Research progress in parametric modeling, intelligent optimization, scheme generation, and intelligent details of bridge structures was systematically reviewed in this paper, and the development trend of the intelligent design of bridge structures was predicted. Current research on bridge modeling mainly includes parametric building information modeling (BIM) and parametric finite-element modeling. The BIM obtained from parametric modeling was mainly used for visualization, which is difficult to convert into a finite element model (FEM) for structural analysis and optimization. The FEM obtained from parametric modeling could be used for the analysis and optimization of structures. However, the layouts of the loads and boundary conditions for a relatively complex FEM were still manually implemented. Moreover, it was difficult to accurately convert FEM into a BIM model. In research on the intelligent optimization of bridge structures, heuristic algorithms remain the dominant approach for optimization, which mainly focuses on reinforced concrete structures. Few studies have been conducted on steel and steel-concrete composite structures. In addition, most studies have focused on single-load cases. The generative approach can be used for the rapid generation of bridge schemes. However, current studies have only considered span design and bridge-type selection. There have been few research results on the intelligent detailed design of bridge structures, and the integration of intelligent detailed design and digital manufacturing has not been considered. It is foreseeable that through intelligent design technology, the main direction of bridge structural design development will be the implementation of the intelligence of scheme generation, modeling, optimization, and detailed design of bridge structures, along with the integration of digital schemes with subsequent digital manufacturing and intelligent construction.

Artificial intelligence (AI) technology has become a core component of national strategic science and technology. Its integration with bridge seismic engineering is emerging as a critical approach to enhancing the seismic resilience of infrastructure. Bridge seismic analysis has long faced challenges such as complex physical models and the difficulty of balancing efficiency with accuracy. Addressing these challenges, this study systematically reviews the application and innovation of traditional machine learning models, deep learning models, and next-generation AI fusion technologies in bridge seismic analysis, including: ① Intelligent synthesis and input of complex ground motions; ② Seismic capacity analysis and demand prediction; ③ Damage assessment and fragility analysis; ④ Resilience evaluation and recovery strategy optimization; ⑤ Seismic analysis of large-scale bridge networks. AI has significantly improved the efficiency and accuracy of bridge seismic analysis, opening new avenues for exploring problems involving multiple parameters and strong nonlinearity. However, existing AI models still face persistent challenges, including insufficient foundation in physical laws, weak model generalization capabilities, and difficulties in effectively integrating heterogeneous data sources. Looking ahead, AI technology will further advance the field of bridge seismic engineering through enhancing the interpretability of physical laws, developing multi-modal sensing technology, building high-fidelity databases, strengthening model generalization capabilities, and developing novel intelligent algorithms. This research facilitates a paradigm shift in bridge seismic studies, moving from reliance on manual expertise towards a deep integration of physical laws with artificial intelligence.

To review the latest technological advancements in the detection of underwater structural defects in bridges, this study focused on innovative applications of underwater robots to improve detection accuracy and efficiency. Underwater robots can carry both non-contact detection devices, such as optical and acoustic sensors, and contact detection devices, such as ultrasonic instruments and rebound hammers, demonstrating their potential for efficient detection in complex underwater environments. This study undertook a detailed analysis of the adaptability and improvement techniques of non-contact detection methods based on optical and acoustic principles, highlighting effective approaches for enhancing image quality and detection accuracy. It also clarified the current status of underwater contact detection research and proposed a solution for the collaborative operation of underwater robots with contact detection devices. This work emphasizes that the future direction of underwater detection lies in the use of underwater robots equipped with contact-detection devices. The challenges faced by current underwater bridge structure detection technologies are summarized, and new underwater detection methods based on intelligent algorithms and multisource data fusion are proposed, offering specific directions and technical paths for future research.

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.

The intelligent cooperative vehicle infrastructure System (ICVIS) enhances existing transportation services and facilitates the development of new applications by enabling flexible, cooperative, and open communication among vehicle subsystems, roadside subsystems, personal systems, and monitoring centers. Vehicle-to-vehicle and vehicle-to-infrastructure communication significantly improve the quality and reliability of information within the cooperative system, thereby optimizing driving conditions and enhancing traffic safety and efficiency. ICVIS features a complex structure, diverse functionalities, and stringent reliability requirements. Comprehensive testing and evaluation are critical to ensure its safe and efficient operation in real-world traffic scenarios. Currently, there is no systematic framework for ICVIS testing tools and evaluation methods. Most existing approaches rely on singular evaluation techniques, and the related theories, testing tools, and technologies are still in their nascent stages, with notable limitations in both breadth and depth. In this paper, existing ICVIS evaluation methods are systematically categorized based on testing objects, tools, and data sources, starting with an overview of the ICVIS system structure and its application scenarios. First, in response to the evolution of the ICVIS system structure from a “centralized” to a “cooperative” model, the hierarchical testing concept of “device level→system level→overall level” is discussed along with its specific implementation. Next, the concept of a multi-layer domain testing toolchain is introduced, categorizing and comparing existing evaluation tools. Thereafter, the advantages and disadvantages of current testing methods are outlined based on simulation software, driving simulators, closed test sites, semi-open roads, and open roads, among other evaluation tools. Furthermore, data-driven ICVIS evaluation methods, including subjective, objective, and combined subjective-objective evaluation approaches, are explored. Finally, through the analysis of three practical ICVIS assessment cases, the application process and outcomes of these evaluation methods in real-world scenarios are demonstrated. This systematic review and synthesis of ICVIS testing and evaluation methods is intended to provide valuable insights into the development and application of ICVIS technologies.

