In the past six years, China has made a tremendous number of achievements in tunnel engineering, which will greatly help China shift dramatically to achieve the great leap from a large tunneling country to a strong tunneling power of the world. In order to promote the sustainable and high quality development of tunnel engineering, the state of the art in tunneling was systematically analyzed in this review paper, including the construction status quo , design technology, survey and forward prospecting technology, excavation and support technology, green approach with Eco-friendly construction and operation technology, mechanized and intelligent construction, lining structure anti-seismic technology, operation support facilities, maintenance and operation technology, and typical tunnel engineering cases of traffic tunnels. Overall, this review paper provides references and insights for researchers and engineers in the field of tunnel engineering.

To advance the development of bridge health monitoring (BHM) technology and ensure the safety of bridge operations, the current academic research status was investigated. The latest progress made by BHM in terms of systems and applicability, damage monitoring algorithms, data preprocessing, safety warning, and digital twin technology was reviewed. Then, current areas of active research and future development directions of BHM technology were identified. Comprehensive analysis shows that, in terms of the BHM system and its applicability, the correlation mechanism between structural response parameters and health indicators must be studied, and smart sensor devices with long-life noncontact automatic collection should be developed. Establishing an automated, networked, and intelligent integrated system for multisource data collection, transmission, storage, analysis, evaluation, and early warning is a key research direction. In terms of structural damage monitoring algorithms, methods of selecting different artificial neural networks and correction methods based on heterogeneous scenes are needed. Meanwhile, the construction of multilevel coupled intelligent algorithms based on data-driven and model correction real-time interaction for multisource information flow is a main focus of research. In terms of monitoring data preprocessing, further research on and development of multi-source heterogeneous data fusion methods based on deep learning are required. Dynamic signal extraction algorithms for damaged structures under the influence of complex environments must be established, and the precise separation of structure monitoring data must be realized. In terms of safety and early warning of damaged structures, early warning indicators and systems should be established to supplement conventional damage safety assessment based on reliability theory and monitoring data. The use of structural monitoring data to reflect overall mechanical behavior, combined with intelligent detection information of local damage for service performance evaluation, is an important development direction. Digital twin technology is still in its infancy in BHM. Integrating digital twin technology into multilevel composite algorithms, establishing a structured multisource heterogeneous big-data intelligent fusion mechanism, developing a digital interconnection, real-time interactive intelligent bridge operation, and maintenance monitoring system, are other important development directions.

In order to improve the development of intelligent detection technologies in highway bridges, the detecting equipment, inspection method, damage identification algorithm, intelligent safety evaluation and maintenance decision were reviewed in this study. After comprehensive analysis, the current situation and development trends of intelligent detection technologies were summarized. According to the testing environments and the arrangements of components, unmanned aerial vehicles, mobile robots, ring-type climbing robots, multi-functional detection robots, cable-climbing robots, underwater robots, sonar device were designed and utilized. To collect the damage information of bridges, the image acquisition devices were installed in most of the intelligent detection equipment.Consequently, the obstacle avoidance, anti-environment disturbance ability and the image acquisition accuracy were of significance in estimating the effectiveness of these devices.In the field of intelligent detection methods, image acquisition technology, laser point-cloud scanning approaches and holography became mature gradually. Besides that, ground penetrating radar, interferometric synthetic aperture radar and sonar detection technology were available in detecting bridge foundation and scouring depth. However, owing to the poor anti-interference ability, the innovative technologies consisted of optical fiber sensing, thermography, acoustic emission technology, ultrasonic detection and electromagnetic sensing needed to be improved and validated further. With the development of intelligent detection equipment,improvement of detection technology and accumulation of detection information, the hierarchical comprehensive safety evaluation algorithm was no longer suitable for bridge estimations.To achieve scientific evaluation of service performance and disaster resistance resilience in regional and network bridges, the synchronous reconstruction used digital twinning methods and estimation with multi-source data fusion technologies were major development orientations in intelligent detection and evaluation of highway bridges.

The traditional scenario enumeration method based on expert experience has failed to meet testing requirements owing to the increasing reliance of autonomous driving on virtual simulation scenarios for testing and verification. The automatic generation of simulation scenarios has substantial technical advantages in terms of scenario diversity, safety, interpretability, and generation efficiency. It plays a crucial role in improving the efficiency of autonomous driving tests, which have become a prevalent research topic. In recent years, researchers have intensively studied automatic scenario generation methods. In the present study, extensive research was conducted on the results obtained in the field of automatic scenario generation. Thus, the latest research progress in scenario definition, scenario deconstruction, scenario generation based on mechanism modeling, scenario generation driven by data, etc., is schematically presented in this paper. In addition, an analysis on some areas worthy of further study was performed, and prospective research directions are presented herein. In terms of scenario deconstruction, given that scenarios are abundant, extremely complex, and inexhaustible, substantial importance should be given to research on the deconstruction of heterogeneous complex scenarios with the coupling of "field-weather-traffic." Regarding mechanism modeling, to meet the requirements of testing scenario diversity and boundary generation, the focus should be on scenario combination generation, edge scenario optimization generation, and adaptive generation. Furthermore, data with rich content must be collected, laying the foundation for research. To fully exploit the test value of scenario data, attention should be paid to the research on scenario reconstruction, thereby accelerating the generation of test scenario databases and dangerous scenarios. Thus, future research should focus on the aspects mentioned above to establish a completely automatic simulation scenario generation system for autonomous driving. This will lay a theoretical foundation for performing large-scale simulation tests of high-level autonomous driving.

Crack detection using deep learning (DL) is important for reducing infrastructure operation risks, saving operation and maintenance costs, and promoting the intelligent transformation of the civil engineering industry. In current practice, algorithms, datasets, and evaluation metrics are the key components of DL-based crack detection. Hence, in this article, four aspects of current research are reviewed systematically. First, the development of the DL method is reviewed, and the applications of deep convolution neural networks in the field of computer vision and their significant advantages over conventional algorithms in image data processing are introduced. Second, three popular DL algorithms for crack detection are described in detail. Third, the available crack image datasets and current evaluation metrics are reviewed. Finally, recent research outcomes in this field are summarized, and future research needs are discussed. The comprehensive analyses show that, based on the backbone of convolutional neural networks, DL algorithms have been widely used to locate and classify cracks on the surface of civil infrastructure with good accuracy, although obtaining quantitative information about cracks still requires auxiliary extraction using traditional image-processing technology. Because of the high cost and technical skills required for pixel-level annotation, there is a lack of large-scale crack semantic segmentation datasets, resulting in crack detection models with poor robustness. Moreover, most researchers created their own datasets for model training and used different metrics to evaluate their model performance, highlighting the need to establish a benchmark dataset for model training and use a set of popular indices to compare the performances of different models. Crack detection robots have been developed for different types of infrastructure, and it is the development trend to improve multiscene adaptability and reduce the application cost of crack detection robots.

