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

Connected and automated vehicles facilitate rapid, collaborative, and shared travel by integrating information flows in the cyber-communication layer with physical entities in the vehicular layer. However, challenges such as channel fading and resource scarcity persist in the cyber communication domain. These issues contribute to system nonlinearity, random disturbances, and behavioral uncertainty during vehicle operation, which thereby undermines platoon stability. Hence, the development of platoon control methods has become a critical area of research for ensuring vehicle safety and mitigating the adverse effects of communication delays on vehicle traveling states. Safety platoon control focuses on considering the spatiotemporal value of the traveling state under communication delays from the perspectives of spatial computing loss and information timeliness. Significant research value and practical implications for enhancing the intrinsic safety of vehicles and the robustness of communication networks are offered. First, by acknowledging the spatial correlation of information transmission, a Spatial Adjustment intelligent driving model (SA-IDM) was developed to normalize the communication structures among multiple preceding vehicles while adjusting the state information based on signal attenuation. Second, by recognizing the temporal correlation of information transmission, the age of information (AoI) was introduced to quantify the timeliness of state information. A frequency domain analysis was conducted to assess the string stability of the SA-IDM and derive an AoI threshold that indicates when information is outdated. This threshold was converted into a time value for the entire transmission process. In addition, a retransmission strategy using an information update roadside unit was proposed. Finally, the model parameters were calibrated using real vehicle tests and a genetic algorithm. Numerical simulations confirm the effectiveness of the proposed model. Results show that SA-IDM can suppress the impacts of disturbances from the leading vehicle of a platoon, and when the AoI threshold is 0.2 s, it can effectively measure the value of time. The state information retransmission strategy ensures the timely acquisition of information by the platoon under a high delay occurrence rate (DOR). When the DOR=20%, the reduction in the average spacing deviation of the SA-IDM compared with that of the IDM is 33.5%. When the DOR=70%, the average spacing deviation reduction achieved by the information update-based roadside unit state information retransmission strategy, compared with the maximum AoI retransmission strategy, is 21%. In addition, vehicles located at the front and rear of the SA-IDM platoon exhibit weaker safety, thus necessitating more frequent information transmission.

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

The field-of-view of a roadside sensing unit (RSU) is limited. In addition, static and dynamic occlusions in traffic scenarios may adversely affect the perception accuracy of an RSU. In such cases, even if multiple RSUs cooperate, the RSU-captured trajectories may deviate from the ground truth and vary with the RSU placement, thereby impairing the performance of the RSU in conflict risk monitoring. Considering the need to optimize the RSU configuration prior to real-world deployment, this study proposes a framework for virtually assessing the RSU risk monitoring performance at intersections using trajectory data. To emulate the real-time risk surveillance process of an RSU, an agent-based method was developed to identify and analyze multimodal conflict risks at intersections. The trajectory data were integrated with a three-dimensional intersection model to reconstruct dynamic traffic scenarios. After the parameterization of the RSU model, a virtual procedure was established to simulate cooperative trajectory perception. To address the time efficiency issue of collecting cooperatively perceived trajectory data using continuous simulations, a deep neural network (DNN) was introduced to generate co-perceived trajectories using prior knowledge (raw trajectory data and probabilistic occupancy maps) and simulated trajectory data as the input and response, respectively. The raw and RSU-captured trajectory data were used separately to compute the time-to-collision (TTC) indices of conflict events, and TTC-based cross-entropy was introduced to quantify the risk monitoring performance of the RSU. The proposed framework was tested on trajectory data collected from Intersections A and E in the open-source CitySim dataset. The results indicate that RSU risk monitoring performance is closely associated with RSU placement. The average root mean square error of the trained DNN model on the test dataset was 0.353, which implies that the DNN model can generate RSU-captured trajectories. The TTC-based cross-entropy obtained by the DNN-based method was significantly and positively correlated with that obtained by time-consuming simulations and outperformed the simplified RSU model that did not consider perceptual constraints. The simulation approach takes approximately 10⁰ -seconds to obtain a single trajectory, whereas the DNN model achieves this in just 0.01 s, thus highlighting its significant speed advantage (16 GB of RAM, Intel^® Core^TM i7-12700H@2.30 GHz CPU, and RTX-3060 GPU). Compared with existing methods, the proposed framework considers both the perceptual constraints of the RSU and multimodal conflict risks at intersections. The proposed framework can be used to pre-estimate RSU risk monitoring at specific intersections and will be valuable in the future construction of digital transportation infrastructure.

