To achieve effective automatic identification of damage edges in ultra-thin asphalt overlays, the characteristics of elevation and gradient data in detecting wear were explored, confirming the feasibility of using a multi-level gradient-direction detector combining quartile, Otsu, and DBSCAN modules. An anomaly detector based on quartiles, within-class variance, and DBSCAN clustering was developed to analyze data size, dispersion, and density. Gradient vectors derived from 3D laser scan elevation data of continuously worn ultra-thin overlays were processed, and anomalies were visualized using a Python environment to identify wear regions. It was found that gradient data revealed significantly clearer edge features of wear regions compared to elevation data. With wear cycles increasing to 10,000, elevation data showed a tendency for wear regions to merge, as indicated by the total wear area, number of wear regions, and average area of wear regions. In contrast, gradient data indicated significantly smaller total wear areas, with minimal changes in the number of wear regions and average area, demonstrating the robustness of edge detection based on gradient data. These findings confirm that the use of multi-level gradient-direction detector for detecting wear in ultra-thin overlays is both feasible and effective, offering a robust method for identifying wear regions.

Airport pavement repair interfaces are susceptible to premature failure under heavy-load impact and hygrothermal cycling. To improve interfacial reliability, this study developed a toughened interfacial agent composed of waterborne epoxy resin (WER), 3-aminopropyltriethoxysilane-grafted rubber powder (APTES-RP), and microsilica. The main contribution of this work is the development of a controllable silane pre-hydrolysis and grafting strategy for rubber powder modification, followed by response surface methodology (RSM)-based optimization to balance interfacial bonding strength, impact resistance, and workability. The results showed that an ethanol/water ratio of 9:1, an initial apparent pH of 6, and 2% APTES provided a stable 120 min processing window for reproducible grafting. The optimized formulation, containing 17.4% APTES-RP, 8.0% microsilica, and 12.7% added water, simultaneously maximized interfacial strength and impact energy while maintaining acceptable workability. After freeze-thaw cycling, the interfacial agent retained more than 80% of its interfacial strength, confirming its durable interfacial integrity. Overall, this work provides a practical and scalable strategy for the design of interfacial agents used in airport pavement repair.

Cold-mix ultra-thin overlay still exhibit deficiencies in durability and skid resistance. This study proposes a mechanism for evaluating and selecting emulsified asphalt. A comparative optimization analysis of CCPA-E, CSBS-E and BC-1 emulsified asphalts was conducted using AHP and TOPSIS. The corresponding mixing ratios were also proposed. The results show that the combined evaporation method can efficiently obtain the evaporated residue of modified emulsified asphalt. The mixture made with CCPA-E emulsified asphalt has significantly better skid resistance and abrasion resistance than the micro-surfacing mixture and is close to the performance of the hot-mix SBS mixture. Compared to micro-surface, the wear value of the CCPA-E cold-mix asphalt mixtures decreased by an average of 21.2% after 1 h of water immersion and 23.4% after 6 days. The experimental road section was constructed with the CCPA-E cold-mix ultra-thin overlay. The skid resistance of the pavement was effectively improved, and the BPN and TD were improved by 92.3% and 175.6% respectively.

Asphalt pavements at high-altitude airports endure prolonged extreme low temperatures and large diurnal swings, imposing stringent demands on crack sealants, whose multi-scale adhesion failure mechanism remains unclear. Three SBS and crumb-rubber-composite-modified sealants, designated A, B and C, were characterized through surface free energy tests, pull-off and shear tests, fluorescence microscopy, FTIR and molecular dynamics simulations. Cross-scale correlation analysis and CRITIC-TOPSIS were applied to link and rank the sealants across scales. Work of cohesion, work of adhesion, pull-off strength and shear strength all rose monotonically with modifier content, and sealant C exhibited a 38.5% higher work of cohesion and a 52.4% lower CVφ than sealant A. Molecular dynamics simulations showed that electrostatic forces drove sealant-aggregate adhesion while van der Waals forces governed sealant-asphalt adhesion, with a simulation-experiment deviation of only 2.88-5.74%. A level-by-level transmission linked phase-morphology uniformity, intermolecular interaction, interfacial energy and macroscopic mechanical performance. Sealant C achieved a CRITIC-TOPSIS index of 1.000, far above 0.271 for B and 0.000 for A, and is recommended as the preferred material for crack sealing of high-altitude airport asphalt pavements.

