Subgrade engineering serves as the critical load-bearing component for pavement structures, which significantly influences the stability, safety and durability of road infrastructure. To further advance the sustainability of subgrade engineering in China, propel its high-quality development toward green low-carbon, sustainability, and intelligent development, and contribute to the national strategy of building a “Transportation Power”, this study systematically synthesizes the latest advancements in scientific and technological innovation within China's subgrade engineering domain in recent years, while comprehensively delineating the priority directions for future research. The research is grounded in an analysis of the industry's current development status and evolving trends, and centers on six thematic pillars, namely, engineering properties of subgrade fillers, durable subgrade design theory, subgrade widening technology, subgrade protection and retaining structures, intelligent construction of subgrade engineering, and subgrade disaster prevention and mitigation. Specifically, it encompasses cutting-edge research areas, including the mechanical behaviors of various subgrade soils (e.g., oversized-grained soil, coarse-grained soil, fine-grained soil, and special soils), subgrade moisture evolution mechanisms and associated design methodologies, approaches for determining subgrade structural modulus, calculation and control criteria for subgrade permanent deformation, indices and standards for uneven settlement control of widened subgrade, splicing techniques for new and existing subgrade segments, waterproofing and drainage systems for reconstructed/expanded subgrade, advanced ground improvement technologies, subgrade slope stability assessment, slope protection measures, anti-slide piles and retaining wall structures, design and rehabilitation of slope anchorage systems and waterproof-drainage facilities, intelligent compaction of subgrades, intelligent detection and real-time monitoring of subgrade performance, classification of subgrade disasters, subgrade defect detection, disaster monitoring and warning systems, prevention and mitigation strategies for subgrade defects and disasters, and evaluation and enhancement of subgrade disaster resilience. For each area, this study analyzes and deliberates on the current state of academic research, prevailing challenges, targeted countermeasures, and future development prospects. This review is intended to provide strategic guidance and reference for the advancement of China's subgrade engineering discipline, while offering novel perspectives and foundational insights for researchers and practitioners in this field.

Automated vehicles encounter significant safety and efficiency challenges within mixed traffic flows involving frequent pedestrian interactions. Precise modeling of this pedestrian-vehicle interaction is therefore crucial. Such modeling is fundamental not only to advancing the vehicle's decision-making intelligence but also to building high-fidelity virtual testbeds, collectively enabling safer navigation, enhanced user experiences, and superior traffic throughput. Despite its importance, the field currently suffers from highly fragmented research and a lack of systematic review. This paper addresses this gap by providing a comprehensive survey of the state-of-the-art progress and key challenges in pedestrian-vehicle interaction modeling. It first deconstructs the essential characteristics of this interaction through the lenses of traffic safety, utility maximization, social norms, and information exchange to establish a formal definition. It then systematically reviews dominant behavioral modeling techniques and interaction quantification methods, further examining the unique attributes and modeling paradigms of automated vehicles as interactive agents. The paper concludes with a summary and outlook on future technological trends. Our review identifies several critical limitations in the current literature: Theoretical: An inadequate understanding of pedestrian cognitive mechanisms and unsystematic insights into the role of communication; Modeling: The constraints of existing physics- or utility-based assumptions and a lack of research into hybrid models; Contextual: A general disregard for the heterogeneity of interaction scenarios, vehicles, and participants; Methodological: The persistent bottleneck of poor explainability in data-driven approaches. To overcome these challenges, future work must focus on deepening the understanding of cognitive processes, exploring the coupling of physics- and utility-driven models, and systematically integrating contextual factors. Significantly, emerging technologies like multimodal large language models and theories of embodied cognition are creating new research paradigms. We argue that substantial progress in this field necessitates deep interdisciplinary fusion and novel applications of these technologies, paving the way for a next-generation intelligent transportation system that is safer, more efficient, and fundamentally human-centric.

To avoid the influence of inter-individual differences on the performance of fatigue driving recognition and improve the personalized level and efficiency of fatigue driving detection, the proposed method combined Principal Component Analysis (PCA) with One Dimensional Convolutional Neural Network (1DCNN) to build a fast and personalized fatigue driving recognition method. Firstly, simulated driving experiments in highway scenarios were conducted, noninvasively collecting driving behavior data, facial landmark coordinates, and fatigue scores (Karolinska Sleepiness Scale, KSS) of 24 participants. Then, the raw experimental data was segmented using a two-layer sliding time window, and 486 fatigue-related measurements were calculated. Finally, a compact deep learning algorithm was used to construct a personalized fatigue driving recognition model based on PCA-1DCNN. We used 5-fold cross-validation to divide the experimental data of each driver, trained and validated personalized fatigue driving recognition models, and obtained personalized fatigue detection thresholds. The proposed personalized fatigue recognition models achieve an average accuracy, sensitivity, and specificity of 99.93%, 99.92%, and 99.94% across all participants, respectively. The recognition performance is relatively stable for all participants, with standard deviations of 0.07%, 0.11%, and 0.09% for accuracy, sensitivity, and specificity, respectively. The proposed method does not require manual feature extraction and use a lightweight 1DCNN to quickly detect fatigue driving, with an average detection time of 0.010 3 seconds. The proposed method aims to avoid the influence of inter-individual differences and improve the accuracy and speed of fatigue driving recognition at an individual driver level, which can support the development of efficient personalized fatigue driving warning systems and promote personalized modeling research on other dangerous driving behaviors.

Maintaining an adequate level of vigilance during driving is crucial for driving safety. Types of vigilance decline during driving can be categorized into fatigue, distraction, and prolonged automated driving monitoring. These types may differ in key features and attention mechanisms, but their heterogeneous characteristics remain unclear. This heterogeneity may contribute to the poor generalization ability and suboptimal performance of current models for detecting impaired driving states. This study systematically examines the characteristics and mechanisms of vigilance decline during driving through a literature analysis on measurement tools, types, features, influencing factors, underlying mechanisms, detection methods, and warning systems. The following conclusions were drawn: ① While measurement tools for vigilance have formed a relatively complete system, their application in traffic scenarios is not yet widespread. ② The characteristics of vigilance decline are generally defined, but research on type differences in fatigue driving is insufficient, and studies on EEG features of cognitive distraction are lacking. The effects of auditory-cognitive distraction, fatigue driving, and their interaction on takeover efficiency need further exploration. ③ Mechanistically, vigilance decline due to sleep-related fatigue is linked to reduced cortical activity, while task-related fatigue, distraction, and automated driving monitoring are associated with insufficient attention resources and arousal levels. ④ Existing detection technologies focus excessively on fatigue driving and visual-manual distraction, with insufficient research on detecting cognitive distraction and comprehensive vigilance assessment. The high cost and complexity of EEG and eye-tracking devices limit their use. ⑤ Current warning systems overlook factors such as driving environment and individual physiological and psychological states, lacking differentiated warning strategies based on vigilance decline mechanisms. The following recommendations are proposed: ① Strengthen interdisciplinary collaboration to develop a vigilance measurement paradigm specific to traffic scenarios and promote empirical research on vigilance measurement tools in transportation. ② Systematically compare the vigilance characteristics across different types of fatigue driving and analyze eye-tracking and EEG feature maps of distracted driving under various cognitive processing combinations. ③ Develop portable, low-invasiveness EEG devices and create real-time monitoring models for cognitive distraction based on eye-tracking features and ERP indicators. ④ Overcome ERP identification challenges through standardized experimental design, innovative data analysis methods, and multimodal data fusion techniques. ⑤ Establish classification-based warning standards for drivers, design personalized warning strategies based on vigilance decline mechanisms, and integrate in-vehicle environmental control warning systems.

