In recent years, the pace of bridge construction in China has been steadily accelerating, with the scale of projects and technical level reaching world-leading standards. The high-quality and innovative development of bridges is an important starting point and basic prerequisite for building China into a country with strong transportation network. To further enhance the strength of the bridge engineering discipline in China, promote the high-quality development of green low-carbon, sustainable and intelligent bridge engineering in China, and support the construction of a transportation powerhouse, this review, based on the analysis of the current industry development status and trends, systematically summarizes the latest scientific and technological innovation achievements in the field of bridge engineering in China in recent years and comprehensively sorts out the future key development directions covering four major themes: bridge engineering structural design and system innovation, disaster prevention and mitigation and structural safety, green construction and intelligent construction, and healthy operation and maintenance and longevity assurance. Specifically, it covers 21 hot research directions, including bridge function and analysis, high-performance materials, steel bridges and composite structure bridges, long-span bridge structures, innovative bridge foundation structures, new progress in bridge seismic resilience research, bridge wind, fire, and blast resistance, bridge engineering collision and protection, water resistance and resilience, multi-hazard coupling of bridges, high-quality bridge construction, green construction technologies and construction technologies, bridge monitoring and assessment, intelligent detection, on-bridge traffic safety, bridge life extension technologies, and integrated construction and maintenance platforms. The review provides guidance and reference for the development of the bridge engineering discipline in China and offers new perspectives and basic materials for researchers and technicians in the field of bridge engineering.

End-to-end autonomous driving methodologies eliminate the need for manually defined rules and explicit module interfaces. Instead, these approaches directly map trajectory points or control signals from raw sensor data, thereby addressing the inherent shortcomings associated with traditional modular methods, such as information loss and cascading errors, and overcoming the performance limitations imposed by rule-driven frameworks. Recent advancements in self-supervised-learning-based generative artificial intelligence have exhibited substantial emergent intelligence capabilities, significantly promoting the evolution of end-to-end methodologies. However, the existing literature lacks a comprehensive synthesis of the advancements in generative end-to-end autonomous driving. Consequently, this paper systematically reviews the research progress, technical challenges, and developmental trends in end-to-end autonomous driving. Initially, the input and output modalities of the end-to-end models are delineated. Based on the historical progression of end-to-end autonomous driving, this paper provides an overview and comparative analysis of the foundational concepts, current research status, and technical challenges of traditional, modular, and generative end-to-end methods. Subsequently, the evaluation methodologies and training datasets utilized for end-to-end models are summarized. Furthermore, this paper explores the challenges currently faced by end-to-end autonomous driving technologies in relation to generalization, interpretability, causality, safety, and comfort. Finally, predictions are made for the future trends of end-to-end autonomous driving, emphasizing the fact that edge scenarios provide critical support for the training of end-to-end models, which can enhance the generalization capabilities. In addition, self-supervised learning can effectively improve training efficiency, personalized driving can optimize user experience, and world models represent a pivotal direction for the further advancement of end-to-end autonomous driving. The findings of this research serve as a significant reference for refining the theoretical framework and enhancing the performance of end-to-end autonomous driving systems.

To evaluate the fire resistance of hanger systems in suspension bridges, vehicle fires are classified into five levels. Levels 1 and 2 represent passenger vehicle fires, levels 3 and 4 correspond to truck fires, and level 5 represents tanker fires. These vehicle fires are characterized by distinct maximum heat release rates and burning durations. The proposed hierarchy was validated using existing vehicle fire experiments. Geometric features of flames are established for Levels 3, 4, and 5 vehicle fires based on previous vehicle fire incidents. For passenger vehicle fires, a cylindrical flame radiation model was employed to compute spatial radiative heat flux, validated through three full-scale car fire tests. In the case of truck fires, a prismatic flame radiation model was used to calculate spatial radiative heat flux. For tanker fires with crosswinds, a computational fluid dynamics method validated by a liquefied natural gas trench fire test was employed to calculate the heat flux envelope on the hanger surface. An incremental temperature calculation formula for hanger cross-sections with radiative heat flux boundary conditions was derived, and validation was performed using finite element models. Using mechanical property tests of high-strength steel wires at high temperatures, a quantitative relationship between critical temperature and design safety factor of hangers is developed based on the ultimate load-carrying capacity at high temperatures. Finally, integrating the above outcomes, a five-step theoretical framework is proposed to evaluate the fire resistance of hanger systems under graded vehicle fires. This algorithm can serve as a reference for the assessment and fire-resistance design of hanger systems in suspension bridges.

With the continuous promotion of strong transportation strategies, the demand for high-quality sand and gravel materials in China's transportation infrastructure remains high. Environmental protection requirements continue to grow, and the shortage of natural resources continues to exacerbate. Therefore, alternatives to sand and gravel aggregates have become the main direction of development in the field of road engineering. China's western region has long been plagued by wind and sand problems, and successive exploratory studies have been conducted on the application of aeolian sand in road engineering, which have confirmed the significance of aeolian sand resource utilization for promoting green and low-carbon transportation in the sustainable development of road engineering industry. Therefore, this study focuses on the problems of large regional differences in aeolian sand and the lack of corresponding standardized research. The physicochemical properties of aeolian sand and its engineering characteristics in different regions are systematically discussed, analyzing its potential activity, excitation mode, and mechanism. The progress of research on the application of aeolian sand in roadbed and pavement engineering is summarized, thereby illustrating the influence and mechanism of aeolian sand on the performance of pavement concrete and semi-rigid bases, comprehensive utilization and treatment technology of aeolian sand in roadbed engineering, prevention and control of wind-blown sand of aeolian sand roadbeds, and performance of aeolian sand roadbeds in resisting scouring and water damage. Finally, development trends in the application of aeolian sand and research focus directions for the future of road engineering are presented.

The purpose of this study was to develop a new application of energy-absorbing materials in the road maintenance field and to produce a preventive maintenance seal that can improve the road surface function and enhance the structural bearing capacity of existing roads. A new road energy-absorbing material was used as the matrix, and a “sandwich” structure was used as the framework. A road maintenance energy-absorbing seal was designed and prepared, considering texture reconstruction. Image-processing analysis and accelerated loading tests were performed to analyze the decay law of the surface texture characteristics and road surface function of the road maintenance energy-absorbing seal and evaluate the durability of the seal. The effect of the road maintenance energy-absorption seal on decreasing the strain at the bottom of an asphalt concrete plate was evaluated using a continuous loading test of wheel rolling, and its load-bearing and buffering effects were investigated. Based on dynamic thermomechanical analysis, the microenergy-absorbing characteristics and damping behavior of the road maintenance energy-absorption seal were described, and its buffering mechanism was revealed. Finally, this study lays a solid foundation for the extensive investigation and promotion of the road maintenance energy-absorbing seal. The results show that the aggregate coverage rate is 40%, based on the seal surface texture and surface functions (wear resistance and sliding resistance). The ratio between the 2.36-4.75 mm and 1.18-2.36 mm aggregates is 25:75. The spraying plans for the energy-absorbing material are 1.0 and 2.0 kg·m^-2 for the upper and lower layers, respectively. After 40 000 cycles of loading and wear cycles, the surface texture of the road maintenance energy-absorbing seal attenuated slightly, and the decline in the durability of its surface was evident. A road maintenance energy-absorbing seal can effectively reduce longitudinal and transverse strains at the bottom of an asphalt concrete plate. Moreover, it can convert the original tensile strain into compressive strain or decrease the value of the original tensile/compressive strain by over 30%-50%. The loss factor [tan(δ)] of the energy-absorbing seal is 0.1-0.3, and the seal can exhibit excellent damping performance within wide ranges of temperature (-50 ℃-200 ℃) and frequency (10^-4-10⁸ Hz).

