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.

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.

Cross-sea transportation infrastructure is characterized by large engineering scale, and high construction complexity. There are still some key problems in traditional operations and maintenance work, such as high comprehensive cost, low efficiency, poor accessibility, and large offshore safety risks. In order to realize the safe, reliable and efficient operation and maintenance of the Hong Kong-Zhuhai-Macao Bridge (HZMB), four common problems of the sea-crossing transportation infrastructure are summarized: low perception ability of service status, low utilization rate of monitoring information value, low efficiency of traffic risk active management and control, and low level of informationization and intelligent decision-making in operation and maintenance management. This paper analyzes the key technologies of intelligent operation and maintenance of the HZMB, and refines nine construction contents of intelligent operation and maintenance of the HZMB: the cross-sea traffic infrastructure operations based on 5th Generation Mobile Communication Technology (5G) and Internet of Things (IoT), the millimeter deformation monitoring and closed space positioning based on Beidou, the intelligent monitoring platform and big data fusion processing system for underwater structure, the inspection, testing, integration of emergency system based on unmanned aerial vehicle, the proximity detection and maintenance system for cross-sea bridges and tunnels based on the inspection robot, the service environment digitization and operational status monitoring and evaluation system for cross-ocean cluster facilities, the digital maintenance and management system for cross-sea cluster facilities based on the life-cycle hypothesis, the intelligent system of full-time traffic safety operation and rapid emergency response, the integrated operation and are summarized management platform for cross-sea cluster facilities. In addition, the overall technical architecture including device awareness layer, communication layer, basic resource layer, data support layer, business support layer, intelligent application layer and user interaction layer are also proposed. Lastly, the main technical features of intelligent operation and maintenance of the HZMB. The intelligent operation and maintenance of the HZMB can provide important reference for the operation and maintenance of other cross-sea transportation infrastructure.

Bio-oil is a green, environmentally friendly, and renewable resource with the potential to restore the physical and rheological properties of aged asphalt and improve road performance of recycled asphalt mixtures. To promote the in-depth research of bio-oil in the field of regeneration of aged asphalt materials, the source, preparation, and physicochemical properties of bio-oil were summarized, the regeneration mechanism of bio-oil on aged asphalt was discussed, the properties of bio-oil regenerated asphalt and bio-oil regenerated asphalt mixture were reviewed, and subsequent research interests were elaborated with regard to the existing deficiencies of bio-oil regenerated aged asphalt materials. The current research shows that pressed oil bio-oil has been most systematically studied for application in the regeneration of aged asphalt materials, followed by rich wood fiber plant-based bio-oil and animal manure bio-oil. However, all three types of bio-oil have broad application prospects in the regeneration of aged asphalt materials. The rich wood fiber plant-based bio-oil and pressed oil bio-oil play the role of “diluting” and “dissolving” in the regeneration of aged asphalt, which can regenerate aged asphalt in terms of both chemical balance and molecular structure repair. The animal manure bio-oil plays the role of “ dissolving” in the regeneration of aged asphalt, which achieves the regeneration of aged asphalt in terms of molecular structure repair by promoting the disintegration of asphaltene aggregates through its rich polar amide-based compounds. In addition, the rich wood fiber plant-based bio-oil and pressed oil bio-oil can be directly used for the regeneration of aged asphalt, while the animal manure bio-oil needs to be applied in combination with oil-rich regenerators to give full play to its regenerative effect. Among these bio-oils, the pressed oil bio-oil demonstrates better efficiency in regenerating aged asphalt materials. Future research is expected to be conducted as follows: establishing the correlation between the source, preparation process, and physicochemical properties of bio-oils to better screen and evaluate the applicability of bio-oils from different sources and preparation processes as asphalt regenerators; further exploring the application potential of rich wood fiber plant-based bio-oil and animal manure bio-oil in the regeneration of aged asphalt materials; focusing on the secondary aging of bio-oil regenerated asphalt and regenerated asphalt mixtures; and exploring the composite application of bio-oil in the field of regeneration of aged asphalt materials.

The continuous increase in bridge spans, innovations in structural systems and the application of lightweight high-strength materials have led to a decline in damping for long-span bridge structures. Consequently, it has intensified vibration sensitivity of bridges to dynamic loads, such as wind, vehicles, and earthquakes. This article is intended to summarize recent research findings on damping characteristics and identification methods for long-span bridges, and to promote progress in excitation technology and damping identification techniques. The review is organized in four aspects. Firstly, a systematic examination of the structural damping theories and a comprehensive review of the state-of-the-art of damping characteristics for long-span bridges are presented. Secondly, an extensive summary of the working principles, excitation systems, and application status of long-span bridge excitation technology are provided. Thirdly, the research progress of time-domain, frequency-domain, and time-frequency domain methods for long-span bridge damping identification based on ambient excitation is deeply analyzed, and the latest achievements in the areas of intelligent, automated, and uncertainty quantification of damping identification are systematically elaborated; in addition, the damping identification methods based on forced vibration and their application status are summarized. Lastly, the paper identifies directions for further research in four areas:damping theory, excitation technology, sensing systems, and identification methods. The findings from this review offer a theoretical foundation for the development of damping theory and identification method for long-span bridge structures. In addition, it is helpful to determine the suitable value of the structural damping for dynamic analysis of long-span bridges, and it may serve as a valuable reference for design and intelligent operation and maintenance of long-span bridges.

Bridges are crucial infrastructure for traffic and transportation. The inspection of bridge apparent defects is important for ensuring public safety, extending the lifespan of bridges, and identifying risks in a timely manner. They also contribute to improving the reliability and durability of bridges during their operational phases. In recent years, with the rapid development of technologies such as computer vision and artificial intelligence, machine vision has gradually emerged as a new approach for bridge apparent defect inspection. This study conducted a detailed analysis of relevant studies in recent years to review the key techniques for bridge apparent defect inspection based on machine vision, including inspection platform development, data acquisition, image processing, 3D reconstruction, defect localization, and defect parameter quantification techniques. By analyzing the inspection process of existing research, a technical framework for bridge apparent defect inspection based on machine vision was summarized, and the functions and connections between each process were analyzed. The above-mentioned review of key techniques and summary of technical frameworks provide a reference for researchers conducting inspection work on bridge structures. Finally, based on the different levels of automation in data acquisition and defect detection observed in existing studies, this study proposes a hierarchical classification for intelligent bridge apparent defect inspection based on machine vision. This classification includes six levels: manual inspection assistance, defect inspection and localization, partially automated inspection, globally automated inspection, high-degree automated inspection, and fully automated inspection. A comparison of existing literature reveals that although research has moved beyond the traditional stage of manual inspection, it still falls short of achieving fully automated inspection. Therefore, this field has strong research value and broad application prospects.