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

To analyze the relationship between drivers' cognitive control and driving, clarify the differences in driving performance among drivers in different cognitive control groups, and explore the mechanism of the influential relationship of “cognitive control-vehicle control ability-driving performance,” this study recruited 49 young subjects and carried out a psychological experiment that simultaneously considered two typical cognitive control abilities: inhibitory control and working memory. Based on the test results for inhibitory control and working memory, drivers were divided into four groups: high inhibitory control and high working memory (Group A), high inhibitory control and low working memory (Group B), low inhibitory control and high working memory (Group C), and low inhibitory control and low working memory (Group D). Based on the driving simulator, a driving experiment was conducted by setting up road risk scenarios, including expected and unexpected event types. The risk event response assessment of the entire road was used to represent the overall driving performance. The average speed on a normal road section without risk events was used to represent the longitudinal control ability of the vehicle, and the standard deviation of the lateral lane position was used to represent the lateral control ability of the vehicle. The results show that the risk driving tendency of unexpected events is 15.1% higher than that of expected events. In terms of risk response assessment, Group A significantly reduces the risk driving tendency by 16.8% compared with Group D. Moreover there is no significant interaction between event type and cognitive control on the risk response. In terms of vehicle control ability, the average speed of Group D is a significant 16.2% higher than that of Group A, whereas there is no significant difference in the standard deviation of the lateral lane position among each group. The vehicle longitudinal control ability plays an intermediary role between cognitive control and the risk response, thus forming a simple relationship framework of “cognitive control-speed control-risk response.” Therefore, the significant differences in risk response and average speed are both caused by simultaneous changes in the two cognitive control abilities, and drivers in different cognitive control groups mainly cause differences in risk response through longitudinal control. These findings provide references for early interventions in driving safety, cognitive training, and the design of vehicle-mounted safety systems.

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

Abnormal driver behavior (ADB) is a common and unsafe driver behavior that can easily lead to traffic accidents and poses a serious threat to road safety. To comprehensively, accurately, and swiftly detect ADB, this study proposes an abnormal driver behavior identification and quantification model based on hybrid contrastive learning (ADB-HCL). The proposed model innovatively incorporates a Mix-up data augmentation strategy into the contrastive learning framework and thereby generates mixed samples of normal and abnormal driver behaviors. This expands the coverage of the driver behavior feature space, thus enhancing the ability of the model to identify unknown or rare abnormal behaviors. In addition, by generating a template feature representing normal driver behavior and calculating the distance between the test driver behavior and the template feature, the model quantifies the degree of abnormality and hence overcomes the limitations of traditional methods that provide only discrete outputs for known abnormal driver behaviors. Experimental results based on the DAD(Driver Anomaly Detection) and AIDE(AssIstive Driving pErception) datasets show that the ADB-HCL method excels at identifying unknown abnormal driver behaviors and achieves an accuracy of 86.73% with an inference time of only 10.75 ms, which represents a 6% to 15% improvement over existing methods. The quantification results of driver behavior abnormalities indicate that this method enables the fine-grained quantification of abnormal driver behaviors. The findings demonstrate that ADB-HCL has significant advantages in terms of the comprehensiveness, accuracy, granularity, and speed of detecting abnormal driver behaviors, this showcasing the potential applicability of ADB-HCL in vehicle active safety technologies.