Conventional texture-based methods for asphalt pavement wear evaluation often suffer from limited spatial representativeness, high data randomness, and a narrow dependence on elevation indicators. To address these limitations, this study proposes a volumetric wear evaluation framework based on three-dimensional laser point cloud data. A new dynamic index (K) is developed by integrating four functional volume parameters (Vmp, Vmc, Vvc, and Vvv) to capture the total material loss across progressive wear stages. Both linear and non-linear statistical analyses, including a linear mixed-effects model and Levene's test, are employed to assess the sensitivity and robustness of these parameters under different Smr thresholds. The results show that Vmc and Vvc, particularly within the Smr = 5 %-80 % range, are highly sensitive to increasing wear cycles. The proposed index K exhibits a stable upward trend with wear progression, maintaining a coefficient of variation of 0.33 and negative kurtosis (-0.82), indicating strong robustness and low concentration bias. In contrast, traditional line-based indices (e.g., WRt, WRMTD, WRSMPD) show higher variability and sensitivity to local noise. Overall, the volume-based index K provides a more comprehensive and stable representation of surface wear. It offers practical value for the durability evaluation of thin asphalt overlays and supports the development of preventive maintenance strategies.

The material bearing ratio (Smr), derived from the Abbott-Firestone curve, is the essential basis for calculating four key functional volume parameters (Vmp, Vmc, Vvc, Vvv), which directly quantify load-bearing capacity, skid resistance, and drainage performance of asphalt surfaces. Accurate determination of Smr1 and Smr2, representing the peak-core and core-valley boundaries, is therefore critical for evaluating the functional evolution of pavement materials under wear.However, conventional approaches that use fixed thresholds (e.g., Smr1 = 10 %, Smr2 = 80 %) ignore the intrinsic link between segmentation boundaries, bearing curve morphology, and wear progression, often resulting in biased parameter calculations and inaccurate performance assessment.This study proposes a dynamic segmentation framework based on Response Surface Methodology (RSM). Smr1 and Smr2 were treated as factor variables, while the four functional volume parameters served as responses. Sensitivity analysis guided the ranking of optimization objectives, and a desirability function was applied to integrate multiobjective optimization and identify optimal Smr combinations at different wear stages.Results show that Smr1 increases from 10 % to approximately 20 % and Smr2 decreases from 95 % to around 85 % as wear progresses, indicating a contraction of the functional bearing region. The corresponding core region height (Hc) shows a continuous downward trend, capturing compaction and surface smoothing more accurately. This dynamic approach establishes a statistically grounded and wear-responsive framework that improves functional parameter calculation, enhances degradation assessment, and supports early-stage pavement performance evaluation.

In order to achieve the co-optimized design of waterborne epoxy resin emulsified asphalt (WEREA) chip sealer for spalling resistance, skidding resistance, and abrasion resistance, this study first determined the basic physical properties of the binder at varying levels of waterborne epoxy resin (WER) usage (0 %-20 %) through pull-off, shear, and tensile tests. Next, prediction models for sealer aggregate spalling rate (ASR), mean texture depth (MTD) and mean texture depth loss (MTD loss) were developed using the response surface methodology (RSM). Furthermore, by integrating the RSM with the desirability optimization method (DOM), environmental efficiency constraints were introduced to establish the co-optimization criterion and range. The results showed that WER significantly improved the bonding properties of the binder, with optimal performance achieved by maintaining the usage below 15%. The p-values for the ASR, MTD, and MTD loss were all less than 0.0001, and all the R2 values exceeded 0.8, demonstrating that the models can significantly explain and predict the effect of material usage changes on sealer's performance. The conflict between the binder's ability to suppress ASR and MTD loss and its negative impact on MTD suggests the need for synergistic optimization of the sealer's performance. The most desirable optimization was carried out within the recommended intervals: 5 %<= ASR <= 10%, 0.8 mm <= MTD <= 1.4 mm, and 10 %<= MTD loss <= 32.1 %. The best balance between performance and environmental efficiency of the sealer was finally determined with the usage rates of emulsified asphalt, aggregates, and WER set at 1.0 kg/m2, 5.4 kg/m2, and 6.6%, respectively.