This study focuses on the challenges associated with real-time dynamic updates in the high-precision, high-reliability, and high-security aspects of highly automated driving (HAD) maps. The aim is to summarize the existing difficulties and challenges encountered in high-definition map updates, accelerate the large-scale commercialization of autonomous driving maps, enhance the safety and stability of intelligent vehicles, and provide important support for high-level autonomous driving systems. Therefore, this article first describes the definition and connotation of HAD maps, highlighting their data characteristics and functional applications. Next, it summarizes the development status and trends of high-definition map updates and evaluates the advantages and disadvantages of a centralized map and multisource updates, indicating that crowdsourced updates have become a new trend in map update development. Furthermore, it summarizes the basic architecture and core modules of crowdsourced updates, analyzes the core technologies involved, and summarizes the current situation and trends of key technologies involved in crowdsourced updates. Results of the analysis of the crowdsourced update technology for HAD maps show that the current technology still faces seven significant challenges, including map modeling, high-precision positioning, 3D reconstruction, fusion updates, data security, fast review, and standardization of laws and regulations. In response to these challenges, the study suggests that addressing the technical, policy, and legal issues of crowdsourced update technology requires the cooperation of multiple departments from governments, industry, academia, and research. The collective effort will accelerate the development and application of crowdsourced update technology for HAD maps.

Regarding the increasing volume of major linear engineering construction roads and the aggravation of the destruction of the surrounding ecological environment, the impact factors of the ecological environment along the line were explored and a corresponding theoretical model was proposed to provide greening measures. Based on texts including journal literature, standards, environmental impact reports, and yearbooks, among others, the LDA model was adopted to extract respective themes, and eight types of impact factors were identified. Based on the ecological factor theory, the theoretical model of the impact factors was established, and the corresponding greening measures were proposed combined with the output results of the Word2vec model. Subsequently, a case analysis was carried out. The results show that: ① The ecological environment along the construction road of major linear engineering is affected by eight kinds of key factors: water environment destruction, soil environment destruction, soil erosion, air pollution, noise pollution, solid waste pollution, social factors, and ecosystem destruction, a total of 17 specific factors. ② These eight key factors can be divided into two categories: abiotic factors and biotic factors. The former can be further classified as climate, soil, topography, atmosphere, and anthropogenic factors, while the latter includes intraspecific and interspecific relationships of organisms. ③ The four specific factors of wastewater sewage pollution, environmental awareness, vegetation resource destruction, and ecological environment sensitivity have the greatest impact on the case construction road. The research conclusions can provide some theoretical ideas and reference for strengthening the ecological restoration along construction roads of major linear engineering.

The innovation of engineering materials is a major driver of the development for civil engineering structures, and the reformation of engineering structures continually promotes the revolution for engineering materials. Ultra high performance concrete (UHPC) is a new class of concrete that has excellent mechanical properties including high strength, high ductility, high durability, high impact resistance, etc., which is suitable for the new generation bridges with long span, light weight, and high performance. To facilitate the UHPC bridge researches and implantations, this paper systematically summarizes the recent research progresses, cutting-edge highlights, current issues, corresponding solutions, and development prospect for UHPC bridges. The paper firstly summarizes the research achievements for UHPC materials, including mix design, mechanical properties, and development of UHPC for bridges; then concludes the design theories for UHPC structures, including the contributions of fibers in flexural and shear design, impact and blast resistance, fatigue design, etc. The achievements of structural system innovations, such as UHPC bridges without stirrups, steel-UHPC composite bridges, UHPC columns for seismic resistances, UHPC bridge overlay, UHPC for bridge retrofit. In lights of the current research and applications, the major challenges and technological path for large-scale application of UHPC in bridge engineering are proposed, aiming to provide new visions and references for UHPC academic researches and large-scale applications in bridge engineering.

Fatigue damage is an important issue in the technical system of asphalt pavement and the corresponding fatigue design models have been established in various design methods. The fatigue evolution behavior of asphalt pavement in the life cycle was studied and 60 million loading tests were applied on the full-scale accelerated loading test track (RIOHTrack). Different fatigue damage conditions from 19 test sections were obtained and a bidirectional fatigue damage mode of asphalt pavements was presented. In this mode, under the action of a driving load, the asphalt pavement undergoes both top-down and bottom-up fatigue damage. The top-down fatigue damage is caused by a compression or shear load and can be generally manifested as the dual damage between the transverse top-down fatigue cracking and rutting deformation. Bottom-up fatigue damage is a traditional fatigue damage mode, caused by the flexural load at the bottom of the whole material structural layer. It is important to note that the flexural fatigue failure of a single structural layer does not lead to the failure of the whole structure, that is, the fatigue life of a structural layer is not equal to the fatigue life of the whole structure. In this paper, based on the bottom-up fatigue damage mode, a layer-by-layer accumulation analysis method of fatigue life is proposed to improve the assessment of flexural fatigue life of asphalt pavement.

Complex wind environments, nonlinear aerodynamics, and wind-structure interactions are the main challenges in wind-engineering research. During the past several decades, the number of data accumulated from wind tunnel tests, numerical simulations based on computational fluid dynamics, and structural health monitoring has become massive, providing valuable resources for addressing these challenges. With the development of deep-learning technology, machine learning (ML) has achieved great success in nonlinear science and engineering problems owing to its nonlinear representation capabilities, powerful optimization algorithms, excellent generalization performance, and flexible network architecture. Emerging data-driven approaches based on ML algorithms have helped address these challenges in wind engineering and increased physical and engineering knowledge based on the available wind-engineering data. The application of ML in wind engineering involves all aspects of the wind-engineering field, such as the wind environment, aerodynamic and aeroelastic forces, wind-induced vibrations, aerodynamic optimization and control, and wind disaster assessment. The purpose of this paper is to introduce the research progress and state-of-the-art technologies in ML and artificial-intelligence applications for bridge wind engineering.

To achieve the desired pavement mean texture depth by adjusting the gradation design of an asphalt mixture, high-precision three-dimensional laser scanning technology was used to collect the surface texture feature information of three typical gradation asphalt mixture specimens: Asphalt Concrete, Stone Matrix Asphalt, and Open Graded Friction Course. After the exception values and outliers were processed by neighborhood interpolation and the sampled data was denoised by mean filtering, the sample surface was reconstructed in three dimensions. A band-pass filter was designed according to the frequency corresponding to the wavelength of the macro texture based on the frequency domain information of the reconstructed surface obtained by the Fourier transform; the macro texture of the pavement was separated and extracted from the reconstructed surface. A Monte Carlo algorithm was used to calculate the mean texture depth of the pavement. The influence of the mixture particle size and passing rate of the sieve on the mean texture depth was considered by using the product of the mass ratio on the sieve and particle size. The prediction models of the product of the mass ratio on sieve and particle size, and the mean texture depth were established using multiple linear regression analysis, random forest, and artificial neural networks; the influence of the mixture gradation on the mean texture depth of the asphalt pavement was studied. The results show that mean filtering not only removes the noise signal but also retains the elevation profile features. The surface features of the three-dimensional reconstructed specimen are consistent with the original surface features. The mean texture depth is affected by other particle sizes in the grading curve, except for the maximum nominal particle size and passing rate of the sieve. The regression model was established using multiple linear regression, random forest, and an artificial neural network, which takes the product of the mass ratio and particle size on the sieve of each mesh size as the independent variable and the mean texture depth as the dependent variable, has an R² of more than 0.95.

