In the process of rapid development of road engineering, the problems caused by subgrade permanent deformation (PD) haven't been completely solved. Clarifying the evaluation and control methods for subgrade PD under long-term cyclic loading can ensure the durable and stable operation of road engineering. Firstly, this paper explored the main conditions and setting methods for PD test of subgrade soil. Subsequently, the constitutive models based on classical soil mechanics and empirical models based on experimental phenomena were sorted out. Next, the calculation process, verification methods, and evolvement rules of subgrade PD were summarized. Then, three methods for controlling subgrade PD were discussed, including critical dynamic stress, structural measures, and failure probability. Through analysis of research progress, it is found that there are four main problems with subgrade PD, namely inaccurate test methods, incomplete prediction models, unreasonable calculation theories, and unclear control standards. The specific problems and potential challenges in each aspect are elaborated in detail. Four prospects for future research are also given. Firstly, it is necessary to establish a static earth pressure coefficients database of subgrade soil to form a unified test method for PD of subgrade soil. Secondly, the influence rules and internal mechanisms of loading action duration and intermittent duration on PD of subgrade soil should be clarified, and the mechanical model for PD of subgrade soil should be derived under the theoretical system of element model and fractional-order calculus. Thirdly, the calculation method of subgrade humidity field considering the influence of dynamic loading should be innovated, and then the fully coupled calculation method of subgrade PD under humidification action based on the mechanical model for PD of subgrade soil should be established, and a comprehensive verification platform of subgrade that can scientifically simulate the climate environment and stress state should be developed. Fourthly, a control standard for subgrade PD based on pavement performance requirements should be determined with reliability as the goal, and then the corresponding relationship between structural failure and material deformation should be quantified, and the granular materials improvement layer structure design method for subgrade performance control should be optimized.

Uncertainty characterization of surrounding rock parameters is the fundamental cornerstone of tunnel long-life design, and the key is to obtain sufficient accuracy under limited data samples. To address this, a novel method for uncertainty characterization of surrounding rock parameters has been proposed by combining the Bootstrap method and the Akechi Information Criterion (AIC), studying the minimum sample size required to obtain sufficient accuracy. Firstly, the mean and standard deviation of surrounding rock parameters was obtained by the Bootstrap method. Secondly, the probability distributions of the sample under this resampling size were identified by the AIC. Thirdly, the confidence intervals for the mean and standard deviation of the parameters with a confidence level of 95% were calculated. Subsequently, the minimum numbers of samples required for an accuracy of 90% were determined. By this way, the curacy of the uncertainty characterization of surrounding rock parameters was ensured. The proposed method was illustrated through Hoek's classical weak rock parameters. Results indicated that the minimum sample sizes for the mean and standard deviation of weak rock parameters are 12 and 22, respectively. These minimum sample sizes derived from the proposed method were validated by real data of weak rocks from two different places, and the results agreed well with the real data. Furthermore, by incorporating the triple standard deviation criterion, this proposed method was applied to conduct uncertainty characterization of surrounding rocks for the third and fourth level rock mass in the rock mass classification standards. The minimum number of samples for weight, deformation modulus, cohesion, internal friction angle, and Poisson's ratio, were obtained. These could provide valuable insights for the uncertainty characterization of surrounding rock parameters in engineering practices, which in turn would aid in tunnel reliability assessments and long-term design considerations.

Existing deep-learning-based methods for structural damage identification rely heavily on massive amounts of labeled data. Therefore, a meta-learning-based approach is proposed for structural damage localization and quantification. First, a structural damage localization and quantification model was established using an artificial neural network. This model was used to learn the nonlinear mapping relationship between structural modal data (frequency and mode shape) and substructure stiffness parameters. Second, a model-agnostic meta-learning strategy was used to train the damage localization and quantification model. The generalizability of the damage localization and quantification models can be improved by optimizing the initial weight parameters of the artificial neural network (ANN). The proposed method utilizes a model-agnostic meta-learning training strategy to acquire prior knowledge, thereby accelerating the learning process for new structural damage localization and quantification tasks with limited training data. The method was verified on a numerical three-span bridge and benchmark project of the Z24 bridge. The results demonstrate that the proposed approach provides efficient and accurate localization and quantification of potential structural damage using limited data. Compared with conventional ANN and transfer learning methods, the method exhibited faster convergence and higher identification accuracy.

