The discipline code system of the National Natural Science Foundation of China (NSFC) serves as the foundation for project application and review management, and provides the framework to guide fundamental research and promote disciplinary innovation. In 2025, the discipline of Architecture and Civil Engineering underwent a systematic review and optimization of its research directions and keyword systems. Specifically, the “E0809 Road and Track Engineering” section has expanded its research scope from six to twenty directions, with a corresponding systematic enhancement of the keyword framework. This revision reflects a strategic transition from conventional engineering disciplines toward green and intelligent systems, resilient safety, systems integration, and engineering solutions for extreme environmental conditions. This paper systematically elucidates the overall ideas and concepts behind this optimization of research directions and keyword systems. Furthermore, it provides an analytical interpretation of future development trends and strategic directions for the discipline.

Recently, significant progress has been made in the construction of domestic and foreign smart highways, which focused on the external perception technologies and vehicle infrastructure collaboration systems. However, there were many limitations including definition, purposes and consistency of framework, making it imperative to optimize the concepts of the traditional road construction, and operation and maintenance. To this end, the development of intelligent transportation system in America, Europe and Japan was summarized in this paper. The experience of these countries was analyzed for reference in our country. And then the typical frameworks of smart highway were given. The physical framework and functional system of auto-intelligence smart highway based on embodied intelligence were proposed, whose intelligent ontology was road infrastructure. Also, it could achieve the empowerment mechanism of whole service life for all-time-all-zone-all-event and meet the functional requirements. First, the auto-intelligence smart highway was seen as an embodied intelligence system. The functional system was established by implementation process analysis method and machine system behavioral control theory. Then, the physical framework was designed based on data types, collection methods, network modes, decisions and service, which could support for five application scenarios. Further, the frameworks and systems of multi-source perception, R2X interaction, and intelligent brain were proposed and their synergistic operation methods were established based on genetic algorithm. Based on embodied intelligence, the data attribute requirements for auto-intelligence smart highway were provided, which could meet the functional purposes. The technology sets and their requirements were also developed, which could achieve fundamental requirements. Finally, the application scenarios were designed, which could lay a good foundation for the sustainable development of construction and operation of smart highway in China. The physical framework and functional system of auto-intelligence smart highway based on embodied intelligence are characterized by clear hierarchy, consistent coordination and good compatibility, whose intelligent ontology is road infrastructure. It could optimize the concepts of the traditional road construction, and operation and maintenance, achieving the empowerment mechanism of whole service life for all-time-all-zone-all-event.

Owing to its significant low-carbon advantages and exceptional mechanical properties, polyurethane binders demonstrate excellent application potential in pavement engineering. However, aging induced by multi-environmental factors, including water, heat, light, and oxygen, represents a critical challenge to the long-term durability of polyurethane binder and pavements. Regarding this issue, this paper systematically reviewed the research progress on the aging and anti-aging of polyurethane materials. The aging mechanism, aging test method, aging evaluation methods, and targeted anti-aging research were summarized and commented based on the requirements of road service. The study showed that the aging of polyurethane is essentially a chemical structural evolution process centered around free radical chain reactions. The generation and transformation paths of active groups are influenced by different environments, thereby affecting the molecular chain breakage, cross-linking, and reconfiguration behaviors. In terms of performance evaluation, existing studies included various methods such as physical properties, static mechanical properties, dynamic viscoelastic properties and microstructure characterization. However, there is a lack of a performance evaluation system that is oriented towards engineering requirements (e.g. high-temperature, low-temperature and fatigue). Molecular structure regulation and targeted anti-aging materials enhance the aging resistance of polyurethane, but the long-term effects on road-use polyurethane binders still require further research. Although substantial progress has been made in aging and anti-aging of polyurethane, the complexity of aging behavior of polyurethane binders used in roadway environments still requires further investigation. Future research should focus on analyzing the multi-source coupled aging mechanisms, constructing and standardizing performance evaluation system towards pavement requirements, designing targeted anti-aging strategy based on aging mechanisms, so as to achieve long-term service of road-used polyurethane binders.

