Method for detecting defects in wood structures using multi-path stress waves

Abstract

The present invention discloses a method for detecting defects in a wooden structure by using multi-path stress waves, belonging to the technical field of non-destructive testing of wooden structures. The present invention establishes a mathematical model between the attenuation coefficient δ on the diameter and the actual defect area y through reverse tests. In use, first, it is determined whether there are defects in the path between the measuring points, and then, according to the defective path and the defect position graph obtained by measurement with a stress wave detection device, the diameter passing through or near the defect center is determined, the path attenuation coefficient on this diameter is calculated and substituted into the mathematical model to directly obtain the corrected defect area, which can significantly improve the defect recognition accuracy. It is beneficial to the safety assessment, timely protection and repair of wooden structures. And when the proportion of the defect area is small, it is difficult for the traditional multi-path stress wave detection method to detect the defect, and the detection method provided by this embodiment can cover the recognition accuracy when the proportion of the defect area is small.

Description

Technical Field

[0001] The present invention relates to a method for detecting defects in wooden structures by using multi-path stress waves, and belongs to the technical field of non-destructive testing of wooden structures. Background Art

[0002] There are a large number of existing historical buildings in China, such as traditional wooden structures and mixed wood-brick structures. Their wood materials, load-bearing skeletons, construction techniques, etc. have extremely high historical, artistic and scientific values. Due to biological damage, environmental impact, growth defects, etc., defects such as insect damage, decay, internal holes, cracks, etc. are generated, seriously affecting the safety of wooden structures, and it is necessary to carry out preventive protection or reinforcement and repair on wooden structures. External defects can be found by the naked eye and repaired in time, but internal cavities cannot be directly observed by the naked eye and the detection results cannot be quantified, so they cannot be protected and repaired in time. Therefore, accurately detecting internal defects is very important for the preventive protection and timely repair of wooden structures.

[0003] At present, non-destructive testing methods such as single-path stress wave testing, ultrasonic testing, micro-drilling resistance testing, and multi-path stress wave testing are used to detect internal defects in wooden structure buildings. Among them, multi-path stress wave testing is lighter in weight, portable, does not require a wired power supply, does not require a coupling agent, is suitable for on-site testing, and has the characteristics of visualization and quantification. However, through research, it is found that although multi-path stress wave testing can roughly determine the defect location, there is a large difference between the detected area and the actual damage degree, and the defect detection accuracy is not enough, which is not conducive to the safety assessment, timely protection and repair of wooden structures. Summary of the Invention

[0004] In view of the above problems existing in the prior art, the present invention provides a method for detecting defects in wooden structures by using multi-path stress waves.

[0005] To solve the above technical problems, the present invention includes the following technical solutions:

[0006] A method for detecting defects in wooden structures by using multi-path stress waves includes the following steps:

[0007] Step 1: Identify the tree species of the wooden component, and select a number of healthy woods of the same tree species as the wooden component;

[0008] Step 2: Use a multi-path stress wave detection device to detect the propagation speed of the healthy wood cross-section; during the detection, 2n measuring points are evenly arranged around the same cross-section of the healthy wood, and the two points with an interval of i measuring points in the middle are recorded as the (i + 1)-th type of path, i = 0, 1, 2,..., n - 1; after the measurement of the selected healthy wood is completed, use statistical tools to analyze the data on the same type of path to obtain the propagation speed threshold range V 0(i+1)min ~V 0(i+1)max , and the average value V of the propagation speed0(i+1)av , determine the velocity attenuation critical value δ 0(i+1) =(V 0(i+1)min -V 0(i+1)av ) / V 0(i+1)av ;

[0009] Step 3: Establish a mathematical model between the attenuation coefficient δ on the diameter and the actual defect area y through reverse test;

[0010] Step 4: Use a multi-path stress wave detection device to measure a certain cross section of the wood component to obtain defect location graphics, propagation path diagrams of each path, propagation speed, defect area ratio and image;

[0011] Step 5: Analyze the propagation speed of each path and calculate the attenuation coefficient δ of each path (i+1) , and the critical value of each path attenuation δ 0(i+1) Make comparisons;

[0012] If δ (i+1) ≤δ 0(i+1) , determining that the path has no defects;

[0013] If δ (i+1) >δ 0(i+1) , determine that the path is defective, and determine the geometric center position of the defect in combination with the defect position graph obtained in step 4; determine the path on the diameter that passes through or is close to the geometric center position of the defect, and substitute the wave velocity attenuation coefficient δ on the path on the diameter into the mathematical model in step 3 to calculate the corrected defect area y.

[0014] Furthermore, the mathematical model in step 3 is a linear model, which satisfies the following formula:

[0015] y=aδ+b;

[0016] Among them, a and b are constants.

