During the long-term service of asphalt pavement, environmental factors cause non-uniform asphalt film aging gradient from the surface inward, leading to distinct performance differences within the asphalt film and directly affecting pavement durability. This study aims to systematically investigate the gradient aging of asphalt film under water, UV, thermal oxidation, and their coupling effects. This study initially designed a standardized asphalt film-aggregate specimen. New laboratory aging gradient simulations were also developed for 2 water aging methods (water bath and moisture) and the coupling aging method of thermal oxidation and UV radiation, with quantifying UV energy and optimizing layering process. Subsequently, FTIR, DSR, and AFM were employed to characterize the aging gradient in terms of functional groups, rheological properties, and microscopic morphology. The results indicated that water aging induces asphalt film gradient through three mechanisms: loss of light components (especially saturates), catalytic promotion of surface oxidation, and isolation of deeper layers by a water film. Under coupling aging, the carbonyl content in the surface layer of asphalt aged for 3 d at 100 degrees C is nearly 13 times that at 30 degrees C. Moreover, the carbonyl content of asphalt subjected to UV following short-term thermal oxidation is 32.1% higher than that after short-term thermal oxidation alone. However, sulfoxide content shows no clear trend due to its low bond energy, while clear gradient behaviors are observed in G*, delta, Nf, R, J_nr, R_diff, J_nr_diff and bee-like structures. UV radiation dominates asphalt aging at low temperatures, whereas thermal oxidation prevails at high temperatures. The aging gradient decreases nonlinearly with depth and diminishes with increasing temperature or aging time. These findings reveal the physicochemical mechanisms of asphalt film aging gradient and provide insights for improving interfacial behaviors to enhance pavement service performance.

The compressive strength of crumb rubberized concrete (CRC) is vital for its structural performance and sustainability. This study integrates XGBoost machine learning with advanced regression and visualization techniques to optimize CRC strength across multiple cement grades. It presents one of the first comprehensive analyses combining feature selection, in-depth outlier detection, uncertainty quantification, and sensitivity analysis across 10-18 input variables and 531 samples. This study emphasizes the significance of robust feature selection, with the OG18 model achieving the highest accuracy (R2=0.979, RMSE=2.63 MPa) and the lowest prediction uncertainty. In contrast, the SSV10 model prioritizes speed, resulting in lower precision and increased prediction uncertainty. Notably, variance inflation factor (VIF) thresholds (5 or 10) were found to be misleading; models with VIF values up to 45 outperformed those adhering to conventional cutoffs. Additionally, the findings indicate that outliers may not represent errors but rather critical data points that enhance model predictions. Furthermore, shapley additive explanations (SHAP) values, permutation importance, and partial dependence plots consistently identified crumb rubber (CR) content, w/b ratio and curing age as dominant strength predictors. Conceptual strength profiles and weight-volume rubber interaction were developed to assist in mix design optimization. Findings show that using <135 kg/m3 (similar to 30%) of CR with G52.5 cement can replace up to 379 kg/m3 of sand while achieving structural-grade strength, whereas G32.5 is better suited for nonload-bearing applications. This study offers a novel, data-driven framework for sustainable CRC design, bridging key knowledge gaps in ML-based material modeling. These outcomes reinforce confidence in adopting practices that align with SDGs 8, 11, and 12. Looking ahead, future model enhancements are proposed to address the identified limitations and further refine predictive accuracy.

This research comprehensively investigates how shape-stabilized phase change materials (SSPCM), when used as backfill, influence the thermal performance of deep buried pipe energy pile (DBP-EP). By integrating field experimental data with numerical simulations, a comparative analysis is conducted between conventional grout and three SSPCM variants. The investigation focuses on how latent heat, thermal conductivity, and phase change temperature affect the performance of the system under intermittent operation. The findings reveal that SSPCM backfill significantly improves the initial heat exchange capacity and overall energy efficiency of DBP-EP, while also reducing the thermal influence zone—though these benefits tend to diminish over extended operation. Under summer conditions, SSPCM enhances early-stage heat extraction in the heat exchange well and effectively cools the pile shaft. Specifically, utilizing the latent heat and thermal conductivity, as well as reducing the phase-change temperature (in cooling mode), all of which contribute to the optimization of the heat transfer efficiency, energy efficiency ratio (EER), and thermal radius. Under intermittent operating conditions, the application of SSPCM materials significantly reduces outlet water temperature compared to conventional grouting materials, with a more substantial enhancement in heat transfer capacity relative to continuous operation modes; however, the heat exchange efficiency of the system demonstrates a decreasing trend as the intermittent ratio increases. Among tested scenarios, an 8 h on/16 h off mode offers the best performance. This study offers new insights into the role of SSPCM in enhancing thermal performance under intermittent operation, providing valuable guidance for material selection and operation strategies in energy pile systems.