Rock freeze-thaw damage is a crucial issue in cold-region tunnel-engineering research. To better understand the mechanical properties of cold-region rocks and the microdamage caused by the freeze-thaw action, compression, acoustic-wave, and computed tomography (CT) scanning tests were conducted on granite under the freeze-thaw action. The physical and mechanical parameters of the rock and the microdamage characteristics were obtained. Based on three-dimensional reconstruction, a quantitative analysis of pore evolution was performed, which reveals the mechanism of frost damage in the cold-region tunnel surrounding rock. Based on continuum damage theory and microelement statistical theory, a mechanical damage constitutive model considering the initial freeze-thaw damage and residual deformation was derived. The results show that after 50 freeze-thaw cycles, the longitudinal wave velocity of the specimen decreases by 16.60% and the total porosity increases from 7.98% to 10.01%. The linear elastic modulus, peak stress, and residual strength decrease as the peak strain increases. The freeze-thaw action can enhance the development of connectivity between pores, intensify seepage effects, and increase the probability of rock ductile failure, thereby exhibiting clear softening characteristics. The parameters of the new constitutive model can be determined easily and present clear physical significance, high accuracy, and practicability. The model is suitable for describing the stress-strain relationship of the frost-rock damage process and for reflecting the residual-strength characteristics of rocks. The results of this study provide theoretical guidance for the service-performance analysis of cold-region tunnels.

Scientific and reasonable suspender maintenance policies play a major role in ensuring the safe operation of cable-supported bridges. This study addressed the decision-making challenges associated with the maintenance and replacement of vulnerable suspenders by considering their appearance and structural damage state. Accordingly, a preventive maintenance decision-making method that minimizes the combined costs of maintenance and risk throughout the bridge's lifecycle was proposed. First, an optimization objective function was constructed based on the maintenance decision-making problem. The suspender service context was defined as the environment, and the bridge operation and maintenance management system acted as the agent. In addition, the state space, action space, state transition probability matrix, and reward function were established. The expectation of the cumulative discount reward replaced the objective function of the maintenance optimization problem, and state prediction and maintenance decision models based on the Markov decision process were constructed. Then, a preventive maintenance decision method for the suspender system was established based on the suspender system maintenance decision model and dueling double deep Q-network (D3QN) algorithm, which incorporates both a target network and an experience replay mechanism. Finally, a maintenance decision-making framework for the suspender system was constructed using the state prediction model and preventive maintenance decision-making method. With a suspension bridge used as a case study, the state prediction model enabled continuous interaction between the agent and environment, simulating the degradation and maintenance processes of the suspenders while generating the necessary data for training the neural network. Based on the interaction data, the D3QN algorithm network model was trained to obtain the optimal maintenance policy, which was then compared with traditional policies. The results show that the proposed method comprehensively considers the maintenance cost and structural risk and dynamically and adaptively adjusts the maintenance policy. Compared with the traditional policy, the maintenance cost of the policy obtained under the proposed method can be reduced by more than 12%.

Under out-of-plane deformation or high fatigue stresses, conventional crack-stop hole repair of fatigue cracks in steel structures is susceptible to crack perforation (i.e., secondary crack initiation), thereby resulting in unsatisfactory fatigue strengthening. This study proposes cold-expanded crack-stop hole technology for repairing fatigue cracks in steel structures. This principle involves a cold-expanded crack-stop hole using a mandrel to induce residual compressive stress around the hole, thereby reducing the fatigue stress level and extending the fatigue life. Cold expansion tests of crack-stop holes and fatigue tests were conducted on steel plates with type Ⅰ cracks. The distributions of residual strain around the hole after cold expansion were obtained, and the evolution of the residual strains during fatigue loading was clarified. The effects of cold expansion rates (0%, 1%, and 2%) and hole-to-crack tip distances (0, 5, and 10 mm) on the fatigue performance of steel plates with type Ⅰ cracks were investigated, and the life-extending mechanisms for the cold-expanded crack-stop holes were revealed. The results indicate that, for a specified hole-to-crack tip distance, increasing the cold expansion rate can enhance the fatigue life. Increasing the cold expansion rate (not exceeding 2%) can extend the distribution range and value of residual compressive stress around the cold-expanded crack-stop holes. Increasing the hole-to-crack tip distance reduces the improvement of fatigue life by cold-expanded crack-stop holes. The maximum fatigue life can be obtained when the cold expansion rate and the hole-to-crack tip distance are 2% and 0 mm, respectively; Compared with the case of conventional crack-stop hole specimens with a hole-to-crack tip distance of 0 mm, the total fatigue life of the specimens increased by 50.82%. Further increasing the cold expansion rate may potentially enhance the fatigue life, which needs more studies. Finally, a model for predicting the initiation life of fatigue cracks around cold expanded crack-stop holes based on nominal S-N curves and fatigue notch factors was proposed. Analysis results show that the predicted values agree well with experimental results in the log-log coordinate system, particularly when the hole-to-crack tip distance is 0 mm, with an error of ±5%; for hole-to-crack tip distances of 5 and 10 mm, the error is ±20%.