In permafrost regions, accelerated permafrost degradation and thaw settlement are induced by prolonged thermal effects from high-intensity solar radiation on black asphalt pavements, leading to distresses including pavement cracking and differential subsidence. To mitigate the thaw-induced settlement, reduction of cyclic heat absorption in subgrades and maintenance of permafrost thermal stability are of strategic significance for preserving transportation infrastructure and ensuring the socioeconomic sustainability of cold regions. Mature cooling technologies like thermal pipes, ventilated ducts, pile-net embankments are widely implemented for subgrade cooling and preservation of permafrost thermal equilibrium, while research on cooling technologies of pavement engineering in the thermal stability protection of permafrost remains limited in cold regions, with long-term durability deficiencies impeding their widespread application and development. This study comprehensively reviewed recent advances in mainstream pavement cooling technologies, which were consisted of porous pavements, heat-reflective coatings, composite coatings, thermal-resistant pavements and phase-change material pavements, along with the laboratory and field evaluation methods for cooling performance. Firstly, the cooling mechanisms, material compositions, and fabrication methods of each technology were introduced. Subsequently, based on the photothermal and other physical properties of cooling materials and their core action mechanism, the cooling effect and pavement performance of various cooling technologies and their key influencing factors were analyzed. The advantages and limitations of indoor and outdoor cooling performance test methods were concluded around the light source characteristics, equipment function modules and test accuracy of photothermal irradiation experiments. Finally, these pavement cooling technologies were summarized according to the application scenarios and adaptability problems in cold regions. Corresponding improvement measures of various cooling technologies and test methods were proposed addressing the existing shortcomings and scientific issues related to the technologies and testing methods. Prospects and future research directions for the development of asphalt pavement cooling technology systems in cold regions were also suggested.

Polyurethane exhibits outstanding advantages such as super-strong adhesion, controllable performance, adaptability to various substrates, and environmental friendliness, demonstrating great potential to overcome the performance limitations of traditional road bonding materials. However, the water erosion resistance of polyurethane mixtures is insufficient, and the mechanical strength degradation rate is high after freeze-thaw cycles, which can easily cause raveling and pothole damage, seriously affecting the durability of polyurethane pavement. To clarify the degradation mechanism of polyurethane mixture performance under freeze-thaw cycles, this study first analyzed the mesoscopic damage characteristics of the adhesive from perspectives such as contact angle. Subsequently, with the aid of atomic force microscopy (AFM), the microscopic deterioration law of the interfacial bonding between polyurethane and aggregate was investigated, using the modulus of the interfacial transition zone as an evaluation index. Finally, combined with the macroscopic physical indices of the polyurethane mixture, the evolution mechanism of pavement performance was elucidated. The research show that freeze-thaw cycles lead to an increase in microscopic voids inside the polyurethane adhesive and their development into micro-cracks, gradually reducing its thermal stability, mechanical properties, and hydrophobicity. The freeze-thaw failure occurring in the interfacial zone between polyurethane and aggregate contributes more to the overall performance degradation of the polyurethane mixture than the self-damage of the polyurethane adhesive. Under freeze-thaw cycles, the pavement performance of the polyurethane mixture exhibits a segmented trend, with significant degradation in the early stage (a reduction of approximately 67.1%) and moderate degradation in the later stage (approximately 14.5%). The strength loss of the polyurethane mixture during the early stage (0-8 cycles) of freeze-thaw is mainly caused by the decline in the hydrophobicity of the polyurethane adhesive. However, after multiple freeze-thaw cycles (8-16 cycles), the contribution of the polyurethane-aggregate interface damage to the performance degradation of the polyurethane mixture gradually increases. The addition of a silane coupling agent can form “molecular bridges” at the interface. By enhancing the interfacial bonding, the freeze-thaw resistance of the polyurethane mixture can be improved by 3-4 times. The research results are not only significant for improving the freeze-thaw resistance of polyurethane mixtures but also lay a foundation for solving the problem of raveling and potholes in polyurethane pavements.