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.

Turning flow at signalized intersections is a fundamental parameter for urban road traffic system modeling.This is essential for traffic state prediction and the traffic management strategies.However，owing to the nonlinear characteristics of traffic flows in urban road networks，turning flow often changes randomly over time.Following the advancement of connected vehicle (CV)technology，route and turning information provided by connected taxis，connected mobility vehicles，and reserved vehicles，has enabled high-resolution turning flow predictions.A key issue that remains to be addressed is how to utilize limited CV individual data to predict accurately the future turning behavior of the mixed traffic flow.This paper decouples and analyzes the stochastic fluctuation characteristics of turning flow from the longitudinal and lateral perspectives.Longitudinally，the randomness of upstream vehicles traveling downstream at different speeds was modeled by a platoon dispersion model.Laterally，a Gaussian process method with deep kernel learning was employed to predict the randomness of turning ratio，leveraging partially observed CV turning data.The proposed model was validated in two intersection scenarios that involved Ningbo and Qinzhou in China.Results indicate that the proposed model accurately predicts high-resolution changes in turning flows.Compared with the traditional cell transmission model and a prediction method based on the CV turning ratio in the literature，the proposed model improved the prediction accuracy by more than 23.81% in the Ningbo experimental scenario and by more than 26.06% in the experimental Qinzhou scenario. Even at low CV penetration rates，the proposed model can achieve more accurate turning flow predictions，demonstrating its potential practical value.

Horizontal swivel construction, characterized by minimal disruption to existing railways and highways, has become a critical method for constructing long-span bridges over operational railways. However, swivel construction presents challenges, such as heavy loads, elevated overturning risks, and rapid state transitions. Therefore, effective monitoring and control are essential to ensure safety, structural stability, and precision. This study clarifies the primary and secondary objectives of swivel-monitoring feedback control and establishes their logical interconnections. The “traction-rotation” process was decomposed into five monitoring components: support system, traction system, swivel posture, structural stress, and surrounding environment. Both corresponding key and general monitoring indicators were proposed. Threshold calculation methods and state evaluation criteria were established for two typical swivel support configurations: swivel hinge-centered support and combined swivel hinge-support foot systems. A data linkage model was developed based on the physical interactions between the external environment, support system, traction system, swivel posture, and structural stress, and a dynamic feedback and control mechanism was established to ensure “mobility, stability, accuracy, and speed.” The ARIMA time-series model was incorporated to facilitate the dynamic prediction of monitoring data, allowing timely control of traction and balance systems. By integrating inertial sensing, image recognition, digital twins, and PLC wireless communication technologies, a lightweight visual monitoring feedback control system was implemented using WebGL, Python, and the B/S architecture. This system was successfully implemented during the swivel construction of a cable-stayed bridge. The results indicate that the primary objectives of monitoring and controlling rotating bridges are “mobility, stability, accuracy, and speed,” which follow a pyramid-shaped logical hierarchy, with mobility forming the foundational tier. The proposed monitoring-feedback control model for the swivel construction of bridges elucidates the interrelationships between control targets, monitoring indicators, state evaluation, and system responses. The developed lightweight, visualized swivel construction monitoring feedback control system enables real-time monitoring and regulation of traction, support, posture, and velocity states, effectively addressing challenges such as data redundancy, fragmented analysis, and state assessment and control.

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.

The impact of complex wind fields on the construction quality of sea-crossing cable-stayed-suspension hybrid system bridges is significant. Digital twin technology serves as an effective approach for holistic integration and simulation of bridge construction environments and processes. This study takes a large-span cable-stayed-suspension hybrid system bridge as an engineering case. By coupling the mesoscale Weather Research and Forecasting (WRF) model with computational fluid dynamics (CFD), the influence of real bridge structures was introduced into the traditional simulation framework. High-precision wind field prediction and structural mechanical analysis methods for construction stages under complex environments were established by combining data fitting and user-defined function (UDF) boundary condition loading techniques. A digital twin scenario construction method was proposed, integrating virtual geographic environments, bridge twin models, and simulation systems. A dynamic closed-loop feedback mechanism (“data acquisition-model calibration-instruction feedback-scheme adjustment”) was developed to address the limitations of traditional static models in dynamically responding to construction changes. A four-layer virtual simulation platform for bridge construction was developed based on the Cesium engine, incorporating modules for construction progress, wind field distribution, and structural mechanical responses. Through 3D GIS-based global visualization and localized detail rendering, the platform assists engineers in decision-making optimization. Case studies demonstrate that this method dynamically maps construction processes and predicts wind-structure interactions. The findings provide strong support for advancing the intelligence and informatization of bridge construction.

