are nonlinear and non-monotonic. In general, the correlation between battery temperature consistency and vehicle speed related parameters is stronger than that between acceleration and pedal state related parameters. Among the four most important driving behavior parameters for VCPT prediction, maximum speed is included for all four types of segments, and maximum negative acceleration is included for segments B, C, and D. Compared to high vehicle speed and speed fluctuation, the increment of temperature inconsistency caused by high deceleration is the most significant. For the four types of segments, the driving behavior parameters having the most significant promoting effect on battery temperature inconsistency are maximum negative acceleration, average negative acceleration, maximum negative acceleration, and standard deviation of speed respectively. For the four parameters, the mean VCPT corresponding to the parameter values above the 85% quantile are 9.44%, 20.36%, 13.05%, and 16.37% higher than those below the 15% quantile, respectively. The research results can support the development of the driving-scenario-adaptive threshold based evaluation and diagnosis method for battery temperature inconsistency, which can improve the accuracy of battery safety warning for EVs.

Distributed electric drive vehicles can improve vehicle mobility through in-situ steering capabilities. All four wheels of the vehicle are experiencing slipping, creating a situation where the vehicle body is prone to drifting or even losing control. To achieve stable and accurate in-situ steering control, this article analyzed the dynamic mechanism of in-situ steering and proposed a control strategy that coordinates yaw and slip rates. This study designed a road adhesion estimation algorithm based on longitudinal dynamics to complete the evaluation of road conditions before in-situ steering. It adopted a hierarchical control architecture. The upper-layer controller coordinated the torque control strategy based on the vehicle state, and the lower-layer control designed a yaw rate decision-making framework. The nominal yaw rate of the original steering was determined by considering the throttle opening, and the four-wheel-drive torque was calculated using a single-neuron adaptive PID (SNAPID) control algorithm. This algorithm, based on a quadratic performance index, was employed to achieve tracking control of the yaw speed. The expected slip rate of the four wheels was obtained by introducing fuzzy logic reasoning, and the drive torque adjustment was calculated using the PID algorithm and was combined with the yaw rate torque control to suppress the deviation of the steering center. Simulation tests and real vehicle tests show that:when the adhesion coefficient is constant, the tire turning in a steady state is regarded as a rigid body, and the side slip angle and the lateral reaction force on the ground are unchanged, the test results are consistent with the inferred steering dynamics. The test results confirmed that the yaw rate tracking control algorithm exhibits excellent tracking performance and robustness across various attachments. In comparison to PID, the algorithm demonstrated a 46% increase in response speed, a 24.0% reduction in maximum overshoot, and a shortened average adjustment time by 1.3 seconds. The average steady-state errors were consistently maintained at 0.01 (°)·s^-1. The control of yaw rate and slip rate contributed to a reduction in horizontal and vertical coordinate offsets by 2.94 meters and 1.69 meters, effectively mitigating steering center offset.

Fast and accurate identification of pavement types in complex driving environments is a crucial prerequisite for vehicle active control systems to generate timely predictions. In response to the problem that existing methods fail to balance accuracy and speed and are deploy-friendly, this study proposes a pavement classification model based on structural reparameterization and adaptive attention that can quickly and accurately identify complex pavements, such as asphalt, cement, snow, dirt, tile, stone, and wet asphalt. First, a feature extraction backbone network centered by multi-branch heterogenous convolutions, including horizontal/vertical/square/point kernels, was constructed. Second, a lightweight and efficient attention structure was proposed to adaptively aggregate spatial contexts depending on the feature scale and perform local cross-channel interactions depending on the feature dimension. This allowed the model to focus on highly correlated features. Accordingly, a structural reparameterization strategy was introduced to decouple the training and inference periods. During training, expressive feature representations were obtained through multi-branch learning. During inference, the multi-branch structure was equivalently transformed into a plain single-branch structure without losing performance, thereby obtaining a lightweight deploy-friendly model with a significantly high inference speed. The experimental results showed that the proposed model effectively recognized pavement types in complex driving environments. It achieves 99.14% all-scene classification accuracy and 96.48% new-scene classification accuracy with 6.57×10⁶ parameters while maintaining a high inference speed of 496.28 frames per second in the server chip and 33.89 frames per second in the edge chip. Compared with other models, the proposed model has higher adaptability to complex and changeable environments and achieves a better balance between efficiency, effectiveness, and lightweight. Therefore, the proposed model is significantly advantageous for pavement-type identification tasks.