To address the problem of lateral control of an intelligent vehicle during trajectory tracking, a trajectory tracking control method for an intelligent vehicle based on the deep deterministic policy gradient (DDPG) method of reinforcement learning is proposed. First, the tracking control of an intelligent vehicle was described as a reinforcement learning process based on the Markov decision process (MDP). The main framework of reinforcement learning was the actor-critic composed of actor and critic neural networks. The reinforcement learning environment included vehicle, tracking, and road models as well as a reward function. Then, the learning agent of the proposed method was updated by DDPG, in which the replay buffer was used to solve the problem of sample correlate on, and the actor and critic neural networks were copied to solve the problem of update divergence. Finally, the proposed method was tested under different scenarios and compared with the deep Q-learning (DQN) and model predictive control (MPC) methods. The results show that the reinforcement learning method based on DDPG has the advantages of a short learning time, small lateral deviation, and small angular deviation, and it can meet the requirements of vehicle tracking at different speeds. When DDPG and DQN are used as the two reinforcement learning methods, both methods can achieve the maximum cumulative reward of training under different scenarios. In the two simulation scenarios, the total learning time of DDPG is 9.53% and 44.19% of DQN, respectively, and the learning time of a single round of training is only 20.28% and 22.09% of DQN. When DDPG, DQN, and MPC are used for control, in the first scenario, the maximum lateral deviation based on DDPG is 87.5% and 50% of DQN and MPC, respectively. In the second scenario, the maximum lateral deviation based on the DDPG method is 75% and 21.34% of DQN and MPC, respectively, and the simulation time is 20.64% and 58.60% of DQN and MPC, respectively.

Human-machine interaction technology of intelligent vehicles (HMIoIVs) is an effective transition method for improving the intelligence level of autonomous vehicles to leap to high automation levels rapidly. It involves different automation techniques of L0-L3 intelligent vehicles, including the advanced driving assistance system (ADAS). This paper presents a review of the concept, structure, and research content of HMIoIVs. Based on the quantity of independent drivers and the number of driving operation involved, existing HMIoIVs can be divided into three categories: the single-driving dual-control structure, serial dual-driving single-control structure (traded control), and parallel dual-driving dual-control structure (shared control). Human driver factors, the driver model, human driver condition monitoring, and driving intention recognition, research methods of traded control and shared control, and the relationship between authority and responsibility are reviewed comprehensively. Finally, the challenges faced by the current HMIoIVs are analyzed and summarized, and prospects for the technological development of HMIoIVs are highlighted.

The primary tasks of route choice modeling are to quantitatively analyze the route choice behavior of traffic participants based on reasonable assumptions, and to estimate and predict their usage of the transportation network. In this paper, the state of the art in route choice modeling was comprehensively reviewed. The characteristics of different kinds of trip data were introduced. The common methods of choice set generation were interpreted. The various discrete choice models proposed in the literature were sorted out and discussed. The two major approaches of model estimation were compared. Finally, the broad prospects of machine learning in route choice modeling were illustrated. The results indicate that route choice studies have made all-round progress with the holographic development of traffic perception technology and the consequent support of massive vehicle trajectory data. Route choice models can be classified into path-based and link-based models. The former takes paths as alternatives and selects a path from a choice set generated by some deterministic or stochastic method. Such models include the multinomial logit (MNL) model, as well as the more accurate MNL-modification models, generalized extreme value (GEV) models, mixed logit models, and non-GEV models. However, the latter takes links as alternatives and dynamically solves the route choice problem without a choice set. Such models include various recursive logit models. The parameters of a route choice model can be estimated using labeled or unlabeled data. The former approach reconstructs the chosen route on the transportation network through map-matching, while the latter considers a series of possible routes by probability. In recent years, machine learning-based route choice models have aroused extensive attention due to better prediction performance. In the future, the discrete choice models and machine learning models should be further combined in order to make the two complement each other.

To provide references for the improvement of existing pavement structure falling weight deflectometer (FWD) technology and improve pavement structure quality evaluation systems, this study began with the axisymmetric dynamic equilibrium equation and applied the Hankel-Laplace transform. An analytical solution for the mechanical responses of pavement structures was then derived. This solution considers the transverse isotropic characteristics of pavement materials and interlayer contact states between pavement structures. A fast calculation method for the mechanical responses of pavement structures is proposed in conjunction with the transfer matrix method. The correctness of the theoretical derivation developed in this study was verified through comparisons to the calculation results of ABAQUS. To this end, 6 728 sets of pavement structures with different vertical moduli, modulus ratios, interlayer slip coefficients, and structural layer thicknesses were randomly generated. The surface displacement responses of each group under the action of an FWD pulse load were calculated using a back-propagation neural network (BPNN) and real-coded multi-population genetic algorithm (MPGA) to back-calculate the parameters of the pavement structures. The results demonstrate that for the BPNN, only the prediction results for the soil-based material parameters and vertical modulus of each structural layer are close to ideal with correlation coefficients above 0.75. Compared to the BPNN, the prediction accuracy of the real-coded MPGA for the vertical modulus is significantly improved and the correlation coefficient is greater than 0.95. However, regardless of the method used, the prediction results for the modulus ratio and bottom slip coefficient are not ideal. In summary, the proposed analytical solutions and calculation methods provide enhanced calculation accuracy and numerical stability, allowing them to facilitate the rapid calculation of the mechanical responses of pavement structures. Additionally, in the calculation of pavement parameter inversion, the incomplete continuity of pavement structures and transverse isotropy of materials should be considered. The results of parameter inversion based on road surface deflection data alone are not ideal. Therefore, it is necessary to make relevant improvements to existing detection technologies.

Road information is the key to the decision-making of digital traffic infrastructure and maintenance behavior. The application of advanced detection equipment is the way to obtain road information. The high-efficiency, and non-destructive detection characteristics of 3D Ground Penetrating Radar (3D GPR) can support the acquisition of road structural defects data. This paper reviews the typical types of road structural defects, detection methods, etc. The major principles of 3D GPR technology, and its application on road engineering are introduced afterwards. Subsequently, this paper concludes the application and development of artificial intelligence technology in GPR image recognition technology according to the structural defect characteristics and recognition methods. According to the development of traffic infrastructure, this paper looks forward to the digital twin technology based on 3D GPR data, mainly introduces the modeling and model simulation methods. The review can provide basic theoretical knowledge and practical methods for 3D GPR road structural defections detection and provide guidance for digital transport infrastructure construction and road maintenance based on 3D GPR data.

A pedestrian trajectory prediction model (VP-LSTM) based on a long short-term memory (LSTM) network was proposed in this study to model the interaction between pedestrians and vehicles that can fit to complex and crowded scenes and social-interaction problems of pedestrian trajectory prediction. The model considers the interaction between pedestrians and vehicles, which is suitable for complex traffic scenarios. The VP-LSTM included three inputs: the direction and speed of pedestrians as historical track sequence inputs, the relative position of pedestrians as human-human interaction information input, and the relative position of pedestrians and vehicles as the human-vehicle interaction information input. First, the fan-shaped human-human and circular human-vehicle interaction neighborhoods were designed to accurately capture the pedestrians and vehicles that interacted with the predicted pedestrians. Second, three LSTM coding layers were established to encode the historical pedestrian-track sequence and the human-human and human-vehicle social information. Third, the anticollision function and direction attention function of human-vehicle and human-human interaction were defined as the weights of human-vehicle and human-human social information, respectively, to improve the accuracy of social information. Then, the information of human-human and human-vehicle interactions was inputted into the attention module to obtain the information focused on by that pedestrians. Finally, the filtered social information and pedestrian history track sequence were inputted into the LSTM neural network to predict the pedestrian trajectory. Finally, the DUT human-vehicle interaction dataset constructed by our group was used to verify the proposed network. The experimental results show that the proposed method can accurately predict the future movement-trajectory of pedestrians in traffic and improve the accuracy of intelligent vehicle decisions.