To improve the decision-making ability of intelligent vehicle in dynamic scenarios, aiming at the decline of decision-making performance caused by insufficient cognition of changes under interactive environment situations, an overtaking decision-making method that considers real-time interactive vehicle prediction information was proposed. Firstly, by analyzing the overtaking behavior of human driver, a decision-making framework for interactive overtaking behavior of intelligent vehicle that integrates multiple factors was constructed. On this basis, a trajectory prediction model that can distinguish the temporal and spatial features of interacting vehicles was established based on Convolutional Neural Network (CNN) and Long Short Term Memory Network (LSTM). Based on Kullback-Leibler (KL) divergence theory, a real-time guidance and correction mechanism combines physical laws of vehicle motion was designed. Thus, the accuracy and interpretability of the prediction model were improved. Furthermore, based on risk field theory and game theory, constraints on the safety and efficiency benefits of ego vehicle were analyzed, and an overtaking behavior decision-making model for intelligent vehicle considering interactive prediction information was proposed. Equilibrium solution of the overtaking game was realized in dynamic scenarios. Finally, overtaking scenario experiments were conducted through numerical simulation and vehicle test. Results show that the proposed decision-making model that considers interactive prediction information, can realize high-precision trajectory prediction. And it can make real-time overtaking decisions based on the prediction results in dynamic scenarios. The trajectory prediction model predicts average displacement errors of 0.993 meter. In addition, the game stability and benefit during the decision-making process increase by 28% and 20%, respectively. The model shows higher decision-making safety and stability performance. This study provides theoretical support for intelligent vehicle trajectory prediction and driving decision-making, and improve the driving decision-making performance of intelligent vehicle.

Highway slope disasters pose a significant threat to the safe operation of road traffic. Clarifying the fundamental causes of these disasters and developing lightweight monitoring and early warning systems have become urgent priorities to ensure the safety of road networks. Based on a comprehensive review of the characteristics and spatial distribution of highway slope disasters, this study systematically summarized five typical failure modes-collapse, landslide, debris flow, subgrade subsidence, and surface slumping-and analyzed the specific features and failure mechanisms of each type. An in-depth analysis was also conducted on the contributing factors and triggering conditions. Building on these insights, the study proposed a framework for lightweight monitoring and early warning of highway slope disasters. This framework consisted of five main components: on-site inspection, selection of monitoring indicators, optimization of monitoring locations, refinement of warning models, and implementation of preventive measures. The study also outlined the essential conditions required for implementing such lightweight monitoring and early warning. Based on this, the paper focused on highway embankment slopes characterized by “long corridors and strong constraints”. A core framework of “pre-screening before measurement, low-cost sensing, and lightweight modeling” was summarized, and a feasible implementation scheme was proposed. This work serves as a reference for the development and application of lightweight monitoring and early warning technologies for highway subgrade slopes and holds important implications for improving the resilience and safety of road infrastructure.

Traffic signs are crucial elements ensuring the safe, efficient, and green operation of the transportation system. However, the effectiveness of traffic signs is often restricted by the adaptability of traffic sign setting standards, the effectiveness of testing methods, and the comprehensiveness of utility analysis. This paper focuses on the three fundamental issues of “unable to find,” “difficult to understand,” and “incorrect navigation” caused by traffic signs, which affect travel quality. It systematically sorts out and summarizes the research hotspots of traffic signs using the scientific knowledge map method. Meanwhile, starting from the driver's cognitive decision-making chain of “discovery, understanding, and execution,” this paper comprehensively reviews the experimental testing platforms, basic theories, and key technologies in traffic sign research. The study further emphasizes the need to deeply characterize the complex impacts of traffic signs on drivers' visual perception, cognitive processing, manipulative behaviors, and vehicle operating states, and to explore their implicit influence pathways on driving behavior. Additionally, it conducts utility assessments and optimizations of the traffic sign system from the perspective of drivers' information needs. Based on this, combining the team's research experience, this paper innovatively constructs a research and application paradigm for traffic signs covering six aspects: “scheme design, feature representation, quantitative evaluation, optimized selection, supporting arrangements, and standard guidelines.” By analyzing typical traffic sign research cases, it elaborates on the specific implementation steps of this paradigm and compares it with similar research methods at home and abroad. The results show that the research and application paradigm of traffic signs integrating human factor needs has obvious advantages. It not only provides theoretical support for sign design research and optimization but also offers a solid basis for solving common issues related to safety facilities in the transportation industry and significant engineering applications.

Environmental perception, as the core technology of the autonomous driving system, directly affects the decision-making level and driving safety of intelligent vehicles. It is the key to achieving high-level autonomous driving for intelligent vehicles. To enhance 3D object detection accuracy and robustness in complex scenarios, aimed at the limitations of the lack of image edge semantics and the interference of point cloud background noise in the current BEV multimodal fusion perception, this paper proposed DDL-BEV, a multi-scale dynamic fusion perception framework based on DepthEdgeNet, Dynamic Queries, and LiDAR-Camera Cross Attention. First, DepthEdgeNet was constructed. The fusion of depth information and edge semantic features was achieved through dual-branch feature extraction and interaction, and the camera Bev space features were optimized. Second, a Dynamic Query module was designed. The LiDAR point cloud was voxelized into cylindrical grids and transformed into BEV features. The dynamic perception of the foreground position effectively reduced the interference of background noise. Finally, LiDAR-Camera-Cross-Attention fusion mechanism was designed. Combined with the Feature Enhancement Module of the multi-branch dilated convolution feature enhancement module, a hierarchical feature interaction architecture was constructed. The BEV features of the LiDAR point cloud and the camera BEV features were fused to achieve the complementary advantages of cross-modal features. The fused features were input into the object detection head to obtain 3D object detection results. Experiments on the nuScenes dataset show that the average detection accuracy (mAP) and comprehensive detection score (NDS) of the DDL-BEV fusion algorithm proposed in this paper reach 69.3% and 71.9% respectively. Compared with the baseline BEVFusion method, they are improved by 1.5% and 1.3% respectively. In special scenarios such as at night, on rainy days, during turns, and at intersections, the mAP of DDL-BEV is increased by 6.7%, 5.42%, 5.45%, and 4.65% respectively, and the scene sensitivity is reduced from 14.11% to 8.33%. Results show that the DDL-BEV detection algorithm has stronger detection robustness in scenarios with insufficient lighting, obstructed environments, and rain-fog interference.

Snow and ice on the road in winter will seriously affect traffic safety and transportation efficiency. It is of great significance to clarify the low-temperature freezing behavior of droplets on the surface of pavement materials for the snow and ice control on pavements. Based on a low-temperature adhesion observation system, this study investigated the influence of ambient temperatures, droplet volumes, and substrate surface conditions on the freezing behavior of adhered liquid (H₂O) on cement concrete surfaces. The result indicated that on the cement concrete surface, the freezing of droplets is mainly controlled by heat conduction, and the freezing process consists of super-cooling stage, phase change stage, papillation stage, and completion stage, based on imageology. During the freezing process, the freezing surface in droplets gradually moves upward from the heat conduction interface, while the volume expands with the frozen undergoing, then finally releases in the form of papillations. According to experiments, as the ambient temperature decreases, between 0 ℃ and -4 ℃, the droplets continue to remain in supercooled state without freezing; when the ambient temperature is lower than -4 ℃, the droplets gradually freeze, and the freezing completion time shortens as the ambient temperature decreases. For the original pavement surface, when the ambient temperature is -15 ℃, the freezing completion time is 39.95% less than that at -8 ℃ and only 6.34% less than that at -12 ℃. The increase of liquid volume affects the heat transfer efficiency of the droplets and prolongs the final freezing time. The freezing completion time of 0.5 mL droplet is 8.40% longer than that of 0.3 mL droplet, and the freezing completion time of 0.8 mL droplet is 44.37% longer than that of 0.5 mL droplet. To the initial surface under -12 ℃, affected by the contact area in heat conduction, the freezing time of the droplet is negatively correlated with the surface roughness. The greater the height variance of the concrete surface micro-structure, the faster the droplet freezing process is and the shorter the freezing completion time is. Compared with the normal concrete surface, the freezing completion time of the sandpaper polished surface is extended by 14.13%-16.90%. For pavement surfaces in cold regions, it is appropriate to achieve a balanced design of surface texture depth considering anti-skid and freezing resistance.