To achieve conflict regulation and synergistic enhancement during waterborne epoxy resin emulsified asphalt (WEREA) micro-surface performance optimization, this study integrates micro-mechanism analysis and macroperformance evaluation. A molecular dynamics (MD) model was developed to characterize WEREA evaporation residues, with glass transition temperature (Tg) and fractional free volume (FFV) serving as key indicators for evaluating thermal stability and impermeability. Furthermore, a multi-objective optimization framework combining response surface methodology (RSM) and desirability optimization method (DOM) was developed to identify formulations maximizing cohesion and dynamic stability while minimizing wet track abrasion value (WTAT) and freeze-thaw dry track abrasion value (DTAT). Results indicate that increasing WER content and crosslinking conversion elevates Tg by 10.72 % and reduces FFV by 7.41 %, confirming enhanced hightemperature stability but compromised permeability. Comparative analysis revealed WER exerted predominant influence over DMP-30 in mixture strength development. And the differences in the optimal regulation ranges for each performance necessitated multi-objective coordination. Through the proposed optimization framework, maximum desirability (0.959) was achieved at 18 % WER and 1.5 % 2,4,6-tris(dimethylaminomethyl)phenol (DMP-30), demonstrating effective balance between competing performance requirements. This study provides a solution framework and formulation basis for design high-performance WEREA microsurfaces.

Asphalt pavements constitute an important part of global transportation infrastructure, and their surface texture directly impact driving safety and comfort. Understanding the evolution of surface texture during wear and its effect on friction degradation is essential for pavement maintenance and safety assessment. This study conducted accelerated wear tests on three typical asphalt pavements (AC-13, SMA-13, and OGFC-13) using a self-developed indoor plate accelerated load wear tester (PALWT). A 3D laser scanner and a dynamic friction tester were used for pavement surface modeling and friction measurement. Based on the ISO 25178-2 standard, areal field parameters and functional volume parameters were innovatively introduced into the pavement wear quantification system to reveal the wear mechanisms of material loss and transfer. Results showed that the macrotexture characteristics of the pavement shifted from rough to flat after wear, with peak features gradually disappearing and texture directionality transitioning from multi-directional to the traffic direction. Wear caused a decrease in material at the surface peaks and valley areas while accumulating material in the core area. The dynamic friction coefficient (DFT40, DFT60, and DFT80) of asphalt pavements initially increased (peaking at around 3,000 wear cycles) before decreasing and eventually stabilizing. Open-graded and gap-graded pavements generally outperform dense-graded pavements in terms of friction retention. Peak material volume (Vmp) and valley void volume (Vvv) effectively reflected material redistribution during wear across peak, core to valley, showing a strong correlation with skid resistance. Mean peak curvature (Spc) significantly improves skid resistance at low to medium speeds, while texture aspect ratio (Str) becomes critical under high-speed conditions by ensuring stable multidirectional tire-road interaction.

Light-coloured epoxy skid-resistant paving technology is an eco-friendly, low-carbon solution extensively used in pavement maintenance. This study addressed performance inconsistencies and vague design parameters by employing an orthogonal experimental approach. Tests were performed using a BM-7A brightness meter, a modified wet wheel abrasion tester, an adhesion pull-off tester and a ZW-D ultraviolet ageing chamber. The effects of epoxy spreading amount, aggregate type, size and distribution on the brightness, abrasion resistance, adhesion and ageing resistance were systematically analysed. Ceramic particles and emery were chosen as skid-resistant aggregates. The optimal proportions identified were a resin-to-curing agent ratio of 3:4 and a pigment paste content of 5%. A minimum total spreading amount of 0.5 kg/m(2) was required for the three-layer system, with the optimal configuration using 0.6 kg/m(2) each for the bottom and middle layers and 0.4 kg/m(2) for the top layer. Skid-resistant aggregates sized 0.6-1.18 mm were the most effective. The optimised mixture achieved a 51% reduction in abrasion and a 23.44% increase in brightness, with a surface texture depth exceeding 0.8 mm, meeting performance standards. These findings established clear design parameters and demonstrated the significant engineering value of light-coloured epoxy skid-resistant paving materials.