Historical earthquake damage reveals that tunnels crossing active faults suffer severe damage and are very difficult to repair and rebuild after the earthquake. Therefore, improving their seismic resilience is a key challenge for tunnel construction in active fault zones with strong earthquakes. This paper firstly summarizes and analyzes the lessons from earthquake damage and identifies the key aspects of seismic design of tunnels crossing active faults, including the assessment of the seismic hazard of the engineering sites, the seismic design strategies of tunnels crossing active faults, and the prevention and control measures. Secondly, based on the literature review, the paper presents the current state of the art of seismic research on tunnels crossing active faults, focusing on the seismic analysis methods, the tunnel structural failure mechanisms, and the seismic control measures. Then, the paper establishes a unified concept of seismic resilience of tunnels crossing active faults and a design framework based on the core idea of "pre-earthquake reserve, mid-earthquake stabilization, and post-earthquake restoration". The paper also proposes the objective of seismic resilience of tunnel structures, considers the strong earthquake-dislocation coupling effect of active faults, develops a zoning guideline of seismic defense for tunnels with "site zoning and structural segmentation" and a design concept of "resisting/mitigating, adapting, and inducing/avoiding/recovering". Finally, the paper discusses the key scientific and technological issues that need to be urgently addressed in the seismic research of tunnels through faults and outlines the future research directions in this field. This study aims to provide a unified concept of seismic resilience defense and design strategy for tunnel construction crossing active faults, and also to point out the focus areas for future seismic research.

The precast socket pier, a type of prefabricated structure, is connected by embedding a precast pier into a groove reserved in a bearing platform and filling concrete or grouting materials in the gap . A corrugated steel pipe is placed in the reserved groove of the bearing platform to strengthen the lateral constraint on the root of the pier. In this study, quasi-static load tests of two groups of specimens of precast socket pier and cast-in-place pier were conducted. The seismic performance of the precast socket pier and the lateral constraint effect of the corrugated steel pipe were studied. And through the simulation analysis of the relevant influence parameters, the influence of socket depth, grouting material strength and connection interface form on the seismic performance of precast socket piers were discussed, providing the basis for the design of precast piers. The results show that the size of the plastic hinge region and failure mode of the corrugated-steel-pipe-restrained precast socket pier are consistent with those of the cast-in-place pier. The bearing capacity of the socket pier is similar to that of the cast-in-place specimen. The ultimate displacement at failure of the socket pier is increased by 20%, and the ductility and energy dissipation capacity are improved. The corrugated-steel-pipe-restrained precast socket pier has good integrity and seismic performance. With the increase of socket depth, the seismic performance of the precast socket pier is improved. However, when the socket depth exceeds 0.6D, the improvement of seismic performance tends to ease. When the strength of the grouting material reaches that of the concrete of the precast socket pier, the strength of the grouting material has little effect on the seismic performance of the pier. Compared with the smooth contact interface, the bearing capacity of the socket pier connected by corrugated key teeth and trapezoidal key teeth is increased by about 30% and 26%, and the cumulative energy consumption is increased by 20% and 15% respectively, where the seismic effect of the socket pier connected by corrugated key teeth is the best.

To clarify the trajectory behavior of vehicles in the lane-keeping stage while driving on freeways and provide a reference for lane width determination, a field driving test of 38 drivers was performed on two freeways in Chongqing, China. Using on-board equipment, the speed, trajectory, and lateral distance of “car body-lane marking” under natural driving conditions were collected. Based on natural driving data, the lateral offset of trajectory and the lateral margin of “car center-lane marking” were calculated, and the characteristics of these two parameters in straight/curved sections of freeways and their influencing factors were analyzed. The results show that there is a difference in the lateral offset of the desired trajectory between curved and straight sections. The characteristics of the vehicle trajectory in curved sections shift to the inside of the curve, whereas the vehicles driving in straight sections tend to shift to the left side of the lane. However, the proportion of vehicle tires close to the lane markings in the curved sections is lower than that in the tangents. The minimum and expected values of the left residual width between the bodywork and lane markings on the tangent are concentrated in [0.2 m, 0.6 m] and [0.3 m, 0.9 m], respectively, while these two indicators measured from the curved section range in [0.2 m, 0.7 m] and [0.5 m, 0.9 m], respectively. The lane position has an impact on the trajectory lateral offset and left lateral margin; the left residual width of the left-hand curves is lower than that of the straight section and right-hand curves. When driving in the left-hand curves of the inner lane, the distance between the car and dividing median is closer. Thus, the crash risk of the left-hand curves is higher. When the speed increases, the vehicles in the inner lane tend to move away from the dividing median. However, in general, the speed has no significant effect on the left lateral margin and trajectory lateral offset. According to the findings of this study, for freeways with a designed speed of 80-120 km·h^-1 and more than six two-way lanes, the width of the passenger car lane can be taken as 3.25 m.

With the rapid development of artificial intelligence, deep learning algorithms for nonlinear propose a new approach for solving the persistent dilemma of tunnels and underground engineering relying on empirical designs. In this study, by fusing multiple indices (mechanical and deformation control indices) with the correlation coefficient of the support system synergy degree, an evaluation standard for support systems, characterized by the degree of fit, was proposed. Using this evaluation standard, the data of 718 highway and 486 railway tunnel sections were collected to build a database for algorithm training. Eight attributes about the background information of tunnel engineering, including rock hardness degree, integrity degree, rock thickness, underground water volume, buried depth level, geological structure, construction method, and internal contour type, were considered input indicators. Eight attributes of the support system, including shotcrete+steel mesh, rock bolt, steel arch, secondary lining, and auxiliary measures, were considered output indicators. The input and output indicators were then quantified. After comparing the characteristics of the PSO-SVM, SA-PSO-SVM, and CLS-PSO-SVM in the application of the intelligent feedback model of the support system, the generated intelligent feedback model was tested. The results show that the evaluation method first eliminates the weak design scheme. The degrees of fit of the strong support and general support schemes are 4.28 and 4.68, respectively, which verifies that the method can evaluate the material utilization rate while ensuring structural safety. Among the three intelligent algorithms, the CLS-PSO-SVM algorithm, with the broadest search range, had the highest feedback accuracy but the longest time consumption, whereas the PSO-SVM algorithm had the shortest time consumption but the lowest accuracy. Finally, the accuracies of the five output labels designed using the CLS-PSO-SVM algorithm are 93.4%, 92.6%, 89.3%, 91.8%, and 94.3%. The collective accuracy of the five output indices is 81.1%.

