Crack detection using deep learning (DL) is important for reducing infrastructure operation risks, saving operation and maintenance costs, and promoting the intelligent transformation of the civil engineering industry. In current practice, algorithms, datasets, and evaluation metrics are the key components of DL-based crack detection. Hence, in this article, four aspects of current research are reviewed systematically. First, the development of the DL method is reviewed, and the applications of deep convolution neural networks in the field of computer vision and their significant advantages over conventional algorithms in image data processing are introduced. Second, three popular DL algorithms for crack detection are described in detail. Third, the available crack image datasets and current evaluation metrics are reviewed. Finally, recent research outcomes in this field are summarized, and future research needs are discussed. The comprehensive analyses show that, based on the backbone of convolutional neural networks, DL algorithms have been widely used to locate and classify cracks on the surface of civil infrastructure with good accuracy, although obtaining quantitative information about cracks still requires auxiliary extraction using traditional image-processing technology. Because of the high cost and technical skills required for pixel-level annotation, there is a lack of large-scale crack semantic segmentation datasets, resulting in crack detection models with poor robustness. Moreover, most researchers created their own datasets for model training and used different metrics to evaluate their model performance, highlighting the need to establish a benchmark dataset for model training and use a set of popular indices to compare the performances of different models. Crack detection robots have been developed for different types of infrastructure, and it is the development trend to improve multiscene adaptability and reduce the application cost of crack detection robots.

To further enhance the strength of the field of automotive engineering and promote the development of automotive technology in China, this study systematically analyzes the academic research status, cutting-edge hot issues, latest research results, and future development prospects in the field of automotive engineering at both domestic and international levels from six aspects:automotive electrification and energy saving, intelligent and connected vehicles, vehicle dynamics and control, automotive NVH (noise, vibration, harshness) control and lightweight control, automotive electronics and electrical (E&E) and software technology, and automotive testing and evaluation technology. Automotive electrification and energy saving constitute key aspects of pure and plug-in hybrid electric vehicles, hydrogen fuel cell vehicles, extended-range electric vehicles, and energy-saving vehicles. Intelligent and connected vehicles are objectives of the research on intelligent driving environment perception technology, autonomous driving positioning technology, intelligent vehicle decision-making and planning, motion control technology, vehicle-road coordination, intelligent safety technology, Internet-of-vehicles safety technology, and intelligent cockpit and human-computer interaction technology. Vehicle dynamics and control are addressed by the research on brake-by-wire, steer-by-wire, suspension-by-wire, and chassis-by-wire cooperative-control technologies. Automotive NVH control and lightweight control involves the prediction and optimization of automotive aerodynamic noise, NVH control of pure electric vehicle systems, acoustic metamaterials and automotive structural vibration control, automotive noise active control, and automotive lightweight and collision safety technologies. Automotive E&E and software technology is addressed by the research on automotive E&E architecture, automotive software technology and OTA (over the air) upgrade, chip and system function integration, etc. Automotive testing and evaluation technology is addressed by the research on testing and evaluation technology of fuel vehicles, new energy vehicles, and intelligent and connected vehicles. This review provides a reference for further development of automotive engineering research in China, and guidance for the innovation in key technologies of the automotive industry.

Highway construction in China has witnessed remarkable achievements, with rapid growth in the national road network and continuous breakthroughs in the key technologies. This review aims to further enhance the influential level of pavement engineering in China, as well to promote its sustainable and high-quality development. The review systematically summarizes the current status, cutting-edge issues, and future development in pavement engineering. Specifically, it covers seven research topics:highway resilience evaluation and recovery, long-life pavement structures and materials, highway energy self-sufficiency, low environmental impact technologies, the genome of pavement materials and high-throughput computations, highway digitalization and intelligence, and highway intelligent inspection and high-performance maintenance. Focusing on the fields of green, resilience, intelligence, longevity, and traffic-energy interaction, the review identifies 20 critical research topics, including factors leading to highway disasters and their mechanisms evaluation and recovery of highway resilience, key technologies for enhancing highway resilience, full-scale tests for long-life pavement structures, technologies for extending the longevity of highway structures and functions, energy harvesting technologies, energy self-sufficient highways designs, environmental impact testing methodologies and evaluations; innovative materials for low-impact pavements, warm mix asphalt recycling technology, genomic studies on pavement materials, multiscale computation for pavement materials, research on the genome of pavement materials and high-throughput computations, digital modeling technologies, digital twin simulation technologies, data-driven technologies for highway maintenance operations, ground-penetrating radar detection technologies; lightweight detection of pavement performance, strategies for detecting and recovering pavement skid resistance, and high-performance preventive maintenance technologies. The review can provide guidance for the pavement engineering development in China, offering valuable reference for the researchers and practitioners in this field.

Road information is the key to the decision-making of digital traffic infrastructure and maintenance behavior. The application of advanced detection equipment is the way to obtain road information. The high-efficiency, and non-destructive detection characteristics of 3D Ground Penetrating Radar (3D GPR) can support the acquisition of road structural defects data. This paper reviews the typical types of road structural defects, detection methods, etc. The major principles of 3D GPR technology, and its application on road engineering are introduced afterwards. Subsequently, this paper concludes the application and development of artificial intelligence technology in GPR image recognition technology according to the structural defect characteristics and recognition methods. According to the development of traffic infrastructure, this paper looks forward to the digital twin technology based on 3D GPR data, mainly introduces the modeling and model simulation methods. The review can provide basic theoretical knowledge and practical methods for 3D GPR road structural defections detection and provide guidance for digital transport infrastructure construction and road maintenance based on 3D GPR data.

Intersections are bottlenecks in urban traffic operations and can cause congestion. Traffic control is a crucial strategy in regulating traffic flow by preventing and alleviating traffic congestion; it is also a cost-effective approach. The development of communication and automation technologies has led to a new type of mixed traffic, comprised of regular vehicles (RVs), connected vehicles (CVs), and connected and automated vehicles (CAVs). This has influenced the revolution of control objects, data environments, and control methods in urban traffic control. Beyond the challenges it brings, intersection traffic control provides opportunities for innovative developments in traffic control theory and methods. Intersection control in mixed traffic situations with CAVs is still in its infancy, but it has already become a hot topic both at home and abroad. According to the right of way, research on traffic signal timing, vehicle trajectory/path planning, and cooperative trajectory-signal control for mixed traffic control at an isolated intersection, in a corridor, and in a traffic network with shared facilities was first summarized; research on intersection control with dedicated facilities for autonomous driving was subsequently introduced, including CAV-dedicated lanes, CAV-dedicated links, CAV-dedicated zones, and dedicated bus rapid transit (BRT) lanes, which provide a separate right-of-way for CAVs. Even though current literature on intersection control in mixed traffic is still scarce, a comprehensive review on the topic has revealed a number of research questions that are yet to be addressed, including:the uncertainty in the driving behaviors of RVs; compliance rates of CVs; coupling of CV/CAV longitudinal and lateral trajectories; and spatial-temporal sparsity of CVs/CAVs. Future research directions are also herein presented, including:trajectory planning of CAVs in mixed traffic; controlling mixed traffic by CAV trajectory planning; and cooperative control of CAVs and mixed traffic. It is expected that these topics will drive forward innovation of traffic control in mixed traffic with CAVs.

