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.

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.

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.

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.

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。

Complex road infrastructure and traffic operation environments on ground roads can significantly affect the lane line perception of automated vehicles. Focusing on ground roads, a test vehicle equipped with a multi-sensor system from the Tongji University vehicle-mounted holographic information collection system was used to conduct LiDAR-based lane detection tests on six accident-prone roads in Shanghai. A hybrid modeling approach (combining machine learning and binary logistic regression models) was used to analyze the key factors that influence autonomous driving lane detection failure and the impacts of their feature changes on three dimensions: road characteristics, lane marking properties, and traffic operation. First, a feature importance analysis was conducted based on the LightGBM model, and the SHapley Additive exPlanations (SHAP) method was used to analyze the impacts of individual feature changes on lane detection failure. Next, a binary logistic regression model was used to determine the significant factors and their interaction effects on the important influential factors. The feature importance results indicate that the 10 critical factors influencing lane line perception failure, in decreasing order of importance, are the type of marking combination, test vehicle operating speed, absence of markings, vehicle occlusion, road width, lane width, marking wear, type of leading vehicle, type of guiding marking, and number of leading vehicles. A narrower lane width (2.6 m) and the presence of large trucks in front of the vehicle increased the probability of lane detection failure. Missing or worn lane markings, double-dashed-line combinations, and pedestrian crosswalk lines increased the probability of detection failure. The results of the binary logistic model indicate that, except for road width, all other factors significantly affect lane line detection failure. Moreover, there are interaction effects between the lead vehicle type and lane width as well as between the lead vehicle type and operating speed. The research findings provide guidance for lane design and optimization in mixed traffic environments, and they offer directions for sensor manufacturers and automakers to optimize LiDAR perception algorithms.

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.

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.

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.

    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。

    截至2023年底,我国公路总里程达544.1万千米,其中高速公路总里程18.4万千米。然而,我国沥青路面的设计使用寿命一般为10~15年,随着服役年限的增加,达到设计使用年限的沥青路面逐年增多。因此,在“交通强国”战略与“双碳”目标下,针对我国公路事业发展所面临的既有沥青路面存量大、老龄化严重等难题,延长既有沥青路面结构寿命,高质量保证既有路面运维水平是交通行业发展的重大需求。近年来,众多学者和科研人员在既有沥青路面结构延寿方法与技术研究方面开展了大量卓有成效的工作,取得了丰富的研究成果,为我国既有沥青路面结构延寿提供了重要理论和技术支撑。
    为充分展现我国既有沥青路面结构延寿方法与技术研究领域的最新研究成果,及时总结该领域的前沿动态,推动延寿方法和技术的不断发展与创新,《中国公路学报》编辑部联合长沙理工大学于新教授、东南大学钱振东教授、长安大学汪海年教授,共同策划了“既有沥青路面结构延寿方法与技术”专栏;并邀请东南大学罗桑教授、同济大学严宇教授、长安大学杨旭教授、长沙理工大学李盛教授、哈尔滨工业大学王大为教授、重庆交通大学何亮教授、河海大学朱浩然副教授、长沙理工大学黄拓副教授作为组稿专家,共同向该领域的知名专家学者约稿,出版本期“既有沥青路面结构延寿方法与技术”专栏。本专栏共收到论文30余篇,最终录用9篇研究内容主要集中于以下2个方面:
    (1)既有沥青路面性能演变规律研究。主要内容包括:长期服役状态下既有沥青路面结构延寿关键技术综述、基于亿次加载试验的长寿命沥青路面性能演化规律及设计体系研究、基于道路结构承载力及环境严酷程度的服役寿命评价指标研究、人工气候加速作用下胶粉再生沥青老化特征及时空演变模型、不同应力状态下沥青混合料疲劳剩余强度及损伤演化规律研究。
    (2)既有沥青路面病害成因及检测技术。主要内容包括:基于温度应力分析的半刚性基层沥青路面拱胀病害成因研究、沥青混合料隐性病害动态共面电容成像优化、基于自适应课程学习的探地雷达道路隐性病害检测增强。
    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既有沥青路面结构延寿设计方法及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。

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.

The development of autonomous vehicle platoons will lead to new accident patterns. Insufficient research exists on occupant injury and protection associated with this new type of accident. To provide a reference for the research and technological development of occupant protection in autonomous vehicle platoon collisions, a continuous crash accident scenario involving a typical three-car autonomous vehicle platoon under high-speed conditions was utilized to determine boundary conditions such as impact time and speed. A full-scale finite element simulation was conducted to obtain the driver kinematics and injury response in each collision condition, and driver injury risk in the autonomous vehicle platoon collision scenarios was analyzed. The results show that although the risk of skull fracture is less than 1%, the risk of severe craniocerebral injury is significant, with the highest predicted risk of AIS 3+ using the BrIC criterion reaching 70.2%. Owing to excessive forward bending and backward extension of the cervical spine, three types of ligaments are at risk of serious injury. Furthermore, the risk of chest rib fracture is relatively low, whereas the risk of viscera damage is contingent on the collision sequence. When the middle car first experiences a frontal collision and is then rear-ended, the maximum principal strain on the driver's heart and liver far exceeds the damage threshold of 0.3, resulting in significant damage risk. Conversely, when the middle car is rear-ended and then collides with the front car, the maximum principal strain on the driver's internal organs is less than 0.3, resulting in a low overall damage risk.