In the process of rapid development of road engineering, the problems caused by subgrade permanent deformation (PD) haven't been completely solved. Clarifying the evaluation and control methods for subgrade PD under long-term cyclic loading can ensure the durable and stable operation of road engineering. Firstly, this paper explored the main conditions and setting methods for PD test of subgrade soil. Subsequently, the constitutive models based on classical soil mechanics and empirical models based on experimental phenomena were sorted out. Next, the calculation process, verification methods, and evolvement rules of subgrade PD were summarized. Then, three methods for controlling subgrade PD were discussed, including critical dynamic stress, structural measures, and failure probability. Through analysis of research progress, it is found that there are four main problems with subgrade PD, namely inaccurate test methods, incomplete prediction models, unreasonable calculation theories, and unclear control standards. The specific problems and potential challenges in each aspect are elaborated in detail. Four prospects for future research are also given. Firstly, it is necessary to establish a static earth pressure coefficients database of subgrade soil to form a unified test method for PD of subgrade soil. Secondly, the influence rules and internal mechanisms of loading action duration and intermittent duration on PD of subgrade soil should be clarified, and the mechanical model for PD of subgrade soil should be derived under the theoretical system of element model and fractional-order calculus. Thirdly, the calculation method of subgrade humidity field considering the influence of dynamic loading should be innovated, and then the fully coupled calculation method of subgrade PD under humidification action based on the mechanical model for PD of subgrade soil should be established, and a comprehensive verification platform of subgrade that can scientifically simulate the climate environment and stress state should be developed. Fourthly, a control standard for subgrade PD based on pavement performance requirements should be determined with reliability as the goal, and then the corresponding relationship between structural failure and material deformation should be quantified, and the granular materials improvement layer structure design method for subgrade performance control should be optimized.

To achieve the integrated performance of asphalt mixtures throughout their design, construction, and service-life periods, this study introduces workability indicators and analyzes their effects on the workability of asphalt mixtures from the following five aspects: cohesion, adhesion, flowability, compactability, and homogeneity. First, the evaluation methods and indicators for each of the five aspects are elaborated separately, and the disadvantages of the existing evaluation system and future research directions are summarized. Second, the intrinsic relationship between the abovementioned five aspects and their effect on the pavement performance of asphalt mixtures are comprehensively analyzed. Finally, based on the workability and pavement performance of asphalt mixtures, a rational, convenient, and highly representative comprehensive evaluation method as well as an indicator for the workability of asphalt mixtures are proposed. This comprehensive indicator is incorporated into the design phase, and a balanced mix-design method based on workability and pavement performance is proposed to adjust and optimize the design of asphalt mixtures. This can promote both the improvement and upgrading of asphalt-mixture design methods as well as the sustainable and high-quality development of asphalt pavements.

Load-carrying capacity is a key index reflecting the service performance of highway bridges. Its reliable evaluation is key for determining the safety risk of bridges in advance, which can provide an important basis for decision-making, such as that pertaining to bridge reinforcement and reconstruction. In this study, the connotations of bridge load-carrying capacity evaluation were analyzed from the aspects of evaluation targets and evaluation methods, the current development of technical standards for load-carrying capacity evaluation in the United States and China was introduced, and the implementation method and evaluation theory of proof and diagnostic load-testing methods were demonstrated. Proof load testing can directly determine whether the bridge load-carrying capacity satisfies the requirements, whereas diagnostic load testing should be further developed in the future as it offers more bridge information and ease of implementation. Additionally, in this study, the verification theory used for the load-carrying capacity in the codes of various countries was systematically compared; three types of resistance correction methods, including direct structural testing, state-parameter mapping, and a time-varying deterioration-prediction model, were summarized; the correction methods for the bridge load effect were described comprehensively in terms of load and bridge models; and the reliability targets of bridge evaluation in various countries were analyzed. The British and American codes have established a multilevel evaluation reliability target by discounting the design load and resistance factor, which is worthy of reference for the Chinese bridge evaluation code. Finally, an outlook into the future development of load-carrying capacity evaluation methods in terms of testing methods, evaluation theories, and technological equipment is presented.

Autonomous driving has garnered considerable attention across various sectors, including government, industry, and academia. Scientific testing and evaluation play pivotal roles in advancing autonomous driving technologies. However, in the R&D realm, several common challenges have emerged, including disagreements among development entities regarding algorithm effectiveness, low coverage of testing scenarios, high entry barriers to testing tools, slow testing iteration speeds, and a lack of standardized evaluation criteria. These challenges have led to a growing demand for the development of public service platforms for online testing in the field of autonomous driving. This study introduced a technical framework for the construction of an autonomous driving testing public service platform known as OnSite. OnSite is characterized by its high scenario coverage, lightweight testing deployment, accelerated testing processes, and standardized evaluation criteria. To further meet the requirements of scenario-based testing in autonomous driving, innovative features such as automatic scenario generation, accelerated testing of critical scenarios, and bidirectional interaction testing between the vehicle under test and the background traffic flow were proposed as unique functionalities of the platform. By analyzing the results of competitions and evaluations hosted on the OnSite platform, this study assessed the weaknesses in autonomous driving planning and decision-making algorithm development and offered insights into fundamental research. Finally, a five-stage development plan was presented, featuring characteristics such as motion-planning-oriented testing, full-stack algorithm-oriented testing, virtual-real fusion testing, collaborative driving testing, and development-testing integration. The OnSite platform provides a “ubiquitous” service for autonomous driving testing, facilitating the transition of theoretical advancements from “shelves” to practical application while overcoming the problem of translating research achievements into real-world implementation.

近年来, 随着“一带一路”合作倡议和“交通强国”战略的不断推进, 我国隧道建设技术不断提升, 隧道和地下工程发展取得了显著成就。国家《“十三五”现代综合交通运输体系发展规划》提出, 要加快城市轨道交通装备关键技术产业化, 提升绿色安全水平, 推动云计算、大数据、物联网、移动互联网、智能控制等技术与交通运输的深度融合, 实现基础设施和载运工具数字化、网络化、智能化发展。在此背景下, 加快隧道及地下工程智能建造水平, 已成为我国地下工程建设的战略重点。隧道智能建造是工程建造领域的发展方向, 也是新形势下隧道工程建设发展的必然趋势, 智能时代的到来给隧道建造技术的创新发展带来了新的机遇与挑战。随着我国工业水平和经济的快速发展, 隧道工程机械行业经历了从无到有、由弱到强的发展历程。近年来, 公路隧道机械化装备配套施工、爆破智能设计、掌子面地质与力学信息智能采集和监测、隧道辅助工序智能机器人与装备、机械化施工工序优化与智能管控等技术的不断创新, 促进了我国隧道智能建造技术与装备的快速发展。
为充分展现我国隧道智能建造技术与装备领域的最新研究成果, 及时总结该领域的前沿动态与技术发展, 推动隧道建设的智能化创新与应用, 《中国公路学报》编辑部联合山东大学李利平教授、广西大学张稳军教授(本刊副主编)共同策划了“隧道智能建造技术与装备”专栏, 并邀请山东大学李术才院士、深圳大学陈湘生院士、同济大学朱合华院士、中国煤炭科工集团王国法院士作为顾问专家, 中国中铁李建斌高级专家、中国铁建股份有限公司程永亮副总工程师、北京交通大学袁大军教授、西南交通大学王明年教授、中国铁道科学研究院集团有限公司马伟斌主任研究员、高端工程机械智能制造全国重点实验室程磊主任、同济大学李晓军教授、长安大学叶飞教授、北京航空航天大学杜博文教授、青岛国信(集团)有限公司曲立清总工程师、山东高速建设管理集团有限公司侯福金总经理、中铁十四局集团有限公司陈健副总工程师作为组稿专家, 共同向该领域的知名专家、学者约稿, 出版本期“隧道智能建造技术与装备”专栏。本专栏共收到相关论文20余篇, 最终录用7篇, 研究内容主要集中于以下3个方面：
(1)钻爆法隧道智能建造技术进展与发展趋势。主要内容包括：钻爆法隧道智能建造进程、智能建造体系、钻爆法隧道智能化围岩评价与爆破设计、智能建造施工装备与管控平台、隧道智能建造辅助工序机器人装备等。
(2)隧道机械化快速施工与施工工序优化。主要内容包括：公路隧道装配式仰拱、仰拱接头变形特征与破坏规律、仰拱接头模型试验与有限元分析、接头设计、全机械化施工装备配套、二次衬砌支护时机动态确定、考虑二衬时机的隧道力学解析与稳定性分析。
(3)隧道典型灾害风险预测评估方法。主要内容包括：岩溶隧道突涌灾害风险预测、基于深度神经网络和属性数学的灾害预测方法、灾害等级划分与概率预测。
在此特向专栏组稿专家、审稿专家、论文作者的辛勤付出致谢, 希望本期专栏的出版可以进一步推动“交通强国战略”和“双碳战略”背景下交通基础设施中隧道智能建造技术与装备的高质量发展。《中国公路学报》将持续关注该领域的国内外最新研究进展, 以期为广大专家、学者及工程技术人员提供学习、交流的平台, 促进我国隧道工程建造的高质量与可持续发展。由于水平及时间有限, 专栏中的不足之处在所难免, 恳请各位专家不吝指出。