More than 800 million end-of-life tires are generated annually, motivating the use of untreated tire rubber aggregates in concrete as a resource-efficient material. Thus, this review integrates more than three decades of experimental and numerical evidence on the mechanical, thermal, interfacial, and durability performance of tire-rubberized concrete. In general, higher rubber contents reduce compressive strength, stiffness, reinforcement bond, shear capacity, and thermal conductivity, but improve deformability and energy dissipation. These responses depend not only on nominal replacement level, but also on rubber density, particle morphology, dosage basis, water-to-cement ratio, processing conditions, and specimen geometry. Findings remain inconsistent regarding particle-size effects, carbonation resistance, shrinkage, energy absorption, and long-term deterioration. Statistical analysis of 137 strength-reduction records and 79 tensile records, supported by bootstrap confidence intervals, residual diagnostics, and repeated hold-out validation, showed that mass- and volume-based dosage metrics perform similarly for long-term predictions, with RMSE values of 0.057 and 0.058, respectively. At early ages, mass-based dosage performs modestly better, producing an RMSE of 0.111 compared with 0.128. Moreover, as compressive strength rises from 7 to 50 MPa, the tensile-to-compressive strength ratio falls from approximately 0.14 to 0.08, consistent with conventional concrete relationships. Resolving contradictory particle-size findings requires numerical models that isolate particle, interfacial, thermal, and time-dependent mechanisms. Current simulations remain constrained by simplified geometries and non-independent calibration data. Future research should standardize rubber characterization, report dosage by both mass and volume, and develop coupled multiscale models validated using independent mixtures.

This study presents a novel investigation into the effect of crumb rubber aggregates (RA) particle size on the compressive strength (CS) of rubberised concrete (RC), utilising five tree-based ensemble machine learning (EML) models validated through rigorous laboratory experiments and innovative analytical evaluations. As one of the few EML modelling studies, this analysis covers 361 literature-based and 25 laboratory-based samples incorporating 15 and 7 input variables, respectively. The findings revealed that the categorical boost model outperformed other EML models, achieving impressive evaluation metrics confirmed by cross-validation and Taylor diagrams. Sensitivity analysis highlighted the superiority of accumulated local effects compared to other techniques, pinpointing fine RA (0 similar to 0.6 mm) as a critical factor influencing CS, as supported by both EML models and laboratory experiments. Higher RA content and water/cement (w/c) ratios reduce CS, but increased w/c can offset strength loss, making it suitable for low-strength and lightweight concrete applications. Furthermore, the developed hybrid expo-linear model combining exponential and Bolomey-based components captures multi-factor interactions well, with +/- 5% accuracy, and outperforming previous models for reliable rapid RC strength prediction. Overall, adapted EML-driven and expo-linear models match the concrete strength theory, clarifying non-linear interactions to optimize RC mixes whilst preserving structural performance and enhancing environmental sustainability.

Ride-pooling (RP) can reduce transport emissions, but its carbon reduction potential across spatial contexts remains unclear, especially at intercity transfer hubs. Using citywide regulatory ride-hailing data from Suzhou, China, this study examines how meso-level path topology features are associated with carbon reductions in railway station pooling. Railway station pooled trips show 62.6% higher carbon reductions per trip than the overall pooled-trip sample average, influenced by nonlinear mechanisms and interaction patterns rather than higher pooling intensity. An explainable machine learning framework shows that carbon efficiency is primarily associated with route quality, as reflected in path topology, operational scenarios, and built environment factors. In particular, detour constraints, common drop-off configurations, and their interactions with distance act as critical thresholds. Building on these insights, we develop a dual-layer classification framework that can identify pooling matches with high carbon reduction potential prior to dispatch (accuracy  ≥  96%). These findings suggest that intercity railway stations may serve as favorable contexts for low-carbon RP when demand concentration and route quality align. This insight can help guide matching strategies that are enabled by AI and informed by carbon efficient in shared mobility systems.