The morphology of aggregates significantly influences the performance of asphalt mixtures. To overcome the limitations of traditional aggregate morphology analysis methods, machine learning algorithms were introduced to achieve rapid identification and prediction of three-dimensional (3D) morphological parameters based on multi-angle, multi-feature two-dimensional (2D) morphological parameters. 3D morphological parameters and 2D morphological parameters from three projection directions for 400 aggregate particles were obtained by Computed Tomography and image processing. Prediction models for 3D parameters were established based on five machine learning algorithms. The predictive performances were evaluated. The contributions of different input parameters were analyzed by Shapley Additive Explanations. The study reveals significant differences in morphological parameter distributions among different 2D projection planes. The maximum difference between 2D and 3D shape parameter distributions reaches 25.0%. It will result in large errors in distribution of aggregate morphology parameters characterized by solely data from a single projection plane. The Extreme Gradient Boosting (XGBoost) algorithm achieves a balance among predictive performance, generalization ability, and interpretability, with determination coefficients (R²) of 0.86 for the training set and 0.83 for the prediction set. Aspect ratio (I_AR) and shape factor (I_SF) are identified as the core parameters for predicting the 3D needle index (contribution: 89.5%), while circularity (I_Cir) dominates the prediction of sphericity (contribution: 92.1%). All 2D morphological parameters make contributions to predicting the 3D angularity index. Increasing the number of projection planes effectively improves model performance. Compared to models using parameters from a single projection plane, models utilizing parameters from three projection planes improve prediction accuracy by at least 20.0%. Parameters from the horizontal projection plane (top view) contributes almost negligibly to the prediction model of 3D needle index, with an R² below 0.10, indicating limited reliability of top-view morphological analysis alone. These results provide an effective approach for rapidly and accurately identifying the 3D morphological characteristics of aggregates during the design phases of asphalt mixtures, thus facilitating the enhancement of performance through optimized aggregate morphology control.

Aiming at the engineering problem that granite aggregate has insufficient adhesion with asphalt, making it difficult to be applied in asphalt surface layers of high-grade highways. Using granite porous asphalt mixture as the matrix and cement-based grouting material as the filler, a granite-based semi-flexible pavement material and its preparation process were proposed, and the grouting rate was used to evaluate the filling degree of the matrix voids by the cement. By means of small-scale pull-out tests, the interfacial bond strength among aggregate, asphalt, and cement was quantitatively analyzed, and combined with macro- and micro-morphological characteristics of the interface fractures, the multi-phase interfacial interaction mechanism was revealed. Moreover, the pavement performance was systematically evaluated through high-temperature rutting, low-temperature bending, and water stability tests. The test results indicate that the grouting saturation ratio of the granite-based material reached 95.27%, and its interfacial bond strength ranged from 0.42 to 0.85 MPa, representing an increase of approximately 70% compared with that of the basalt-based counterpart. This finding demonstrates that granite can serve as a viable alternative to commonly used basalt as the skeleton aggregate for semi-flexible pavement materials. The interface fractures of the granite-based material are composed of cement, asphalt, and aggregate, and the interaction among the three heterogeneous materials forms a multi-phase interface. Physical adhesion between asphalt and aggregate forms the initial interfacial bonding, while the coupling of physical adsorption, chemical bonding, and mechanical synergy among cement, asphalt, and aggregate enhances the interfacial adhesion; moreover, cement hydration gels interpenetrate with asphalt, forming an asphalt-cement interpenetrating network, which strengthens the interfacial bonding. The Marshall stability, dynamic stability, residual stability ratio, and freeze-thaw splitting strength ratio of the granite-based semi-flexible pavement material are 38.8 kN, 78 535 times·mm^-1, 98.3%, and 99.3%, respectively, which are basically the same as those of the commonly used basalt-based material, but its maximum flexural tensile strain decreases by 6.4% compared with that of the basalt-based material. This study provides an innovative solution and technical support for the efficient application of acidic aggregates in semi-flexible pavements.

To address the limited ultraviolet (UV) aging resistance of raw lignin in asphalt, laccase-activated lignin was prepared using a mild and controllable laccase-catalyzed oxidation method. The regulatory effects of laccase activation on the chemical structure and antioxidant activity of lignin were analyzed. The dispersion state and compatibility of laccase-activated lignin in asphalt were evaluated. The influences of laccase-activated lignin on the rheological properties and aging behavior of asphalt were further investigated. On this basis, the cracking resistance of asphalt mixtures under UV aging conditions was assessed. The results show that the laccase-catalyzed treatment preserves the aromatic backbone structure of lignin while promoting the exposure of phenolic hydroxyl groups, thereby enhancing its free-radical scavenging capacity. At the same dosage, laccase-activated lignin modified asphalt exhibits superior high-temperature rutting resistance, fatigue resistance and cracking resistance potential compared with raw lignin modified asphalt. Laccase-activated lignin effectively retards the photo-oxidative hardening and embrittlement of asphalt. Comprehensive evaluation of rheological properties and aging resistance shows that the optimal dosage of laccase-activated lignin is 6%. Laccase-activated lignin modified asphalt mixtures show significantly lower reductions in pre-peak fracture energy and fracture toughness after UV aging than base asphalt mixtures. Their cracking resistance retention is comparable to that of SBS-modified asphalt mixtures, demonstrating good UV aging resistance and engineering application potential. The results confirm that laccase-activated lignin is a promising biomass-based anti-UV aging modifier for asphalt, providing theoretical support and technical guidance for the development of renewable high-performance road materials.

