228 Dahle et al.: Measuring Modulus of Elasticity with Acoustical Stress Waves logs and lumber have shown that the modulus of elasticity of the log correlates well to the average modulus of elasticity of the lumber (Ross et al. 1997). Gao et al. (2012; 2013) found that wood tempera- (Grabianowski et al. 2006; Downes and Drew 2008; Kretschmann 2010) without the need for destruc- tive sampling. Research has led to the understanding that acoustic stress-wave velocity is an accurate pre- dictor of the dynamic modulus of elasticity (DMOE) of wood that is derived from standing trees (Lind- ström et al. 2002; Grabianowski et al. 2006; Auty and Achim 2008; Gao et al. 2013; Chen et al. 2015). Yet much of this work has not directly tested the DMOE against ES . Stress-wave relationships between sawn of elasticity without the need for destructive sam- pling. This research was designed to determine if the acoustic technology can be an applied when mea- suring E in standing trees, thus allowing the use of a rapid, non-destructive method of obtaining E that can be used in prediction stability in urban trees. MATERIALS AND METHODS ture had a significant effect on how acoustic sound travels through clear wood in standing trees. As the temperature of wood reaches freezing, velocity of sound waves increase as temperature decreases (Gao et al. 2012; Gao et al. 2013). The speed of sound slowly decreases as branch wood warms above freezing (Bächle and Walker 2006; Kretschmann 2010). Although branches tend to include small imperfections, such as knots, the one-way flight path of the stress-wave timer will circumvent these areas reasonably well (Chauhan and Walker 2006). Acoustic stress wave technology has been used for nondestructive materials in fields resonance testing as an indicator of material properties (Vary and Lark 1978; Vary and Bowles 1979; Henneke and Stinchcomb 1986; Halabe et al. 1997; DeVallance et al. 2011). In standing trees, advanced stress wave