    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。

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。

To solve the problems of easy rusting of assembled segmental pier reinforcements in coastal erosion environments and large residual displacements of bridge piers after strong earthquakes, hybrid reinforced precast segmental assembled concrete piers with stainless steel reinforcement and unbonded prestressed glass fiber-reinforced polymer (GFRP) bars are proposed. The damage modes of the hybrid-reinforced piers were revealed through the proposed static tests. In addition, the seismic performance parameters including hysteresis performance, bearing capacity, energy dissipation, residual displacement, prestressing change, stiffness degradation, rebar strain, and joint opening of the piers were analyzed and compared with the seismic performance of the hybrid-reinforced segmental-assembled piers with bonded non-initial prestressed GFRP tendons. The results show that the unbonded GFRP reinforcement applied 50% of the vertical axial compression ratio of the top of the pier preload, and the residual displacement of the specimen could be reduced by nearly 40%; however, the peak bearing capacity was reduced by approximately 20%. With an increase in the reinforcement rate of the GFRP reinforcement and a reduction in the reinforcement rate of the stainless-steel reinforcement, the residual displacement of the specimen was reduced by 38%, and the bearing capacity was reduced by 10%. The vertical axial pressure ratio at the top of the pier, i.e., the effect of P-Δ effect on the maximum bending moment at the bottom of the pier, is more apparent. With sufficient initial tensile force and reinforcement rate of unbonded prestressed GFRP tendons, while considering the influence of the ultimate tensile strain of GFRP tendons, the same horizontal bearing capacity as the bonded case can be achieved, but the residual displacement of the pier column can significantly reduce.

To systematically analyze and summarize the current research status and development trends in the operational management of emergency medical service (EMS) vehicles, this study organizes the research framework of EMS vehicle operational management into three levels: strategic, tactical, and operational, based on 1 502 articles indexed from the Web of Science database. The findings reveal that at the strategic level, research on EMS vehicle location focuses on continuous improvement of coverage definition and accurate characterization of inherent uncertainties within the system. Key research methods include stochastic planning and robust optimization as uncertainty modeling and optimization approaches. At the tactical level, EMS vehicle relocation is categorized into multiperiod and dynamic relocation based on the triggering of relocation decisions. Given the complexity of relocation with respect to location, the research emphasizes the application of heuristic and reinforcement learning algorithms in addressing real-world large-scale problems. Decisive issues at the operational level include EMS vehicle dispatch, destination selection, and route planning. Research on EMS vehicle dispatch has evolved from rule-to model-based and from independent to joint optimization in relocation. Destination selection involves coordinated optimization with hospital workload, and route planning primarily addresses special scenarios such as disaster response. In future research, optimization in EMS vehicle operational management should focus on the dual research threads of dynamics and uncertainty. This entails accurately characterizing the sources of system uncertainty while leveraging finer-grained data to assist real-time decision-making. In terms of specific modeling and solving techniques, joint optimization of multiple decision problems across different levels should be conducted to progress from local to system optimum EMS vehicle location and dispatch schemes. However, efficient algorithms for handling real-world large-scale scenarios continue to pose a challenging research direction.

    截至2023年底，我国各类桥梁总数已超过120万座，隧道总数突破4万座，桥梁与隧道作为交通基础设施的关键节点工程，其安全是交通强国建设的重要支撑。然而，桥隧结构在其运维期间常遭遇各类极端荷载作用，包括爆炸、火灾、飓风、碰撞、水毁、地震以及高地应力、高地温、断层活化位错、超高水压、低温冻涨等，其诱发的垮塌、倾覆、岩爆、大变形、突水涌泥、塌方、开裂、掉块、渗漏水、挂冰等灾害将会对桥隧结构造成严重破坏。桥隧结构一旦遭遇这些极端荷载，将导致不可逆的严重损伤甚至垮塌破坏，因此，极端荷载是桥梁与隧道工程安全运维研究领域的世界性难题之一。为保障桥隧结构安全，提升国家对交通基础设施的应急响应能力和管理水平，近年来，众多科研人员围绕桥隧结构极端荷载分析理论与计算方法、桥隧结构遭遇极端荷载时（后）的破坏机理和安全评估及其抵抗极端荷载的安全运维理论与方法，开展了大量卓有成效的工作，取得了丰硕的研究成果。
    为系统展示我国桥隧结构极端荷载与安全运维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，保障交通基础设施的安全性和耐久性，《中国公路学报》编辑部联合长安大学张岗教授（我刊青年编委）和石家庄铁道大学徐飞教授共同策划了“桥隧结构极端荷载与安全运维”专栏，并邀请石家庄铁道大学杜彦良院士作为顾问专家，同济大学李国强教授、广西大学韩林海教授、深圳大学任伟新教授、西南交通大学李永乐教授、东南大学郭彤教授、石家庄铁道大学赵维刚教授、山东大学李利平教授、东南大学宗周红教授、东南大学王浩教授、同济大学闫治国教授、大连理工大学许福友教授、北京航空航天大学杜博文教授、铁道科学研究院马伟斌教高、长安大学韩万水教授、长安大学罗彦斌教授、浙江工业大学彭卫兵教授、哈尔滨工业大学李忠龙教授、深圳大学周海俊教授、内蒙古大学李国栋教授作为组稿专家，共同向该领域的知名专家学者约稿，出版本期“桥隧结构极端荷载与安全运维”专栏。本专栏共收到相关论文40余篇，最终录用13篇，研究内容主要集中于以下3个方面：
    (1) 桥隧火灾行为、抗火机制、预测预警、安全防护研究与灾后评估。主要内容包括*分级车辆火灾下吊索系统的抗火性能计算方法、油轮爆火环境悬索桥安全性能预测与预警方法研究、强降雨作用下隧道火灾烟气蔓延特性及控制试验研究、火灾后盾构隧道衬砌结构残余承载力评估模型研究。
    (2) 桥隧碰撞、冰毁、涌水与抗风研究。主要内容包括*现浇及装配式超高墩连续刚构桥碰撞响应及其影响试验研究、内填砂粒的桥梁钢管桩围堰船撞试验与模拟、寒区冰激桥墩动力模型试验、基于桥梁监测系统振动响应的浮吊船悬索桥主梁碰撞荷载估算与分析、盾构隧道联络通道涌水涌砂诱发结构倒塌试验、极端台风下特大悬索桥的GNSS实时振动监测及动力响应分析。
    (3) 桥隧健康监测关键技术与算法。主要内容包括：基于改进Prony理论的结构非缩放位移柔度识别、考虑模态振型影响的斜拉桥非线性内共振能量转换研究。
    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交通基础设施中桥隧结构极端荷载与安全运维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。

