China has a large stockpile of iron ore tailings. Long-term accumulation not only occupies substantial land resources, but also poses environmental risks such as dust dispersion and heavy metal migration. Meanwhile, the production of traditional cementitious materials, such as cement, is energy-intensive and emits large amounts of carbon dioxide. This hinders the development of green and low-carbon construction. Utilizing iron ore tailings as cementitious materials offers an effective solution to ease both resource and environmental pressures. This paper systematically reviews the particle characteristics, chemical and mineral compositions, and potential cementitious properties of iron ore tailings. It summarizes the mechanisms of activity enhancement through mechanical activation, chemical stimulation, and thermal treatment. The mechanical properties, durability, and hydration products of iron ore tailings-based cementitious materials under different activation methods are discussed. The synergistic reaction potential of iron ore tailings with slag and fly ash in alkali-activated systems is highlighted. The advantages and challenges of applying iron ore tailings in the development of green construction materials are analyzed. Finally, the paper proposes that future research should focus on mineral reaction mechanisms, combined activation strategies, and multi-scale performance evaluation systems, to promote the efficient utilization and engineering application of iron ore tailings in low-carbon cementitious materials.

To promote large-scale and high-value resource utilization of iron tailings sand in the multi-layer structure of asphalt pavement for highways in Xinjiang, iron tailings sand asphalt mortar and mixtures were prepared. The optimum surface modification scheme of iron tailings sand was determined. The road performance of iron tailings sand asphalt mixtures under different content ratios was analyzed. The optimal content scheme of iron tailings sand was finally recommended. The heat transfer characteristics of iron tailings sand asphalt mixtures in heating and cooling environments were clarified. The influence laws of different factors on the damage healing performance of iron tailings sand asphalt mixtures were revealed. The service effect of iron tailings sand asphalt pavement was evaluated. The results show that 1% silane coupling agent can significantly improve the adhesion between iron tailings sand and asphalt, and the optimal modification content of modified iron tailings sand is recommended to be 60% for both AC-25 and AC-16 asphalt mixtures. Under heating conditions, the heat transfer efficiency of the iron tailings sand asphalt mixture with 60% content is higher than that of ordinary asphalt mixture, and the average temperatures of its upper and lower surfaces are 1.55 ℃ and 0.78 ℃ higher than those of the latter respectively. The light self-healing performance of iron tailings sand asphalt mixture is strongly correlated with the temperature in the crack propagation zone, and the higher the content of iron tailings sand, the better the light self-healing effect. Under the condition of 4 h light irradiation and 8 h healing, the peak load healing rate (H_P) of 60% iron tailings sand asphalt mixture reaches 54.5%. When the microwave power is 900 W and the heating time is 60 s, the H_P of the iron tailings sand asphalt mixture reaches 68.3%. In practical engineering, the average heating rate of the lower surface layer of the iron tailings sand asphalt mixture ranges from 0.157 ℃·h^-1 to 3.477 ℃·h^-1, showing better heat absorption capacity. Using it in alpine regions with large temperature differences is beneficial to improving the self-healing performance of the asphalt surface layer.

With the development of transportation infrastructure, asphalt is widely used as a key material due to its excellent performance. However, large-scale construction has led to a surge in demand for petroleum asphalt, and its non-renewability and cost pressures from price fluctuations pose challenges to industry development, making the development of environmentally friendly and cost-effective alternative materials an urgent need. Polyurethane, as a high-performance polymer material, has become an important source of solid waste due to its extensive applications in industrial and domestic fields, and its recycling has attracted significant attention. Among various methods, the alcoholysis is widely used because it can convert polyurethane into polyols for recycling through a simple process. However, the By-products generated during polyurethane alcoholysis (BPF) increase the burden of industrializing polyurethane recycling due to difficulties in their treatment. BPF is highly similar to asphalt in physical properties and apparent morphology. This paper proposes an in-depth study on using BPF as a partial substitute for asphalt. The main chemical components of BPF as ethylene oxide/propylene oxide copolyether (EO/PO copolyether) and aromatic amine compounds. Through experimental characterization and molecular dynamics methods, it was found that the blending between BPF and asphalt is primarily based on physical compatibility. The polar hydroxyl groups of the EO/PO copolyether and the amino groups of the aromatic amines in BPF form associations with asphalt molecules through non-covalent hydrogen bonding. Additionally, π-π conjugation and stacking effects occur between aromatic amines and the aromatic and colloidal components of asphalt, promoting the compatibility of BPF with asphalt. As the BPF dosage increases, the aging resistance of BPF-asphalt gradually improves, while the low-temperature crack resistance and fatigue performance show a trend of initially decreasing, then increasing, and subsequently decreasing again. In contrast, the high-temperature performance is more strongly correlated with the composition of BPF. EO/PO copolyether is the main component causing a decline in the high-temperature performance of BPF- asphalt. However, when BPF contains small-molecule polystyrene, its thermal polymerization effect becomes key to enhancing high-temperature performance. At a BPF dosage of 10%, it can serve as an effective alternative to asphalt, enabling the modified asphalt to exhibit comprehensive performance that is comparable to or even surpasses that of base asphalt in all aspects except high-temperature performance. This study validates the feasibility of using BPF as an asphalt extender, significantly promoting the resource utilization of solid waste while reducing dependence on petroleum asphalt, thereby offering both engineering applicability and sustainable development value.

Structural Health Monitoring (SHM) technology has become a key tool for ensuring the operational safety of bridges and supporting maintenance decision-making. However, the conventional bridge SHM paradigm relies on high-cost commercial sensors, closed data acquisition devices, and centralized analysis, resulting in high costs, limited scalability, and data transmission burdens. In this context, edge intelligence, with on-site information extraction and rapid response as its core, provides a new system-level pathway for bridge SHM to alleviate constraints related to cost, data transmission, and scalability. Low-cost sensors and embedded platforms respectively constitute the front-end sensing foundation and edge computing carrier of this paradigm, jointly promoting the transition of bridge SHM from the conventional paradigm characterized by high-cost device dependence, centralized raw data uploading, and cloud-diagnosis-oriented analysis toward a lightweight edge-intelligent paradigm characterized by low-cost dense sensing, scalable and controllable deployment, and edge-side information extraction. Following this main thread, this paper systematically reviews edge-intelligence-based lightweight bridge health monitoring. Firstly, the research status and paradigm evolution of lightweight bridge SHM are summarized. Secondly, from the sensing layer, the applications and technical boundaries of three types of low-cost sensors in bridge SHM are reviewed, including inertial and mechanical sensors, environmental and acoustic sensors, and optical imaging sensors. The common error sources and error calibration strategies of low-cost sensors are also summarized. Thirdly, from the transmission and analysis layer, the hardware performance of embedded platforms is systematically organized, based on which an in-depth analysis is provided of three typical architectures in lightweight bridge SHM systems: edge acquisition nodes, edge gateway nodes, and edge computing nodes. Subsequently, the system characteristics of lightweight bridge SHM is comprehensively reviewed from three dimensions: communication mechanisms, deployment architectures, and application scenarios. Finally, current technical bottlenecks are summarized, and future development trends are discussed in terms of multimodal sensing, edge-adaptive intelligence, and intelligent maintenance decision-making.