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.

During the service life of asphalt pavements, the aging and hardening of asphalt deteriorates the pavement performance because of the long-term effects of natural factors, such as light, temperature, moisture, and oxygen. The development of antiaging technology for asphalt pavements is significant in improving the performance of asphalt pavements. The research status of aging and antiaging technology was reviewed to promote the advancement of asphalt pavement aging and antiaging technology. First, the aging test methods for asphalt and asphalt mixtures were introduced, including hot oxygen aging, ultraviolet aging, and water aging. Second, the aging parameters of asphalt were reviewed, including the softening point, penetration, viscosity, and rheological properties in macroperformance, and the asphalt component, functional group, molecular weight, and surface morphology in microperformance. In addition, the high-temperature and low-temperature performance, fatigue performance, and moisture susceptibility of asphalt mixtures with aging were described. Finally, previous research on the antiaging of asphalt and asphalt mixtures was reviewed, and the antiaging materials, technology, and effects were described. This study demonstrates that the aging and antiaging technology of asphalt pavements can be further investigated based on comprehensive aging tests combining various environmental factors, including microscopic mechanism, antiaging processes, and evaluation methods.

Intersections are bottlenecks in urban traffic operations and can cause congestion. Traffic control is a crucial strategy in regulating traffic flow by preventing and alleviating traffic congestion; it is also a cost-effective approach. The development of communication and automation technologies has led to a new type of mixed traffic, comprised of regular vehicles (RVs), connected vehicles (CVs), and connected and automated vehicles (CAVs). This has influenced the revolution of control objects, data environments, and control methods in urban traffic control. Beyond the challenges it brings, intersection traffic control provides opportunities for innovative developments in traffic control theory and methods. Intersection control in mixed traffic situations with CAVs is still in its infancy, but it has already become a hot topic both at home and abroad. According to the right of way, research on traffic signal timing, vehicle trajectory/path planning, and cooperative trajectory-signal control for mixed traffic control at an isolated intersection, in a corridor, and in a traffic network with shared facilities was first summarized; research on intersection control with dedicated facilities for autonomous driving was subsequently introduced, including CAV-dedicated lanes, CAV-dedicated links, CAV-dedicated zones, and dedicated bus rapid transit (BRT) lanes, which provide a separate right-of-way for CAVs. Even though current literature on intersection control in mixed traffic is still scarce, a comprehensive review on the topic has revealed a number of research questions that are yet to be addressed, including:the uncertainty in the driving behaviors of RVs; compliance rates of CVs; coupling of CV/CAV longitudinal and lateral trajectories; and spatial-temporal sparsity of CVs/CAVs. Future research directions are also herein presented, including:trajectory planning of CAVs in mixed traffic; controlling mixed traffic by CAV trajectory planning; and cooperative control of CAVs and mixed traffic. It is expected that these topics will drive forward innovation of traffic control in mixed traffic with CAVs.

To further enhance the strength of the field of automotive engineering and promote the development of automotive technology in China, this study systematically analyzes the academic research status, cutting-edge hot issues, latest research results, and future development prospects in the field of automotive engineering at both domestic and international levels from six aspects:automotive electrification and energy saving, intelligent and connected vehicles, vehicle dynamics and control, automotive NVH (noise, vibration, harshness) control and lightweight control, automotive electronics and electrical (E&E) and software technology, and automotive testing and evaluation technology. Automotive electrification and energy saving constitute key aspects of pure and plug-in hybrid electric vehicles, hydrogen fuel cell vehicles, extended-range electric vehicles, and energy-saving vehicles. Intelligent and connected vehicles are objectives of the research on intelligent driving environment perception technology, autonomous driving positioning technology, intelligent vehicle decision-making and planning, motion control technology, vehicle-road coordination, intelligent safety technology, Internet-of-vehicles safety technology, and intelligent cockpit and human-computer interaction technology. Vehicle dynamics and control are addressed by the research on brake-by-wire, steer-by-wire, suspension-by-wire, and chassis-by-wire cooperative-control technologies. Automotive NVH control and lightweight control involves the prediction and optimization of automotive aerodynamic noise, NVH control of pure electric vehicle systems, acoustic metamaterials and automotive structural vibration control, automotive noise active control, and automotive lightweight and collision safety technologies. Automotive E&E and software technology is addressed by the research on automotive E&E architecture, automotive software technology and OTA (over the air) upgrade, chip and system function integration, etc. Automotive testing and evaluation technology is addressed by the research on testing and evaluation technology of fuel vehicles, new energy vehicles, and intelligent and connected vehicles. This review provides a reference for further development of automotive engineering research in China, and guidance for the innovation in key technologies of the automotive industry.

In recent decades, crumb rubber has been widely used as a sustainable and recycled material in road construction. To further promote the use of crumb rubber binder/rubber asphalt mixtures in road applications, overview its development, and investigate the requirements of using this material, this study reviewed the development, manufacture, performance, and impediments of using crumb rubber in asphalt modification. First, the development of crumb rubber binders abroad and in China was reviewed, along with the criteria of crumb rubber materials in related national/state specifications for crumb rubber binder/rubber asphalt mixtures. Then, the methods of incorporating crumb rubber into binder/mixtures were illustrated, including the dry(retained in a 1.0 mm sieve) and wet(pass through a 1.0 mm sieve) processes. The interaction reaction between crumb rubber particles and base asphalt binder was summarized. According to the literature, the effects of crumb rubber characteristics (such as crumb rubber particle sizes and crumb rubber contents) on the performance of crumb rubber binder/rubber asphalt mixtures were summarized. Finally, the impediments of using crumb rubber binder/rubber asphalt mixtures were discussed with solutions accordingly.