The new quality of productive forces is a contemporary advanced force that is fostered by revolutionary breakthroughs in technology, innovative allocation of production factors, and deep transformation and upgrading of industries. Transportation industry development focuses on the quantity of expansion. Development of quality and efficiency is not high if the traditional path of development constrains the socio-economic development needs and eliminates the original development path which is key in the mental productivity of empowerment. Therefore, this paper proposes the connotation and characteristics of the new quality of productive forces in transportation from the perspective of the development of transportation and transportation productive forces, thereby constructing an evaluation index system for the level of new quality of productive forces in transportation, which better serves economic and social development as a measurement standard. Taking the port in the field of transportation as a case study, the study evaluates and measures the new quality of productive forces in port enterprises using the analytic hierarchy process (AHP) and the entropy weight method. Based on theoretical and empirical research and analysis, the transportation industry is believed to be in a key period of innovative development in the digital economy era. The development of transportation should fully leverage the new quality of productive forces to achieve high-quality development. The improvement of the level of new quality of productive forces in transportation should be promoted from three aspects: labor force, means of labor, and objects of labor, to strengthen the coupling of the system. Empowering digital elements, optimizing the allocation of all factors, enhancing the resilience of the transportation system, carrying out full-process transportation services, building a green and low-carbon transportation system, and strengthening the safety protection capabilities of transportation should be systematically and comprehensively promoted.

This study investigated the material parameter values for the long-life design of concrete cable-stayed bridges and target reliability index values for PC main beam components. In addition, it investigated the random degradation law of concrete strength, a method for determining material parameter values considering the design service life, and a calculation method for the target reliability index considering the material performance degradation of cable-stayed bridge PC main beam components. First, based on relevant measured data, a random process identification of concrete compressive strength degradation under freeze-thaw cycles was conducted. The results show that the degradation law of concrete strength under freeze-thaw cycles followed the Gamma process, and a Gamma process identification method was established. Second, a method was proposed to determine the required material performance values by considering the design service life. Based on the conclusion that the degradation of the concrete compressive strength follows the Gamma process, a one-dimensional probability distribution of the concrete performance degradation process at any time was obtained. The concrete compressive strength design value that meets the requirements of the design service life was determined based on the probability density function with α^' = 0.05 percentile value when the structure is in service until the design service life. Finally, the time-varying flexural bearing capacity of the cable-stayed bridge PC main beam was calculated by considering the degradation of concrete strength, rebar corrosion, and collaborative working capacity degradation of the rebar and concrete. The time-varying reliability index of a cable-stayed bridge PC main beam was calculated using the checkpoint method. Based on the degradation of the service period reliability index, a method was established to determine the target reliability index of the cable-stayed bridge PC main beam, considering the design service life under the condition of a long-life design. This study provides a reference for reasonably determining target reliability index values for cable-stayed bridges.

The fatigue damage process of asphalt is a complex variable-rate physical reaction process. This study aimed to accurately characterize the fatigue damage process of asphalt. Controlled stress fatigue tests were conducted on SBS-modified asphalt and base asphalt at different temperatures. The dissipated pseudo strain energy was applied to eliminate the interference of viscoelastic dissipated energy on fatigue damage and characterize the asphalt fatigue damage. The representative rates of the fatigue damage processes were determined by analyzing the damage evolution equation described by dissipated energy. The Arrhenius kinetics equation was used to correlate the representative rates of asphalt fatigue damage at different temperatures for determining the fatigue damage activation energy of asphalt. The results show that dissipated pseudo strain energy can accurately characterize the asphalt fatigue damage. The damage evolution equation parameter, β, can be used as a representative rate indicator of the fatigue damage process of asphalt to quantify its overall rate of the fatigue damage process. The fatigue damage activation energy of asphalt is the minimum energy required for fatigue damage, which can characterize the degree of difficulty of the fatigue damage process. The higher the fatigue damage activation energy of asphalt, the lower the probability of fatigue damage occurring in asphalt. The fatigue damage activation energies of SBS-modified asphalt and base asphalt are 59.92 and 28.91 kJ·mol^-1, respectively. The polymer network formed by SBS modifiers can increase the fatigue damage activation energy, thereby improving the fatigue performance of asphalt. In summary, the fatigue damage process of asphalt can be accurately characterized by the two indicators of the representative rate of fatigue damage and fatigue damage activation energy.

To investigate the dynamic response characteristics of loess under impact loading, a series of impact compression tests with varied strain rate were conducted on loess samples with four different moisture contents of 13%, 16%, 19%, and 22% using the split Hopkinson pressure bar (SHPB) testing technique. The design speeds of the impact rod were 4, 6, 8, and 10 m·s^-1. The experimental results show that the dynamic stress-strain relationship of loess under impact loading exhibits obvious subsection characteristics. It can be divided into three stages: elastic deformation, plastic flow, and failure. The strain rate and moisture content significantly influence the dynamic characteristics of the loess samples. An increase in strain rate prolongs the plastic flow process of the loess, while an increase in moisture content leads to a transition from a predominantly plastic flow characteristic to a brittle failure in the dynamic stress-strain development of the loess. The yield strength, failure strength, and failure strain of the loess increase with the increase in the strain rate and decrease with the increase in the moisture content. The compressive wave velocities of the loess samples are primarily influenced by the moisture content. The higher moisture content results in higher compressive wave velocities. Additionally, a Z-W-T component model considering damage evolution was used to simulate the dynamic response of the loess. The simulated stress-strain relationship of the loess shows a good agreement with the SHPB experimental results. Further analysis of the model parameters reveals that the response of the loess is considerably more sensitive to high strain rates than to low strain rates.