technology is used to measure decay that cannot be seen from the outside (Gilbert and Smiley 2004; Wang and Allison 2008; Wang et. al 2009; Johnstone et al. 2010a; Johnstone et al. 2010b). Along with pro- viding a good estimation of the amount of internal decay, this technology can be used to estimate the testing of flexural modulus of living trees, but comes with a large cost associated with the equipment and a lengthy setup due to the number of probes. Stress-wave timers, such as the Fakopp® micro- second stress wave propagation timer used in this study, allow for simplistic acoustical testing of living trees and prevent the need for destructive sampling. A UTM is a direct measure of materials elasticity (ASTM 2005), yet this requires destructive testing and takes a significant amount of time. The abil- ity to use a portable acoustical stress-wave system can allow a rapid estimation of flexural modulus ©2016 International Society of Arboriculture [2a] [3] Northern red oak (Quercus rubra L.) stump sprouts arising aſter a three-stage shelterwood harvest were collected in February 2013 and September 2014. The samples were growing in a 29.95 ha site within the Research Forest of West Virginia Uni- versity (Monongalia County, West Virginia, U.S.). One hundred and twenty sprouts were harvested with only one sprout per stump cut. Sprout di- ameter ranged from 2.7 to 4.6 cm, and all sprouts were cut to a length of 55.9 cm beginning at the proximal end of the sprout. The sprouts were randomly separated into two groups: sixty were placed at room temperature (warm), estimated at 21.1°C, and sixty at -6.7 °C (frozen), for five days in a CSZ-H/AC environmental unit (model ZPH- 32-2-2-H/AC, Cincinnati Sub-Zero, Cincinnati, Ohio, U.S.). Two of the frozen samples were subse- quently discarded during processing due to damage. Dynamic Modulus of Elasticity The DMOE was evaluated using a Fakopp Micro- second Timer (frequency 23 kHz, Fakopp Enter- prises, Agfalva, Hungary) by