Road traffic safety analyses and evaluation involve the entire process of road design, operation, maintenance, and reconstruction. The objective and content of road safety analysis and evaluation vary significantly in different stages and situations. Therefore, appropriate traffic safety analysis and evaluation methods must be selected for specific problems. To identify the characteristics and application of different road safety analysis and evaluation methods, in this study, four typical road traffic safety evaluation methods, including traffic crash statistics, vehicle operating speed, traffic conflict, and driving behavior analyses, were investigated. The characteristics and applications of different evaluation methods for data collection, characteristic indexing, modeling, and practical application, along with development trends, are summarized. In the literature, certain road traffic safety analysis and evaluation methods have formed relatively mature theoretical models and have been successfully applied to road traffic safety evaluations, such as methods based on traffic accident statistical and vehicle operating speed analyses. However, safety evaluation methods based on traffic conflicts and driving behavior have problems such as data sources and indicator consistency. There are still issues to be studied in the construction of data-sharing platforms, data integrity, temporal and spatial correlation of data, and analysis of data characteristics. The operational speed analysis method must be further studied in terms of precise data acquisition, model development and calibration, and zero-collision segment reliability threshold selection. In addition, traffic conflict analysis methods have unresolved problems in estimating conflict sample size statistics, estimating the construction of dynamic traffic conflict models, screening traffic conflict discrimination indicators, and assessing the impact of driver factors on traffic conflict modeling. Driving behavior analysis requires in-depth research on data collection consistency, collection time, and index consistency. Simultaneously, with the rapid development of intelligent and connected vehicles, more data related to road traffic safety operations may appear in the future, which may change our existing consensus on road safety analysis and evaluation methods and bring opportunities and challenges to the development of new road traffic safety analysis and evaluation methods. This review improves the understanding of theoretical systems of road traffic safety analysis and evaluation methods and enables the accurate evaluation of road traffic safety problems.

Battery state estimation is the core technology of battery management system (BMS) and plays a vital role in ensuring safe and reliable battery use, thereby maximizing the battery capacity, and prolonging the service life. The battery model is the basis of the state estimation technique, which significantly affects the accuracy and timeliness of the state estimation. In this review, the most commonly used battery modeling and state estimation methods are summarized. First, various battery models, including the electrical characteristic, thermal, electrothermal coupled, and aging models, and modeling methods were systematically reviewed. Second, through a literature review, methods for estimating the state of charge (SOC), state of health (SOH), state of energy (SOE), state of function (SOF), state of power (SOP), state of temperature (SOT), and state of safety (SOS) were developed from the perspectives of remaining capacity, function estimation, power prediction, health assessment, temperature monitoring, and safety assurance. Finally, future research directions and trends in the state estimation of batteries are proposed, with the aim of providing references for the advanced and intelligent development of the state estimation of electric vehicle power lithium batteries.

The existing macroscopic homogeneous model cannot reflect the impact of multiple scales, phases, and components on the mesoscopic heterogeneity of asphalt mixtures. Therefore, for the digital transformation of traffic infrastructure, it is important to construct a digital twin model of asphalt mixture performance from the mesostructure characteristics of asphalt mixtures to realize the accurate analysis, control, and optimization of pavement performance. Based on the digital twinning technology framework of asphalt mixture performance, in this article, the digital twin model of asphalt mixture performance is reviewed based on mesostructure characteristics, the digital extraction of the asphalt mixture mesostructure and the construction of the digital twin model are introduced, and digital characterization methods of image-based technology and artificial random generation are summarized. The evaluation methods for the asphalt mixture mesostructure composition are discussed, and the characteristic parameters of the mesostructure in the asphalt mixture digital twin model are summarized. The application of the digital twin model of asphalt mixture performance based on mesostructure characteristics in macro-meso performance correlation is described in detail. Finally, the development trends, model construction, parameter characterization, and performance evaluation of the asphalt mixture performance digital twin technology are discussed. The related work can provide valuable references and insights for the construction and application of digital twinning technology systems and technology platforms for asphalt mixture performance based on mesostructure characteristics.

Cross-sea transportation infrastructure is characterized by large engineering scale, and high construction complexity. There are still some key problems in traditional operations and maintenance work, such as high comprehensive cost, low efficiency, poor accessibility, and large offshore safety risks. In order to realize the safe, reliable and efficient operation and maintenance of the Hong Kong-Zhuhai-Macao Bridge (HZMB), four common problems of the sea-crossing transportation infrastructure are summarized: low perception ability of service status, low utilization rate of monitoring information value, low efficiency of traffic risk active management and control, and low level of informationization and intelligent decision-making in operation and maintenance management. This paper analyzes the key technologies of intelligent operation and maintenance of the HZMB, and refines nine construction contents of intelligent operation and maintenance of the HZMB: the cross-sea traffic infrastructure operations based on 5th Generation Mobile Communication Technology (5G) and Internet of Things (IoT), the millimeter deformation monitoring and closed space positioning based on Beidou, the intelligent monitoring platform and big data fusion processing system for underwater structure, the inspection, testing, integration of emergency system based on unmanned aerial vehicle, the proximity detection and maintenance system for cross-sea bridges and tunnels based on the inspection robot, the service environment digitization and operational status monitoring and evaluation system for cross-ocean cluster facilities, the digital maintenance and management system for cross-sea cluster facilities based on the life-cycle hypothesis, the intelligent system of full-time traffic safety operation and rapid emergency response, the integrated operation and are summarized management platform for cross-sea cluster facilities. In addition, the overall technical architecture including device awareness layer, communication layer, basic resource layer, data support layer, business support layer, intelligent application layer and user interaction layer are also proposed. Lastly, the main technical features of intelligent operation and maintenance of the HZMB. The intelligent operation and maintenance of the HZMB can provide important reference for the operation and maintenance of other cross-sea transportation infrastructure.

The continuous increase in bridge spans, innovations in structural systems and the application of lightweight high-strength materials have led to a decline in damping for long-span bridge structures. Consequently, it has intensified vibration sensitivity of bridges to dynamic loads, such as wind, vehicles, and earthquakes. This article is intended to summarize recent research findings on damping characteristics and identification methods for long-span bridges, and to promote progress in excitation technology and damping identification techniques. The review is organized in four aspects. Firstly, a systematic examination of the structural damping theories and a comprehensive review of the state-of-the-art of damping characteristics for long-span bridges are presented. Secondly, an extensive summary of the working principles, excitation systems, and application status of long-span bridge excitation technology are provided. Thirdly, the research progress of time-domain, frequency-domain, and time-frequency domain methods for long-span bridge damping identification based on ambient excitation is deeply analyzed, and the latest achievements in the areas of intelligent, automated, and uncertainty quantification of damping identification are systematically elaborated; in addition, the damping identification methods based on forced vibration and their application status are summarized. Lastly, the paper identifies directions for further research in four areas:damping theory, excitation technology, sensing systems, and identification methods. The findings from this review offer a theoretical foundation for the development of damping theory and identification method for long-span bridge structures. In addition, it is helpful to determine the suitable value of the structural damping for dynamic analysis of long-span bridges, and it may serve as a valuable reference for design and intelligent operation and maintenance of long-span bridges.

