With the continuous promotion of strong transportation strategies, the demand for high-quality sand and gravel materials in China's transportation infrastructure remains high. Environmental protection requirements continue to grow, and the shortage of natural resources continues to exacerbate. Therefore, alternatives to sand and gravel aggregates have become the main direction of development in the field of road engineering. China's western region has long been plagued by wind and sand problems, and successive exploratory studies have been conducted on the application of aeolian sand in road engineering, which have confirmed the significance of aeolian sand resource utilization for promoting green and low-carbon transportation in the sustainable development of road engineering industry. Therefore, this study focuses on the problems of large regional differences in aeolian sand and the lack of corresponding standardized research. The physicochemical properties of aeolian sand and its engineering characteristics in different regions are systematically discussed, analyzing its potential activity, excitation mode, and mechanism. The progress of research on the application of aeolian sand in roadbed and pavement engineering is summarized, thereby illustrating the influence and mechanism of aeolian sand on the performance of pavement concrete and semi-rigid bases, comprehensive utilization and treatment technology of aeolian sand in roadbed engineering, prevention and control of wind-blown sand of aeolian sand roadbeds, and performance of aeolian sand roadbeds in resisting scouring and water damage. Finally, development trends in the application of aeolian sand and research focus directions for the future of road engineering are presented.

Transportation sector is one of the major sources of global carbon emissions, and as a major transport country, China is facing a huge challenge to reduce transportation carbon emissions. Researchers have conducted extensive research on carbon emission assessment and emission reduction measures in recent years. This paper focuses on the highway construction stage, reviewing previous studies on three aspects: carbon emission assessment, carbon reduction, and carbon sequestration compensation. The results show that: most carbon emission assessment methods use the Life Cycle Assessment(LCA) model throughout the highway construction stage, in which the basic data accounting focuses on the emission factor method, as well as software, platforms, and other tools combined with artificial intelligence; emission side of the carbon reduction is mainly centered on green geo-technology, solid waste resource utilization technology, and green-efficient project management, to completely accomplish the carbon reduction goal; carbon compensation research includes compensation measures and effect evaluation of the carbon sequestration, and its carbon compensation measures are mainly in using slope vegetation photosynthesis to sequester carbon and new materials to increase carbon sinks. Moreover, carbon sequestration effects are evaluated using the subjective-objective combination analysis method. The analysis reveals that unsolved problems still exist: lack of unified standards for emission factors, leading to considerable errors in carbon emission assessment; lack of comprehensive assessment methods for the specific emission reduction ratio of emission reduction measures and the poor combination with artificial intelligence; the carbon compensation measures are largely insufficient and the carbon sequestration effect assessment method is subjective. Future research should focus on exploring the accuracy of the assessment method; proposing more effective carbon emission reduction measures and enhancing the research on the assessment of emission reduction effect; adopting more diversified carbon compensation measures, and establishing a universally applicable compensation effect assessment method. This review paper provides a comprehensive reference for carbon emission reduction research in transportation geotechnical field and assists in realizing the goal of the “carbon peaking and carbon neutrality” strategy.

Load-carrying capacity is a key index reflecting the service performance of highway bridges. Its reliable evaluation is key for determining the safety risk of bridges in advance, which can provide an important basis for decision-making, such as that pertaining to bridge reinforcement and reconstruction. In this study, the connotations of bridge load-carrying capacity evaluation were analyzed from the aspects of evaluation targets and evaluation methods, the current development of technical standards for load-carrying capacity evaluation in the United States and China was introduced, and the implementation method and evaluation theory of proof and diagnostic load-testing methods were demonstrated. Proof load testing can directly determine whether the bridge load-carrying capacity satisfies the requirements, whereas diagnostic load testing should be further developed in the future as it offers more bridge information and ease of implementation. Additionally, in this study, the verification theory used for the load-carrying capacity in the codes of various countries was systematically compared; three types of resistance correction methods, including direct structural testing, state-parameter mapping, and a time-varying deterioration-prediction model, were summarized; the correction methods for the bridge load effect were described comprehensively in terms of load and bridge models; and the reliability targets of bridge evaluation in various countries were analyzed. The British and American codes have established a multilevel evaluation reliability target by discounting the design load and resistance factor, which is worthy of reference for the Chinese bridge evaluation code. Finally, an outlook into the future development of load-carrying capacity evaluation methods in terms of testing methods, evaluation theories, and technological equipment is presented.

The reinforcement effect of prestressed anchor cables with small diameter on the surrounding rock of soft rock tunnels was systematically investigated. First, the bearing arch effects of the surrounding rock of a tunnel strengthened using small-diameter prestressed anchor cables were simulated and analyzed based on a stratum-structure model. A generalized “load-structure” mechanical analysis model of the anchored surrounding rock was established. A formula for calculating the bearing capacity of the anchored surrounding rock with the combined support of long and short anchor cables was derived. Subsequently, numerical analysis of the simulated loading of the anchored surrounding rock was performed. The development process of a plastic zone in the anchored surrounding rock and the main factors influencing the ultimate bearing capacity were studied. The effectiveness of anchor cable support schemes was also explored. Finally, the reinforcement effect of small- diameter prestressed anchor cables on the surrounding rock of a soft rock tunnel was verified and summarized through on-site testing. The results indicate that a superimposed arch composed of a shallow and a deep bearing arch is formed in the surrounding rock under the combined support of long and short anchor cables. In this case, the diffusion range of the pre-tensioning force is higher than that obtained with the short anchor cable scheme, and the engineering economy can be considered comparable to that of the long anchor cable scheme. After the anchored surrounding rock is loaded, its inner surface first enters a plastic state. Considering the corresponding load when the inner surface enters the plastic state as the bearing capacity of the anchored surrounding rock tends to be conservative. The ultimate bearing capacity of the anchored surrounding rock can be obtained using numerical calculation methods, which mainly depend on the strength of the surrounding rock and the anchoring force of the anchor cable. The active support obtained with small- diameter anchor cables and high pre-tension can significantly increase the overall stiffness of the anchored surrounding rock. Under the conditions of relatively soft rock and soft rock strata, small- diameter (Φ21.8) prestressed combined long-short anchor cables (5 m+10 m, 19 per ring of upper and middle benches, spacing of 80 cm, design anchoring force of 450 kN, design pre-tensioning force of 350 kN) were used, and the maximum deformation of the tunnel was basically controlled within 30 cm according to actual measurements. For extremely soft rock formations, ensuring that small-diameter anchor cables have a sufficient anchoring force is a key technical problem that must be solved urgently.