A digital model of the human body is an important tool for vehicle collision-safety research, such as injury mechanism research and protection-device development. It is also the core means of virtually evaluating vehicle safety in the future. The constitutive parameters of the bone materials are key to determining the accuracy of the digital model. However, for a long time, the material constitutive parameters used in various digital human-body models, both domestic and foreign, have been derived from testing data from European and American bodies. Systematic research on the biomechanical characteristics of the Chinese human body is lacking, which greatly limits the development and efficient application of digital human-body models in China. To clarify the differences in skeletal biomechanical characteristics between Chinese and foreign individuals, and to obtain mechanical data on Chinese human-skeletal materials, a bone-material test study was conducted on a Chinese male cadaver. The femur, tibia, fibula, skull, ribs, ilium, and sternum were dissected. The cortical-bone regions of each bone were sectioned, and a large number of cortical-bone specimens were prepared using high-precision processing methods, such as metallographic grinding-polishing machines and CNC (Computer Numerical Control) engraving machines, with dimensions of approximately 12 mm×2 mm×0.5 mm. The elastic modulus, yield stress, tangent modulus, and effective plastic strain of the specimens were automatically and effectively obtained using the three-point bending test and improved beam-theory method. The results show that the material constitutive parameters of the Chinese-human cortical bone obtained in this study are within the data range of European and American human bodies in the literature, and the material parameters of the different bones differ significantly (p<0.05). The constitutive parameters of the key skeletal materials provided by the Chinese human body offer preliminary data as a reference for developing a complete automotive crash-safety digital model with independent intellectual-property rights that conforms to the characteristics of the Chinese population.

Recently, autonomous vehicles (AVs) have gradually reached the stage of real-world testing and demonstration on public roads. AVs interact frequently with humans, and their driving performance requirements have shifted from a functional level of “safe and stable driving” to an interactive level of “driving like a human.” The construction of evaluation indices and methods that can accurately characterize the human cognition of driving behavioral ability is necessary to guide the continuous development and improvement of automated driving technology to demonstrate human-like or superhuman driving ability. This study focused on the construction of an evaluation indicator system for the driving behavioral ability of autonomous vehicles. This paper first elaborates on the definition and boundaries of the driving behavioral ability of autonomous vehicles. Then, the current status and existing problems of the evaluation index system for driving behavioral ability are presented. Moreover, an STCER-H index system is proposed, which includes five dimensions of instantaneous indicators: Driving Safety, Travel Efficiency, Driving Comfort, Energy Efficiency, and Regulatory Compliance, as well as a comprehensive “Humanoid Level of Driving Behavior” indicator. Consequently, the existing modeling methods for individual indicators are reviewed, the connotations of each dimension indicator are clarified, and the current status and problems of each dimension indicator are summarized. At the level of statistical evaluation in multidimensional aggregation, the definition and modeling suggestions of “Humanoid Level of Driving Behavior” indicator are primarily discussed. Finally, a summary of the challenges and future research prospects of the existing evaluation index system for driving behavioral ability is presented to provide a reference for academic and industry research.

Driver condition, as a crucial component of the “human-vehicle-road-environment” transport system, significantly affects driving behavior. Electroencephalogram (EEG) serves as a direct indicator of brain activity and can accurately reflect a driver's current state during driving. This paper begins by outlining the inherent relationship between EEG and adverse driving conditions, such as distraction, fatigue, and emotion based on the literature. Subsequently, key aspects of the test environment, data processing, and analysis methods employed in EEG research are summarized. The summary reveals that the essence of most studies can be interpreted as an exploration of the qualitative and quantitative relationships between various driver states and EEG. EEG data is collected through the simulated driving by volunteers, the EEG characteristic values are extracted by linear or nonlinear analysis methods, and then the driver's state is identified by mathematical models or neural network models. Furthermore, to enhance the accuracy of recognition models, research on multi-source information fusion based on EEG in scenarios like unsatisfactory driving state has gradually increased. The application of EEG in driving state recognition system is progressively moving towards commercialization. This indicates that current driving state recognition algorithms based on EEG possess promising safety application potential and prospects. Nonetheless, there remains significant room for improvement in areas, such as EEG feature extraction, real-time processing, and recognition accuracy across various driver states.

To address the limitations in generalization capability and environmental adaptability of existing vehicle weighing systems based on computer vision, this paper proposes a non-contact weight-in-motion method based on large vision model and machine learning. Initially, an innovative edge detection algorithm using vision large model was designed for the precise acquisition of tire deformation parameters. Subsequently, a universal recognition model comprising a complete tire character database was developed based on Mask-RCNN (Mask-Region-based Convolutional Neural Networks), capable of accurately labeling and extracting features from the tire sidewalls. Furthermore, a machine learning model for predicting the contact force between vehicle tires and the road surface was constructed using LightGBM (Light Gradient Boosting Machine), and its accuracy, feasibility, and effectiveness were verified. Notably, through testing on SUV vehicles and heavy-duty trucks, this method has been demonstrated to have a maximum error of less than 5% compared to traditional weight-in-motion systems, showcasing high precision and superior performance. This study introduces a vehicle weighing technology that does not require the installation of sensors, offering a user-friendly and cost-effective solution. The potential applications of this technology are extensive, particularly within the realms of toll stations and highways, and other traffic engineering sectors.

Oil-tanker explosion fire has enormous power and poses a severe threat to the safety performance of crossing sea bridge. In order to study the structural response of suspension bridges exposed to complex extreme fire environments caused by oil-tanker explosions, and to clarify the safety of suspension bridges under complex extreme fire loads, a large-span suspension bridge was selected as the research object. The prediction process of suspension bridges safety performance (fire resistance) during oil-tanker explosion fires was provided. Firstly, the computational fluid dynamics-finite element method (CFD-FEM) coupling method was used to reconstruct the oil-tanker explosion fire environment. A three-dimensional multi-scale numerical prediction model for local girder segment and the entire bridge structure were established. The heat transfer mode of bridge segment and performance evolution of the entire bridge structure during oil-tanker explosion were revealed in depth. Subsequently, the high-temperature response and failure mode of steel box girder (stiffening girder) under oil-tanker explosion were studied, and the effects of different fire positions, distance from the fire surface to bottom plate of steel box girder, and wind speed on the fire response behavior of suspension bridge were analyzed. A fire resistance limit warning method for suspension bridges exposed to oil-tanker explosion environment was proposed. The research results indicate that the deformation of local suspension bridge segment under oil-tanker explosion continues to increase. And the fire affected bridge segment shows a failure mode of overall downward deflection followed by upward bowing in middle area, resulting in a development trend of first increasing and then decreasing for suspension cable force in the middle area. The fire position has a significant impact on the overall structural performance of the suspension bridge. As fire position approaches the mid span area, the deflection of girder segment in the middle area increases by 62% compared to the girder segment adjacent to the tower. When the distance from fire surface to steel box girder is reduced from 50 m to 20 m, the peak deflection and total bowing amplitude (the difference between peak values of deflection and bowing) of local girder segment increase by more than 19%, and the structural failure time is advanced by 10 minutes. Wind speed would change the shape of deflagration flame, significantly affecting the distribution of heating surfaces and high temperature response characteristics on both sides of box girders. When wind speed is 8 m·s^-1, fire intensity of the windward side box girder is significantly reduced, and the total bowing amplitude of bottom plate is reduced by 17% compared to 2 m·s^-1. The critical temperature during the bending deformation of steel box girder bottom plate is between 510 ℃-550 ℃, and limit temperature during the buckling instability of steel box girder bottom plate is between 685 ℃-715 ℃. The critical temperature and limit temperature can be used as two-stage warning temperatures for safety performance, thereby achieving two real-time warnings before steel box girder failure. The research conclusion can provide theoretical bases for the safety performance monitoring and early warning of cable supported steel bridges in complex fire environments, and further guide the safe operation and maintenance of similar bridges.

Currently, the widely used snow-melting and deicing methods cause damage to road surfaces and the environment. To study the impact of asphalt-pavement snow-melting and deicing technology on the environment during its life cycle, three different snow-melting and deicing technologies were investigated: pavement containing salt, cable-heated pavement, and mechanical ice removal. Based on the life-cycle assessment method, the three snow-melting and deicing technologies were divided into five processes: raw-material mining, mixing, transportation, paving compaction, and snow removal. The impact of environmental emissions over the life cycle of the three snow-melting and de-icing technologies was quantified. Four environmental impacts-global-warming potential (GWP), acidification potential (AP), photochemical ozone creation potential (POCP), and human toxicity potential (HTP)-were considered to evaluate the environmental load of snow-melting and deicing technology. The results show that CO₂ emissions are the largest in all stages of snow melting and deicing. The environmental loads of the three snow-melting and de-icing technologies are ranked throughout the life cycle: GWP>HTP>AP>POCP. Of the four impact categories, GWP accounts for more than 74% of the environmental load of the different snow-melting and de-icing technologies. To compare the environmental loads of different snow-melting and deicing technologies, their analysis results were normalized. It is concluded that the environmental load generated by the cable-heated pavement is the largest, while that generated by the pavement containing salt is the smallest.