The construction of urban road infrastructure inevitably generates significant and diverse environmental impacts. Therefore, improving the comprehensive environmental efficiency of urban roads while meeting transportation needs has become an urgent issue to address. To develop a systematic method for analyzing the environmental efficiency of urban roads, this study integrates Material Flow Analysis (MFA), Life Cycle Assessment (LCA), and Data Envelopment Analysis (DEA). MFA quantifies the material flows within the road system and generates the life cycle inventory (LCI); LCA then uses these data to evaluate the environmental impacts of materials throughout their life cycle; DEA finally takes the LCA results as inputs and traffic services as outputs to assess the relative environmental efficiency of different road segments or structures. This integrated framework enables the quantification of the comprehensive environmental efficiency of urban roads by considering nine different environmental impact categories. Using Nanjing, China, as a case study, the research found that expressways exhibited the highest environmental efficiency, followed by branch roads, while arterial and collector roads had the lowest efficiency. Specifically, expressway efficiency rose from 0.526 in 2014 to the efficiency frontier (≈ 1.000) in 2018–2020, then slightly declined to 0.984 in 2021, remaining the highest. Arterial roads declined from 0.275 to 0.204, with a modest rebound to 0.238 in 2021. Collector roads fell to 0.172 in 2017 but recovered to 0.290 by 2021, while branch roads fluctuated and improved slightly to 0.349 in 2021. These results demonstrate that the proposed MFA–LCA–DEA integration provides an effective framework for evaluating temporal and structural variations in environmental efficiency, supporting policymakers and planners in integrating environmental considerations into urban road network planning and pavement design.

Compared to open roads, highway tunnels’ continuous semi-enclosed wall structures exacerbate sight distance challenges for vehicles equipped with automation systems (AV) in curved segments. However, AV’s adaptability to existing road geometry in tunnels, which was primarily tailored for traditional human-driven vehicles, remains inconclusive. Therefore, this study aims to investigate the influence of horizontal curved-tunnel geometry on AV’s available sight distance (ASD) and analyze AV’s sight distance safety (SDS). To this end, we established a virtual co-simulation platform for emulating AV’s ASD in diverse tunnel scenarios, which include light detection and ranging (LiDAR)-based sensing configurations and tunnel geometry combinations. On this basis, the important features related to ASD were extracted using the recursive feature elimination algorithm and several widely-used machine-learning models were developed to predict ASD estimation. The Shapley additive explanation analysis was conducted on the most performant model to interpret the feature effects. Moreover, reliability analyses under varying driving automation levels, speeds, and pavement conditions were conducted to quantify the probability of noncompliance with SDS in scenarios focusing on circular curves, followed by proposing an SDS evaluation framework. The results show that: i) random forest model outperforms other machine-learning models in predicting ASD estimation; ii) higher-type tunnel geometry, higher-end sensing configurations, driving on the outside curve lane, and higher mounting height of LiDAR achieve longer ASD; iii) lower-end sensing configurations cause ASD to be less sensitive to the tunnel geometry; iv) SDS deteriorates in the orders of moist, dry, and wet pavement conditions and automation levels 4, 3, 1, and 2; v) AV adapt to low-type highway tunnels’ horizontal curves from the sight distance perspective, but may fail in high-type designs. These findings shed light on the impact mechanism of tunnel curves on AV’s ASD and serve to improve AV’s road-oriented operational design domain and identify the tunnel segment that is significantly non-compliant with SDS.