Road traffic safety analyses and evaluation involve the entire process of road design, operation, maintenance, and reconstruction. The objective and content of road safety analysis and evaluation vary significantly in different stages and situations. Therefore, appropriate traffic safety analysis and evaluation methods must be selected for specific problems. To identify the characteristics and application of different road safety analysis and evaluation methods, in this study, four typical road traffic safety evaluation methods, including traffic crash statistics, vehicle operating speed, traffic conflict, and driving behavior analyses, were investigated. The characteristics and applications of different evaluation methods for data collection, characteristic indexing, modeling, and practical application, along with development trends, are summarized. In the literature, certain road traffic safety analysis and evaluation methods have formed relatively mature theoretical models and have been successfully applied to road traffic safety evaluations, such as methods based on traffic accident statistical and vehicle operating speed analyses. However, safety evaluation methods based on traffic conflicts and driving behavior have problems such as data sources and indicator consistency. There are still issues to be studied in the construction of data-sharing platforms, data integrity, temporal and spatial correlation of data, and analysis of data characteristics. The operational speed analysis method must be further studied in terms of precise data acquisition, model development and calibration, and zero-collision segment reliability threshold selection. In addition, traffic conflict analysis methods have unresolved problems in estimating conflict sample size statistics, estimating the construction of dynamic traffic conflict models, screening traffic conflict discrimination indicators, and assessing the impact of driver factors on traffic conflict modeling. Driving behavior analysis requires in-depth research on data collection consistency, collection time, and index consistency. Simultaneously, with the rapid development of intelligent and connected vehicles, more data related to road traffic safety operations may appear in the future, which may change our existing consensus on road safety analysis and evaluation methods and bring opportunities and challenges to the development of new road traffic safety analysis and evaluation methods. This review improves the understanding of theoretical systems of road traffic safety analysis and evaluation methods and enables the accurate evaluation of road traffic safety problems.

The virtual trial assembly based on point cloud data includes the data acquisition, control point extraction, and control point matching. To address the difficult data acquisition and high-cost processing of complete point cloud data, partial point cloud data were utilized in the virtual trial assembly. To address the fact that current control point extraction methods are software dependent, ineffective, and subjective, an intelligent control point extraction method was developed for both the cross sections and sides of large and complex components based on the point cloud data, random sample consensus, Hough transform, and image-processing techniques. To address the fact that these relationships between control points are manually determined, an intelligent control point matching method was developed based on the super four-point congruent sets, iterative closest point, and generalized Procrustes analysis. The proposed methods were adopted in the intelligent virtual trial assembly of a large and complex steel bridge, where a beam was assembled to an arch rib, and one arch rib was assembled to another arch rib. The proposed intelligent virtual trial assembly method is found to be software independent, effective, and highly automated, providing supports for improving the construction quality and efficiency of steel bridges with theories and algorithms.

To address the large-deformation problem of tunnels in squeezing rock, a new adaptive steel arch joint was proposed for large-deformation tunnels based on the concept of consuming energy for yielding support. The adaptive joint yields through the sliding of the joint, which can significantly reduce the surrounding rock pressure and take advantage of the performance of supporting materials. The axial load-bearing behavior of the adaptive joint was studied by means of theoretical calculations and experiments, and the feasibility and yielding performance of the adaptive joint were verified. The mechanical response of an adaptive joint can be divided into four stages: the elastic stage, constant resistance stage, compaction stage, and plastic stage. A 3D refined model of the adaptive joint was established. A comparative analysis with and without an adaptive joint was examined through theoretical analysis and experimental results. The results show that the adaptive joint can maintain a constant resistance, bear a large deformation, and have high compressibility and good flexibility. The sliding resistance of the adaptive joint increases with an increase in the bolt torque. For bolt torques of 70, 80, 90, and 100 N·m, the numerical simulation results agree well with the theoretical and experimental results. The supporting effects of the adaptive joint and traditional steel arch were compared. The results show that, when the adaptive joint is set, the internal force of the steel arch is significantly lower than without the adaptive joint under the same convergence, but there is a stress concentration at the joint. The limit convergence of the steel arch with the adaptive joint reaches approximately 10 times that of the traditional steel arch, thus it can better resist large deformation. The new adaptive joint can easily obtain materials and is simple in structure. It provides new ideas for the research of large-deformation support technology for soft rock highway tunnels in high-ground-stress areas. However, the interaction between the adaptive joint and surrounding rock has not yet been proved, and further study is required for its application in actual engineering.

Steel fiber is the main reinforcing fiber of ultra-high-performance concrete (UHPC). It is better than other fibers in improving the mechanical properties of UHPC. To promote the optimization of UHPC properties by steel fibers further and accelerate the popularization and application of UHPC, important achievements in the research of steel fibers in UHPC are introduced and reviewed concerning orientation and distribution, shape characteristics, content, modification, and mixing with other fibers. The results show the following:① The mechanical model of steel fibers along the length and random distribution of steel fibers, the nondestructive testing technology of fiber orientation, and the rheological control technology need to be further studied; ② The coupling effect of the steel-fiber shape and loading rate on the mechanical properties of the matrix, the matching relationship between the aggregate with different content in UHPC and the length-diameter ratio of steel fibers, and the critical point of the fiber length-diameter ratio under the premise of certain fiber content and performance need to be further studied as well; ③ The cost, control threshold, and possible side effects of fiber modification need to be further explored; ④ An effective and reasonable numerical method to predict the fracture behavior of steel-fiber UHPC is still in the exploration stage; it has a high research value; ⑤ Research on steel fiber mainly focuses on the influence of single factors on different properties. It is suggested to divide the proportion according to the actual situation of the UHPC project, performance demand, site environment, and cost, and carry out research on fiber characteristics and content based on this proportion; ⑥ The quantitative description of the synergistic effect between steel fibers and synthetic fibers, the matching of the high-strength matrix and flexible fiber, and research on hybrid fibers based on the coupling of multi-scale fibers and different performance fibers are valuable from a research viewpoint.

To demonstrate the effect of a monotonous road environment on driving fatigue, real vehicle driving experiments were conducted in this study along the G109 Golmud-Tibet route using devices, such as an eye tracker and electroencephalograph. The multi-source data of 19 drivers including their physiological behaviors, driving behaviors, and subjective KSS scales were obtained. First, the change rules of each characteristic parameter under different fatigue degrees in different time windows were analyzed. Second, the different vegetation levels corresponding to different altitudes were defined as the quantified indices of the monotonous road environment, and the driving fatigue level was classified into three levels, namely, sobriety, mild fatigue, and severe fatigue. Third, according to the test results of Pearson correlation and independent variable collinearity, the (α+θ)/β, average blink duration, blood oxygen, and vegetation level were selected as independent variables. Meanwhile, the degree of driver fatigue was considered as the dependent variable. Finally, a model of the relationship between the driving fatigue and monotonous road environment based on the ordered multi-class logistics was established, and the driving fatigue generation mechanism in the monotonous environment was analyzed. The results indicate that in a high-altitude monotonous road environment, short-term driving fatigue is mainly related to the degree of monotonous environment, and the driving time is not the main factor causing short-term driving fatigue. α/β and heart rate demonstrate no significant correlation with driving fatigue. While (α+θ)/β and average blink time are positively correlated with driving fatigue, oxygen content is inversely related to the degree of fatigue. In addition, when the vegetation coverage rate changes from relatively sparse to sparse, the drivers' alertness is higher and the fatigue degree increases by 5.9%. As the vegetation coverage changes to the extremely sparse state, drivers overcome their own fatigue, and the degree of fatigue increases by 5.8%. Furthermore, as the environmental monotony intensifies, vegetation coverage changes to severe sparseness. Thus, the drivers' fatigue level increases significantly and enters the state of severe fatigue. The findings of this study can lay a theoretical foundation for the prevention of driving fatigue in a monotonous road environment.