Accurate identification of cable force in cable-stayed bridges is essential for ensuring the safe operation of the structure. The vibration method is a commonly used approach in the field of cable force identification. However, traditional force-frequency formulas are often derived based on the assumption of ideal boundaries, making it difficult to account for practical issues such as boundary disturbances at the pylon and girder ends, as well as interference from embedded pipes at both ends and internal and external dampers. To address these issues, this study proposes a cable force identification method that accounts for the coupled vibration of the tower-cable-beam-damper system, aiming to mitigate the effects of complex boundary changes and vibration-control devices on cable dynamics. First, the transmissibility functions among the tower-cable-damper responses are derived, effectively eliminating the interference of tower-beam-damper vibrations in cable spectrum analysis. Second, the partial coherence functions among the tower-cable-damper responses are formulated to quantify the contributions of different excitation sources to the cable vibration response. Third, a cable model updating method is developed, using frequency targets identified from the transmissibility function. Subsequently, by designing an optimization space for cable force and bending stiffness, time-varying cable forces are identified. Finally, field tests on a cable-stayed bridge validate the accuracy of the proposed method, the test results show that, for cables with an external damper, the calculation error of existing cable force identification formulas exceeds 40%, whereas the proposed method maintains a calculation error of less than 8% for both long and short cables, thereby providing an effective and practical method for tracking and identifying cable forces in cable-stayed bridges equipped with an external damper.

Cable force is a key indicator for evaluating the in-service state of stay cables. However, fluctuations in cable force caused by temperature variations can mask anomalous changes in cable force due to damage. To address this, this paper proposes a cable force prediction and anomaly assessment method based on nonlinear modeling of “multi-point temperature-cable force”. First, the nonlinear mapping mechanism between temperature changes in components (pylon, girder, cables) and changes in cable force was analyzed, with full consideration of the influence of multi-section and multi-point temperature variations. The basic form of the “multi-point temperature-cable force” prediction model was presented. Then, an objective function for evaluating the prediction effectiveness of the model was constructed. An intelligent optimization method for key parameters of the random forest (RF) algorithm, based on the particle swarm optimization algorithm, was proposed. A dimensionality reduction strategy for multi-point temperature monitoring data using principal component analysis was formulated, thereby establishing a method for constructing the cable force prediction model based on the optimized RF algorithm. Furthermore, a method for establishing cable force anomaly thresholds based on the probability distribution of modeling residuals from the cable force prediction model was proposed, along with a probabilistic assessment method for cable force anomalies. Finally, the accuracy of the cable force prediction model and the effectiveness of the cable force anomaly assessment method were verified using monitoring data from a single-pylon cable-stayed bridge. The results show that, compared to linear models, the proposed method more accurately characterizes the relationship between temperature and cable force in cable-stayed bridges. It provides an effective technical approach for identifying early-stage cable force anomalies under temperature variations. This study is expected to serve as a decision-making reference for the preventive maintenance and targeted inspection of cable-stayed bridges.

Due to multiple factors such as environmental disturbances and sensor drift, the actual monitoring data noise of bridges exhibits significant non constant characteristics, which limits the accuracy of traditional temperature-induced strain prediction models based on the assumption of constant noise. This paper is proposing a temperature-induced strain prediction method for bridges using variational heteroscedastic Gaussian process regression (VHGPR). A two-layer probabilistic framework that integrates structural response learning with noise-variance estimation was developed, and a heteroscedastic modeling strategy was introduced to explicitly capture non-constant noise. High-dimensional posterior inference was transformed into a low-dimensional optimization problem relying only on a small number of parameters by structural reconstruction of variational parameters. On this basis, a marginal variational free-energy-based modeling and optimization strategy was proposed, and a variational-inference-driven analytical method for approximating the joint posterior was developed. The non-constant time-varying law of strain noise level and amplitude of main girder under temperature effect was revealed. The effectiveness of the proposed method was verified by long-term monitoring data of an existing cable-stayed bridge. The results show that the VHGPR model enables probabilistic prediction of temperature-induced strain under non-constant noise, provides accurate estimates of the main-girder mean strain, and effectively quantifies predictive uncertainty. The predictions are reliable, and the model exhibits strong capability in uncertainty quantification. The time-varying noise characteristics are effectively captured through the integration of variational inference and a heteroscedastic modeling strategy. The proposed method outperforms conventional Gaussian process regression across multiple evaluation metrics. The root-mean-square error decreases by 14.4%, the mean absolute error decreases by 12.7%, and the coefficient of determination increases to above 0.96.