The sight distance reliability of LiDAR-based automated vehicles (LAVs) in complex road environments is critical for their deployment. Road geometry and weather conditions are two key factors affecting the perception capabilities of LAVs. However, current studies rarely analyzed the combined effects of these two factors on sight distance performance. This study investigates LAVs' sight distance reliability on curved roads in adverse weather, considering various design speeds, curve radii, and scenarios including clear weather, rain, and fog. Available sight distances (ASDs) are extracted using defined LiDAR point cloud thresholds to develop sight distance reliability functions. Moreover, the Monte Carlo simulation quantifies sight distance failure risks associated with different levels of LAVs in varied operational contexts. The results unveil a significant impact of weather conditions on ASDs, highlighting that decreased visibility and increased rainfall adversely affect ASD, with a notable 56.64% probability of sight distance failure under certain conditions. Additionally, the study finds that shorter perception-reaction times can mitigate sight distance risks when LAVs navigate on curved roads, whereas higher speeds exacerbate these risks. Furthermore, the study reveals that lower automation levels struggle to maintain adequate sight distances on existing curved roads under adverse weather conditions. These insights remind road managers to determine appropriate speed limits for LAVs on curved roads, enhancing operational safety from a sight distance perspective.

This study investigates the significance and prospects of utilizing high-performance fiber-reinforced concrete in airport pavements. With the rapid development of the aviation industry, higher performance demands are placed on airport pavements. Traditional airport pavements are predominantly made from ordinary cement concrete, but this material exhibits significant issues of brittleness and low durability when subjected to heavy aircraft and harsh weather conditions. Consequently, fiber-reinforced concrete, known for its exceptional strength and toughness, has garnered considerable attention. This paper discusses in detail the application of various types of fiber materials in strengthening concrete, including steel and basalt fibers. These fibers, based on their chemical and physical properties, play distinct roles in the concrete, thereby enhancing its overall performance. For instance, steel fibers possess a high modulus of elasticity and tensile strength, but are prone to corrosion in acidic environments, while carbon fibers are renowned for their light weight, high strength, and stability. Additionally, the paper emphasizes the importance of mix design methods, as the incorporation of fibers alters the composition and structure of the concrete. Appropriate concrete mix design methods need to be selected. Although fiber-reinforced concrete is widely used in the field of construction engineering, its research and application in airport pavements are not yet sufficiently extensive. Studies should go beyond laboratory tests to explore the evolution of concrete performance in actual usage environments. Consideration should also be given to the unique usage scenarios and stress characteristics of airport pavements to select suitable types of fibers. Moreover, research on the dynamic mechanical properties of fiber-reinforced concrete is a key aspect in enhancing the performance and service life of airport pavements.

The incorporation of waste tire rubber particles as concrete aggregate presents a sustainable solution to mitigate environmental challenges. This study developed hydrophilic rubber particles through polydopamine (PDA) surface modification inspired by mussel adhesion mechanisms. Rubber particles were treated with dopamine hydrochloride (1-3 g/L) under varying durations (1-12 h), with optimal conditions identified as 2 g/L for 6 h. The results demonstrated significant hydrophilicity enhancement: water contact angle decreased from 134.0 degrees to 72.7 degrees (45.75 % reduction), while activation index decreased by 66.6 %. FTIR analysis confirmed successful PDA deposition through emerging -OH and C-N functional groups. In cement mortars with 30 % modified rubber replacement, 28-day compressive strength showed 88.1 % improvement compared to untreated counterparts and flexural strength increased by 31.3 %. Microstructural analysis revealed 42.7 % porosity reduction and 60 % decrease in harmful pores (>200 nm). The PDA coating facilitated denser interfacial transition zones through Ca-2(+)-catechol interactions, reducing interfacial cracks from 8 mu m to complete bonding. This green modification method enables high-value recycling of 30 % waste tire content while maintaining a high compressive strength providing an eco-efficient pathway for sustainable materials.