With the rapid increase in the demand for automated driving testing, quickly selecting critical scenarios from numerous testing scenarios has become a top priority. Due to the scarcity and low occurrence probability of critical scenarios, which result in low testing efficiency, there is an urgent need to develop accelerated methods for critical scenario identification. To provide a comprehensive review of accelerated methods for critical scenario identification in automated driving testing, this review is explored in three dimensions: functional scenarios, logical scenarios, and concrete scenarios. In the functional scenario dimension, the research primarily focuses on scenario configuration, selecting the combinations of factors that constitute critical scenarios. In the logical scenario dimension, the research focuses on the scope of scenarios, selecting the range of values for the factors that define critical scenarios. In the concrete scenario dimension, the research emphasizes scenario instances, selecting the specific values of factors that constitute critical scenarios. It is noteworthy that current research on functional and logical scenarios is still insufficient, requiring more scholars to engage in this area. Furthermore, existing methods face multiple challenges, including insufficient scenario authenticity, limited acceleration effects, and incompatibility with automated driving functions. Future research should focus on addressing these issues, particularly in the areas of functional and logical scenarios, and continuously optimizing the accelerating technology for critical scenario identification to provide robust support for the ongoing advancement of automated driving testing technology.

Steel bridges exposed to the atmospheric environment inevitably suffer from corrosion damage under the combined effects of temperature, relative humidity, and pollutants, posing a global challenge. To deepen the understanding of atmospheric corrosion, this study summarizes existing research findings from three perspectives: the macro-environment near bridge sites, the local environment around components, and the micro-environment on component surfaces, while also exploring future research directions. Current research shows that the macro-environment has reached a stage where it can be characterized and classified, along with a classification method based on standard coupon exposure test results and climatic parameters. The local environment only reflects the influence of long-term exposure to corrosive media at different parts of steel bridges but lacks characterization parameters such as the intensity and duration of corrosive media effects, making corrosivity level determination reliant on engineering experience. The micro-environment focuses on the mechanism of steel atmospheric corrosion, dynamically characterizing the corrosivity of different points on steel bridges through parameters such as surface temperature, surface humidity, and surface pollutant deposition. The existing corrosion environment zoning map of China has limitations, including low data density and exposure test stations located far from bridge sites, making it difficult to accurately reflect the corrosivity level of the macro-environment at bridge locations. It is recommended to establish a gradient-based atmospheric corrosion exposure monitoring network along Chinese highway system to obtain multi-source atmospheric corrosion data and develop a corrosion environment zoning map tailored for steel bridges. Future research should aim to build a micro-environment research system that is characterizable, quantifiable, and applicable. Theoretical studies on micro-environment calculations should be conducted to clarify the interaction mechanisms of micro-environment parameters and establish quantitative analysis methods. A predictive model for atmospheric corrosion rates at the micro-environment level should be developed to provide scientific principles for precise detection of localized corrosion, corrosion-resistant structural design, and targeted maintenance strategies.

Human factors, such as insufficient driving experience and the tendency toward high-speed driving, are the main causes of brake failure in heavy-duty trucks on Long Steep Downhill (LSD) sections of highways. Guiding driver to maintain reasonable speeds through information-based intervention is an effective solution. This involves optimizing active speed control strategies to meet the information needs of drivers during their sequential decision-making process. Based on reinforcement learning, the paper establishes an Excellent Driver Model (EDM) with sequential decision-making capability, extracts significant differences in driving behavior between naturalistic drivers and the EDM, and maps these differences into an optimized active speed intervention strategy. First, an interactive environment was built based on a real highway LSD section. Second, considering the cumulative effect of driving risks on LSD sections, a single-step reward function and a global reward function were designed, incorporating driving safety, efficiency and comfort. Then, the Dueling Deep Q-Learning Network (DuDQN) was used to train the EDM. Finally, driving simulation experiments were conducted to collect operation data from naturalistic drivers. Spatiotemporal differences in driving behavior between naturalistic drivers and the EDM were identified using speed trend tests, which helped determine the timing and targets of speed intervention. Numerical results show that EDMs based on DuDQN and DQN perform similarly, with only a slight advantage for DuDQN. Compared to naturalistic driving, the EDM improved the safety performance by 4%, and its maximum acceleration change rate was 0.12 m·s^-3, lower than the 0.22 m·s^-3 observed in naturalistic driving. Significant difference in driving behavior between naturalistic driving and the EDM often occurred in sections with frequent slope changes. By extracting these differential features, a reasonable speed intervention strategy can be developed. The proposed framework innovatively supports the formulation of driving speed intervention strategies in a model-driven manner, which will help enhance active safety prevention and control capabilities on highway LSD sections.

To explore a high-value utilization pathway for red mud in road base courses, this paper proposes a technical solution for preparing red mud-based geopolymer-lightweight aggregate pavement base material (RGAC) through the synergistic combination of red mud-based geopolymer (RMG) and red mud-based lightweight aggregate (RA). RMG was prepared using red mud, fly ash, and ground granulated blast-furnace slag as precursors, activated by a composite alkali activator. RA was prepared using red mud and slag as the matrix through extrusion granulation, and was used to replace 4.75-9.5 mm natural aggregates at volume substitution ratios of 15% and 23%. The effects of RMG content (5%-9%) and RA content on the mechanical strength at different ages, microstructure, leaching characteristics of hazardous substances, and carbon footprint of RGAC were investigated. The results show that the 7-day unconfined compressive strength of RGAC ranges from 4.42 to 7.65 MPa, and the 28-day strength reaches 82.7%-90.7% of the 90-day strength. With increasing RMG content, the unconfined compressive strength, splitting tensile strength, and elastic modulus at all ages increase significantly, but the increase tends to flatten when RMG content exceeds 7%. When RA content increases from 15% to 23%, the unconfined compressive strength, splitting tensile strength, and elastic modulus of RGAC at different curing ages generally showed a decreasing trend. Microscopic analysis indicates that hydration products of RMG, such as N—A—S—H and C—A—S—H gels, fill pores, coat particles, and enhance interfacial bonding. When the red mud content is 40%-60% and the Si/Al ratio of the system is 1.6-2.3, the gel network is denser and the macroscopic mechanical properties are better. Excessively high red mud content (e.g., 80%) leads to insufficient active components, resulting in reduced gel formation, a loose structure, and strength deterioration. The leaching concentrations of heavy metals in all RGAC mixtures are below the Class III limits of the Groundwater Quality Standard (GB/T 14848—2017). Increasing the RMG content significantly enhances the heavy metal immobilization effect, which involves multiple synergistic mechanisms, including physical adsorption, chemical bonding, ion exchange, and precipitation reactions. Compared with cement-stabilized macadam base courses, the CO₂-equivalent emissions associated with the cementitious materials of RGAC are reduced by approximately 46%.

Current vehicle collision and danger warning system primarily rely on vehicle-road cooperation using LiDAR sensors. However, LiDAR is generally expensive, and the computational demands for point cloud processing are extremely high, resulting in costly implementations. Cameras, characterized by low unit cost and minimal computational requirements, have become an effective platform for collision and danger warning systems. Existing Camera-based collision warning systems operate in the pixel domain, which suffers from low localization accuracy and a high error probability. To address these issues, a novel 3D tracking algorithm utilizing roadside cameras was proposed. The object-level vehicle-road cooperation was executed in order to extend the vehicle's perception area and improve blind-spot detection performance. To optimize the system for roadside scenarios, the 3D object detection module was modified, by introducing normalized training labels based on camera imaging theory. Using supervised learning, the module effectively learned the relationship between target pixels and depth, significantly improving localization accuracy. In the motion prediction module, a multi-class motion prediction method was proposed, which employed specific nonlinear models to describe the movements of different traffic participants. This ensured precise fitting of motion trends. The Generalized Intersection over Union metric was used to measure the motion similarity between different participants, which enhanced differentiation in dense roadside scenarios and improved association accuracy. Spatial alignment of vehicle-road perception results was achieved through affine transformation. The final fusion results was performed by jointly using the similarity metric and the Hungarian matching algorithm. Experimental results on the V2X-Seq dataset demonstrate that our algorithm outperforms other advanced 3D tracking methods in terms of accuracy and effectiveness. By leveraging vehicle-road cooperation, the system correctly triggers crash warnings in “ghost probe” scenarios, significantly improving the warning success rate and enhancing intelligent vehicle safety.