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.

The conventional design process of bridge structures relies heavily on the experience of designers. The structural configuration proposal is repeatedly modified, resulting in low modelling efficiency. In addition, the optimal results are hard to achieve optimality. To this end, the intelligent modelling and optimization method of half-through steel box arch bridge with composite bridge deck system was proposed in this paper. First, the structural intelligent modelling method based on the human-computer collaboration was proposed. The automated layer classification method was adopted to extract the key information such as the arch rib, K brace, bridge deck system, and bridge pier abutment in the initial condition drawings. The cross-sectional information module of the bridge components was defined. The spatial information reasoning was conducted combined with the extracted information and the length and spatial coordinates of components were determined. The dividing criteria of elements and sectional fiber was established and the number of nodes and elements was determined, realizing the automatic definition of nodes and elements. The boundary condition module was defined. According to the structural parameter module, the load input module and structural output module were defined, realizing the intelligent modelling. Based on the parametric FE model, the structural intelligent optimization method was proposed. Parameters were input with combination of the intelligent modelling technology, forming the FE model to calculate the pseudo-objective function. The standard genetic algorithm, strengthen elitist genetic algorithm, and differential evolution algorithm were employed to optimize the structural cost. The proposed intelligent FE modelling and optimization method was verified with combination of the practical engineering case. The results indicate that the parametric FE model could be established by only marking the initial condition drawings using the proposed intelligent modelling method, which can significantly improve the modelling efficiency and quality. Compared with the manual structural optimization method, the cost of structural material is reduced by about 37.9% and the optimization period is reduced by about 74% using the proposed optimization method.

To investigate the long-term structural damage mechanism of asphalt mixtures under complex humidity conditions, this study focused on asphalt mixtures with 100% replacement of natural aggregates by steel slag. Constant humidity curing environments (60%RH, 80%RH, and 95% RH) were established. A multiscale coupling analysis was conducted by combining X-ray CT image analysis and long-term dynamic modulus testing. The results showed that the pore structure of steel slag asphalt mixtures under high humidity followed a three-stage evolution: micropore formation, development of small and medium pores, and coalescence into large pores. Valid pores dominated the volumetric expansion and performance degradation process. Significant differences were observed between mixtures with different gradations in pore evolution patterns and damage responses. In the SMA-13 gradation, strong pore coalescence formed large connected networks (average volume reached 47.12 mm³). In contrast, the AC-13 gradation showed micropore development (pore count increased by 91.4%), resulting in better structural stability. Dynamic modulus testing revealed that increased humidity and extended curing time significantly reduced the mixture stiffness. The hydration reactions of free calcium oxide (f-CaO) and free magnesium oxide (f-MgO) were the primary damage-inducing factors. In the micro-macro correlation analysis, the Mantel test was introduced to quantify the relationship between the pore structure parameter matrix and the dynamic modulus response matrix. The results confirmed that porosity and average coordination number were significantly negatively correlated with the dynamic modulus. The coupling relationship between microstructural parameters and macro performance varied with gradation. This study provides a theoretical basis and data support for optimizing the performance and promoting the efficient application of steel slag asphalt mixtures in road engineering.

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.

Reflection cracks are a typical disease of asphalt pavement with semi-rigid base, which constantly evolve under load and environmental effects, leading to secondary pavement damage. However, as cracks develop within the pavement with small dimensions and strong concealment, it is difficult to detect and track them. Therefore, these fundamental issues are not being effectively resolved. The development of the discrete element method provides a powerful simulation tool for studying the cracking behavior of asphalt pavements, whereas the advancement in the ground penetrating radar (GPR) technology provides an effective means for detecting and tracking cracks. This study utilized a discrete element simulation method and 3D GPR detection technology to explore the expansion process of reflection cracks under vehicle loads, and the deterioration process of reflection cracks under hydraulic coupling. The 2D convex hull algorithm and the circumcircle collision detection algorithm were proposed to construct a coarse aggregate template, establishing a full-scale microscopic model of asphalt pavement that met the grading requirements. The expansion law of reflection cracks under vehicle loads was studied, and the expansion of reflection cracks towards the pavement surface was mainly divided into three stages: rapid expansion, stable expansion, and failure. The deterioration process of reflection cracks after expansion to the pavement surface under hydraulic coupling was analyzed. Compared with the situation where only the load acts, under the action of hydraulic coupling, the reflection cracks form secondary cracks and expand laterally, leading to interlayer debonding, base-layer loosening, and fragmentation. It was observed that water is the primary factor in the deterioration of reflection cracks. Based on the radar detection results, a large number of actual pavement crack core analyses were conducted, calibrating the typical morphological characteristics of cracks in six grades, from “none” to “severe.” The accuracy of the discrete element simulation results was further verified, and the stage characteristics of the entire development process of reflection cracks was revealed, from “initiation,” to “expansion,” to “deterioration.” The comprehensive presentation of the entire development process of reflection cracks, including secondary diseases, facilitates more accurate identification, evaluation, and tracking of reflection cracks and their development trends.

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.