Compensatory behavior is crucial for drivers to balance their workloads and driving tasks, maintain driving performance, and enhance safety in complex traffic scenarios. To investigate the characteristics of drivers' compensatory behaviors under high-workload scenarios and quantify the effectiveness of compensatory behaviors in enhancing driving safety, a driving simulation experiment with a combination of 3 secondary tasks and 3 follow-up scenarios was designed, during which 42 subjects were recruited to perform driving trials. First, six indicators, including the braking deceleration, time headway, and subtask completion rate, were selected to construct an index mechanism for measuring compensatory behavior, and a multiway analysis of variance was performed. Then, a safety margin index was extracted, and a generalized linear mixed model for predicting the collision risk was constructed using the logit connection function. Finally, the drivers' compensatory behavior positivity was ranked using a technique for order preference based on similarity to an ideal solution, and the drivers were categorized into two groups using a clustering method. Subsequently, a driving safety evaluation model was constructed based on the extreme gradient boosting model, which used the optimization algorithm to find the optimal hyperparameters, including the maximum depth, learning rate, and number of trees. The results show that subjects adopt compensatory strategies such as decreasing speed, increasing headway, elevating effort, and interrupting subtasks in high-load scenarios. The model results verify the effectiveness of the compensatory behavior. By increasing the headway by 46.2% and 49.5% under the general and emergency braking scenarios of the front vehicle, respectively, drivers can maintain the same safety status. The model recall and accuracy increased by 7.13% and 3.08%, respectively, after incorporating driver compensatory behavioral characteristic variables. These findings provide a reference for driving behavior regulation and traffic safety evaluation in complex environments.

As typical weaving sections, freeway diverge areas often experience hazardous behaviors, such as sudden braking and lane changes, that can easily lead to severe traffic conflicts. To effectively evaluate the safety levels of freeway diverge areas, this study addresses the optimization of conflict data and proposes a conflict dataset screening method as well as an extreme value modeling approach that integrates both conflict probability and severity. For conflict probability, the time difference to collision (TDTC) was utilized as an indicator to analyze three collision scenarios in freeway diverge areas and calculate the time thresholds for conflict events. Regarding conflict severity, delta-V was introduced to filter out conflict events with the potential to result in fatalities. To validate the effectiveness of the proposed fusion conflict data in enhancing the accuracy of safety assessments, the proposed dataset was compared with a traditional dataset that considered only the conflict probability. Block maximum extreme value models were constructed for both datasets to conduct safety assessments. The results show that the extreme value model constructed using the fused dataset achieves extreme value reproducibility, exhibiting a mean absolute error of 0.046 and a root mean square error of 0.058. Its estimation accuracy and fit to actual conflicting data outperform those of traditional datasets. When applied to predict the collision frequency in each diverge areas, the proposed model demonstrates enhanced evaluation reliability and more accurate accident predictions, aligns better with actual conflict situations, and improves prediction reliability.

To investigate mental rotation effects on drivers navigating a highway spiral tunnel, a naturalistic driving study was conducted with 30 participants. Visual and electrocardiographic (ECG) data were collected from drivers traversing curved and spiral tunnels. A factor model was constructed to identify the sensitive indicators of the drivers' mental rotation effects. Subjective workload intensity and curvature-slope illusion severity were compared, and a data envelopment analysis (DEA) model was established to identify and investigate the characteristic impacts of the driving environment in a highway spiral tunnel on the drivers' mental rotation effects. The results revealed that the sensitive visual indicators of the drivers' mental rotation effects included fixation duration, pupil diameter, saccade duration, and saccade amplitude. Sensitive ECG indicators included the HR, SDNN, LF/HF, and SampEn. Significant differences in visual and ECG performance were observed between curved and spiral tunnels. Drivers in a spiral tunnel exhibit longer fixation durations, larger pupil diameters, longer saccade durations, smaller saccade amplitudes, higher HR, higher LF/HF ratios, lower SDNN, and lower SampEn values than do those in a curved tunnel. Moreover, drivers in spiral tunnels demonstrate increased visual attention levels, longer recognition response times, greater difficulty in information perception and processing, increased mental effort, decreased visual search efficiency, and weakened discrimination abilities. In addition, they are more prone to feelings of tension and anxiety, which leads to decreased autonomic nervous system regulation and reduced heart rate variability. Furthermore, subjective evaluations by the drivers indicate that the curvature slope illusion severity and subjective psychological workload are greater in the spiral tunnel than in the curved tunnel. A comprehensive evaluation based on DEA reveals that the curved tunnel outperforms the spiral tunnel in terms of technical, scale, and overall benefits. These results suggest that the driving environment in a spiral tunnel adversely affects the visual and psychological performance of drivers and thus necessitates the implementation of improvement measures.