Light detection and ranging (LiDAR) sensors are crucial for the environmental perception of intelligent networked vehicles, and multi-coordinate system spatial calibration is a prerequisite for the accurate environmental perception of these sensors. Aiming to address the problems arising from single sensor observations in space synchronization between the LiDAR and vehicle body coordinate systems, this study proposed a two-step calibration method based on planar and linear motion constraints imposed on the LiDAR sensor and vehicle. To construct the motion constraint, the LiDAR motion pose information was obtained based on the laser odometry, and the plane driving recognition was conducted based on the LiDAR motion trajectory information and multi-frame ground plane fitting information in the time domain. The plane motion constraint calibration was constructed under plane road conditions; subsequently, the roll angle and pitch angle were calibrated. Based on the pitch and roll angles, the vehicle trajectory was corrected, a linear driving discrimination model was established using the laser motion trajectory to determine the vehicle motion state, and a linear motion constraint was constructed to meet the straight driving requirements of the vehicle based on the road conditions to calibrate the yaw angle. Finally, a real vehicle test was conducted using an intelligent driving test vehicle, and the feasibility of the proposed method was verified using data collected from the real vehicle. The test results showed that the proposed method was superior to the target-based method. The rotation error was reduced to 0.61 on the original data, and the error rate was reduced to 47.4% by the proposed method. The rotation error after calibration was reduced to 1.64 on manually expanded data, and the error rate was reduced to 40.6%. Compared with the method based on the calibration object, the rotation error was reduced, and no special calibration object or calibration field were needed, which reduced the model's dependence on the environmental data. Finally, the effectiveness and robustness of the proposed method were demonstrated through comparative ablation experiments.

Due to the ignoring of structural and aerodynamic nonlinearities, traditional linear flutter theory of long-span bridges can only be applied to the critical flutter state prediction, not suitable for post-flutter state analysis. Besides, the superposition principle of aerodynamic forces is usually directly assumed to be true when analyzing bending-torsional coupled flutter. Therefore, this paper firstly verified superposition principle of aerodynamic forces and its applicable interval by forced vibration wind tunnel test, and then realized nonlinear flutter analysis by introducing the amplitude dependent structural damping ratio and flutter derivatives into the complex mode eigenvalue algorithm. The predicted post-flutter amplitude was verified by comparing with wind tunnel test results. The results show that the superposition principle of bending-torsional coupled aerodynamic forces is approximately satisfied for streamline box girder in the test range (vertical amplitude A_h/B ≤ 1.0, torsional amplitude A_α ≤ 12°). The predicted post flutter amplitudes show good agreement with experimental results when the amplitude dependence of both structural damping ratio and flutter derivatives are concerned, which also indicating that the amplitude dependence of structural damping and aerodynamic forces is the major nonlinearity of section model wind tunnel test system.

The full-field dynamic identification of cracks is very important to reveal the failure mechanism of engineering structures. To address the problems that the traditional single-point displacement and strain measurement methods are difficult to achieve full-field crack detection, this paper proposes a method for full-field detection and reconstruction of concrete structure cracks based on digital image correlation (DIC). By setting the crack detection threshold, the displacement bouncing change point representing the crack location is detected line by line and point by point in the horizontal direction in the DIC displacement field, then the horizontal crack width is obtained by the difference of displacement vectors on both sides of the crack, and then the crack direction is obtained by local least squares fitting, and the crack width is obtained according to the relationship between the horizontal crack width and the crack direction. Global detection and dynamic reconstruction of crack geometry can be realized by global operation of the displacement field. Aiming at noise contamination to image, the necessity of noise reduction in the displacement field is verified by simulation tests. Mean square root error, signal-to-noise ratio and smoothness index are used to quantitatively analyze the noise removal effects of different methods. Aiming at the selection of the optimal crack detection threshold in practical engineering, the information entropy theory and correlation coefficient are introduced, and the determination method of the optimal threshold is proposed through the curve of crack width distribution entropy and the curve of identification loss correlation coefficient. Finally, the loading test of concrete beams strengthened with ultra-high performance concrete is carried out to verify the results. The results show that the proposed method realizes the automatic measurement and visual display of the crack development process of concrete structures. The comparison results with the crack observation instrument show that the measurement error of the crack width is within 0.01 mm, which meets the engineering requirements. This study provides a non-contact, visual and accurate method for full-field crack measurement of loading tests for engineering structures.