To integrate and analyze pavement distress information inspected by non-destructive testing (NDT) and build a distress digital twin model, this study proposed a new building information modeling and geographic information system (BIM + GIS) framework for pavement distress integration and modeling. In this framework, surface and contour fitting methods were developed for modeling different types of surface diseases. Compared with traditional 2D picture patching or 3D fixed parameter modeling methods, the proposed method achieves the 3D modeling of distress. Another part of the framework proposes a 3D modeling method for concealed diseases in the pavement structure. Compared with traditional analysis and modeling methods, the proposed method transforms ground penetrating radar (GPR) data into distress models, efficiently integrates the model with the BIM platform, and lowers the threshold for the practical application of GPR data for road pavements. Based on the disease characteristics of two GPR images, the 3D reconstruction of suspected and local key areas of concealed diseases were identified and extracted. Finally, a digital twin model including global diseases was automatically constructed, which integrated the NDT data efficiently and transformed the disease in the virtual space. A heat map was drawn by GIS to feedback disease distribution and development and guide maintenance management. The experimental results show that the framework can complete the 3D digital transformation of full-field diseases. The modeling accuracies of the crack and pothole models were 80.13% and 98.17%, respectively. The model of road internal distress was compared with on-site coring to determine the position and type of disease accurately. For road surface diseases, the surface fitting model is suitable for scenes that require spatially distributed diseases. The model has network cracks and is loose, which makes it suitable for modelling diseases. The contour fitting model is suitable for scenarios that require simplicity and high efficiency. Its modeling effect is better for distresses that can expand, such as transverse cracks, longitudinal cracks, and potholes, owing to the simple physical information of the distress contour. The distress modeling based on the C-scan image is efficient for internal distress identification; however, it cannot distinguish distress types, which is suitable for the overall structural quality analysis of roads. Distress modeling based on B-scan images can subdivide distress categories; however, because it is limited by modeling efficiency, it is more suitable for the quality analysis of local key roads.

To study the dynamic response mechanism of a vibratory roller during the process of subgrade compaction and provide theoretical support for the harmonic ratio index in continuous compaction control technology, based on the finite-element method, two elastic and elastic-plastic models were established, and the acceleration signal during compaction was simulated and analyzed. The effects of geometric nonlinearity, contact nonlinearity, and material nonlinearity on the distortion of the acceleration signal were studied. The generation mechanism of the harmonic and subharmonic components in the acceleration signal spectrum was illustrated, and their variations with soil parameters, such as modulus and cohesion, were analyzed simultaneously. During this process, the elastic model was used to determine the influence of geometric nonlinearity and contact nonlinearity caused by the change in contact area before jump vibration occurred. After the jump vibration, it was utilized to illustrate the impact of the contact nonlinearity caused by separation. Furthermore, an elastic-plastic model was established to reflect the effects of material nonlinearity. The results show that geometric nonlinearity has little effect on the distortion of the acceleration signal. Material nonlinearity and contact nonlinearity caused by contact area change lead to harmonic components in the acceleration signal, and contact nonlinearity caused by jump vibration induces subharmonic components. In addition, during compaction, the modulus and yield limit of soil increase at the same time, and the contact nonlinearity caused by the change in contact area is enhanced. However, the influence of material nonlinearity is weakened. Owing to the dominant elastic factor of soil in the middle and late stages of compaction, the plastic deformation is very small. Because of the influence of local disengagement, the second harmonic component increases during the compaction process. Finally, jump vibration not only causes subharmonic components, but also leads to a decrease in the harmonic components. Simultaneously, the acceleration waveform before and after the jump vibration does not change continuously but changes suddenly.

To widen our understanding of concrete-filled steel tubular (CFST) bridge towers and promote the application of CFST structures in cable-supported bridge towers, the engineering applications and general structure details of CFST bridge tower were first sorted out. The main problems existing in the design of CFST bridge towers were discussed, and the existing CFST bridge tower structure was optimized from the perspective of structure simplification and construction efficiency. Subsequently, the local buckling behavior of steel panels and the mechanical properties of CFST towers was reviewed, and the recommended design methods were provided. Finally, the technological characteristics and economy of the CFST bridge tower were compared with those of the traditional reinforced concrete bridge and steel bridge towers. The results showed that owing to the lack of an in-depth understanding of the common bearing mechanisms of steel and concrete, the relatively weak research on the local buckling theory of steel plates is subject to unilateral concrete restraints, unclear force transfer performance for the steel-concrete interface, complexity of the current structure of the CFST bridge tower, and poor construction efficiency. The optimized PBL-stiffened CFST bridge tower has a simpler stiffening structure, a steel-concrete connection structure, and steel panels that have a stronger restraint effect on the concrete without the need for reinforcement design; thereby reducing steel consumption, simplifying the steel structure manufacturing process and on-site installation process, and improving the level of industrial manufacturing and assembly construction of bridge towers. A structural design method for stiffened steel plates that considers the influence of local buckling and the calculation method of the bearing capacity of the CFST bridge towers is safer and more reasonable. CFST bridge towers have design flexibility, construction efficiency, and high disaster-bearing toughness. The cost of construction and maintenance is much lower than that of steel bridge towers, and it can economically compete with traditional reinforced concrete bridge towers, which has broad application prospects.

Bio-oil is a green, environmentally friendly, and renewable resource with the potential to restore the physical and rheological properties of aged asphalt and improve road performance of recycled asphalt mixtures. To promote the in-depth research of bio-oil in the field of regeneration of aged asphalt materials, the source, preparation, and physicochemical properties of bio-oil were summarized, the regeneration mechanism of bio-oil on aged asphalt was discussed, the properties of bio-oil regenerated asphalt and bio-oil regenerated asphalt mixture were reviewed, and subsequent research interests were elaborated with regard to the existing deficiencies of bio-oil regenerated aged asphalt materials. The current research shows that pressed oil bio-oil has been most systematically studied for application in the regeneration of aged asphalt materials, followed by rich wood fiber plant-based bio-oil and animal manure bio-oil. However, all three types of bio-oil have broad application prospects in the regeneration of aged asphalt materials. The rich wood fiber plant-based bio-oil and pressed oil bio-oil play the role of “diluting” and “dissolving” in the regeneration of aged asphalt, which can regenerate aged asphalt in terms of both chemical balance and molecular structure repair. The animal manure bio-oil plays the role of “ dissolving” in the regeneration of aged asphalt, which achieves the regeneration of aged asphalt in terms of molecular structure repair by promoting the disintegration of asphaltene aggregates through its rich polar amide-based compounds. In addition, the rich wood fiber plant-based bio-oil and pressed oil bio-oil can be directly used for the regeneration of aged asphalt, while the animal manure bio-oil needs to be applied in combination with oil-rich regenerators to give full play to its regenerative effect. Among these bio-oils, the pressed oil bio-oil demonstrates better efficiency in regenerating aged asphalt materials. Future research is expected to be conducted as follows: establishing the correlation between the source, preparation process, and physicochemical properties of bio-oils to better screen and evaluate the applicability of bio-oils from different sources and preparation processes as asphalt regenerators; further exploring the application potential of rich wood fiber plant-based bio-oil and animal manure bio-oil in the regeneration of aged asphalt materials; focusing on the secondary aging of bio-oil regenerated asphalt and regenerated asphalt mixtures; and exploring the composite application of bio-oil in the field of regeneration of aged asphalt materials.