Structural damping is a key parameter for bridge dynamic analysis and vibration-mitigation design. However, under low damping, short free-decay records, and multimodal coupling, damping estimates are often scattered and highly noise-sensitive. Building on our previous Power Spectrum Decrement Method (PSDM), this study proposes an Enhanced Power Spectrum Decrement Method (EPSDM) by integrating the power-spectrum decrement concept with Frequency Domain Decomposition (FDD). A multi-channel power spectral density (PSD) matrix is first constructed and subjected to singular value decomposition, where the first singular value spectrum and associated singular vectors used to extract modal frequencies and mode shapes. An analytical relationship is then established between the temporal decay of the first singular value amplitude and the modal damping ratio, enabling multimodal damping estimation without time-domain modal separation or inverse transformation. Moreover, the Modal Assurance Criterion (MAC) is introduced for effective band selection, enhancing robustness against closely spaced modes and high noise levels. The eight-story shear frame example demonstrates that EPSDM achieves high identification accuracy for damping ratios across multiple modes, outperforming PSDM and the logarithmic decrement method overall. Compared with ERA, EPSDM exhibits better robustness under strong noise conditions and offers the advantage of eliminating the need for repeated model-order tuning. Further validation is conducted through a moving-vehicle excitation test on a long-span steel box-girder cable-stayed bridge, where short free-decay responses are used to identify the frequencies, mode shapes, and damping ratios of the first 12 vertical bending modes, yielding physically reasonable and highly repeatable results. The proposed EPSDM provides an accurate and efficient tool for multimodal damping identification from short-duration free vibration and multi-channel monitoring data, supporting bridge performance assessment and the calibration and evaluation of vibration-control parameters.

Constructing a lightweight structural health monitoring and safety assessment method for urban bridge groups is essential for ensuring efficient urban traffic operation and improving overall safety. However, existing studies have mainly focused on individual bridges, and a systematic framework applicable to bridge groups has not yet been established. This gap limits the effectiveness of network-level management and risk identification at the urban scale. Spaceborne Interferometric Synthetic Aperture Radar (InSAR) provides high-precision time-series observations with wide spatial coverage and offers a new paradigm for large-scale bridge safety inspection. This study proposed a multi-scale safety assessment method for urban bridge groups by integrating multi-source remote sensing data and Distributed Scatterer InSAR (DS-InSAR) time-series deformation analysis. Structural parameters, including bridge location, type, and geometric dimensions, were first extracted from high-resolution optical remote sensing data. The line-of-sight (LOS) time-series displacements derived from DS-InSAR were then used as the primary observations. The coupling between LOS deformation and air temperature was analyzed across different bridge types. Deformation rate and thermal expansion response were introduced to preliminarily identify bridge service deterioration. Subsequently, a comprehensive safety evaluation framework was developed based on the Analytic Hierarchy Process (AHP), enabling the quantitative mapping from macroscopic deformation to structural risk states. A case study was conducted on 48 representative bridges in Changsha. Based on the InSAR-derived time-series deformation, six bridges exhibiting potential abnormal responses were identified. Their structural risks were further quantified and classified using the proposed framework. The results show that the method enables efficient identification and grading of safety states at the bridge group scale and provides reliable support for urban bridge maintenance and risk early warning.