Accurate measurement of suspender force is important in construction control, health monitoring, damage diagnosis, reinforcement and maintenance of suspension bridges. However, at present, there is still a lack of effective methods for short suspenders in the mid span area. Therefore, A laser scanning-based method for testing the suspender forces of suspension bridges, referred to as the scanning method, is proposed in this paper. Firstly, to address the difficulty of measuring the cable shape in suspension bridges, a method of quickly capturing the geometric shape of the main cable using three-dimensional laser scanning is proposed, and a precise method for calculating the cable shape based on the scanned point cloud is given. Secondly, the formula for calculating the suspender force of the suspension bridge is derived based on the measured shape of the main cable. The suspender force is solely determined by the geometry of the main cable, its specific gravity, and the horizontal force within it, independent of factors such as stiffness and boundary conditions in the suspenders. This approach effectively addresses challenges associated with accurately measuring cable forces in short suspension bridges using frequency-based methods. Then, field tests were conducted on the Guihua Bridge in Wushan to verify the accuracy of the proposed cable force calculation method and the reliability of the proposed laser scanning cable force testing method. The test results indicate that, in terms of measurement accuracy, approximately 50% of the suspender force measurements obtained using the traditional frequency method exhibit an error exceeding 10%, whereas only 2 out of the 62 suspender force test samples in the proposed method have an error exceeding 5%; in terms of measurement efficiency, it took about 330 minutes to complete a full-bridge hoisting cable test using the frequency method, while the proposed method requires only about 70 minutes. The field test efficiency of the proposed method is nearly four times higher than that of the traditional frequency method. Finally, the proposed method was applied to the Egongyan Railway Bridge in Chongqing, where the true cable forces before the cable failure were retrospectively evaluated. The evaluation results based on the historical laser scanning data show that the cable forces of the broken cable and adjacent cables of the Egongyan Railway Bridge are within 10% deviation from the designed cable forces, which proves that cable forces are not the main cause of the bridge cable failure.

Lateral collaborative performance is a key indicator for evaluating the service condition of prefabricated beam bridges. The timeliness and applicability of traditional methods for obtaining lateral collaborative performance evaluation indicators can still be further improved. To address this issue, this paper proposed a dynamic characterization model for the lateral collaborative performance of prefabricated beam bridges based on the analysis of vehicle-induced response mapping relationships. This method can use the health monitoring data to realize the dynamic characterization of the changes in the lateral collaborative performance of bridges. It established a vehicle-induced response mapping model for prefabricated beam bridges using a Bayesian optimization Natural Gradient Boosting (Bo-NGBoost) model, and evaluated the location and extent of lateral connection damage based on the error distribution and magnitude of the response mapping models at different positions caused by lateral connection damage. The effectiveness of the proposed method was verified. The results showed that the Bo-NGBoost had certain advantages in terms of accuracy and robustness compared with standard NGBoost and Long Short-Term Memory (LSTM) networks. Under typical working conditions, its average coefficient of determination (R²) reached 0.986, representing improvements of 4.4% and 36.4% over standard NGBoost and LSTM, respectively. In particular, in the emergency lane region with sparse effective data, the model maintained a high R² of 0.953, whereas the R² of LSTM was only 0.374. Numerical simulations were conducted to consider various combined working conditions of different pavement roughness and traffic flow density. The R² of the deflection response was higher than 0.951, and the root mean square error (RMSE) was below 0.153 mm. In addition, by analyzing the error distribution and magnitude of the response mapping models at different positions, the location of lateral connection damage can be identified and its extent evaluated. In actual monitoring scenarios, the mapped strain responses of the model have an R² exceeding 0.981 with the true data, and an RMSE below 2.381×10^-6, with the true values generally falling within the 95% confidence interval. This indicates that the method not only accurately reflects the mapping relationship of the lateral vehicle-induced response of bridges, but also can locate and evaluate lateral connection damage, thereby providing strong support for the maintenance of prefabricated beam bridges.

To reveal the effect of lane-changing intentions on vehicle trajectories and ensure the efficiency and safety of highway traffic, this study constructed a vehicle trajectory-prediction model while considering lane-changing intentions. The model considers the intention of a vehicle to change lanes and integrates the attention mechanism into a convolutional bidirectional long short-term attention network. To predict the interaction between vehicles and surrounding vehicles and the chronology dependence of driving trajectory data, spatial-temporal features were extracted using a convolutional neural network and bidirectional long short-term memory network. Additionally, an attention mechanism was incorporated to facilitate the model in focusing on important time series. The heading angle was used to define the vehicle lane-changing process. Lane-changing intention labels were added to the data based on the change in the heading angle and then transformed into a one-hot vector spliced with the trajectory information as the model input. Model-validation comparison experiments were performed using the NGSIM and HighD freeway datasets. The results show that, compared with the current mainstream trajectory-prediction models, the long-time domain (3-5 s) root mean square error and the average and final displacement error metrics of CI-CBLA show different degrees of reduction, and that the optimization for the HighD dataset is more significant than that of the NGSIM dataset. Considering that the vehicle lane-changing intention effectively improves the trajectory-prediction accuracy, the CI-CBLA model performs well in freeway vehicle-trajectory prediction. To verify the generalizability of the model, the Ubiquitous Traffic Eye dataset was selected for training and validation. The results show that the model can predict the trajectory of urban roads in a short-time domain.