With the development of intelligent vehicles and modern communication technology, vehicle optimization control based on intelligent network information has become one of the important research topics in the field of intelligent transportation. To fully understand the research progress of vehicle optimization control based on intelligent network, this paper summarizes the key problems of vehicle energy consumption and emission optimization control using intelligent network information. First, the existing studies of energy consumption and emission control in the level of vehicle kinematics are classified according to different combinations of information transmission. The key problems and research methods under each combination are described in terms of four kinds of combinations: vehicle-vehicle, vehicle-infrastructure, vehicle-vehicle-infrastructure, and platoon-platoon-infrastructure. The research on ensuring safety and timeliness in vehicle energy consumption and emission optimization is also summarized. Second, the research status of vehicle energy consumption control, comprehensive control of energy consumption, and emission combined with engine characteristics are introduced in details. The importance of taking the underlying system characteristics in vehicle optimization into account is discussed. Third, according to the key distribution of existing research and the operation performance of the vehicle, engine, and after-treatment system, an integrated optimization control framework of vehicle energy consumption and emission in an intelligent network environment is given. The application method and reference research of the framework are described. Finally, the supplementary significance of the above framework to the existing research is summarized, and the challenges encountered in the application of intelligent vehicle networks are proposed in the future, which provides a reference for the follow-up wider research.

To fill the gap in the research of truck weighing method in terms of its timeliness, reliability, convenience, transferability, and precision, this study proposed a deep learning-based real-time on-line weight estimation technique framework. The proposed framework consists of a connected on-broad unit and data acquisition module, a cloud platform and data process module, and an AI-based weight estimation module. The vehicle dynamic theory was considered as the basis of the whole technique. Based on the theories of AI techniques, a deep learning-based model was built to learn the relationships between vehicles’ dynamic and their weight, which allowed a real-time estimation relying merely on detecting the vehicle’s motion state. To serve multi-vehicle motion data collection and integrated processing, on-board motion sensor units were developed. These unites were interconnected by a cloud platform. Concerning the time series feature in the vehicle motion data and the non-linear trait in the motion-weight relations, the Long Short-Term Memory model, which had a good performance in accounting for time series data and learning its non-linear trait, was applied to develop the weight estimation model. The results showed the accuracy of the truck weight estimation. In the single-vehicle-testing experiment specifically, the per-minute pointwise estimation generates 3.58% mean relative error for 80% samples, and the trip-based estimation produces 3.15% error. In the cross-vehicle testing experiment, the mean relative error of per-minute pointwise estimation is 5.29%, and the error of trip-based estimation is 3.42%. Compared to the existing vehicle estimation methods, the proposed technique has a good performance. It provides highly automatic and precise estimations, shows broader applicability among different types of vehicles, and allows for traffic surveillance and management to be timely and extended. It also has many potential applications in the domains of transport scheduling, vehicle safety monitoring, and energy consumption detecting.

In this study, flow and pressure equations for a pipeline between a load-sensing (LS) pump and a flow control valve (FCV) were derived to improve the reliability of the LS hydraulic system with an FCV closing rapidly and frequently. The cause of pressure impact on the LS hydraulic system was analyzed, and the influence factors on the peak value of the impact pressure were evaluated. An antishock loop was set between the FCV and the LS pump to suppress the pressure effect. A simulation model of the LS hydraulic system was established in AMESim software. The factors influencing the peak value of the impact pressure on the LS hydraulic system, such as the hydraulic component performance, hydraulic system parameters, and operational parameters, were compared and analyzed. In addition, simulations were performed to investigate the effect of the antishock loop on the pressure impact on the LS hydraulic system. An LS hydraulic system test rig was developed, and the theoretical analysis and simulation results were verified. The results show that the variation in the LS pump discharge flow lags the FCV flow. This lag increases the net flow in the pipeline between the LS pump and FCV when the FCV is closed suddenly, inducing pressure impact. The larger the initial LS pump displacement or the faster the FCV closing speed, the higher the peak value of impact pressure on the LS hydraulic system. An antishock loop was set at the LS pump outlet. The opening or closing of the unloading valve was controlled by the difference between the LS and discharge pressures of the pump to reduce the net flow increase in the pipeline between the pump and the valve. This reduction can significantly restrain the peak value of impact pressure on the LS hydraulic system. The antishock loop reduces the peak value of impact pressure by more than 68% when the initial displacement of the LS pump is large.

The resilience of public transportation systems is one of the core contents of traffic safety research. As a powerful tool for analyzing large-scale complex systems, complex network theory provides a new perspective and direction for studying the resilience of public transportation systems. This paper provided a compressive literature review on the application of complex network theory in the field of public transportation resilience. Firstly, this paper analyzed the characteristics of the literature related to public transportation network resilience, such as the trend of the number of documents, the distribution of publications and hot keywords combined with the bibliometric analysis method. This paper sorted out the development process of public transportation network resilience, and summarized the research hotspots in the field of public transportation network resilience. Secondly, based on the definition of public transportation network resilience, this paper reviewed the application and research status of complex network theory in public transportation resilience assessment and resilience optimization. On the one hand, the core content of public transportation resilience assessment including resilience assessment indicators, assessment methods, interruptions modeling and redistribution of interrupted traffic were systematically analyzed. On the other hand, this paper sorted out the related research on the resilience improvement strategy of public transportation from the two aspects of pre-disaster prevention and post-disaster recovery. Finally, the main problems and challenges faced by existing researches were summarized, and the development direction and research trend of future public transportation resilience were analyzed from the aspects of resilience assessment method innovation, interruptions modeling improvement, and recovery model exploration.

With the rapid development of the Chinese economy at the beginning of the 21st century, above-ground space resources have become increasingly saturated, and cities are faced with rapid expansion. With the construction of tunnels, metro stations, and complex underground projects, underground engineering structures exhibit unique characteristics, such as micro deformation, small spacings, large sections, deep buried depths, high precision, extended distances, and large sizes. These projects face three significant technical problems:water, softness, and deformation. These factors are difficult to predict and lead to challenges for underground construction technology and equipment. Therefore, this paper reviews and analyzes the technical progress and innovation in underground engineering in recent years, particularly construction technology, construction equipment, and digitalization technology. Displacement and deformation control techniques for water-rich strata in soft soil were analyzed in detail. The ground freezing technology of water-rich strata and comprehensive auxiliary construction control techniques for shield tunnels passing beneath metro tunnels are proposed. Typical super underground engineering equipment with green, intelligent, and independent intellectual property rights, which integrate artificial intelligence, the Internet of Things, and other leading-edge and core technologies for equipment, were described. The characteristics of modern underground space digitalization technology were established based on the following aspects:design, construction, monitoring, operation, maintenance, and application. The construction of a visual underground space information platform is the basis of achieving "transparent underground space" through co-construction and sharing of underground space information. The advanced forecasting and control of adverse geological, environmental disasters can ensure underground engineering safety. A control and management system based on the Internet of Things, big data, and other modern information technologies is critical in constructing the security of smart city infrastructure. Future underground engineering should strengthen the application of digital intelligence technology and achieve refinement, digitization, intelligence, and information of the entire construction.