The stability analysis of a slope reinforced with a frame-prestressed anchor rod structure under an earthquake action is required for practical applications. Pseudodynamic and limit equilibrium methods were used, and the unit horizontal spacing of the anchor rod was adopted as the calculation unit. The dynamic safety coefficient of a slope under an earthquake action and the formula for determining the variation in the anchor rod axial force were derived, considering the dynamic variation in the anchor rod axial force under the earthquake action. The stability of the slope reinforced with a frame-prestressed anchor rod structure under an earthquake action was analyzed by programming a solution using MATLAB. The results show that the traditional methods of calculating the slope stability coefficient based on the initial prestress or ultimate pullout bearing capacity of anchor rods may underestimate or overestimate slope stability. The method proposed in this study considers the gradual transition of the axial force on the anchor rod from the prestress to ultimate pullout bearing capacity, providing a more accurate evaluation of the stability of the slope reinforced with the frame-prestressed anchor rod structure. Within a cycle, slip may occur when the stability coefficient of the slope is lower than 1, and the anchor rod undergoes elastic deformation, with its axial force increasing periodically. The reinforcement force provided by the frame-prestressed anchor rod structure increases, stabilizing the slope and resulting in a gradual decrease in the axial force growth rate of the anchor rod. The stability coefficient within a cycle first decreases to a minimum value and then increases, showing an overall cosine variation pattern. With time, the stability coefficient improves within a cycle. The horizontal spacing of the anchor rod is the most significant factor that influences the stability of the slope reinforced with the frame-prestressed anchor rod structure during an earthquake, followed by the internal friction angle of the soil, acceleration coefficient, and soil cohesion. The amplification coefficient of the acceleration amplitude has the second most significant impact on the slope stability, whereas the inclination angle of the anchor cables has the least impact.

To explore the key issues and future directions in the study of asphalt pavement textures, this paper provides a comprehensive review of the research progress in three-dimensional (3D) reconstruction and evaluation methods of asphalt pavement textures, both domestically and internationally. First, 3D reconstruction methods of asphalt pavement textures are systematically reviewed. The 3D reconstruction techniques for asphalt pavement surface tomographic images are summarized, and active laser scanning and passive image-based methods for the 3D reconstruction of pavement surface textures are introduced. The algorithm principles and characteristics of the monocular, binocular, and multi-view image depth estimations are compared, and the application of pavement texture 3D generation technology is analyzed. Subsequently, based on computational principles, the pavement surface texture evaluation methods are categorized into the geometric statistical index method, spectral index method, fractal index method, and image feature method, and the corresponding evaluation indices are further classified into 2D evaluation indices based on the pavement surface profile and 3D evaluation indices based on the pavement surface texture. Multidimensional and multiscale analyses are conducted on the applicability conditions, advantages, and limitations of the different evaluation indices. Finally, future research directions concerning the evaluation and reconstruction techniques of pavement surface textures are discussed, and the development trends of intelligence, digitization, and informatization in the evaluation and 3D reconstruction of asphalt pavement textures are anticipated. This study provides a reference and source of inspiration for academic research on the functionality of pavement surfaces and development of modern high-quality pavements.

This study aims to determine and intelligently predict the optimal vibration frequency of subgrade-filler compaction. The natural frequencies of different material-compaction degrees were determined through vibration-compaction experiments and the hammer modal method. The correlation between the natural frequency and optimal vibration frequency was determined, based on the maximum dry density of the materials and the dynamic stiffness. The relationship between the material characteristics and optimal vibration frequency was established, and the key control-feature parameters of the optimal vibration frequency were proposed. A machine-learning prediction model based on the key control-feature parameters was constructed and the prediction model was optimized using a bilevel evaluation method. The results indicate that the vibration frequency at the natural frequency results in the largest maximum dry density and the most optimal state for dynamic stiffness. The key controlling features influencing the optimal vibration frequency include the maximum particle size of the material, gradation parameters, content of needle-shaped particles in the coarse aggregate, and Los Angeles abrasion value. In a comparative analysis of the prediction models, the artificial neural network (ANN) model is considered to have the best goodness-of-fit and can be used as the core model for optimal vibration-frequency prediction. The research findings provide a novel approach for determining the optimal vibration frequency in subgrade compaction and offer theoretical guidance and support for intelligent subgrade construction.

Vehicle trajectory prediction is a core function of autonomous driving systems and serves as a critical foundation for downstream decision-making and planning modules, enabling safe and effective driving behaviors. To achieve accurate long-term trajectory prediction of surrounding vehicles in structured road scenarios, a hierarchical, interactive vehicle multi-modal trajectory prediction method, S-VectorNet, was proposed based on the classical VectorNet model. First, gated recurrent unit (GRU) was introduced to encode historical trajectory data and map information, thereby enhancing the temporal representation capability of the encoded features. Second, a two-layer interaction model incorporating attention blocks and graph neural network (GNN) was constructed to model interactions between traffic agents (target vehicles and surrounding agents) and the map. This approach improves the model's ability to capture long-range dynamic interactions. Next, dynamic scene representation module, which is updated over time, was proposed to capture the temporal correlations of individual motion states and interactions using multi-head attention mechanisms and time-series models. This allows the model to learn rich scene memory information. Finally, a two-stage trajectory generation method was employed to generate multi-modal trajectories, enhancing the model's ability to predict trajectory endpoint. Experiments conducted on the Argoverse dataset show that S-VectorNet reduces the minimum average displacement error by 12% and the minimum final displacement error by 22% compared to the baseline model on the validation set. On the test set, the minimum average displacement error is 0.83 m, and the minimum final displacement error is 1.23 m, demonstrating significant comprehensive performance advantages over other existing trajectory prediction models.