    截至目前，中国机动车保有量超4.6亿辆、驾驶人超5.5亿、公路通车里程达550万千米，道路交通系统体量巨大，交通安全面临着极大的压力和挑战。与此同时，自动驾驶、人工智能等新技术快速发展，也为应对交通安全问题带来新的变化和机遇。为全面推进交通安全治理工作，国务院安委会发布了《“十四五”全国道路交通安全规划》，提出“科技赋能、智慧治理”的基本原则，要求提升交通安全治理现代化、信息化、智慧化水平。“交通强国五年行动计划2023~2027”进一步强调，要健全交通运输安全生产体系，推动安全生产向事前预防转型。针对中国特有的复杂交通系统，同时结合国家战略发展方向，学术界、工业界和行业主管部门都进行了大量的实践与攻关研究，在驾驶行为、安全设施优化、安全风险评估、自动驾驶交通安全等方面取得了一系列研究成果。
    为帮助政府、研究者和工业界等及时跟进道路交通安全领域理论与技术研究的最新成果，聚焦道路交通安全研究的前沿方向，促进道路交通安全理论与技术创新，《中国公路学报》编辑部联合同济大学王雪松教授（本刊副主编）、同济大学方守恩教授、公安部道路交通安全研究中心王长君研究员、公安部道路交通管理科学研究所孙正良研究员、交通运输部公路科学研究院交通安全研究中心周荣贵研究员、东南大学刘攀教授、西南交通大学闫学东教授、武汉理工大学吴超仲教授、吉林大学李世武教授、中南大学黄合来教授、长安大学付锐教授、东北林大大学裴玉龙教授、北京工业大学赵晓华教授共同策划了“道路交通安全”专栏，并邀请武汉理工大学吕能超教授、东南大学郭延永教授、长安大学王畅教授、长安大学王秋玲副教授、清华大学裴欣副研究员、西南交通大学胥川副教授、北京工业大学李佳副教授、上海海事大学王晓梦助理教授、上海理工大学丰明洁助理教授作为组稿专家，共同向该领域的知名专家、学者约稿，出版本期“道路交通安全”专栏。
    本专栏共收到论文117篇，最终录用16篇，研究内容主要集中于以下4个方面：
    （1）自动驾驶交通安全研究。研究成果包括：基于自动驾驶安全视域的交叉口右转适驾性研究、基于混合模型的地面道路车道线识别和影响因素分析、考虑路侧感知限制的交叉口冲突风险监测效能虚拟评价方法、基于多约束自适应模型预测控制的智能车路径跟踪与稳定性集成控制、通信时延下考虑行驶状态时空价值的编队安全控制。
    （2）交通行为、心理与安全分析。研究成果包括：异常驾驶人行为识别与异常度量化研究、考虑驾驶人补偿行为特征的行车安全评价方法、基于自然驾驶试验的公路螺旋隧道驾驶人心理旋转效应分析、驾驶人认知控制能力对交通风险事件响应的影响特性、考虑客货交互压迫的小汽车换道风险预测研究、融合冲突可能性和严重性的高速公路分流区极值模型构建及应用。
    （3）事故伤害评估与防护。研究成果包括：碰撞减速工况下低龄儿童脑挫伤损伤特性试验与仿真、基于真实事故重建的头部保护测评方法评估与优化、基于人体落地机制预测的人地碰撞损伤防护方法。
    （4）交通安全设施评估与优化。研究成果包括：基于分布鲁棒优化的危化品运输事故应急救援站选址分配问题研究、汽车波形梁护栏碰撞非线性理论计算方法研究。
    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！道路交通安全基础理论与应用技术的创新发展，对中国交通强国建设具有重要支撑作用。《中国公路学报》将持续关注道路交通安全领域的最新研究进展，涵盖人、车、路、管理等多方面内容，聚焦前沿理论、关键技术和工程实践。期望通过搭建学术交流与知识共享的平台，为专家、学者及工程技术人员提供有价值的研究成果和实践经验，推动中国公路交通行业向更安全、更高效、更可持续的方向发展。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。

Due to the construction convenience and cost-effectiveness, non-excavation grouting technology is widely employed for road defect rehabilitation. As a core component of road repair, the performance of grouting material is critical to the effectiveness of filling and reinforcement. This paper systematically reviews research progress concerning the classification, applicability, composition design, key properties, repair mechanisms, and microstructural characterization of road grouting materials. It focuses on analyzing the influence mechanisms of material composition and typical dosage ranges on performance. The potential for synergistic or inhibitory effects between mineral admixtures and chemical admixtures is highlighted, emphasizing the need for research into their interaction mechanisms to mitigate adverse effects. Furthermore, the limitations of current design methodologies prioritizing single-material performance optimization are discussed. The necessity of establishing a predictive model integrating grouting material properties, subgrade characteristics, and construction processes is underscored. This model aims to achieve optimal repair outcomes by addressing multifactor coupling issues. Additionally, the repair mechanisms of different grouting materials are analyzed. In the macroscopic perspective, void filling and cementation dominate. In the microscopic perspective, repair is achieved through reactions such as pozzolanic activity, geopolymerization, grout-soil interaction, and polymer solidification. Cement-based and geopolymer-derived grouts primarily rely on cementitious reactions and ion exchange with soil/rock particles. In contrast, polymer grouts is mainly dependent on foam compaction and cementation effects.