This research investigates the viscoelastic properties of subgrade soil, which cause the subgrade to show significant differences in dynamic resilient modulus (M_R) when subjected to loads of different duration. To accurately predict the M_R of subgrade soil, this study employed an improved dynamic triaxial test method to investigate the relationship between M_R and factors such as load duration, confining pressure, and cyclic deviator stress. Two typical subgrade soils with high liquid limit silt and low liquid limit clay were selected for this study, and specimens with different working conditions were prepared for M_R testing. Subsequently, the influence of different factors on the M_R was analyzed. Analysis of the test results shows that as load duration increases, the M_R of both soil samples gradually decreases. Moreover, the M_R under different load durations displays different sensitivities to cyclic deviator stress. Grey relational analysis was then applied to assess the impact of factors, such as load duration, cyclic deviator stress, and confining pressure on the M_R. Subsequently, combined with the Kelvin model, a comprehensive viscoelastic M_R prediction model was established considering the compaction degree, moisture content, stress state, and load duration. Finally, the test results of other subgrade soils were used to verify the established prediction model and compared with conventional models that did not consider viscoelasticity. The validation results show that the newly established M_R prediction model, which considers the viscoelastic properties of subgrade soil, has high accuracy and applicability. The research results provide valuable references for subgrade design and engineering practices.

To study the blast resistance of reinforced concrete (RC) bridge piers when subjected to explosive loads, this study conducted an field test on a three-span simply supported bridge featuring circular-section double-column piers. The analysis focused on the failure characteristics, damage level, and dynamic response of the double-column piers with circular-section subjected to both in-contact and non-contact explosive loads. The findings indicate that the failure mode of the bridge piers might shift from a local shearing failure mode to a bending failure mode due to variations in explosive equivalent and structural dimensions. Nevertheless, the extent of rupture in transversal and vertical directions is found to be dependent on the sectional stiffness in the corresponding directions, entailing local compressive and inclined shear cracks. In addition, the reinforcement shape after yielding at the location of rupture deviate from those observed in conventional bending failure. Compared to contact explosions, non-contact explosions result in significantly less damage at the equivalent of explosive mass due to the faster dissipation of energy. The diffraction effect of the shockwave, which is attributed to the large dimensions of the bridge columns and their large energy consumption capacities in the blast-facing surface, can be neglected in the anti-blast analysis of pier bodies. However, the confinement effect in the space beneath the bridge and the reflection of the ground and embankment must be taken into account in the analysis. For simplified design and analysis, double-column piers can be modeled longitudinally as pin supports at the top with two fixed ends when conducting transversal analysis.

To investigate the shear performance of fiber reinforced polymers (FRP)-ultra high performance concrete (UHPC) combined beams, four-point bending tests were conducted. The effects of interface slip were reduced by using the FRP shear key (FSK) and epoxy resin bonded hybrid connection. The failure mode, load-displacement response, strain response, and slipping response of the composite beams were analyzed to study the effect of concrete type, FSK spacing, and concrete width and height on the shear performance of the composite beams. The results indicate that the failure mode of the FRP-UHPC composite beam is shear failure, while the failure of the FRP-normal strength concrete composite beam is bending-shear failure. The composite beams exhibit shear lag effect, which become more apparent with increased concrete slab width. Using UHPC, reducing FSK spacing, and increasing concrete slab size can effectively improve the shear performance of the composite beams and reduce interface slipping. Based on reasonable assumptions, a calculation method for shear bearing capacity and a calculation method for deflection that consider shear deformation were proposed for the FRP-UHPC combined beam. The predicted results were in good agreement with the experimental results.

The selection of ground motion intensity measures is the basis for seismic analysis of bridges. This study investigates suitable intensity measures for isolated curved girder bridges subjected to pulse-like ground motions using a three-span isolated curved continuous girder bridge as a prototype. We selected 90 typical pulse-like ground motions and employed the finite element method to conduct seismic dynamic analyses of the bridge at various incidence angles. We examined the correlation between 19 intensity measures (categorized into three types) as well as the seismic responses of the bridge. The efficiency and sufficiency indices in the probabilistic seismic demand model were utilized to compare these intensity measures. In addition, we fitted the relationship between β_D|IM and the intensity measure. The results reveal that velocity-related intensity measures, such as peak ground velocity (PGV) and peak spectral velocity (PSV), exhibit a strong correlation with the seismic dynamic responses of the isolated curved girder bridge, demonstrating good efficiency and sufficiency across different incidence angles. These findings suggest their suitability as primary parameters for selecting and adjusting pulse-like ground motions. Compared with the bending moment of the pier, the peak shear deformation of the isolation bearing is deemed more suitable as the seismic demand parameter. Finally, we found that a power function can effectively characterize the relationship between the mean value of the intensity measures and the seismic response when conducting probabilistic seismic demand analysis. These findings provide valuable insights for selecting seismic intensity measures in the analysis of isolated curved girder bridges.