Given the substantial volume and significant aging of existing asphalt pavement structures, extending their service lives has become a strategic requirement for the sustainable development of the transportation industry. This study focuses on existing asphalt pavement structures in long-term service and provides a comprehensive review of the research progress in four key technologies for extending service life: health condition detection and assessment, performance evolution and life prediction, life extension design methods, and research and development of specialized structures and targeted materials. First, the current applications of nondestructive testing techniques, including ground-penetrating radar, are presented to evaluate the internal conditions and bearing capacities of pavement structures. Second, the advantages and limitations of full-scale testing, the finite element method, and the discrete element method for studying the mechanical performance responses and degradation of pavement structures are systematically summarized. Based on these results, design strategies and parameter acquisition methods for extending the life of existing asphalt pavement structures are reviewed. This paper also summarizes the verification models for fatigue and permanent deformation as well as life-extending structural combinations. Finally, life-extension materials and technologies for different pavement layers and functional requirements are summarized. Future research is recommended to integrate comprehensive evaluation indicators, analyze the performance degradation characteristics of existing asphalt pavement structures, establish remaining life prediction models and life extension design methods, and develop high-performance strengthening materials to extend the service lives of asphalt pavement structures.

Transportation sector is one of the major sources of global carbon emissions, and as a major transport country, China is facing a huge challenge to reduce transportation carbon emissions. Researchers have conducted extensive research on carbon emission assessment and emission reduction measures in recent years. This paper focuses on the highway construction stage, reviewing previous studies on three aspects: carbon emission assessment, carbon reduction, and carbon sequestration compensation. The results show that: most carbon emission assessment methods use the Life Cycle Assessment(LCA) model throughout the highway construction stage, in which the basic data accounting focuses on the emission factor method, as well as software, platforms, and other tools combined with artificial intelligence; emission side of the carbon reduction is mainly centered on green geo-technology, solid waste resource utilization technology, and green-efficient project management, to completely accomplish the carbon reduction goal; carbon compensation research includes compensation measures and effect evaluation of the carbon sequestration, and its carbon compensation measures are mainly in using slope vegetation photosynthesis to sequester carbon and new materials to increase carbon sinks. Moreover, carbon sequestration effects are evaluated using the subjective-objective combination analysis method. The analysis reveals that unsolved problems still exist: lack of unified standards for emission factors, leading to considerable errors in carbon emission assessment; lack of comprehensive assessment methods for the specific emission reduction ratio of emission reduction measures and the poor combination with artificial intelligence; the carbon compensation measures are largely insufficient and the carbon sequestration effect assessment method is subjective. Future research should focus on exploring the accuracy of the assessment method; proposing more effective carbon emission reduction measures and enhancing the research on the assessment of emission reduction effect; adopting more diversified carbon compensation measures, and establishing a universally applicable compensation effect assessment method. This review paper provides a comprehensive reference for carbon emission reduction research in transportation geotechnical field and assists in realizing the goal of the “carbon peaking and carbon neutrality” strategy.

A system reliability analysis model is developed using an improved vector projection response surface method (IVPRSM) to enhance the efficiency and accuracy of calculating the static system dependability of long-span continuous steel-truss bridges. A novel approach was developed to enhance the efficiency of computing the component dependability index by optimizing the sampling process of the vector projection response surface technique. The limit state functions of steel truss bridge components can be effectively rebuilt, the design points can be promptly searched, and the failure probability of the components can be evaluated using the IVPRSM. The candidate failure components can be screened using the β-unzipping method. Considering the structural topology model was modified by assuming failure in the potential failure elements, the reliability indices of the remaining components were calculated using IVPRSM. Therefore, identifying the primary failure modes of steel-truss bridges and constructing a fault tree is possible. The structural system reliability index was determined using the differential equivalent recursion technique, which relied on the equivalent linear functions of the failure modes and the correlation coefficients between the failure modes. The efficacy and precision of the IVPRSM were confirmed through a reliability study of three numerical arithmetic cases. Considering a double-deck continuous steel truss bridge with a main span of 300 m as an engineering example, the proposed system reliability analysis method was used to calculate the reliability indices of the key components of the steel truss bridge in each failure stage. The study findings demonstrate that IVPRSM exhibits superior computational efficiency and accuracy compared with conventional approaches. At the ultimate limit condition of the load-carrying capacity, the reliability index for all types of critical members ranges from 4.1 to 4.8. The lower chord at the pivot of the main span of the truss girder, the upper chord and web member at approximately 1/4 of the main span (300 m), and the strengthened vertical bar in the center of the main span pose a significant danger of failure. Consequently, twenty primary failure modes affecting the load-carrying capability of the steel truss bridge were identified, resulting in a system dependability index of 4.6. This study proposes an IVPRSM-based reliability method for the static systems of continuous steel truss bridges. This algorithm may assist in designing continuous steel truss bridges by considering system reliability.

To monitor anger while driving in real time and provide timely and effective intervention and adjustment, an accurate and efficient method for classifying anger while driving is proposed. Based on low-intrusive driving behavior and voice features, this study adopted semi-supervised learning methods to build a model to reduce the dependence on labels and improve classification accuracy. The driving data were obtained from a high-fidelity driving simulation experiment involving 30 participants. A sliding time window was set to intercept anger events, and a driving anger dataset was formed through feature extraction and computation. On this basis, a model called SSL-GBM was developed by combining a pseudo-labeling algorithm in semi-supervised learning (SSL) with a gradient boosting machine (GBM), thus fully exploring the internal information of the data to reduce the dependence on manual labels. Data processing, feature engineering, model searching, and parameter optimization were automated within an automated machine framework, enabling the classification of driving anger levels. The results indicate that the driving anger emotion classification model has an accuracy of 90.3% in predicting five-level driving anger scores, which is an improvement of 3.7% compared to the best-performing model among the existing models. In particular, the recognition accuracy for levels 2-5 improves by more than 2.5%, significantly reducing the detection failure to misjudge the angry state as normal. As shown by the prediction of anger levels throughout the driving duration, the algorithm is fully equipped with the characterization ability and generalization performance applied to real-time driving anger state recognition, thereby verifying the effectiveness and rationality of the proposed approach. This study has significant application value in discriminating driving anger states and enhancing the capacity of driving assistance systems to monitor dangerous driving behaviors.

This study investigates the effect of heavy rainfall on the smoke-movement characteristics and smoke control of tunnel fires using a reduced-scale (1∶15) experimental platform based on the Froude criterion. A series of tunnel fire tests was conducted based on four different rainfall intensities, four heat-release rates, and six longitudinal ventilation velocities. The results show that the flow field at the tunnel entrance is affected by raindrop behaviors, such as diffusion and drag, when heavy rainfall occurs. Induced airflow is directed downstream of the tunnel. When the rainfall intensity increases, the induced airflow velocity increases and smoke is restricted upstream of the tunnel. The first part of the longitudinal ventilation velocity was used to eliminate back-layering caused by the hot-smoke driving force, and the other part was used to offset the effect of induced airflow in the tunnel. The back-layering length increases with the rainfall intensity. The accuracy of the critical wind speed was verified based on a previous model. Under fire-source power levels of 3.03, 6.06, 9.09, and 12.12 kW, the critical ventilation velocity increased by 25.6%, 17%, 14%, and 9% under the effect of heavy rainfall, respectively. The higher the heat-release rate, the less affected is the critical ventilation velocity by heavy rainfall. Based on the temperature-distribution law of a tunnel ceiling subjected to heavy rainfall, the mechanism by which heavy rainfall affects smoke movement was clarified, and the effect of heavy rainfall on the key parameters for tunnel-disaster prevention and ventilation design was investigated.