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.

Welding is a common connection method widely used in the construction of steel structure bridges. Identification and tracking of weld seam is one of key technologies for achieving automated and intelligent welding. However, Steel surfaces typically have high brightness and reflectivity, which severely interfere with seam recognition. Moreover, tack welding is usually required before welding to fix the relative positions of the workpieces in steel structure. Existing seam tracking technologies struggle to handle intermittent welding at tack points, and repeated welding may lead to numerous adverse issues. Therefore, a method for intermittent fillet seam identification and tracking based on line structured light vision is proposed. Firstly, for weld seam images containing laser stripe patterns, median filtering is employed to remove speckle noise from the weld seam images. Secondly, the center points of the laser stripes are extracted based on image grayscale features. Finally, an improved RANSAC algorithm is used to fit the extracted laser stripe center points, obtaining the feature lines and their intersections (i.e., weld seam feature points), thereby achieving precise discrimination between continuous and intermittent weld seams. Experimental results show that compared with the robot's taught path, the maximum error of the weld seam path extracted by this method is 1.56 mm, with an average processing time of 0.17 ms per frame. This study provides a feasible solution for robot jump welding of intermittent fillet welds in steel structures.

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.

To address the demand for full-time monitoring of traffic loads on long-span freeway bridges, a full-time spatiotemporal traffic load identification method was developed using computer vision and data fusion technologies. First, an integrated approach combining YOLO-v8, ByteTrack, and LPRNet algorithms was established to recognize vehicle parameters and license plate characters from on-bridge roadside camera videos. A fuzzy matching algorithm based on Levenshtein distance was employed to match vehicle license plate data from roadside camera videos with electronic toll collection (ETC) records, providing a cost-effective solution for vehicle weight identification. Second, utilizing the same field-of-view and image coordinate transformation relationships between daytime and nighttime scenes, a labeled dataset was generated based on video-identified vehicle parameters captured during the daytime. This dataset was used to train an artificial neural network to predict vehicle wheel positions at night, enabling the identification of vehicle load sequences in camera-monitored regions at night. Third, a previously developed hybrid virtual-real traffic simulation approach was used to reproduce the full-time spatiotemporal distribution of traffic loads across the entire bridge deck. Additionally, a dynamic time-warping algorithm was employed to fine-tune the vehicle trajectories to align the theoretical and measured bridge responses. Finally, the proposed method was comprehensively validated using monitoring data from a cable-stayed bridge. The results indicated that the machine vision approach achieves full-time multi-target recognition accuracy of ≥92%, full-time vehicle license plate recognition and matching accuracies of 94% on average, and nighttime vehicle axle position prediction error of 0.2 m in both the transverse and longitudinal directions. The hybrid virtual-real traffic simulation approach provides average weighted vehicle longitudinal location matching errors of 0.89 m and 0.49 m, and vehicle traffic lane matching errors of 7.7% and 5.2%, for the left and right bridge parts, respectively. The deflections calculated from the identified full-time traffic loads closely match the measured static deflections caused by traffic loadings, yielding a correlation coefficient of 0.999. This work provides a cost-effective and highly efficient methodology for full-time monitoring of spatiotemporal traffic loads on long-span freeway bridges in China.

The gradation of aggregate has a significant impact on the performance of cement-stabilized crushed aggregate base. An in-depth and systematic analysis of the differences in the design theories of different gradation design theories and the influences of the strength and shrinkage characteristics of has important engineering guiding significance. In this paper, the methods of computer vision, discrete element simulation, and laboratory tests were conducted to compare and verify the differences in the optimal gradation void ratio of different gradation design theories (n method, k method, fractal theory, stepwise blending method). The influence of different maximum particle sizes (31.5, 26.5, 19.0 mm) on the strength, temperature shrinkage and dry shrinkage characteristics of the material were studied. The research results show that among the four gradation design methods, the design gradation of the k method (k=0.73) can achieve the minimum void ratio and the maximum 7-day compressive strength under the same compaction degree conditions. The void ratio of cement-stabilized crushed stone materials is linearly negatively correlated with the strength. Under the same compaction degree, the maximum particle size shows a significant negative correlation with the strength. With the increase of the maximum particle size, cement-stabilized crushed aggregate materials have better anti-dry shrinkage characteristics. Below 0 ℃, the larger the maximum particle size, the smaller the temperature shrinkage. The delay time from mixing to compaction of cement-stabilized crushed stone materials has a significant impact on its strength. Within 1 hour after mixing, the 7-day strength decreases significantly with the extension of the compaction delay time. The research results reveal the influence of the maximum particle size on the performance of cement-stabilized crushed aggregate materials, and also provide certain references for the optimization of gradation design, improvement of construction technology, and prediction of shrinkage characteristics of cement-stabilized crushed aggregate base materials with dense skeletons.