The contact explosion test of two columns of the middle pier of the two-span prestressed concrete continuous girder bridge model was carried out successively, and the refined three-dimensional finite element model of the bridge model was established to explore the dynamic response, failure mode and collapse mechanism of the bridge model under contact explosion. The results shows that: ① The bottom of the column mainly suffers local breaching under contact explosion. The magnitude of the axial force has a significant influence on the damage degree of the column. After the pier loses its bearing capacity, the bridge model will collapse under the action of its own weight; ② Except for the measuring point at the fixed support, the variation trend of acceleration time history curve of other points on bridge deck is basically the same in the two scenarios. The variation trend of the peak acceleration distribution of the bridge deck is basically the same along the longitudinal and transversal direction. The maximum tension fluctuation value of the measuring point in the positive bending moment region is obviously smaller than that in the negative bending moment region; ③ The nonlinear numerical model can well simulate the punching shear failure mode of the bottom of the column and the distribution of cracks in the column body under the action of contact explosion, and can reproduce the collapse failure phenomenon of the bridge model under the action of its own weight during the test. The research results can provide references for the global blast-resistant collapse of the continuous girder bridge structure and its explosion safety protection to some extent.

Highway vehicle-bridge interaction (VBI) models generally simulate tire loads using a single-point force, which ignores the spatial load effect of tires on the vehicle-bridge interaction. This may lead to errors in the bridge response calculated by the drive-by vehicles. To solve this problem, a VBI modeling method is proposed herein that considers the influence of the tire contact surface. First, according to the actual contact area of vehicle tires, a multipoint force tire model comprising uniformly distributed spring-damping elements was established to replace the traditional single-point force tire model. Tire patch loads were used to connect the vehicle and bridge models to construct the vehicle-bridge interaction formula. Subsequently, based on the new modeling method, VBI simulation models of the simply supported beam and slab bridge were established using LS-DYNA. The proposed method was then verified via comparison with the existing research. Finally, based on field tests and simulations of a steel deck bridge, the accuracy and feasibility of the multipoint tire load contact model were demonstrated. The results show that the spatial load effect of the tires can weaken the dynamic response to some extent for simply supported beams and slabs. This phenomenon becomes increasingly evident with a decrease in bridge stiffness and the deterioration of road profile conditions. The maximum dynamic response of the multipoint force model for a slab bridge with Class E roughness is 4.94% lower than that of the single-point force model. For steel deck bridges with a smaller local stiffness, the results of the single-point model without considering the tire contact significantly overestimate the dynamic response of the bridge. Furthermore, the error of the maximum dynamic response of the measuring points at the bridge deck reaches 8.83%. In contrast, the results considering the effect of the tire contact surface are closer to the measured values. The errors at different fatigue details are less than 2%, and the error at the U-rib is only 0.33%. Therefore, considering the tire contact surface, the vehicle-bridge interaction system can accurately reflect the dynamic performance of bridges, particularly steel deck bridges.

The prediction and classification of large deformation of the soft rock tunnel in the field of tunnel engineering are important and complex. The limitations of previous predictions and classifications of large deformation of soft rock tunnels were summarized. Considering its advantages in simulating the fracture of the tunnel surrounding rock mass, the combined finite-discrete element numerical method (FDEM) was used to simulate the deformation and failure process of soft rock with different strength-stress ratios. The large deformation mechanism of the surrounding rock under high in-situ stress as well as the deformation or failure mode of surrounding rock under different in-situ stresses were revealed. In addition, the tunnel deformation prediction equation and new classification of tunnel deformation based on the strength-stress ratio were proposed. The following findings were obtained from the present study. ① Critical hysteresis damping can be adopted to simulate the progressive large deformation process of soft rock tunnel, which can yield the final deformation of the unsupported tunnel and prevent a dynamic response. ② As the strength-stress ratio decreases, the deformation or failure modes of surrounding rock can be divided into four categories: elastic-plastic deformation, closed fracturing, shear dilation, and broken expansion. In addition, new classification of rock deformation based on different surrounding rock failure modes, as well as the corresponding support measures, were proposed. ③ A prediction equation for tunnel deformation based on the deformation of surrounding rock obtained from different strength-stress ratio was proposed; compared with previous prediction models, this prediction equation can improve prediction accuracy and applicability, and makes up for the deficiency of the previous models that the prediction error is great at low strength stress ratio, which indicates that this equation is particularly suitable for the prediction of large deformation of surrounding rock with low strength-stress ratio, and can predict the total deformation of unsupported tunnel surrounding rock, which is essential for guiding the design of the support scheme, selection of support parameters, and prediction of the tunnel boring machine (TBM) jamming state.