The cable is susceptible to corrosion, vehicular-induced fire, and other detrimental effects during bridge services. The current studies on the structural safety assessment methods of in-service cable-stayed bridges under complex environmental conditions are not complete. Therefore, this study focuses on the cables of a cable-stayed bridge with a service life exceeding 25 years and conducts tensile tests at elevated temperatures to obtain corresponding degradation patterns and computational models. After defining the degradation rule of in-service cable at high temperatures, a fire source model caused by a 300 MW tank car was created using the Fire Dynamics Simulator. The heat transfer analysis model and the structural model of the in-service cable-stayed bridge were established using ANSYS, and structural thermodynamic coupling analysis was conducted. The results indicate that under the most unfavorable conditions, the fire from the 300 MW oil tanker leads to the failure and rupture of three groups (six) of cables near 1/4 of the span of the bridge, and the failure times of the groups are 551 s, 592 s, and 1 064 s, respectively. Owing to the effects of the bridge dead weight, the maximum deflection of the main girder decreases by 0.08 m, the deflection span ratio is 1/1250, and the maximum deflection rate is 9.6 mm·min^-1. All the outcomes are within the limits of the JTG/T D65-01—2007 standard. The 300 MW tanker truck fire cannot cause a complete collapse of the entire bridge structure but results in severe and irreversible damage to the cable. The study's findings provide a reference for structural safety analysis and evaluation of in-service bridges in complex environments.

As an inherent attribute of bridges, damping can greatly reduce the induced dynamic responses and can further improve the safety and comfort of driving. However, the effect of bridge damping is often ignored when solving the dynamic equations of space bridges under moving vehicle conditions. For this reason, the analytical solution for the vehicle-bridge interaction vibration was derived by considering the effects of the beam's damping using the example of a single-axle vehicle passing over a thin-wall box girder. The feasibility of identifying the torsional-flexural frequencies and vertical frequencies was verified using the analytical solution and the vehicle or vehicle-bridge contact responses. Additionally, the characteristic of the bridge's monosymmetric cross-section caused the center of mass and shear center to be offset, thus implying that its lateral and torsional movements were coupled, while the vertical motion was relatively independent. Additionally, the bidirectional damping characteristics that allow the assignment of various damping ratios for vertical and transverse-torsional motions were considered. Meanwhile, the rocking and vertical contact responses of the vehicle were derived based on the cross-sectional rigidity hypothesis, which realized that the torsional-flexural frequencies and vertical frequencies of the thin-walled beam were separated. The negative influences of road roughness in the effort to retrieve the bridge frequencies were eliminated by using residual contact response technology. The results show that: ① The first several torsional-flexural and vertical frequencies of the thin-walled beam can be respectively identified by the rocking and vertical contact responses; ② The damping ratios of vertical and torsional-flexural directions only affect the visibility of their directional frequencies, especially higher-order frequencies; ③ The residual contact response technology yields a good performance in identifying the beam frequencies, even under poor pavement conditions; ④ A running speed of 10 m·s^-1 (36 km·h^-1) for the test vehicle is recommended in practical applications.

A long short-term memory (LSTM) neural network model for predicting slope displacements based on maximum mutual information coefficients (MICs) and the XGBoost algorithm (MIC-XGBoost LSTM) was established to accurately predict slope displacements. First, the effects of different rainfall conditions on the slope were investigated. The maximum MIC was used to analyze the correlation between different rainfall conditions and the cumulative displacement of the slope, and the rainfall-influencing factors with significant correlations were determined. Next, based on the XGBoost algorithm, feature construction was performed on the influencing factors with high correlation using the cumulative displacement data of the slope, and the construction features were normalized with the original features. The normalized data were divided into training and validation sets. LSTM was used to predict the displacement of the Shangluo rock slope on the G312 National Highway. The XGBoost, LSTM, and MIC-XGBoost-LSTM prediction models were used to train and predict the cumulative displacement value of the slope, and the prediction accuracy was evaluated based on the root-mean-square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) indicators. In addition, the RMSE was used to determine the longest prediction cycle and minimum training sample size for the MIC-XGBoost LSTM model. Finally, the displacement data of the Baishui River landslide were used to further validate the model. The results show that the correlations between daily displacement increment, evapotranspiration, net rainfall, cumulative seven-day rainfall, and cumulative displacement at the monitoring point are higher than those of other factors, and the MIC of the feature values constructed using four related factors and the output feature values is 0.97. The RMSE, MAE, and (MAPE) of the predicted results obtained using the MIC-XGBoost-LSTM model are 0.25%, 0.185%, and 0.024%, respectively, which are lower than those of XGBoost and LSTM. Based on the RMSE, the longest prediction cycle and minimum training sample size of the MIC-XGBoost-LSTM model are 56 and 675, respectively. Finally, the displacement data of the Baishui River landslide were used for verification. The evaluation indicators are lower than those of the XGBoost and LSTM models, demonstrating that the MIC-XGBoost-LSTM slope displacement prediction model has high reliability.

    我国交通基础设施网络规模居世界前列，公路与铁路桥梁总数已超过120万座。然而，随着经济的迅速发展，行车密度日益增加、荷载逐渐加重，如何保障桥梁安全运营和车辆行驶安全是当前面临的巨大挑战。车辆与桥梁的动力相互作用是影响桥梁结构性能和车辆舒适性、安全性的重要因素，深入开展车桥耦合振动相关研究，对桥梁设计建造与安全运维都具有重要的理论价值和工程意义。
    近年来，在车桥耦合振动理论与应用研究领域，众多学者和科研人员开展了大量卓有成效的工作，取得了丰富的研究成果，为我国桥梁设计与建造技术的提升和交通基础设施的安全运营提供了重要的理论和技术支撑。为充分展示我国车桥耦合振动理论及其应用领域的最新研究成果，引领该研究领域的发展方向，推动车桥耦合振动的理论与应用技术创新，《中国公路学报》编辑部联合湖南大学孔烜教授（我刊青年编委）、邓露教授（我刊编委）共同策划了(车桥耦合振动理论与应用新进展)专栏，并邀请东南大学蔡春声教授、长沙理工大学韩艳教授、长安大学韩万水教授（我刊编委）、西南交通大学李小珍教授、重庆大学王志鲁副教授、同济大学夏烨副教授（我刊青年编委）、北京交通大学张楠教授作为组稿专家，共同向该领域的知名专家学者约稿，出版本期(车桥耦合振动理论与应用新进展)专栏。本专栏共收到车桥耦合振动及其应用新进展相关理论、技术、方法及试验研究等论文40余篇，最终录用10篇。研究内容集中于以下3个方面：
    （1）车桥耦合振动理论研究。主要内容包括：考虑薄壁箱梁阻尼下的车-桥耦合振动解析理论、时变空间路面模型建立及其对车-桥耦合振动的影响、横风下公铁两用双层钢桁梁桥中汽车气动特性等。
    （2）车辆荷载识别与动态称重。主要内容包括：大跨桥梁车辆追踪与荷载时空分布智能识别、基于最大熵正则化的桥梁动态称重算法与试验验证、基于视觉大模型和机器学习的非接触式车辆动态称重方法等。
    （3）基于车桥耦合振动的桥梁性能研究。主要内容包括：基于过桥重载车辆动力响应的桥梁影响线识别、基于随机车流-桥梁耦合振动的板梁桥铰接缝裂缝扩展分析、改进型波形钢腹板组合箱梁桥构造细节的疲劳应力研究、考虑非平稳因素的大件车与普通车流混行中小跨径桥梁安全评估。
    在此特向专栏组稿专家、审稿专家、论文作者的辛勤付出致谢！希望本期专栏的出版可以进一步推动车桥耦合振动领域的理论创新与技术进步。《中国公路学报》将持续关注该领域的国内外最新研究进展，以期为广大专家、学者及工程技术人员提供学习、交流的平台，促进我国桥梁建设的高质量与可持续发展。由于水平及时间有限，专栏中的不足之处在所难免，恳请各位专家不吝指出。

Congestion is a common phenomenon that affects the operational performance of expressway networks. However, previous studies have focused on congestion delays in regional road networks due to section failures within a short period of damage, whereas the effect of dynamic congestion caused by macro-traffic flow fluctuations is disregarded. To address the characteristics of road-network resilience based on the generation-cluster-recovery process of dynamic congestion, this study integrates network topology, spatiotemporal characteristics of dynamic flow, and road-infrastructure attributes to devise a percolation-theory-based methodology for the congestion resilience of expressway networks. This approach was demonstrated and analyzed in the expressway network of a province in China. The results show that the road-network resilience and congestion cluster scale demonstrate an approximate power distribution and a scale-free law under different spatiotemporal conditions. Under speed thresholds (q_l) of 0.2 and 0.7, the congestion cluster reaches the transition state and the road-network resilience is reduced by approximately 65%. Comparing the resilience curves for different time periods, the congestion recovery time of evening peak hours is approximately 50% higher than that of other periods. Additionally, the transportation infrastructure type significantly affect the congestion resilience of road networks. The recovery time of congested bridges and tunnels is 25% longer than that of normal sections. When the proportion of bridges and tunnels exceeds 65%, the road-network resilience decreases by approximately 50% under different spatiotemporal conditions. The findings of this study could provide the theoretical evidence and practical implications for traffic management to mitigate and prevent frequent congestion.