The development status and application prospects of intelligent motorway traffic application technologies are analyzed to promote the intelligent transformation and upgrading of motorways and realize the effective integration of new technologies and traffic applications. First, by combining local and international intelligent road development experience and technical achievements, an intelligent motorway system is defined by drawing on the technological evolution process of the intelligent vehicle highway system (IVHS). Furthermore, the positioning of the intelligent motorway demand is discussed based on traffic applications, functions and technologies. Subsequently, problem-oriented, demand-driven, and technology-supported, it focuses on the three aspects of precise perception of road network status, cooperative control of traffic operation, and intelligent service of user interaction to review traffic application technologies. Based on the review summary, the challenges in the development and application of related technologies and the research focus and development direction of the future field are discussed. It is found that the synergistic development and integration of these transportation application technologies is an essential condition for the coupled development of multiple fields, such as road network integration and perception, cooperative traffic management and control, safety demand management, infrastructure management and maintenance, and intelligent service interaction. In future technological developments, vehicle-road-cloud cooperative perception and multisource asynchronous heterogeneous data fusion are technological breakthroughs, and intelligent cooperative management and control, operation and maintenance, and service interaction are technological transformations. In engineering practice, specific experimental scenarios should be gradually transitioned to complex motorway scenarios. The traffic monitoring system should be synergized with intelligent terminals, and efficient traffic prediction, hybrid traffic synergy, and preventive intelligent maintenance are challenges of these applications. Through the review and analysis of this study, it can provide valuable references for technology research and development and engineering applications of local intelligent motorways, as well as the technology application transformation of future demonstration projects.

Recognition and monitoring of cracks is an important part of the current research on the structural health monitoring of bridges. In the field of inspection and monitoring of bridge structures, traditional crack recognition and monitoring techniques, particularly crack monitoring techniques, hardly meet the timeliness and accuracy requirements of practical projects. Crack recognition based on deep learning has greatly improved the efficiency and accuracy of crack detection; however, it can only obtain crack information at a specific moment, and the ability to monitor the process of crack generation and evolution, which is crucial for a more reasonable evaluation and safety quantification of concrete structures, is lacking. In view of this, a systematic study of crack recognition and monitoring methods based on deep learning was performed. In this study, we analyze and discuss the construction benchmark of a crack dataset, improve and optimize the crack detection and semantic segmentation algorithms, propose a real-time recognition algorithm for multitask integration, establish an evaluation method for the model inference effect, and optimize the calculation method of crack parameters, ultimately forming crack recognition and automatic real-time monitoring algorithms for crack dynamic expansion. The results show that the proposed method for intelligent recognition and monitoring of cracks can effectively track the generation of new cracks and the global evolution of existing cracks, and the monitoring data can provide support for a reasonable and quantitative assessment of the current service performance of bridge structures.

Battery state estimation is the core technology of battery management system (BMS) and plays a vital role in ensuring safe and reliable battery use, thereby maximizing the battery capacity, and prolonging the service life. The battery model is the basis of the state estimation technique, which significantly affects the accuracy and timeliness of the state estimation. In this review, the most commonly used battery modeling and state estimation methods are summarized. First, various battery models, including the electrical characteristic, thermal, electrothermal coupled, and aging models, and modeling methods were systematically reviewed. Second, through a literature review, methods for estimating the state of charge (SOC), state of health (SOH), state of energy (SOE), state of function (SOF), state of power (SOP), state of temperature (SOT), and state of safety (SOS) were developed from the perspectives of remaining capacity, function estimation, power prediction, health assessment, temperature monitoring, and safety assurance. Finally, future research directions and trends in the state estimation of batteries are proposed, with the aim of providing references for the advanced and intelligent development of the state estimation of electric vehicle power lithium batteries.

As vehicle-infrastructure collaboration technologies gain traction in the transportation sector, traffic control is increasingly characterized by automation, proactiveness, and cooperative functionality. In this evolving landscape, Trajectory based Traffic Control (TTC) has been introduced as an avant-garde traffic control approach. TTC orchestrates the amalgam of connected and automated vehicles (CAV) and human-driven vehicles (HV) by fine-tuning the trajectories of CAVs to optimize traffic flow efficiency. Despite its global recognition as a pivotal research area in transportation, TTC remains embryonic in its theoretical stages with fragmented research content. This study offers a comprehensive review of extant literature on TTC, elucidating its foundational concepts and distinguishing features. A tripartite analytical structure is employed, delving into the TTC framework, local coordination control system, and the global coordination control system. The advancements and modular intricacies within the TTC framework are explored. A salient observation from this review is that, while TTC theoretical research has matured, many foundational assumptions in studies remain robust. Factors, such as swarm characteristics, operational risks, and the inherent heterogeneity of semi-automated and connected traffic in tangible traffic scenarios, have been marginally addressed. Consequently, transitioning this theoretical knowledge into large-scale, practical applications remains challenging. Highlighting prospective avenues for further exploration, the study emphasizes the significance of traffic group game theory, consensus mechanisms, robust TTC approaches addressing diverse risks, and the rigorous testing and validation of TTC within large-scale heterogeneous traffic environments. These types of recommendations aim to shepherd future TTC research endeavors towards more pragmatic and holistic outcomes.

Decision-making and planning are the core functions of automated driving systems and the key to improving the driving safety, driving experience and travel efficiency of automated vehicles. The main challenges faced by decision-making and planning are how to meet the extremely high reliability and safety requirements for automated driving, and how to effectively deal with scenario complexity, environmental variability, traffic dynamicity, game interactivity, and information completeness, as well as how to generate human-like driving behavior, so that vehicles can integrate into the traffic ecosystem naturally. A systematic and overall review of the technical points of decision-making and planning is presented in this paper to gain a comprehensive understanding of their frontier issues and research progress. Firstly, the research progress of situational awareness-oriented behavior prediction is reviewed from three perspectives, namely data-driven driving behavior prediction, probabilistic model driving behavior prediction, and personalized driving behavior prediction. Secondly, behavior decision-making is summarized into reactive decision-making, learning decision-making and interactive decision-making, all of which are analyzed in turn. Thirdly, motion planning and its applications are compared and analyzed from a methodological perspective, including graph search methods, sampling methods, numerical methods, interpolation and curve fitting methods, etc. Additionally, the key scientific issues and major research progress of end-to-end decision-making and planning are summarized and analyzed. Finally, the significant impact of decision-making and planning on improving the intelligent level of automated vehicles is summarized, and the future development trends and technical challenges are prospected.

Aiming to meet the demands of multi-level fortification, this paper proposes a variable hysteresis rotational friction damper (VH-RF). The damper was applied to a bridge pier structure, and its multi-stage seismic performance was investigated. The configuration of the new damper, its working mechanism, and its variable hysteretic principle were discussed. In addition, a simplified analysis model was established. A finite element model of the new damper was established in the ABAQUS software. Based on the simplified mathematical model and finite element model, the hysteretic behavior and corresponding influence law of the damper were systematically studied. A hysteretic behavior model was developed for the new damper in OpenSees to reflect the multi-stage seismic performance of the double-column pier. The results are as follows:① The VH-RF can present different hysteretic behaviors under different deformations. It has a stable energy dissipation capacity under small deformations and great self-centering capability under large deformations. ② By changing the design parameters of VH-RF, the hysteretic performance of VH-RF can be adjusted. ③ A double-column pier with the VH-RF can provide a staged seismic response, thereby enhancing the seismic resilience of the bridge structure.