Optical flow methods in computer vision have significant potential in the field of structural health monitoring. However, traditional optical flow methods often lack robustness and accuracy when faced with complex environmental changes. Therefore, this paper proposes an adaptive feature point recognition algorithm based on computer vision to improve the accuracy of feature point detection and tracking under challenging conditions, such as variations in lighting, perspective, noise interference, and rain. The algorithm dynamically adjusted the parameters for feature point extraction, matching, and optimization to adapt to the motion and variation of feature points in different scenarios and environmental conditions. Experimental results show that the root mean square error (RMSE) of the algorithm is significantly lower than that of the traditional LK optical flow method under various conditions, including lighting variation (bright and low light), perspective variation, noise interference (strong and weak noise), and rain interference (heavy and light rain), thus verifying the robustness and accuracy of the adaptive feature point recognition algorithm in complex environments. Furthermore, a comparison of processing time and the number of feature points demonstrates the algorithm's advantage in real-time performance. This study provides an effective method for the real-time monitoring of bridge structures, contributing to improved construction quality and operational safety.

Sunken tunnels are low-lying sections of urban roads. It is crucial to minimize or even eliminate urban flood disasters and ensure traffic safety by preventing the accumulation of excessive water under heavy rainfall conditions. In this study, road surface conditions and rainfall loss were considered as the starting points, and a formula was derived to predict the water depth in low-lying sections characterized by inflow, drainage capacity, and road slope functions. Furthermore, the storm water management model was used to construct a drainage network model to predict the water depth under different rainfall and drainage conditions and to verify the feasibility of the theoretical formula. Finally, based on the simulation and prediction results, a risk-level assessment of the water depth within the tunnel was conducted. The research results show the following. Under ideal drainage conditions, the maximum water depths in the tunnel for rainfall recurrence periods of 10, 50, and 100 years are 14.93, 20.85, and 22.74 cm, respectively. The simulated water depth prediction curves are consistent with the results obtained using the theoretical formula. The curves initially exhibit a rapid increase, followed by a gradual increase, reaching a peak value, and then a rapid decrease. The peak water depth occurs after peak rainfall intensity. At a pipe blockage rate of 25%, the water accumulation risk level is III when the rainfall recurrence period is 10 years and IV for both the 50- and 100-year recurrence periods. The research findings provide a reference for the monitoring, early warning, and risk assessment of water accumulation depth in sunken tunnels.

To address the challenges of multi-target detection accuracy degradation and robustness deterioration caused by insufficient vehicle environmental perception under adverse weather conditions, this paper proposes an enhanced YOLOv8-MRDE algorithm to improve target detection performance in foggy driving scenarios. The algorithm achieves performance optimization through three key improvements: First, a MixDehaze dehazing module is integrated at the front-end of the backbone network to effectively enhance image feature visibility. Second, a multi-scale feature fusion architecture based on Reparameterized Generalized-FPN(RepGFPN)is constructed, which strengthens cross-scale feature representation through hierarchical feature reproduction mechanisms. Third, a dynamic attention mechanism is introduced in the detection head to establish a Dynamic Head(DyHead) structure for improved capture of critical features. For training optimization, the EIoU loss function replaces the conventional CIoU to accelerate network convergence, while structural pruning techniques are applied to eliminate model redundancy and achieve lightweight deployment. Experimental results on both the RTTS real-world fog dataset and Foggy Cityscapes dataset demonstrate the superior detection accuracy of YOLOv8-MRDE. Compared to the baseline YOLOv8 model, the proposed algorithm achieves mean Average Precision (mAP) improvements of 2.6% (RTTS) and 4.2% (Foggy Cityscapes), with 25% and 22% reductions in model parameters and computational costs, respectively. The findings validate the effectiveness of YOLOv8-MRDE in foggy driving conditions, demonstrating its potential to enhance detection accuracy and robustness. This work provides both theoretical foundations and technical support for improving safety in low-visibility autonomous driving systems.

This paper endeavors to enhance the crack resistance of the negative moment regions in continuous composite beams by integrating Hybrid Fiber Reinforced Concrete (HFRC) on the basis of prestressed application. Four composite beams with different concrete slab were initially designed and fabricated: conventional concrete slab (CB-1), HFRC slab (CB-2), prestressed conventional concrete slab (PCB-1), and prestressed HFRC slab (PCB-2), to conduct static load tests. During the test, the accelerometers were utilized to acquire the frequency and corresponding mode shapes at three static load stages: LS-1 (0 kN), LS-2 (100 kN), and LS-3 (ultimate load). The results revealed that the maximum crack widths in specimens CB-2, PCB-1, and PCB-2 were reduced by 19.2%, 42.5%, and 59.2% respectively, while the cracking loads increased by 43%, 129%, and 189% respectively. At the LS-1 stage, relative to specimen CB-1, the first-order frequencies of specimens CB-2, PCB-1, and PCB-2 increased by 2.4%, 2.9%, and 7.6% respectively, the second-order frequencies by 3.6%, 5.9%, and 6.8% respectively, and the third-order frequencies by 0.6%, 1.4%, and 3.5% respectively. This indicates that the integration of prestressing technology and hybrid fibers can improve the crack resistance and stiffness of the negative moment regions in continuous composite beams, take advantage of the synergy of the prestressing technique and HFRC. Subsequently, the damage extent in the negative moment regions of composite beams was quantified using the enhanced Coordinated Modal Assurance Criterion (eCOMAC), where the eCOMAC values at mid-span of specimens CB-2, PCB-1, and PCB-2 showed a decrease of 22.5%, 34.3%, and 54.8% respectively compared to specimen CB-1, aligning well with the experimental results. Finally, the finite element software ABAQUS and eCOMAC were used to investigate the effect of prestressing and the volume content of steel fiber and polypropylene fiber on the cracking performance of the negative moment zone. The results show that PF has a slight improvement on concrete damage, while SF has a more obvious improvement effect. And prestressing is best.

As a typical high-risk vehicle type, the coordination between truck driving speed and highway geometric design directly affects the operational safety level of expressways. To clarify the influence of highway geometric design on exiting truck speeds, this study analyzed the operating speeds of floating trucks on expressway segments from the mainline to the ramps and constructed a prediction model for exiting truck operating speeds on expressways. First, by examining the speed variation trends of trucks during the exit process and considering highway geometric design factors, the key characteristic cross-sections of truck speed within the diverging area were identified. Subsequently, a variable importance projection analysis was conducted to determine the critical geometric design factors affecting truck speed variation at these cross-sections. Finally, based on the measured data from 12 cases, the partial least squares regression method was employed to establish operating speed prediction models for trucks at three characteristic cross-sections, and the models were validated using the measured data from four cases. The results indicate that truck speeds undergo significant changes at the taper point, diverging point, and gore area, with the initial deceleration typically occurring approximately 500 m upstream of the exit. The results indicate that the speed of the trucks changes significantly at three cross-sections: the taper point, divergence point, and gore area. The deceleration behavior of trucks typically first emerges approximately 500 m upstream of the exit, implying that the geometric design and traffic signs exert a significant influence on truck speed. Critical design factors affecting truck speed variations include the length of the taper section, length of the deceleration lane, taper rate of the transition section, length of the taper line, length of the guide line, and radius of curvature at the end of the gore area. The developed prediction model can relatively accurately describe the law of truck speed changes in the divergence area, with the mean absolute percentage error of its prediction results being less than 10%. These findings offer theoretical support and practical guidance for evaluating alignment consistency, formulating speed-limit schemes, and deploying traffic safety facilities in highway interchange areas.