Driver state monitoring technology, as a key means for improving vehicle intelligence and safety, aims to accurately identify and deeply understand the driver's actions, emotions, and attention states. Although significant progress has been made in this field, a systematic summary of the principles of deep learning algorithms is lacking. In view of this, this paper systematically reviews driver state monitoring algorithms based on images and deep learning to meet the needs of the continuous development of intelligent vehicle technology. First, the methodology in the literature is elaborated upon. The existing publicly available datasets are then organized and described. Subsequently, in-depth exploration is conducted from the aspects of data selection and processing, model architecture, model training and evaluation, and optimization. Finally, the shortcomings of the current research are summarized, and the main future development directions are outlined. The results show that: ① the research on driver state monitoring based on image and deep learning has progressed to a certain depth; ② data selection and processing techniques show diversity; ③ model architectures continue to evolve in the direction of multi-modal, multitasking, lightweight, and high robustness, gradually beginning to adopt training strategies for incomplete supervision and multi-objective optimization. However, most research methods lack systematic testing of actual driving scenarios neither fully considering the behavioral characteristics of drivers under natural driving conditions nor the changes in the human-computer interaction patterns of intelligent vehicles, making it difficult to construct an all-around monitoring function for various driving scenarios and driver personalities. The further development of driver state monitoring algorithms is mainly limited by two factors. First, the current deep learning methods still have deficiencies in their domain adaptation, interpretability, and operational efficiency. Second, large-scale high-quality datasets under natural driving environments are lacking. This review is dedicated to providing effective guidance and important references for further development of high cognitive driver state monitoring systems.

To improve the response speed of task-dependent applications in Internet of Vehicles (IoV), a delay-aware-dependent task scheduling and computational offloading strategy was investigated that included the following three design points. First, a vehicle-side task-dependency application model was constructed based on a directed acyclic graph (DAG), which characterizes in detail the dependencies between tasks in each application while constructing the total task DAG for multi-vehicle multi-application scenarios. Second, a local computational and offloading model was designed based on the partial offloading mode, which considers multiple delay terms such as queuing time, computational time, and result transmission time. Expressions for the execution waiting time and delay minimization optimization problem were also formulated. Third, based on the design principle of “completing more tasks in less time” and the characteristics of task dependency, the execution and waiting time priority indicators of tasks were designed. An improved heterogeneous earliest finish time task scheduling algorithm was then designed that fully considers these time priority indicators. Next, an optimal task scheduling order to improve the delay performance was obtained. Finally, to obtain the optimal offloading decision for each task, a Markov decision process was constructed for task calculation. A task offloading algorithm based on a deep deterministic policy gradient was designed, and the optimal computational offloading decision was obtained. Simulation experiments were conducted under different network settings. Results show that compared with existing delay minimization schemes, the proposed scheme has obvious delay performance advantages and is more suitable for IoV with strict low-delay requirements.

Bridge accidents caused by ship collisions can be sudden or unpredictable. During these incidents, parameters such as the mass of the colliding object, collision speed and angle, and contact stiffness are unknown, making it impossible to determine the actual impact force. Subsequently, safety assessments and repair decision-making for bridges after accidental collisions are difficult. Given the widespread application of long-term structural health monitoring systems in large-scale bridges, the vibration responses of bridges during sudden ship-bridge collisions can be recorded effectively. Therefore, estimating the sudden impact force using only structural vibration responses has become an urgent problem that needs to be solved. This study considered a collision accident between the jib of a floating crane vessel and the main steel-boxed girder of a suspension bridge as an example. The established finite element model of the suspension bridge was used to calculate the vibration responses of the designated girder measurement points for 24 different impact loads in the semi-sine impact load mode. Data fitting indicated a linear relationship between the peak vibration response peaks of the girder and the impact force peaks. Moreover, the slope of the linear curve depends on the impact duration. Consequently, a relational expression was established among the peak values of the structural vibration responses, impact forces, and impact duration times. Thus, the peak value and duration of the impact force can be estimated based on the peak values of the vibration responses at any two points on the structure. Finally, the impact force was estimated using the proposed approach from the vibration responses of an unexpected collision accident between a floating crane and the main girder of a suspension bridge recorded in a bridge monitoring system. The estimated impact force was then applied to the finite element model of the suspension bridge for collision calculation. The results show that the calculated collision damage depth and width of the steel-boxed girder are consistent with those of the actual collision damage zone, verifying the proposed approach for estimating the accidental impact force based solely on the peak values of the structural collision vibration responses. This method does not involve complex theoretical calculations of dynamic inverse problems and is simple, practical, and applicable for estimating the impact force of unexpected ship-bridge collisions using long-term structural health monitoring systems.

Several tunnel collapse accidents caused by water and sand inrush during the construction of shield tunnels have resulted in significant economic losses. Due to the sudden and concealed nature of these accidents, research on the development process and mechanism of shield tunnel collapses is still in its early stages, making it challenging to propose effective preventive measures. To explore the mechanism of segmental tunnel collapse induced by water and sand inrush in connecting passages and provide a theoretical basis for subsequent prevention measures, a model test was designed and conducted, using the accident case of Shanghai Metro Line 4 as the research background. The development process of tunnel collapse was reproduced, and the responses of the strata and tunnel during the collapse were analyzed. The study proposed the development process and causes of structural collapse induced by tunnel leakage. The results indicate that: ① Seepage erosion following tunnel leakage creates soil caves in the external strata, which develop to the ground through a process of formation, destabilization, and reproduction; ② The soil arching effect causes load redistribution outside the tunnel, leading to significant deformation of the tunnel structure; ③ When the soil caves reach a critical height, the top soil destabilizes and falls, and the impact load causes the tunnel structure to collapse. The maximum impact load observed in the test was 13 times the normal soil and water load; ④ A preliminary theoretical formula for calculating the impact load on the tunnel is proposed.

Existing deep-learning-based methods for structural damage identification rely heavily on massive amounts of labeled data. Therefore, a meta-learning-based approach is proposed for structural damage localization and quantification. First, a structural damage localization and quantification model was established using an artificial neural network. This model was used to learn the nonlinear mapping relationship between structural modal data (frequency and mode shape) and substructure stiffness parameters. Second, a model-agnostic meta-learning strategy was used to train the damage localization and quantification model. The generalizability of the damage localization and quantification models can be improved by optimizing the initial weight parameters of the artificial neural network (ANN). The proposed method utilizes a model-agnostic meta-learning training strategy to acquire prior knowledge, thereby accelerating the learning process for new structural damage localization and quantification tasks with limited training data. The method was verified on a numerical three-span bridge and benchmark project of the Z24 bridge. The results demonstrate that the proposed approach provides efficient and accurate localization and quantification of potential structural damage using limited data. Compared with conventional ANN and transfer learning methods, the method exhibited faster convergence and higher identification accuracy.

To clarify the driving behavioral patterns and vehicle operational characteristics of urban underground helical ramps, a real vehicle driving test was conducted on the Jiefangbei Underground Ring Road-Hongyamen Underground Road in Yuzhong District, Chongqing City. Operating parameters, including driving speed and the longitudinal and lateral accelerations of 20 drivers under natural driving conditions, were collected by on-board instruments, and the speed characteristics and longitudinal/lateral acceleration cumulative frequency of vehicles in the helical ramp range were obtained. The relationships among the longitudinal and lateral accelerations and speed were analyzed, and a G-G diagram method was introduced to identify dangerous driving behaviors. The results reveal the following. ① The measured speed of the helical ramp is much higher than the design speed, the deceleration of the driver in the continuous downhill section is higher than the acceleration, and the lateral acceleration amplitudes of continuous curves and independent curves differ in different driving directions. ② The distribution of longitudinal acceleration data for the upward ramp curve (R=80 m) is elliptical, the longitudinal deceleration for the upward ramp curve (R=40 m) and the downward ramp curve (R=80 m) decreases with an increase in speed, and the longitudinal acceleration (deceleration) for the rest of the helical ramp curve first increases and then decreases with an increase in speed. ③ The lateral acceleration of the helical ramp curve is positively correlated with the speed, whereas the lateral acceleration of the upward ramp curve (R=80 m) first increases and then decreases with an increase in speed. ④ The risk section of the helical ramp curve is mainly located in the exit acceleration section, and only the downward ramp curve (R=40 m+30 m) also has a risk section in the entrance deceleration section. These results on the operational characteristics of passenger cars on urban underground helical ramps can be used to inform future speed limit schemes and curve geometry designs for urban underground helical ramps.