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.

The volume expansion characteristics and water-damage risks of steel slag restrict its engineering applications as a potential substitute aggregate for asphalt pavements. To address the challenge of predicting the volume expansion and water stability of steel slag asphalt mixtures, this study established a machine-learning prediction model that incorporated multiple factors. Based on immersion expansion tests and 300 water stability tests covering variables-such as asphalt type, steel slag content, f-CaO content, gradation, and environmental conditions, a backpropagation neural network model was developed based on water-induced volume expansion and a CatBoost prediction model was optimized using Bayesian optimization and cross-validation. SHapley Additive exPlanations (SHAP) theory was employed to analyze the feature importance and parameter sensitivity that affect water stability. The results indicate that the volume expansion of the steel slag asphalt mixtures was significantly correlated with the gradation composition, f-CaO content, and immersion time. The CatBoost model achieved the highest prediction accuracy for the residual stability and tensile strength ratio (TSR) and effectively reflected the prediction error, with R² >0.997 and MSE<0.344 5. Among the material factors influencing water stability, the f-CaO content of the steel slag coarse aggregate (mean SHAP values: 2.05, 1.21, 1.17, and 4.62, 1.44, and 0.77, respectively) was the most crucial, followed by the asphalt type (0.84 and 0.82), steel slag content (0.36 and 0.32), and asphalt content (0.12 and 0.38). There was an interactive effect between the feature combinations of steel slag f-CaO content-asphalt content and f-CaO content-steel slag content on water stability. To satisfy water stability requirements, the steel slag content in the surface layer of the asphalt pavement should not exceed 75%. Additionally, the f-CaO content thresholds for steel slag with particle sizes of 2.36, 4.75, and 9.5 mm should be controlled within 2.0%, 2.25%, and 2.0%, respectively. This study provides theoretical support for controlling steel slag expansion and predicting water stability, thereby promoting the resourceful utilization of steel slag in asphalt pavements.

In order to improve the safety of bridge erecting machine construction and reduce the operation risk, this paper constructs a bridge erecting machine operation state intelligent monitoring system based on Digital Twin (DT) and Virtual Reality (VR). The system includes four main functional modules: bridge erecting machine construction process simulation, multi-source heterogeneous data fusion, structural safety analysis and monitoring, and virtual reality panoramic roaming. Firstly, Unity 3D platform is used to create a virtual construction environment, and the simulation of key construction processes is realized through C#; subsequently, the fusion of heterogeneous data from multiple sources is carried out based on CASREL model, mapping the whole life cycle of the physical entity, and real-time monitoring of the operation status of the bridge erecting machine. The system also integrates the Collider component, which monitors the risk of collision by synchronizing reality with reality through laser range sensors and serial communication; Subsequently, a dynamic finite element analysis module is developed to combine with APDL command flow to analyze the safety of the bridge erecting machine structure; Finally, immersive panoramic roaming is realized through HTC Vive to enhance the operator's interactive experience. The construction of 32 m-span simply supported box girder of high-speed railway is taken as the test object to verify the effectiveness of the system. The results show that the key construction parameters of SLJ900/32 mobile bridge erector (such as the distance of heavy load crossing, the distance of main outrigger moving forward and the height of falling and placing box girder) are all within the safe construction range, which provides systematic engineering implementation guidance for bridge erecting machine operations. Based on the fusion of heterogeneous data from multiple sources, the system realizes dynamic finite element analysis, accurately evaluates structural mechanical properties, and applies collision detection algorithms to monitor potential risks in real time and issue timely early warnings. In addition, the virtual reality panoramic navigation function enables operators to gain real-time insight into the overall situation of the construction process. which enhances the depth of knowledge of the construction environment and decision-making ability.

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 address the common technical challenge of controlling convergence deformation at the arch feet of initial supports in soft rock tunnels, this study explores the occurrence conditions of convergence deformation at the arch feet of the initial supports in construction via the bench cut method from the perspective of structural load and constraint conditions. This method proposes the use of prestressed feet-lock cables to actively control the convergence deformation at the initial support arch feet. The supporting mechanical and deformation control effects of prestressed foot-lock cables on the arch feet of the initial supports in soft rock tunnels are studied by combining theoretical calculations and field tests. Moreover, a construction method for the prestressed feet-lock cable is proposed. The results show that the convergence deformation at the arch feet caused by the horizontal load of the surrounding rock is larger than the expansion deformation at the arch feet caused by the vertical load of the surrounding rock. This is the fundamental reason for the convergence deformation at the arch feet of the initial support. The traditional feet-lock pipe (bolt) has a limited restraint effect on the arch feet of the initial support and cannot play an axial anchoring role. The initial support has an insufficient horizontal restraining effect on the arch feet, which explains the significant convergence deformation of the arch feet. After setting up a 10 m-long end resin anchored prestressed feet-lock cable with a diameter of 21.8 mm and applying a high pretension force (i.e., a designed pretension force of at least 300 kN), the deformation rate at the arch feet of the initial support significantly decreased. The working principle is that the prestressed feet-lock cable plays a strong, fast, and active control role in the convergence deformation at the arch feet of the initial support while forming a “prestressed anchor cable retaining wall” with a steel rib and shotcrete initial support, which plays an active reinforcement role in the rock surrounding the tunnel. For the severe collapse of the tunnel face and vault after tunnel excavation, the “anchor first and then support” scheme is difficult to implement, considering its high support cost, long process time, and inability to use prestressed anchor cables in large quantities, the prestressed feet-lock cable support provides an alternative with high promotion and application value.