To address the issue of decreased safety performance of support structure in loess tunnels due to uneven loading, this study was based on the measured data from 55 monitoring sections of 27 shallow-buried loess tunnels in China. It elucidated the distribution patterns of surrounding rock pressure in shallow-buried loess tunnels under different geographical zones. By combining numerical calculation numerical and model test, the study revealed the causes of uneven loading in shallow-buried loess tunnels and proposed tunnel load control techniques. Additionally, the effectiveness of these load control techniques was analyzed. The results show that the surrounding rock pressure of shallow-buried loess tunnels is mainly distributed in a butterfly shape, with stress concentration commonly observed at the 60° position of the arch and arch foot. The average value of surrounding rock pressure is 112 kPa, with a median value of 68 kPa. The loads at the arch foot and the 60° position of the arch circle can be up to 1.9-2.2 and 1.6-1.9 times the load of the vault, respectively. When the tunnel is located in the transition from clayey loess area to general loess area and sandy loess area, the butterfly-shaped distribution of surrounding rock pressure becomes increasingly prominent. Uneven settlement of strata, continuous expansion of fracture surfaces, and weaker soil strength at corresponding locations are the main causes of uneven loading in shallow-buried loess tunnels. By applying a load control layer composed of “grouting small conduits + grouting reinforcement layer” at certain positions on the tunnel arch shoulder and arch foot, control of surrounding rock pressure can be achieved. Load control results in a reduction of approximately 20%-30% in the load on the tunnel arch shoulder and arch foot, the settlement deformation of the soil above the vault decreased by 10%-20%. The tension on the vault is alleviated, and the compression on the arch shoulder and side wall is weakened. The proposed load control technique effectively improves the stress of the support structure and achieves favorable results.

The rapid detection of tunnel fires provides an important guarantee of tunnel operation safety and critical decision-making information for tunnel emergency responses. However, existing fire smoke detection methods using video image have issues regarding accuracy and timeliness in complex highway tunnel environments, and lack basic video image data. Therefore, in this study, physical highway tunnel fire experiments were conducted to obtain high-definition video image datasets considering smoke video images simulated under real tunnel scenarios as the research object. An intelligent detection algorithm based on improved YOLOv5s was realized. The enhanced mosaic method is incorporated for training data augmentation, while the Transformer Encoder module is introduced to enhance the network's global feature extraction capabilities as well as improve the problem of difficult feature extraction for small smoke targets, thereby improving the network performance. The latest lightweight convolution method, GSConv, was further utilized to replace some of the Conv modules, reducing the network parameters while maintaining network performance to achieve network compression. Furthermore, a lightweight efficient channel attention (ECA) module is added to alleviate issues such as missed detections of smoke targets at long distances and small targets using a local cross-channel interaction strategy, further improving network performance without increasing the number of parameters. A combination of the CIoU loss function and SiLU activation function are considered to allow the network to converge more quickly. To verify the effectiveness of the proposed algorithm, seven target-detection models were selected for comparison and analysis: YOLOv3, YOLOv3-efficientnet, YOLOv5s, YOLOX, YOLOv7, YOLOv7-tiny, and SSD. The results indicate that regarding the self-built highway tunnel fire smoke dataset, the proposed algorithm achieves the detection accuracy of 97.27% and mAP@0.5 of 97.85%. Although the improvements over the original network are only 1.83% and 1.13%, respectively, compared with the other seven algorithms, the algorithm developed in this study exhibits better detection performance for small smoke targets at long distances and in the early stages of fires, significantly improving missed detections. Additionally, the detection speed of this algorithm is 86.2 FPS, meeting the timeliness requirements for tunnel fire detection. The reliability of this algorithm was also validated using video footage of a fire in the Zhenwu Mountain Tunnel in Chongqing to provide technical support for realizing rapid fire perception in complex tunnel environments.