To address high system complexity, prohibitive costs, and scalability challenges in monitoring short- and medium-span bridges, this study proposes a lightweight monitoring system and evaluation framework based on a single deflection indicator. First, a hierarchical architecture integrating “precise perception, intelligent processing, and dynamic evaluation” is established using deflection as the core monitoring indicator. Second, a signal decomposition method combining variational mode decomposition (VMD) and low-pass filtering is introduced to decouple temperature effects and high-frequency noise, thereby extracting live load-induced deflection components. Simultaneously, an assessment model is developed for structural equivalent load back-calculation and stiffness evaluation based on real-time deflection data, complemented by a three-level (blue, yellow, and red) early warning threshold scheme. Furthermore, a long-term performance evaluation and service life prediction model is constructed by incorporating reliability theory and extreme value statistics. Field validation on an actual bridge demonstrates that the system ensures stable data acquisition and accurate load effect separation, with early warning responses aligning with structural behavior and long-term reliability indices satisfying regulatory standards. The results indicate that the proposed method significantly reduces system cost and complexity without compromising monitoring accuracy, providing an effective solution for the economical and intelligent monitoring of short- and medium-span bridges.

To enable real-time online monitoring of the service condition of bridge substructures under unknown operational loads, a lightweight monitoring method for substructures based on the joint load-parameter-response estimation and sparse observation responses is proposed. Firstly, a modified adaptive unscented Kalman filter with unknown input algorithm was developed, integrated with a dynamic analysis model of the bridge substructure to construct a joint load-parameter-response estimation framework suitable for real-time online assessment of substructures. Subsequently, numerical simulations were conducted to validate the effectiveness of the algorithm under noise interference and random load conditions, and the impact of initial structural parameter errors on the robustness of the algorithm was investigated. Finally, the feasibility and effectiveness of the proposed method in complex operational environments were verified using field monitoring data from an actual bridge substructure. The results demonstrate that, when only acceleration responses at the pier top and bottom are observed, the proposed algorithm can effectively achieve simultaneous estimation of unknown loads, structural parameters, and responses of the bridge substructure, with identified structural responses and unknown random loads showing high consistency with true values, and the drift phenomena caused by noise interference and cumulative errors is effectively suppressed. Under a 5% noise level and 50% initial structural parameter error, the identification errors for structural parameters, responses, and unknown loads are all within 5%, and the pier foundation stiffness and pier body stiffness converge accurately and rapidly to their true values. Field monitoring tests further indicate that the method can effectively identify the structural parameters and dynamic characteristics of bridge substructures under unknown operational loads, with the identified time-frequency domain responses exhibiting good agreement with measured data.

To address the urgent need for constructing a digital foundation with unified standards, layered decoupling, and converged integration during the digital transformation of expressways, this study systematically defines the core connotation of the expressway digital foundation and proposes a practical architecture system and evolution path. First, by systematically reviewing relevant research and practices on digital foundations in China and internationally, the connotation and characteristics of the digital foundation were analyzed from three dimensions: new-type expressway infrastructure, industry digital transformation, and operating systems. On this basis, combined with current information technology development trends and the specific characteristics of expressway operations, an overall architecture of “Cloud-Network-Map-Data-Intelligence” was constructed from the perspective of an operating system, clarifying the interaction relationships and functional positioning of each element. Following the basic principles of “unified standards and universal benefits; goal orientation and scenario-driven; overall planning and intensive reuse of existing resources; open-source openness and iterative evolution，” an implementation framework for the digital foundation construction was designed. Furthermore, the evolution form of the expressway digital foundation from Version 1.0 to Version 3.0 was proposed, and the promotion path was clarified. The results show that the constructed “Cloud-Network-Map-Data -Intelligence” architecture system achieves the transformation of the digital foundation from concept to structure, and the evolutionary forms from Version 1.0 to Version 3.0 provide actionable implementation guidance for the phased construction of the expressway digital foundation. This research systematically defines the connotation and architecture of the expressway digital foundation, forming a verifiable and practical theoretical framework and implementation reference, which can provide technical support for the digital transformation and upgrading of expressway infrastructure.

Against the backdrop of the rapid development of China's highway network, frequent geological disasters pose a serious threat to the safety and resilience of highway lifeline engineering. Traditional manual inspection and static evaluation methods are incapable of fine hazard identification on a large spatial scale and efficient processing of massive multi-source data, which can no longer meet current disaster prevention and control requirements. Accordingly, this paper proposes an integrated new prevention and control system for highway geological disasters, framed as “risk identification-targeted monitoring-accurate prediction”. By constructing a high-quality multi-source database integrating engineering design data and historical disaster records, the system adopts AI models for in-depth data learning and training, realizing geological hazard identification from linear regional screening to precise point-scale localization. On this basis, Newton force monitoring system are deployed at identified high-risk locations. Combined with accurate prediction results, active prevention and control measures including advanced early warning, pre-reinforcement and proactive regulation are implemented. The research results indicate that Newton force monitoring at high-risk sites identified by AI models enables real-time and high-precision mechanical perception of disaster evolution, and delivers effective advanced prediction. The intelligent prevention and control system established in this study promotes the intelligent transformation of highway geological disaster management from passive response to active prevention, and provides a systematic solution for improving the safety and resilience of highway lifeline infrastructure.

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.