Vehicle movements can be highly random, and the driving style used becomes complex in urban traffic environments. To overcome the difficulties in accurately predicting vehicle trajectory in complex traffic environments, the Social Generation Adversarial Network (Social GAN) machine-learning model was used to develop a vehicle trajectory prediction algorithm named SIA-GAN. This developed algorithm was based on a spatial-temporal attention mechanism by considering a vehicle's speed, acceleration, course angle driving state, and shape size, and an interaction influence force field between the different vehicles was derived. Based on the magnitude of the interaction influence force that characterized each vehicle at the scene, different spatial attention weighing factors were assigned to the vehicles, along with a component of stressed "attention" that incorporated the information of vehicles having a greater impact on each other's driving pattern. The time attention mechanism was then combined to mine the time dependence of the vehicle under consideration in terms of the trajectory's feature vector during the observation period. To verify its effectiveness, the proposed algorithm was iteratively trained on an open-source dataset and compared with three trajectory prediction algorithms (long short-term memory (LSTM), Social LSTM, and Social GAN). The results show that SIA-GAN not only improves the convergence speed during training but also significantly reduces the average displacement error (ADE), final displacement error (FDE), average velocity error (AVE), and average course angle error (ACAE) when compared with other existing algorithms for trajectory prediction. At predicts 3.2 s, each of the aforementioned indexes decreases by 51.25%, 60.1%, 37.84%, and 13.75%, respectively, on average. The average reduction at predicts 4.8 s is 52.78%, 61.47%, 35.92%, and 9.57%, respectively. Thus, the proposed SIA-GAN trajectory prediction algorithm can accurately and effectively reflect complex spatial interaction characteristics between vehicles, enhancing the accuracy, rationality, and interpretability of trajectory predictions.

A general framework from semantic segmentation to geometric information extraction and integrated modeling was proposed based on LiDAR data to rapidly and automatically extract road geometric information and complete digital modeling. The local maximum and neighboring point mean features were concatenated as local features based on a fundamental foundation of spatial contextual features. Three-dimensional coordinates and radial distribution were combined to describe the global contextual features, and a semantic segmentation network was established. Additionally, the voxel grid filter and radius outlier removal methods were used to minimize the amount of point cloud data and remove outliers. The adaptive radius variable alpha-shapes method (VA-Shapes) was then employed to extract the road boundary based on semantic segmentation results. Furthermore, the geometric data of the road, including the road width, longitudinal gradient, and cross-slope, were obtained from the horizontal and vertical coordinates of the boundary. The in shape function and interpolation method were then applied to establish a digital elevation model. Subsequently, road routes were generated from the extracted road geometric information using Dynamo for Revit, and adaptive road components and various infrastructure components were constructed using Revit, developing a detailed digital road model. The Semantic3D dataset was utilized for training and testing to analyze and evaluate the extracted road geometric information. The overall accuracy (OA) of the proposed net is 95%, whereas the intersection-over-union (IOU) of segmented pavement is 97.9%, indicating that the proposed net could accomplish superior performance on semantic segmentation of point clouds. Compared with the traditional fixed radius A-Shapes method, the temporal complexity of the VA-Shapes method is low. In addition, the VA-Shapes method can efficiently extract the road boundary. The mean absolute errors between the extracted and manually measured geometric information are slight, demonstrating the effectiveness and accuracy of the proposed methods. The proposed process from semantic segmentation of the point cloud for geometric information extraction and building information modeling for digital modeling has the potential to build a digital model of a road in reverse, which is critical for the intelligent management of existing road infrastructures.

Aiming to meet the demands of multi-level fortification, this paper proposes a variable hysteresis rotational friction damper (VH-RF). The damper was applied to a bridge pier structure, and its multi-stage seismic performance was investigated. The configuration of the new damper, its working mechanism, and its variable hysteretic principle were discussed. In addition, a simplified analysis model was established. A finite element model of the new damper was established in the ABAQUS software. Based on the simplified mathematical model and finite element model, the hysteretic behavior and corresponding influence law of the damper were systematically studied. A hysteretic behavior model was developed for the new damper in OpenSees to reflect the multi-stage seismic performance of the double-column pier. The results are as follows:① The VH-RF can present different hysteretic behaviors under different deformations. It has a stable energy dissipation capacity under small deformations and great self-centering capability under large deformations. ② By changing the design parameters of VH-RF, the hysteretic performance of VH-RF can be adjusted. ③ A double-column pier with the VH-RF can provide a staged seismic response, thereby enhancing the seismic resilience of the bridge structure.

An asphalt mixture is a multi-level and multi-phase granular material, in which the complicated aggregate characteristics, interface effect, and migration behaviors determine the segregation properties, compaction quality, and mechanical responses. Elucidating the mesoscopic mechanism of asphalt mixtures and providing a theoretical basis for the design optimization, construction control, and performance analysis of asphalt mixtures helps improve the durability of asphalt pavement. This paper reviews the outcomes of existing research on aggregate-asphalt systems. Asphalt mixtures used in paving, compaction, and service processes are regarded as different states of aggregate-asphalt systems with different degrees of freedom of migration. First, focusing on the aggregate characteristics, the effects of geometric morphologies and size of the aggregates on the performance of the asphalt mixture are analyzed; based on this analysis, the method for the calculation of composite geometric characteristics is described. Subsequently, considering the loose aggregate-asphalt system, the mesoscopic properties are described, including the contact-friction properties of the particle system and the interface interaction of the aggregate-asphalt system. Subsequently, the evaluation methods used for the segregation of asphalt mixture are summarized; the effect of particle migration on the segregation tendency of asphalt mixture is analyzed, and the forming mechanism of mixture segregation is discussed. Considering the compaction process of asphalt mixtures, the dynamic compaction properties of asphalt mixtures and the migration behaviors of particles are discussed; the influence of the geometric characteristics and the interface effect on the particle migration are analyzed, and the compaction process of the asphalt mixture is discussed based on the migration behaviors. Considering the compacted asphalt mixture, the spatial migration behavior of the aggregate-asphalt system is summarized, and the influence of the micromigration behavior on the mechanical strength properties of the asphalt mixture at the mesoscopic level is analyzed. Finally, the asphalt mixtures used in the transportation and paving, compaction, and service processes are defined as aggregate-asphalt systems with large-degree-of-freedom, small-degree-of-freedom, and micro-degree-of-freedom migration properties, respectively. The development tendency of interface behaviors and the migration dynamic properties of aggregate-asphalt systems are summarized, and prospects for further research are discussed. This review provides a reference for the basic theoretical research on the microscopic and mesoscopic characterization and mechanical response of asphalt mixtures, which will help improve mixture design, construction techniques, and maintenance quality.

To study the cooperative performance of the sensor and the asphalt pavement structure, reveal the sensor's perceived efficiency and the evolution of the performance of the sensor's cooperative work with the mixture. Through the fusion of the asphalt mixture specimens with built-in sensors, the mechanical response test under multiple modes is carried out, and the characteristics of the strain response law under different loading modes and environmental conditions are analyzed. By using ABAQUS software, a finite element model of beam specimens with built-in sensors is established. Combing with numerical simulation and four-point bending fatigue test, the interactive influence behavior and durability under the built-in strain sensor works with asphalt mixture are explored. The results show that the sensor exhibits a good sense of timeliness under progressive loading and dynamic loading. Because of the influence of the viscoelasticity of the mixture, the strain response has obvious hysteresis. The greater the loading frequency f, the more obvious the adverse effects of hysteresis. When the information collection frequency f^' is 120 times the loading frequency f, the data collection effect is the best. The strain sensor can maintain a stable strain sensing function under working conditions such as temperature changes, water-heat coupling, and different speeds. And the measured strain response curve accords with the mechanical behavior characteristics of the asphalt pavement structure. There is obvious stress concentration in the mixture in contact with the sensor flange and the sensor's force measuring rod, which leads to fatigue cracking of the beam specimen and reduces the fatigue life of the mixture. Under repeated loads, the formation and expansion of cracks, and the slippage of the sensor and the matrix material, which make the measured strain difficult to reflect the true deformation of the mixture. And the sensing function presents three-stage characteristics of cooperative work, sensing inaccuracy, and sensing failure.