The longitudinal joint is the weakest part of the prefabricated utility tunnel and is easily damaged under external force and foundation settlement. The mechanical performance of the socket joints of the prefabricated utility tunnel on a clay foundation is essential to the safety evaluation of the utility tunnel joint or structure. Based on the utility tunnel project in the Xindu District of Baotou City, the failure characteristics and deformation rules of prefabricated utility tunnel joints under shear action were investigated using the shear model test. The mechanical characteristics and working mechanism of all components of utility tunnel joints were revealed. The results showed that the horizontal cracks first appeared at the hub of the utility tunnel. As the vertical loading displacement increased, oblique and radial cracks successively appeared horizontally. When the loading displacement reached 28 mm, the concrete was broken off at the hub, and tensile cracks appeared at the concrete at the socket. The top and bottom plate of joints within the cabin were deformed, and the side wall bulged. The deformation of the large cabin was higher than that of the small cabin. The maximum stress of the high-strength bolts was increased by 0.382 GPa and the elongation reached 2.6 mm. The bolt was subjected to the combined action of tension-bending-shear during loading. The stress of the tunnel joint was divided into three stages. The foundation, joint concrete, and high-strength bolts are gradually involved in the shear process of the joint. The shear stiffness of the joints in the second and third stages decreased by 73% and 57.9%, respectively. The joint hub of prefabricated utility tunnel, particularly at the tapered edge, should be locally reinforced. High-strength bolts should be incorporated as early as possible to prevent premature failure of the socket joint.

To evaluate the readiness of freeways in plain regions for automated vehicles (AVs) from the perspective of lane detection performance, field tests were conducted on the Beijing-Shanghai Freeway and Shenyang-Haikou Freeway in Shanghai using a test vehicle equipped with the Tongji University Road and Traffic Holographic Data Acquisition System. By considering scenarios in which the test vehicle and surrounding vehicles in adjacent lanes could potentially collide laterally as safety-critical conditions, the upper and lower thresholds for lane width detection were calculated, and lane-detection failure events were extracted. The lane-detection failure type was used as the label. Five feature types, namely road geometric design, road section, road marking, vehicle operation, and environment, were considered as input features. Using an XGBoost ensemble learning model, the relationship between the lane detection failure type and the features was established. As a post hoc interpretation technique, the SHapley Additive exPlanations (SHAP) was used to analyze the feature importance and the impacts of individual and interaction features on failure types. The results show that features including speed, segment type, leading-truck distance, lane location, special marking type, average curvature, rate of change of vertical curve, marking condition, and longitudinal line type affect failure probabilities, with feature importance decreasing in order. Specifically, the failure probability increases when the average curvature is smaller than 0.4 km^-1, the change rates of crest and sag curves exceed 10%·km^-1 and 30%·km^-1, respectively, vehicles are on the mainline at entrance or exit or in the right-most lane, lane markings are connected with acceleration or deceleration tapers, special markings are present or poorly maintained, or the leading truck distance ranges from 0 to 55 m. Moreover, approaches such as continuous connection of markings at acceleration or deceleration taper extensions, better maintenance of worn-out and unerased markings, and the use of solid lines on important lanes can reduce the probability of failure. These findings can be applied in conducting readiness evaluations on freeways from the perspective of lane-detection performance for AVs, providing quantitative references for traffic departments to manage AVs' operation design domain, and guiding lidar performance optimization for sensors and automobile manufacturers.

In connected and automated environments, implementing feedback control on key connected and automated vehicles in a vehicle platoon can indirectly influence the operation of human-driven vehicles to thereby optimize the overall traffic flow in terms of efficiency and safety. Hence, this study proposes a spectral clustering-based pinning control (SC-PC) strategy to optimize the microcontrol effects of vehicle platoons in a connected and automated environment and enhance the overall traffic flow performance. First, a network model and definition of pinning control is proposed for vehicle platoons in a connected and automated environment. Second, by comprehensively considering the static network topology information and the dynamic information of vehicle dynamics, a key control node identification method based on the spectral clustering algorithm is proposed to determine the implementation objects of pinning control. Subsequently, a feedback control method targeting pinning node vehicles is designed, guided by multiple objectives, such as safety and efficiency. Finally, numerical simulation experiments are conducted using the TOD dataset of vehicle following behavior in real scenarios. The effects of different pinning node identification methods and pinning rates on the pinning control were compared and analyzed, and the effectiveness of the proposed SC-PC strategy was verified. The results show that, compared with other pinning control strategies, the proposed SC-PC strategy can more accurately identify key control nodes within the vehicle platoon to improve traffic oscillation, synchronization, and safety indicators by at least 5.3%, 11.7%, and 16.0%, respectively. Thus, the proposed method can enhance the anti-interference ability of traffic flow while simultaneously optimizing efficiency and safety to serve as a balance between resource input and control effects in traffic flow optimal-control issues in connected and automated environments.