This study aims to enhance the seismic performance of seismically isolated bridges by combining the advantages of variable curvature pendulum bearing (VCPB) and shape memory alloy (SMA) cables. We propose a novel SMA variable frequency pendulum bearing (SVFPB) and parameter design method specifically for bridges with SVFPBs. The hysteretic model of the SVFPB is derived, and numerical models for the VCPB and SMA cables are developed using OpenSees software. The accuracy of these numerical models is verified against experimental data, and the SVFPB is further modeled. We then optimally design the parameters of the SVFPB for an isolated bridge based on the validated model and proposed method. We determine the optimum scale and distribution coefficients for the SMA cables, along with their corresponding yield forces. The effectiveness of the proposed method and the performance enhancement of the SVFPB system in bridges are demonstrated through the optimum parameters. The analysis considers the efficacy of the SVFPB system in mitigating seismic responses in bridges. Results show that the optimum model parameters for the SMA cables can be achieved using the proposed method. The seismic performance of bridge equipped with SVFPBs using these optimum parameters shows significant improvement. Specifically, the girder peak displacement, bearing residual displacement, and base forces in piers are fully controlled. The girder peak displacement and bearing residual displacement are reduced, and the reduction reached 6.7% and 51.3%, respectively. The maximum increments in base force and bending moment of the piers are limited to 7.4% and 7.0%, respectively. These findings provide reliable foundation for enhancing the performance of bridges with SVFPBs.

Unmanned mining trucks, as the primary carriers for transportation in mining areas, have seen rapid development in recent years. However, due to their large size and numerous blind spots, these trucks are often equipped with multiple LiDARs for surround perception. Achieving high-precision calibration of multiple LiDARs on unmanned mining trucks is crucial for efficient autonomous driving perception. In light of this, this paper proposes a joint self-calibration algorithm for multiple LiDARs on unmanned mining trucks based on a coarse-to-fine calibration (CTFC) approach. Firstly, to address the issue of uneven terrain in unstructured environments, a site usability validation algorithm is proposed, ensuring the primary usability of the input data stream. Secondly, to tackle the problem of inconsistent point cloud sparsity and significant differences in overlapping regions among heterogeneous LiDARs, a multi-LiDAR registration algorithm based on iterative hierarchical reorganization is designed. This algorithm improves joint registration accuracy by extracting identity constraints and aligning the data from coarse to fine multiple times. Finally, to address the weak constraints of non-overlapping LiDAR calibration, a non-overlapping registration algorithm based on bilateral equal-distance constraints is proposed. This algorithm constructs calibration relationships between non-overlapping LiDARs by assuming the identity of calibration board positions observed by multiple LiDARs with overlapping regions. To validate the effectiveness of the proposed algorithm, experiments were conducted in typical feature-degraded scenarios, selecting multiple mining area scenes. The performance of the proposed algorithm was verified based on Root Mean Square Error (Root Mean Square Error, RMSE) and center point matching error metrics. The experimental results show that the proposed algorithm positively impacts the final outcomes. In typical degraded scenarios, the RMSE for multi-LiDAR calibration was 0.048 m, and the center point matching error was 0.028 m. The overall efficiency improved by 120 times compared to manual calibration and multi-stage calibration methods, demonstrating significant advantages.

Faced with the severe challenges of heavy-load traffic and extreme environments, the research and development of modified asphalt is undergoing a paradigm shift from macro-empirical trial-and-error to molecular-level precision customization. This paper reviews the evolutionary history, microscopic mechanisms, and frontier research of polymer-modified asphalt technology. Firstly, the application of modern separation and advanced characterization techniques in constructing chemistry-rheology cross-scale correlation model is discussed from the classic SARA four-fraction model, revealing the influence of microscopic components on macroscopic mechanical responses. Secondly, the generational evolution of modifier technology is systematically reviewed from early thermoplastic resins and thermoplastic elastomers to the eco-friendly and high-value utilization of waste rubber and plastic materials, emphasizing the pivotal role of micro-dose chemical compatibilizers in improving the compatibility of multiphase systems. Furthermore, based on thermodynamic compatibility theory and the evolution laws of microscopic phases, the physicochemical essence governing the storage stability of modified asphalt is deeply analyzed. Finally, the paper provides an outlook on frontier directions, including the construction of asphalt genetic databases based on the Materials Genome Initiative, the application of virtual laboratories through molecular dynamics simulations, and digital inverse design integrated with Physics-Informed Neural Networks (PINNs). The convergence of these emerging technologies is driving a fundamental transformation in the development of modified asphalt from traditional, inefficient empirical trial-and-error models toward a future of molecular-level precision tailoring and intelligent design.

In order to improve the bonding performance of weak surfaces between layers in 3D printed concrete structures and enhance the flexural capacity of printed bridges, especially the main girder components, and promote the further application of 3D printing technology in bridge engineering, a herringbone (HB) stiffener was proposed to strengthen the interlayer interface of 3D printed concrete. Various reinforced specimens were designed and fabricated with subsequent tests, including direct tensile test, tensile splitting test and direct shear test respectively. Results show that the implantation of HB stiffeners significantly enhanced the tensile and shear properties of the printed specimens. Compared with the unreinforced control group specimens, the tensile strength of the specimens in the direction perpendicular to the interlayer interfaces (Z-direction) increased from 0.832 MPa to 1.823 MPa, with an increase of 119%. The splitting strength along the printing path direction (X-direction) increased from 1.262 MPa to 2.387 MPa, with an increase of 89%. The splitting tensile strength in the direction perpendicular to the printing path (Y-direction) increased from 1.179 MPa to 2.212 MPa, with an increase of 87%. The shear strength in the X-direction increased from 1.487 MPa to 3.819 MPa, with an increase of 156%; and the shear strength in the Y-direction increased from 0.897 MPa to 3.350 MPa, with an increase of 273%. It is demonstrated that the use of HB stiffener can effectively improve the interfacial bonding performance of printed components, especially in tensile and shear resistance. In addition, the typical loading feature of unreinforced and reinforced structure were revealed through load-displacement curve analysis. It is further proved that the implantation of stiffeners not only improves the mechanical strength of the interlayer interface, but also enhances the structural toughness of the 3D printed specimen. Finally, based on the results, calculation formulas for the tensile and shear strength of 3D printed reinforced specimens were derived with the semi-theoretical, semi-empirical approach, providing theoretical support for the engineering applications of 3D printed reinforced concrete structures.

To promote the development of the reasonable arch axis design theory for arch bridges, the evolution process of this theory was reviewed, the current research status and main problems faced in the calculation methods of reasonable arch axis were summarized, and the future research focuses and directions were discussed. Research results show that the development of arch bridges is intrinsically linked to the improvement of reasonable arch axis design theory. Identifying an arch axis that aligns with the constant load distribution mode and approximates the constant load thrust line is crucial for arch bridge design, which enhances the efficient synergy between material properties and structural force, improving the overall performance and load-bearing efficiency of arch bridges. Calculation methods for reasonable arch axis are generally divided into the analytical equation method and the curve fitting method. Determining the constant load distribution mode of main arch rib and spandrel structures, establishing and solving arch axis equation is the main focus in analytical equation method. Furthermore, selecting the type of curve to fit the arch axis, determining the position and number of control points on the curve, considering curve fitting methods and optimization objectives are the main focus in curve fitting method, the catenary, the high-order parabola and the spline curve are the commonly used fitting curves. The analytical equation method expresses the reasonable arch axis through design parameters of arch bridge, such as constant load intensity and horizontal thrust. This approach provides direct guidance for optimizing the structural configuration of the main arch ribs and spandrel structures, thereby significantly enhancing mechanical performance. In contrast, although the curve fitting method generates geometrically smooth arch axis curves, it inevitably induces substantial local bending moments at concentrated load sections or intermediate sections between adjacent concentrated loads. In order to provide theoretical support for maintaining the reasonable design state of long-span arch bridges, future studies should focus on addressing the design challenges of reasonable arch axis under the synergistic interaction of three scenarios, namely the application of lightweight and high-strength materials, optimization of complex structural layouts, and adaptation of special construction methods. These challenges are in line with the collaborative innovation trends of long-span arch bridges.