Permafrost, as a special geotechnical medium highly sensitive to temperature, poses severe challenges to the construction, operation, and maintenance of highway engineering due to its frost heave and thaw settlement behaviors. The extreme cold climate conditions on the Qinghai-Xizang Plateau in China further intensify these challenges. This study focuses on the research status and development course of highway engineering in the plateau permafrost regions of the Qinghai-Xizang Plateau in China. It systematically reviews the core challenges faced by plateau permafrost highways in terms of engineering theory, materials, structural design, construction, and operation and maintenance, and identifies key technical difficulties in each research direction. Based on this, the three-stage evolution of China's plateau permafrost highway engineering, characterized by passive heat blocking, active regulation, and energy balance, is summarized. The technological essence, development path, and practical effectiveness of each stage are analyzed in depth, clearly presenting the iterative upgrading process of the related technologies in China. Emphasis is placed on the construction practices and key technological breakthroughs of two landmark projects: the Qinghai-Xizang Highway and the Gonghe-Yushu Highway, which demonstrate China's profound expertise and notable international influence in permafrost highway engineering. Finally, considering the current research status, national strategic demands for major projects, and the background of a warming and humidifying climate on the plateau, the paper discusses future research directions and development trends in permafrost highway engineering. It aims to provide a reference for theoretical innovation, technological upgrading, and engineering practice in plateau permafrost highway engineering.

The closed-section structure composed of steel-concrete composite wallboard can be applied to structures with ultra-large cross-sections, and have high load-bearing efficiency and constructability efficiency. To deepen our understanding of composite wallboard and promote their engineering application, the mechanical characteristics of composite wallboard structures was first analyzed in this paper, and the basic structure and design concept of various composite wallboard were reviewed systematically. Subsequently, the various connection structure of composite wallboard was compared and the formation characteristics of structural was summarized. Furthermore, the mechanical performance of composite wallboard along with its main influencing factors and corresponding design theories were reviewed. Finally, a novel steel-ultra-high performance concrete (UHPC) composite wallboard. was proposed, and the current research status on steel-UHPC composite wallboard were introduced. The results showed that steel-concrete-steel composite structure, multi-cell concrete filled steel tube, and steel shell concrete all fall within the category of composite wallboard, yet their design concept and formation characteristics are different significantly. The connection structure of composite wallboard can be classified into “direct connections” and “indirect connections”. And the strongest connection performance and ensure sufficient stiffness of the steel structure during construction are provided by diaphragm as a type of “direct connection”, albeit with certain requirements on the structural thickness. For composite wallboard with ultra-large cross-sections, the hybrid connection structure with diaphragms and “indirect connections” structure was recommended. However, the force distribution mechanisms among different types of connection structure need be further studied. Based on the related experimental research, the advantage of composite wallboard in mechanical performance is demonstrated adequately, but the design theoretical system is still incomplete. The design detail of the connection structure is the critical factor affecting the mechanical performance of composite wallboard, but current design practices do not clearly distinguish the distinct roles of connection structure. Leveraging the superior compressive strength-to-weight ratio of Ultra-High Performance Concrete (UHPC), the Ultra-High Performance Concrete Filled Steel Tube (UCFT) panel has the lighter weight and higher strength compared to conventional steel plates. Research about steel-UHPC composite wallboard has just begun. And the future work should focus on the material property for dedicated UHPC and expand experimental studies that consider connection structure as a key parameter.

To address the significant vulnerability exhibited by cable-supported bridges (including suspension bridges and cable-stayed bridges) under traffic-induced fire scenarios, and to promote the development of new theories and methodologies for fire prevention and control, as well as to enhance their overall capacity to withstand traffic-related fires, this paper provided a systematic review of the current state of research on the fire resistance of cable-supported bridges. Key scientific and technological challenges in bridge fire safety that urgently need to be addressed were also identified and summarized. Through literature review and accident sampling and data analysis, the causes of traffic fire incidents on cable-supported bridges and the damage characteristics of their key components were reviewed. The determination of fire scenarios, the analysis methods of temperature field and the main influencing parameters of cable-supported bridges were elucidated. The spatiotemporal heat transfer patterns of bridge components located in open-environment were analyzed. The damage characteristics and failure modes of cables, main girders, and towers under complex fire scenarios were investigated. Existing issues in enhancing the fire resistance of cable-supported bridges and in fire prevention and control were examined. Research findings indicate that the fire risk sources of cable-supported bridges mainly include accidents caused by vehicle collisions, spontaneous combustion and rollovers on the bridge deck, as well as accidents involving oil terminals and oil tankers. The evolution of fire scenarios and bridge disaster is influenced by multiple factors, such as the type of ignition source, fire spread patterns, fire scale, ignition location, wind field characteristics (wind direction, wind speed and wind regime), and the structural characteristics in the bridge. Key challenges in fire resistance analysis include calculating the temperature rise in the cross-section of large-diameter cable components, buckling failure of the main girder, stability of the bridge tower, and the dynamic failure mechanisms of the entire structure. Critical issues remain in constructing temperature field databases for fires, quantitatively controlling in comprehensive fire protection measures, and enhancing structural resilience for cable-supported bridges.

China has achieved remarkable milestones in road construction, with the functional connotation of roads expanding beyond basic traffic passage to a multi-dimensional system. As a core component of transportation system, road infrastructure is driving the coordinated evolution of transportation systems toward greater efficiency, safety, and comfort through iterative advancements. This paper systematically reviews the development trends and pathways of road engineering technologies, with a focus on pavement engineering, from the perspectives of durability, green sustainability, intelligence, energy integration, and autonomous operation, while identifying future directions and challenges for each domain. For asphalt pavement durability and longevity, this paper summarizes key technologies spanning material performance enhancement, pavement structure optimization, maintenance level and safety resilience improvement. Based on green development principles, this paper analyzes technological advancements in eco-friendly pavements, including permeable pavements for stormwater management, low-noise pavements for acoustic comfort, de-icing/anti-snow pavements for winter safety, low-heat-absorption pavements for urban heat island mitigation, low-carbon pavements and waste-recycled pavements. Regarding intelligent road engineering, this paper clarifies development pathways based on four core features of self-sensing, self-regulation, self-repair, and self-power supply. For energy integration, this paper examines progress in harvesting regional renewable energy and constructing self-sustaining energy systems to support roadside infrastructure and electric vehicle charging. Finally, this paper outlines pathways for autonomous road traffic operation, including multi-stakeholder collaborative governance, full-element digital twin modeling, and traffic flow autonomous optimization. The holistic advancement of road engineering relies not only on innovations in traditional pavement technologies but also on the development and breakthroughs of various cutting-edge technologies. In the era of deep integration of transportation facilities, information systems, and energy networks, this paper synthesizes core theories of each technological domain, analyzes current technical bottlenecks, and provides theoretical references and directional guidance for future road engineering research and practice.