These incidents now represent a serious threat to bridge structures, public safety, and the sustainable development of maritime navigation. Vision-based ship detection and tracking serve as key approaches for the early identification of potentially hazardous ships. However, the collection and annotation of large-scale image datasets is time-consuming and labor-intensive, resulting in limited detection performance during the initial deployment phase due to sample scarcity. In addition, the diversity of ship types, significant scale variations, and complex background interference within waterways further hinder the accuracy of ship recognition. To address the aforementioned challenges, this study initially constructs a three-dimensional proxy virtual environment to simulate navigable bridge waterways. Using this environment, a synthetic image dataset is generated to enrich the training data, featuring six typical ship categories and diverse complex scenarios. Subsequently, Contextual Transformer modules are embedded into the You Only Look Once version 8 (YOLOv8) to enhance contextual awareness. The Wise Intersection over Union version 3 loss function is introduced to improve the model's focus on critical positive samples. Thereafter, the improved YOLOv8 is pretrained using the synthetic dataset and integrated with ByteTrack to build a baseline ship detection and tracking framework. Ultimately, a supervised domain adaptation strategy is employed to fine-tune the model using a mixture of synthetic and real-world images, enabling robust multi-object ship detection and tracking in real navigation scenarios. The results demonstrate that the improved ship detection and tracking framework outperforms the original model. When tested on real-world videos, the model adapted through supervised domain adaptation achieves a Multiple Object Tracking Accuracy of 82.9%, indicating strong tracking accuracy and continuity. Moreover, the use of virtual-to-real-world transfer techniques effectively reduces the reliance on large quantities of real-world samples for ship detection and tracking tasks.

For steel structures under long-term vibration loads, bolt connections are prone to loosening, and under complex working conditions, existing visual inspection techniques often fail to identify minor loosening due to texture blur and geometric distortion. By integrating super-resolution reconstruction and multi-scale perception, an intelligent bolt loosening detection method based on YOLOv26-CABU-Net was proposed. First, the YOLOv26 model was trained using a data augmentation strategy that simulated haze and motion blur, enabling accurate localization of bolt head regions of interest against complex backgrounds. Second, to overcome the limitation of existing methods that rely on regular arrangements, a RANSAC (Random Sample Consensus) perspective correction algorithm based on bolt head center point matching was proposed, effectively eliminating geometric distortion caused by irregular arrangements and large tilt angles. Then, the CABU-Net semantic segmentation network was constructed, which innovatively embedded a Real-ESRGAN super-resolution module and a convolutional block attention module (CBAM), achieving active detail restoration and high-precision contour segmentation for low-resolution bolt images. Finally, abandoning traditional straight-line fitting schemes, a corner positioning algorithm based on contour centroid distance spectrum peaks was designed, enabling loosening detection by quantifying the angular difference of corners. Test results show that the method achieved an object detection accuracy (AP) of 0.996 and a mean intersection over union (mIoU) for contour segmentation of 0.993. Under conditions including shooting distances within 2.0 meters, large tilt angles up to 60°, and varying lighting, the maximum error in loosening angle detection was strictly controlled within 4.0°. Compared to traditional algorithms, this method significantly improves robustness and detection accuracy under complex shooting conditions.

To address the challenges of scarce high-quality annotated data and poor generalization performance in vision-based bridge structural crack detection, this paper proposed a zero-shot interactive prompt segmentation method. Firstly, the visual foundation model SAM2 (Segment Anything Model 2) was introduced to construct a zero-shot segmentation framework for bridge cracks. Secondly, the relationship between different prompt strategies and model performance was systematically investigated to provide corresponding optimal prompt strategies for cracks of different morphologies, and the effect of iterative mask refinement on model performance improvement was explored. Finally, the proposed method was systematically compared with image processing and deep learning methods on multiple public crack datasets, and the model's robustness and generalization performance under noisy data and cross-domain crack scenarios of concrete road and steel bridge were tested. The results show that: model performance exhibits a trend of first increasing, then stabilizing, and finally decreasing as the number of prompt points increases; multi-point prompt strategies should be prioritized for crack segmentation tasks, and using prediction results as mask prompts for iterative refinement can further improve segmentation accuracy. Without relying on annotated data, the proposed method can achieve 87.8% of the performance of supervised deep learning methods, demonstrating excellent zero-shot segmentation capability. For typical noisy datasets, the SAM2-based model exhibits stronger robustness compared with deep learning methods. When migrated to the concrete road crack dataset, the performance of all methods declines to varying degrees, primarily due to the complex crack morphology and strong environmental interference in the images; when migrated to the steel bridge dataset, the performance of the proposed method increases by 22.45%, while deep learning methods decrease by an average of 38.02%, demonstrating superior generalization performance. The research results provide a new technical approach for structural health monitoring.