To systematically analyze and summarize the current research status and development trends in the operational management of emergency medical service (EMS) vehicles, this study organizes the research framework of EMS vehicle operational management into three levels: strategic, tactical, and operational, based on 1 502 articles indexed from the Web of Science database. The findings reveal that at the strategic level, research on EMS vehicle location focuses on continuous improvement of coverage definition and accurate characterization of inherent uncertainties within the system. Key research methods include stochastic planning and robust optimization as uncertainty modeling and optimization approaches. At the tactical level, EMS vehicle relocation is categorized into multiperiod and dynamic relocation based on the triggering of relocation decisions. Given the complexity of relocation with respect to location, the research emphasizes the application of heuristic and reinforcement learning algorithms in addressing real-world large-scale problems. Decisive issues at the operational level include EMS vehicle dispatch, destination selection, and route planning. Research on EMS vehicle dispatch has evolved from rule-to model-based and from independent to joint optimization in relocation. Destination selection involves coordinated optimization with hospital workload, and route planning primarily addresses special scenarios such as disaster response. In future research, optimization in EMS vehicle operational management should focus on the dual research threads of dynamics and uncertainty. This entails accurately characterizing the sources of system uncertainty while leveraging finer-grained data to assist real-time decision-making. In terms of specific modeling and solving techniques, joint optimization of multiple decision problems across different levels should be conducted to progress from local to system optimum EMS vehicle location and dispatch schemes. However, efficient algorithms for handling real-world large-scale scenarios continue to pose a challenging research direction.

Recently, autonomous vehicles (AVs) have gradually reached the stage of real-world testing and demonstration on public roads. AVs interact frequently with humans, and their driving performance requirements have shifted from a functional level of “safe and stable driving” to an interactive level of “driving like a human.” The construction of evaluation indices and methods that can accurately characterize the human cognition of driving behavioral ability is necessary to guide the continuous development and improvement of automated driving technology to demonstrate human-like or superhuman driving ability. This study focused on the construction of an evaluation indicator system for the driving behavioral ability of autonomous vehicles. This paper first elaborates on the definition and boundaries of the driving behavioral ability of autonomous vehicles. Then, the current status and existing problems of the evaluation index system for driving behavioral ability are presented. Moreover, an STCER-H index system is proposed, which includes five dimensions of instantaneous indicators: Driving Safety, Travel Efficiency, Driving Comfort, Energy Efficiency, and Regulatory Compliance, as well as a comprehensive “Humanoid Level of Driving Behavior” indicator. Consequently, the existing modeling methods for individual indicators are reviewed, the connotations of each dimension indicator are clarified, and the current status and problems of each dimension indicator are summarized. At the level of statistical evaluation in multidimensional aggregation, the definition and modeling suggestions of “Humanoid Level of Driving Behavior” indicator are primarily discussed. Finally, a summary of the challenges and future research prospects of the existing evaluation index system for driving behavioral ability is presented to provide a reference for academic and industry research.

Driver state monitoring technology, as a key means for improving vehicle intelligence and safety, aims to accurately identify and deeply understand the driver's actions, emotions, and attention states. Although significant progress has been made in this field, a systematic summary of the principles of deep learning algorithms is lacking. In view of this, this paper systematically reviews driver state monitoring algorithms based on images and deep learning to meet the needs of the continuous development of intelligent vehicle technology. First, the methodology in the literature is elaborated upon. The existing publicly available datasets are then organized and described. Subsequently, in-depth exploration is conducted from the aspects of data selection and processing, model architecture, model training and evaluation, and optimization. Finally, the shortcomings of the current research are summarized, and the main future development directions are outlined. The results show that: ① the research on driver state monitoring based on image and deep learning has progressed to a certain depth; ② data selection and processing techniques show diversity; ③ model architectures continue to evolve in the direction of multi-modal, multitasking, lightweight, and high robustness, gradually beginning to adopt training strategies for incomplete supervision and multi-objective optimization. However, most research methods lack systematic testing of actual driving scenarios neither fully considering the behavioral characteristics of drivers under natural driving conditions nor the changes in the human-computer interaction patterns of intelligent vehicles, making it difficult to construct an all-around monitoring function for various driving scenarios and driver personalities. The further development of driver state monitoring algorithms is mainly limited by two factors. First, the current deep learning methods still have deficiencies in their domain adaptation, interpretability, and operational efficiency. Second, large-scale high-quality datasets under natural driving environments are lacking. This review is dedicated to providing effective guidance and important references for further development of high cognitive driver state monitoring systems.

In recent years, the pace of bridge construction in China has been steadily accelerating, with the scale of projects and technical level reaching world-leading standards. The high-quality and innovative development of bridges is an important starting point and basic prerequisite for building China into a country with strong transportation network. To further enhance the strength of the bridge engineering discipline in China, promote the high-quality development of green low-carbon, sustainable and intelligent bridge engineering in China, and support the construction of a transportation powerhouse, this review, based on the analysis of the current industry development status and trends, systematically summarizes the latest scientific and technological innovation achievements in the field of bridge engineering in China in recent years and comprehensively sorts out the future key development directions covering four major themes: bridge engineering structural design and system innovation, disaster prevention and mitigation and structural safety, green construction and intelligent construction, and healthy operation and maintenance and longevity assurance. Specifically, it covers 21 hot research directions, including bridge function and analysis, high-performance materials, steel bridges and composite structure bridges, long-span bridge structures, innovative bridge foundation structures, new progress in bridge seismic resilience research, bridge wind, fire, and blast resistance, bridge engineering collision and protection, water resistance and resilience, multi-hazard coupling of bridges, high-quality bridge construction, green construction technologies and construction technologies, bridge monitoring and assessment, intelligent detection, on-bridge traffic safety, bridge life extension technologies, and integrated construction and maintenance platforms. The review provides guidance and reference for the development of the bridge engineering discipline in China and offers new perspectives and basic materials for researchers and technicians in the field of bridge engineering.

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.