Under the backdrop of rapid intelligentization and informatization development in bus systems, bus en-route operation control has gradually become an important research highlight in bus operation optimization. Such research helps to enhance the efficiency of bus operation and passenger travel experiences, and it provides strong support for the development of the public transportation industry. To analyze the problems and challenges in bus en-route operation control, this study systematically reviewed and analyzed the status and development achievements of relevant research in terms of research direction, scope, and practical application to provide a clear research context, theoretical framework, and methodological guidance for subsequent researchers. This study followed the research approach of “state diagnosis-control optimization”, considered a bus bunching incident as an example of a poor en-route operation state, provided a focused summary of the influencing factors that lead to such incidents, and addressed other possible poor en-route operation states as well as related identification and diagnosis technologies. Hence, a comprehensive examination of the methodologies employed in bus en-route operation control is undertaken. First, the methodologies and limitations of existing research on the optimization objectives, control objects, and constraint conditions are analyzed. This is followed by a comprehensive analysis of the control strategies from disparate dimensions, including stop control and interval control, as well as an examination of the characteristics and applicable scenarios of the various control strategies. Furthermore, this paper presents a summary of commonly used optimization model-solving algorithms and discusses their applicable conditions and optimization effects. Finally, this paper offers a prospective outlook on future research directions and development trends to provide new ideas and system optimization directions for future work to improve bus operation efficiency and service quality.

    重大交通基础设施是国家经济发展的重要支撑。由于现有设计方法不能充分考虑其长寿命期荷载和抗力的不确定性，无法清晰表征环境、荷载等因素的长期耦合效应，以致其设计寿命难以满足绿色长寿发展需求。加之施工、超载等因素影响，我国重大交通基础设施使用寿命普遍低于国际先进水平，导致全寿命周期建设运维成本增加，安全事故频发，严重制约了其服务保障能力。
    目前，重大交通基础设施长寿命设计已成为世界主流研究方向，许多发达国家先后发起了相关战略性研究计划,如美国路面长期性能研究计划（Long-term Pavement Performance Program）、欧盟长寿命桥梁（Long Life Bridges）项目等。然而，我国仍缺乏重大交通基础设施长寿命设计理论与方法的体系化研究。
    为此，本专栏依托国家重点研发计划项目“复杂条件下重大交通基础设施长寿命设计理论与方法（2021YFB2600900）”，针对我国重大交通基础设施服役寿命短等问题，解决极端环境、不稳定地质条件和大交通量等耦合作用下重大交通基础设施性能劣化、退化的基础科学问题，攻克长寿命设计中荷载与抗力的多指标多层次概率演化的理论难题，建立基于性能目标的全寿命、全概率设计方法体系，提出材料-结构-功能-环境协同的长寿命设计理论与试验验证方法，以期为我国重大交通基础设施长寿命设计提供坚实的理论基础和技术支撑，从而进一步服务国家“交通强国”战略实施和综合立体交通网高质量建设。
    本专栏旨在展示该项目的部分研究成果，并为重大交通基础设施的设计提供新理念、新方法，从而助力我国公路交通基础设施建设向更高质量、更高效能的方向发展。

To study the effect of the load on bearing characteristics of slender energy piles, a centrifuge model test was conducted with different loads applied on slender frictional energy piles, and the energy piles were subjected to 30-year thermal-cold cycles. The centrifuge acceleration was 70g. The model pile was composed of aluminum tube. Five groups of strain gages and six groups of thermistors were set uniformly throughout the pile body to measure the axial force and temperature. Standard Fengpu sand was used as the test material around the pile and bearing layer of the pile bottom. 30 hot-cold cycles were applied to the energy piles with free and top loads. In a slender pile with a length-to-diameter ratio of 50, the maximum axial force occurs at 3/4 of the depth in the middle and lower parts of the pile approximately, as a result of the coupling effect between the change in temperature and the friction resistance at the lower part of the pile. The influence of the circulating temperature on the axial force of the pile is primarily concentrated in the first cycle. The slender pile body causes the tip of the frictional pile to exhibit a certain end-bearing effect. In the thermal cycle stage, the end of the slender energy pile exhibits a large embedment depth, and the axial force of the lower part changes significantly. During the cold circulation process, slender piles with load on the top have a deeper embedment distance at the lower part of the pile body. The application of pile-top loads can reduce the attenuation value of the pile shaft axial force and the negative friction resistance on the pile side caused by heating. For energy piles subjected to a combination of top load and long-term cycle temperature, the bearing capacity of the energy pile foundation is reduced to a certain extent. The bearing capacity of the pile head reduces by 14.8% compared with that of the energy pile with a free pile head, while keeping the pile head load unchanged. Therefore, the long-term bearing characteristics of energy piles should be considered in the design.

Water content at the root-soil interface is often unsaturated due to transpiration and water absorption in plants. To quantify the contribution of interfacial suction to the shear strength of the root-soil interface, a shear-strength model of the root-soil interface considering interfacial suction was developed based on the principle of unsaturated effective stress. The model divides the shear strength of the root-soil interface into two parts: one caused by the net normal stress, and the other by the interfacial matric and tension suctions. The root-soil interface shear strength of the roots of four plants, Magnolia multiflorum, Privet microphylla, vetiver, and Robinia pseudoacacia, was measured under different interfacial suction and normal load conditions using a single pull test. The relative error of the test and model values was less than 9%, verifying the accuracy and reliability of the model. The root-soil interface friction coefficient was significantly correlated with root surface roughness by analyzing the influencing factors of the model. Among them, the average height of the root surface roughness index had the largest correlation with the root-soil interface friction coefficient, with a correlation coefficient of 0.96 and a weight of 0.35. The effective stress parameters were closely related to the bending surface radius, saturation radius, and filling and contact angles, and increased with the increase in interfacial volume water content. The interfacial suction stress depends on the relative magnitude of the matrix suction and the effective stress parameter; it increased first and then decreased with the increase in the interfacial volume moisture content. The above results provide reference for further exploring the mechanical mechanism of root-oil interface interaction and optimizing the ecological protection design of slope.

