Assessing internal soundness of hardwood logs through acoustic impact test and waveform analysis

  • Feng Xu
  • Yunfei LiuEmail author
  • Xiping WangEmail author
  • Brian K. Brashaw
  • Lon A. Yeary
  • Robert J. Ross


The objective of this study was to explore the potential of an acoustic impact test as a nondestructive evaluation procedure to assess the soundness of hardwood logs in terms of internal decay, void, and defect ratio. Fifteen logs of four hardwood species (black cherry, white oak, red oak, and cottonwood) were obtained and subjected to acoustic impact testing. The logs were subsequently dissected for visual examination and physical mapping of the internal defects. The acoustic response signals were analyzed to derive acoustic velocity (through FFT), time centroid (through moment analysis), and the first- and second-order damping ratio (through continuous wavelet transform). The longitudinal acoustic velocity was found to have a negative, nonlinear relationship with defect ratio (R2 = 0.72), but it lacked sensitivity to small defects and was affected by species. Time centroid proved to be a better predictor than acoustic velocity with an improved correlation with log defect ratio (R2 = 0.87). Wavelet-based damping ratio was found to have a close linear relationship with log defect ratio. Comparing with the first-order damping ratio, the second-order damping ratio had a better predicting power (R2 = 0.92) and was not affected by type and location of defects. The results further indicated that a combination of acoustic velocity, time centroid, and the first- and second-order damping ratios could yield the optimal prediction of log defect ratio. However, considering the sensitivity and simplicity of the waveform analysis, the second-order damping ratio of the response signals could be the best single predicting parameter for assessing the internal soundness of hardwood logs.



This research was conducted through research cooperation between the USDA Forest Service Forest Products Laboratory (FPL) and Nanjing Forestry University (NFU), China, and was supported in part by the National Natural Science Foundation of China (Grant No. 31170668), the Priority Academic Program Development (PAPD) of Jiangsu Higher Education Institutions, and the NFU Innovation Grant for Outstanding PhD Dissertations (Grant No. 163070682). The authors acknowledge the technical support from the Engineering Mechanics and Remote Sensing Laboratory (EMRSL) at the FPL.

Compliance with ethical standards

Conflict of interest

On behalf of all authors, the corresponding authors state that there is no conflict of interest.


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© This is a U.S. government work and its text is not subject to copyright protection in the United States; however, its text may be subject to foreign copyright protection 2019

Authors and Affiliations

  1. 1.College of Information Science and TechnologyNanjing Forestry UniversityNanjingChina
  2. 2.Forest Products LaboratoryUSDA Forest ServiceMadisonUSA
  3. 3.Mississippi State UniversityStarkvilleUSA

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