The purpose of this study was to develop a new application of energy-absorbing materials in the road maintenance field and to produce a preventive maintenance seal that can improve the road surface function and enhance the structural bearing capacity of existing roads. A new road energy-absorbing material was used as the matrix, and a “sandwich” structure was used as the framework. A road maintenance energy-absorbing seal was designed and prepared, considering texture reconstruction. Image-processing analysis and accelerated loading tests were performed to analyze the decay law of the surface texture characteristics and road surface function of the road maintenance energy-absorbing seal and evaluate the durability of the seal. The effect of the road maintenance energy-absorption seal on decreasing the strain at the bottom of an asphalt concrete plate was evaluated using a continuous loading test of wheel rolling, and its load-bearing and buffering effects were investigated. Based on dynamic thermomechanical analysis, the microenergy-absorbing characteristics and damping behavior of the road maintenance energy-absorption seal were described, and its buffering mechanism was revealed. Finally, this study lays a solid foundation for the extensive investigation and promotion of the road maintenance energy-absorbing seal. The results show that the aggregate coverage rate is 40%, based on the seal surface texture and surface functions (wear resistance and sliding resistance). The ratio between the 2.36-4.75 mm and 1.18-2.36 mm aggregates is 25:75. The spraying plans for the energy-absorbing material are 1.0 and 2.0 kg·m^-2 for the upper and lower layers, respectively. After 40 000 cycles of loading and wear cycles, the surface texture of the road maintenance energy-absorbing seal attenuated slightly, and the decline in the durability of its surface was evident. A road maintenance energy-absorbing seal can effectively reduce longitudinal and transverse strains at the bottom of an asphalt concrete plate. Moreover, it can convert the original tensile strain into compressive strain or decrease the value of the original tensile/compressive strain by over 30%-50%. The loss factor [tan(δ)] of the energy-absorbing seal is 0.1-0.3, and the seal can exhibit excellent damping performance within wide ranges of temperature (-50 ℃-200 ℃) and frequency (10^-4-10⁸ Hz).

A long short-term memory (LSTM) neural network model for predicting slope displacements based on maximum mutual information coefficients (MICs) and the XGBoost algorithm (MIC-XGBoost LSTM) was established to accurately predict slope displacements. First, the effects of different rainfall conditions on the slope were investigated. The maximum MIC was used to analyze the correlation between different rainfall conditions and the cumulative displacement of the slope, and the rainfall-influencing factors with significant correlations were determined. Next, based on the XGBoost algorithm, feature construction was performed on the influencing factors with high correlation using the cumulative displacement data of the slope, and the construction features were normalized with the original features. The normalized data were divided into training and validation sets. LSTM was used to predict the displacement of the Shangluo rock slope on the G312 National Highway. The XGBoost, LSTM, and MIC-XGBoost-LSTM prediction models were used to train and predict the cumulative displacement value of the slope, and the prediction accuracy was evaluated based on the root-mean-square error (RMSE), mean absolute error (MAE), and mean absolute percentage error (MAPE) indicators. In addition, the RMSE was used to determine the longest prediction cycle and minimum training sample size for the MIC-XGBoost LSTM model. Finally, the displacement data of the Baishui River landslide were used to further validate the model. The results show that the correlations between daily displacement increment, evapotranspiration, net rainfall, cumulative seven-day rainfall, and cumulative displacement at the monitoring point are higher than those of other factors, and the MIC of the feature values constructed using four related factors and the output feature values is 0.97. The RMSE, MAE, and (MAPE) of the predicted results obtained using the MIC-XGBoost-LSTM model are 0.25%, 0.185%, and 0.024%, respectively, which are lower than those of XGBoost and LSTM. Based on the RMSE, the longest prediction cycle and minimum training sample size of the MIC-XGBoost-LSTM model are 56 and 675, respectively. Finally, the displacement data of the Baishui River landslide were used for verification. The evaluation indicators are lower than those of the XGBoost and LSTM models, demonstrating that the MIC-XGBoost-LSTM slope displacement prediction model has high reliability.

A system reliability analysis model is developed using an improved vector projection response surface method (IVPRSM) to enhance the efficiency and accuracy of calculating the static system dependability of long-span continuous steel-truss bridges. A novel approach was developed to enhance the efficiency of computing the component dependability index by optimizing the sampling process of the vector projection response surface technique. The limit state functions of steel truss bridge components can be effectively rebuilt, the design points can be promptly searched, and the failure probability of the components can be evaluated using the IVPRSM. The candidate failure components can be screened using the β-unzipping method. Considering the structural topology model was modified by assuming failure in the potential failure elements, the reliability indices of the remaining components were calculated using IVPRSM. Therefore, identifying the primary failure modes of steel-truss bridges and constructing a fault tree is possible. The structural system reliability index was determined using the differential equivalent recursion technique, which relied on the equivalent linear functions of the failure modes and the correlation coefficients between the failure modes. The efficacy and precision of the IVPRSM were confirmed through a reliability study of three numerical arithmetic cases. Considering a double-deck continuous steel truss bridge with a main span of 300 m as an engineering example, the proposed system reliability analysis method was used to calculate the reliability indices of the key components of the steel truss bridge in each failure stage. The study findings demonstrate that IVPRSM exhibits superior computational efficiency and accuracy compared with conventional approaches. At the ultimate limit condition of the load-carrying capacity, the reliability index for all types of critical members ranges from 4.1 to 4.8. The lower chord at the pivot of the main span of the truss girder, the upper chord and web member at approximately 1/4 of the main span (300 m), and the strengthened vertical bar in the center of the main span pose a significant danger of failure. Consequently, twenty primary failure modes affecting the load-carrying capability of the steel truss bridge were identified, resulting in a system dependability index of 4.6. This study proposes an IVPRSM-based reliability method for the static systems of continuous steel truss bridges. This algorithm may assist in designing continuous steel truss bridges by considering system reliability.

Joint bolts reduce the longitudinal and transverse bending stiffnesses of shield tunnels. Based on the classical longitudinal equivalent continuous beam theory for shield tunnels, as well as considering the reduction effect of the transverse bending stiffness of shield segments and the combined action of axial force and bending moment, this study proposes a model for calculating the longitudinal bending stiffness of shield tunnels based on an elliptical cross-section and a strict elliptical-integration derivation. The governing equation of the proposed model is a transcendental equation, and a numerical method is used to solve the governing equation. The proposed model is compared with existing models derived from circular and elliptical cross-sections, but not strictly elliptical integration. Additionally, the effects of the transverse bending stiffness of shield segments and the material stiffness of shield segments and joint bolts on the longitudinal equivalent bending stiffness of shield tunnels are analyzed. The findings show that for a circular cross-section, the results of the proposed model are consistent with those of existing models. Existing models derived based on a circular cross-section are specific cases of the proposed model; when the geometric and material parameters are provided, the longitudinal equivalent bending stiffness of shield tunnels and the ratio between the axial force and bending moment present nonlinear and positive correlations within the range of positive and negative critical ratios between the axial force and bending moment. The longitudinal equivalent bending stiffness is constant outside the range of positive and negative critical ratios between the axial force and bending moment. Meanwhile, it decreases with the transverse bending stiffness of the shield segments or the material stiffnesses of the shield segments and joint bolts. These findings provide a theoretical reference for the longitudinal-deformation analysis of shield tunnels.