The asphalt binder is one of the most commonly used construction materials in pavement engineering, and is widely used in highways and urban roads in China. During pavement maintenance, asphalt materials exhibit aging behavior, such as hardening and brittleness, which significantly affects the operational quality and service life of the pavement. To promote the development of asphalt binder aging evaluation methods, this paper considers aging evaluation methods such as macroscopic performance testing, microscopic structure detection, and numerical simulation techniques. The macro- and micro-mechanisms that drive asphalt binder thermo-oxidative aging and ultraviolet aging were analyzed multidimensionally. The scales and application advantages of various evaluation methods were compared. The results show that current laboratory investigation methods for the thermo-oxidative aging of asphalt binders fail to accurately determine the long-term service durability. Thus, there is a great need for standardized laboratory research methods on ultraviolet aging. Thermal-oxidative aging is mainly due to high-temperature thermal decomposition that causes chemical bonds to break. Ultraviolet aging is triggered by the absorption of energetic UV light by groups in the molecule and their conversion from the ground state to the excited state, which leads to the breaking of chemical bonds. Macroscopic performance testing is a direct way to establish a link between asphalt binder aging and performance deterioration. The macroscopic performance testing methods and evaluation indicators for the two types of aging cannot be equated or replaced. Microscopic detection techniques, such as fluorescence microscopy, atomic force microscopy, scanning electron microscopy, Fourier-transform infrared spectroscopy, and transmission electron microscopy, can be used to qualitatively or quantitatively analyze the microscopic evolution process of asphalt binder aging and the aging response of modifiers. The results of molecular dynamics and micromechanical models correspond to the precise conclusions of microscopic investigations, and establish correlations with macroscopic mechanical tests to numerically characterize the aging process of asphalt binders. The results of this study can provide a reference for the microscopic evolution law and macroscopic performance properties of asphalt binders. In actual asphalt pavement maintenance, ultraviolet aging and thermal-oxidative aging occurs simultaneously. Multifactor composite aging simulations based on outdoor asphalt pavement aging monitoring should be the focus of laboratory asphalt binder aging simulation. The establishment of a multiscale asphalt aging dynamic simulation, which combines multilevel aging evaluation indicators and numerical simulation techniques, is the main research direction for the cross-applications of asphalt binder materials and numerical simulation.

To promote the intelligentization of road infrastructure and realize the intelligent perception function of road paving structure, the research progress and development trend of road paving structure design system integrating perception characteristics were reviewed. First, the connotation of road paving perception information was clarified, including road environment perception, vehicle load perception, road surface function perception, and structure response perception. On this basis, the design idea of hierarchical perception of road paving structure was proposed. Aiming at the current imperfect design of road paving perception, a preliminary construction of a road paving structure design system that integrates intelligent perception characteristics was proposed. Finally, the problems existing in the design and application of road pavement structure integration with intelligent perception characteristics were identified, and its development and application were prospected. It is found that the perception design of road paving structure should comprehensively consider factors such as road grade, regional environment, service demand, project cost and development trend, and carry out hierarchical design based on the idea of “safety first, hierarchical progression”, and integrate the characteristics of intelligent perception. The road paving structure design system should cover the perception system, material design, layout combination, perception performance retention, calculation theory and design specifications and requirements. In the road paving structure design and application process that integrates the characteristics of intelligent perception, there were problems of the design connotation unclear, the implementation standards of perception technology not uniform, the performance maintenance technology of the perception pavement structure imperfect, and the perception data processing and application not standard. In the future, it has broad application prospects in the construction of the long-term performance observation scientific network of highways, the formulation of scientific and reasonable road maintenance decisions, and the research and development of long-life pavement design methods suitable for our country.

Bridges are crucial infrastructure for traffic and transportation. The inspection of bridge apparent defects is important for ensuring public safety, extending the lifespan of bridges, and identifying risks in a timely manner. They also contribute to improving the reliability and durability of bridges during their operational phases. In recent years, with the rapid development of technologies such as computer vision and artificial intelligence, machine vision has gradually emerged as a new approach for bridge apparent defect inspection. This study conducted a detailed analysis of relevant studies in recent years to review the key techniques for bridge apparent defect inspection based on machine vision, including inspection platform development, data acquisition, image processing, 3D reconstruction, defect localization, and defect parameter quantification techniques. By analyzing the inspection process of existing research, a technical framework for bridge apparent defect inspection based on machine vision was summarized, and the functions and connections between each process were analyzed. The above-mentioned review of key techniques and summary of technical frameworks provide a reference for researchers conducting inspection work on bridge structures. Finally, based on the different levels of automation in data acquisition and defect detection observed in existing studies, this study proposes a hierarchical classification for intelligent bridge apparent defect inspection based on machine vision. This classification includes six levels: manual inspection assistance, defect inspection and localization, partially automated inspection, globally automated inspection, high-degree automated inspection, and fully automated inspection. A comparison of existing literature reveals that although research has moved beyond the traditional stage of manual inspection, it still falls short of achieving fully automated inspection. Therefore, this field has strong research value and broad application prospects.

Auto-extraction of traffic safety information from traffic alarm reception data is of great significance for handling traffic crashes and improving traffic management. This paper established an auto-processing method based on a multi-task transfer learning algorithm that includes a text pre-training model as a shared parameter layer upstream, and a multi-task parallel processing method to automatically extract traffic safety information, types, and semantics. A total of 120 191 traffic alarm reception data items were collected over two years from a traffic control center in Suzhou Jiangsu, and a standard traffic safety information database was constructed with them. The experimental results show that the key information extraction method developed in this study can better extract the time, address, and license plate information from traffic alarm reception data. The performance of traffic crash classification achieved with the method proposed in this study is better than that of the conventional deep learning model: the classification accuracy is 93%. The traffic-safety semantic analysis method is based on local feature enhancement and focus on identifying the severity of crashes and rescue demands; its recognition accuracy is 87%. The results also demonstrate that the auto-processing method of traffic safety information proposed in this study has great portability and practicality.

Effective measuring of full pavement performance is the key basis for comprehensive and systematic implementation of maintenance decisions. In this paper, we propose an automated method for pavement full-area performance measuring based on deep learning and virtual model. The method first generates pavement virtual entities based on UAV real measurement data modeling; then uses a deep semantic segmentation network to finely detect pavement performance from real measurement data; finally, the output performance feature detection results are matched and UV mapped with virtual entity data to obtain the positioning information of each performance in the virtual space and deploy them one by one to obtain damage state sensing model for the full area of the pavement. The results show that the average cross-merge ratio (MIoU) of the fully trained U-Net network reaches 0.86, showing an excellent segmentation accuracy of the pavement performance areas collected by the UAV. The pavement damage state sensing model established in this paper effectively senses 91 actual existing injuries in a pavement area of 236 m in length and 20 m in width, which enables a more systematic global characterization of pavement-wide injuries compared with the traditional two-dimensional inspection results, facilitating efficient inference of injury characteristics and location information, and realizing accurate and dynamic pavement service state assessment.