[0017] Furthermore, the mathematical model in step three is a neural network model.

[0018] Furthermore, the step 5 further includes the following steps:

[0019] Step 6. Repeat steps 4 and 5 to test other cross-sections of the wood component. After all cross-sections of the wood component to be tested have been tested, make a comprehensive assessment of the safety of the wood component.

[0020] Furthermore, in step three, when the mathematical model is established through reverse testing, at least five samples are set for the actual defect area, and the defect area is evenly set from small to large.

[0021] Further, before detecting a wooden component using a multi-path stress wave detection device, visually observe, tap, and measure the wooden component with tools to preliminarily determine whether there are external damages and whether the external damages have spread to internal damages. If so, use the damaged part as the key detection section.

[0022] Further, in step five, if no diameter passes through or is close to the center position of the defect, based on the proportion of the defect area measured by the multi-path stress wave detection device, the image, and the defective paths calculated, re-layout the measuring points so that two measuring points on one of the diameters pass through the defect center. Re-detect the attenuation coefficient δ on this diameter, and then substitute it into the mathematical model in step three to calculate the corrected defect area y.

[0023] Due to the adoption of the above technical solutions, the present invention has the following advantages and positive effects compared with the prior art: The present invention establishes a mathematical model between the attenuation coefficient δ on the diameter and the actual defect area y through reverse experiments. In use, first determine whether there are defects in the paths between the measuring points, and then based on the defective paths and the defective position graph obtained by measuring with the stress wave detection device, determine the diameter passing through or close to the defect center, calculate the path attenuation coefficient on this diameter and substitute it into the mathematical model to directly obtain the corrected defect area, which can significantly improve the defect recognition accuracy. It is beneficial for the safety assessment, timely protection, and repair of wooden structures. And when the proportion of the defect area is small, it is difficult for the traditional multi-path stress wave detection method to detect the defect, and the detection method provided by this embodiment can cover the recognition accuracy when the proportion of the defect area is small. Description of the Drawings

[0024] Figure 1 It is a flowchart of a method for detecting wooden structure defects using multi-path stress waves in an embodiment of the present invention. Detailed Embodiments

[0025] The following further details a method for detecting wooden structure defects using multi-path stress waves provided by the present invention in conjunction with the drawings and specific embodiments. In combination with the following description, the advantages and features of the present invention will be clearer. It should be noted that the drawings are all in a very simplified form and use non-precise scales, only for the purpose of conveniently and clearly assisting in explaining the purpose of the embodiments of the present invention.

[0026] Embodiment 1

[0027] As shown in the figure, a method for detecting wooden structure defects using multi-path stress waves provided in this embodiment includes the following steps:

[0028] Step 1: Confirm the tree species of the wooden component and select several healthy woods of the same tree species as the wooden component;

[0029] Step 2: Use a multi-path stress wave detection device to detect the propagation speed of the healthy wood cross-section; during the detection, evenly arrange 2n measuring points around the same cross-section of the healthy wood. The distance between two points with an interval of i measuring points in the middle is recorded as the (i + 1)-th type of path, where i = 0, 1, 2, …, n - 1; after the measurement of the selected healthy wood is completed, use statistical tools to analyze the data on the same type of path to obtain the propagation speed threshold range V 0(i+1)min ~V 0(i+1)max , the average value V 0(i+1))av of the propagation speed, and determine the critical value δ 0(i+1) of the speed decay = (V 0(i+1)min ~V 0(i+1)av ) / V 0(i+1)av ;

[0030] Step 3: Establish a mathematical model between the attenuation coefficient δ and the actual defect area y through reverse experiments;

[0031] Step 4: Use a multi-path stress wave detection device to measure a certain cross-section of the wooden member to obtain the defect position graph, the propagation path graph of each path, the propagation speed, the proportion of the defect area, and the image;

[0032] Step 5: Analyze the propagation speed of each path and calculate the attenuation coefficient δ (i+1) of each path, and compare it with the attenuation critical value δ 0(i+1) of each path;

[0033] If δ (i+1) ≤δ 0(i+1) , it is determined that there is no defect in the path;

[0034] If δ (i+1) >δ 0(i+1) , it is determined that there is a defect in the path, and in combination with the defect position graph obtained in Step 4, determine the geometric center position of the defect; determine the path on the diameter passing through or close to the geometric center position of the defect, substitute the wave speed attenuation coefficient δ on the path on this diameter into the mathematical model in Step 3, and calculate the corrected defect area y.