To investigate the impact of the behavioral preference and dynamic spatio-temporal relationship of pedestrian-vehicle interaction (PVI) on potential traffic conflict, a safety analysis approach incorporating machine learning and ordered logistic modeling is proposed. The pedestrian-vehicle crossing data at unsignalized crosswalks was collected through unmanned aerial vehicle (UAV). The 876 pairs of PVI were extracted from of trajectories road users. Considering the dynamics of interactions, the interaction indicators based on competition for right-of-way and collision relationships were proposed. A representation learning model was trained to capture time-series data of PVI, transformed these into potential representations, and clustered them to identify and analyze typical interaction patterns and features. Utilizing these patterns and varying conflict severities, multiple ordered logistic models were developed to investigate the factors influencing conflict risk and to explore differences in risk causation across distinct interaction patterns. The findings reveal that PVI can be categorized into three distinct patterns: near-interaction, far-soft-interaction and far-hard-interaction; Reductions in relative pedestrian-vehicle distances, increases in lower vehicle speed limits and upper pedestrian speed limits are common factors that reduce conflict severity; For near-interactions, rapid pedestrian deceleration, right-of-way competition, and high-speed vehicles reduce conflict risk, and both extreme vehicle deceleration and rapid pedestrian acceleration increase the danger; For far-interactions, an increase in the pedestrian speed lower limit leads to an increase in conflict risk; Rapid vehicle deceleration and right-of-way competition reduce the conflict risk for far-soft-interactions; Pedestrian sharp deceleration and high-speed vehicles are the main factors that reduce the conflict risk of far-hard-interactions, while pedestrian sharp acceleration raises the danger. The application of the combined approach reduces the influence of the heterogeneity of PVI behavior on the analysis results. The conclusions of the study provide a theoretical basis for enhancing safety in pedestrian crossing on urban roads.

End-to-end autonomous driving methodologies eliminate the need for manually defined rules and explicit module interfaces. Instead, these approaches directly map trajectory points or control signals from raw sensor data, thereby addressing the inherent shortcomings associated with traditional modular methods, such as information loss and cascading errors, and overcoming the performance limitations imposed by rule-driven frameworks. Recent advancements in self-supervised-learning-based generative artificial intelligence have exhibited substantial emergent intelligence capabilities, significantly promoting the evolution of end-to-end methodologies. However, the existing literature lacks a comprehensive synthesis of the advancements in generative end-to-end autonomous driving. Consequently, this paper systematically reviews the research progress, technical challenges, and developmental trends in end-to-end autonomous driving. Initially, the input and output modalities of the end-to-end models are delineated. Based on the historical progression of end-to-end autonomous driving, this paper provides an overview and comparative analysis of the foundational concepts, current research status, and technical challenges of traditional, modular, and generative end-to-end methods. Subsequently, the evaluation methodologies and training datasets utilized for end-to-end models are summarized. Furthermore, this paper explores the challenges currently faced by end-to-end autonomous driving technologies in relation to generalization, interpretability, causality, safety, and comfort. Finally, predictions are made for the future trends of end-to-end autonomous driving, emphasizing the fact that edge scenarios provide critical support for the training of end-to-end models, which can enhance the generalization capabilities. In addition, self-supervised learning can effectively improve training efficiency, personalized driving can optimize user experience, and world models represent a pivotal direction for the further advancement of end-to-end autonomous driving. The findings of this research serve as a significant reference for refining the theoretical framework and enhancing the performance of end-to-end autonomous driving systems.

Fault diagnosis of power battery systems is key to ensuring the safe and reliable operation of electric vehicles, in which the avoidance of false alarms not only reduces the driver's anxiety regarding vehicle safety but is also necessary for the practical application of the diagnostic method. Therefore, it is crucial to improve the reliability of the method. Abnormal voltage fluctuations in a power battery system are critical signals released by the deterioration of battery performance; hence, entropy methods, which can satisfactorily assess the degree of data dispersion, have been widely studied in battery fault diagnosis. However, when the classical Shannon entropy method based on interval probability was validated in engineering practice, many primary and secondary false-alarm single cells were found in the results. Vehicle voltage data with thermal runaway accidents were first used to analyze the fault diagnosis principle of the model to improve the accuracy of the method. Furthermore, based on normal vehicle operation data, the false-alarm mechanisms of the model in two typical scenarios were investigated. Under the above conditions, two measures were proposed to mitigate the false and missing alarm problems of the original method: the data optimization method and the kernel density estimation and entropy fusion method. Finally, real fault samples with different fault characteristics were selected to test the generalization ability of the algorithms, and their validity and reliability were verified separately. Based on a large amount of normal in-service vehicle data, a comparative analysis of the performance before and after model optimization was conducted. The results show that the relative false alarm rates of the two methods on normal vehicles decrease by 90% and 98%, respectively. Thus, this study significantly improves the reliability of the diagnostic strategy, promotes the online real-vehicle application of the methods, and provides ideas for analyzing and optimizing the accuracy of other fault diagnostic strategies.

To evaluate the fire resistance of hanger systems in suspension bridges, vehicle fires are classified into five levels. Levels 1 and 2 represent passenger vehicle fires, levels 3 and 4 correspond to truck fires, and level 5 represents tanker fires. These vehicle fires are characterized by distinct maximum heat release rates and burning durations. The proposed hierarchy was validated using existing vehicle fire experiments. Geometric features of flames are established for Levels 3, 4, and 5 vehicle fires based on previous vehicle fire incidents. For passenger vehicle fires, a cylindrical flame radiation model was employed to compute spatial radiative heat flux, validated through three full-scale car fire tests. In the case of truck fires, a prismatic flame radiation model was used to calculate spatial radiative heat flux. For tanker fires with crosswinds, a computational fluid dynamics method validated by a liquefied natural gas trench fire test was employed to calculate the heat flux envelope on the hanger surface. An incremental temperature calculation formula for hanger cross-sections with radiative heat flux boundary conditions was derived, and validation was performed using finite element models. Using mechanical property tests of high-strength steel wires at high temperatures, a quantitative relationship between critical temperature and design safety factor of hangers is developed based on the ultimate load-carrying capacity at high temperatures. Finally, integrating the above outcomes, a five-step theoretical framework is proposed to evaluate the fire resistance of hanger systems under graded vehicle fires. This algorithm can serve as a reference for the assessment and fire-resistance design of hanger systems in suspension bridges.