Fatigue cracking at the diaphragm cutout is one of the major diseases of orthotropic steel decks (OSDs). Sufficient fatigue tests are needed to validate the fatigue resistance, fatigue evaluation method, and crack treatment techniques of the cutout detail. Fatigue tests on full-scale OSD models were carried out to investigate the fatigue performance of polished cutouts, defective cutouts (with artificial defects), and defective cutouts strengthened with carbon fiber-reinforced polymer (CFRP). Combined with the finite element method, the fatigue assessment methods for the polished cutout details were also studied. The results show that the fatigue lives of polished cutouts exceed 50 million cycles under standard fatigue vehicle loads, indicating that fatigue cracks will not occur during the service period. There is a significant concentration of compressive stresses around the cutout under the effect of wheel load, and the combined effect of the wheel load stress and thermal residual stress constitutes an external driving force of the fatigue cracking. Moreover, the initial geometric defect is also an important reason for this fatigue cracking. For the fatigue evaluation of polished cutouts, the principal stress 6 mm away from the free edge of the cutouts on the surface of the transverse diaphragm can be used as the nominal stress, and the fatigue resistance is proved to be higher than fatigue grade A (with an constant-amplitude fatigue threshold of 165 MPa) in the specification of American Association of State Highway and Transportation Officials (AASHTO). The externally bonded CFRP reinforcement is effective in arresting the fatigue crack propagation at the defective cutouts. If a 6.5-mm-long fatigue crack is taken as the damage tolerance, the fatigue life of the diaphragm cutouts strengthened with CFRP on one side is more than 14.5 times that of the un-strengthened ones. The fatigue life can be further improved if double-side CFRP strengthening is applied, which should be studied in the future.

In recent years, hydraulic failures have occurred more frequently and have become the primary causes of bridge collapse and failure. Combined with historical data, this paper first introduces the scour and flood, which are the three main hydrological factors, and presents an analysis and comparison of their mechanism and impact on hydraulic bridge failure. Second, the existing research achievements and methods of hydraulic bridge failure were summarized based on different collapse modes caused by the two types of hydrological factors investigated previously. Finally, existing monitoring methods and countermeasures are comprehensively reviewed from the viewpoint of applications. From the review, the following conclusions are drawn. ① Scour is the primary cause of hydraulic bridge failure, principally resulting in the failure of beam, truss, and arch bridges. The scour degree of the bridge is significantly correlated to service time, structural state, and annual mean runoff. ② Three-dimensional numerical simulation of scour space morphology still shows differences with experimental data, with the sand model minimally reflecting the graduation. The empirical formula is expected to solve the limitation of the calculation dimension and improve the scour depth prediction with the time factor and cohesion soil. ③ The formula for analyzing the flood lift force currently neglects pulsating pressure. Moreover, the relationship between wave behavior and force is not clearly verified based on the water channel experiment of the wave load. The combined effect analysis with scour is the majority of reliability research on bridges under multiple disasters, but there is a lack of extensive investigations on the combined effects of wave current, wave force, and earthquake hydrodynamic force. ④ The bridge water resistance is still limited to the independent study of the flow field or structural domain, as there is no bridge failure mode analysis based on the multifield interaction of the flow field structural domain under different hydrological factors. ⑤ Radar, sonar, and underwater detection by divers are the current main methods of bridge scour monitoring. Bridge scour dynamic identification is suitable for regional large-scale inspections under complex environments, but further research is required. Existing countermeasures of hydraulic damage should be adjusted to local conditions during the applications to prevent intensifying the damage.

To study the depth of a vertical crack in a loess slope and improve the flaws in the traditional crack method, single- and multi-crack sliding models were established. In addition, the limit equilibrium method was used to analyze the stress of the sliding models, considering that the vertical tension section formed behind the crack could be stabilized after sliding. The calculation formula for the limit depth of the vertical crack on the top of the slope was derived by analyzing the limit state equation of the slope using the optimal value method. According to the calculation program, the variations in crack depth by the effects of different factors could be obtained. The upper-bound theorem of limit analysis and practical engineering examples were used to verify the formula. Results show that the limit depth of the vertical crack is twice that of the traditional crack depth for a single-crack slope and is independent of the slope inclination angle β. However, for a multi-crack slope, the limit depth of vertical cracks is more complex than that of a single-crack slope, where its value is related to the soil bulk density γ, cohesion c, internal friction angle φ, slope inclination angle β, and geological survey parameter L₂. The crack influence coefficient k_K decreases with an increase in β and increases with an increase in φ and γL₂/c, whereas the angle α between the sliding surface and horizontal plane increases with an increase in β and φ and decreases with an increase in γL₂/c. However, β has little influence on k_K and α. The accuracy of the formula is verified by the upper-bound theorem of limit analysis. With a sliding slope along the Huang-Yan Expressway taken as an example, the relative error between the calculation results and field measurement results is 3.76%, thus proving the reliability of the formula.

Distributed-drive electric vehicles have the advantages of high control flexibility, short transmission chain, compact structure, high transmission efficiency, and high space layout utilization. These unique structure and traction characteristics enable the exploitation of the full potential of vehicle dynamics control; moreover, they enhance vehicle safety, improve drive efficiency, and simplify chassis structure, thus making distributed-drive electric vehicles a promising hardware carrier for high-performance vehicle controller development. However, as distributed drive electric vehicles are over-driven, multi-constraint, strongly nonlinear, and longitudinal-lateral-vertical motion coupling systems, they face the challenges of vehicle dynamics control, vehicle driving economy control, and cooperative control. In this study the state of the art in torque distribution control of distributed-drive electric vehicles was reviewed and analyzed, including control framework, stability control, energy efficiency control, and control of stability and energy efficiency. Moreover, application cases were presented, and the development direction of torque allocation strategy was outlined to provide a reference for advanced high-performance strategy design of distributed-drive electric vehicles.

Cracks often adversely influence the slope stability. Crack development is not only related to the soil properties but also depends on the slope geometry. To study the influence of the inclination angle of an upper slope on the stability of slopes with cracks, this study considered an upper non-horizontal slope with cracks as a research object using the kinematic approach of limit analysis and logarithmic spiral rotating mechanism of plane strain. The effects of static and seismic forces were considered, and the velocity compatible motion field and corresponding energy balance equation were developed. Three different types of calculation models were employed, namely, unspecified depth and location of cracks, cracks with known depth but unknown location, and cracks with known location but unknown depth. The stability factor of the soil slope with cracks under different slope parameters (slope angle β, inclination angle of the upper slope α, and internal-friction angle φ) and different seismic forces (k_h=0.1, 0.2, and 0.3) was calculated using nonlinear sequential quadratic programming. The results demonstrate that the greater the inclination angle of the upper slope, the greater the percentage reduction in the slope-stability factor. The overvaluation of the stability factor is as high as 15%, if the upper slope inclination angle.is not considered. The larger the seismic force, the more significant the influence of the inclination angle of the upper slope on the percentage reduction in the stability factor. With the increase in the inclination angle of the upper slope, the depth of the critical crack gradually increases, and its position gradually moves far from the crest. Only when the crack is within a certain range does the stability factor decrease, and this range increases owing to the effect of the inclination angle of the upper slope and the seismic force. Under static forces, as the slope angle increases, the reduction percentage in the stability factor first increases and then decreases. In comparison with the results of OPTUMG2, the stability analysis of soil slopes with cracks considering the upper slope inclination angle in this work is reasonable.