At complex intersections, such as large and skewed intersections, autonomous vehicles (AVs) face the challenge of multitarget collision avoidance when performing right-turn driving tasks that require the real-time perception of dynamic changes in a road traffic environment. To evaluate the readiness of right turns at intersections from the perspective of areas from where AVs must accurately perceive the right turn (i.e., the safe sight zone), field tests were conducted at six smart intersections in Shanghai, China. This study aims to reveal the relationship between the road traffic environment and autonomous driving perception and to clarify intersection boundaries. A safe sight zone evaluation method based on driving tasks and perceived targets is proposed. Through driving task analysis, perceptual subtasks for “intersection entry” and “execute turn” were identified. The static road infrastructure and dynamic traffic participants that needed to be detected were identified to form dynamic safe sight zones. The perception capabilities of safe sight zones were evaluated based on whether the AVs accurately detected the perceptual targets. The intersection design and traffic environment were considered as the input features, and a perception prediction model was constructed using the CatBoost ensemble learning model. The relationship between perception capability and contributing factors were revealed using the SHapley Additive exPlanations (SHAP) post hoc interpretation technique. The results indicate the following. ① Dashed-dashed types of lane lines have a great effect on detection failure. The failure probability increases for right-turn radii greater than or equal to 8 m. Large intersections, such as those with wider lanes (15.5 m), and more lanes (four lanes) at the entrance are more likely to lead to failure. ② Failure probability increases in dusk conditions. The failure probability increases when there are more than two non-motorized vehicles or more than one motorized vehicle in the front. Large trucks in the front affect failure to a greater extent than do small vehicles. The results support readiness evaluations for intersections with similar road traffic environments from the perspective of sight-zone perception, guide the management of smart intersection reconstruction and expansion, and the management of AV-testing open roads. These results also provide the contributing factors and thresholds for the optimization of autonomous driving perception algorithms.

To solve the declining path-tracking accuracy and stability of intelligent vehicles, an integrated path-tracking and stability-control method based on multiple-constraint adaptive model predictive control (CAMPC) was proposed. A predictive model was established based on vehicle-dynamics and preview-error models, and the effects of the road adhesion coefficient on the nonlinear characteristics of the tire lateral force and cornering stiffness were analyzed. A corrective coefficient for tire cornering stiffness based on the Magic Formula was designed to correct the cornering stiffness of the predictive model in real time. Based on the phase-plane method, vehicle stability was analyzed to obtain the limit values of the yaw rate and sideslip angle for constructing the envelope constraints of vehicle stability. Subsequently, a stability index was designed based on the distance from the vehicle phase trajectory to the envelope boundaries to represent the degree of vehicle stability. A weight-adaptive mechanism was designed based on the stability index. By adding multiple constraints, such as the envelope constraints of vehicle stability and road environment, and then combining them with the weight-adaptive mechanism, a CAMPC control method was proposed to realize integrated path tracking and stability control. The effectiveness of the CAMPC control method was verified using joint simulation platforms MATLAB/Simulink and CarSim. The results show that the corrective coefficient for the tire cornering stiffness can improve the model mismatch caused by a change in the adhesion coefficient and improve the path-tracking performance. On roads covered by snow, compared with the conventional model predictive control (MPC), the CAMPC can reduce the maximum yaw rate and maximum sideslip angle by 10.8% and 59%, respectively, whereas it can reduce them by 59.6% and 71.5%, respectively, on roads covered by ice and snow, thus improving the vehicle stability and path-tracking accuracy. When the adhesion coefficient changes abruptly and the conventional envelope-constraint effect is insignificant, the CAMPC can effectively reduce the sideslip angle and improve the sideslip degree of the vehicle. Compared with sliding mode control, the linear quadratic regulator, and Stanley control, the proposed control method can improve the path-tracking accuracy and vehicle stability under variable speeds and adhesion coefficients. The proposed CAMPC provides a new approach for investigating autonomous-driving control technologies.

    截至2023年底,我国公路总里程544.1万千米,高速公路总里程18.4万千米。随着人工智能、大数据等技术的发展,如何利用新技术提高道路运维效率和质量成为我国道路建设运维领域的重要课题。为此,科技部立项国家重点研发计划项目《智慧道路建设运维关键技术(2023W1B2403500)》,旨在形成智慧道路建设运维技术标准,推动我国智慧道路高质量发展。发展道路数字化对提高道路通行效率、提升行车安全、促进经济发展具有深远的意义,智慧道路建设运维相关研究成果可为推动交通行业高质量发展,加快建设交通强国提供重要支撑。
    近年来,在智慧道路建设运维理论、方法与关键技术研究领域,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,为我国道路建设与运维技术的提升和交通基础设施的运营安全提供了重要的理论和技术支撑。为充分展示我国智慧道路建设运维理论、方法与关键技术研究领域的最新研究成果,引领该研究领域的发展方向,推动智慧道路建设运维理论、方法与关键技术的创新,《中国公路学报》编辑部联合长沙理工大学吕松涛教授、哈尔滨工业大学王大为教授、同济大学杜豫川教授、西南交通大学艾长发教授、长安大学杨旭教授、德国亚琛工业大学刘鹏飞教授,共同策划了“智慧道路建设运维理论、方法与关键技术”专栏,并向该领域的知名专家学者约稿,出版本期专栏。专栏共收到论文27篇,最终录用9篇。研究内容集中于以下2个方面:
    (1)道路运维数字化技术。主要内容包括:智慧道路设计建造运维中的数字化技术综述、车辆荷载激励下的水泥混凝土路面振动信号时频能量解析及车速估算、高精度三维路面纹理超分辨率重构及测评方法、考虑HV-AV交互的动态换道轨迹规划方法。
    (2)道路病害智能检测研究。主要内容包括:道路窨井盖-井周路面的病害处治与智慧检测监管综述、基于YOLOM算法的路面病害轻量化检测方法、考虑完整性分割的超轻量化路面裂缝检测方法、基于SmRtRock的沥青路面隐蔽性裂缝智能识别与定位、基于CNN和尺度自适应Transformer融合网络的路面裂缝分割方法。
    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智慧道路建设运维理论、方法与关键技术的创新与发展。《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,促进我国道路建设运维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。