Oil-tanker explosion fire has enormous power and poses a severe threat to the safety performance of crossing sea bridge. In order to study the structural response of suspension bridges exposed to complex extreme fire environments caused by oil-tanker explosions, and to clarify the safety of suspension bridges under complex extreme fire loads, a large-span suspension bridge was selected as the research object. The prediction process of suspension bridges safety performance (fire resistance) during oil-tanker explosion fires was provided. Firstly, the computational fluid dynamics-finite element method (CFD-FEM) coupling method was used to reconstruct the oil-tanker explosion fire environment. A three-dimensional multi-scale numerical prediction model for local girder segment and the entire bridge structure were established. The heat transfer mode of bridge segment and performance evolution of the entire bridge structure during oil-tanker explosion were revealed in depth. Subsequently, the high-temperature response and failure mode of steel box girder (stiffening girder) under oil-tanker explosion were studied, and the effects of different fire positions, distance from the fire surface to bottom plate of steel box girder, and wind speed on the fire response behavior of suspension bridge were analyzed. A fire resistance limit warning method for suspension bridges exposed to oil-tanker explosion environment was proposed. The research results indicate that the deformation of local suspension bridge segment under oil-tanker explosion continues to increase. And the fire affected bridge segment shows a failure mode of overall downward deflection followed by upward bowing in middle area, resulting in a development trend of first increasing and then decreasing for suspension cable force in the middle area. The fire position has a significant impact on the overall structural performance of the suspension bridge. As fire position approaches the mid span area, the deflection of girder segment in the middle area increases by 62% compared to the girder segment adjacent to the tower. When the distance from fire surface to steel box girder is reduced from 50 m to 20 m, the peak deflection and total bowing amplitude (the difference between peak values of deflection and bowing) of local girder segment increase by more than 19%, and the structural failure time is advanced by 10 minutes. Wind speed would change the shape of deflagration flame, significantly affecting the distribution of heating surfaces and high temperature response characteristics on both sides of box girders. When wind speed is 8 m·s^-1, fire intensity of the windward side box girder is significantly reduced, and the total bowing amplitude of bottom plate is reduced by 17% compared to 2 m·s^-1. The critical temperature during the bending deformation of steel box girder bottom plate is between 510 ℃-550 ℃, and limit temperature during the buckling instability of steel box girder bottom plate is between 685 ℃-715 ℃. The critical temperature and limit temperature can be used as two-stage warning temperatures for safety performance, thereby achieving two real-time warnings before steel box girder failure. The research conclusion can provide theoretical bases for the safety performance monitoring and early warning of cable supported steel bridges in complex fire environments, and further guide the safe operation and maintenance of similar bridges.

The cable is susceptible to corrosion, vehicular-induced fire, and other detrimental effects during bridge services. The current studies on the structural safety assessment methods of in-service cable-stayed bridges under complex environmental conditions are not complete. Therefore, this study focuses on the cables of a cable-stayed bridge with a service life exceeding 25 years and conducts tensile tests at elevated temperatures to obtain corresponding degradation patterns and computational models. After defining the degradation rule of in-service cable at high temperatures, a fire source model caused by a 300 MW tank car was created using the Fire Dynamics Simulator. The heat transfer analysis model and the structural model of the in-service cable-stayed bridge were established using ANSYS, and structural thermodynamic coupling analysis was conducted. The results indicate that under the most unfavorable conditions, the fire from the 300 MW oil tanker leads to the failure and rupture of three groups (six) of cables near 1/4 of the span of the bridge, and the failure times of the groups are 551 s, 592 s, and 1 064 s, respectively. Owing to the effects of the bridge dead weight, the maximum deflection of the main girder decreases by 0.08 m, the deflection span ratio is 1/1250, and the maximum deflection rate is 9.6 mm·min^-1. All the outcomes are within the limits of the JTG/T D65-01—2007 standard. The 300 MW tanker truck fire cannot cause a complete collapse of the entire bridge structure but results in severe and irreversible damage to the cable. The study's findings provide a reference for structural safety analysis and evaluation of in-service bridges in complex environments.

This study investigates the effect of heavy rainfall on the smoke-movement characteristics and smoke control of tunnel fires using a reduced-scale (1∶15) experimental platform based on the Froude criterion. A series of tunnel fire tests was conducted based on four different rainfall intensities, four heat-release rates, and six longitudinal ventilation velocities. The results show that the flow field at the tunnel entrance is affected by raindrop behaviors, such as diffusion and drag, when heavy rainfall occurs. Induced airflow is directed downstream of the tunnel. When the rainfall intensity increases, the induced airflow velocity increases and smoke is restricted upstream of the tunnel. The first part of the longitudinal ventilation velocity was used to eliminate back-layering caused by the hot-smoke driving force, and the other part was used to offset the effect of induced airflow in the tunnel. The back-layering length increases with the rainfall intensity. The accuracy of the critical wind speed was verified based on a previous model. Under fire-source power levels of 3.03, 6.06, 9.09, and 12.12 kW, the critical ventilation velocity increased by 25.6%, 17%, 14%, and 9% under the effect of heavy rainfall, respectively. The higher the heat-release rate, the less affected is the critical ventilation velocity by heavy rainfall. Based on the temperature-distribution law of a tunnel ceiling subjected to heavy rainfall, the mechanism by which heavy rainfall affects smoke movement was clarified, and the effect of heavy rainfall on the key parameters for tunnel-disaster prevention and ventilation design was investigated.

The accuracy and speed of evaluating the residual bearing capacity of the tunnel structure after a fire event directly affects the reliability and economy of emergency disposal and repair work. Herein, the shield tunnel was regarded as the research object and the structural damage index system after a fire event was determined by combining the analytic hierarchy process and numerical simulations. The influence of each index on the residual bearing capacity of tunnel structure was studied. The damage degree was divided into five grades: mild, moderate, severe, extreme, and damage, based on cluster analysis. The mechanical performance evaluation model of the shield tunnel structure after a fire event was based on a neural network. The results show that the damage area, spalling depth, concrete deterioration depth, concrete strength reduction, and bolt strength reduction are the main factors affecting the residual bearing capacity of shield tunnel after a fire event. Following the increase in the deterioration degree of each factor, the mechanical properties of the structure decrease, but the decrease range and manifestation differ significantly. The BP neural network can be effectively used to evaluate the performance of the shield tunnel after a fire event, and the average error is less than 10%.