Pedestrian injuries caused by the ground in pedestrian-vehicle accidents cannot be ignored. Hence, a method of pedestrian injury protection based on pedestrian landing mechanism prediction is proposed to reduce the risk of pedestrian injuries caused by the ground in pedestrian-vehicle collisions. In this study, a pedestrian landing mechanism prediction model was constructed based on 1 300 groups of pedestrian-vehicle collision accident data. Vehicle braking rules were established according to the prediction model, and vehicle motion was controlled according to the established rules. The 720 simulation results show that the proposed method can accurately predict 87.5% of the pedestrian landing mechanisms, and the prediction accuracies of M1, M2, M3, M4, and M5 are 75.29%, 95.75%, 91.3%, 94.55%, and 100%, respectively. The ground-related WIC and HIC were reduced to 61.7% and 37.5%, respectively, and the risk was reduced by 8.33%. Further analyses were conducted to determine the reasons for inaccurate predictions of specific landing mechanisms, and the vehicle braking rules were updated based on these results. The improved method can significantly improve the prediction accuracy of M1: its prediction accuracy is 92.94%, and its use risk is reduced to 5.83%. The WIC and HIC caused by the ground decreased to 70.3% and 43.6%, respectively. Thus, the results of this research provide new ideas for predicting pedestrian ground collision injuries and low-risk methods for pedestrian protection against intelligent vehicles.

Vulnerable road users (VRUs) face high risks of injury and death from traffic accidents. The current VRU head protection program relies on a single head impact velocity and injury assessment criteria that fail to account for brain tissue strain. This limitation affects the effectiveness of simulating real-world impacts and the accuracy of head injury risk assessments. In this study, the VRU head impact boundary conditions were extracted based on the reconstruction of 40 real-world pedestrian VRU head impacts. Using the Total Human Model for Safety (THUMS) head finite element model and headform impactor, this study explored the effects of real head impact boundary conditions on head kinematics and injury under procedural test scenarios and compared these conditions with test procedure scenarios. The results indicate that the peak linear acceleration in the current test procedure scenarios is higher; however, the peak rotational velocity is significantly lower than that observed in real-world accidents. Different impact locations have significant effects on the head kinematics and injury response parameters, particularly in stiffer areas, such as the windshield edges and lower right corner, where the injury risk under regulatory conditions is higher than that in real accident cases. In contrast, the opposite is true in other windshield areas. This study suggests that future programs or virtual assessments should diversify the head impact boundaries and injury assessment criteria to consider the differences in impact locations and the effects of head rotation on brain tissue injuries. For most windshields (non-edge areas), increasing the linear velocity enhances head rotation, and rotational injury assessment criteria should therefore be introduced. For future virtual assessments, injury criteria based on brain tissue strain should be used to assess VRU head injury risk in real accidents more comprehensively and accurately.

Head-impact deceleration conditions resulting from traffic accidents and falls have a significant probability of inducing brain contusions, posing a significant threat to the lives of low-age children. However, specific characteristics of brain contusions in low-age children remain unclear. Given the challenges in obtaining samples from child cadavers, this study utilized piglets as surrogates to investigate the characteristics of brain contusions under head-impact deceleration conditions through a combination of tests and simulations. First, free-fall drop impact tests were conducted on piglets to induce brain contusions, and a detailed analysis of their characteristics was performed. Subsequently, a finite element model of the piglet head was developed to simulate the impacts based on the experimental conditions. By comparing the simulation results with the brain contusion areas obtained from the tests, mechanical metrics that could describe the brain contusions were identified. The injury-induced process was also analyzed based on the contours of these identified metrics within the brain tissue. Finally, the characteristics of brain contusions in piglets were compared with those observed in real-world cases of low-age children obtained from hospitals. These results indicate that brain contusions typically manifest in the surrounding areas beneath the impact site and can be categorized as coup injuries. Three mechanical metrics-intracranial pressure, maximum shear strain, and maximum principal strain of the brain tissue-are found to be effective in describing brain contusions. The brain contusion area is mainly influenced by the relative displacement between the skull and the brain caused by skull deformation. Higher positive intracranial pressure and larger shear strain or principal strain in brain tissue can lead to brain contusions. Additionally, the inertia of brain tissue can exacerbate the severity of brain contusions. The characteristics of brain contusions in piglets are consistent with those observed in real-world cases of low-age children obtained from hospitals. This study contributes to the development of injury criteria for brain contusions in low-age children and proposes relevant prevention strategies.

