Pavement surface texture significantly affects tyre–pavement friction and noise characteristics. The traditional methods for evaluating pavement surface texture result in a single index called mean profile depth (MPD). Although this index can reflect the overall texture properties, it cannot reveal the range and distribution of pavement surface texture, which play a critical role in prediction of tyre–pavement interaction characteristics. In this paper, a cost-effective and relatively precise image-based texture analysis method (ITAM) was developed based on digital image processing and spectral analysis technologies. Mixture sample produced using surperpave gyratory compactor (SGC) is cut into three sections which are scanned using a standard commercial scanner. Mixture surface profile is then identified from the scanned cut section images by applying a series of image analysis technics. Afterwards, a discrete Fourier transform is applied on the mixture surface profiles to calculate the texture distribution indicators through the ITAM software. Additionally, the traditional texture indicator (MPD) is derived. Previous researchers have shown that the stationary laser profilometer (SLP) serves as an effective method to characterise pavement texture properties as this method correlates well with traditional texture testing methods. In this study, the ITAM analysis results are verified by comparing with those from the SLP method. It is shown that ITAM results correlate well with SLP and therefore considered as an effective method to characterise pavement surface texture properties. The results indicate that this method is a promising and powerful tool for future application in mixture designs to estimate texture as related to noise and friction.

The traditional asphalt mixture surface texture measurement methods cannot provide the distributions of the texture, which is necessary for studying the influence mechanism of texture on tyre/pavement interaction performance. A non-destructive 3-dimensional image-based texture analysis method (3D-ITAM) is developed based on the 3D surface reshaping and spectral analysis techniques. Mixture 3D surface is reshaped based on the photometric stereo method using the acquired tricolour digital images. Spectral indicators representing mixture surface texture distribution properties are calculated from 3D-ITAM software. Additionally, the traditional texture indicator (i.e. mean texture depth) is also calculated. The 3D-ITAM results are confirmed with both macro and micro-texture indicator values from the sand patch method (SPM) and hand-push friction tester (HFT), which are two widely used in practice though they cannot capture the distribution property of surf ace texture. It was demonstrated that the 3D-ITAM results correlate well with SPM and HFT and can be considered as an effective method to characterise the mixture surface texture.

Tire–pavement friction plays a key role in traffic safety. With the development of auto vehicle industry, most of the new vehicles are equipped with Anti Braking System (ABS). Therefore, a prediction model representing the braking process of vehicles equipped with ABS is deemed necessary. In this paper, the tire–pavement friction is measured by Dynamic Friction Tester (DFT) and Hand Friction Tester. The surface texture of asphalt pavement is acquired using a recently developed programme named as 2-Dimensional Image Texture Analysis Method (2D-ITAM). Tire–pavement friction at optimum design slip speed corresponding to the maximum tire–pavement friction is calculated with a widely used model. Then a prediction model correlating the tire–pavement friction at optimum design slip speed with the macro-texture and micro-texture of pavement is established using multivariate non-linear regression analysis. This prediction model is validated through laboratory test indicating its effectiveness of predicting the tire–pavement friction. The model is anticipated to be an improved tool which can be considered by practitioners in an optimised asphalt mixture design including the evaluation of skid resistance of pavement.

Pavement skid resistance plays a key role in traffic safety. Meanwhile, tire-pavement noise is a major source of traffic noise in urban areas. Current asphalt mixture design methods, however, mostly focus on volumetric and mechanical properties and pay little attention to the skid resistance and noise reduction performance of asphalt mixtures, which are significantly affected by the surface textures of asphalt mixtures. Incorporating the evaluation of surface texture into the mixture design would aid in a more rational selection of materials considering both mechanical and functional properties of asphalt mixtures. In this paper, the surface texture properties of several types of asphalt mixtures are measured using a recently developed 2-Dimensional Image Texture Analysis Method. A prediction model correlating the mixture surface texture levels at different central texture wavelength in octave band with the important mix design parameters is established using a multivariate non-linear regression analysis. The model is validated through laboratory test and imaging measurement indicating its capability of predicting the level and distributions of mixture surface texture. The prediction model is anticipated to provide a basis of optimised mixture design considering the skid resistance and noise reduction performances of asphalt pavement.

The operating temperature plays a key role in the service reliability of ball bearings used in the traction motor of high-speed trains. Studies on the heat generation and temperature distributions of the bearing under the vehicle vibration environment are critical for designing the high-speed train traction motor bearing and to monitor its operation. In this paper, an SKF friction torque model that considers the inlet shear heating reduction and kinematic replenishment/starvation reduction was used to calculate the power loss of the bearing operating in a high-speed train. A thermal analysis model of the bearing, which takes into account the vehicle vibration due to track irregularities, was developed to study the thermal characteristics of the bearing operating in a high-speed train. Experiments were conducted on a service high-speed train (i.e. CRH380B) to validate the proposed bearing thermal analysis model. The thermal characteristics of the bearing and the influence of key factors on the operating temperature of the bearing under vehicle vibration due to track irregularities were studied with the proposed bearing thermal analysis model. The results show that the thermal failure of the bearing subassemblies most likely occurs at the balls region owing to the highest temperature in that region, compared to the inner and outer raceway of the bearing. The method of applying grease with appropriate kinematic viscosity should be adopted to reduce the power loss of bearing while meeting the lubrication requirement. The vehicle vibration due to track irregularities significantly influences the thermal characteristics of high-speed train motor bearings, which cannot be ignored in the thermal analysis of bearings operating in a high-speed train.