Continuous rigid-frame bridges with cast-in-place or prefabricated super-high piers with grouted sleeve connections (CSHP-B or PSHP-B) exhibit significantly high flexibility. The earthquake-induced pounding and its influence on the seismic performances of CSHP-B and PSHP-B remain unclear. In this study, 1/20-scaled models of a CSHP-B and PSHP-B and relevant adjacent approach model bridges were designed and manufactured. Subsequently, shaking table tests were conducted regarding the pounding response and its relevant influence on the seismic performances of the CSHP-B and PSHP-B under excitations of non-long-period ground motions, non-pulse-like long-period ground motions, and near-fault pulse-type (NFPT) ground motions. The experimental results show that when considering pounding, the peak displacements of the main beams (pier top) of the CSHP-B and PSHP-B decreased, while the relevant peak displacements of the adjacent bridge model increased. Moreover, considering pounding, the peak displacements of the main beams (pier top) of the PSHP-B are larger than those of the CSHP-B; the decreasing and increasing ratios of the peak displacements of the PSHP-B (-8.6%--19.8%) and adjacent model bridge (+6.9%-+17.5%) are both higher. The peak pounding force and pounding number between the PSHP-B or CSHP-B and the adjacent model bridge both increase with increasing excitation intensity. However, the pounding number between the main bridge and the adjacent model bridge decreased under the excitations of the NFPT ground motions. In addition, compared with the CSHP-B, the peak pounding force and pounding number between the PSHP-B and the adjacent model bridge are relatively larger and smaller, respectively. Furthermore, when considering pounding, the influence of the high-order modes on the displacement response of the main beams increases, while the strain and moment responses of the pier bottoms decrease, and the development of the damage mode of PSHP-B changes more significantly.

A collision-avoidance facility consisting of an internal sand-filled permanent cofferdam combined with a steel pipe pile is proposed for installation at bridge piers to enhance transportation safety and prevent collisions between ships and bridges. This collision avoidance facility was designed and built considering a bridge at the scale of 1∶25. Horizontal impact tests were conducted to compare the depth of impact and impact force-time curves of scaled bridge piers with and without a collision avoidance facility under impact loading. Non-linear finite element models of the bridge pier were established to simulate the dynamic response and damage process, with the finite element results compared with experimental results to verify their reliability. Combined with the actual project, a finite element model of the collision avoidance facility was established, with numerical simulation studies conducted to calculate the reduction efficiency of the collision avoidance facility on the peak impact force under the four groups of working conditions. The research results show that, compared with a bare bridge pier, a steel-pipe-pile cofferdam can effectively protect the ship's bow after a collision, and the reduction rate of the collision depth is 34.25%. The errors in the peak impact force and impact depth were under 10% and 5%, respectively, thus confirming the accuracy of the finite element model. With the increase in mass and impact speed for the ship, the peak impact force reduction rate is larger, wherein the 200 000 t ship with a 3.97 m·s^-1 reduced load positively impacted the SZ02# south tower and the peak impact force reduction rate reached 38.11%. Therefore, a steel-pipe-pile cofferdam collision avoidance facility filled with energy-dissipating sand can better reduce the impact force and damage to the ship bow, bridge pier, and this facility during collisions, thus providing a reference for bridge collision-avoidance design.

Ice-induced vibrations significantly affect the comfort and safety of bridges in cold regions. This study considers a typical simply supported girder bridge in the upper Songhua River, exposed to ice. Using a scaled bridge model established using similarity theory, we investigate the impact of an ice row's impact location and impact velocity on dynamic ice loading and the resulting response of the bridge pier structure. We decipher the time-course characteristics of the ice-induced force and determine the law of ice-induced vibration coupling. The test results show that extrusion crushing occurs first after the ice row impacts, followed by longitudinal cleavage. The ice-row crushing process occurs simultaneously with the extrusion of crushed ice and spalling. The ice-breaking prism structure is very efficient: the ice force value of the ice platoon in the test for an impact speed of 7 cm·s^-1 is approximately twice the value for 52.63 cm·s^-1. The structural response to impact on the ice-breaking prism is significantly smaller than that to impact on the round pier. The transverse acceleration response peaks at the moment when the ice row splits, and the strain and displacement response amplitudes do not change significantly with the ice-row velocity. The strain and displacement curves at the ice loading stage agree well with the ice force action process. The strain and displacement responses can therefore be utilized to invert the ice force time course, thus providing a solution to the difficult challenge of deploying force sensors. Hence, this modeling study of ice-induced vibrations in bridge piers in cold regions provides a data basis for the safety assessment, structural design, and abnormal warning.

As an inherent attribute of bridges, damping can greatly reduce the induced dynamic responses and can further improve the safety and comfort of driving. However, the effect of bridge damping is often ignored when solving the dynamic equations of space bridges under moving vehicle conditions. For this reason, the analytical solution for the vehicle-bridge interaction vibration was derived by considering the effects of the beam's damping using the example of a single-axle vehicle passing over a thin-wall box girder. The feasibility of identifying the torsional-flexural frequencies and vertical frequencies was verified using the analytical solution and the vehicle or vehicle-bridge contact responses. Additionally, the characteristic of the bridge's monosymmetric cross-section caused the center of mass and shear center to be offset, thus implying that its lateral and torsional movements were coupled, while the vertical motion was relatively independent. Additionally, the bidirectional damping characteristics that allow the assignment of various damping ratios for vertical and transverse-torsional motions were considered. Meanwhile, the rocking and vertical contact responses of the vehicle were derived based on the cross-sectional rigidity hypothesis, which realized that the torsional-flexural frequencies and vertical frequencies of the thin-walled beam were separated. The negative influences of road roughness in the effort to retrieve the bridge frequencies were eliminated by using residual contact response technology. The results show that: ① The first several torsional-flexural and vertical frequencies of the thin-walled beam can be respectively identified by the rocking and vertical contact responses; ② The damping ratios of vertical and torsional-flexural directions only affect the visibility of their directional frequencies, especially higher-order frequencies; ③ The residual contact response technology yields a good performance in identifying the beam frequencies, even under poor pavement conditions; ④ A running speed of 10 m·s^-1 (36 km·h^-1) for the test vehicle is recommended in practical applications.

The pavement roughness model plays a crucial role in vehicle-bridge coupled vibration analysis. The transverse distribution and degradation of pavement roughness have a substantial impact on the analysis outcomes. However, existing research often overlooks the influence of time-varying factors related to vehicular load during pavement deterioration and neglects the transverse distribution characteristics of the pavement. To address these limitations, this study proposes a method to establish a time-varying three-dimensional pavement model and investigates its effects on the vehicle-bridge coupled system. Firstly, a time-varying vehicular load model is developed based on traffic volume development law, and data are acquired from typical highways. By integrating the degradation of pavement roughness and the lateral distribution of vehicular trajectories, a simulation method for the time-varying pavement is established. An analysis is then conducted on a practical bridge engineering case to assess the influences of the simulation process on the response of the vehicle-bridge coupled system. The research findings demonstrate that the proposed time-varying vehicular load model effectively captures the evolution patterns of traffic volume and vehicular mass distribution. Over time, the displacement power spectral density of the pavement steadily increases. The transverse displacement of the pavement exhibits a concave-convex distribution pattern, which corresponds to the distribution of cumulative-equivalent axle loads. Notably, the impact coefficient and vehicular acceleration increase rapidly, yielding an increasing rate as time progresses. Utilizing the proposed non-uniform excitation pavement model reveals its sensitivity to changes in the vehicle loading position. Specifically, as vehicles approach the passing lane, both the impact coefficient and vehicular acceleration exhibit smaller values. The calculated impact coefficient obtained using the non-uniform excitation pavement model falls within the range between the worst and best cases of the uniform excitation pavement. Furthermore, the non-uniform excitation pavement model has a greater influence on lateral vehicular acceleration compared with that induced by vertical acceleration and causes an amplifying effect on lateral acceleration. These research findings provide valuable insights for assessing the dynamic performance of bridges and establishing appropriate impact coefficient values.

