Ultrasonic wave velocity as a universal metric for defect detection in timber structures: A case study on Japanese cedar wood (<i>Cryptomeria japonica</i>)

Chun-Won Kang; Masumi  Hasegawa; Haradhan  Kolya

doi:10.59400/sv2463

Ultrasonic wave velocity as a universal metric for defect detection in timber structures: A case study on Japanese cedar wood (Cryptomeria japonica)

Chun-Won Kang Department of Housing Environmental Design and Research Institute of Human Ecology, College of Human Ecology, Jeonbuk National University, Jeonju 54896, Republic of Korea
Masumi Hasegawa Faculty of Agriculture Department of Agro-Environmental Sciences, Kyushu University, Fukuoka 819-0395, Japan
Haradhan Kolya Department of Housing Environmental Design and Research Institute of Human Ecology, College of Human Ecology, Jeonbuk National University, Jeonju 54896, Republic of Korea

Article ID: 2463

DOI: https://doi.org/10.59400/sv2463

Keywords: ultrasonic wave; wood defects; propagation velocity; Japanese cedar wood; non-destructive testing

Abstract

This study makes significant contributions to the field of ultrasonic testing (UT) by offering a novel approach to the identification of artificially introduced defects within Japanese cedar wood (Cryptomeria japonica). The findings are of particular relevance for the heritage conservation and construction sectors, where non-invasive defect detection is paramount. The study establishes a robust framework for assessing the structural integrity of timber by correlating ultrasonic wave velocity reductions with defect size and distribution. Big-sized defects led to more substantial decreases in wave velocity. The study establishes a robust framework for assessing the structural integrity of historical timber by correlating ultrasonic wave velocity reductions with defect size and distribution. This framework has the potential to be applicable to diverse wood species and defect types.

Published

2025-05-29

How to Cite

Kang, C.-W., Hasegawa, M., & Kolya, H. (2025). Ultrasonic wave velocity as a universal metric for defect detection in timber structures: A case study on Japanese cedar wood (Cryptomeria japonica). Sound & Vibration, 59(2), 2463. https://doi.org/10.59400/sv2463

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Issue

Vol. 59 No. 2 (2025)

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Article

This work is licensed under a Creative Commons Attribution 4.0 International License.

References

[1]Asdrubali F, Ferracuti B, Lombardi L, et al. A review of structural, thermo-physical, acoustical, and environmental properties of wooden materials for building applications. Build and Environment. 2017; 114: 307–332. doi: 10.1016/j.buildenv.2016.12.033

[2]Gayathri R, Mahboob S, Govindarajan M, et al. A review on biological carbon sequestration: A sustainable solution for a cleaner air environment, less pollution and lower health risks. Journal of King Saud University-Science. 2021; 33: 101282. doi: 10.1016/j.jksus.2020.101282

[3]Kang CW, Hashitsume K, Kolya H. Surface topography analysis of Cedrela sinensis and Korean Paulownia boards using stylus and 3D optical profilometry. Int. J. Adv. Manuf. Technol. 2024. 134: 2431–2437. doi: 10.1007/s00170-024-14292-2

[4]Kawasaki T, Kawai S. Thermal insulation properties of wood-based sandwich panel for use as structural insulated walls and floors. J. Wood. Sci. 2006; 52: 75–83.

[5]Shukla SK. Thermal Evaluation of Indoor Climate and Energy Storage in Buildings. CRC Press; 2024.

[6]Nenning T, Tockner A, Konnerth J, et al. Variability of mechanical properties of hardwood branches according to their position and inclination in the tree. Constr. Build. Mater. 2024; 419: 135448. doi: 10.1016/j.conbuildmat.2024.135448

[7]Yazzie KC, Torgersen CE, Schindler DE, Reeves GH. Spatial and temporal variation of large wood in a coastal river. Ecosystems. 2024; 27: 19–32.

[8]Yang H, Wang S, Son Y, et al. Global patterns of tree wood density. Glob. Chang. Biol. 2024; 30: e17224.

[9]Jiang X, Wang J, Zhang Y, Jiang S. Defect Detection in Solid Timber Panels Using Air-Coupled Ultrasonic Imaging Techniques. Appl. Sci. 2024; 14(1): 434. doi: 10.3390/app14010434

[10]Hajian E, AJ Huber J, Hansson L, Sandberg D. High temperature drying of sawn timber—A review. Dry. Technol. 2024: 42(9): 1–18.