In order to accommodate the growing demand for cable force in cable-supported structures, increasing the diameter and number of individual bars is considered an effective method for augmenting cable force, thereby resulting in increased cable and coil diameters. Therefore, this paper proposes a dispersed anchoring system (DAS) that utilizes multiple high-strength and small-diameter CFRP bars in conjunction with the previously developed variable-stiffness load transfer component (LTC) to simultaneously enhance cable force and bending behavior. A simplified stress-releasing model (SRM) based on equivalent rings was proposed to address the challenges of complex modeling and low computational efficiency in multi-tendon cable systems. The reliability of the SRM and effectiveness of the DAS were validated through a full-scale experiment. The results indicate that adjusting the spacing between tendons is effective in reducing axial tensile stress discrepancies between the inner and outermost layer tendons in a parallel-tendon anchoring system, while having minimal impact on axial and radial stresses. The SRM effectively addresses the "hoop effect" of the closed ring model, resulting in circular extrusion stress for inner cable tendons that more closely approximates a real multi-tendon model. The high-strength CFRP cable demonstrates a burst failure overall, while the LTC exhibits minimal visible extrusion and shear damage. The load-displacement curve and axial displacement can be accurately simulated using SRM. The ultimate tensile load of the high-strength CFRP cable was measured at 3 393 kN with an anchoring efficiency of 91%. The relatively low anchoring efficiency may be attributed to the uneven distribution of stress and the type of anchorage employed. The axial strain in the cable increases as load is applied, particularly at the free end, and excessive differences in axial strain can be caused by factors such as gauge placement, adhesive layer thickness, and errors in tendon length. Under the same load, the axial strain of the central tendon in the anchoring zone exhibits a nearly linear decrease from the loading end to the free end. Meanwhile, within a range of 30-360 mm, there is only a slight increase in average shear stress of the central tendon. These findings suggest that variable-stiffness anchoring design can effectively alleviate shear stress concentration.

In view of the weak anti-noise performance and low identification efficiency of the traditional curvature index in bridge damage identification method, a data-driven bridge damage identification method based on statistical moment curvature index and vehicle bridge coupling vibration theory was proposed. Firstly, under the drag of the tractor, two test vehicles were used to synchronously collect acceleration response signals from adjacent designated measuring points when it was stationary; and the statistical moment curvature of the corresponding signals were calculated. The two test vehicles were dragged to repeat the operation until the collection and calculation of all measuring points of the whole span bridge were completed, and then the statistical moment curvature values of each measuring point of the bridge were compared in the previous state. After subtracting the curvature values of measuring points in different states, the statistical moment curvature difference curve varying along the longitudinal direction of the bridge can be drawn, and the damage position of the bridge can be obtained based on the measuring points with prominent changes in the curve. Further, the objective function with statistical moment curvature as the parameter was constructed to modify the bridge model and obtained the damage degree of the bridge. In this paper, the theoretical relationship between statistical moment curvature and vibration mode shape and its stiffness was derived for the first time, and the influence of different parameters on this method was studied through numerical simulation. Finally, the practical operability of this method was verified by practical bridge test. The results show that compared with vibration mode curvature and flexibility curvature index, the statistical moment curvature index has shown better anti-noise performance and higher recognition efficiency. At the same time, compared with the traditional transmission rate damage identification method, this method has behaved better effect on the location and degree of damage, and the identification error is smaller.

Rectangular-like sections are prone to wind-induced self-excited vibration, commonly known as "soft galloping", due to their blunt aerodynamic shapes. Unlike classical galloping, the vibration will not diverge infinitely but converge to a limit cycle oscillation with a stable amplitude, which shows significant nonlinear characteristics of its self-excited force. Moreover, the time-varying feature of galloping vibration also reveals notable unsteady characteristics. Such unsteady and nonlinear galloping may happen on steel and rectangular-like columns, hangers, and girders used in real bridge engineering due to their blunt shapes, low-mass, and low-damping characteristics. Therefore, it is necessary to accurately predict their responses of unsteady and nonlinear galloping. To this end, by taking a rectangular cross-section with a side ratio of 3:2 as the object, the parameters of a nonlinear mathematical model for the two-dimensional galloping self-excited force were first identified. Then, considering that the nonlinearity of unsteady galloping is mainly reflected in its aerodynamic damping effect, while the nonlinear effect of its aerodynamic stiffness is relatively weak, a three-dimensional nonlinear analysis method for the unsteady galloping responses of blunt columns, considering the influence of structural mode shape and vertical mean wind profile was proposed by the modal decomposition. On this basis, case studies of theoretical analysis and aeroelastic model wind tunnel tests were carried out on the nonlinear and unsteady galloping responses of a rectangular column with a side ratio of 3:2 under two typical wind profiles. The feasibility and reliability of the proposed three-dimensional nonlinear analysis method for the unsteady galloping responses of the blunt column were verified by comparing the experimental and theoretical results.

