Effect of wood anisotropy in ultrasonic wave propagation: A ray-tracing approach

Espinosa, Luis; Prieto, Flavio; Brancheriau, Loïc; Lasaygues, Philippe

doi:10.1016/j.ultras.2018.07.015

Cited by 25 publications

(21 citation statements)

References 30 publications

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“…e index C of each method is developed by bringing the five index values in Table 5 into equation (12). Figure 11 shows that the comprehensive evaluation index of each combined forecasting model is significantly higher than that of the two single detection methods, indicating that the combined forecasting model can improve the forecasting precision of the internal defects of the timber components.…”

Section: Forecasting Effect Evaluationmentioning

confidence: 99%

“…In recent years, national and international scholars have conducted many studies in detecting the internal damage of timber components in ancient buildings and have achieved good results. Commonly used nondestructive testing methods [2] for detecting the internal damage of timber components mainly include stress wave [3][4][5], X-ray scanning [6][7][8], drilling resistance [9][10][11], and ultrasonic wave [12][13][14]. However, it is difficult to achieve precise detection by using only one single detection method because each of these methods has its own advantages and disadvantages [15].…”

Section: Introductionmentioning

confidence: 99%

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Detection and Prediction of Internal Damage in the Ancient Timber Structure Based on Optimal Combined Model

Wang

Qian

et al. 2019

Advances in Civil Engineering

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It is currently known that using stress wave and drilling resistance to detect the internal damage in the ancient timber structure is not a highly precise process. To improve the detection precision of this process, a simulation test was used to detect the internal damage of poplar and elm in ancient buildings. In this empirical study, we compared the detection precision of these two detection methods. Based on the idea of variable weight, we introduced three combined forecasting models based on the IOWA operator, IOWGA operator, and IOWHA operator to predict the internal damage in the ancient timber structure. The results show that the combined forecasting model based on the IOWA operator is more effective in predicting compared to a single detection method and other combined forecasting models. To be more specific, the results show that the detection precision of the combined model is increased by 25.8% and 4.7%, respectively, compared to the precision of the stress wave and drilling resistance tests. The error indicators of the combined forecasting model based on the IOWA operator are better than those of the other combined forecasting models. In addition, the analysis results based upon cross-validation theory show the combined forecasting model based on the IOWA operator has the best applicability, which provides a new practical method for evaluating internal damage of timber components in ancient buildings.

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Section: Forecasting Effect Evaluationmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Detection and Prediction of Internal Damage in the Ancient Timber Structure Based on Optimal Combined Model

Wang

Qian

et al. 2019

Advances in Civil Engineering

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“…TOF numerical measurements (input data for the image reconstruction) were obtained for a wood cross-section, using a raytracing approach [17]. For the simulation, the disk diameter was set to 30 cm, with an eccentric hole with a diameter of 10 cm, located halfway between the disk center and the bark (Fig.…”

Section: A Forward Problem and Sinogram Interpolationmentioning

confidence: 99%

“…First, the typical approach using straight lines trajectories (isotropic material) was done by using the filtered back-projection (FBP) method [19]. Second, we proposed a reconstruction method adapted to the wood anisotropy condition, that iteratively obtains the curved rays to reduce the difference between the input TOF and values obtained via the forward model [17]. This method allowed us to obtain a parametric image that represents the inner mechanical values, in this case the comparison was made using a radial Young modulus (ER) image, assuming that ρ is known.…”

Section: B Image Reconstructionmentioning

confidence: 99%

“…In this study, we are interested on numerically evaluating the influence of two factors in the reconstruction of ultrasonic tomographic images for standing tree decay detection: the image reconstruction method and the interpolation of the TOF measurements to simulate a higher number of sensors. First, considering the orthotropic condition of wood, TOF numerical estimations for a cross-section of wood were obtained using a raytracing approach [17], a method used mainly in the field of exploration seismography to simulate wave fronts in elastic media. Images were thus obtained using two reconstruction methods: one adapted to curved rays (anisotropy) and other considering straight-line trajectories (isotropic).…”

Section: Introductionmentioning

confidence: 99%

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Ultrasonic imaging of standing trees: factors influencing the decay detection

Espinosa

Prieto

Brancheriau

et al. 2019

2019 XXII Symposium on Image, Signal Processing and Artificial Vision (STSIVA)

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Ultrasound Computed Tomography is a widely used technique for nondestructive control of materials. One application is the evaluation of the inner state of standing trees in urban areas. The quality of the tomographic image depends on several factors, such as the number of probes used and the image reconstruction algorithm. Here we are interested in evaluating the influence of using a reconstruction algorithm adapted to wood anisotropy, compared to the classic methods using an isotropic model, and the effect of numerically increasing the number of sensors and the time-of-flight measurements using an interpolation method, known as sinogram interpolation. A numerical configuration was tested, simulating the presence of an eccentric defect in a wood section. Reconstructed images were compared before and after the interpolation process, and a thresholding was applied to quantify the decay area estimation. A more detailed defect identification was obtained with the reconstruction algorithm adapted to wood anisotropy and the interpolation of the sensors.

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