The corrugated beam guardrail is one of the most widely used types of guardrails in China. When the vehicle deviates from the normal lane as a result of malfunctions, accidents or improper operations, the guardrail prevents the vehicle from running off the road, thereby mitigating the occurrence of severe accidents. Therefore, the eligible containment performance of the guardrail is critical for ensuring roadway safety. At present, full-scale impact tests and finite element analysis are often employed to evaluate the containment performance of guardrail, which are costly. To address this issue, a simplified computational model was developed to estimate the maximum lateral displacement of the corrugated beam guardrail under the vehicle impact in this paper. The global coordinate system of the guardrail and the local coordinate system fixed to the vehicle were established, and the corrugated beam guardrail was simplified into a cable that only bears tension. Then, by integrating equilibrium conditions of forces, constitutive relationships of materials and members, and displacements coordination conditions, the static analysis of the rails and posts and dynamic analysis of the vehicle were carried out and then a nonlinear equation set for solving the dynamic response of the overall system was established. The nonlinear equations were solved by the Newton method and central difference method. Finally, the simplified model proposed was validated using the general-purpose finite element software LS-DYNA. The theoretical calculation results were all lower than the simulation results, with an average error of 13.11%. The results show that the proposed theoretical model can accurately estimate the dynamic maximum lateral deflection of the corrugated beam guardrail and greatly reduce the time cost of the calculation, which provides a convenient method for designing and evaluating the containment performance of the corrugated beam guardrail.

This study investigates the location allocation problem of emergency response stations for hazardous material transportation accidents. First, to accurately describe the full coverage of rescue stations on roads without redundant coverage, a nonincreasing continuous form of the road full coverage degree function was proposed, and an improved generalized maximal arc-covering location allocation (IGMACLA) model was established under deterministic conditions. Second, considering the uncertainty of the emergency response time and the feasibility of the newly proposed full-coverage road degree function in addressing uncertain response times, an IGMACLA model was constructed based on a distributionally robust optimization (DRO) approach with fuzzy chance constraints. Third, an approximation method was employed to formulate the original DRO model as an integer second-order cone programming model under both zero-mean bounded perturbation and Gaussian perturbation ambiguous sets, which were further solved using the branch-and-cut algorithm. Finally, the effectiveness and reliability of the proposed models were verified using numerical examples, and the advantages of the distributional robust optimization method over traditional robust optimization and stochastic programming methods were analyzed. The computational results of the IGMACLA model based on DRO are relatively conservative compared with those of the deterministic IGMACLA model but present strong robustness. As the tolerance level increases, the optimal objective value of the IGMACLA model based on DRO, which is called the lower bound value of the total coverage effect of emergency response stations, increases (or decreases). By incorporating partial probability distribution information, the DRO approach significantly outperforms the traditional RO approach. Compared with the stochastic programming method, the DRO method pays a small price to resist the uncertainty associated with unknown complete probability distribution information. As more information about the partial probability distribution in the fuzzy set is utilized, the DRO method becomes less affected by changes in the tolerance level and distributional ambiguity.

