Imaging of Structural Timber Based on In Situ Radar and Ultrasonic Wave Measurements: A Review of the State-of-the-Art

被引:4
|
作者
Pahnabi, Narges [1 ]
Schumacher, Thomas [1 ]
Sinha, Arijit [2 ]
机构
[1] Portland State Univ, Civil & Environm Engn, Portland, OR 97201 USA
[2] Oregon State Univ, Wood Sci & Engn, Corvallis, OR 97331 USA
基金
美国农业部;
关键词
structural timber; durability; ultrasonic testing; ground penetrating radar; imaging; synthetic aperture focusing technique; tomography; in situ examination; damage detection; condition evaluation; GROUND-PENETRATING RADAR; CROSS-LAMINATED TIMBER; NONDESTRUCTIVE ASSESSMENT; MOISTURE-CONTENT; WOOD; DECAY; DEFECTS; RECONSTRUCTION; DURABILITY; INSPECTION;
D O I
10.3390/s24092901
中图分类号
O65 [分析化学];
学科分类号
070302 ; 081704 ;
摘要
With the rapidly growing interest in using structural timber, a need exists to inspect and assess these structures using non-destructive testing (NDT). This review article summarizes NDT methods for wood inspection. After an overview of the most important NDT methods currently used, a detailed review of Ground Penetrating Radar (GPR) and Ultrasonic Testing (UST) is presented. These two techniques can be applied in situ and produce useful visual representations for quantitative assessments and damage detection. With its commercial availability and portability, GPR can help rapidly identify critical features such as moisture, voids, and metal connectors in wood structures. UST, which effectively detects deep cracks, delaminations, and variations in ultrasonic wave velocity related to moisture content, complements GPR's capabilities. The non-destructive nature of both techniques preserves the structural integrity of timber, enabling thorough assessments without compromising integrity and durability. Techniques such as the Synthetic Aperture Focusing Technique (SAFT) and Total Focusing Method (TFM) allow for reconstructing images that an inspector can readily interpret for quantitative assessment. The development of new sensors, instruments, and analysis techniques has continued to improve the application of GPR and UST on wood. However, due to the hon-homogeneous anisotropic properties of this complex material, challenges remain to quantify defects and characterize inclusions reliably and accurately. By integrating advanced imaging algorithms that consider the material's complex properties, combining measurements with simulations, and employing machine learning techniques, the implementation and application of GPR and UST imaging and damage detection for wood structures can be further advanced.
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页数:37
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