To investigate the mechanical behavior of a curved continuous steel-concrete composite twin I-girder bridge subjected to flexure-torsion combined action, a steel-concrete composite bridge model was designed with a curve radius of 200 m and a 17.5+17.5 m span combination. Static loading tests were performed, including two-point asymmetric loading in the elastic stage of the bridge model and four-point symmetrical loading to structural failure. The load-deflection relationship and strain development on the steel girders, bridge slabs, and reinforcements were measured at key sections. The failure process and characteristic load of the bridge model, crack distribution, and crack width of concrete slabs were also recorded. The experimental results show that, under the symmetrical loading, the outer structure carries more load owing to the curvature effect. After the flange of the key sections is yielded, the outer web at the second span loading section is buckled owing to shear, indicating that the plastic rotation capacity of the section is limited by the local buckling of the steel plate. The concrete slab at the mid-support section is cracked beyond ±20% of the effective span. The inner structure continues to carry the load after the outer structure fails, and its ductility coefficients are significantly smaller than that of the other side, indicating that the bridge model is redundant in the lateral direction. Under the vertical load, the normal stress of the cross-section is primarily developed by vertical bending and non-uniform torsion. In addition, the bottom flange of steel girders is developed with normal stress caused by lateral bending. Finally, a simplified method for calculating the lateral bending moment of the bottom flange is provided. Based on Vlasov's thin-walled structure theory, formulas are provided for calculating the non-uniform torsional section properties of the steel-concrete composite twin I-girder bridge.

In order to enhance the toughness and durability of roller compacted concrete (RCC) and improve its road performance, rubber particles with sizes of 1-2 mm were modified by 5% NaOH solution, and a control group without treatment was set up. Then, 5%, 10%, 15%, and 20% volume of sand were replaced by RCC. The working performance, mechanical properties, and durability of rubber RCC were analyzed by improving the VC value, compressive strength, flexural strength, drying shrinkage, and frost resistance. The pore structure and microstructure of the mixture before and after modification were compared and analyzed by industrial CT and SEM, and the microscopic mechanism of rubber particles on the mechanical properties of the RCC mixture was revealed. The results show that the incorporation of rubber particles reduces the early strength growth rate of modified rubber RCC and has a negative impact on the mechanical properties of road RCC, but improves its plasticity. With the same rubber content, the mechanical properties of modified rubber RCC are better than those of unmodified rubber RCC, and the content of modified rubber particles should not exceed 15%, which has little effect on the bearing capacity of the road pavement structure. When the modified rubber content exceeds 15%, the dry shrinkage performance of RCC is improved. When the content of modified rubber particles is 5%-20%, the higher the modified rubber content, the more significant improvement in the frost resistance of the RCC. Therefore, the addition of rubber particles reduces the pore volume in RCC and optimizes the pore structure. The surface of the modified rubber particles is rougher, the crack width between the rubber particles and cement mortar is reduced, and the bonding strength between rubber particles and aggregate in the modified rubber RCC mixture is improved.

Conditionally automated driving systems, though advanced, are not universally adept at managing all driving scenarios and require driver intervention when necessary. The efficacy of driver take-over is paramount for the safety, user experience, and broader acceptance of such automated vehicles. A plethora of recent studies rigorously examined driver take-over performance, but certain challenges persist. This study presented a systematic review of extant literature concerning driver take-over performance, encapsulating the influencing factors, the models, and the various evaluation methodologies employed. The determinants influencing take-over performance span driver-specific factors, traffic environment parameters, and features of the automated driving systems. Concerning the modeling of take-over performance, distinctions were drawn between classical statistical models, machine learning approaches, and structural equation models. The study further encapsulated prevailing evaluation indices specific to take-over performance, alongside holistic evaluation methodologies. Findings from the review pinpoint that current indicators for influencing factors lack comprehensiveness. Additionally, a discernible imbalance between interpretability and predictive accuracy is observed in the existing models. Furthermore, the present evaluation methods for take-over performance necessitate refinement. As a roadmap for future inquiries, this study advocates for the initiation of comprehensive measures of take-over performance based on subjective evaluation of human drivers. Then, there is an imperative to develop quantitative indicators of the influencing factors of take-over performance from human-machine-environment aspects. Conclusively, calls are made for crafting high-precision predictive models for take-over performance that duly recognize the intricate interdependencies of myriad influencing factors. Pursuing such avenues of research is vital to provide theoretical support for elevating driver take-over performance, thus propelling the evolution of conditionally automated driving.

Real-time traffic accident detection is crucial to improve traffic emergency management. Vision-based methods can effectively reduce the manual monitoring costs and achieve promising results. However, existing methods typically fail to identify traffic accidents in congested traffic. To this end, this study proposes an improved two-stream network to boost detection performance in congested traffic. Specifically, the improved two-stream network utilizes two parallel convolutional neural networks (appearance and motion) to extract the appearance and motion features of traffic accidents. To guide the appearance network in capturing the prominent crash appearance features in congested traffic, we developed an auxiliary network and triplet loss to train the appearance network. Moreover, this study establishes an optical flow extraction network in a motion feature extraction network to capture subtle optical flow information. Experimental results show that the proposed method effectively improves accident detection in congested traffic, reducing the false and miss identification rates by 24.56% and 25.00%, respectively, compared to traditional methods. This method is applicable to high-volume mixed urban traffic scenarios (for example, urban intersections, expressways, and main roads).

To analyze the compatibility improvement of Benzaldehyde (BENZ) and/or Dioctyl Phthalate (DOP) effecting on direct coal liquefaction residue (DCLR) and asphalt, this paper selected saturate, aromatic, resin, and asphaltene to represent the four components of asphalt, and used molecular simulation software (Materials Studio, MS) to build SK-90 asphalt, DCLR, BENZ, DOP monomer molecular model. Using the above molecular models to construct 14 systems, the difference in solubility parameters, Flory-Huggins parameters, and the interaction energies of the 14 systems were calculated to explore the influence mechanism of BENZ and/or DOP on the compatibility of DCLR and SK-90 asphalt. The results showed that the addition of BENZ and/or DOP could improve the solubility parameter of SK-90 asphalt, thereby reducing the difference in solubility parameter between DCLR and SK-90 asphalt. It meant that the compatibility of DCLR and SK-90 asphalt became better. The order of BENZ and/or DOP to improve the compatibility of DCLR and SK-90 asphalt was BENZ and DOP>BENZ>DOP. Moreover, the van der Waals potential energy and non-bonding energy were more excellent than electrostatic potential energy, which as indicators to evaluate the compatibility between DCLR and SK-90 asphalt.

