## 6 Surface Characteristics Assessment
### Surface Texture Assessment  
Surface texture measurement can be used as an indirect tool for friction assessment and includes both contact and non-contact methods. Depending on scale, surface texture is categorized into four classes: microtexture, macrotexture, megatexture, and uneven  
Figure 21: Correlation between Grip Number and temperature [70].  
Figure 22: Combined effect of surface and water temperature on the coefficient of friction [110].  
ness [111]. Figure 23 presents the wavelengths of texture classes and their influence on rolling tires, according to PIARC, with microtexture and macrotexture directly relating to aircraft skid resistance during wet surface conditions. Furthermore, unevenness and megatexture are undesirable and limited, due to other negative effects during landing and take-off on aircrafts, so only microtexture and macrotexture assessment and control are required for aircraft skid resistance and runway surface regulation.  
Different authorities use different limits for the definition of microtexture and macrotexture. For example, ICAO [7] states that the threshold between microtexture and macrotexture is 0.1 mm, whereas PIARC reports 0.5 mm wavelength to be a threshold [112]. In general, microtexture can be described as a hardly detectable texture of individual stones, and macrotexture is a texture among individual stones that can be judged approximately by the naked eye. In practical measurements, the difference between macrotexture and microtexture is evaluated by using different algorithms, such as the Fourier transformation, the Butterworth filter [113], the Gaussian smoothing filter [114], or other algorithms, to effectively isolate the microtexture from the broader macrotexture.  
In a state-of-the-art review [112], surface texture parameters were separated into two-dimensional amplitude and shape-related parameters, three-dimensional amplitude and shape-related parameters, spectral characteristics of a surface wave, and fractal and multifractal characteristics. Amplitude and shape-related parameters are widely used during the texture assessment. However, the other parameters have limited use. In one study [115], macrotexture, obtained by a circular track meter, was characterized using wavelet analysis, which allowed the analysis of the influence of different aggregate sizes on surface texture. In a similar study [116], asphalt surface friction was characterized as a set of fractal parameters of texture. Fractal mathematics was also used for the texture analysis by [PERSON] et al. [117]. The authors report that this technique has potential for a skid resistance assessment, but this has not yet been introduced into common practice.  
Figure 24 presents methods currently used for texture assessment, with the methods recommended by ICAO shown in red. The methods are divided by measurement principle and purpose. Tactile and visual assessments can only be used for approximate texture monitoring. Visual assessment can be used for the detection of runway wear [118], rubber build-up [119], and pavement deformation [120], but not for direct  
Figure 23: PIARC classification for pavement surface characteristics according to wavelength [112].  
friction characterization [66]. This type of assessment allows for the identification of areas with potential microtexture and macrotexture deterioration, but it is not a quantitative nor a precise assessment.