To evaluate the safety of the double-deck highway-railway steel truss bridges in strong winds, wind-tunnel tests were used to obtain accurately the mutual aerodynamic characteristics of a double-deck highway-railway truss bridge and vehicle under crosswind conditions. Using the highway-railway double-deck truss and the double-deck bus as the research objects, a rigid pressure model of the bridge and vehicle with a geometric scale ratio of 1:50 was designed and implemented. The wind pressure distribution on the surface of the vehicle under the vehicle-bridge combination condition was tested based on the wind-tunnel test, and the influence of the lateral distance of the lane on the bridge deck of the double-deck truss bridge on the aerodynamic characteristics of the vehicle was analyzed. Furthermore, the wind pressure coefficient distribution on the vehicular surface was analyzed to explore the mechanism responsible for the changes in the vehicle's aerodynamic coefficient. The results show that the change of lateral lane distance in the double-layer truss bridge has a considerable influence on the side and the lift force coefficients followed by the yawing and the rolling moment coefficients. However, the influences on the drag force coefficients and the pitching moment coefficients of the vehicle are small and have a finite discreteness. Owing to the wind-tunnel effect, the side force coefficients of the double-deck bus in the double-deck truss bridge are always greater than those of a stationary bus on the bridge deck. Because the deflection moment is the result of the yawing moment coefficients, its change rule is the same as that of the side force coefficients. The side force coefficient is mainly determined by the size of the windward side. Moreover, The lift coefficient (CL) of a double-deck passenger car when situated on a truss bridge is greater than its lift coefficient when inside the truss bridge.

Vehicle load is one of the most important loads of long-span bridges, and it is also the main cause of fatigue deterioration of most bridges. However, the bridge weigh-in-motion system is expensive and cannot be distributed across the bridge, which means the dynamic identification of bridge vehicle load distribution information is still a challenging problem. This paper introduced computer vision and deep learning technologies to meet the needs of long-span bridge structural health monitoring, and established an integrated intelligent identification system for bridge vehicle load spatio-temporal distribution. Firstly, we studied the vehicle identification method based on traffic monitoring data and deep target detection network, trained the YOLOv7 deep network for vehicle target detection tasks, and obtained vehicle images containing information such as vehicle type and time in single camera through the trained model. Then, we introduced the HardNet depth feature descriptor to establish an image point feature matching method, designed a searching and matching strategy through distributed surveillance video data to achieve the matches of vehicle image data corresponding to multiple monitors in the traffic flow direction, and the vehicle position was estimated by linear interpolation of the monitoring blind area to obtain the spatio-temporal distribution of vehicles on the bridge. Finally, the methods were integrated to establish the vehicle load spatio-temporal distribution identification system. This system can automatically output the spatio-temporal distribution of vehicle load and visualization results combining with dynamic weighing data, realizing an integrated process from monitoring data to vehicle load spatio-temporal distribution. In this paper, the monitoring data of Jiujiang Yangtze River Bridge was used for verification. The results show that the system can achieve vehicle identification and tracking based on video data, with computational time less than the duration of the input video and an accuracy rate of 97.62% for large vehicle matching, allowing for rapid and accurate identification of vehicle load distribution. The system is of great significance to ensure the safety of bridge service and has broad application prospect.

To address the limitations in generalization capability and environmental adaptability of existing vehicle weighing systems based on computer vision, this paper proposes a non-contact weight-in-motion method based on large vision model and machine learning. Initially, an innovative edge detection algorithm using vision large model was designed for the precise acquisition of tire deformation parameters. Subsequently, a universal recognition model comprising a complete tire character database was developed based on Mask-RCNN (Mask-Region-based Convolutional Neural Networks), capable of accurately labeling and extracting features from the tire sidewalls. Furthermore, a machine learning model for predicting the contact force between vehicle tires and the road surface was constructed using LightGBM (Light Gradient Boosting Machine), and its accuracy, feasibility, and effectiveness were verified. Notably, through testing on SUV vehicles and heavy-duty trucks, this method has been demonstrated to have a maximum error of less than 5% compared to traditional weight-in-motion systems, showcasing high precision and superior performance. This study introduces a vehicle weighing technology that does not require the installation of sensors, offering a user-friendly and cost-effective solution. The potential applications of this technology are extensive, particularly within the realms of toll stations and highways, and other traffic engineering sectors.

Bridges play a crucial role in transportation systems. Therefore, it is necessary to evaluate the health conditions of bridges owing to damage and deterioration during long-term operation. In addition to bridge modal parameters, the influence line of a bridge is required for bridge condition evaluation. Bridge displacement or strain induced by a moving inspection vehicle is typically adopted in conventional methods to extract the influence line of a bridge; hence, sensors should be installed on the bridge, and traffic should be blocked during measurements. In this study, the coupling effect of heavy vehicles and bridges was investigated to derive the dynamic response of a heavy vehicle passing over a bridge. Moreover, a method for identifying the bridge influence line based on a weighted polynomial chirp-let transform was proposed, which was verified using numerical simulations and experiments. The proposed method uses the vertical acceleration of a passing heavy vehicle instead of the displacement of the bridge to extract the influence line of the bridge; therefore, it is a typical indirect method that can be easily implemented in practical applications. The numerical simulation results show that the analytical solution of the dynamic responses of the bridge and vehicle obtained in this study is more accurate than the classical solution in which the coupling effect of the vehicle and bridge is ignored. The passing vehicle is simplified as a moving constant load, and the bridge influence lines can be identified accurately using the proposed method. The experimental results show that the proposed method performs satisfactorily at different driving speeds and vehicle masses.