[0035] In this embodiment, a mathematical model between the attenuation coefficient δ on the diameter and the actual defect area y is established through reverse experiments. In use, first determine whether there is a defect in the path between the measuring points, and then according to the defective path and the defect position graph measured by the stress wave detection device, determine the diameter passing through or close to the defect center, calculate the path attenuation coefficient on this diameter and substitute it into the mathematical model to directly obtain the corrected defect area, which can significantly improve the defect recognition accuracy. It is beneficial to the safety assessment, timely protection and repair of the wooden structure. And when the proportion of the defect area is small, it is difficult for the traditional multi-path stress wave detection method to detect the defect, and the detection method provided by this embodiment can cover the recognition accuracy when the proportion of the defect area is small.

[0036] Further, the mathematical model in step three is a linear model, which satisfies the following formula:

[0037] y = aδ + b;

[0038] where a and b are constants.

[0039] Further, the mathematical model in step three is a neural network model. The input data of the neural network model is the attenuation coefficient on the diameter of the cross-section, and the output data is the corrected defect area. Before use, the neural network model is trained first. After the parameters meet the requirements, the trained neural network model is obtained.

[0040] Further, after step five, the following steps are also included:

[0041] Step six: Repeat step four and step five to detect other cross-sections of the wooden component. After all the cross-sections to be measured of the wooden component are detected, comprehensively judge the safety of the wooden component. When it is determined that there are problems with the safety of the wooden component, preventive protection and reinforcement and repair measures are taken for the wooden component.

[0042] Further, in step three, when establishing the mathematical model through reverse experiments, at least 5 samples of the actual defect area are set, and the defect areas are evenly set from small to large.

[0043] Further, before detecting the wooden component with a multi-path stress wave detection device, first measure the wooden component through visual observation, knocking and tools to initially judge whether there are external damages and whether the external damages spread to internal damages. If so, the damaged part is used as the key detection cross-section.

[0044] Further, in step five, if no diameter passes through or is close to the center position of the defect, then combine the proportion of the defect area measured by the multi-path stress wave detection device, the image and the defective paths calculated, re-lay out the measuring points so that two measuring points on one of the diameters pass through the defect center, re-detect the attenuation coefficient δ on this diameter, and then substitute it into the mathematical model in step three to calculate the corrected defect area y.

[0045] Measure the length of the wooden component in advance. Determine the detection positions and intervals according to the type, height or length of the wooden component, and the surface decay condition, and then conduct the detection of the specific cross-section of the wooden component. The multi-path stress wave detection equipment is equipped with matching detection software. It is necessary to record the detailed dimensions, shape and detection positions of the cross-section of the wooden component. Set the tree species in the software matched with the multi-path stress wave detection instrument, input the dimension information, set the layout method, and arrange the sensors according to the software schematic and positions. The distance between adjacent sensors should not be greater than 100 mm. The specific knocking method is to use a hammer of the same weight to knock on each sensor one by one, repeating 3 times. When measuring the propagation speed, at least 10 stress wave sensors are required.

[0046] Embodiment 2

[0047] In this embodiment, the tree species of the wooden component is Douglas fir.

[0048] Use the FAKOPP 3D Acoustic Tomograph stress wave detector to measure the stress wave propagation speed of 14 healthy Douglas fir wooden components with a diameter of 40 cm. A total of 12 sensors are evenly arranged around the cross-section. The propagation rate in the specimen cross-section is divided into 6 types according to the different propagation paths (distances). The average value of the propagation rates between two measurement points with an intermediate interval of i points is uniformly denoted as V (12+i)-0 , where 0 ≤ i ≤ 5. For example, the average value of the propagation rates between two adjacent points is uniformly denoted as V 12-0 ; the average value of the propagation rates between two points with an intermediate interval of five points is uniformly denoted as V 17-0 .

[0049] Obtain the maximum value, minimum value, and average propagation speed of the healthy wood. After testing, the stress wave propagation speed range of healthy Douglas fir is 935 - 1713 m / s. The average propagation speed on the diameter is 1523 m / s, and the speed range is 1380 - 1713 m / s. Calculate the critical attenuation coefficient |1380 - 1523| / 1523 * 100% = 9.4%.

[0050] Through reverse tests, simulate different area defects. The hole sizes are 0, 1 / 25, 1 / 8, 1 / 4, 1 / 3, 1 / 2 of the area of the healthy wood, and the hole positions are at the center of the cross-section. Use the FAKOPP 3D Acoustic Tomograph stress wave detector to measure each defective component, and obtain the defect position graph, propagation path graph of each path, propagation speed, defect area ratio and image, etc.

[0051] Analyze the propagation speed of each path to determine whether each path is within the threshold range V 0(i+1)min ~V 0(i+1)maxThe attenuation coefficient of each path is calculated, and the paths with a coefficient greater than the critical attenuation coefficient are marked, so that the defect situation and path can be preliminarily determined.