The original pavement surface must meet roughness specifications to ensure the quality of resurfacing or reconstruction projects. Therefore, it is crucial to use appropriate roughness detection equipment and evaluation indicators to represent the surface's roughness characteristics. This study adopted the shot blasting and milling machine to treat the cement concrete base surface at four levels increased with the shot blasting times and texture depth respectively. The 3D laser detection equipment with 0.55 mm measurement accuracy captured raw data from the surface. The 8-neighbor cyclic filling algorithm was applied to correct invalid data, followed by Butterworth filtering to reduce noise. The Kriging interpolation method was utilized to reconstruct the 3D model of the surface. The surface roughness characteristics were assessed through three categories of indicators: geometric, spectral, and fractal indicators. A comprehensive roughness index (TPCA) based on these indicators was performed using the principal component analysis. The three categories of roughness indicators exhibit varying degrees of change with the variation of the treatment parameters. At the second level of shot-blasting treatment, the TPCA reached its peak, showing a 240.1 % increase over the control group. Under the fourth milling treatment level, TPCA achieved its highest recorded value, marking a 1357.8% increase compared to the control.

Automated pavement condition survey is of critical importance to road network management. There are three primary tasks involved in pavement condition surveys, namely data collection, data processing and condition evaluation. Artificial intelligence (AI) has achieved many breakthroughs in almost every aspect of modern technology over the past decade, and undoubtedly offers a more robust approach to automated pavement condition survey. This article aims to provide a comprehensive review on data collection systems, data processing algorithms and condition evaluation methods proposed between 2010 and 2023 for intelligent pavement condition survey. In particular, the data collection system includes AI-driven hardware devices and automated pavement data collection vehicles. The AI-driven hardware devices including right-of-way (ROW) cameras, ground penetrating radar (GPR) devices, light detection and ranging (LiDAR) devices, and advanced laser imaging systems, etc. These different hardware components can be selectively mounted on a vehicle to simultaneously collect multimedia information about the pavement. In addition, this article pays close attention to the application of artificial intelligence methods in detecting pavement distresses, measuring pavement roughness, identifying pavement rutting, analyzing skid resistance and evaluating structural strength of pavements. Based upon the analysis of a variety of the state-of-the-art artificial intelligence methodologies, remaining challenges and future needs with respect to intelligent pavement condition survey are discussed eventually.

In order to enhance the texture and particle retention capabilities of the anti-abrasive seal layer, this paper focuses on the interlocking pattern between the seal and the particles of the pavement, and for the first time considers the base pavement texture, the particle size of the seal, and the amount of seal particles as design variables. The study investigates the relationship between these factor variables and response variables such as texture loss and mass loss. An optimization of the plan design was conducted using the I-optimization method in the mixture design of Design Expert software. The study involved a long-term wear test of 20,000 wear cycles on anti-abrasive seals. The response surfaces corresponding to each response variable were calculated using RSM (response surface method), thereby providing design solutions with high wear-resistant performance. Finally, the rationality and scientific nature of the optimized solutions were verified using three-dimensional texture indices, preliminarily elucidating the texture characteristics of the high wear-resistant performance. The results indicate that combinations of 0.7 kg/m 2 aggregate usage, 0.8 mm -1.0 mm texture depth, 0.6 mm -1.18 mm aggregate particle size, and 0.75 kg/m 2 aggregate usage, 1.2 mm -1.3 mm texture depth, 0.3 mm -0.6 mm aggregate particle size experienced the least quality and TD loss. The general texture characteristics of high wear-resistant seal layers include a small Sq value (Sq less than 1.8), a moderate Ssk value (Ssk approximately 0), and a slightly higher Sku value (Sku greater than 3). However, if Ssk is not close to zero (and the absolute value of Ssk is too large), even higher Sku values can result in increased large losses.

针对在嵌入式设备上部署神经网络模型存在受限于设备体积与计算性能的影响而难以保证神经网络模型的推理实时性的问题,提出了一种基于YOLOv5-nano的前车检测改进方法(HS-YOLO).首先,采用硬拟合函数h-swish来取代SiLU激活函数,在激活关系相似的情况下提高模型推理速度;此外,引入SIOU边界框回归损失来替代CIOU损失,提高模型的训练速度与推理精度.为进一步验证改进模型的性能,使用SSD、YOLOv4-tiny、基础模型YOLOv5-nano与改进的HS-YOLO网络在相同训练条件下进行训练,得到最优模型并在测试集上进行推理测试.结果表明:HS-YOLO模型的精确率、召回率及AP0.5较原模型YOLOv5-nano分别提升了0.76%、0.43%、0.41%;在推理速度方面,HS-YOLO模型的单张图片推理耗时为7.8 ms,实时推理帧数为128 FPS,在所有模型中表现最优,较原模型分别提高了0.7 ms和10 FPS.