The ceiling and middle slabs of an atrium-style subway station are both replaced with flat-beams to form a large atrium (the opening area is about 50% of the floorage of each story). There is no column at the station hall floor and thin-walled columns with a width-to-height ratio of 7.5 are adopted at the platform floor. With such a building style, the capabilities of the station to withstand lateral action such as seismic action become a major concern. In this study, a series of 1 g shaking table tests were carried out on an atrium-style subway station embedded in artificial soil under different earthquake intensities. Experiments were conducted to investigate the seismic characteristics of the soil and subway station including the influence of earthquake intensity. Experimental results show that the two ends of the beams on the station hall floor, with the largest dynamic tensile strains, are the most vulnerable sections of the atrium-style subway station when an earthquake occurs. The differences in horizontal acceleration amplitude between the sidewall and adjacent soil vary at different depths. It was also discovered that the intensity of the earthquake has a significant influence on the seismic responses of both the station structure and surrounding ground. With increasing earthquake intensity, the predominant frequencies at different soil depths tend to be less significant while the high-amplitude spectra tend to lie mainly in a quite wider frequency band. The differences in acceleration amplification factor at the depth of the ceiling slab between the sidewall and the adjacent soil decrease with increasing earthquake intensity. The distribution of dynamic soil normal stresses along the sidewall may change its shape as earthquake intensity increases. The peak stresses are asymmetric for the left and right sidewalls. Under horizontal earthquake excitation, there is a rocking mode of vibration for the atrium-style station. The higher amplitude of horizontal input motion leads to larger vertical accelerations of the station ceiling slab. The conclusions of the experiment contribute to understanding the seismic characteristics of atrium-style underground structures better, and also provide references for the seismic design.

Using traffic data to establish a real-time crash-risk prediction model (RTCPM) is the basis of active traffic-safety management. Vehicle data and surrogate safety measures (SSMs) extracted from roadside sensors have potential value in the RTCPM field. This study used high-precision roadside data to generate SSMs as inputs, and proposed an RTCPM for road sections that distinguishes collision types. Based on the traffic data of the studied road section, the traffic parameters were extracted to construct a detailed database. These parameters included the vehicle-motion parameters and the SSMs. A traffic-conflict extraction method, based on vehicle-avoidance behavior and spatiotemporal proximity, was developed to obtain lateral and longitudinal traffic conflicts. These labeled traffic-conflict events were used as the type labels for samples in the modeling. The extreme gradient boosting algorithm (XGBoost) was used for RTCPM. The edited nearest neighbor (ENN) method was adopted to eliminate sample-size imbalances, and the Shapley additive explanations (SHAP) method was employed to explain the contribution of the model features. The traffic parameters before a traffic conflict occurred were aggregated with a 30 s-time window as the sample feature, and input into the XGBoost model for training and testing. The established XGBoost model predicts the collision risk and type 30 s before the collision. The model achieves an overall accuracy of 97.4%, predicting 93.0% of longitudinal conflicts with a false-positive rate of 0.13%, and 61.8% of lateral conflicts with a false-positive rate of 0.12%. The interpretation results of the SHAP model show that SSMs play an important role in prediction. The 5%-quantile 1/modified time to collision (MTTC) has the greatest impact on the longitudinal-conflict prediction, while the average traffic flow and acceleration are the most important features for lateral-conflict prediction. The proposed model framework can provide a basis for active traffic management in the affected area of an interchange.

High quality development of transportation is a complex system engineering. Based on the "Outline for Building China's Strength in Transportation" which proposes the scale of transportation infrastructure, quality of transportation products, adaptability to economic development, time value, mode cost, security demand, and benefit requirements, this study analyzed the connotation of high-quality development of transportation from five dimensions: basic conditions, fundamental pursuit, important expression, value embodiment, and balance point. Considering the situation of economic development and China's national conditions, this paper proposes the relevant requirements for high-quality development of transportation. Among these requirements, the key focus is to improve the quality and efficiency of the construction of transportation infrastructure. Therefore, this paper discusses the significance of the aforementioned aspect and proposes two core methods to achieve this goal. Finally, this paper presents the required policy and countermeasures for high-quality development of transportation from the following five perspectives: ① promoting the intensive use of traffic channel space and zero-distance transfer between high-speed railways and major airports; ② improving the green traffic share in accordance with multi-way and multi-measures; ③ reducing transportation costs and realizing integration and innovation of transportation; ④ providing diversified and multi-level transportation service systems satisfying the expected demand, and establishing world-class transportation services; ⑤ fully realizing the integration of transportation and land use and creating representative TOD (Transit Oriented Development) models in China, based on the scopes of urban agglomeration, metropolitan area, and city and from the perspectives of region, corridor, and hub node. The paper concludes by highlighting directions for further research on transportation development in China.

Herein, the raw material selection and preparation process of the asphalt were summarized to further promote the development of SBS/CR asphalt technology in this study. A reasonable mixing scheme and preparation method were recommended. The mechanism of SBS/CR modification was discussed. Moreover, the rheological and basic properties of the asphalt were investigated. The performance differences between SBS/CR-modified asphalt, base asphalt, SBS asphalt, and rubber asphalt were compared, and the performance grades of SBS/CR-modified asphalt were divided based on the statistical results and specifications. The results show that SBS/CR-modified asphalt is mainly prepared by high-speed shear or colloidal milling. The common mixing methods are as follows:2%-3.5% SBS, 10%-20% crumb rubber, 170℃-180℃ asphalt heating temperature, and 4 000-5 000 r·min^-1. In addition, the composite modification process of SBS/CR on asphalt involves mainly physical action supplemented by chemical reactions, asphalt components, and crumb rubber treatment methods, which significantly affects the dispersion state of modified materials. Compared with base asphalt, rubber asphalt, and SBS asphalt, the comprehensive performance of SBS/CR-modified asphalt is better, and its rheological classification basically meets the requirements of PG 76 and PG-22. According to the box chart data and asphalt specifications, the performance of the modified asphalt can be divided into four grades:excellent, good, medium, and poor. The performance requirements of SBS/CR-modified asphalt suitable for cold, warm, and hot regions have been recommended. In view of the great progress in the research of SBS/CR-modified asphalt technology, it is urgent to establish the relationship between the indoor modification process and factory-end production, explore the performance evolution law under coupling conditions, and optimize its storage stability technology and construction supporting technology.