Automated pavement crack repair offers a promising approach to significantly extend road lifespan and is crucial for intelligent road maintenance. To tackle challenges associated with real-time crack trajectory extraction and substantial sealing errors, the Automated Pavement Crack Sealing Robot (APCSbot) was developed. APCSbot integrates a real-time crack trajectory segmentation network (S2TNet) and a cross-entropy-based adaptive fuzzy control method (CEAFC) for crack sealing repair. The S2TNet incorporates Anchor Ratio IoU Sampling (ARIS) and Balanced Fine-Grained Features (BFGF) to enhance the detector's capability in predicting bounding boxes and segmenting instance binary masks, consequently improving crack trajectory extraction accuracy. The CEAFC method employs cross-entropy optimization iterations to tune controller parameters and constructs fuzzy logic to enhance repair control robustness. Furthermore, an unmanned wheeled robot framework based on four-wheel independent differential drive was established, integrating the crack segmentation network and tracking repair control methods. Extensive experiments conducted on DeepCrack, CFD, and S2T-Crack datasets demonstrate a real-time pavement crack segmentation accuracy of 80.21%. The crack sealing repair process achieves a speed of approximately 0.05 m·s^-1, with an average sealing error for slender cracks of 5.17 mm. The APCSbot showcases its accuracy and robustness in pavement crack sealing repair, thus providing technical support for intelligent road maintenance.

In order to realize the high value and diversified utilization of muddy slag, a new artificial granulation technology that used alkali-activated blast furnace slag (GGBS) was developed to solidify muddy slag and achieve high strength and high water resistance under standard curing conditions. This paper experimentally explored the effects of factors such as alkali activator type, dosage and curing time on the mechanical strength and water resistance of solidified soil particles, and determined Ca(OH)₂ and Na₂SiO₃ as alkali activators and the optimal mixing ratio. On this basis, Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM) were further used to microscopically characterize the artificial particles, revealing the strength formation mechanism and evolution law of alkali-activated GGBS solidified soil particles. The study shows that GGBS undergoes hydration reaction under the alkali activation of Na₂SiO₃; when the Na₂SiO₃ dosage reaches more than 10%, the particle strength reaches more than 5 MPa after 7 days of standard curing, and the softening coefficient is higher than 0.75. By adding Ca(OH)₂ to replace part of Na₂SiO₃, the alkali activation effect can be further enhanced, and the optimal mixing ratio is 1∶3. The particle strength is increased by more than 20% compared with the use of Na₂SiO₃ alone. Microscopic experiments show that when alkali-excited GGBS generates hydrated calcium silicate (C—S—H) and hydrated calcium aluminosilicate (C—A—S—H), an inorganic material M_x{—(SiO₂)_zAlO₂—}_n·wH₂O with a high degree of polymerization and a three-dimensional network structure is synthesized, which can fill pores and bond soil particles, significantly enhancing the strength of the soil particle blank. In this paper, artificial aggregate is made by alkali-activated GGBS solidified sludge, which has the characteristics of light weight (1.8-2.0 g·cm^-3), high strength (≥5 MPa), water resistance (softening coefficient＞0.75), low energy consumption (20 ℃ cold curing), green and environmental protection (solid waste utilization rate ≥85%), etc. It can be used to replace natural fillers in traffic roadbed base, backfill of cross-sea bridge pedestals, and protective structures of coastal highways.

The rapid construction of infrastructure will inevitably lead to large-scale maintenance. To address the issues of maintaining the structural performance and enhancing the bearing capacity of long-span bridges, based on a summary of methods for enhancement, as well as closely related technologies such as structural damage simulation, optimization algorithms, and process safety monitoring, this paper focuses on the current status and development trends of improving bridge performance by changing the structural system, systematically reviews the research status and typical engineering applications of enhancement methods such as the transformation of simple-supported into continuous structures, adding support points, cable-stayed composite systems, suspension-composite systems, hanging systems, and beam-arch composite systems, identifies two main types: the method of increasing constraints and the method of additional structures, deeply analyzes the active transformation behavior of the structural system of long-span bridges, as well as the scientific mechanisms behind changes in structural states. The paper outlines the key issues, major challenges, and future development trends related to structural system modification and reinforcement. It highlights that this method involves both benefits and risks, and points out that the compatibility between the old and new systems requires systematic and in-depth research. The establishment of mathematical models for bridge damage remains a shortcoming limiting the research on structural system modification and reinforcement. Issues such as the construction of multi-variable and multi-objective functions and the formulation of optimal solution criteria still need exploration, and breakthroughs in solving algorithms for complex stress processes remain key. With the help of smart devices and digital twins, interactive collaborative design and intelligent construction methods that involve real-time monitoring, analysis, control, and feedback should be further explored.

To thoroughly investigate the fatigue performance of the orthotropic steel-UHPC composite bridge decks with large-size U-ribs, and to address the issues of scale fragmentation and information barriers inherent in traditional full-scale model fatigue tests, a multiscale integrated experimental research method was proposed by taking the interrelationships of performance indicators among the component, subassembly, and structural scales as the starting point. This method adopted a strategy of progressive advancement, integration, and feedback of multiscale performance indicator information, designed multiscale coordinated fatigue tests for components, subassemblies, and structures, established a modular experimental research pathway, and provided effective support for multiscale experimental studies under the same research objective. The results indicate that the proposed multiscale integrated experimental research method, through the information transfer and integration pathways among components, subassemblies, and structures, forms a systematically closed-loop research framework, which can effectively resolve the problem of data fragmentation in multiscale testing. At the component scale, typical fatigue-prone details such as the UHPC material, stud connectors, and steel bridge deck all exhibit performance degradation and fatigue damage accumulation characteristics. These can be quantitatively characterized through mechanical performance degradation models, S-N curves, and damage accumulation criteria, serving as fundamental inputs for upper-scale assessments. At the subassembly scale, segment model tests can elucidate the fatigue damage evolution paths of each segment model, determine their fatigue failure modes, identify the controlling locations of the UHPC layer, stud connectors, and typical fatigue-prone details of the steel bridge deck, and establish mapping relationships between local responses and component performance indicators. This provides experimental basis and theoretical support for design parameter optimization and structural-scale fatigue response analysis. At the structural scale, in-situ monitoring results of the actual bridge show that the strain responses at key controlling locations are stable and the degree of damage is low. The performance indicators demonstrate good applicability and consistency across the component, subassembly, and structural scales.