To improve the efficiency of traffic at a signalized intersection under mixed traffic conditions, a vehicle queue estimation method with a low penetration of connected and automated vehicle (CAV) is proposed. Based on the random arrival characteristics of the mixed traffic flow in the upstream area of the signalized intersection, a queue estimation of mixed traffic flow was constructed considering the composition of CAV and human-driven vehicle (HV). With the displacement difference, speed difference, and acceleration difference of CAV as an input and displacement difference of HV as an output, a Seq2seq architecture-based vehicle microscopic trajectory forward/backward reconstruction under low penetration conditions was established. The model uses the temporal attention mechanism to determine the key time domain of the vehicle driving state change, and improves the ability to reconstruct the “stop-and-go” waves for the model. Additionally, with the number of vehicles queuing at the current signal cycle as an input and vehicle queuing length as an output, an XGBoost-based vehicle queue length estimation model was developed, which can accurately estimate the vehicle queue length under the condition of low historical sample data. The experiments were based on the NGSIM dataset for model training. The performance of the proposed method was verified under different conditions including different penetrations of CAV, single signal cycle, and multisignal cycle. Under the low penetration rate of 10%-30%, the loss function converges faster and has a better stability compared to the classical time series prediction models of RNN (recurrent neural network), LSTM (long short-term memory), Seq2seq (sequence to sequence), and CNN (convolutional neural network). The root mean square error (RMSE) of the vehicle trajectory is reduced by 8.9%-71.7%. The method could accurately describe the stop-and-go waves at the signalized intersection. Compared to the queue length estimation methods based on KNN (k-nearest neighbors), random forest, and polynomial regression model, the RMSE of the proposed method is reduced by 13.56%-91.99%, and the running time of the queue length estimation is reduced to approximately 8 ms, which effectively demonstrates the accuracy and real-time performance of the proposed method.

To overcome the limitations of existing regional green wave coordinated control models, which are primarily oriented toward the single-cycle control mode, a regional green wave coordinated control model suitable for unequal double-cycle control demand has been proposed. This model provides a calculation for the regional common signal cycle range and a method for determining double-cycle intersections. Based on the main characteristics of the double-cycle control mode, the constraints for phase initial point, phase execution time, phase sequence, as well as initial point and passage time of the coordinated traffic flow were established for both single- and double-cycle intersections. The objective function of the regional green wave coordinated control, which maximizes the weighted sum of the green wave proportions for each coordinated traffic flow and its volume, was established using the projection calculation method. The proposed model was applied to a partial real-world network in the Futian District, Shenzhen City, Guangdong Province, China. To compare the control effects of the status quo, Synchro, and the proposed model, two evaluation indicators-average delay time and average number of stops-were chosen to evaluate them from the perspectives of intersection, coordination path chain, and the entire network, respectively. The simulation results indicate that the proposed model effectively reduces the average delay time at intersections and in the entire network. This scheme reduces the regional average delay time and the average number of stops by 13.94% and 9.71% compared to the Synchro scheme in double-cycle control mode, respectively. Additionally, the proposed model improves the overall control effect of the coordination path chain in the region, reducing the coordination path chain average delay time and the average number of stops by 66.41% and 55.73% compared to the Synchro scheme in double-cycle control mode, respectively. Thus, the proposed model can achieve coordinated optimization for both single- and double-cycle intersections in the coordinated network, making it suitable for complex traffic scenarios in a coordinated network where there exists a road section crosswalk and branch road intersecting with a main road or artery. Moreover, the proposed model achieves a significant coordinated control effect in the control network, particularly in the coordination path chain.

This study proposes a novel traffic state estimation method that leverages convolutional neural networks (CNNs) for the adaptive smoothing of cross-sectional traffic flow data to reconstruct a complete traffic state. Unlike traditional adaptive smoothing methods, which depend on empirical selection for traffic propagation characteristics, the proposed method utilizes three distinct anisotropic convolution kernels. These kernels are carefully designed to align with the specific propagation patterns of traffic waves (forward, backward, and bidirectional), thereby effectively distinguishing between free-flow and congested states. Furthermore, a novel fundamental diagram (FD)-based weight operator is introduced that adaptively weighs different traffic features to accurately depict traffic equilibrium conditions. Unlike traditional adaptive smoothing methods that use sigmoid functions to determine weighting, this operator explicitly incorporates the physical meaning of traffic flow fluctuations and clearly represents first-order characteristics of continuous traffic flow. Validation against field freeway trajectory data demonstrates that the proposed framework, with its anisotropic convolutional kernels, maintains high estimation accuracy and reduces the number of trainable parameters by approximately one-third, thus ensuring results more closely aligned with the physical properties of traffic flow. The proposed method significantly outperforms traditional adaptive smoothing techniques, reducing the overall mean absolute error (MAE) and mean absolute percentage error (MAPE) by 22.3 and 31.35%, respectively. Remarkably, under congested conditions, the proposed approach shows even greater precision, with MAE and MAPE reductions of 31.1 and 37.58%, respectively. Moreover, the proposed model demonstrates lower sensitivity to minor traffic disturbances, and a slower rate of error increase as the distance between the estimation locations and observation cross-sections increase. Across various observation section spacings, the proposed method consistently surpasses baseline methods, delivering the lowest estimation errors for any given spacing. Notably, as the section spacing increases, this method shows the most gradual increase in estimation errors. These findings collectively underscore the effectiveness and superiority of the proposed traffic state estimation method.