Electromagnetic waves emitted by ground penetrating radar are easily attenuated by the external environment. Complex underground municipal facilities in urban areas further increase the difficulty of cavity detection. Currently, the amplitude feature in the time domain cannot fully reflect the structure and dielectric parameters of the cavity, leading to cavity omission and misjudgment during automatic detection. To fully utilize the multi-attribute information of the ground penetrating radar signal, improve the accuracy and efficiency of automatic detection of urban cavities, the amplitude, frequency, and phase features of the reflected signal at a specific time can be extracted, and the feature fusion can improve the accuracy of cavity detection. First, the signal is converted from the time domain into the time-frequency domain using the Hilbert transform, and the amplitude, frequency, and phase profiles at a specific time are obtained by time-frequency domain calculations. Four single-feature datasets containing the original profile are obtained. Second, IA+IF, IA+IP, IF+IP, and IA+IF+IP are fused using the 2D wavelet transform method. In addition, the maximum fusion rule is used in the high-frequency domain while the mean fusion rule is used in the low-frequency domain. Finally, the YOLOv7 algorithm is used to train on these 8 datasets, and the performances of the training models are compared. The results showed that the models trained on the IA+IP and IA+IF+IP datasets had better performance than the model trained on the OP datasets; the model trained on the IA+IP datasets showed the best performance; its precision, recall, F1_score, and AP_0.5 rates were 5.0%, 7.6%, 7.8%, and 5.9% better than those of the original profile, respectively. The proposed method can depict other detailed information beyond the amplitude of the cavity and strengthen the reflection characteristics of the signal at the location of a cavity, which can improve the performance of the automatic detection algorithm.

Distributed drive electric vehicles have a high degree of controllable freedom, fast response time, high chassis drive-by-wire integration, and compact vehicle structure, making them the best platform for realizing advanced vehicle dynamics control technology. The widespread use of wired control systems, such as steer-by-wire, drive-by-wire, brake-by-wire, suspension-by-wire, and different levels of driver assistance systems, such as anti-lock brake systems, lane keeping systems, adaptive cruise control, and lane change assist systems, result in redundancies and conflicts in the control of the vehicle chassis. The distributed driving format provides more possibilities for efficient and cooperative control between the multiwire control and auxiliary driving systems. This paper highlights the latest progress in distributed drive electric vehicle integrated control technology in terms of integrated control strategy architecture, longitudinal-lateral integration control, lateral-vertical integration control, longitudinal-vertical dynamic integrated control, longitudinal-lateral-vertical dynamic integrated control, fault-tolerant control, and chassis dynamics integrated control for distributed drive intelligent electric vehicles. It also provides an outlook on the development direction from multiple perspectives, aiming to provide a reference for developing high-performance integrated chassis control technology for distributed electric vehicles.

The innovation of engineering materials is a major driver of the development for civil engineering structures, and the reformation of engineering structures continually promotes the revolution for engineering materials. Ultra high performance concrete (UHPC) is a new class of concrete that has excellent mechanical properties including high strength, high ductility, high durability, high impact resistance, etc., which is suitable for the new generation bridges with long span, light weight, and high performance. To facilitate the UHPC bridge researches and implantations, this paper systematically summarizes the recent research progresses, cutting-edge highlights, current issues, corresponding solutions, and development prospect for UHPC bridges. The paper firstly summarizes the research achievements for UHPC materials, including mix design, mechanical properties, and development of UHPC for bridges; then concludes the design theories for UHPC structures, including the contributions of fibers in flexural and shear design, impact and blast resistance, fatigue design, etc. The achievements of structural system innovations, such as UHPC bridges without stirrups, steel-UHPC composite bridges, UHPC columns for seismic resistances, UHPC bridge overlay, UHPC for bridge retrofit. In lights of the current research and applications, the major challenges and technological path for large-scale application of UHPC in bridge engineering are proposed, aiming to provide new visions and references for UHPC academic researches and large-scale applications in bridge engineering.

Intelligent hydraulic excavators are mostly used for unmanned construction in special environments. The trajectory planning and tracking of the bucket tip of intelligent excavator working devices must meet stringent requirements when performing specific excavation tasks, such as straight-line scraping, slope leveling, and fixed-point excavation. Therefore, this paper presents a trajectory tracking control strategy using variable universe fuzzy multi-parameter self-tuning (VUFMS)-proportional-integral-derivative (PID) for the intelligent excavator movement control. First, the trajectory planning of straight-line scraping and fixed-point excavation was presented in a joint space by using the cubic non-uniform rational B-splines curve interpolation method. The position sequences of each joint angle, angular velocity, and angular acceleration during the excavation operation were obtained. Second, a VUFMS-PID trajectory tracking control method capable of handling nonlinear and time-varying systems was proposed based on the variable universe algorithm. Finally, the effectiveness of the VUFMS-PID control method was verified by using AMESim and MATLAB/Simulink joint simulations and the hardware-in-the-loop (HiL) experiment platform. The results indicate that during the bucket tip motion, the planned trajectory is smooth and continuous within a small region and the error between the planned and desired trajectory is small. The VUFMS-PID control method yields good response speed and tracking accuracy, better than those produced by other methods such as conventional PID control and fuzzy PID control. In the analysis of the HiL experiment, the tracking error of the proposed control method remains confined within 20 mm during the tracking of straight-line scraping and fixed-point excavation trajectory, whereas the tracking of planned trajectory is accurate.

Electrified Road (e-Road), which is mainly characterized by Inductive Power Transfer (IPT), has been developing rapidly in recent years. It can provide dynamic wireless charging for moving electric vehicles, effectively solving the problems of long charging time and insufficient range of electric vehicles, and is an important reserve technology to support the development of electrification of road transportation.This review details the operating principles and performance characteristics of the IPT system and summarizes the charging performance parameters and TRL of existing e-Road test sections.On this basis, the main engineering problems and related research progress of e-Road are further analyzed from the perspective of infrastructure, including: ① an in-depth analysis of the impact of electromagnetic loss caused by high-frequency magnetic field through dielectric pavement materials on the charging efficiency of the IPT system and possible solutions; ② in view of the mechanical incompatibility between charging unit and asphalt pavement, the causes of the mechanical damage to the e-Road composite structure are inquired and potential optimization measures are proposed; ③ the life-cycle environmental performance of e-Road and conventional roads were assessed and compared, from which the environmental performance of e-Road is important and should be included in the full life-cycle sustainability framework for assessing the benefit of using electric vehicles.In addition, a comprehensive feasibility analysis of e-Road is conducted,in terms of policy support, safety, and price factors. An outlook is made on the future development of charging road infrastructure, including discussions over possible means to integrate e-Road with other new smart road technologies.