Bridge displacement can directly reflect bridge condition, and its accurate measurement provides crucial information for monitoring bridge condition. Existing UAV-based vision methods for displacement measurement usually rely on stationary references or artificial targets. For bridges spanning rivers or roads, insufficient stable references and inconvenient target installation limit their practical application. This paper proposed a target-free UAV-based approach for bridge displacement measurement aided by multi-laser projection. First, parallel laser beams were used to project multiple laser points onto the bridge surface to construct a stationary reference with a physical scale. By analyzing the motion of the target relative to this reference, the influence of UAV translation and rotation can be effectively reduced without camera pose information. Subsequently, a joint subpixel localization method and an improved Kanade-Lucas-Tomasi (KLT) algorithm were developed. These techniques improved the robustness of laser spot positioning and natural feature tracking under complex environmental conditions. Finally, the image scale factor is calculated in real time using the geometric features of the projected multi-laser points. This enabled the conversion from pixel displacement to physical displacement without requiring any additional known-size objects. Experimental results show that the method performs well under different laser projection conditions and environmental disturbances. The maximum Root Mean Square Errors (RMSEs) of the horizontal and vertical displacement are 0.031 mm and 0.028 mm, respectively. In a field test on a three-span continuous girder bridge, the proposed method effectively captured the bridge deflection response induced by vehicle loads, thereby verifying its practical applicability. The proposed method provides an effective technical solution for non-contact displacement measurement of bridge.

To study the corrosion evolution characteristics and corrosion resistance of the zinc based multi-element alloy anchoring system for bridge cables, based on copper salt ice acetate salt spray corrosion tests, combined with scanning electron microscope (SEM) microscopic morphology observation , energy dispersive spectroscopy (EDS) techniques and high resolution X-ray diffraction (XRD) corrosion product analysis, the corrosion macroscopic morphology, corrosion products, corrosion quality changing, corrosion microscopic morphology, and variation of the corrosion microcracks' number and width of the cable anchoring system specimens were studied. The Arrhenius equation was introduced to analyze the corrosion resistance. The results showed that at the interface between the anchor material and the steel wire, the corrosion distribution of the anchor body is radial with the steel wire as the center, and the corrosion of the anchor body is denser and faster near the steel wire, with flaky corrosion points appearing at 15 days, continuous corrosion points at 30 days, and the formation of corrosion pits at 60 days. The anchor material-anchorage interfaces are mainly uniformly corroded, and the accumulation of corrosion products in the gaps exacerbates the outer corrosion. The corrosion quality changing rate of ZnAl9Cu1MgRE alloy anchor specimens is lower than that of ZnAl6Cu1 alloy, the former has stronger corrosion resistance. Observation of surface microstructure (SEM) shows that the number of microcracks at the “anchor material-steel wire” interface and the “anchor material-anchorage” interface increases rapidly and then slows down with the corrosion process. The growth rate is the fastest at 15-60 days, and decreases after 60 days due to the widening and merging of microcracks. Energy dispersive spectroscopy (EDS) analysis shows that the corrosion morphology characteristics of the anchor material, anchorage surface, “anchor material-steel wire” interface, and “anchor material-anchorage” interface are basically the same. The corrosion mechanism is that Cl^- pitting corrosion causes honeycomb like depressions, tumor like protrusions under the electric couple effect, volume expansion and ion diffusion interact to generate sheet-like stacking morphology, and hydrogen embrittlement and internal stress coupling promote microcracks. The X-ray diffraction (XRD) indicates that the main corrosion products on the surface of the anchor material are ZnO, Zn(OH)₂, Cu₆Al₂ (OH)₁₆CO₃ et al. The main corrosion products of the anchorage are Fe₃O₄ and Fe₅CuO₈, and the corrosion of Zn is accelerated at the “anchor material-steel wire” and “anchor material-anchorage” interfaces due to electrode effects. Based on the Arrhenius equation calculation, the apparent activation energies of ZnAl9Cu1MgRE alloy and ZnAl6Cu1 alloy at the “anchor material-steel wire” interface are 114.32 and 89.37 kJ·mol^-1, respectively, both of which meet the A-level high corrosion resistance standard, and the former has relatively better corrosion resistance.