The reinforcement effect of prestressed anchor cables with small diameter on the surrounding rock of soft rock tunnels was systematically investigated. First, the bearing arch effects of the surrounding rock of a tunnel strengthened using small-diameter prestressed anchor cables were simulated and analyzed based on a stratum-structure model. A generalized “load-structure” mechanical analysis model of the anchored surrounding rock was established. A formula for calculating the bearing capacity of the anchored surrounding rock with the combined support of long and short anchor cables was derived. Subsequently, numerical analysis of the simulated loading of the anchored surrounding rock was performed. The development process of a plastic zone in the anchored surrounding rock and the main factors influencing the ultimate bearing capacity were studied. The effectiveness of anchor cable support schemes was also explored. Finally, the reinforcement effect of small- diameter prestressed anchor cables on the surrounding rock of a soft rock tunnel was verified and summarized through on-site testing. The results indicate that a superimposed arch composed of a shallow and a deep bearing arch is formed in the surrounding rock under the combined support of long and short anchor cables. In this case, the diffusion range of the pre-tensioning force is higher than that obtained with the short anchor cable scheme, and the engineering economy can be considered comparable to that of the long anchor cable scheme. After the anchored surrounding rock is loaded, its inner surface first enters a plastic state. Considering the corresponding load when the inner surface enters the plastic state as the bearing capacity of the anchored surrounding rock tends to be conservative. The ultimate bearing capacity of the anchored surrounding rock can be obtained using numerical calculation methods, which mainly depend on the strength of the surrounding rock and the anchoring force of the anchor cable. The active support obtained with small- diameter anchor cables and high pre-tension can significantly increase the overall stiffness of the anchored surrounding rock. Under the conditions of relatively soft rock and soft rock strata, small- diameter (Φ21.8) prestressed combined long-short anchor cables (5 m+10 m, 19 per ring of upper and middle benches, spacing of 80 cm, design anchoring force of 450 kN, design pre-tensioning force of 350 kN) were used, and the maximum deformation of the tunnel was basically controlled within 30 cm according to actual measurements. For extremely soft rock formations, ensuring that small-diameter anchor cables have a sufficient anchoring force is a key technical problem that must be solved urgently.

To study long-term debonding effects in the engineering field and promote the development of concrete-filled steel tubular bridges, the interfacial performance, interfacial force transfer, and bearing capacity of concrete-filled steel tubes are reviewed from the point of material differences between steel and concrete. The mechanisms of debonding, interfacial force transfer failure, and composite action failure are explained. Novel structures called concrete-filled steel tubes with internal studs and concrete-filled steel tubes stiffened with PBL are proposed. The interfacial performance, interfacial force transfer, joint mechanical behavior, and bearing performance of these structures are reviewed to demonstrate the feasibility of achieving composite steel tube and concrete core action. The results indicate that steel and concrete have large differences in the aspects of heat conduction, shrinkage and creep, Poisson's ratio, elastic modulus, strength, cross-sectional dimensions, and forming ways. These differences constitute the primary reason for steel-concrete interface debonding, interfacial force transfer failure, and composite action failure. The interfacial bond strength results are highly scattered. Both the tangential and normal bond strengths of the steel-concrete interface are not higher than 1.5 MPa. The steel-concrete interface debonding cannot be avoided for bridges in the service stage owing to multiple factors, including hydration heat, sunshine temperature difference, concrete shrinkage and creep, axial loading, and fatigue loading. The interfacial force transfer for the different bridge systems can be divided into interfacial force transfer of members and joints. To this day, no interfacial force transfer model exists for bridges. Different types of debonding have different effects on the bearing capacity of concrete steel tubes. The spherical cap gap has the least effect followed by the partial circumferential gap. The circumferential gap has the largest effect. Regarding the concrete-filled steel tube with internal studs, it has been demonstrated that the internal studs can work as the shear connector to guarantee the interfacial performance and force transfer in the joint. In the case of the concrete-filled steel tube stiffened with PBL, it has been demonstrated that the PBLs can function as both the shear connector and stiffeners. From one viewpoint, this can guarantee the interfacial performance and force transfer in the joint. Conversely, it can enhance the buckling behavior of steel plates and the bearing capacity of members.

In this study, a method for accelerating the vehicle-bridge coupling vibration analysis based on an improved Gibbs-Poole-Stockmeyer algorithm was proposed to address the efficient analysis requirements of kilometer-scale dual-layer random vehicle-bridge coupled vibrations to reduce the storage and computational costs of the bridge subsystem. First, the improved Gibbs-Poole-Stockmeyer algorithm was tested using a structural discretization model to verify its accuracy. Subsequently, the improved method was applied to optimize the storage analysis of a super-long-span dual-layer steel-truss bridge model, while incorporating the existing analysis system for random vehicle flow over a bridge response analysis. Finally, the calculation efficiencies were compared. The results show that when the same bandwidth is obtained as in existing research methods, the improved Gibbs-Poole-Stockmeyer algorithm can achieve a lower tree width without the need for backtracking during node numbering. The proposed method improves the storage space of the long-span dual-layer steel-truss bridge model, and the maximum bandwidth of the stiffness matrix and the length of the one-dimensional variable-bandwidth storage array are significantly reduced. Under equivalent computational conditions, the improved analysis system significantly improves computational efficiency in performing transient analysis and handling high-traffic vehicle-bridge coupled vibration analysis compared to existing analysis systems. The proposed method significantly increases the processing capacity and computational efficiency of existing analysis systems for large-scale finite element models.