The traffic safety of ice and snow pavement has always been the difficult problem to be solved in transportation industry. Intelligent management of operation risk is an effective way to ensure traffic safety. The research progress of ice and snow pavement intelligent management at home and abroad was overviewed to promote the development of ice and snow pavement intelligent management technology and clarify the key issues and future direction. First, the impact of ice and snow on traffic were expounded, which reveals the relationship between people, vehicles, pavement and traffic accidents. It is clear that the root cause of the accident is the decline of pavement friction caused by ice and snow. Subsequently, the mechanism, influencing factors, prediction model and evaluation method of tire-ice-pavement friction on ice and snow pavement are discussed. Moreover, the operating principle and pavement engineering applicability of pavement snow and ice state sensing technology were compared and analyzed to put forward the technical requirements.Finally, the static and dynamic risk assessment and management methods are summarized. The static methods include meteorological method, historical accident data method and mechanical method, and the dynamic methods include vehicle dynamic method, traffic flow method and comprehensive risk assessment method. The comprehensive risk assessment method can represent the multi-factor dynamic change of road operation risk, which will be the way for intelligent management of ice and snow pavement in the future.

To verify the safety of the decision-making results of autonomous vehicles (AVs), a method for generating driving models with autonomous decision-making and interaction capabilities was proposed, and the driving models were as background vehicles (BVs) and used to build a self-evolution simulation scenario to test the continuous decision-making capability of AVs. First, based on reinforcement learning and a combination of inheritance and evolution ideas, different driving styles with autonomous decision-making and interaction capabilities were designed in this study. Second, in the model-building stage, three styles of driving models, namely, conservative, general, and aggressive, were generated and trained. The simulation training parameters for the general-style driving model were derived from the parameter distribution of a naturalistic driving dataset named highD to ensure fidelity. Finally, based on this, an aggressive-style driving model with significant aggressive features was designed and trained to enhance the complexity and testing effect of the self-evolution scenario. The results show that the distributions of parameters such as the car-following speed, distance headway, and lane-change moment time-to-collision obtained by using the highD dataset are in agreement with real data. An average similarity of 88% is observed between the general-style driving model generated and the corresponding real data, which is an improvement of 20.3% on the results obtained from the rule-based intelligent driver model (IDM). The proposed self-evolution scenario is seven times more testable than the baseline scenario composed of IDMs for the different driving models generated, as confirmed by the number of collisions in which the system under test is primarily responsible. Thus, self-evolution scenarios composed of the driving models designed and generated in this study can effectively support simulation tests for aiding the decision-making system in AVs.

Currently, China uses Vehicle Infrastructure Cooperation (VIC) as a main development path for autonomous driving. Relevant achievements are constantly updated and iterated, and the VIC industry is developing rapidly. Smart cars and intelligent roads are the core elements of the VIC industry, and an efficient synergy between the two is needed for a good foundation. VIC has the characteristics of wide coverage, a long industrial chain, and outstanding cross-border integration. Firstly, an overview of the VIC is described, and the key technologies and overall architecture are introduced. Secondly, China's basic advantages in the VIC industry are systematically presented. In contrast to America and developed countries in Europe, the advantages of state-owned land and road facilities, institutional advantages under the new national system, basic advantages of the manufacturing industry with completed categories, and potential market advantages of the world's largest autonomous driving market make a coordinated development of the VIC, the preferred way to develop intelligent transportation in China. Then, the problems in VIC development were analyzed in depth from several perspectives, such as planning, law, technology, and industry standards. Finally, countermeasures and suggestions for the development of China's VIC industry are proposed to accelerate development and promote the construction of intelligent transportation. The aim of this study is to achieve the development goal of a country with a strong transportation network, implement a dual-carbon development strategy, ensure national transportation information security, and promote a leapfrog development in transportation.

The wide promotion and application of new-energy vehicles, especially electric ones, has resulted in huge charging demands. However, owing to the unreasonable planning of some charging stations, the spatial layout and service capacity of charging stations do not match the actual charging demands, and the coexistence of a low utilization rate of charging stations and inconvenient charging for users has become a significant problem in the electric vehicle industry. Reasonable location and deployment planning of charging stations is essential to optimize the usage and charging experience of electric vehicles, alleviate the “mileage anxiety” of users, improve the utilization rate of charging piles, and optimize the urban transportation and power grid network. This problem must also be solved in the field of intelligent transportation. To provide a more comprehensive overview of the problem, the development, types, and standards of charging stations are reviewed, and the charging demand estimation and prediction methods used on the “vehicle side” “ and “station side” are described. Then, charging station planning methods are analyzed from the perspectives of siting, sizing, and solutions. Research results on multiobjective, multiconstraint, and multiperiod covering models and flow-based models, as well as their application to simulation networks and actual city environments, are listed. Finally, existing problems in current research, including the accuracy of charging demand estimation and the match of charging stations to demands, are summarized. The coordinated planning for orderly charging guidance, new energy consumption, and “cut peak and fill valley” of the grid to make full use of the characteristics of distributed energy storage and flexible charging and discharging of electric vehicles in the era of vehicle-pile-road-grid interconnection is also discussed.

To evaluate the potential of polyurethane solid-solid phase change materials (PUSSPCMs) as asphalt modifiers, the influence of PUSSPCMs soft segments on the rheological properties and mechanism of asphalt was investigated. Therefore, PUSSPCMs with different soft-segment mass fractions (P70, P75, P80, P85, and P90) and the corresponding PUSSPCMs modified asphalt were prepared as testing materials in this study. Temperature regulating properties, dynamic shear rheology (DSR), and bending beam rheology (BBR) tests were used to investigate the temperature regulation and rheological properties of PUSSPCMs modified asphalt, and the modified mechanism was analyzed by differential scanning calorimetry (DSC), Fourier transform infrared (FTIR), and atomic force microscopy (AFM). The results show that PUSSPCMs modified asphalt exhibits better temperature-regulating properties than base asphalt, as well as greater deformation resistance and high-temperature performance, while low-temperature performance is lower. The temperature regulating properties and low-temperature performance of PUSSPCMs modified asphalt are greatly enhanced as the soft segment mass fraction increases, while the deformation resistance and high-temperature performance are correspondingly reduced. P90 asphalt has the best temperature-regulating properties and low-temperature performance, whereas P70 asphalt has the highest deformation resistance and high-temperature performance. PUSSPCMs have excellent heat storage-release properties, and P90 has a greater enthalpy and a lower initial temperature than P70. Furthermore, the enthalpy of PUSSPCMs is closely linked to the temperature-regulating properties of PUSSPCMs modified asphalt. In addition, there is no new functional group between the PUSSPCMs and asphalt, which means that it is a physical modification. As the soft segment mass fraction of PUSSPCMs increases, the "bee-like structure" of the asphalt expands, while the difference between the peripheral phase state and Young's modulus decreases. These are inextricably related to the high- and low-temperature performances of PUSSPCMs modified asphalt.

An accurate and reliable evaluation is required prior to large-scale marketing of automated vehicles (AVs). However, as the system complexity increases and the operational designed domain expands, the evaluation methods employed for traditional vehicles can no longer meet the requirements of AVs. To solve the logical scenario phase safety evaluation problems, a method based on naturalistic driving data (NDD) is proposed in this paper. First, the construction process of logical scenarios based on NDD is developed, and the description parameters are analyzed. An NDD collection platform is used to gather related data, and the distribution of the different parameters is described by a Gaussian distribution. Next, the logical scenarios are discrete to obtain the concrete scenarios, and the tested algorithm is traversed in the obtained concrete scenarios using a joint simulation platform, which is a combination of PreScan, CarSim, and MATLAB. Hazardous concrete scenarios are clustered using Gaussian to build a dangerous domain. Finally, the scenario risk index, which considers both the parameter space of logical scenarios and the hazardous domain of the tested algorithm, is proposed, and evaluation examples are provided in leading braking and cut-in scenarios. The scenario risk index of the tested algorithm in the leading braking scenario is 0.409 8 and 1.08×10^-5 in the leading cut-in scenario. This means that the algorithm is more dangerous in the leading braking scenario, which is consistent with the visual results of the virtual test. By making a comparison of the scenario risk index and simulation test results, it can be found that the proposed evaluation method can be used to ensure that the safety of AV algorithms in the logical scenario phase is quantifiable and easy-to-operate, and verify that it is close to actual driving conditions.