The viscoelasticity of asphalt mixtures should be carefully considered while conducting mechanical response analysis and structural design of pavements. The objectives of this study were to reveal the viscoelasticity evolution of rubberized asphalt rejuvenated reclaimed asphalt pavement (RAP) binder under moisture-thermal-radiation multifield-coupling effect from the physical-chemical perspective, to investigate the viscoelasticity of rubberized asphalt rejuvenated RAP binder based on rheological tests, and to study the viscoelasticity evolution of rubberized asphalt rejuvenated RAP. First, a Xenon lamp environmental aging chamber was developed to conduct indoor moisture-thermal-radiation multifield-coupling accelerated aging. Next, the physicochemical evolution characteristics of the binders at various aging stages were analyzed using Fourier-transform infrared spectroscopy and gel permeation chromatography. Subsequently, basic rheological tests, frequency sweep tests, and small-strain creep tests were conducted to investigate the viscoelasticity of rubberized asphalt rejuvenated RAP binders. Furthermore, the viscoelasticity evolution of asphalt mixture at various aging stages were revealed under dynamic- and static-loading conditions. The results show that the incorporation of crumb rubber decreases carbonyl factor, sulfoxide factor, and long-chain factor levels of the asphalt binder by at least 5%, and decreases the variation rate of large molecular content by more than 25%. For the asphalt binder, adding crumb rubber and RAP can increase the failure temperature by one performance grade on average, decrease the phase angle by 8% on average, decrease nonrecoverable creep compliance by 80% on average, increase recoverable creep by 300%, and increase the complex modulus between 10^-3-10 Hz by more than 30%. For the asphalt mixture, adding crumb rubber and RAP can increase the dynamic modulus in the high-frequency range by an average of 14%, decrease the phase angle between 0.1-10³ Hz by an average of 7%, and decrease the critical frequency of the viscoelastic state by at least 50%. This study provides a foundation for the material design and engineering application of rubberized asphalt rejuvenated RAP.

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

A response surface model based on response surface methodology (RSM) was established to investigate the feasibility of using fluidized industrial-solid-waste solidified loess in roadbed engineering. Granulated blast furnace slag powder (GBFS), circulating fluidized bed desulphurization fly ash (CFBFA), and flue gas desulphurization gypsum (FGD) were used as the influencing factors, and the 7 and 28 d unconfined compressive strengths (UCS) of specimens were used as the response values. This study examined the influence of various solid waste materials on the strength of fluidized solidified loess when 10% ordinary Portland cement (OPC) was incorporated into the curing agent. The mixing ratio of the curing agent was optimized, and the hydration mechanism of the strength formation was analyzed using X-ray diffraction, Fourier-transform infrared spectroscopy, thermogravimetric analysis-derivative thermogravimetry, and scanning electron microscopy. The results show that with an increase in the amount of GBFS and a decrease in the amount of CFBFA, the UCS at 7 and 28 d increases significantly, and the interaction between GBFS and CFBFA significantly influences the UCS. With an increase in the FGD dosage, the 7 d UCS first increases and then decreases, whereas the 28 d UCS decreases. The effect of the interaction between FGD and GBFS on the UCS changes from significant to nonsignificant as the curing age increases from 7 to 28 d, whereas the interaction with CFBFA has the opposite effect. On the basis of the optimal ratio determined using RSM, the strength requirements and raw material costs were considered. At a binder-soil ratio of 0.15 and a water-solid ratio of 0.51, the recommended dosages of GBFS, CFBFA, and FGD are 43%-50%, 25%-32%, and 8%-15%, respectively. At the beginning of the reaction, OH^- released via OPC hydrolysis and Ca²⁺ and SO₄^2- dissolved from FGD can stimulate volcanic ash activities on the surfaces of GBFS and CFBFA. This promotes the formation of ettringite (AFt) and calcium silicate (aluminum) acid (C—S—(A)—H), which binds loess particles and fills interparticle pores, increasing the 7 d UCS of the test specimens. In the later stage of the reaction, GBFS and CFBFA continue to dissolve Ca²⁺, [SiO₄]^4-, and [AlO₄]^5- to undergo volcanic ash reactions, generating additional C—S—H to fill structural pores and cracks and further increasing the 28 d UCS of the specimen. In practical engineering applications, fluidized solidified loess prepared by adjusting the ratio of curing-agent raw materials or binder-soil ratio can fully satisfy the strength requirements of general abutments, culvert backfills, and general highway subgrades.