To investigate the effect of cement-sodium silicate(C-S) synchronous grouting, the time-varying electrical parameter characteristics of a single-liquid/C-S grout were tested and analyzed in the context of the Jiangyin-Jingjiang river-crossing shield tunnel. The results show that the electrical parameters of C-S grout have a more stable trend than that of single-liquid grout. The dielectric constant tends to stabilize within 12 to 48 h, while the conductivity values distributed range from 4.12 to 5.47 mS·m^-1. A full-scale model test was conducted consisting of a multilayered medium of segments, grouting body, and soil layer, while two defects, grouting cavity and thin thickness, were also involved in the tests. Ground-penetrating radar (GPR) detection tests were conducted on the grouting body at 24 h and 48 h after injecting along five different detection monitoring lines. Combined with a gprMax numerical simulation comparison, the imaging rule and detection effect of the GPR were analyzed under varying frequencies and detection durations. This indicates that the change in the electrical parameters of the C-S grout within 48 h after grouting has little impact on the phase and amplitude of the GPR reflection waveform, and the two defects of thin thickness and cavity cause interruption and fluctuation of the in-phase axis in the GPR reflection wave. GPR can be used for good identification, with better identification effect at 400 MHz. However, the 900 MHz waveform has a limited effect on grouting detection at 14 ns and deeper.

Regarding the inadequacy of a small amount of let pressure and poor synergy in a soft-rock tunnel under high ground stress, based on the reasonable release of surrounding rock stress and synergistic deformation mechanism, a tunnel graded yielding support structure with a two-stage pressure release function is proposed herein. According to the characteristics of two-stage deformation, a simplified mechanical model was constructed in stages based on the load structure method to reveal the interrelationship between structural deformation and surrounding rock pressure. A numerical analysis model was established using the convergence constraint method to compare the deformation characteristics of the surrounding rock under the action of strong support and yielding support, and to reveal the stress distribution law of the support structure. The results show that the yield pressure point determines the time when the structure enters the yield pressure stage:too small a yield pressure point will lead to rapid destabilization of the structure, which does not meet the early deformation control requirements of the surrounding rock; too high a yield pressure point is not conducive to the complete release of the structure yield pressure deformation, which delays the stabilization time of the structure and affects the stability of the surrounding rock; increasing the let pressure amount can significantly release the surrounding rock stress, reduce the structural force, and ensure the stability of the support structure. Reasonable regulation of the two stages of pressure sharing can improve the stability of the inner arch, reduce the stress concentration in the inner arch, and improve the structural bearing capacity. The proposed graded pressure-let support structure can improve the pressure-let volume, release the surrounding rock stress, and reduce the structural stress through two stages of pressure let, which can guarantee the stability of the tunnel surrounding rock and provide a reference for the support measures in the high ground stress soft-rock environment.

In order to improve the construction efficiency of segmental joints and the prefabrication level of immersed tunnels, and to solve the problems in existing segmental joint connection schemes, such as low utilization rate of joint sections, installation difficulties of buried waterstops, low work efficiency and poor pouring quality of shear keys, etc., a new type of steel shear rod composite structure is proposed to replace the traditional shear key in segment joints. By carrying out single-bar and multi-bar shear experiments of the shear rod composite structure, the shear load, the relative displacement of the joint, the strain of the steel sleeve and the stress of the steel bar are measured, and the shear resistance of the shear rod is studied. Relying on the Dalian Bay submarine immersed tunnel, based on the material plastic damage constitutive model, considering the detailed structure of the segment joints, the effectiveness of the numerical simulation method is verified according to the shear experiment of the shear rod composite structure. The deformation law and failure characteristics of the horizontal and vertical shearing, bending around the horizontal and vertical axes of the segment joints were studied by numerical simulation, and a three-dimensional simulation model was further established to study the working conditions of the shear rod composite structure in Dalian Bay submarine immersed tunnel. The research shows that the failure of the shear rod composite structure is controlled by the shear failure of the shear rod, and the failure ductility characteristics are obvious. The failure load-displacement relationship of shear rods can be divided into three stages:elasticity, plastic strengthening and plastic failure. When there is a gap in the joint, the failure yield characteristics of the shear rod composite structure are obvious. The numerical results of the shear test are in good agreement with the field experimental results. Numerical experiments of segmental joints reveal that the failure process of joints under shearing and bending is complex, and all of them exhibit staged characteristics. Based on the joint damage analysis, a safety evaluation index of segmental joint displacement is proposed. The safety evaluation of the displacement index of the segment joints of the three-dimensional full-length numerical model under the control conditions is carried out. The evaluation results show that the shear rod composite structure is suitable for the Dalian Bay submarine immersed tunnel project and has good economy.