采用自开发算法和自研发的轮式加速加载磨耗仪,研究了集料形态对碎石封层嵌挤结构与抗剥落性的影响.结果表明:80.7%的针状集料以平铺形式存在,对碎石封层嵌挤结构的影响较小,为保证良好的抗剥落性,其含量不宜高于16%;片状集料多以平铺或交叠形式存在,易引起相邻集料的移位和交叠,对集料嵌入率和保持能力不利,含量应控制在10%以下;当针状与片状集料同时存在时,碎石封层经历磨耗后的质量损失增加约2%;集料嵌入面积百分率兼具良好的稳定性与敏感性,是评价碎石封层抗剥落性的理想指标.

The objective of this paper is to examine the reliability of three-dimensional (3D) laser detection technology data density for crack width detection results of cement slabs. Four groups of cement concrete crack elevation data with a laser data density of 0.5–1.5 mm were obtained using an indoor 3D laser detection system, and 3D models were established. The nonlinear least squares method was applied to fit the fracture section, and the crack width was determined by the peak value analysis. The results demonstrate that the lateral spacing of laser points exerts a large impact on the mean and discrete degrees of cement concrete crack width detection results. The laser point spacing is positively correlated with crack identification errors. Insufficient laser accuracy leads to an overestimation of crack severity level and affects the accuracy of pavement damage condition evaluation. High-precision laser equipment exhibits certain reliability for detecting cement concrete crack width above 3 mm. In the actual pavement crack width detection process, the appropriate transverse spacing of laser points can be selected according to different error limit requirements to fulfill the requirements of both detection reliability and data processing efficiency. Suggestions for future research include expanding the experimental conditions, increasing the 3D laser point spacings, and selecting more road lanes and pavement materials to further examine the influential factors of pavement crack width measurement.

High-modulus asphalt mixture (HMAM) is one of the most effective materials to enhance the rutting resistance of asphalt pavement and upgrade pavement sustainability. The objectives of this study are to investigate the modulus properties of different HMAMs and their correlation with the rutting resistance, to propose reasonable modulus evaluation indicators, and to analyze the rutting resistance mechanisms of different materials (hard asphalt, polyethylene, dissolved polyolefin). The effect of three HMAMs and two styrene-butadiene-styrene (SBS) modifiers on asphalt mixtures’ rutting resistance were evaluated by dynamic modulus test and wheel track test, and the results were simulated and further analyzed via ABAQUS. The results indicate that the dynamic modulus of the mixtures showed a gradual increase and decrease with the increase of loading frequency and testing temperature, respectively. The ratio of dynamic modulus in low frequency to that in high frequency correlates well with dynamic stability under high-temperature conditions, and the wider the frequency coverage, the higher the correlation between this ratio and dynamic stability. The rutting resistance of asphalt pavements can be improved by reducing the frequency sensitivity of HMAMs under high temperatures or by increasing the modulus’ absolute value of the pavement structural layer. Therefore, two indicators, the absolute value of the modulus and the ratio of 0.1 Hz dynamic modulus to 25 Hz dynamic modulus at 55 °C, are recommended for the evaluation of rutting resistance of HMAMs. Based on the evaluation indexes proposed in this paper, a comparative analysis of the rutting resistance mechanism of HMAMs prepared with different materials was carried out, and it was concluded that the mixture with high-modulus agents had the best rutting resistance, which is consistent with the test road observations, thus verifying the feasibility of the modulus evaluation indexes recommended in this paper for the evaluation of the rutting resistance of different types of HMAMs.