To study the deterioration characteristics of limestone in dry and wet slope areas, uniaxial compression tests were conducted on limestone with varying dry and wet cycle levels at different strain rates using a 50 mm diameter split Hopkinson pressure bar (SHPB) test system. The deterioration law and mechanism of the uniaxial compression mechanical properties of limestone were investigated, and a macro-micro composite damage constitutive model of limestone under the coupled action of dry and wet dynamic loads was established based on the Lemaitre strain equivalence principle. The effectiveness of the proposed model was verified. The experimental results show that as the number of dry-wet cycles increases, the internal microstructure of the sample weakens, which is reflected in the gradual decrease of longitudinal wave velocity, compressive strength, and elastic modulus, as well as in the increase in porosity and peak strain. As the strain rate increases, the sample dissipates more energy, leading to the formation and activation of additional microcracks. The strength of the sample exhibits a significant strain rate enhancement effect, whereas the elastic modulus, peak strain, and degree of damage only gradually increase. The microscopic damage caused by the wet and dry cycles and the macroscopic damage caused by the dynamic load superimpose, jointly aggravating the damage and destruction of the sample. The macro-micro composite damage evolution curve of limestone, established based on the Lemaitre strain equivalence principle, can be divided into four stages: initial damage retention, slow damage increase, accelerated damage, and damage deceleration and termination for a complete and unified simulation of the entire process of composite damage evolution. The established constitutive model can effectively represent the stress-strain characteristics of limestone deformation and failure under the coupled effect of dry and wet dynamic loads, demonstrating a high degree of agreement with the experimental data. This model provides a valuable reference for estimating the strength of rocks subjected to dry and wet cycles, as well as for studying rock deformation, damage, and failure.

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

The shear performance of a steel inner core-UHPC composite box girder was studied by testing five steel-UHPC composite web specimens by three-point loading. We observed the entire process of crack evolution using DIC and analyzed the effects of the stirrup ratio and the height of the steel web on the shear resistance of the composite web. The test results showed that the pure UHPC, semi-steel-UHPC, and full-steel-UHPC webs underwent shear failure, whereas the UHPC web with stirrups and the full-steel-UHPC web underwent flexural failure. The height increase of the steel web affected the shear performance of the steel-plate-UHPC web significantly. When the steel web height increased from 0 to 300 and 600 mm, the ultimate bearing capacity of the composite web increased by 9.3% and 57.6%, respectively. The additional stirrup effectively inhibited the development of diagonal cracks in the web, so that the failure mode of the composite web changed from shear to a much more ductile flexural failure mode. According to the proposed theoretical formula for the cracking load of the steel-plate-UHPC composite web, the average relative error between the calculated and test values was within 6%. The shear capacity of shear-damaged composite webs calculated based on the French code had a relative error of 3%-13% with respect to the test values. These results provide a reference for the design and engineering application of steel inner core-UHPC composite box girders.

This paper addresses the evaluation of common diseases at the hinged joints of assembled slab beam bridges. A mechanical model of a multi-beam system is used to investigate the theoretical relationship between the bridge's modes and the stiffness of the hinged joints. A stiffness identification method based on the solution of the characteristic equation is proposed, aiming to assess the condition of assembled slab beam bridges through monitoring data. Firstly, the multi-beam system was used as a simplified mechanical model of the assembled slab-girder bridge. The characteristic equation of the general multi-beam system was derived, and the relationship between the mode and the stiffness of the hinged joint was obtained. Then, the influence of hinge joint damage was explored by numerical simulation, and the accuracy of the proposed method was verified by simulating different damage scenarios. Finally, a typical real bridge was tested and analysed to evaluate the damage of the hinged joints based on over a year of measured data. The results show that the proposed method can accurately locate the damage location and quantify the damage degree of the hinge joint only by the modal and frequency of the bridge. The actual bridge evaluation results are consistent with the visual inspection results, and the new damage of the hinge joint is successfully identified, which verifies the accuracy and reliability of the proposed method.