In recent years, with increasingly harsh tunnel construction environments and the labor-force population aging, the replacement of human labor with machines has become an inevitable trend in current tunnel construction developments. As one of the two mainstream tunnel construction methods currently employed, the drill-and-blast method possesses greater flexibility and adaptability than the tunnel-boring machine method because of its ability to accommodate various tunnel cross-sectional shapes and geological conditions. Consequently, this method is being more widely applied. Moreover, with the rapid advancement and profound integration of technologies such as big data, Internet of Things, 5G communication, and artificial intelligence, intelligent construction techniques for drilling and blasting methods have witnessed rapid progress. Significant achievements have been made in the intelligent evaluation and blasting design of tunnel surrounding rock, intelligent construction equipment, intelligent construction management platforms, and auxiliary process equipment. Thus, through further integration of information technology, tunnel construction technology, and intelligent equipment, a new model for tunnel construction has emerged, namely, intelligent tunnel construction. This study presents a comprehensive account of the evolutionary trajectory of the drilling and blasting methods in tunnel construction, delving into the intricacies of the current intelligent construction system employed in this method. Subsequently, the notable advancements in intelligent evaluation of the surrounding rock and intelligent blasting design are elucidated. Furthermore, the development and enhancement of intelligent rock-drilling, anchor, arch, wet spraying, and lining trolleys are traced. The current state and application of intelligent control platform technology in tunnel construction is provided. Building upon prior research achievements, this article also discusses the development of tunnel-face surveying and measurement robots as well as collapse-warning robots for tunnel rock masses, with some of these innovations already being successfully implemented on site. Finally, research ideas are proposed to address the challenges of autonomous navigation, control, and operation of intelligent robots in long, large, and deeply buried tunnels, with the aim of providing guidance for the development of tunnel automation technology and proactive disaster prevention and control in China.

With burial depth, the section size and length of tunnels gradually increase, and the physical and mechanical parameters of the tunnel face surrounding the rock directly affect the tunnel face stability. The traditional rock laboratory test method consumes considerable manpower and material resources, making it challenging to guide the intelligent design and construction of tunnels. Based on drilling parameters, this study proposes an intelligent analysis method for the uniaxial compressive strength and elastic modulus (R_b-E) of rocks surrounding a tunnel face. First, a formula for calculating the mechanical specific energy during the combined impact-thrust-rotation rock-breaking process of a computer three-arm drilling jumbo is proposed from an energy perspective. Subsequently, a numerical simulation was conducted to simulate the rock-breaking process of the computer three-arm drilling jumbo. Using dynamic analysis methods, the drilling speed and dynamic response patterns of the energy inside the dense rock unit and the uniaxial compressive strength and elastic modulus of the surrounding rock per unit volume were analyzed under different uniaxial compressive strength and elastic modulus conditions of the surrounding rock. Subsequently, an analytical model of the uniaxial compressive strength and elastic modulus of the surrounding rock at the tunnel face based on drilling parameters (DP-R_b-E) was established using the energy method. Field drilling experiments and tests of the mechanical parameters of the surrounding rock were performed to verify the reliability of the method, relying on drilling and blasting method tunnel projects such as the Zhengwan high-speed railway and the Yixing liaison line. The results indicate that under the same conditions of thrust pressure, impact pressure, and rotational speed, the greater the uniaxial compressive strength and elastic modulus of the surrounding rock, the slower the drilling speed, and the higher the average internal energy of the dense units of the surrounding rock. The results of field experiments showed that the variation rate between the analytical results and the measured results of uniaxial compressive strength ranged from -5.52% to -20.89%, while the variation rate between the analytical results and the measured results of the elastic modulus ranged from -9.01% to -22.03%.

To address the issues of insufficient recognition accuracy, low robustness, and slow detection speed in existing tunnel face joint and fissure recognition methods, this paper proposes a novel algorithm called mask-region convolutional neural network-EfficientNet (Mask R-CNN-E) based on the Mask R-CNN instance segmentation algorithm for tunnel face joint and fissure recognition. This algorithm incorporates the advanced EfficientNet as the backbone network to enhance the feature extraction capability of Mask R-CNN, thereby significantly improving recognition accuracy. EfficientNet employs a compound scaling method to effectively balance network depth, width, and resolution, achieving an optimal tradeoff between computational efficiency and accuracy. During the model training process, multiscale training and poly-learning rate adjustment strategies were adopted to enhance the robustness of the algorithm. The performance of the algorithm was evaluated using the mean average precision (A_m) metric, and comparative experiments were conducted using the traditional Mask R-CNN algorithm. In addition, a skeleton algorithm was employed to refine the joint and fissure mask outputs of the model to obtain more precise quantitative information on joints and fissures. The results show that the improved algorithm achieved a bounding box mean average precision (b_A_m) of 0.656 and a segmentation mean average precision (s_A_m) of 0.436, with both significantly higher than those of the traditional method, indicating superior recognition accuracy. The improved Mask R-CNN-E algorithm significantly enhances tunnel face joint and fissure recognition, exhibiting stronger robustness and anti-interference capabilities in complex tunnel environments. In terms of joint and fissure length measurements, the algorithmic error was controlled within the range of 1.5%-9.8%, which satisfies engineering requirements. This method not only offers high theoretical accuracy and robustness but also provides more reliable support in practical applications, which is crucial for improving the safety and efficiency of tunnel engineering.

To achieve rapid, automated, and high-precision overall monitoring of the tunnel face, a method based on three-dimensional (3D) laser point clouds is proposed to monitor the tunnel face extrusion displacement field. This includes a simple and highly accurate rigid-body reference introduction method and nonrigid face deformation path-tracking technology for the tunnel. First, a stable rigid-body reference based on the initial support longitudinal direction was proposed based on the assumption of plane strain. The wave characteristics of the initial support were expressed based on two-dimensional continuous wavelet transform, assuming a local small-deformation rigid body. A multilevel iterative registration algorithm was applied to correct the position of the initial support and achieve rigid body reference registration. Finally, the point-cloud normal vector clustering algorithm was used to segment the tunnel face. Based on the assumption of a small local deformation rigid body, the path-tracking algorithm was used to obtain the displacement path of each tunnel face, resulting in the extrusion displacement field of each tunnel face. The results of this study were experimentally verified in highway tunnels. The tunnel face extrusion displacement estimated using the proposed method, was qualitatively compared with that obtained with and without deformation path tracking under traditional rigid body reference. In addition, a quantitative comparative analysis was conducted using the extrusion results monitored by the total station. The results indicate that the research findings of this study can accurately monitor the extrusion displacement field of the tunnel face, and the root mean square error with respect to total station monitoring was 0.83 mm. The results of this study can be used to monitor and analyze the stability of the tunnel face and effectively warn of geological disasters, thereby improving the safety of the tunnel excavation process.