Guided by low-carbon development policies, many provinces and municipalities across the country have begun to popularize electric buses. However, the characteristics of the technical performance and operating environment of electric buses, such as their range, charging time constraints, and random road network environment, create new challenges for electric bus vehicle scheduling and charging plans. Stochastic travel times lead to delays in the connection of trips, and because of the interdependence of successive trips, delays in the upstream trips may cause delays in the downstream trips. This leads to the knock-on effect of delay propagation, making the risk tolerance of the trips and charging schedules very vulnerable and preventing the effectiveness of bus scheduling. In this study, we consider the effect of delay propagation in the electric bus scheduling problem, analyze the effect of stochastic travel times on bus trips and charging schedules, and develop optimization models from single-line to regional scheduling modes to obtain an economical and reliable bus scheduling solution. First, the network flow model was used to describe the electric bus scheduling process, and a Markov process was introduced to portray the delay propagation effect. On this basis, service quality indicators, such as expected waiting time and expected delay time, were calculated and added to the objective function, thereby developing a mixed-integer linear programming model. Then, a multi-commodity flow model was applied to extend the single-line scheduling model into a generic regional scheduling model, and a 'delay state layer’ was designed to calculate the delay time distribution and save computational expenses. Finally, a case study was conducted with actual data from two electric bus lines in Guangzhou, and the commercial solver Gurobi was used to obtain the exact solution. The results show that the optimal time window interval for the charging schedule is 40 min. Under the optimal scheme, the vehicles can make full use of the idle time in daytime operation for charging, and this characteristic is not affected by the duration of the time window interval. As the delay penalty factor increases, the average value of the expected delay time first decreases and then keeps fluctuating, and when the delay penalty coefficient ≥ 2 CNY·min^-2, the average value of the expected delay time is less than 15 s. Thus, this model can effectively reduce the delay. Under this condition, the average value of the expected waiting time increases and then fluctuates, which indicates that the model can intelligently adjust the order of connection between trips by increasing the waiting time as buffer time, so as to reduce the occurrence of delay.

The urban multimodal transportation system is a highly complex and diverse transportation network designed to efficiently meet the mobility needs of people, goods, and services within a city. Its complexity originates from many factors including the coupling between different transportation modes, complex interactions between transportation demand and supply, and intrinsic stochasticity and self-organization of an open, heterogeneous, and adaptive system. Therefore, understanding and managing such a complex system is a nontrivial task. However, with the increasing availability of multisource big data in multimodal transportation and other sectors, enhanced computational hardware capabilities, and rapid development of machine learning models, the concept of large models has been applied in various fields, including computer vision and natural language processing. In this study, a conceptual framework, multimodal transportation generative pretrained transformer (MT-GPT), of a data-driven foundation model for multifaceted decision-making in complex multimodal transportation systems was conceived. Considering the characteristics of different transportation modes, the core technologies and their integration methods were investigated to realize this conceptual framework. An expansive data paradigm is envisioned for a foundation model tailored to transportation, along with improvements in hierarchical multitask learning, hierarchical federated learning, hierarchical transfer learning, and hierarchical transformer framework. Application cases of MT-GPT within the “spots-corridors-networks” three-layer large model framework are discussed by constructing “task islands” and “coupling bridges”. MT-GPT aims to provide an intelligent support for tasks such as multiscale multimodal transportation planning, network design, infrastructure construction, and traffic management.

The embedded intelligent sensing device has the interaction with the asphalt concrete material of the pavement. In order to study the influence of the layout depth, number, shape and dispersion of the device on the mechanical properties and failure form of the asphalt pavement material, this paper uses the PFC^2D numerical simulation software. In the experiment, the discrete element numerical simulation method was used to establish the discrete element model of the asphalt sample embedded in the intelligent sensing device under the condition of the uniaxial compression simulation experiment. By simulating the failure mode of the asphalt concrete sample under different influence conditions, the influences of different pavement burial depths, device numbers, device shapes and dispersion states on the mechanical properties and failure modes of asphalt concrete samples with intelligent sensing devices were analyzed. The results show that the mechanical properties and failure modes of asphalt concrete samples containing intelligent sensing devices are greatly affected by the number of devices, and the stress concentration phenomenon generated during the experiment mostly occurs at the edge of the sensing device and the junction of the asphalt mixture. The shape and outline of the unit will guide the development of cracks in the sample: the cracks of asphalt concrete samples under the influence of the rectangular sensing device are mostly developed in a “V” shape, while the trapezoidal shape of the intelligent sensing device is in the same condition. It is easy to induce 45° oblique cracks along the side of the trapezoid. In terms of all the influencing factors, the asphalt concrete sample with intelligent sensing device has three devices in a diagonal distribution state in the sample. With the lowest strength and the least stability, this dispersion will result in the greatest degree of failure of the specimen.

Asphalt mixture is a kind of granular pavement materials which is dominated by granular aggregates in volume and exhibits obvious time-temperature dependency. Recognizing and understanding of asphalt mixture from the perspective of granular matter is the basic foundation of approaching and clarifying its matter attributes and behavior characteristics. This paper attempts to clarify the granular matter nature of asphalt mixture materials based on the theories of amorphous material and their glass transition as well as granular materials and the jamming transition models. It introduced the concept and connotation of granular matter materials, elucidated the granular matter attributes of asphalt mixture, reviewed the advances in the research of granular pavement materials, concluded the four essential features of granular pavement materials (geometric feature, packing effect, interfacial feature, and rheological feature), and discussed the future research directions of granular pavement materials. The results show that the behavior characteristics of asphalt and its effect on the whole system of asphalt mixture can be identified from the perspective of amorphous materials. The research of amorphous materials should be carried out in both the multiple individual scales and a coupling of them. The future research concerns include revealing the evolution rule of the microstructure of asphalt mixture in the process of jamming transition from both time and space dimensions, and exploiting a deeper understanding of the rheological behavior of asphalt mixture. Clarifying the conditions of stable packing and methods of high-efficiency packing for asphalt mixture from the perspective of granular material and multi-scale methodology, and identifying the thixotropic mechanism of asphalt mixture jamming under different working conditions. Re-understanding the rheological process of asphalt mixture subject to the different temperature, packing density and stress mode conditions, and establishing the jamming phase diagram of asphalt mixture covering the actual pavement service conditions.

Of the various types of bridges, suspension bridges have the largest span capacity. With a continuous increase in the span, the dynamic rigidity of the structure decreases, leading to a reduction in the wind resistance of the structure. In this study, the development of the beam section type and the proposal of a structure system meeting the requirements for structural stress and wind resistance stability were used as the key control factors for the design and construction of a 4 km suspension bridge. First, a section model wind tunnel test was conducted for a steel box girder section with a laminar flow damper (V-shaped damper, Y-shaped damper) and new turbulence damper; in addition, the ultimate span of a conventional plane cable suspension bridge was discussed. By building a three-dimensional finite element model, the variation characteristics of the structural torsional base frequency with the span were calculated and summarized. The influences of the sag ratio of the main cable, main cable spatialization, and setting of the torsional auxiliary cables were studied. Correspondingly, a new suspension bridge structure system was proposed. Based on the conclusions, the conceptual design of the 4 km suspension bridge was conducted. The results show that the span of a conventional suspension bridge can reach 2 700 m when the wind speed in the flutter inspection exceeds 80 m·s^-1 at the new stiffening beam section. The dynamic stiffness of a long-span suspension bridge can be easily improved by setting torsional cables between the main cables; this can increase the torsional frequency of the structure by 47.5%. The main span of a suspension bridge with the turbulent vibration control theory and new suspension bridge structure system can reach 4 000 m with an 80 m·s^-1 critical wind speed for the flutter. The span of a suspension bridge can be further increased to 6 000 m by adding structural measures such as wind-resistant cables, providing the possibility to construct a larger span cross-sea project in the future.