To address the issues facing the integrated brake-by-wire (I-BBW) system in relation to hydraulic system stiffness mutation and inadequate brake fluid after wheel cylinder pressure control intervention, an I-BBW wheel cylinder pressure control strategy based on motor-solenoid valve coordinated fluid replenishment logic was proposed. First, the working principle of the I-BBW was analyzed. Subsequently, essential mathematical models of the key subsystems were constructed. A strategy for regulating the I-BBW wheel cylinder pressure was then designed. The motor-solenoid valve coordinated replenishment logic dynamically adjusts the integrated parameter status of the pressure regulator according to changes in the servo cylinder fluid pressure-piston position stiffness characteristics to maintain sufficient I-BBW brake fluid. The servo cylinder pressure regulator uses a pressure loop based on the feedforward of the hydraulic system variable stiffness fitting, robust sliding mode position loop, and motor current loop to accurately regulate the pressure of the I-BBW high pressure source and overcome sudden changes in the stiffness of the hydraulic system. The wheel cylinder pressure regulator condenses the pressurization/depressurization characteristics of the inlet/outlet solenoid valve using the test data and converts the complex pressure-regulating logic between the wheel cylinder and solenoid valve into a simple-structured characteristic mapping model to achieve precise control of the wheel cylinder pressure. Finally, a hardware-in-the-loop experimental bench was constructed to verify and test the designed algorithm. The results show that the proposed algorithm can effectively control the I-BBW and accurately respond to a variety of target pressure demands. The average pressure tracking errors of the servo cylinder and wheel cylinders are controlled to within 0.15 and 0.25 MPa, respectively. Furthermore, should there be a shortage of brake fluid, a quick response is given, demanding brake fluid replenishment within 100 ms, thus ensuring the I-BBW retains stable and accurate brake pressure control capabilities.

To ensure the reliability of vehicle dynamics simulation results based on road irregularity input, a two-wheel road irregularity model using wavelet-reconstructed white noise and regulated by a parallel coherent/incoherent function pair was proposed. First, a standard white noise signal was reconstructed by removing the signal below the lower cut-off frequency after completing wavelet decomposition to obtain white noise above the lower cut-off frequency. The reconstructed white noise was then input into an integration transfer function with a precision correction term to obtain a nonstationary single-wheel road irregularity. Finally, three independent wavelet-reconstructed white noise precision correction integrals were modulated using a pair of parallel coherent/incoherent transfer functions to generate nonstationary two-wheel road irregularities. The wavelet decomposition and reconstruction of standard white noise improve the modeling accuracy of the road irregularity's power spectral density around the lower cut-off frequency using an integration transfer function with a precision correction term to adapt to different road irregularity frequency exponents. The modulation of the parallel coherent/incoherent transfer functions ensures the modeling accuracy of the coherence function between two-wheel road irregularities. The PSDs and coherent function curves of the modeled road irregularities closely agree with those of the actual ones, as demonstrated by two modeling applications on actual roads.

With improving digitalization and intelligence, intelligent compaction technology has become the future development trend for filling compactions in areas such as airports, roads, and ports. However, the current impact-rolling construction process still requires a human to plan and control the path, which seriously affects the compaction efficiency and quality. To address this problem, an impact-roller compaction-path optimization and control method is proposed. First, the impact-roller compaction process was described in detail, and the essence of the impact-roller path-planning problem was revealed. Next, the impact-roller path-planning problem was regarded as a classic traveling salesman problem. A mathematical model was established, with the shortest turning distance as the target, and the corresponding model solver was designed to autonomously optimize the compaction path. Second, combining technologies such as databases, GMap.NET's second development, and the Internet of Things, a path-navigation platform based on the.NET framework was developed to control the compaction path in real time. Finally, the proposed method was verified using an example simulation and field test. The results show that the proposed method can obtain the optimal compaction path, based on the working-surface condition and basic parameters of the impact roller. Moreover, it can reduce the compaction path and improve the compaction efficiency compared with the traditional path-planning method. In addition, the developed path-navigation system can stably visualize the compaction path during compaction and control the path in real time, thereby guaranteeing the compaction quality.