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.

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 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.

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.

Electric vehicle wireless charging systems on urban roads have been widely studied because of their convenience and high efficiency. An improved DD coil structure is proposed to address the issue of high-efficiency energy transmission when different types of primary-and secondary-side coils are matched and when the primary-and secondary-side coils are offset and deflected. This structure employs semi-circular coils instead of rectangular coils. It adds an orthogonal Q coil based on a semi-circular DD coil. Based on the basic structure of the improved DD coil, an embedded core structure was designed, and the improved DD coil was optimized from the perspective of a lightweight coupling mechanism. The COMSOL finite element simulation software and MATLAB were utilized to conduct joint simulations, and the interoperability and anti-bias performance of the unipolar DD, improved DD, and DDQ coils were compared and analyzed. A system model using an improved DD coil was established, and the energy transmission characteristics of the system during the rotation and migration of the secondary coil were analyzed. The results demonstrated that, compared with the traditional unipolar DD coil, the mutual inductance of the improved DD coil is increased by over 70%. Compared to the DDQ coil, especially when the secondary-side coil is circular, the mutual-inductance value of the improved DD coil is increased by more than 4%, and the material consumption is reduced by 2%. The maximum output power of the electric vehicle wireless charging system using the improved DD coil can reach 3.46 kW, with an efficiency of 94.73%. During the process of 0°-45° rotation deviation of the secondary coil, the system output power fluctuation was only 0.12%. The efficiency fluctuation was only 0.01%, indicating that the system had high energy transmission stability.

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.

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.

This study aims to establish a dynamic theoretical characteristic model for Air springs with an additional chamber (ASAC) to elucidate the mechanism of the amplitude-frequency dependency, dynamic stiffness resonance peaks and amplitude-independent fixed-point characteristics. Firstly, the variation of dynamic stiffness under different excitation amplitudes and frequencies was experimentally studied. An analytical dynamic characteristic model of ASAC was established and nondimensionalized, based on thermodynamic principles. Then, the proposed model was verified by experiments, followed by derivations of closed-form expressions for the frequency and amplitude of the amplitude-independent point and the resonance peaks. Finally, the impact of the air chamber stiffness ratio and damping coefficients on the amplitude-independent point was analyzed. The results show that the nonlinearity of dynamic characteristics is caused by the amplitude-frequency dependency of air damping; the resonance of the air mass at a specific frequency causes resonance peaks in dynamic stiffness, whose magnitude is related to the pipe length, diameter, and excitation amplitude. Besides, the frequency of the amplitude-independent point is solely dependent on the stiffness ratio of the two air chambers, while the amplitude is equal to the stiffness of the main chamber. The model can characterize amplitude and frequency dependence and is applicable to common road excitation frequencies and amplitude ranges. Some design suggestions for ASAC were also provided.

Intersection optimization is a crucial measure for enhancing urban road traffic efficiency. Existing methods rely on analytical expressions of intersection performance evaluation metrics at the aggregate level, making it difficult to integrate optimization involving different design patterns. To improve the flexibility and scalability of intersection design optimization models, we propose a simulation-based coordinated optimization model for intersection layouts and signal control. The optimization problem employs an iterative process to develop a lane-based intersection optimization design model that generates decision variables for intersection geometric layout and signal control. Simulations by Simulation of Urban MObility (SUMO) were used as an evaluation model to provide performance metrics for the corresponding design solutions. During the optimization process, the decision variables were transferred to the simulation model through an interface and automatically adjusted to the geometric configuration and signal timing parameters in the simulation environment. A simulation was performed to derive an optimization objective value, which was used as a fitness function for the particle swarm optimization algorithm to update the decision variables of the current scheme. The updated variables were then returned to the intersection optimization model. Thus, the optimal allocation of spatial and temporal resources for the intersection was obtained through continuous iterations. The optimization model was decoupled from the analytical expression of the evaluation indicators by embedding the SUMO simulation module into the optimization framework. Through case studies in high- and low-traffic scenarios, the feasibility and optimization benefits of the model were compared and verified. Compared to traditional optimization algorithms, this method reduces delays by 5.7% and 21% under low- and high-traffic conditions. The experimental results show that the optimal geometric design and signal timing scheme derived from this method are more effective in improving traffic efficiency and reducing the environmental burden under high-flow conditions, and that the simulation-based optimization model for geometric design and signal control co-optimization is free from reliance on analytical models for operational evaluation, which makes it easy to extend the model to various design modes.