As core factor in the complex system of human-vehicle-road-environment, drivers play the most critical role in traffic safety. Focusing on the impact of drivers' familiarity on road traffic accidents, the paper systematically combs and analyzes the relationship between drivers' familiarity and traffic accidents and the mechanism of its influence on safe driving. Firstly, based on the criterions using for identifying drivers' familiarity of distance-based scale and frequency-based scale, the correlation between drivers' familiarity with the road, the environment and the vehicle and the probability of traffic accidents was analyzed. Secondly, the mechanism that drivers' familiarity affects their safe driving was summarized from the perspectives of drivers' control behavior, route choice behavior and visual behavior. Finally, the challenges and future trends in the field were analyzed and discussed, at the same time, the method that further standardizes the index of drivers' familiarity was proposed, which provides a theoretical basis for the evaluation of the degree of driving safety and the improvement of traffic safety. Aimed at the limitation of the existing research-related and unclear problems in these study on the mechanism, further research should explore the mechanism that drivers' familiarity, such as road familiarity, vehicle familiarity, drivers' familiarity with the environment and so on, influence their safe driving from the aspects of cognitive psychology. Then drivers' familiarity needs to be quantified according to visual characteristics and physiological indicators. At the same time, drivers' familiarity should be included in the route choice model. Meanwhile, the impact of drivers' familiarity on their comfort should also be objectively measured from the perspective of physiological indicators, which provides a strong theoretical basis for the improvement of the acceptance of autonomous driving technology, traffic safety and vehicle safety.

Compared to scenarios under conventional ground motions, bridge structures subjected to across-fault ground motions have more complicated dynamic responses, larger seismic demands, and more severe seismic damage. With the development of super-infrastructure, such as the Sichuan-Tibet railway, an increasing number of bridges are facing the hazard of crossing potential faults; therefore, relevant studies are urgently needed. Based on the seismic damage of fault-crossing bridges, this paper first demonstrates the basic characteristics of near- and across-fault ground motions and introduces the relevant simulation methods. Then, studies on the analysis theory, numerical simulation, and model tests of fault-crossing bridges are reviewed, and coping strategies against the fault-crossing effect are summarized. The results show that the current simulation methods of across-fault ground motions are generally reasonable and feasible, but certain application conditions exist and improvements are needed; the studies on fault-crossing bridges mainly focus on middle- and small-span girder bridges, and the seismic responses and damage modes of bridges are affected by multiple factors, including fault type, fault-crossing location, fault-crossing angle and permanent displacement. The seismic isolation technique and the anti-collapse displacement restriction measurements for fault-crossing bridges have achieved preliminary improvements. Finally, development orientations for future studies on fault-crossing bridges are proposed in this study. In particular, more accurate and efficient simulation techniques for across-fault ground motions need to be developed; shake table array tests and numerical studies on long-span fault-crossing bridges need to be conducted, the difficulties with the experimental technique of shake table array tests for fault-crossing bridges need to be solved, the influences of ground motion parameters on different types of fault-crossing bridges need to be explicitly pointed out, and seismic isolation, anti-collapse, and resilient techniques should be further developed and experimentally validated. This study can provide a reference for the simulation of across-fault ground motions and the analysis and design of fault-crossing bridges.

Fatigue cracking of orthotropic steel deck is a common steel bridge hazard. Accurately and quickly identification and characterization of the crack geometry is important for reducing structural operation and maintenance costs and developing operation and maintenance strategies. To address the problems such as low efficiency of traditional manual inspection and harsh inspection environment, a computer vision-based method for identifying cracks and their characteristics was proposed. A combination of the object detection network YoloV5 and the semantic segmentation network U-Net++ was used for crack recognition. The objects in the images were labeled according to the structural properties of the two networks, and the parameters in the networks were trained separately. The trained YoloV5 and U-Net++ were used to detect and segment the crack image to be tested in stages. Then, the U-Net++ segmentation results were optimized by threshold segmentation and the crack skeleton lines were obtained after skeletonization. After determining the crack morphology, the calibration block identified by YoloV5 was used to solve the perspective transformation matrix and the pixel scale coefficient, followed by image correction of the crack skeleton line and determination of crack geometry features. The results indicate that YoloV5 can accurately detect cracks and calibration blocks with good stability. By optimizing the pixel size of the input for U-Net++ training, the convergence speed of the training is improved, and the network loss is reduced from 0.121 to 0.096. When solving the perspective transformation matrix, fitting the least squares solution of the matrix with all corner coordinates improves the accuracy of the image correction calibration. The corner projection error increases when the image is acquired at a greater distance and angle, and the error is more sensitive to the angle. The maximum error in the calculation of crack features for different image acquisition distances and angles is 7.2%, indicating high recognition accuracy and stability. The proposed method for identifying crack features in steel bridges can accurately calculate crack geometries and is of great practical value.

Fatigue damage is an important issue in the technical system of asphalt pavement and the corresponding fatigue design models have been established in various design methods. The fatigue evolution behavior of asphalt pavement in the life cycle was studied and 60 million loading tests were applied on the full-scale accelerated loading test track (RIOHTrack). Different fatigue damage conditions from 19 test sections were obtained and a bidirectional fatigue damage mode of asphalt pavements was presented. In this mode, under the action of a driving load, the asphalt pavement undergoes both top-down and bottom-up fatigue damage. The top-down fatigue damage is caused by a compression or shear load and can be generally manifested as the dual damage between the transverse top-down fatigue cracking and rutting deformation. Bottom-up fatigue damage is a traditional fatigue damage mode, caused by the flexural load at the bottom of the whole material structural layer. It is important to note that the flexural fatigue failure of a single structural layer does not lead to the failure of the whole structure, that is, the fatigue life of a structural layer is not equal to the fatigue life of the whole structure. In this paper, based on the bottom-up fatigue damage mode, a layer-by-layer accumulation analysis method of fatigue life is proposed to improve the assessment of flexural fatigue life of asphalt pavement.

The safety factor has the advantages of clear meaning, ease of understanding, and engineering applications. In order to facilitate readers' understanding and explanation of specific problems, this study analyzed and discussed the problems existing in the safety factor value principle and design specification of bridge structures. First, starting from many uncertain factors affecting structural safety, this study defines the base-level safety factor and adjustment factor. Second, the adjustment principle of the safety factor was discussed based on material, member, connection and structure, main bearing structure and deck structure, superstructure and substructure, structural restraint system, permanent and non-permanent members, construction stage and operation stage, structural analysis method, and value of load. Finally, based on the established principles, from the perspective of structural resistance, load action, failure mode, and analysis methods, the safety factors of the bridge design specifications were compared and analyzed. Furthermore, the rationality of the safety factor value of the specifications was discussed. In addition, the problems and suggestions for the safety factor value were put forward.