This paper develops the Partitioned Independent Internal Substructure (PIIS) method, which aims to enhance computational efficiency when analyzing the seismic response of boundary-soil-multi-span bridge structures while ensuring the objective continuity of the soil and maintaining computational accuracy. We first discuss the boundedness and unscientific nature of local foundation truncation models. This clarifies the reasons for the relatively large computational burden involved in the seismic analysis of boundary-soil-multi-span bridge systems. We next introduce a creative concept for the PIIS method and elaborate on its nature and underlying theory. In essence, this approach divides the site system into different regions without truncation. Independent internal substructures are then established for each region, and equivalent seismic loads are calculated independently. This process achieves the accurate input of seismic motion for the boundary-soil-multiple-span bridge system. Finally, we consider the examples of a free field and a boundary-soil-multi-span bridge structure to verify its accuracy and high-efficiency. The results show that the PIIS method achieves exactly accurate results, significantly improving computational efficiency and avoiding the conventional method of local truncation modeling that comes at the cost of decreasing accuracy. Furthermore, this developed method overcomes the excessive computational burden of the earlier methods for calculating equivalent seismic loads and promotes the development of seismic analysis techniques for soil-structure interaction.

Square ultrahigh-performance concrete-filled high-strength steel tubes (S-UCFSTs) exhibit significant potential for use in long-span bridges and super high-rise buildings. To investigate the axial compressive behavior of S-UCFST short columns, tests were conducted on 16 S-UCFST short columns. The effects of several parameters, including the compressive strength of concrete, yield stress of steel, and width-to-thickness ratio of the steel tube, on axial compressive performance were considered in this study. Nonlinear finite element and parametric analyses of the specimens were conducted using ABAQUS to investigate the mechanism of the S-UCFST short columns under axial compression. The results show that the S-UCFST short columns exhibit favorable axial compressive behavior. Increasing the compressive strength of concrete improves the axial compressive strength and stiffness but minimally affects the ductility. The axial compressive strength is improved by increasing the yield stress of steel, whereas the effects of stiffness and ductility are minor. Reducing the width-to-thickness ratio of the steel tube improves the axial compressive strength, stiffness, and ductility while delaying the occurrence of local buckling and shear failure in S-UCFST members. Finally, the existing statistics and experimental results were compared with theoretical results calculated according to Chinese code GB 50936—2014, European code EC4 (2004), and American code AISC 360-22. The comparisons indicate that GB 50936—2014 presents a significant prediction bias for the axial compression strength of S-UCFST short columns. The predictions of European code EC4 (2004) are not sufficiently safe and fail to account for the local buckling of the steel tube. The results calculated according to American code AISC 360-22 are more similar to the experimental results and consider the local-buckling effect of the steel tube.

To effectively improve the durability of bridge pier column structures, a new type of composite structure, ultra-high-performance concrete (UHPC) normal concrete (NC) composite columns, was developed in this study. Axial compression tests were conducted on eight UHPC-NC composite columns, one composite column without a stirrup protection layer, and one composite column without a steel bar. The effects of different casting methods, UHPC thicknesses, stirrup spacings, and steel bar arrangements on the axial compression performance of composite columns were investigated in this study. The results show that the UHPC outside the stirrup maintains a certain integrity, and no large-scale spalling occurs when UHPC-NC composite columns reach the peak load. The casting method significantly affects the bearing capacity of the composite columns. Casting the internal NC first and then casting the exterior UHPC can make the specimen increase its bearing capacity. For composite column specimens with the same cross-sectional area, the relative amplitude of the peak load increase gradually decreased with an increase in the UHPC thickness. When the UHPC thickness was increased from 30 to 40 mm and from 40 to 50 mm, the relative amplitudes of the peak load increase were 11.8% and 9.5%, respectively. The stirrup spacing had little effect on the initial stiffness of the composite column. However, the bearing capacity of the composite column increased with decreasing stirrup spacing. When the stirrup spacing was reduced from 60 to 40 mm, the ultimate bearing capacity of the specimen increased by 11.0%. Spiral stirrups were arranged in the composite columns to avoid brittle failure of the UHPC-NC composite columns. A finite-element analysis model of the UHPC-NC composite column was established in this study. It produces results in good agreement with the experimental results. The strength reduction coefficient of UHPC outside the stirrup, α, and the influence coefficient of the casting method, β, were introduced based on the axial compression tests and numerical simulation. The calculation formula for the axial compression bearing capacity of composite columns is highly accurate and can be used to calculate the axial compression bearing capacity of UHPC-NC composite columns.