This study aimed to establish construction parameters for on-site use of dry ice powder thermal shock breaking and conduct a comprehensive safety evaluation of critical engineering structures. The first field test of dry ice powder thermal shock rock-breaking for tunnel excavation was conducted in an open-cut tunnel near a residential area. Pressure variation curves were captured from the dry ice powder fracturing cylinder using pressure sensors. Concurrently, the tunnel structure's noise and vibration responses during the breaking process were monitored using specialized systems. The Hilbert Huang transform (HHT) was used to decompose and extract features from the vibration signal, and the vibration safety evaluation parameters were determined from the vibration energy time-domain distribution and frequency-domain distribution features. Results show that:① the maximum vibration speed of dry ice pneumatic rock breaking reduces to less than 50 mm·s^-1 at a distance of 8 m from the crown beam, complying with safe mass vibration speed requirements. Additionally, the noise level at a distance of 4 m is 83 dB, posing minimal impact on the life of nearby residents; ② overall, the vibration speed induced by dry ice rock breaking decays in alignment with the multiplicative power function decay. This decay can be categorized into a zone of slowly decreasing vibration speed (2.5-10 m) and a stable zone (>10 m); ③ the rupture pressure of the dry ice powder fracture tube ranges between 40-60 MPa. Dry ice powder thermal shock rock breaking is a collective action of a shock wave and high-energy gas, which fractures the rock and occurs in three stages. The gas wedge of high-energy gas plays a key role in causing rock damage; ④ compared to Fourier transform analysis, the HHT is better suited for processing vibration signals. By calculating the vibration's maximum displacement after identifying the vibration velocity peak and main frequency, the safety of the tunnel structure can be more effectively assessed.

The rapid development of information technology in the mid-to-late 20th century and its penetration into various industries and sectors of society has promoted the construction of intelligent transportation systems (ITS). As key nodes in integrated transportation systems, intelligent airports have received widespread attention, and their development has begun to flourish. However, current intelligent airports in China mainly focus on the application of information technology with diverse scopes and objectives. This indicates a lack of a unified understanding of the concept of intelligent airports, which urgently demands the establishment of an intelligent airport system architecture that can clarify the contents and objectives of intelligent airports and thereby promote their coordinated and standardized development. Therefore, a system engineering approach is adopted to address this requirement. In this study, four types of physical entities in 17 areas were identified, including business production facilities, business production equipment, business coordination centers, and transportation vehicles. Three types of users in 20 departments were categorized, including production operation support users, management users, and social users. Nine major functional domains were classified, including airspace management, flight area management, passenger service, aviation logistics, emergency support, operation management, commercial management, road traffic management, and enterprise management. Subsequently, a top-level logical architecture consisting of nine logical subsystems and the corresponding information flows was designed for an intelligent airport. Building upon this logical architecture, a top-level physical architecture with eight physical centers and the corresponding information flows was designed. Finally, an intelligent airport system architecture that encompasses all aspects of airport operations is proposed. The proposed intelligent airport system architecture will help clarify the understanding of intelligent airports, promote the development of their basic theories, and drive their standardized and collaborative development.

The truck-drone delivery mothership system refers to a joint delivery pattern in which a truck carries drones to locations close to customers, launches these drones to serve multiple customers, and then retrieves the drones. This is an important development direction with potential in the field of traffic engineering. Owing to the demands of some customers related to the load capacity of a drone and locations outside the flight range of a drone, a truck-drone joint delivery system that considers customers with greater demands and at greater distances (TDJD-CGDGD) based on the mothership system is proposed, where the truck is allowed to serve customers in question and drones can be retrieved at different locations from where they are launched. This delivery pattern can be viewed as a traveling salesman problem for drones. An MILP model aimed at minimizing the total delivery cost was formulated. To solve large-scale instances efficiently, an algorithm hybridizing the greedy randomized adaptive search procedure (GRASP) and adaptive large neighborhood search (ALNS) was developed. This algorithm first routes trucks and drones with an additional constraint that can simplify truck-drone simultaneous routing. This additional constraint is then relaxed, and the algorithm focuses on adjusting the drone routes to further reduce the total cost. It was found that our algorithm had good performance; TDJD-CGDGD achieved an average cost saving of 19% compared to truck-only delivery, allowing drones to be launched and retrieved to service customers with high demands, resulting in an average cost saving of 5% compared to not allowing this function.