Thoms, A W; Brosnan, J T; Sorochan, J C; Saxton, A M
Exploring relationships in surface-hardness data collected with different instruments Journal Article
In: Journal of Testing and Evaluation, vol. 44, no. 2, pp. 945–951, 2016.
Abstract | Links | BibTeX | Tags: Bermudagrass, Bermudagrasses, Clegg Impact Soil Tester, Concussion, F355, injury, Mechanical engineering, REGRESSION analysis, Synthetic turf, Testing
@article{Thoms2016,
title = {Exploring relationships in surface-hardness data collected with different instruments},
author = {Thoms, A W and Brosnan, J T and Sorochan, J C and Saxton, A M},
doi = {10.1520/JTE20140423},
year = {2016},
date = {2016-01-01},
journal = {Journal of Testing and Evaluation},
volume = {44},
number = {2},
pages = {945--951},
abstract = {Two instruments are used to measure impact attenuation on athletic field playing surfaces: the F355 Apparatus A (F355) and the Clegg Impact Soil Tester (CIST). Although both devices use weighted missiles equipped with accelerometers to measure impact attenuation, Gmax, little information is available in the peer-reviewed scientific literature comparing data collected with these devices on natural and synthetic turf athletic field playing surfaces. A 2-year field study was conducted at the University of Tennessee Center for Athletic Field Safety in 2012 and 2013 to determine whether data collected with a CIST could be used to predict values with the F355. Ten different synthetic turf and four natural turfgrass surfaces constructed over four root zone types were subjected to 30 simulated traffic events at two rates (three events wk-1 and 10 events wk-1). Three impact attenuation samples were collected with both devices on all surfaces for each rate of simulated traffic. Two regression analyses were conducted: one using all 252 data points collected annually and a second that incorporated blocking to account for within surface sampling. In both years, associations between impact attenuation data collected with the CIST and the F355 were weak. CIST values only explained 9 % of the variability in F355 data in 2012 (R2=0.09) and 24 % in 2013 (R2=0.24). When accounting for surface sampling, CIST values only explained 46 % of the variability in F355 data in 2012 (R2=0.46) and 56 % in 2013 (R2=0.56). Residuals around these best-fit regression lines were 625 Gmax, indicating that the CIST cannot accurately predict impact attenuation values with the F355 within this range. Future research should continue to explore relationships between the F355 and CIST across diverse playing surface types and environments. © 2016 ASTM Int'l (all rights reserved).},
keywords = {Bermudagrass, Bermudagrasses, Clegg Impact Soil Tester, Concussion, F355, injury, Mechanical engineering, REGRESSION analysis, Synthetic turf, Testing},
pubstate = {published},
tppubtype = {article}
}
Thoms, A W; Brosnan, J T; Sorochan, J C; Saxton, A M
Exploring relationships in surface-hardness data collected with different instruments Journal Article
In: Journal of Testing and Evaluation, vol. 44, no. 2, pp. 945–951, 2016.
@article{Thoms2016,
title = {Exploring relationships in surface-hardness data collected with different instruments},
author = {Thoms, A W and Brosnan, J T and Sorochan, J C and Saxton, A M},
doi = {10.1520/JTE20140423},
year = {2016},
date = {2016-01-01},
journal = {Journal of Testing and Evaluation},
volume = {44},
number = {2},
pages = {945--951},
abstract = {Two instruments are used to measure impact attenuation on athletic field playing surfaces: the F355 Apparatus A (F355) and the Clegg Impact Soil Tester (CIST). Although both devices use weighted missiles equipped with accelerometers to measure impact attenuation, Gmax, little information is available in the peer-reviewed scientific literature comparing data collected with these devices on natural and synthetic turf athletic field playing surfaces. A 2-year field study was conducted at the University of Tennessee Center for Athletic Field Safety in 2012 and 2013 to determine whether data collected with a CIST could be used to predict values with the F355. Ten different synthetic turf and four natural turfgrass surfaces constructed over four root zone types were subjected to 30 simulated traffic events at two rates (three events wk-1 and 10 events wk-1). Three impact attenuation samples were collected with both devices on all surfaces for each rate of simulated traffic. Two regression analyses were conducted: one using all 252 data points collected annually and a second that incorporated blocking to account for within surface sampling. In both years, associations between impact attenuation data collected with the CIST and the F355 were weak. CIST values only explained 9 % of the variability in F355 data in 2012 (R2=0.09) and 24 % in 2013 (R2=0.24). When accounting for surface sampling, CIST values only explained 46 % of the variability in F355 data in 2012 (R2=0.46) and 56 % in 2013 (R2=0.56). Residuals around these best-fit regression lines were 625 Gmax, indicating that the CIST cannot accurately predict impact attenuation values with the F355 within this range. Future research should continue to explore relationships between the F355 and CIST across diverse playing surface types and environments. © 2016 ASTM Int'l (all rights reserved).},
keywords = {},
pubstate = {published},
tppubtype = {article}
}
Thoms, A W; Brosnan, J T; Sorochan, J C; Saxton, A M
Exploring relationships in surface-hardness data collected with different instruments Journal Article
In: Journal of Testing and Evaluation, vol. 44, no. 2, pp. 945–951, 2016.
Abstract | Links | BibTeX | Tags: Bermudagrass, Bermudagrasses, Clegg Impact Soil Tester, Concussion, F355, injury, Mechanical engineering, REGRESSION analysis, Synthetic turf, Testing
@article{Thoms2016,
title = {Exploring relationships in surface-hardness data collected with different instruments},
author = {Thoms, A W and Brosnan, J T and Sorochan, J C and Saxton, A M},
doi = {10.1520/JTE20140423},
year = {2016},
date = {2016-01-01},
journal = {Journal of Testing and Evaluation},
volume = {44},
number = {2},
pages = {945--951},
abstract = {Two instruments are used to measure impact attenuation on athletic field playing surfaces: the F355 Apparatus A (F355) and the Clegg Impact Soil Tester (CIST). Although both devices use weighted missiles equipped with accelerometers to measure impact attenuation, Gmax, little information is available in the peer-reviewed scientific literature comparing data collected with these devices on natural and synthetic turf athletic field playing surfaces. A 2-year field study was conducted at the University of Tennessee Center for Athletic Field Safety in 2012 and 2013 to determine whether data collected with a CIST could be used to predict values with the F355. Ten different synthetic turf and four natural turfgrass surfaces constructed over four root zone types were subjected to 30 simulated traffic events at two rates (three events wk-1 and 10 events wk-1). Three impact attenuation samples were collected with both devices on all surfaces for each rate of simulated traffic. Two regression analyses were conducted: one using all 252 data points collected annually and a second that incorporated blocking to account for within surface sampling. In both years, associations between impact attenuation data collected with the CIST and the F355 were weak. CIST values only explained 9 % of the variability in F355 data in 2012 (R2=0.09) and 24 % in 2013 (R2=0.24). When accounting for surface sampling, CIST values only explained 46 % of the variability in F355 data in 2012 (R2=0.46) and 56 % in 2013 (R2=0.56). Residuals around these best-fit regression lines were 625 Gmax, indicating that the CIST cannot accurately predict impact attenuation values with the F355 within this range. Future research should continue to explore relationships between the F355 and CIST across diverse playing surface types and environments. © 2016 ASTM Int'l (all rights reserved).},
keywords = {Bermudagrass, Bermudagrasses, Clegg Impact Soil Tester, Concussion, F355, injury, Mechanical engineering, REGRESSION analysis, Synthetic turf, Testing},
pubstate = {published},
tppubtype = {article}
}