determining the time it takes for a stress wave, generated by a hammer tap on an spike transducer (Figure 1), to travel from one end of the sample to the other end. This was replicated five times per sample for each group following a protocol that assumed that measur- ing each sample with three hits at the same lo- cation would be sufficient in assessing different stands (Carter et al. 2005a; Carter et al. 2005b). Longitudinal DMOE was calculated as Equation 1: [1] [1] where DMOE = dynamic longitudinal modulus of elasticity in gigapascals c = stress-wave velocity (cm/second) [1a] [1] [1b] [1a] [1b] [2] [2a] [2] 𝐷𝐷弶ﭒ 𝐷𝐷弶ﭒ 𝐷𝐷弶ﭒ 𝐷𝐷弶ﭒ = 𝑐𝑐弶ﭒ 2 × 𝜌𝜌弶ﭒ , (Equation 1, DeVallance et al. 2011) � 𝑛𝑛弶ﭒ 𝑚𝑚弶ﭒ 2� 𝑥𝑥弶ﭒ 109 � 𝑛𝑛弶ﭒ 𝜌𝜌弶ﭒ = density �980 cm/𝑚𝑚弶ﭒ 2∗volume 𝑐𝑐弶ﭒ 𝑚𝑚弶ﭒ 3 [1a] 𝑚𝑚弶ﭒ 2� 𝑥𝑥弶ﭒ 109 𝑚𝑚弶ﭒ 𝑚𝑚弶ﭒ 𝑚𝑚弶ﭒ 𝑚𝑚弶ﭒ (𝑔𝑔弶ﭒ ) 𝜌𝜌弶ﭒ = density �980 cm/𝑚𝑚弶ﭒ 2∗volume 𝑐𝑐弶ﭒ 𝑚𝑚弶ﭒ 3 𝐷𝐷弶ﭒ 𝑆𝑆弶ﭒ = (4×∗𝑚𝑚弶ﭒ 𝑠𝑠弶ﭒ 𝑠𝑠弶ﭒ 𝑠𝑠弶ﭒ 𝑠𝑠弶ﭒ ∗×(𝑠𝑠弶ﭒ 𝑠𝑠弶ﭒ 𝑛𝑛弶ﭒ 𝑔𝑔弶ﭒ 𝑙𝑙弶ﭒ ℎ3)) 𝐷𝐷弶ﭒ 𝑆𝑆弶ﭒ = (4×∗𝑚𝑚弶ﭒ 𝑠𝑠弶ﭒ 𝑠𝑠弶ﭒ 𝑠𝑠弶ﭒ 𝑠𝑠弶ﭒ ∗×(𝑠𝑠弶ﭒ 𝑠𝑠弶ﭒ 𝑛𝑛弶ﭒ 𝑔𝑔弶ﭒ 𝑙𝑙弶ﭒ ℎ3)) 𝐷𝐷弶ﭒ 𝑆𝑆弶ﭒ = (4×∗𝑚𝑚弶ﭒ 𝑠𝑠弶ﭒ 𝑠𝑠弶ﭒ 𝑠𝑠弶ﭒ 𝑠𝑠弶ﭒ ∗×(𝑠𝑠弶ﭒ 𝑠𝑠弶ﭒ 𝑛𝑛弶ﭒ 𝑔𝑔弶ﭒ 𝑙𝑙弶ﭒ ℎ3)) � 𝑁𝑁弶ﭒ 𝑚𝑚弶ﭒ 2� 𝑥𝑥弶ﭒ 109 � 𝑁𝑁弶ﭒ 𝐷𝐷弶ﭒ 𝑀𝑀弶ﭒ = 𝑤𝑤弶ﭒ 𝑠𝑠弶ﭒ 𝑙𝑙弶ﭒ 𝑤𝑤弶ﭒ 𝑠𝑠弶ﭒ 𝑟𝑟弶ﭒ 𝑔𝑔弶ﭒ ℎ𝑙𝑙弶ﭒ −𝑟𝑟弶ﭒ 𝑟𝑟弶ﭒ 𝑑𝑑弶ﭒ 𝑤𝑤弶ﭒ 𝑠𝑠弶ﭒ 𝑟𝑟弶ﭒ 𝑔𝑔弶ﭒ ℎ𝑙𝑙弶ﭒ 𝑚𝑚弶ﭒ 2� 𝑥𝑥弶ﭒ 109 𝑟𝑟弶ﭒ 𝑟𝑟弶ﭒ 𝑑𝑑弶ﭒ 𝑤𝑤弶ﭒ 𝑠𝑠弶ﭒ 𝑟𝑟弶ﭒ 𝑔𝑔弶ﭒ ℎ𝑙𝑙弶ﭒ [3] 48∗×𝜋𝜋弶ﭒ ∗×(𝑟𝑟弶ﭒ 𝑚𝑚弶ﭒ 𝑟𝑟弶ﭒ 𝑟𝑟弶ﭒ 𝑟𝑟弶ﭒ 𝑚𝑚弶ﭒ 4) [2] 48∗×𝜋𝜋弶ﭒ ∗×(𝑟𝑟弶ﭒ 𝑚𝑚弶ﭒ 𝑟𝑟弶ﭒ 𝑟𝑟弶ﭒ 𝑟𝑟弶ﭒ 𝑚𝑚弶ﭒ 4) [2a] , (Equation 2, ASTM 2005) � 𝑁𝑁弶ﭒ 48∗×𝜋𝜋弶ﭒ ∗×(𝑟𝑟弶ﭒ 𝑚𝑚弶ﭒ 𝑟𝑟弶ﭒ 𝑟𝑟弶ﭒ 𝑟𝑟弶ﭒ 𝑚𝑚弶ﭒ 4) 𝑚𝑚弶ﭒ 2� 𝑥𝑥弶ﭒ 109 ∗ 100 𝑤𝑤弶ﭒ 𝑠𝑠弶ﭒ 𝑙𝑙弶ﭒ 𝑤𝑤弶ﭒ 𝑠𝑠弶ﭒ 𝑟𝑟弶ﭒ 𝑔𝑔弶ﭒ ℎ𝑙𝑙弶ﭒ −𝑟𝑟弶ﭒ 𝑟𝑟弶ﭒ 𝑑𝑑弶ﭒ 𝑤𝑤弶ﭒ 𝑠𝑠弶ﭒ 𝑟𝑟弶ﭒ 𝑔𝑔弶ﭒ ℎ𝑙𝑙弶ﭒ � 𝑚𝑚弶ﭒ 𝑚𝑚弶ﭒ 𝑚𝑚弶ﭒ 𝑚𝑚弶ﭒ (𝑔𝑔弶ﭒ ) 𝜌𝜌弶ﭒ = density �980 cm/𝑚𝑚弶ﭒ 2∗volume 𝑐𝑐弶ﭒ 𝑚𝑚弶ﭒ 3 [1b] 𝑚𝑚弶ﭒ 𝑚𝑚弶ﭒ 𝑚𝑚弶ﭒ 𝑚𝑚弶ﭒ (𝑔𝑔弶ﭒ ) , (Equation 2, ASTM 2005) � , (Equation 2, AS [1] 𝐷𝐷弶ﭒ 𝐷𝐷弶ﭒ 𝐷𝐷弶ﭒ 𝐷𝐷弶ﭒ = 𝑐𝑐弶ﭒ 2 × 𝜌𝜌弶ﭒ , (Equation 1, DeVallance et al. 2011) � 𝑛𝑛弶ﭒ (DeVallance et al. 2011) 𝐷𝐷弶ﭒ 𝐷𝐷弶ﭒ 𝐷𝐷弶ﭒ 𝐷𝐷弶ﭒ = 𝑐𝑐弶ﭒ 2 × 𝜌𝜌弶ﭒ , (Equation 1, DeVallanc 𝑚𝑚弶ﭒ 2� 𝑥𝑥弶ﭒ 109 �
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