To address the inefficiency, heavy workload, and high omission rates of the traditional fatigue crack detection methods for orthotropic steel bridge decks, a novel tracked climbing robot is proposed for high-efficiency image acquisition and high-precision size measurement for fatigue cracks. The robot consists of a mechanical mobility system, an image measurement system, and an electronic control system. It demonstrates reliable magnetic adsorption and climbing maneuverability, enabling stable adsorption to the top plate, diaphragm plate, and U-rib plate, as well as transfer between plate surfaces. The robot system is equipped with a brushless motor-driven single-axis gimbal which can adjust the camera's tilt angle according to the crack location. Additionally, a laser rangefinder enables rapid image self-calibration and accurate crack dimension measurement. Static and dynamic analyses were conducted to evaluate the robot's mechanical performance under adverse motion conditions. The locomotion stability and measurement accuracy of the robot were further validated on a full-scale model of steel bridge decks. The results indicate that the tracked climbing robot can track along the pre-defined inspection paths and effectively collect the images of various fatigue cracks across all critical regions of the bridge deck. The crack dimension measurement exhibits high accuracy with a maximum error of only 1.36%, and a sub-millimeter resolution level is reached.

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

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

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

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

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

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

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

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

Faced with the severe challenges of heavy-load traffic and extreme environments, the research and development of modified asphalt is undergoing a paradigm shift from macro-empirical trial-and-error to molecular-level precision customization. This paper reviews the evolutionary history, microscopic mechanisms, and frontier research of polymer-modified asphalt technology. Firstly, the application of modern separation and advanced characterization techniques in constructing chemistry-rheology cross-scale correlation model is discussed from the classic SARA four-fraction model, revealing the influence of microscopic components on macroscopic mechanical responses. Secondly, the generational evolution of modifier technology is systematically reviewed from early thermoplastic resins and thermoplastic elastomers to the eco-friendly and high-value utilization of waste rubber and plastic materials, emphasizing the pivotal role of micro-dose chemical compatibilizers in improving the compatibility of multiphase systems. Furthermore, based on thermodynamic compatibility theory and the evolution laws of microscopic phases, the physicochemical essence governing the storage stability of modified asphalt is deeply analyzed. Finally, the paper provides an outlook on frontier directions, including the construction of asphalt genetic databases based on the Materials Genome Initiative, the application of virtual laboratories through molecular dynamics simulations, and digital inverse design integrated with Physics-Informed Neural Networks (PINNs). The convergence of these emerging technologies is driving a fundamental transformation in the development of modified asphalt from traditional, inefficient empirical trial-and-error models toward a future of molecular-level precision tailoring and intelligent design.

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

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

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

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

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

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

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

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

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

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

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

Unmanned mining trucks, as the primary carriers for transportation in mining areas, have seen rapid development in recent years. However, due to their large size and numerous blind spots, these trucks are often equipped with multiple LiDARs for surround perception. Achieving high-precision calibration of multiple LiDARs on unmanned mining trucks is crucial for efficient autonomous driving perception. In light of this, this paper proposes a joint self-calibration algorithm for multiple LiDARs on unmanned mining trucks based on a coarse-to-fine calibration (CTFC) approach. Firstly, to address the issue of uneven terrain in unstructured environments, a site usability validation algorithm is proposed, ensuring the primary usability of the input data stream. Secondly, to tackle the problem of inconsistent point cloud sparsity and significant differences in overlapping regions among heterogeneous LiDARs, a multi-LiDAR registration algorithm based on iterative hierarchical reorganization is designed. This algorithm improves joint registration accuracy by extracting identity constraints and aligning the data from coarse to fine multiple times. Finally, to address the weak constraints of non-overlapping LiDAR calibration, a non-overlapping registration algorithm based on bilateral equal-distance constraints is proposed. This algorithm constructs calibration relationships between non-overlapping LiDARs by assuming the identity of calibration board positions observed by multiple LiDARs with overlapping regions. To validate the effectiveness of the proposed algorithm, experiments were conducted in typical feature-degraded scenarios, selecting multiple mining area scenes. The performance of the proposed algorithm was verified based on Root Mean Square Error (Root Mean Square Error, RMSE) and center point matching error metrics. The experimental results show that the proposed algorithm positively impacts the final outcomes. In typical degraded scenarios, the RMSE for multi-LiDAR calibration was 0.048 m, and the center point matching error was 0.028 m. The overall efficiency improved by 120 times compared to manual calibration and multi-stage calibration methods, demonstrating significant advantages.