Accurate International Roughness Index (IRI) deterioration patterns are key to quantifying carbon emissions (CEs) in the use and maintenance and rehabilitation (M&R) phases of pavement life cycle assessment (LCA). Despite evidence that initial IRI has a significant impact on IRI deterioration, most LCA studies still used an ideal fixed value for initial IRI. Therefore, enhancing initial IRI data quality is essential for improving LCA model. To develop an initial IRI prediction model, a dataset containing data on surface layer material, pavement structure, and construction conditions was collected from the Shanxi Department of Transportation. The best of four machine learning models used in this study has been able to achieve an excellent performance (R2 = 0.831). Factors such as compaction number and mixture ratio significantly affect initial IRI. Based on three real cases, the integration of best initial IRI prediction model with LCA and heuristic optimisation algorithm was used to obtain mixture ratios corresponding to the optimal life cycle CEs. For three cases, the total CEs of the optimized results were reduced by 22.7 % (314,942 Kg CO2-eq), 16.7 % (242,565 Kg CO2-eq) and 17.1 % (251,931 Kg CO2-eq) compared to the real results, with the use and M&R phases contributing over 90 % of the total reduction. This illustrates the need to consider the impact of materials on the use and M&R phases when evaluating material benefits using pavement LCA. These processes can also build a framework for designing surface layer mixture to minimize CEs, which can provide a reference for other material production.

To provide a comprehensive foundation for maintenance strategy decision-making, this study integrates Building Information Modeling (BIM), Environmental Life Cycle Assessment (LCA), and Life Cycle Cost Analysis (LCCA) to perform automated Net Present Value (NPV) analysis by monetising the multidimensional impacts of various maintenance strategies. A case study on provincial roads in China shows that preventive maintenance strategies, especially those using emerging technologies like Ultra-Thin Overlay (UTO), are more cost-effective than traditional strategies and should be prioritised when conditions allow. Additionally, the study identifies the optimal maintenance frequencies for each strategy: once every 5 years for the UTO strategy, once every 9 years for the Milling and Filling (M&F) of the Surface Course strategy, and once every 7 years for the Hot In-Place Recycling (HIR) strategy. In summary, the BIM-LCA-LCCA approach offers a versatile, efficient, and real-time method for supporting decision-making in pavement maintenance strategies. It is applicable to most mainstream asphalt pavement structures in China and is expected to provide valuable insights for optimising the design and management of future infrastructure projects.

With the development and maturity of new generation digital technologies such as artificial intelligence, Internet of Things, and 5G mobile communication, their integration with physical products is becoming increasingly seamless. Automobiles serve as a prime example in this regard. In recent years, automated vehicle (AV) technologies have emerged as a prominent focal point, witnessing an escalating acceptance in the market and a growing number of self-driving vehicles on the roads, existing roads are primarily designed for traditional human-driven vehicles (HVs). Due to the differences in perception between automated systems and human drivers, it is essential to assess AVs' feasibility to current road infrastructure. This paper analyzes the safety and comfort of automated vehicles equipped with adaptive cruise control systems (ACC-AVs) on longitudinal road profiles from the perspective of vehicle dynamics. Firstly, a co-simulation platform integrating PreScan, CarSim, and Simulink software is established, providing a comprehensive environment for simulating AV behavior. Secondly, an evaluation system is developed to assess AV’s feasibility on longitudinal roads, based on safety indicators (rear-end collisions occurrence) and comfort indicators (axial acceleration, vertical acceleration, and vertical acceleration change rate). Lastly, the feasibility of ACC-AVs on existing longitudinal profile roads is simulated and evaluated. The results indicate that, on straight slope section, ACC-AVs may experience rear-end collisions on downhill sections with design speeds below 100 km/h; when safety requirements are met, both uphill and downhill sections exhibit good comfort levels. For the vertical curve section, comfort is also favorable in segments with low design speeds and large vertical curve radii, however, as curve radii decrease or design speeds increase, comfort deteriorates. The findings of this study provide a reference for optimizing highway profile design for AVs.

The laser welding plan for a lock bottom construction is suggested in order to meet the development needs of high-quality, high-efficiency, and high-reliability aerospace pressure vessels. The weld porosity issue comes next. In this paper, by introducing the inhibition state of titanium alloy porosity, laser welding technology and the influence of process parameters on porosity, the laser welding scheme of lock bottom structure is compared and analyzed.