Cross-sea deep-water piers are often subjected to wave action. Because this cannot be ignored, wave force should be considered in deep-water pier design. During an earthquake, the wave field near piers is disturbed by the dynamic water pressure, and the wave action is also affected. Therefore, coupling interaction between an earthquake and wave should be considered in dynamic response analysis. However, when an earthquake begins, the phase of the seismic excitation at the bottom of piers can be simply regarded as 0, whereas the phase of the wave on the surface of piers may vary from 0 to 2π. The phase difference between the earthquake and wave will directly affect the magnitude of wave action and in turn affect the coupled earthquake-wave action. Therefore, studying the dynamic coupling effect of earthquakes and waves on deep-water piers and considering the influence of phase difference on the dynamic responses of piers are of great significance. In this study, a refined model of composite piers and potential fluid element model of water bodies near piers were established using the finite element analysis software ADINA to simulate fluid-structure interaction. The dynamic responses of piers under independent earthquake action, independent wave action, and coupled earthquake-wave action were analyzed, and the differences in ground motion, wave height, and phase difference between the earthquake and wave were considered. Results show that the coupling interaction between an earthquake and wave must not be ignored and its influence on total hydrodynamic pressure is approximately 15%. The phase difference between an earthquake and wave has a significant effect on the dynamic responses of piers. When the phase difference varies between 0 and 2π, the maximum response amplitude varies nearly 54%. The most unfavorable phase difference is closely related to the frequency of earthquake excitation, natural frequency under water, and pier structural type. A method for identifying the most unfavorable phase difference using MATLAB software and a simplified method for calculating the maximum coupled dynamic responses of piers using a coupling coefficient were proposed, which allow for convenient application to actual engineering design.

During mobile phone call (MPC) behavior, drivers are prone to falling into a cognitive distraction (DCD) state. In this regard, a DCD image recognition method based on head and eye behavioral characteristics is proposed in this paper. An online skin color model based on the YCbCr color space was established to adapt to the fluctuating illumination and complex backgrounds during natural driving. The principal component analysis and histogram of oriented gradients (HOG) feature of the candidate skin region was extracted and a support vector machine classifier was used for MPC hand gesture recognition. Additionally, the multi-scale local modulus maximum method was used to detect significant mouth edges, and the driver's speech behavior was identified by calculating the activity of these edges. The discrimination logic of MPC behavior was then established by the integration of MPC hand gestures and speech behavior. On this basis, eyeball activity, blink index, and head rotation activity were obtained within a 5-second-time window. Finally, a DCD recognition method was established using the Dempster-Shafer evidence theory based on the feature fusion of head-eye movements. The results of driving experiments show that the MPC recognition rate based on the fusion of hand gesture and speech behavior is 92.8%. For drivers without glasses, eye activity is the single index with the highest recognition rate of DCD, and the fusion evidence of “eye activity-head yaw activity-head pitch activity” reaches the highest DCD recognition rate of 86.2%. For drivers with glasses, the fusion of “head sway activity-head pitch activity” reaches the highest DCD recognition rate of 83.2%. In addition, the DCD recognition rate of skilled drivers is slightly higher than that of unskilled drivers.

A flexural pile foundation in integral abutment jointless bridges (IAJBs) is usually used in practical engineering to absorb the horizontal reciprocating deformations induced by the effects of ambient temperature or earthquakes. Therefore, a pseudo-static test on different types of piles in IAJBs was carried out to study their seismic performance and deformation capability under low-cycle horizontal reciprocating displacement loads. The behaviors of piles, in terms of damage modes, horizontal deformations and bearing capacities, and distributions of strain and bending moment, were compared. The test results indicate that with an increase in the reinforcement ratio and pre-stressed level, the pile damage position develops downward to a larger embedded depth. The effect of soil-pile interaction and crack resistance can be enhanced by increasing the reinforcement ratio and pre-stressed level. Moreover, a rectangular pile has better pile-soil interaction and energy dissipation capacity than a circular pile. The inflection points of pile deformation will be deeper with increases in the reinforcement ratio and section size, which can increase the effective length and horizontal deformation capacity of the pile. Compared with the circular pile, the steel H-pile has a better elastic-plastic deformation capacity, ductility and energy dissipation capacity. The bearing ratio is a suitable indicator to evaluate the effect of pile-soil interaction. The test results also indicate that the skeleton curves of the concrete pile are expressed using displacement characteristic points of elastic ultimate displacement, unobservable cracking displacement, observable cracking displacement, crushing displacement, peak displacement and limiting displacement. The displacement characteristic point improves with the increase in the reinforcement ratio and pre-stressed level. Based on the skeleton curves of the concrete pile, a displacement-based seismic design criterion “three-stage seismic fortification levels and five-grade damage levels” in IAJBs is proposed, which can provide a reference for the design and formulation of existing specifications.

Surface crack detection of bridge structures provides important condition information and decision basis for condition identification, disease regulation, and safety assessment of bridge structures. To address the problems of traditional manual detection methods, such as high risk, traffic impact and high cost, an intelligent crack identification method for bridge structures is proposed based on unmanned aerial vehicles(UAVs) and deep learning. A multi-rotor DJI M210-RTK UAV was used to acquire high-resolution images of the concrete surfaces of bridge structures. Using the SDNET crack dataset and other image resources, a training image library for the deep-learning was established for deep learning, covering 1133 images of precisely marked crack aeras. The deep-learning recognition model was trained and established using the mask region-based convolutional neural network (Mask R-CNN) algorithm. Based on the Mask R-CNN identification model, cracks were automatically identified and located by scanning high-resolution concrete surface images in rectangular sliding windows. A post-processing procedure was developed to identify crack shapes and widths, covering image binarization, connected domain denoising, edge detection, crack skeletonization and width calculation. A set of instruments consisting of a DJI M210-RTK UAV, a ZENMUSE X5S camera and an Olympus lens with a focal length of 45 mm was used in the verification experiment. The crack widths identified by the UAV method agree well with those measured by the crack width test equipment when the distance from the UAV to the surface of the bridge structure was 10 m. The absolute errors are less than 0.097 mm, and the relative errors are less than 9.8%. The UAV method was applied to the surface crack detection of the bridge tower of the Changsha Hongshan Bridge, with a height of 136.8 m. The deep learning of the Mask R-CNN algorithm was used to identify cracks automatically from the images with an accuracy and a recall rate of 92.5% and 92.5%, respectively. The UAV method, which is highly efficient, safe, and inexpensive, is capable of remote, non-contact and automatic detection of cracks in high-rise bridge structures, which has important value for scientific research and engineering applications.

Distributed drive electric vehicles have a high degree of controllable freedom, fast response time, high chassis drive-by-wire integration, and compact vehicle structure, making them the best platform for realizing advanced vehicle dynamics control technology. The widespread use of wired control systems, such as steer-by-wire, drive-by-wire, brake-by-wire, suspension-by-wire, and different levels of driver assistance systems, such as anti-lock brake systems, lane keeping systems, adaptive cruise control, and lane change assist systems, result in redundancies and conflicts in the control of the vehicle chassis. The distributed driving format provides more possibilities for efficient and cooperative control between the multiwire control and auxiliary driving systems. This paper highlights the latest progress in distributed drive electric vehicle integrated control technology in terms of integrated control strategy architecture, longitudinal-lateral integration control, lateral-vertical integration control, longitudinal-vertical dynamic integrated control, longitudinal-lateral-vertical dynamic integrated control, fault-tolerant control, and chassis dynamics integrated control for distributed drive intelligent electric vehicles. It also provides an outlook on the development direction from multiple perspectives, aiming to provide a reference for developing high-performance integrated chassis control technology for distributed electric vehicles.