To meet the perception requirements of intelligent connected vehicles in complex traffic environments, a cooperative perception enhancement method based on pose error calibration is proposed to address the issues of limited single-vehicle perception field of view, inconsistent availability of connected interaction data, and inaccurate pose information of multi-source cooperation. Focusing on the cooperation effectiveness, the data availability, and the perception precision, we designed a distributed architecture of cooperative perception enhancement for intelligent connected vehicles, including the determination of cooperative agents, the screening of interaction data, and the calculation of cooperative perception. The Transformer cross-attention mechanism was used to fuse the LiDAR point cloud and image to optimize the autonomous perception ability of connected traffic agents. With the constraint of communication distance, the cooperative graph model was constructed based on the principle of perception enhancement in the safety-critical area of the ego vehicle and perception complementarity among cooperative agents to determine the agents for cooperation. Then, to avoid the influence of abnormal perception results, spatial topological consistency and perceived result continuity were used to filter interactive data. Finally, the pose error calibration was completed through the association matching of high-availability perception interaction data. The redundant perception results and new perception results are cooperated based on the cooperative gain calculation and object visibility distinction, respectively. Cooperative perception enhancement of V2X multi-source interaction data was achieved. To verify the effectiveness of the proposed method, tests and verifications were conducted based on the OPV2V dataset and the V2X-Real dataset. The experimental results show that compared with single-vehicle perception, the cooperative perception enhancement method proposed in this paper improves the AP50 by 22.2% and 21.7% on the OPV2V and V2X-Real datasets, respectively. Compared with mainstream cooperative perception methods, the method proposed in this paper has the smallest drop value of AP50 under the pose error interference. Moreover, for the OPV2V and V2X-Real datasets, the reduction value of AP50 is only 48.4% and 52.3% of that of the RobustV2V method, respectively. It was verified that the proposed method improves perception precision while reducing the interference of pose errors on cooperative perception performance.

To comprehensively reflect the influence of sandstone geological characteristics, in-situ environment, and loading effects on sandstone damage and deformation-permeability mechanisms, triaxial seepage tests were conducted on rock samples containing weak interlayers. A multi-factor seepage-stress coupled damage constitutive model for interlayered sandstone was established based on the Drucker-Prager (D-P) failure criterion and the effective stress principle. The results indicate that, on one hand, the peak strength and elastic modulus of the interlayered sandstone are reduced compared to those of intact rock samples, exhibiting a U-shaped distribution with variations in the interlayer dip angle and a linear attenuation with increasing osmotic pressure levels. On the other hand, the mechanical behavior and permeability characteristics of sandstone are jointly influenced by the interlayer dip angle and osmotic pressure, demonstrating a complex interaction. The initial permeability shows a positive correlation with both the interlayer dip angle and osmotic pressure. The permeability of samples with a 30° interlayer dip angle undergoes a four-stage variation throughout the loading process. Furthermore, to overcome the limitations of empirical damage analysis, based on classical damage theory, the initial value of the total damage quantity D₀ was found to exhibit an inverted U-shaped distribution with increasing interlayer dip angle, with the overall trend following an S-shaped growth pattern. Finally, by integrating the mechanical parameters obtained from experiments and employing a combination of qualitative description and quantitative analysis, a damage constitutive model for sandstone under Seepage-Stress coupling was developed. This model accurately captures the mechanical response characteristics of sandstone under various conditions, and its rationality was validated through comparisons between experimental data and theoretical curves.

Centrifuge model tests and finite element analysis were carried out to study the deformation development characteristics and failure process of subgrade during earthquakes under the influence of rainfall infiltration, where the dynamic response characteristics of the subgrade under seismic action after rainfall, the development laws of displacement and strain, as well as the characteristics of instability and failure were investigated. Research results show that the previous rainfall causes the development of seepage and water migration within the subgrade, resulting in the significant dynamic response and further development of displacement of the subgrade. After the rainfall ends, the seepage inside the subgrade continues to develop towards the toe of the slope, during which the saturation of the soil at different positions changes. The deformation law and sliding characteristics of the subgrade induced by earthquakes are affected by the water migration caused by the previous rainfall. When seepage causes the soil near the toe of the subgrade to become saturated due to infiltration, the deformation and damage of the subgrade under the action of earthquakes are the most obvious. When the total amount and duration of prior rainfall are the same, as the rainfall intensity decreases, the infiltration rate of rainwater in the early stage of rainfall is higher, the development of seepage inside the subgrade accelerates, the migration of internal moisture is faster, the soil suction rapidly decreases and the soil shifts from the unsaturated state to the saturated state, causing the effective stress to continuously reduce and eventually leading to the larger scale of subgrade deformation caused by earthquakes as well as the wider range of sliding surfaces upon the occurrence of failure. Among the three cases featuring prior rainfall with decreasing, constant, and increasing intensity, under the same seismic loading, the cumulative deformation of the subgrade is the most significant when the prior rainfall has the decreasing intensity, followed by the constant rainfall intensity cases, and the cumulative deformation of the subgrade is the smallest when the prior rainfall has the increasing intensity.

Scour is one of the primary causes of hydraulic bridge failure. However, although classical scour analysis theory has seen new advancements in numerical simulations and turbulence phenomenological models, it still fails to properly balance computational accuracy and efficiency. The theory struggles to efficiently and accurately compute equilibrium scour depth and spatial morphology, thereby impeding advancements in underwater structural safety design and operational maintenance for bridges. On one hand, mainstream equilibrium scour depth calculation formulas are often overly conservative and differ significantly from actual bridge measurements. On the other hand, classical single-phase-flow computational methods still exhibit considerable differences in morphology and depth compared to flume test observations, while two-phase-flow models that can more accurately describe sediment transport are computationally expensive. We proposed a theoretical system and technical roadmap for intelligent bridge scour analysis that proceeds from three perspectives: optimization of scour calculation formulas, intelligent single-phase flow, and intelligent two-phase flow simulations. This was achieved through the synergistic integration of multi-source data, multi-dimensional domain knowledge, and advanced artificial intelligence algorithms. In related intelligent analysis cases, this theoretical system and technical roadmap demonstrated significant advantages over traditional mainstream empirical formulas and simulation methods across multiple key dimensions, including computational accuracy, efficiency, safety, and generalization capability. It effectively alleviated the long-standing contradiction in traditional scour assessment where accuracy and efficiency were difficult to balance. This approach not only provides a feasible solution for high-accuracy and high-efficiency scour assessment but also offers technical support for full life-cycle bridge design and disaster prevention and mitigation.

To address the poor visual recognition of traditional reflective road markings, a water-based long afterglow self-luminous road marking paint was prepared using strontium aluminate [SrAl₂O₄∶Eu²⁺,Dy³⁺ (SAO)] as the phosphor, titanium dioxide and calcium carbonate as fillers, and dodecanol ester as a film-forming agent. The durability of the long-afterglow photoluminescent road marking coating was investigated through wear tests, water immersion tests, and ultraviolet aging tests, combined with infrared spectroscopy and scanning electron microscopy to reveal its microscopic degradation mechanisms. The results indicate that the main form of wear for long-afterglow photoluminescent road markings is the peeling of fillers, with titanium dioxide being more prone to wear and peeling than calcium carbonate. Immersion in water does not reduce the photoluminescent effect of the coating, but it slightly decreases the skid resistance of the road marking, with the immersion process being primarily a physical interaction without causing chemical changes. Ultraviolet radiation enhances both the initial brightness and afterglow duration of the coating, mainly because strontium aluminate can absorb part of the ultraviolet light, converting it into heat or activating its own luminescent properties, thus increasing the brightness and afterglow time of the coating. At lower ultraviolet irradiation energy, UV radiation can enhance adhesion, but as the irradiation energy increases, the adhesion decreases. Additionally, the silicone-acrylic emulsion has excellent film-forming properties, effectively isolating the coating from moisture and ultraviolet radiation damage; titanium dioxide enhances the reflectivity and UV resistance of the coating, improving its visibility and durability; and calcium carbonate improves the hardness and wear resistance of the coating. The synergistic effect between the materials significantly enhances the overall performance of the long-afterglow photoluminescent road marking coating.

As a core component of bridge operation and maintenance (O&M), bridge inspection is increasingly heading towards digitalization and intelligence. Digital twins technology, with its capabilities of virtual-real interaction, data-driven processes, and multi-physical field simulation, has become a critical element in supporting the digitalized and intelligent development of bridge O&M. This study provides a comprehensive review of the technological system for bridge inspection under the digital twin framework. Firstly, a digital twin-integrated framework for bridge inspection were proposed, including data acquisition, processing, and expression. The connotations as well as latest advancements in structural 3D modeling, defect recognition and expression technologies were detailed introduced. Secondly, focusing on the applications of digital twin technology in bridge inspection, the research status and developmental relationships of unmanned autonomous inspection systems and remote real-scene inspection were analyzed. Finally, the key issues including structural digital twin expression, bridge scene data enhancement, and the construction method of digital twin-based bridge inspection systems were discussed, along with prospects for future developments. The review demonstrates that digital twin technology provides a necessary pathway for achieving scientific, refined, and efficient inspection in response to the demand for long-term, comprehensive assessments of bridge structural conditions. While current researches have made significant progress in the development of inspection hardware and software, there still remains substantial scope for foundational theories and technological breakthroughs to be innovated in addressing the critical requirement of O&M under digital twins.