[11]Gao Y, Fu Z, Zhou Y, et al. Moisture changes and surface checking development during drying of Masson pine wood. Dry. Technol. 2024: 42(8): 1–11.

[12]Chauhan S, Donnelly R, Huang C, et al. Wood quality: multifaceted opportunities BT. In: Walker JCF (editor). Primary Wood Processing: Principles and Practice. Springer; 2006. pp. 159–202.

[13]Reinprecht L. Wood deterioration, protection and maintenance. John Wiley & Sons; 2016.

[14]Mahalil RA, Mydin MAO, Omar R. Exploration of Timber Dry and Wet Rot Defects in Buildings: Types, Causes, Effects and Mitigation Methods. J. Adv. Res. Fluid Mech. Therm. Sci. 2024; 119: 196–217.

[15]Marais BN, Brischke C, Militz H. Wood durability in terrestrial and aquatic environments—A review of biotic and abiotic influence factors. Wood Material Science & Engineering. 2022; 17(2): 82–105.

[16]Arriaga F, Wang X, Íñiguez-González G, et al. Mechanical properties of wood: A review. Forests. 2023; 14: 1202.

[17]Mirmahdi E. Numerical and Experimental Modeling of Spot Welding Defects by Ultrasonic Testing on Similar Sheets and Dissimilar Sheets. Russ. J. Nondestruct. Test. 2020; 56: 620–634. doi: 10.1134/S1061830920080069

[18]Mai TC, Razafindratsima S, Sbartaï ZM, et al. Non-destructive evaluation of moisture content of wood material at GPR frequency. Constr. Build. Mater. 2015; 77: 213–217. doi: 10.1016/j.conbuildmat.2014.12.030

[19]Li W, Van den Bulcke J, Mannes D, et al. Impact of internal structure on water-resistance of plywood studied using neutron radiography and X-ray tomography. Constr. Build. Mater. 2014; 73: 171–179.

[20]López G, Basterra LA, Acuña L. Estimation of wood density using infrared thermography. Constr. Build. Mater. 2013; 42: 29–32.

[21]Pahnabi N, Schumacher T, Sinha A. Imaging of Structural Timber Based on In Situ Radar and Ultrasonic Wave Measurements: A Review of the State-of-the-Art. Sensors. 2024; 24(9): 2901. doi: 10.3390/s24092901

[22]Wang L, Zhang Q, Yi J, Zhang J. Effects of coral aggregate properties on the ultrasonic pulse velocity of concrete. J. Build. Eng. 2023; 80: 107935. doi: 10.1016/j.jobe.2023.107935

[23]Kot P, Muradov M, Gkantou M, et al. Recent advancements in non-destructive testing techniques for structural health monitoring. Appl. Sci. 2021; 11: 2750.

[24]Gupta M, Khan MA, Butola R, Singari RM. Advances in applications of Non-Destructive Testing (NDT): A review. Adv. Mater. Process. Technol. 2022; 8: 2286–2307.

[25]Dwivedi SK, Vishwakarma M, Soni A. Advances and researches on non destructive testing: A review. Mater. Today Proc. 2018; 5: 3690–3698.

[26]Zhao K, Ge Z, Huo L, et al. Application Progress and Prospect of Defect Detection Technology for Timber Structure Members. Russ. J. Nondestruct. Test. 2024; 60: 455–469. doi: 10.1134/S1061830924600217

[27]Xu X, Ran B, Jiang N, et al. A systematic review of ultrasonic techniques for defects detection in construction and building materials. Measurement. 2024; 226: 114181. doi: 10.1016/j.measurement.2024.114181

[28]Hasegawa M, Takata M, Matsumura J, Oda K. Effect of wood properties on within-tree variation in ultrasonic wave velocity in softwood. Ultrasonics. 2011; 51: 296–302. doi: 10.1016/j.ultras.2010.10.001

[29]Park JC, Hong SI. Determination of localized defects in wood by the transfer time of ultrasonic waves. J. Korean Wood Sci. Technol. 2008; 36: 61–68.