Currently, China has a large stock of industrial waste materials (IWM) but a low recycling rate. The comprehensive utilization of IWM is of great significance for promoting the sustainable development of society. Owing to increasing environmental protection regulations and natural resource shortages, the application of IWM to treat poor subgrade soil has become an important approach to alleviate the road material shortages and accelerate green construction material development. This study used a typical IWM to provide a detailed overview of the entire subgrade soil modification process. The water washing method was used to process the red mud, slag, and fly ash, whereas the steam granulation method was employed to treat the steel slag. Subsequently, the preprocessed IWM was used to modify the engineering properties of the problematic soil subgrade via either the individual addition of IWM, joint addition of IWM and cement/lime, or alkaline activation of IWM. To address the different engineering properties of expansive soils, loess, and heavy-metal-contaminated soils, IWM is typically used to improve expansive soils and loess, and compound addition methods are used to treat heavy-metal-contaminated soil. Improved IWM technologies significantly reduce the liquid limit and plasticity index of problematic soils while improving their strength and durability and effectively suppress the migration of heavy metal ions. Thereafter, the macroscopic and microscopic physicochemical properties of the IWM-treated soil were summarized in terms of mechanical properties, microscopic morphology, chemical composition, and reaction mechanism. The mechanism by which IWM-treated subgrade soils improved was analyzed and improvement plans for different problematic soil subgrades was proposed. Currently, few studies consider the long-term performance evolution of IWM-modified problematic soil subgrades. Therefore, further extensive research in this field is recommended.

To study the high-temperature response of continuous multichamber steel box bridge girders under rubber plate support conditions, two double-span continuous steel box bridge girders with twin chambers were designed and manufactured, and fire experiments were conducted under single-span fire and middle fulcrum fire conditions. The bending-torsion coupling effect was realized via transverse eccentric loading, and the plate rubber support was customized to determine the degradation of the bearing performance during fire exposure. The sectional temperature distribution, high-temperature deformation, buckling mode, and crack development of the bridge girders were obtained through the fire experiments, and the postfire properties of the steel and rubber supports were tested. Finally, a numerical model was established to verify the measured data, the internal force redistribution and failure processes were analyzed using model calculations, the failure process was analyzed as a function of the negative moment zone, and a parametric comparison analysis was conducted to determine the evolution mechanism of the fire resistance of continuous steel box girders. The results show that the temperature between the mid-web and side-web of steel box bridge girders exposed to fire exceeds 160 ℃, and the sectional temperature gradient distribution is significantly affected by the fire intensity. For single-span fire, the fire-exposed span deflects downwards, and the non-fire-exposed span first arches up before deflecting downwards; and for middle fulcrum fire, a sudden increase in the displacement is observed at the end of fire exposure. Moreover, the steel box bridge girder under the bending-torsion coupling effect exhibits evident transverse torsional deformation at high temperatures, where the difference in the deflection between the two sides of the section is 94 mm at the end. In the early stages of fire exposure, the internal force of the continuous steel box bridge girders is violently redistributed, and the region of negative bending moment expands sharply. Additionally, the reaction force of the middle support suddenly increases to more than twice that at room temperature. For single-span fire, plastic area expansion first occurs near the middle fulcrum followed by the eventual collapse of the fire-exposed span. In middle fulcrum fire, the compression buckling of the steel girder at the fulcrum leads to failure. Under the same heating mode, the failure of middle fulcrum fire occurs earlier than that of single-span fire, and the fire resistance limit reduces by approximately 40%. Moreover, the residual strength of steel on the bottom flange is approximately 67% and 78% at the maximum temperatures of 933 ℃ and 825 ℃, respectively. Although the rubber supports have a good thermal insulation performance, fire protection should be provided outside the rubber supports because the outer rubber reaching the ignition point can cause burning and the carbonization of the rubber, which leads to a loss in the bearing capacity and depression of the support. Therefore, this study provides guidance for the theoretical analysis of fire resistance and structural performance improvement in steel bridges.

In order to investigate the evolution law of modal parameters throughout the entire process of vortex-induced vibration (VIV) of a bridge, the 12-day measured acceleration data of a suspension bridge with vortex-induced vibration were analyzed. Firstly, the distribution of vortex-induced vibration and modal parameters of bridge were identified and tracked based on the characteristic index of vortex-induced vibration and the improved random subspace method. Then, the frequency and damping ratio were classified according to the alarm level of vortex-induced vibration, and the variation rules of modal parameters before, during and after vortex-induced vibration were studied. Finally, the correlation between modal parameters and characteristics of vortex-induced vibration were analyzed. The results show that, during the 12 days, the suspension bridge discontinuity occurred multiple, multi-order and multi-degree vortex-induced vibrations. The average of the first six orders of frequency fluctuated only in a small amplitude due to environmental influences. During vortex-induced vibration, the dispersion of each order frequency is smaller, and the fluctuation range is only about 0.002 Hz. The mean line of the first six damping ratios basically stays in the stable interval, but the local fluctuation and variation are great. During the vortex-induced vibration period, due to the influence of aerodynamic damping, the damping ratio corresponding to the order of the vortex-induced vibration locking frequency of the bridge decreases obviously at first and then recovers quickly. The damping ratio of other order has no obvious change. The vortex-induced vibration has great influence on the damping ratio identification value, but little influence on structure damping ratio. In this paper, the frequency and damping ratio of the suspension bridge during vortex-induced vibration and the relationship between them and vortex-induced vibration are revealed. It provides reference for finding out the cause and control of vortex-induced vibration more accurately.

Shield tunnel construction is essentially a process that uses a shield machine for dynamic tunneling in a three-dimensional space. In order to study the influence of shield tunneling on the displacement of the soil in front of the excavation face, three-dimensional coupled continuum-discrete numerical calculations were carried out based on the finite difference method (FDM) and discrete element method (DEM). Construction suggestions were then proposed based on the results, combined with data measured in the field. First, based on the elastic mechanics solution of Mindlin, this study comprehensively considered the joint action of the shield thrust, cutterhead friction, and shield shell friction. Based on a three-dimensional mirror image method and spherical cavity shrinkage problem, the analytical solution of the three-dimensional soil displacement induced by the ground loss was derived, and thus the complete analytical solution of the three-dimensional soil displacement in front of the excavation face was established. Second, in order to satisfy the needs on an engineering analysis scale and reflect the micro-mechanical properties of soil, a new coupled continuum-discrete technique was introduced. Based on practical engineering projects, the microscopic parameters of particles in the discrete domain were verified using three-dimensional single shear simulation tests and laboratory direct shear tests. Finally, based on the three-dimensional displacement analytical solution and coupled continuum-discrete model, the characteristics of the soil displacement in front of the excavation face were analyzed under different values for the shield thrust, rotational speed of the cutterhead, and advancing speed. The results indicated that the calculated results of the three-dimensional analytical solution and coupled continuum-discrete method were both in good agreement with the measured data. In the process of shield tunneling, the surface settlement gradually increases, and this settlement is temporarily controlled after the excavation face passes. A partial uplift of the ground surface occurs in the range of H/4-5H/4 in front of the excavation face as a result of the excessive thrust force. An increase in the cutterhead rotational speed will increase the surface settlement, and the asymmetry of settlement trough will be more significant. The thrust speed of the shield will affect the horizontal lateral and longitudinal displacement of the soil when the unit volume of excavated soil remains unchanged in the same stratum. The soil in front of the excavation face and that behind the shield tail have the opposite trends for the horizontal lateral displacement. In practical engineering, the grouting at the shield tail should be dynamically adjusted according to the displacement characteristics of the soil in front of the excavation face. The three-dimensional analytical solution and coupled continuum-discrete calculation method proposed in this study could provide important references for predicting the soil displacement in front of a shield excavation face.