During the construction of curved sections in super-large cross-section shield tunnels, eccentric jack loads can easily cause joint opening and offset deformation in segment joints. This subsequently induces the degradation of joint waterproofing performance. To address this problem, a complete research framework was established in this study based on a multiple sealing gasket waterproofing system ranging from the “local waterproofing mechanism” to the “global load response”. A fluid-solid coupling analysis model for joint waterproofing performance was set up, and a mechanical analysis model for multi-ring segments under complex construction loads was also established. The study systematically revealed the deformation characteristics of segment joints in super-large shield tunnels caused by eccentric jack loads as well as the degradation pattern of the waterproofing performance of multiple sealing gaskets. The results show that the multiple sealing gasket system exhibits a “gradient barrier and functional synergy” waterproofing mechanism.The waterproofing performance of the three-gasket system is improved by 23.6%~35.3% compared to the double-gasket system, and by 62.5%~76.2% compared to the single-gasket system. The waterproofing performance degradation of the circumferential joint under eccentric loads reaches 21.0%~24.0%. This degradation is most significant when the shield machine adopts an upward attitude. Finally, the degradation level of the waterproofing performance of multiple sealing gaskets in segment joints caused by eccentric jack loads was quantified. A waterproofing safety factor correction method based on the degradation rate of waterproofing performance has been established, and the adjustment values of the corresponding waterproofing safety factors considering the influence of eccentric loads have been clarified. The research can provide theoretical support for the refined design of segment joint waterproofing in shield tunnels with super-large cross-sections under ultra-high water pressure, and provide a scientific basis for shifting the safety factor of joint waterproofing performance from “empirical judgment” to “data-driven”.

This paper proposed Risk Explanation Ability Constructed Technology, an architecture designed to elicit human-like driving risk reasoning capabilities in lightweight pre-trained large language models (LLMs). The method aimed to promote their application in driving risk segment recall and automated analysis of risk causes. This method consisted of a pre-training phase and an iterative optimization phase. In the pre-training phase, a chain-of-thought (CoT) is designed according to the reasoning framework of driving risk, namely risk factor identification, interaction behavior inference, and potential risk determination. A lightweight LLM is then guided using a few-shot learning approach to generate this CoT, enabling it to initially assess driving risk levels and generate corresponding reasoning. In the iterative optimization phase, a guided learning strategy is employed. During the initial optimization stages, a “teacher” model is used to regenerate reasoning for samples with incorrect driving risk level assessments. Correct samples and regenerated samples are collected to conduct supervised fine-tuning. Experiments were conducted using the LLAMA 3-8B model as the base model and Qwen2-72B as the “teacher” model, with 7 000 naturalistic driving segments. The results show that this method improve the risk level assessment accuracy of the lightweight pre-trained model from 0.527 to 0.783. Furthermore, by comparing the similarity between manually constructed risk reasoning and model-generated reasoning, this method improves the ROUGE-L (Recall-Oriented Understudy for Gisting Evaluation-Longest Common Subsequence) metric from 0.517 to 0.616 compared to the baseline model. These results indicate that the proposed method effectively enhances the consistency between the model's reasoning and human risk reasoning. This method provides a feasible approach to automatically analyze the causes of risk, supporting the creation of driver safety profiles and the delivery of targeted safety education.

To enhance decision-making safety in highway obstacle avoidance and overtaking scenarios, and to address the limitations of existing Deep Reinforcement Learning (DRL) methods, which rely on short-term observations and lack trajectory prediction for surrounding traffic participants. This paper proposes a DRL-based driving decision-making method integrated with trajectory prediction information. First, an interactive trajectory prediction module based on a Spatio-temporal Transformer is constructed, which incorporates a spatial attention mechanism and a temporal convolutional network to extract multi-vehicle interaction features and predict the future trajectories of surrounding vehicles. Combined with these prediction results, a dynamic driving risk field is established to achieve a quantitative evaluation of potential collision risks and long-horizon driving safety. Subsequently, a DRL driving decision-making framework integrated with trajectory prediction is designed. This framework explicitly introduces predicted trajectories into the state space and utilizes long short-term memory networks to extract temporal features for optimizing the Actor-Critic architecture. Concurrently, a safety reward function is constructed based on the dynamic driving risk field, and an interpretable safety constraint mechanism is introduced to further ensure decision-making safety. Finally, experiments are conducted using the CARLA simulation platform and a self-developed Hardware-in-the-Loop testbench. The results demonstrate that the proposed Trust Region Policy Optimization with Trajectory Prediction information (TRPO-P) algorithm improves safety and traffic efficiency by 14.80% and 6.39%, respectively, compared to baseline reinforcement learning algorithms. These findings verify the effectiveness of the proposed method in enhancing vehicle safety and traffic efficiency within complex dynamic highway driving scenarios.

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.

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.

With the growing demand for comprehensive perception in vehicle-infrastructure cooperative systems, roadside multi-modal perception has become a key approach to overcoming the limitations of onboard sensing. This paper proposes an adaptive balanced optimization framework for multi-modal perception guided by a vision-language model (VLM) to enhance the performance of roadside sensing systems. The framework introduces a dynamic weight allocation module that achieves spatially adaptive multi-modal fusion through cross-modal attention and frame-level residual modeling. To address the convergence imbalance among modalities, a gradient-sensitive asynchronous optimizer is designed to finely regulate modality-specific learning rates. In addition, a lightweight gated scheduling mechanism dynamically triggers VLM calibration based on modality states and scene semantic entropy, thereby reducing computational overhead. Experimental results demonstrate that the proposed method achieves 3D object detection mAPs of 79.20% and 80.16% on the DAIR-V2X-I and RCooper datasets, respectively, outperforming comparable methods by an average of 3.9% (up to 7.51%). Meanwhile, the gated scheduling mechanism reduces the average VLM invocation frequency by 41.2%, effectively cutting redundant computation, while the overall GPU memory usage increases by only about 4.0% compared with the baseline. This work provides a novel, efficient, and scalable solution for advancing intelligent perception in vehicle-infrastructure cooperative systems.