The digitalization of road engineering faces problems such as weak digital foundation, incomplete information acquisition methods, insufficient data sharing, unbalanced development, and insufficient business collaboration. Modern digital technologies, such as big data, cloud platforms, and mobile edge computing networks, can be integrated into the entire process of road design, construction, maintenance, and operation. An important strategy is to improve the levels of informatization and digitalization in the road engineering field and to empower the high-quality development of transportation. Based on this, digital technologies in the processes of smart road design, construction, maintenance, and operation were reviewed. The applications and advantages of digital technology in the process of smart road design, including road surveys, roadbeds, and pavement design, were summarized. The current research status of digital technology in road construction was summarized, mainly involving the intelligent control and precise management of various procedures in roadbeds and pavement construction. Strategies that use digital technology to improve vehicle-road coordination level, traffic management efficiency, and road traffic efficiency were introduced. Methods for improving road condition measurement efficiency and pavement maintenance strategies were presented to realize digitalization updates during road maintenance procedures. The development conditions and trends of digital technology applied to smart road design, construction, maintenance, and operation were summarized from multiple perspectives. Related work could provide guidance and suggestions for improving the intelligence level of road design, construction, maintenance, and operation and promote the high-quality development of the transportation industry.

Traffic state completion methods can provide comprehensive holographic traffic network information for traffic management systems, supporting the formulation of urban signal control strategies and dynamic balancing of traffic flow. Leveraging the advantages of real-time acquisition of multi-source traffic data through intelligent and connected technologies, this paper proposes a real-time traffic state completion method based on graph convolutional neural networks. First, a holographic traffic perception system with an “end-edge-cloud” information interaction architecture was constructed, enabling feature-level fusion of multi-source traffic data. Second, an undirected graph model of the road network was built based on the road network topology. Anomaly data identification and interpolation methods were applied to correct the raw data, forming an effective dataset. The hidden layer weights of the completion network were determined according to the spatiotemporal relationships of the actual road network. Third, the spatial features of intersections were clustered by mapping the original data to the spatial dimension through the graph convolutional approach, which incorporates adjacency relationships and the traffic states of intersections. The gate recurrent unit (GRU) was used to traverse the data along the time series, extracting temporal features for state data completion calculations. Finally, field tests were conducted at typical intelligent and connected intersections in the Beijing High-level Automated Driving Demonstration Area. The test results show that for long-term sequence data, the method achieved an error of no more than 10.64% compared to the real values. The overall performance, as measured by the reduction in root mean-squared error (RMSE), was 17.2% lower than that of existing methods such as the long short-term memory (LSTM) neural network. This completion method provides a theoretical foundation and implementation solution for the application of traffic holographic perception technology in future intelligent and connected environments.

Urban expressways, as essential transportation corridors, present unique challenges for traffic management, especially in merging zones. This study proposes a lane change decision-making model for connected and automated vehicles (CAVs) designed to optimize their operation in these complex environments. Specifically, the model addresses the unique lane change dynamics in urban expressway merging zones, focusing on safety, efficiency, and comfort. The analysis begins with a spatiotemporal overlap evaluation between lane-changing vehicles and adjacent traffic, providing the basis for identifying potential collision risks. For CAVs and neighboring vehicles exhibiting spatiotemporal overlap, the lane change time to collision (LCTTC) is computed, enabling dynamic risk assessment. The resulting risk metrics are integrated into a multi-objective reward function to optimize the deep q-network (DQN) architecture, which balances vehicle safety, operational efficiency, and passenger comfort. A novel, risk-aware lane change strategy, termed the security improvement deep q-network (SIDQN), is then proposed. Simulation experiments validate the effectiveness of this strategy, with results demonstrating a lane change success rate exceeding 95% and an average speed of no less than 21.008 m·s^-1. Moreover, the SIDQN strategy improves safety performance in complex merging scenarios, reducing the LCTTC ratio to just 9.56%, a substantial decrease compared to baseline strategies. The accident rate remains minimal. Additionally, the SIDQN strategy limits the number of lane changes to four and minimizes ineffective consecutive lane changes, reducing extreme acceleration and deceleration events and thereby enhancing passenger comfort. In conclusion, the proposed lane change decision-making model significantly improves performance in urban expressway merging zones and provides a valuable reference for advancing CAV safety and comfort in intelligent, connected environments.

    在“交通强国”“一带一路”等国家重大战略与合作倡议持续推进实施的背景下，公路基础设施建设亟需由快速发展向高质量发展转变。路基作为支撑公路健康运行的基础，在服役期长期承受着复杂的交通和环境荷载，易发生变形沉降，这将对交通安全运行构成威胁。路基的稳定性及其长期服役性作为影响道路安全、服役质量和全寿命周期成本的关键，已成为行业发展的重要关注点。近年来，围绕路基土固化、路基结构性加固及路基性能保持等重点研究方向，新理论、新材料、新技术与相应装备不断涌现，有效提升了路基服役寿命，对其长期服役性能保持形成了重要支撑。
    为充分展现我国路基加固与长期性能保持技术领域的最新研究成果，引领路基高质量建造方向，推动路基固化与加固新材料、新结构及新工艺的发展与创新，《中国公路学报》编辑部联合重庆大学崔新壮教授（我刊编委）、长沙理工大学张军辉教授（我刊路基方向副主编）共同策划了“路基加固与长期性能保持技术”专栏，并邀请北京交通大学李旭教授、东南大学庄妍教授、长沙理工大学顾凡教授（我刊青年编委）、上海大学刘飞禹教授、东南大学邓永锋教授、河北工业大学肖成志教授、长安大学包含教授（我刊青年编委）、中南大学肖源杰教授（我刊青年编委）、西南交通大学刘凯文教授、重庆大学周航教授（我刊青年编委）、湖北工业大学李丽华教授等作为组稿专家，共同向该领域的知名专家学者约稿，出版本期“路基加固与长期性能保持技术”专栏。本专栏共收到相关论文50余篇，最终录用19篇，研究内容主要集中于以下3个方面：
    （1）复杂条件下路基长期服役性能演化机理与控制技术。主要内容包括：路基湿度测量方法、演化规律及调控技术研究进展、循环荷载下路基黏土永久变形特性及力学模型、循环荷载下筋材对桩承式低路堤荷载传递机制的影响试验、湿化作用下高速公路红黏土路基动力特性现场试验、软土中XCC刚性桩复合地基承载特性时效性研究、毛细屏障在黄土路基干裂防控中的应用及设计参数影响分析。
    （2）特殊土路基加固关键技术。主要内容包括：矿渣-白泥固化黄土的力学性能与微观机理、生物胶-纤维固化黄土的三轴剪切特性研究、淤泥质钻渣土碳化造粒方法及强度增长机理试验、聚醚胺-230处治膨胀土的膨胀-压缩-收缩试验、浮泥-流泥路基中防淤堵排水板排水行为、碳化复合桩透水混凝土-MgO固化土界面摩擦特性试验、考虑颗粒破碎影响的隧道宕渣循环动载累积变形特性试验、CFB灰-钢渣粉-矿渣-脱硫石膏全固废公路下伏采空区注浆材料特性研究。
    （3）路基边坡稳定性分析及生态防护技术。主要内容包括：地震及降雨作用下路堤边坡变形破坏特性研究、膨胀土路堑边坡柔性加固方法及现场监测、红黏土裂隙湿化自愈行为及强度影响机制、黄土边坡典型护坡植被的根系加固力学效应演化分析、不同含水率下残积土-织物界面动力剪切特性研究。
    在此特向专栏组稿专家、审稿专家、论文作者的辛勤付出致谢！希望本期专栏的出版可以进一步推动我国交通基础设施中路基加固与长期性能保持技术的不断发展与创新。《中国公路学报》将持续关注该领域的国内外最新研究进展，以期为广大专家、学者及工程技术人员提供学习、交流的平台，促进我国交通基础设施建设事业的高质量与可持续发展。由于水平及时间有限，专栏中的不足之处在所难免，恳请各位专家不吝指出。