Aiming at the ultra-high performance concrete (UHPC) shrinkage effect of a steel-UHPC composite bridge deck, the strain and temperature of full-scale specimens of three composite bridge deck segments with different area ratios of steel-UHPC and UHPC-free shrinkage specimens were tested during a curing process. The development rule of shrinkage strain and the influence of steam curing temperature were analyzed. Based on the obtained UHPC-free strain, the UHPC-restrained strain of a composite bridge deck and time-varying-stop effect, the UHPC elastic modulus, and the shrinkage stress of the composite bridge deck during the curing process were solved. The results show the following: ① The total free shrinkage of UHPC is about 756με. The higher the internal temperature of UHPC during steam curing, the faster the completion of shrinkage. From time-zero of the shrinkage, steam curing is started about at -1 h, and the internal temperature of UHPC reaches 90 ℃ about 5 h after time-zero and lasts for 48 h. Subsequently, more than 52%, 85%, and 91% of the total shrinkage are completed at 5, 25, and 35 h after time-zero, respectively, and all the shrinkage is completed at the age of 12 d. ② The elastic modulus of UHPC and the shrinkage stress of the composite bridge deck are consistent with the development rule of the shrinkage strain. ③ Throughout the curing process, the UHPC shrinkage stress of the steel-UHPC composite bridge deck is significantly lower than the axial tensile strength at that time, and no shrinkage cracks occur, which is consistent with the observed phenomenon. ④ The permanent shrinkage tensile stress value of the UHPC upper edge of steel-UHPC composite bridge deck is about 2 MPa, which is consistent with the reduction value of negative-bending-moment cracking stress obtained from a static load test of the steel-UHPC composite bridge deck compared with axial tensile strength. ⑤ Based on the free shrinkage strain of UHPC and the constrained shrinkage strain of the composite section, the time-varying-stop effect method for calculating the elastic modulus of UHPC and the shrinkage stress of the composite section is feasible.

To satisfy the requirements for safe driving of autonomous vehicles in a networked environment, it is necessary to achieve high-precision localization of vehicles in both time and space. In response to the shortcomings of the single-vehicle localization (SVL) method, a cooperative localization framework for connected and autonomous vehicles based on a double-layer filtering structure was proposed. First, the state of each vehicle was corrected based on the Kalman filter. Then, a cooperative localization estimation method based on the federal Kalman filter was designed. A main filter and multiple local filters were constructed to fuse the state of the ego vehicle and the corrected state of the neighboring vehicle. Using a variety of data fitting methods, a transmission delay probability model was constructed based on real data, and a processing delay probability model was constructed based on a Gaussian distribution. In addition, a communication delay compensation method was proposed and integrated into a cooperative localization framework. Finally, five groups of simulation experiments and three groups of real-vehicle experiments were designed to evaluate the localization performance of SVL, the cooperative localization method without delay error compensation (CLWC) and the cooperative localization method based on delay error compensation (CLC). In addition, the influences of speed and driving direction on the localization results were analyzed in detail. The research results showed that in an urban road environment, the accuracy of CLWC was improved by 15%-23% compared with SVL. In an open-road environment, when the communication delay was small, CLWC was better than SVL, and CLC further improved the accuracy by 5%-13% based on CLWC. In scenarios such as long straight roads, curves, and tunnels, CLC could ensure that the localization trajectory was smooth, and its accuracy was significantly higher than that of SVL. Simultaneously, it further verified the influence of vehicle speed on cooperative localization in the presence of communication delay. The proposed method not only overcomes the defect of SVL error accumulation but also effectively reduces the influence of communication delay and provides a continuous and stable high-precision position for vehicles.

With the rapid development of intelligent and connected vehicle technology, intelligent following and efficient queue driving of vehicles can be effectively achieved through following driving control. This study focuses on the speed planning problem for intelligent and connected vehicles in urban and suburban road conditions to improve the fuel economy, comfort, and safety of the vehicles. To achieve this, based on the following speed limit and vehicle powertrain system information, an initial value optimization-based sequential quadratic programming (SQP) algorithm based on initial value optimization was designed to dynamically determine the optimal speed trajectory of vehicles during the following process. In this research, real-time driving data, such as the information of the speed, acceleration, and the position of the front vehicles, were obtained through vehicle-to-vehicle communication and vehicle-to-infrastructure communication technology within the vehicle network environment. Furthermore, real-time road traffic information was collected. Based on the collected front-following information, the SQP algorithm based on initial value optimization was used to determine the optimal target speed. The aim was to reduce the dynamic energy consumption loss, minimize the required traction force, and travel the corresponding driving distance within a specified time interval. Additionally, boundary constraint conditions based on the dynamic road traffic scene were considered. A rolling time-domain method was used to realize online rolling optimization of the target vehicle speed at each sampling moment, ensuring energy saving and safety while driving the target vehicle. Finally, the effectiveness and real-time performance of the algorithm were validated through simulations. The results show that the SQP algorithm based on initial value optimization can quickly determine the economically optimal speed trajectory for the following vehicle, indicating good following performance. This approach ensures driving safety and reduces unnecessary speed fluctuations during the following process, ultimately achieving improved fuel economy and driving comfort for the following vehicles.

In this study, a method based on strain energy analysis is developed to calculate the equivalent modal damping ratio of steel-concrete composite bridges. By using a steel-concrete composite girder suspension bridge as an example, the damping ratio of the vertical bending mode of the bridge was calculated based on the strain energy principle and compared with in-situ measured results to verify the feasibility of the method. Moreover, the damping characteristics of steel-concrete composite girder suspension bridges for the lateral bending and torsional modes were further studied. The results show that the strain energy ratios of the steel main girder, bridge tower and main cable are significantly different for different modes, where the strain energy of the main cable is the largest in the vertical bending mode, and the total strain energy of the steel main girder and concrete bridge deck panels is dominant in the lateral bending mode, the strain energy ratio of the torsional mode is dependent on the mode order. The equivalent modal damping ratio of the steel-concrete composite girder suspension bridge in lateral bending is the largest, and the equivalent modal damping ratio in vertical bending is the smallest, and the damping ratio of the vertical bending modal is much smaller than the recommended value of the wind-resistant design specification. Finally, the differences of vertical bending mode, lateral bending mode and torsional mode damping ratio between steel-concrete composite girder suspension bridge and cable-stayed bridge were studied, and the equivalent mode damping ratio of steel-concrete composite girder cable-stayed bridge was larger than that of steel-concrete composite girder suspension bridge except for the lateral bending mode.The proposed method can be used for the calculation of modal damping ratio of steel-concrete composite bridge, and the calculated results can provide reference for wind analysis.