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.

In autonomous driving tasks, the main challenge faced by visual large language models is how to perceive the surrounding world and handle complex tasks. However, currently available open-source visual large language models have not been specifically trained during the pre-training stage, resulting in weak spatial understanding and perception capabilities, making them difficult to be directly applied to trajectory planning tasks. In this paper, a dual-enhanced end-to-end trajectory planning framework featuring “spatial question-answering fine-tuning+BEV perception input” is proposed to address the trajectory planning task. Firstly, the visual large language model is trained to recognize different obstacles and spatial messages encountered in autonomous driving based on the annotations of the dataset. Subsequently, bird eye view images are generated from the surround-view cameras to reconstruct spatial information. Finally, the bird eye view, surround-view cameras, and text prompt are input into the spatial enhanced visual large language model. The model is trained through question-and-answer pairs to obtain trajectory data in a standardized format. The effectiveness of the method was verified on the nuScenes dataset and the NAVSIM dataset in this paper. The test results demonstrate that this method has excellent trajectory planning capabilities in real-world scenarios, is more in line with the driving habits of real human drivers, and has generalization capabilities across multiple scenarios.

Safety remains a core challenge in automotive engineering. In recent years, autonomous driving technology has demonstrated potential for improving traffic efficiency and driving safety. However, challenges persist, such as insufficient generalization to long-tail scenarios. Within the current connected driving environment, operational risks primarily stem from control command failures and information uncertainty. Existing safety control methods were often based on single-environment assumptions and they had difficulty handling complex scenarios with overlapping risk. In this work, a driving safety assurance framework based on a safety sandbox was proposed. The framework monitored control commands from autonomous driving algorithms, cloud instructions or human drivers in real time in a non-intrusive way. The system first performs risk assessment based on the current operational state. Then, a resilient arbitration module integrates historical arbitration outputs with the current control commands, applying a multi-level arbitration strategy and resilient intervention mechanism to ensure driving safety while preserving the original driving intent as much as possible. A scaled vehicle-road-cloud co-simulation platform and a real-vehicle testing platform were built. Experimental results show that the proposed method ensures safety under complex conditions, including static and dynamic obstacles. It also maximizes driving comfort. The method provides a feasible and efficient solution for safety assurance in intelligent and connected vehicles.

Compliance with traffic regulations by automated driving systems (ADS) is not only a fundamental prerequisite for ensuring the safe operation of automated vehicles but also a critical factor in enabling automated vehicles to adopt socially integrated driving strategies. However, researchers have yet reached a consensus on the technical requirements, improvement strategies, and evaluation methodologies for ensuring the traffic regulation compliance of ADS. To address this issue, this study systematically reviewed the current research status and challenges regarding the traffic regulation compliance of ADS. First, after clarifying the definition of traffic regulation compliance of ADS, this study presented specific requirements for such compliance and reviewed the existing traffic regulations applicable to both traditional and automated driving. It then conducted a comparative statistical analysis, using a real-world dataset, of common scenarios where traffic regulations were violated by human-driven vehicles versus automated vehicles. Second, a framework for enhancing the traffic regulation compliance of ADS was proposed, comprising four layers: ① a temporal logic-based method for formalizing traffic regulations; ② perception optimization for traffic signal recognition in ADS; ③ vehicle trajectory prediction under traffic regulation constraints; and ④ compliance decision-making in ADS. Research progress on enhancement strategies was reviewed from these four perspectives. Subsequently, regarding the evaluation of traffic regulation compliance of ADS, solutions to three key challenges were summarized: diverse evaluation metrics, feasible experimental methodologies, and the determination of evaluation thresholds. Finally, the challenges faced by the traffic regulation compliance of ADS and future development trends were analyzed and discussed. The obtained results indicate that the traffic regulation compliance of ADS is the underlying support for promoting the large-scale deployment of automated vehicles. To advance this field, future research should focus on several key breakthroughs: establishing a dynamic processing mechanism for hard and soft traffic regulations, designing a dynamic regulation update mechanism integrating natural language processing and knowledge graphs, constructing hierarchical formal models, building a fusion decision-making framework of reinforcement learning and game theory, developing an evaluation system for the traffic regulation compliance of ADS, and innovating the generation of compliance trap scenarios as well as the construction of cross-regional knowledge bases.

Aiming at the energy-saving cooperative control problem of connected vehicles, this paper proposes a hierarchical distributed model predictive control (DMPC) method to achieve multi-objective optimization of both tracking performance and fuel economy. Firstly, a state-space model for individual vehicle longitudinal dynamics and a power polynomial-based fuel consumption model are established. Considering two information flow topologies, predecessor-leader following and two-predecessor following, a complete connected vehicle system model is formulated. On this basis, a hierarchical DMPC controller architecture is designed. The upper-layer controller aims for optimal tracking performance to derive the optimal tracking control input, while the lower-layer controller focuses on minimizing fuel consumption to obtain the optimal economic control input. A novel constrained-domain construction method is introduced to coordinate these objectives, ensuring system stability while enhancing economic efficiency. Design conditions for guaranteeing platoon stability are derived and rigorously proven. Finally, simulation results under sinusoidal disturbance and emergency braking conditions demonstrate that the proposed scheme reduces fuel consumption by 2.92% and 14.75%, respectively, compared to the benchmark scheme, while maintaining satisfactory tracking performance. Overall performance improvements of 4.27% and 14.44% are achieved in the respective scenarios. This study confirms the superiority of the proposed method in terms of energy saving and system stability, providing theoretical support for energy-efficient control of connected vehicles.

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.