Several approaches have been implemented to extract road geometric information from point clouds originating from different LiDAR systems. However, they are unsuitable for scenarios lacking trajectory data and involving road widening and complex alignment combinations, particularly in the case of curved ramps. This article proposes an automated framework to process discrete LiDAR point clouds and extract geometric information for these ramps. The framework primarily contributes in three key areas: 1) A node identification method is proposed to accurately segment the horizontal and vertical alignments, especially for fluctuating curvature and varying longitudinal grade; 2) By determining road axis points using road markings and boundaries, the framework supports road widening and all types of ramp cross sections; 3) Cross sections are extracted without slicing and rotating, allowing width calculation within each section. Test results show that the framework achieves geometric extraction accuracies between 90.79 % and 100 %, demonstrating its effectiveness for curved ramps.

Establishing a comprehensive and systematic method for quantifying carbon emissions (CEs) of asphalt pavement is challenging due to the multifaceted and diverse nature of the involved processes. Utilizing a life cycle assessment framework, this study introduces a practical model system for capturing CEs across the entire lifespan of asphalt pavement. A pivotal aspect of our approach involves calculating activity levels. Material quantities are ascertained based on mixture proportions and specific pavement information. We employ Ridge Regression and the quota method to approximate the energy consumption of construction machinery. A comparative analysis is performed between two CE computation models that use energy consumption and operational time as activity levels, respectively. Additional vehicle fuel consumption is translated based on shifts in the International Roughness Index, as predicted by the Mechanistic-Empirical Pavement Design Guide. CEs from traffic disruptions are calculated collaboratively through VISSIM simulations and MOVES emission modeling. Our findings reveal that material production, mixture preparation, and maintenance duration are the principal factors contributing to CEs throughout the asphalt pavement lifecycle. Notably, emission estimates for machinery in the construction phase can vary by as much as 8.3 times across different models. The study suggests that incorporating the use of recyclable materials and designing more durable pavement structures can offer effective avenues for CE mitigation.

Lightweight LiDAR, characterized by its ease of use and cost-effectiveness, offers advantages in road intersection information acquisition. This study used lightweight LiDAR to collect 3D point cloud data from an urban road intersection and propose a semantic segmentation model based on the improved RandLA-Net. Initially, raw data from multiple positions and perspectives were obtained, and complete road intersection point clouds were stitched together using the iterative closest point algorithm for sequential registration. Subsequently, a semantic segmentation method for point clouds based on the improved RandLA-Net was proposed. This method included a spatial information encoding module based on feature similarities and a feature enhancement module based on multi-pooling fusion. This model optimized the feature aggregation capabilities during downsampling with the weighted cross-entropy loss function applied to reduce the impact of input sample scale imbalances. In comparisons of the improved RandLA-Net with PointNet++ and RandLA-Net on the same dataset, our method showed improved segmentation accuracy for various categories. The overall prediction accuracy on two road intersection point cloud test sets was 87.68% and 89.61%, with average F1 scores of 82.76% and 80.61%, respectively. Most notably, the prediction accuracy for road surface areas reached 94.48% and 94.79%. The results show that our model can enrich the spatial feature expression of input data and enhance semantic segmentation performance in road intersection scenarios.

Automated vehicles equipped with adaptive cruise control systems (ACC-AVs) are prevalent and the ensuing issue is the feasibility of ACC-AVs' operation on the roads. This study investigates the feasibility of road horizontal curve designs for ACC-AVs from a vehicle dynamics perspective. Following the scenario generation framework, we created and tested several scenarios featuring horizontal geometric elements and design speeds, conducting a safety evaluation based on the critical adhesion coefficient, lateral acceleration, lateral-load transfer rate, together with driving comfort indicators. Results indicate that ACC-AV can navigate on road curves designed with a common minimum radius (Rmin_com) effectively at speeds over 60 km/h, comparable to conventional vehicles. However, both Rmin_com and limited minimum radius (Rmin_lim) designs show limitations. Additionally, the feasible radius ranges for ACC-AV reveal the capability to safely handle sharper curves and maintain higher speeds, suggesting potential for adaptable road design in complex environments. Finally, minimum radius ranges were summarized for ACC-AV safe and comfortable operation on road curves, unveiling the potential risks and reminding designers in curve design controls for ACC-AVs.