Fiber reinforced polymer has the potential to replace traditional steel cables in long-span cable-bearing bridges due to its advantages of high strength, light weight, corrosion resistance and low creep. To achieve long-term and reliable application of carbon fiber-reinforced polymer (CFRP) tendon anchorage in civil engineering-such as the cables in cable-stayed bridges, suspenders of arch bridges, and others- long-term performance experiments of eight CFRP tendon composite anchorage specimens with pure epoxy resin and epoxy resin incorporating basalt fiber or talc powder as bonding media were conducted for at least 1 000 h. The effects of different admixtures on the long-term performance of the anchorage were investigated, and the residual anchoring performance after the long-term performance experiments was tested. Results reveal that the bonding state of the tendons and bonding medium of the eight tested specimens remain intact after long-term performance experiments, thus indicating that the novel composite anchorage has excellent long-term performance. Compared with the specimen with pure epoxy resin, the slippages of single-tendon specimens with 0.5 % basalt fiber or talc decreased by 37% and 29.5%, respectively, and their residual load increased by 11% and 6.8%, respectively. When the content was 1%, the corresponding values were almost unchanged, thus indicating that adding an appropriate amount of basalt fiber or talc to the epoxy resin can effectively improve the long-term performance of the composite anchorage. In addition, because the specimen with talc powder can only reduce the creep deformation of the bonding medium and cannot improve the bonding performance between the bonding medium and the CFRP tendon, the improvement effect is not as obvious as that of the basalt fiber. After the long-term performance experiments, the failure mode of the eight specimens in the static load test was attributed to tendon fracture, and the residual anchorage efficiency was greater than 95%, indicating that the novel composite anchorage can still effectively anchor the CFRP tendons after long-term performance experiments (lasting 1 000 h) and can work safely and effectively for a long time, thus demonstrating its capability for use in practical engineering.

To address the issues of high testing costs and poor accuracy caused by individual differences in subjective perceptions and objective physiological information of passengers in traditional motion sickness comfort evaluations for intelligent electric vehicles, a motion sickness evaluation model based on vehicle motion parameters is proposed. First, based on the mechanism of motion sickness, real-vehicle tests were conducted to collect passengers' subjective perceptions of motion sickness along with corresponding physiological data, such as galvanic skin response (GSR), respiratory rate, and pupil diameter. Simultaneously, vehicle motion parameters, including longitudinal, lateral, and vertical accelerations, were collected. Second, passengers' subjective assessments of motion sickness comfort combined with objective physiological data to analyze significant differences and degrees of association using the Kruskal-Wallis (K-W) non-parametric test and partial effect equivalence methods. A motion sickness evaluation model was established based on multiple objective physiological signals such as GSR and pupil diameter variations. Furthermore, Pearson correlation analysis method was employed to construct a correlation matrix linking subjective motion sickness perceptions, objective physiological signals, and vehicle motion parameters. The relationships and sensitivities of vehicle motion parameters, including longitudinal, lateral, and vertical accelerations, and their rates of change with passenger motion sickness on real-world open roads were explored. Using a ridge regression analysis, weights for the impacts of different vehicle motion parameters and cumulative time on motion sickness comfort were determined, facilitating the development of a motion sickness evaluation model based on vehicle motion parameters. Finally, experimental validation was conducted to compare the motion sickness evaluation model developed based on multiple physiological signals with that based on vehicle motion parameters. The results showed that the overall prediction accuracy of the motion sickness evaluation model developed based on the vehicle motion parameters was 88.7%. The proposed evaluation model effectively mitigates the impact of individual physiological differences in traditional testing and achieves accurate comfort evaluations for intelligent electric vehicles. This study provides theoretical support and a practical foundation for the design and optimization of decision-making algorithms and control execution strategies for future intelligent electric vehicles.

Under mixed traffic of cars and trucks, differences in physical performance and driving behavior between cars and trucks can easily lead to dangerous driving behaviors, such as the sudden acceleration, deceleration, or overtaking of cars, which can affect the stability of traffic flow and increase the risk of traffic accidents. Therefore, this study focuses on car-truck mixed traffic scenarios, innovatively proposes the “Oppression of Truck” concept, and explores the lane changing behaviors and driving risks of cars under the influences of car-truck interactions. First, the oppression measurement of trucks (OM_T), considering driving style, was constructed by introducing molecular interaction forces to quantify the oppression of trucks on cars. Then, using the truck oppression measurement to optimize the car lane change intention identification method, a two-stage lane change crash risk prediction model was developed by integrating the OM_T, which comprised a lane-change intention identification model based on a CNN-LSTM (Convolutional Neural Network-Long Short-Term Memory) hybrid neural network and a crash risk prediction model for car lane changing behavior based on LightGBM. A vehicle trajectory data set consisting of data from real traffic scenarios was used to verify the validity of the model. The results indicate that lane changing cars are generally more strongly oppressed by trucks. Moreover, skilled drivers can withstand high truck pressure, whereas cautious drivers are more sensitive to truck oppression and tend to keep driving under low pressure. Additionally, there is a time-lag correlation between truck oppression and driving risk, where stronger oppression can affect vehicle driving behavior and lead to changes in driving risk. Models that incorporate the truck oppression indicator show higher accuracy in lane change intention identification and crash risk prediction. Truck oppression has a higher feature contribution in the crash risk prediction model, which provides a new perspective and effective theoretical support for the microscopic modeling of complex interaction scenarios as well as active safety management and control.

Recent national strategies for developing comprehensive multidimensional transportation systems in China have prompted bridge construction projects to connect islands and crossing straits along the coast. Deepwater floating bridges have attracted considerable attention as potential technical solutions. Analyzing the structural failure probability of deep-water floating bridges under extreme wave loads is important for conceptual design. Accordingly, a computational framework was established in this study for estimating the extreme response and structural failure probabilities of floating structures under narrow-banded random waves using an active learning approach. A deep-water floating bridge segment finite element model was developed for the dynamic analysis. Active learning was employed to examine failure probability learning patterns under various failure modes based on displacement and tension leg forces with different thresholds, and finally obtain failure probability predictions. The active learning method requires only limited samples to achieve convergent failure probability predictions, requiring a low computational cost. The active learning framework was applied to both the single-and multi-objective failure criteria. For the floating bridge studied, under random waves with a return period of 100 years, the failure probability based on the midspan displacement was 2.5% at the threshold ratios of w_S/w_B=2.2 and 1.3% based on the tension leg force at the threshold ratio of F_S/F_B=2.5. These rapid computational failure probability estimates can provide information for feasibility assessments, designs, disaster prevention, and protection technologies for future deepwater floating bridge projects.