To address the challenge of recycling waste tires and to mitigate the problem of differential settlement in fill-cut roadbeds, this study proposed a composite reinforced roadbed system consisting of waste tire cells and geogrids. Through field experiments and theoretical analysis, the settlement evolution and stress distribution characteristics of this technology in fill-cut roadbeds were systematically investigated. Field tests were conducted along the Fenshi Expressway, where the settlement and stress responses of the roadbed were monitored during construction and post-construction stages. The results show that settlement mainly occurs during construction, while post-construction settlement is significantly reduced. Under applied loading, settlement develops more slowly and gradually stabilized. Compared with geogrid reinforced roadbeds, the composite reinforced sections exhibit markedly reduced settlement, and the stress peak was lower than that predicted by the Boussinesq elastic solution. This indicates that the circumferential confinement of waste tire cells and the tensile reinforcement of geogrids work synergistically to achieve lateral load diffusion and improve the uniformity of stress distribution. Furthermore, by treating waste tire cells and geogrids as compressive and tensile units, respectively, a settlement calculation method for the composite reinforced roadbed was established based on the two-parameter elastic foundation beam model. Validation against field measurements confirmed the reliability and applicability of the proposed method. Parametric analysis further revealed that the tensile modulus of tires has a limited effect on settlement, primarily providing lateral confinement; higher tensile stiffness of geogrids more effectively reduced settlement; and the influence of subgrade soil parameters is the most significant, with larger deformation modulus leading to smaller settlement, while higher Poisson's ratio enhances lateral diffusion and improve settlement uniformity.

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 address the issues of false detection, missed detection, poor anti-interference ability and low detection accuracy in existing object detection algorithms during the process of tunnel lining crack detection, this paper proposes a tunnel lining crack detection algorithm RSwin tailored for practical working conditions. The innovation points of this algorithm were: ① It was the first to propose the Residual Swin Transformer Block (RSTB), which had the ability to globally model and locally extract features for complex lining crack characteristics, enhancing the fusion and representation of multi-scale lining crack features and improving the model performance and generalization ability; ② It was the first to integrate the Shape-IoU loss function, optimizing the evaluation method for shape matching problems, comprehensively considering the characteristics of bounding boxes and calculating the loss value based on this, thereby improving the target box matching performance of the model in the task of tunnel lining crack recognition. To verify the effectiveness of the proposed algorithm, a total of 11 classic target detection models (YOLOv7, YOLOv8, YOLOv9, YOLOv10, Cascade Mask R-CNN, Cascade R-CNN, Faster R-CNN, FSAF (Feature Selective Anchor-free Module), FCOS (Fully Convolutional One-stage Object Detection), NAS FCOS (Neural Architecture Search Fully Convolutional One-stage Object Detection), Mask R-CNN) were used on a self-collected tunnel inspection dataset for model comparison, training, validation and testing. The training results and visualization results show that the mAP50 of the RSwin algorithm is 97.6%, which is 14.51%, 5.57%, 4.41%, 2.98%, 3.2%, 2.5%, 6.43%, 11.7%, 3.1%, 4.7%, and 2.4% higher than that of the seven comparison algorithms respectively; at the same time, it has the fastest inference speed, with a frame rate of 9.3 frames·s^-1 under the condition of 807 pixels×606 pixels. The RSwin algorithm has the highest recognition accuracy and the best comprehensive performance, and can be effectively applied to actual tunnel crack detection tasks.

Driven by autonomous driving technology, future roads will inevitably witness a traffic flow pattern of mixed autonomous and human-driven vehicles. In this human-machine mixed environment, the human-machine interaction process requires drivers to adapt, leading to adaptive behaviors in drivers that differ from those in traditional driving environments. Such behavioral changes in turn affect the operation of the entire human-machine mixed traffic flow.This study focuses on data acquisition, behavioral characteristic analysis, and micro-behavior modeling under mixed human-machine traffic flow, reviewing the research status and future prospects of adaptive behaviors in human-driven vehicles. Combining bibliometric methods, this study reviews four mainstream methods of acquiring driving behavior data, analyzes the typical characteristics and influencing factors of drivers' car-following and lane-changing adaptive behaviors in the mixed human-machine traffic flow, and based on this, summarizes three micro-behavior modeling methods for drivers. The research summary reveals that existing data acquisition methods have their own advantages and disadvantages and should be flexibly selected according to different acquisition needs and scenarios. Drivers' adaptive behaviors are mainly reflected in the process of car-following and lane-changing. This behavior is closely related to subjective factors such as drivers' trust in autonomous driving technology and driving style, as well as objective factors such as the penetration rate of autonomous vehicles and the driving environment.Currently, micro-modeling of driver behavior in mixed human-machine environments is still lacking. Existing related models can be categorized into three dimensions: adjusting parameters, introducing human factors indicators, and constructing new models. Based on this, this study further proposes prospective research directions of great academic value, including high-precision multi-modal driving behavior data acquisition, research on adaptive behavior in multi-scenario human-machine interaction, and micro-behavior modeling based on multi-level human factor characterization. These efforts aim to provide a scientific basis for the development of autonomous driving technology, realize harmonious co-driving between humans and machines, and promote the transportation system towards intelligence, safety, and efficiency. aiming to provide a scientific basis for the development of autonomous driving technology, realize harmonious co-driving between humans and machines, and promote the traffic system towards intelligence, safety, and efficiency.

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.

To explore the distortion-induced fatigue mechanism of welded details under different stiffening ribs configurations and welding types in steel bridge decks, a systematic summary and analysis of domestic and international physical fatigue tests on steel bridge decks and welded stiffening rib details were conducted. The digital twin fatigue test of the welded details between the steel bridge deck and stiffening ribs was carried out, based on numerical fracture mechanics theory and extended finite element method(XFEM). A single-deck steel-truss suspension bridge was selected as the engineering case study. A refined digital twin model of the bridge was established, incorporating 12 typical welded connection configurations between top plates and ribs. The digital twin fatigue test simulated welding thermal effects and realized the complete crack propagation process simulation under the Fatigue Load Model Ⅲ specified in the Chinese code JTG D64—2015. The fundamental concept of crack area expansion rate along with its mathematical formulation is proposed, thus providing a more comprehensive characterization of fatigue crack propagation behavior. Comparative analyses of various structural details were conducted by indicators including loading cycles and crack area expansion rate. The results were cross-validated with physical fatigue tests, achieving data symbiosis and virtual-physical integration within the fatigue metaverse. Physical fatigue test results reveal that: the fatigue strength of the welded toe and root for 8 mm thick and single-sided welded U-ribs is higher than the Category 55 and 50 specified in the Chinese code JTG D64—2015, respectively. For the 8 mm thick double-sided welded U-ribs, no crack initiation was observed at the welded root, and the fatigue strength of the welded toes on the inner and outer sides was classified as Category 70 and 90, respectively. For double-sided welded U-ribs with thickness ranging from 12 mm to 16 mm, the fatigue strength of welded toes on both sides can reach Category 100. The fatigue strength of open-rib welded details exhibits significant variability, but it is generally higher than Category 60. The results of the digital twin fatigue test indicate that, taking crack penetration through the bridge deck as the failure criterion, the fatigue strength of the 8 mm thick and single-sided welded U-ribs corresponds to Category 60 of JTG D64—2015. For the 8 mm thick and double-sided welded U-ribs, the welded toe reaches Category 80. For double-sided welded U-ribs with thickness ranging from 12 mm to 16 mm, the fatigue strength is improved to Category 90. However, the significant welding residual stress and deformation caused by high heat input should not be neglected, as negatively impact the long-term performance of the steel bridge deck. In contrast, the fatigue strength of the open-rib details is only Category 50. The results of the digital twin fatigue test extend the long-life region that cannot be covered by physical fatigue tests, revealing the intrinsic principles of the fatigue metaverse's native data for the steel bridge deck details. These findings provide important insights for analyzing the distortion-induced fatigue mechanisms of steel bridge decks and serve as key evidence for fatigue-resistant design.