During metro station construction, holes are created in the main structure of the station to meet functional requirements such as interchange and ventilation, leading to a greater weakening of the bearing capacity of the main structure in the hole-opening area. To investigate the influence of a hole on the arch structure of a metro station constructed using the pile-beam-arch method on the overall mechanical properties, this study considered a newly built metro station in Guangzhou. First, the damage mode, crack extension, and deformation law of the opening in the arch structure of the metro station constructed using the pile-beam-arch method were studied in depth through indoor model tests. Subsequently, the stability and influence of the full-scale opening in the arch structure were investigated using numerical simulation. The results of the study show that although the beam in the region of the opening is in three-dimensional torsion, its horizontal bending stiffness is increased, with vertical bending as the main deformation characteristic. Moreover, the longitudinal girder without an arch causes a 'Y’ type tensile-shear composite penetration crack to form in the center of the longitudinal section, and a 45° penetration diagonal crack forms in the middle of the span. Furthermore, significant stress concentrations are observed at the opening boundaries, the overall force transmission path of the station structure changes, and the center arch does not directly influence the rest of the structure in the opening. Compared with the complete structure, the influence range of the station arch opening is 80% times that of the length of the opening, with a deformation difference of less than 10% as the criterion. The order of influence of each structural internal force is as follows: beam， arch，floor slab， side wall. These results provide a reference for underground engineering construction under similar geological conditions.

To address the challenges of complex spatiotemporal evolution, diverse triggering mechanisms, and delayed responses of conventional treatments for highway-associated landslides, this study proposed an integrated full-process “identification-early warning-control” approach for landslide hazard management. In the hazard identification stage, a rapid landslide detection and evolution-tracking method was developed by integrating multi-source data including remote sensing imagery, Interferometric Synthetic Aperture Radar (InSAR) interferometry, Unmanned Aerial Vehicle (UAV) photogrammetry, and Light Detection and Ranging (LiDAR), combined with geophysical prospecting and borehole inclinometers to accurately delineate the geometric characteristics of sliding surfaces. In the monitoring and early warning stage, a multi-parameter monitoring system was established by integrating Global Navigation Satellite System (GNSS), inclinometers, stress meters, microseismic sensors, and meteorological instruments, and a hierarchical early warning model was constructed based on deformation-mechanical control-environmental responses, enabling dynamic monitoring and accurate early warning under complex geological conditions. In the control strategy stage, a phased rapid emergency treating strategy was proposed, integrating structural optimization with components such as micropiles and intelligent anchoring systems, to achieve highly targeted and efficiently deployable countermeasures. The proposed methodology was validated through its application to the Naliang landslide treatment project along the Duba Expressway. The results demonstrate its strong engineering adaptability and high potential for broader implementation in integrated “identification-early warning-control” treatment of landslides along highway.

Driven by global carbon neutrality goals, the clean energy supply for highways presents a critical pathway to decarbonize transportation. This study systematically reviews the characteristics, collection pathways, and utilization potential of green energy in the road area from the perspective of synergistic development between transportation and energy. First, green energy in the road area was categorized into two types based on energy sources: natural energy, such as solar energy, natural wind energy, geothermal energy, and hydro energy; and traffic-induced energy, including mechanical vibration energy, pavement thermal energy, and convective wind energy. Then, the study reviews various energy collection technologies, including photovoltaic cells, wind turbines, heat pumps, hydro and wave energy conversion devices, vibration energy harvesters, and thermoelectric generators, as well as their conversion efficiency and technical challenges. Finally, by establishing a potential assessment model under a unified scenario, the study conducted a comparative analysis of output power, economic viability, and carbon reduction benefits, and summarized typical application scenarios. The study noted that while the potential for green energy is significant, its development and utilization face multi-dimensional challenges, including precise energy assessment, core technology efficiency and durability, and system integration and economic viability. This study aims to provide a theoretical framework and decision-making reference for constructing a clean, low-carbon, efficiently integrated, transportation energy ecosystem.

To investigate the mechanical behavior of perfobond leiste (PBL) shear connectors in coarse aggregate ultra-high performance concrete (CA-UHPC) thin slabs, push-out tests were conducted to study the influence and mechanisms of factors such as the presence of through reinforcement, perforated steel plate thickness, hole diameter, concrete strength, and connector configuration. The test results indicate that, in specimens without through reinforcement, the concrete tenons had to independently resist the relative slip forces, resulting in shear failure accompanied by the rapid development of vertical cracks, which prevented the utilization of their excellent compressive strength. Compared to specimens with through reinforcement, the shear capacity and stiffness decreased by 34.5% and 28.8%, respectively, displaying a brittle failure mode. Using the equivalent replacement theory for area and stiffness, the shear contribution of through reinforcements can be represented by their equivalent concrete tenon area. Due to the use of 13 mm and 20 mm steel fibers, increasing the thickness of the perforated steel plate from 12 mm to 20 mm reduced the number of fibers distributed along the shear interface beneath the concrete tenons by 52.9%, weakening the pin resistance and resulting in a 17.2% decrease in shear capacity. However, with the increase in perforated steel plate hole diameter from 40 mm to 50 mm and the compressive strength of CA-UHPC from 146 MPa to 163 MPa, the shear capacity increased by 32.8% and 62.2%, respectively. At the same time, compared with ordinary UHPC, the addition of coarse aggregate promotes the improvement of the compressive bearing capacity and stiffness of CA-UHPC concrete tenon, so that the PBL connector has better shear bearing performance. These two factors were identified as the primary contributors to the improvement of shear capacity and stiffness. This study provides valuable data and theoretical support for the performance design and engineering application of PBL connectors in the field of CA-UHPC.

In corrosive environments, prestressed concrete (PC) structures frequently suffer from varying degrees of localized corrosion, which reduces their load-bearing capacity. To investigate the impact of the location and degree of localized corrosion on the flexural performance of PC beams, three post-tensioned PC beams were designed, with two beams subjected to localized electrochemically accelerated corrosion tests at the mid- and quarter-span positions. After achieving the designed corrosion levels in the localized regions, bending tests were performed on all three beams. The effects of localized corrosion on beam deflection, crack propagation, section strain, and failure modes were analyzed, and numerical simulations were conducted using 3D scanning technology and ABAQUS models. The results indicate that localized corrosion significantly reduces the bending stiffness of the PC beams, weakening their load-bearing capacity, ductility, and deflection performance. The effect of mid-span corrosion on bending performance was greater than that of quarter-span corrosion. Additionally, localized corrosion significantly reduced the crack resistance of the corroded regions, leading to an increased number of cracks and a faster crack propagation rate. At the mid-span, localized corrosion primarily affects the width of the cracks in the flexural zone of the beam, whereas quarter-span corrosion mainly influences the width of the diagonal cracks. However, mid-span corrosion has a more significant effect on the maximum crack width at the ultimate load. Compared to the uncorroded beams, the cracking load in the localized corrosion areas decreased by 10%-20%, and the number of cracks increased with the corrosion level. The maximum crack width at the ultimate load decreased by 32%-50%. In corroded beams, nonuniform corrosion tends to facilitate local stress concentration more than uniform corrosion, leading to more severe crack damage in localized areas, which influences crack development and width. The nonuniform corrosion model obtained through 3D scanning more accurately reflects the impact of corrosion on the local mechanical properties of the beams.