Due to the traditional crack segmentation algorithm is difficult to identify narrow cracks and the segmentation edge is not accurate. This paper proposes a pavement crack detection method based on improved U-Shaped Network（Unet） to increase detection accuracy. Since traditional Unet is a type of“shallow”neural network, it is not good for extracting complex crack features. The Oxford University Visual Geometry Group Network（VGG16）is therefore used for feature extraction, in order to improve the accuracy of crack feature extraction. In addition, the fusion of high-and low-order features generate several useless features. The compression and excitation unit（SE block） is added to the decoding part of the model to develop a crack attention unit which allows the network to focus on the crack features under different channels. Moreover, an improved Unet is proposed by combining SE block with VGG16（SE-VUnet）. In addition, a transfer learning method is used to transfer the pre-trained VGG16network weight on ImageNet for crack detection. By selecting the Crack500 data set and using the camera to collect images to develop1600 pavement crack data sets, the SE-VUnet model is trained again to obtain the crack segmentation results. The weighted harmonic mean F1 of Precision and Recall and Jaccard similarity coefficient are used as quantitative evaluation indicators. The segmentation effect and real-time performance of SE-VUnet are compared with Unet and three other representative models. Study results show that the comprehensive F1 and the Jaccard coefficient of SE-VUnet model is 0.840 3 and 0.722 1, which is 1.04% and 1.51% higher than Unet respectively, as well as other three comparison models. The time for the SE-VUnet to screen a single-frame image is 89ms, which is only 5ms slower than the Unet but with a significant improvement over the crack segmentation and detection process.

In the context of the present lack of aggregate spalling identification methods and the imprecise recognition of images of aggregate spalling points owing to aggregate stacking and coverage in the course of chip-seal paving, this paper proposes an aggregate spalling recognition method based on 3-D laser elevation data points in order to improve the recognition accuracy of spalling aggregate in chip seals. In this study, 3-D point elevation data on the surface of a chip seal specimen were fed into the 3-D laser detection equipment, the raw data were smoothed, the super four-point fast and robust matching method was used on the 3-D laser points for registration, and 3-D reconstructions of the chip seal were built based on the laser point after registration, and 3-D reconstructions before and after spalling were overlaid with the different values of the surface elevations before and after spalling. The spalling plane curve was then obtained using contours from the differences in the values of the surface elevation before and after spalling, and the spalling perimeter Ln, spalling area Sn, and spalling volume Vn were then calculated on the basis of the 3-D coordinates of the spalling curve. The results showed that the relative errors of the spalling area measured by the laser method and the OTSU method (an image segmentation algorithm proposed by Otsu (1979)) were #1 3.1% vs 4.0%, #2 4.4% vs 6.4%, #3 3.3% vs 58.2%, respectively, indicating that the bitumen attached to the aggregate and the overlap of plane images of aggregate due to aggregate stacking are the main reasons for the greater errors in the recognition of image method. The relative errors in the spalling perimeter of the three specimens are 12.61%, 1.60%, and 0.94%, the relative errors of spalling area are 3.13%, 4.36%, and 3.31%, and the relative errors of spalling volume are 6.69%, 6.57%, and 8.71%, which are less than the approximately same level 10% indicating that the accuracy and the stability of aggregate spalling recognition based on 3-D laser elevation data is excellent.

Over the course of storm or rainfall event,water thickness builds up on road surface resulting in a loss of contact between vehicle tires and road surface and puts drivers into immediate danger especially at high speeds.Therefore this is a considerably dangerous condition of the road and the realistic measurements and prediction model of water film thickness（WFT） on pavement surface is crucial for determining the road friction coefficient and evaluating the impact of rainfall on traffic safety.A review of the principle as well as critical evaluation of current detection methods of pavement WFT were compared for consistency and accuracy in this paper.The method selection guidelines are given for different road surface water film thickness detection requirements.This paper also introduces the latest development of WFT detection and prediction models for asphalt pavement,and gives the calculation elements and conditions of different WFT prediction models from different modeling ideas,which provides a basis for the selection and optimization of WFT models for future researchers.This article also suggests a few insights as further research directions on this topic.（1） The research can consider the influencing factors of WFT to conduct research on the delineation standard of pavement WFT.（2） In order to meet the future traffic safety dynamic early warning needs,road factors of different material types,disease conditions and linear conditions should be studied,as well as a comprehensive and accurate real-time water film thickness detection and evaluation method considering meteorological factors of rainfall timing,scale and intensity.（3） The prediction model of WFT should be further studied by the analytical method to clarify the influence of the pavement WFT on the driving safety.