[30]Hwang WJ, Lee HM, Park YR, Lee DH. The change of ultrasonic transmission velocity by wood decay. J. Korean Wood Sci. Technol. 2014; 42(2): 214–221.

[31]Ciang CC, Lee JR, Bang HJ. Structural health monitoring for a wind turbine system: A review of damage detection methods. Meas. Sci. Technol. 2008; 19: 122001.

[32]Annamdas VGM, Bhalla S, Soh CK. Applications of structural health monitoring technology in Asia. Struct. Heal. Monit. 2017; 16: 324–346.

[33]Oh SC. Comparison of ultrasonic velocities between direct and indirect methods on 30 mm × 30 mm spruce lumber. J. Korean Wood Sci. Technol. 2020; 48: 562–568.

[34]Bertholf LD. Use of elementary stress wave theory for prediction of dynamic strain in wood. Technical Extension Service, Washington State University; 1965.

[35]Lee CJ, Wang SY, Yang TH. Evaluation of moisture content changes in Taiwan red cypress during drying using ultrasonic and tap-tone testing. Wood Fiber. Sci. 2011; 57–63.

[36]Sakai H, Minamisawa A, Takagi K. Effect of moisture content on ultrasonic velocity and attenuation in woods. Ultrasonics. 1990; 28(6): 382–385.

[37]Mousavi M, Taskhiri MS, Holloway D, et al. Feature extraction of wood-hole defects using empirical mode decomposition of ultrasonic signals. NDT & E International. 2020; 114: 102282. doi: 10.1016/j.ndteint.2020.102282

[38]Vössing KJ, Gaal M, Niederleithinger E. Imaging wood defects using air coupled ferroelectret ultrasonic transducers in reflection mode. Constr. Build. Mater. 2020; 241: 118032. doi: 10.1016/j.conbuildmat.2020.118032

[39]Tiitta M, Tiitta V, Gaal M, et al. Air-coupled ultrasound detection of natural defects in wood using ferroelectret and piezoelectric sensors. Wood Sci. Technol. 2020; 54: 1051–1064. doi: 10.1007/s00226-020-01189-y

[40]Fathi H, Nasir V, Kazemirad S. Prediction of the mechanical properties of wood using guided wave propagation and machine learning. Constr. Build. Mater. 2020; 262: 120848. doi: 10.1016/j.conbuildmat.2020.120848.

[41]Bucur V. Theory of and Experimental Methods for the Acoustic Characterization of Wood BT. In: Acoustics of Wood. Springer; 2006. pp. 39–104.

[42]Bucur V. The Acoustics of Wood (1995). CRC Press; 2017.

[43]Mishiro A. Ultrasonic velocity in wood and its moisture content I. Effects of moisture gradients on ultrasonic velocity in wood. Journal of the Japan Wood Research Society. 1995; 41(12): 1086-1092.

[44]Sandoz JL. Moisture content and temperature effect on ultrasound timber grading. Wood Sci. Technol. 1993; 27: 373–380.

[45]Ono K. A Comprehensive Report on Ultrasonic Attenuation of Engineering Materials, Including Metals, Ceramics, Polymers, Fiber-Reinforced Composites, Wood, and Rocks. Appl. Sci. 2020; 10(7): 2230. doi: 10.3390/app10072230

[46]Groby JP, Ogam E, De Ryck L, et al. Analytical method for the ultrasonic characterization of homogeneous rigid porous materials from transmitted and reflected coefficients. J. Acoust. Soc. Am. 2010; 127(2): 764–772.

[47]Kerherve S. Transport of ultrasonic waves in strongly scattering or absorbing heterogeneous media [PhD thesis]. University of Manitoba; 2018.

[48]Malyarenko EV, Hinders MK. Ultrasonic Lamb wave diffraction tomography. Ultrasonics. 2001; 39: 269–281. doi: 10.1016/S0041-624X(01)00055-5

[49]de Oliveira FGR, Sales A. Relationship between density and ultrasonic velocity in Brazilian tropical woods. Bioresour. Technol. 2006; 97: 2443–2446. doi: 10.1016/j.biortech.2005.04.050

[50]Bucur V. Acoustics of wood. Springer; 2006. pp. 101–108.

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