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The pavement base and subbase are the main load-bearing structures of asphalt pavement, and their materials need to have sufficient bearing capacity. Therefore, in the development of LSAM-50 mixtures with higher bearing capacity, after significant research and engineering practice, conventional particle size asphalt mixtures have formed their own excellent mineral gradation and have been incorporated into relevant specifications, while LSAM-50 mixtures, including mineral gradation, have not been involved in related research and engineering applications. According to the strength composition mechanism of asphalt mixtures, under the same circumstances of asphalt, due to the large nominal maximum particle size of LSAM-50 and the small amount of asphalt used, the strength of mineral grading is more important than that of asphalt, which is one of the key issues to be solved in the research of LSAM-50 mixtures. This study aims to enhance the road performance of asphalt mixtures with a maximum nominal particle size of 50 mm (LSAM-50). The variation of void ratios in coarse aggregate skeletons was investigated when aggregates of 37.5–53 mm (designated as D1), 19–37.5 mm (designated as D2), and 9.5–19 mm (designated as D3) were mixed in different proportions. Meanwhile, the effects of fine aggregate gradation on the strength of asphalt mortar and the influence of the ratio of coarse to fine aggregates on the mechanical strength of LSAM-50 were examined. A densely graded structure with strong interlocking for LSAM-50 was proposed, and its road performance was verified. The results indicate that when the ratio of D1, D2, and D3 is 5:2:3, the void ratio of the mixed coarse aggregate is minimized. When the decrement factor i is 0.75, the compressive strength and splitting strength of asphalt mortar reach their maxima. Compared with the densely graded asphalt-stabilized aggregate mixture (ATB-30) with a maximum nominal particle size of 37.5 mm, the dynamic stability of LSAM-50 with the proposed gradation is increased by 400%, the low-temperature bending strain by 3%, the SCB bending strength by 47%, and the residual SCB strength by 90%. [ABSTRACT FROM AUTHOR]

In intelligent vehicles, road environment perception technology is a key component of autonomous driving assistance systems. This component is the foundation for vehicle decision‐making and control, and is a guarantee of safety during the driving of the vehicle. The existing environment perception technology mainly targets well‐lit environments and requires visible light imaging equipment. Therefore, in low visibility environments, this technology cannot make good judgments about the external environment. Many existing perception systems mainly rely on sensors. Under low visibility conditions, these sensors weaken their effectiveness due to signal transmission, reflection, or absorption, resulting in incomplete or distorted data collection. Reduced visibility often affects the sensing range of various sensors, hindering the system's ability to detect and recognize distant objects, thereby limiting the necessary advance warning and response time for safe navigation. In response to this issue, this study proposed a combined method of infrared imaging and polarized imaging to collect feature data on road conditions in low visibility environments. Then, the obtained images were denoised and enhanced. The processed images were input into the system for recognition, and the images were analyzed and recognized using a low visibility road situation semantic segmentation algorithm based on deep learning. The research outcomes denoted that the pixel accuracy, average pixel accuracy, and average intersection ratio of the variable weight combination model in polarized degree images were 91.2%, 89.1%, and 71.6%, respectively. Those in infrared images were 83.6%, 90.6%, and 62.1%, respectively. The various indicators of the variable weight combination model were higher than those of the U‐shaped neural network model, indicating its performance is relatively excellent. The research results indicated that infrared imaging helps to acquire information at night or in low light conditions, while polarized imaging can provide better adaptation to cluttered light and reflections, enabling the system to provide more robust environmental sensing in complex weather conditions. It fills a critical gap in perception for autonomous driving systems in adverse weather conditions. [ABSTRACT FROM AUTHOR]

Foamed ceramics with high closed porosity were prepared using granite scrap as the raw material and silicon carbide as a foaming agent, and the effects of Na2O, K2O, and MgO on the pore structure and properties of the foamed ceramics were investigated. The results show that both Na2O and K2O could reduce the viscosity of the melt and promote the formation of the liquid phase, and the increase in content could enhance the foaming ability of the blank. When the dosage of Na2O was 4–6 wt% and the dosage of K2O was 6–8 wt%, the homogeneity of the pore structure of the foamed ceramics could be effectively improved, and the samples exhibited an optimal performance, including a bulk density of 510.36–593.33 kg/m3, a closed porosity of 68.24–78.04%, a compressive strength of 1.33–2.66 MPa, and a water absorption capacity of 0.57–1.31%. A further increase in the Na2O and K2O dosages destroyed the uniformity of the pore structure, resulting in a large number of irregular macropores. MgO had a slight effect on regulating the pore structure of the foamed ceramics, and the increase in dosage promoted the precipitation of forsterite crystals, creating conditions suitable for the preparation of foamed ceramics with small pores. [ABSTRACT FROM AUTHOR]

In this study, core‐shell structured cationic waterborne polyurethane (WPU) dispersions with high solid content were prepared. To this end, prepolymer A was synthesized from polytetramethylene ether glycol (PTMG), isophorone diisocyanate (IPDI), castor oil (CO) and 1,4‐butanediol (BDO) without integration of the hydrophilic groups. Prepolymer B with hydrophilic groups was prepared from PTMG, IPDI, CO, BDO and N‐methyldiethanol amine (MDEA). WPU dispersion with a core‐shell structure could be generated by mixing, neutralizing, and emulsifying of the prepolymer A and the prepolymer B. The results indicated that the generation of WPU dispersions through this technique exhibited a milky appearance while the pH values range from 5.30 to 5.60. The optimal combination of prepolymer A and prepolymer B (at a ratio of 5:5) resulted in a dispersion with the highest solid content (50.4%), lowest viscosity (69 mPa·s), and narrowest particle size distribution. As the proportion of prepolymer A to prepolymer B decreases, the tensile strength of WPU film reduces while the elongation at break and glass transition temperature increases. Moreover, initially the contact angle with water was decreased instead of increase. However, modifications in a ratio of prepolymer A and B was not showed any significant impact on the thermal stability performance of the WPU films. [ABSTRACT FROM AUTHOR]

The variation in road cross slope with service life affects the pavement drainage and has an adverse effect on vehicle operation safety. This paper describes a cross-slope variation prediction method influenced by the coupling effect of traffic load and soil consolidation, considering characteristics of embankment to cover the shortage for insufficient consideration of compacted embankment. First, the traffic load-induced settlement equation of a highway on soft soil foundation was introduced, which considers the effects of traffic load stress, confining pressure, soil structure, strength weakening and cyclic loading times on different positions along the cross-section. Then, the finite element model of a highway on soft soil foundations under soil consolidation is established, considering the influence of soil seepage. Finally, predictions of long-term settlement and cross-slope variation generated by coupling action of traffic load and soil consolidation were carried out with contrastive analysis with measured data. We find the following: (1) the long-term settlement was mostly from the consolidation of natural soft subsoil, while the cross-slope variation was mainly affected by traffic load; (2) variation in the cross slope of highway in soft soil areas mainly occurs within 1 year of operation. The effect of passenger cars and trucks on cross-slope variation shows diametrically opposite results, as the passenger car lane decreases while the truck lane increases; (3) the relative error of the cross-slope variation prediction results with the measured data are 2.86% and 2.5% for the left and right lane cross slopes, respectively. [ABSTRACT FROM AUTHOR]