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.

To address the limited perceptual range of physical sensors in the digital twins of engineering structures, a smart virtual sensing technology based on an AI-enhanced reduced-order model (AI-ROM) is proposed. This technology integrates multi-source physical sensor data to predict the global response of complex structures in real time. Methodologically, the structural response is represented as a linear combination of bases using reduced-order modeling theory. This formulation transforms virtual sensing into the optimization of the combination coefficients constrained by the measured response data. Accordingly, a deep-learning loss function capable of handling multi-source physical virtual sensing was constructed. An intelligent model based on a standard attention mechanism was developed for predicting the combination coefficients, enabling the accurate reconstruction of the global structural response using limited local monitoring data. The effectiveness of the proposed method was validated through virtual sensing of a full-scale compression-bending test of the steel-concrete composite tower wall of the Shiziyang Bridge. A refined numerical model was used to generate the full-process response data under combined compression and bending loading conditions to train and deploy the AI-ROM model. In the experiment, measurements from six displacement meters and 17 strain gauges were used as the constraints for response reconstruction. The results show that for highly discrete strain fields, the relative reconstruction error of the AI-ROM is only 9.1%, representing a 63.5% improvement in the accuracy of the refined finite element model. Using the intelligent virtual sensing technology, a sensor optimization method that considers the regional clustering of the analysis domain is further proposed. The spatial distribution of key monitoring points was determined by iteratively evaluating the reconstruction accuracy. In the full-scale tower wall test, this algorithm reduced the number of strain sensors by 58.8%. The proposed intelligent virtual sensing technology facilitates the integration of wide-area perception and real-time simulation in the digital twins of engineering structures, offering more comprehensive information support for safety risk assessments in the operation and maintenance of buildings and infrastructure.

Influenced by complex environmental loads, the mechanical properties of structures evolve throughout their service life. Accurately identifying the characteristic parameters of in-service structures and evaluating their safety performance remain major challenges. This study proposes a hierarchical Bayesian dynamic updating method for time-varying reliability assessment tailored to structural dynamic digital twins. A digital twin model of the physical structure is first established using finite element theory. Based on the monitored dynamic responses of the physical structure, the modal parameters of the digital model are identified through Bayesian inference. A hierarchical Bayesian framework is then constructed to estimate the time-varying physical parameters, whose statistical characteristics are extracted using the expectation-maximization algorithm. The digital model is subsequently updated using the principle of maximum entropy. Leveraging probability density evolution theory, a new approach for evaluating the dynamic reliability of time-varying structures is developed. Compared with traditional methods, the proposed method more comprehensively accounts for the temporal variability of service-related parameters and their associated uncertainties. The effectiveness of the approach is validated through numerical studies on shear-type structures, bridge models, and laboratory experiments, with comparisons drawn against conventional methods. Results demonstrate that the proposed method provides a more accurate acquisition of the parameters of the digital model and significantly improves the handling of parameter variability, thus ensuring the robustness of structural dynamic reliability assessment results.

This study proposed a smart diagnostic method based on Digital Twin (DT) for achieving precise perception and rapid evaluation of in-service bridge safety. Considering structural characteristics and deterioration trends, a “data-driven and knowledge-constrained” DT model for bridge safety assessments was established. The developed model incorporates virtual-real mapping, dynamic interaction, and iterative updating mechanisms. Driven by high resolution 2D images, 3D point cloud data, and other multi-source data, a multi-modal data fusion system was constructed for bridge safety assessments. An object detection algorithm with coordinate attention mechanism and point cloud registration techniques were established to enable rapid and accurate identification and analysis of surface defects and their spatiotemporal evo-lution. By utilizing bridge inspection standards and expert knowledge, an expert knowledge model based on fuzzy logic reasoning was developed for diagnosing intelligent diagnosis of bridge service states, significantly improving the efficiency and accuracy of structure deterioration perceptions and safety assessments. A case study on a bridge in Anhui Province demonstrated that the proposed method could effectively identify defects and deformation conditions, and conduct intelligent diagnosis of bridge safety. The proposed framework is of great significance for advancing intelligent operation and maintenance of bridges.

To develop a low-carbon and sustainable life-cycle maintenance strategy for network-level bridges and enhance the comprehensive benefits in environmental, economic, and safety dimensions, this study proposed an optimization method based on Deep Deterministic Policy Gradient (DDPG) reinforcement learning. This approach integrates bridge structural characteristics, network topology, traffic data, and risk attitudes to construct a reinforcement learning agent that systematically optimizes bridge maintenance decisions. Environmental, economic, and safety indicators were evaluated by comprehensively considering resource consumption, the consequences of potential structural failures, and impacts of vehicle detours, thereby quantifying the contribution of bridge maintenance to sustainability performance. In constructing the reward function for reinforcement learning, sustainability indicators were transformed into monotonically decreasing utility values to reflect the preferences and constraints in the optimization process. Based on a reinforcement learning framework, a DDPG agent with deep neural networks was designed, leveraging the structural degradation features and traffic data of network-level bridges for trial-and-error learning to progressively optimize maintenance decision strategies. The validation results indicated that the reinforcement learning method developed in this study achieves a better balance between environmental, economic, and safety metrics. Through trial-and-error learning, the agent captures the performance variation characteristics of bridges, optimizes maintenance priorities, and allocates resources efficiently. This approach provides a scientific basis for advancing intelligence and sustainability in infrastructure management.