To achieve resource disposal and utilization of lithium slag (LS), LS, ground granulated blast-furnace slag (GGBS), coal gangue (CG), calcium carbide slag (CCS), and desulfurization gypsum (DG) were used to construct an all-solid-waste cementitious system. Lithium slag lightweight aggregates (LS-LA) were prepared by cold-bonding pelletization. Four LS contents of 30%, 50%, 70%, and 90% were designed. The physical properties, single-particle compressive strength, long-term water stability and heavy metal leaching of LS-LA were tested, and its economic performance was analyzed. X-ray diffraction (XRD), scanning electron microscopy (SEM), Fourier transform infrared spectroscopy (FTIR), thermogravimetry-derivative thermogravimetry (TG-DTG), and mercury intrusion porosimetry (MIP) were used to analyze the hydration products and pore structure evolution. The 7 d unconfined compressive strength of lightweight aggregate-hydraulically bound mixture (LA-HBM) was evaluated based on Talbot gradation. The results show that the loose bulk density of LS-LA is 946-1 021 kg·m^-3, and the apparent density is approximately 1 600 kg·m^-3, indicating a lightweight characteristic. When the LS content increases from 30% to 90%, the density and single-particle compressive strength of LS-LA decrease, whereas the water absorption and immersion mass loss rate increase. After continuous immersion for 120 d, the mass loss rates of LS-30 and LS-50 are both lower than 5%. The leaching test results show that cold-bonding pelletization reduces the release risk of heavy metals. The Mn leaching concentration decreases from 0.827 mg·L^-1 in raw LS to a minimum of 0.002 mg·L^-1 in LS-LA. Microstructural analysis shows that calcium (alumino)silicate hydrate (C—(A)—S—H) gel and ettringite (AFt) fill the particle pores and refine the pore structure. The chemical binding of gel phases and the physical encapsulation of the particle structure jointly limit heavy metal migration. The 7 d unconfined compressive strength of LA-HBM is significantly affected by gradation. The mixture with a Talbot exponent of n=0.5 shows the highest strength, but it is lower than that of the natural aggregate control group. The economic analysis shows that the material cost of LS-LA is 18.56-67.76 CNY·t^-1, and LS-50 has the lowest cost per unit strength. In summary, cold-bonding pelletization based on an all-solid-waste cementitious system can convert LS and various solid wastes into lightweight artificial aggregates with low leaching risk. LS-50 shows better comprehensive performance and economic performance, and can be used as an alternative aggregate for base or subbase layers in low-grade highways.

Hybrid electric vehicles exhibit substantial advantages in energy conservation, emission reduction, and alleviation of range anxiety. As the core technology of hybrid systems, energy management strategy (EMS) has emerged as a critical research focus in the field of electric vehicles. With the rapid advancement of machine learning, its capability to process multi-source, high-dimensional data in intelligent connected environments offers novel pathways for optimizing the energy efficiency of electric vehicles. This paper systematically reviews the application and research progress of machine learning in EMS for hybrid systems, with a focus on two primary approaches: intelligent predictive EMS based on classical machine learning and self-learning EMS based on reinforcement learning. Research demonstrates that classical machine learning methods, including supervised and unsupervised learning, effectively extract key features from connected traffic environments and vehicle operational data, playing a crucial role in driving condition preprocessing for intelligent predictive EMS and supporting the optimization of traditional control algorithms. Deep reinforcement learning, which integrates the perception capabilities of deep learning with the decision-making strengths of reinforcement learning, exhibits unique advantages in real-time optimization control for electric vehicles in complex and dynamic traffic scenarios. Building on current research advancements, this paper also provides insights into future directions for machine learning applications in energy management, offering theoretical references for subsequent research.

The Vehicle-road-cloud Integrated System (VRCIS), as a key technological pathway for supporting large-scale deployment of high-level autonomous driving and the transformation and upgrading of intelligent transportation systems. Current research predominantly focuses on breakthroughs in individual technologies, validation in localized scenarios, or engineering demonstration applications, lacking a systematic review of the VRCIS technological framework, evolutionary trajectory, and application practices from the perspective of artificial intelligence (AI) empowerment. From the standpoint of AI empowerment and grounded in the paradigm shift from data-driven to generative intelligence, this paper systematically elaborates on the overall architecture, key technologies, and application practices of VRCIS. First, it identifies the core components of VRCIS and further elucidates its intelligent evolution mechanisms and key development stages. Second, it analyzes the technical paradigm evolution of VRCIS across two stages: VRCIS-1.0 (data-driven stage), which leverages methods such as multi-source data fusion and deep learning to overcome the limitations of single-vehicle perception and coordination mechanisms, exploring the evolutionary paths and breakthrough directions of key technologies including collaborative perception, collaborative decision-making and planning, collaborative control, and communication at different levels; and VRCIS-2.0 (generative intelligence stage), which employs generative large models to achieve generative collaborative reasoning, modeling, and handling of long-tail problems in complex environments. Finally, it summarizes application practices of VRCIS across multiple scenarios such as ports, Robotaxi, logistics shuttles, and mines, and reviews the progress in establishing VRCIS standards and evaluation systems. This paper provides theoretical support and reference for the formulation of technical routes, establishment of standards and specifications, and engineering implementation of VRCIS.

To address the limitations in risk perception and analysis methods for complex highway environments, this study introduces a Gaussian risk pulse waveform to quantify risk energy. By combining the energy interaction characteristics of the field, it achieves a theoretical fusion of risk pulses and driving risk fields. Firstly, by defining the maximum/minimum influence ranges of the risk field and considering the underlying constraints of vehicle geometry on the risk interaction space, a dynamic risk field is constructed in combination with vehicle motion characteristic variables, enabling the spatial expression of “behavioral constraints”. Secondly, a static risk field model is built based on functional differences in road markings (boundary, solid, and dashed lines) to quantify the spatial effects of “rule constraints”. Then, utilizing the superimposition of risk energy, a unified risk field perception model with dynamic-static dual-field coupling is constructed. Finally, the ET-SSE algorithm is employed to calculate the risk field strength threshold and classify risk levels. Empirical data from the Kunming-Mohan and Gongxiao highways in Yunnan are selected to validate the model in complex traffic flow scenarios. Comparisons with Time-to-Collision Inverse (TTCI), Time Headway Inverse (THWI), Artificial Neural Network (ANN), Spatial-Temporal deep learning (ST-Transformer), deep reinforcement learning (DRL), and traditional Driving Risk Field (DRF) models show that the proposed unified risk field model considering the risk pulse effect (RP-DRF) exhibits excellent performance in stability, accuracy, continuity, and real-time perception capabilities, with an average risk perception accuracy of 92.77%. Based on the empirical results, risk levels are classified into three categories: low [50, 443), medium ［443, 1 537], and high (1 537, 2 500］. Furthermore, sensitivity analysis reveals the field strength evolution mechanism. This research provides a quantified safety posture tool for risk perception, decision-making, and control of intelligent connected vehicles in highway scenarios, and the established risk level mechanism can be applied to early warning strategy formulation.

This study proposes a Traffic Conflict Risk Field (TCRF) model to address three limitations of traditional conflict indicators: Inadequate representation of behavioral stochasticity, insufficient modeling of multi-vehicle interactions, and challenges in quantifying spatiotemporal risk continuity. The TCRF employs a Gaussian Mixture Model (GMM) to capture the probabilistic distribution of vehicle trajectories, integrating behavior cloning with variational inference to estimate multi-vehicle trajectory probability density functions. The functional inner product of these density functions defines the risk field's intensity, quantifying the likelihood of vehicle conflicts. The density in multi-vehicle interactions is defined as a linear superposition of pairwise risk field intensities. Conflict risk between two vehicles is calculated as the integral of the field intensity over the spatiotemporal domain, admitting a closed-form solution that serves as a novel conflict indicator. Extensive experiments using real-world motorway trajectory data evaluate scenarios including merging, lane-changing, parallel driving, car-following, and multi-vehicle interactions. Comparative analysis with traditional conflict indicators demonstrates that the TCRF overcomes point-based, discretized risk measurements, eliminating “detection blind zones” and enabling continuous risk characterization across space and time, even in complex multi-vehicle settings. This approach shifts conflict risk assessment from discrete, deterministic evaluations to continuous, probabilistic modeling, enhancing proactive motorway safety management and autonomous driving decision-making.