This study investigated the layer hydraulic characteristics of a double-layered porous asphalt (PA) pavement to better determine the critical design indices. A new two-directional permeameter was developed to measure the hydraulic velocity in the horizontal and vertical directions simultaneously. Based on the permeability test results, the influences of target air void, layer thickness, and nominal maximum aggregate size (NMAS) of mixture on the hydraulic velocity were systematically studied. Subsequently, a hydraulic velocity prediction model was established based on the XGBoost algorithm, and the significance of the influencing factors to hydraulic velocity was evaluated. After that, the bottom layer of a double-layered PA pavement from Suizimei Highway was redesigned based on the established model, which achieved the equivalent hydraulic velocity between top and bottom layers and kept layer thickness unchanged. Finally, the hydraulic velocity and critical service performance of the optimized PA were evaluated. The results indicated that the hydraulic velocity in the horizontal direction played a critical role in the hydraulic characteristic of PA pavement. In this study, the hydraulic velocity in the horizontal direction was greater than that in the vertical direction, even when the target air void increased to 21% with a test sample diameter of 150 mm. In addition, the hydraulic velocity in the horizontal direction was negligibly influenced by the factors including target air void, layer thickness, and NMAS of mixture. Second, the target air void is the most significant factor affecting the overall hydraulic characteristics of the PA mixture. Moreover, the layer thickness and NMAS also affected the hydraulic velocity, contributing as much as 25.2% and 18.5%, respectively. Thus, considering the layer thickness and NMAS into the mix design can significantly decrease the target air void of the bottom layer of a double-layered PA pavement while satisfying the hydraulic demand. Thereby, this optimization approach will help improve the service performance of a double-layered PA pavement.

To investigate the impact of the behavioral preference and dynamic spatio-temporal relationship of pedestrian-vehicle interaction (PVI) on potential traffic conflict, a safety analysis approach incorporating machine learning and ordered logistic modeling is proposed. The pedestrian-vehicle crossing data at unsignalized crosswalks was collected through unmanned aerial vehicle (UAV). The 876 pairs of PVI were extracted from of trajectories road users. Considering the dynamics of interactions, the interaction indicators based on competition for right-of-way and collision relationships were proposed. A representation learning model was trained to capture time-series data of PVI, transformed these into potential representations, and clustered them to identify and analyze typical interaction patterns and features. Utilizing these patterns and varying conflict severities, multiple ordered logistic models were developed to investigate the factors influencing conflict risk and to explore differences in risk causation across distinct interaction patterns. The findings reveal that PVI can be categorized into three distinct patterns: near-interaction, far-soft-interaction and far-hard-interaction; Reductions in relative pedestrian-vehicle distances, increases in lower vehicle speed limits and upper pedestrian speed limits are common factors that reduce conflict severity; For near-interactions, rapid pedestrian deceleration, right-of-way competition, and high-speed vehicles reduce conflict risk, and both extreme vehicle deceleration and rapid pedestrian acceleration increase the danger; For far-interactions, an increase in the pedestrian speed lower limit leads to an increase in conflict risk; Rapid vehicle deceleration and right-of-way competition reduce the conflict risk for far-soft-interactions; Pedestrian sharp deceleration and high-speed vehicles are the main factors that reduce the conflict risk of far-hard-interactions, while pedestrian sharp acceleration raises the danger. The application of the combined approach reduces the influence of the heterogeneity of PVI behavior on the analysis results. The conclusions of the study provide a theoretical basis for enhancing safety in pedestrian crossing on urban roads.

The vibration-based road roughness detection technology, which estimates road roughness by measuring vehicle vibrations induced by road profiles, has been of interest in recent years. To address the challenges of labor-intensive calibration and limited stability in existing vibration-based road roughness detection methods, a rapid calibration method based on parameter estimation is proposed, accompanied by a sensitivity analysis of the vehicle vibration parameters' impact on the detection performance. This approach begins by constructing a seven-degree-of-freedom full-vehicle dynamics model and introduces a calibration method based on impact testing, with a model updating approach. Subsequently, a virtual simulation platform is established to simulate various road evenness conditions, replacing the traditional labor-intensive calibration process and obtaining multiple regression models for vehicle vibration indicators, vehicle speed, and international roughness index (IRI). Relying on the calibrated vehicle model, the influences of vehicle suspension parameters and fluctuation in vehicle mass distribution on the IRI detection performance were investigated. Field tests and research results demonstrate that the calibrated vehicles' evenness detection results align well with the laser profiler, with detection errors significantly correlated with the segment length and detection speed. Under conditions with segment lengths of 100 m or larger and vehicle speeds of 30 to 60 km·h^-1, the IRI detection errors are within 0.5 m·km^-1. A sensitivity analysis reveals that the tire equivalent stiffness coefficients and suspension stiffness coefficients exhibit the highest sensitivity, which highlights the significant impact of tire pressure variations and suspension spring wear on the evenness detection. This research provides a methodological support to advance the widespread application of the vibration-based road evenness detection technology.

截止2023年底，我国各类桥梁总数已经突破120万座。桥梁结构作为重要交通生命线咽喉工程，平时容易受到恐怖袭击、燃油气及危化品意外爆炸作用和船撞、车撞及滚石冲击等冲击作用的威胁，战时则是敌方首要攻击和重点打击的重要经济目标之一。爆炸冲击作用具有持时短、强动载、多场耦合机理复杂且能量巨大等特征，桥梁结构一旦遭受这些爆炸冲击作用，将导致不可逆的严重损伤甚至垮塌破坏，修复困难以及修复代价巨大。面对日益突出的桥梁抗爆抗冲击问题，近年来越来越多的学者开始关注并倾力开展桥梁抗爆抗冲击相关研究，这极大地增进了人们对爆炸与冲击荷载特征、高应变率下材料动态本构模型、爆炸与冲击作用下桥梁动力响应与失效模式、损伤评估及应急抢修抢建等方面的认知，促进了桥梁抗爆抗冲击防护新材料与新技术、抗爆抗冲击设计理论与方法等方面的快速发展。
为充分展现我国桥梁抗爆抗冲击的最新研究成果，及时总结该领域的前沿动态与技术发展，推动桥梁抗爆抗冲击理论与防护技术的不断发展与完善，《中国公路学报》编辑部联合东南大学宗周红教授(我刊编委)、北京工业大学韩强教授(我刊桥梁方向副主编)、西南交通大学郭健教授(我刊编委)和湖南大学樊伟教授(我刊青年编委)共同策划了“桥梁抗爆抗冲击”专栏，并邀请同济大学王君杰教授、太原理工大学王蕊教授、南京工业大学方海教授、同济大学吴昊教授、北京工业大学金浏教授、武汉理工大学胡志坚教授、澳大利亚科廷大学陈文苏副教授、长安大学张景峰副教授(我刊青年编委)和澳大利亚科廷大学张僖洪高级讲师作为组稿专家，共同向该领域的知名专家学者约稿，出版本期“桥梁抗爆抗冲击”专栏。本专栏共收到相关论文50余篇，最终录用15篇，研究内容主要集中于以下3个方面：
(1)桥梁爆炸作用、损伤机理与防护技术。主要内容包括：桥梁结构抗爆安全防护研究综述、装药形状对爆炸作用下钢桥面板损伤的影响、双柱墩混凝土梁桥爆破拆除倒塌过程与机理等。
(2)冲击作用下桥梁结构动力行为与防护技术。主要内容包括：桥梁船撞研究进展综述、落石撞击下双柱式RC梁桥的倒塌破坏分析、拱桥断索冲击响应模型试验与有限元分析、货车撞击双柱式RC桥墩的损伤机理与计算方法、新型再生发泡混凝土墙式护栏防车撞试验与模拟等。
(3)混凝土材料动力本构及撞击-撞后构件行为。主要内容包括：混凝土帽盖模型参数标定及在桥梁冲击损伤模拟中的应用、基于PIV技术的透明黏性泥石流冲击桥墩模型试验、侧向冲击与静力加载下小剪跨比RC柱的剪切损伤行为、多次冲击下RC梁损伤特征及剩余承载性能、高强方钢管UHPC构件轴向冲击与冲击后剩余性能、冲击荷载下节段拼装梁的响应与破坏特征研究、水平冲击作用下非新建桥梁桩基动力响应及损伤特性。
在此特向专栏组稿专家、审稿专家、论文作者的辛勤付出表示诚挚的感谢！希望本期专栏的出版可以进一步推动“交通强国战略”和“双碳战略”背景下交通基础设施中桥梁工程抗爆抗冲击设计计算理论及技术的不断发展与完善。《中国公路学报》将持续关注该领域的国内外最新研究进展，以期为广大专家、学者及工程技术人员提供学习、交流的平台，促进我国桥梁建设事业的高质量与可持续发展。
由于水平及时间有限，专栏中的不足之处在所难免，恳请各位专家不吝指出。