Experimental design and optimization of artificial turf playing surfaces is costly and time-consuming. Although contemporary finite element (FE) models can offer insight into design choices, they have not been validated for a wide range of turf design parameters. In the present study, 14 physical turf designs with varying infill composition (different infill mixture ratios), fiber areal density (fibers per square inch), mixture composition (different infill particle diameters), turf height (varying both fiber length and infill depth), infill depth (using the same fiber length), and fiber type (monofilament vs slit film) were constructed. The turfs were tested using the Biocore Elite Athlete Shoe Turf (BEAST) test apparatus under translation and rotation loading, with the results typically showing low sensitivity to variations in turf design parameters (<10% absolute difference for most metrics), except for extreme case, such as 100% sand infill and very low areal fiber density (0.5 fibers/cm 2 ). Corresponding FE models were developed using a recently validated methodology and captured the variation in response under both translational and rotational modes of loading with a CORA objective rating between 0.53 and 0.93 (mean 0.75). The models demonstrated the ability to reproduce the main effects from the experimental tests and to predict the rank order of the effects measured experimentally. Importantly, the model was able to quantify the effect of varying parameters and show potential for use in future turf design and optimization.
Watson et al. (Thu,) studied this question.
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