[0052] Taking the actual defect area as the dependent variable y and the attenuation coefficient of the wave velocity on the healthy material diameter as the independent variable, there is a statistically linear relationship between the actual defect area and the attenuation coefficient. The regression equation is y=1.1502δ-5.724, the determination coefficient is R2=0.9465, and the determination coefficient R2>0.5, which is a strong correlation. Significance F=0.0011, and the linear relationship is significant.

[0053] As shown in Table 1, by comparing the actual area, the defect area detected by the software of the FAKOPP 3D Acoustic Tomograph stress wave detection device, and the defect area corrected by the attenuation coefficient obtained in this embodiment, it can be seen that the defect area corrected by the attenuation coefficient can identify defects (4%) that the software cannot identify, and can improve the recognition effect of wooden components with a small defect area ratio; and when the actual hole area ratio is greater than 12.5%, the defect area can be more accurate. When the detection method in this embodiment is adopted, the detection efficiency and accuracy can be improved.

[0054] Table 1 Comparison of three types of area ratios

[0055]

[0056] The technical features of the above-described embodiments may be arbitrarily combined. To make the description concise, not all possible combinations of the technical features in the above-described embodiments are described. However, as long as there is no contradiction in the combination of these technical features, they should be considered to be within the scope of this specification.

[0057] The above-mentioned embodiments only express several implementation methods of the present invention, and the descriptions thereof are relatively specific and detailed, but they cannot be understood as limiting the scope of the invention patent. It should be pointed out that, for ordinary technicians in this field, several variations and improvements can be made without departing from the concept of the present invention, and these all belong to the protection scope of the present invention. Therefore, the protection scope of the patent of the present invention shall be subject to the attached claims.

Claims

1. A method for detecting wood structure defects using multi-path stress waves, characterized in that, it includes the following steps: Step 1: Identify the tree species of the wood component and select several healthy woods of the same tree species as the wood component; Step 2: Use a multi-path stress wave detection device to detect the propagation speed of the healthy wood cross-section; during the detection, evenly arrange 2 n measurement points around the same cross-section of the healthy wood, and the distance between two points with i measurement points in between is recorded as the i +1 type of path, i = 0, 1, 2, …, n - 1; After measuring the healthy materials to be selected, use statistical tools to analyze the data on the same type of path, and obtain the threshold range V of the propagation speed for each path 0(i+1)min ~V 0(i+1)max , the average value V of the propagation speed 0(i+1)av , and determine the critical value of speed decay δ 0(i+1) = (V 0(i+1)min - V 0(i+1)av ) / V 0(i+1)av ; Step 3. Establish a mathematical model between the attenuation coefficient on the diameter and the actual defect area through reverse experiments δ and the actual defect area y through reverse experiments Step 4: Use a multi-path stress wave detection device to measure a certain cross-section of the wood component to obtain a defect position graph, propagation path graphs of each path, propagation speed, defect area ratio, and an image; Step 5. Analyze the propagation speed of each path and calculate the attenuation coefficient of each path δ (i+1) , and compare with the attenuation critical value of each path δ 0(i+1) for comparison; If δ (i+1) ≤ δ 0(i+1) , it is determined that the path has no defect; If δ (i+1) > δ 0(i+1) , it is determined that the path is defective, and the geometric center position of the defect is determined by combining with the defective position graph obtained in Step 4; determine the path on the diameter passing through or near the geometric center position of the defect, and use the wave velocity attenuation coefficient δ substituted into the mathematical model in Step 3 to calculate the corrected defect area y .

2. The method for detecting wood structure defects using multi-path stress waves according to claim 1, characterized in that, the mathematical model in step 3 is a linear model and satisfies the following formula: y = a δ +b; where a and b are constants.

3. The method for detecting wood structure defects using multi-path stress waves according to claim 1, characterized in that, the mathematical model in step 3 is a neural network model.

4. The method for detecting wood structure defects using multi-path stress waves according to claim 1, characterized in that, after step 5, the following steps are further included: Step 6: Repeat step 4 and step 5 to detect other cross-sections of the wood component. After all the cross-sections to be measured of the wood component are detected, comprehensively determine the safety of the wood component.

5. The method for detecting wood structure defects using multi-path stress waves according to claim 1, characterized in that, in step 3, when establishing a mathematical model through reverse experiments, at least 5 samples of the actual defect area are set, and the defect areas are evenly set from small to large.

6. The method for detecting wood structure defects using multi-path stress waves according to claim 1, characterized in that, before detecting the wood component with a multi-path stress wave detection device, first measure the wood component through visual observation, knocking, and tools to initially determine whether there are external damages and whether the external damages spread to internal damages. If so, take the damaged part as the key detection cross-section.

7. The method for detecting wood structure defects using multi-path stress waves according to claim 1, characterized in that, In Step 5, if no diameter passes through or is close to the center position of the defect, then based on the proportion of the defect area measured by the multi-path stress wave detection device, the image, and the defective paths calculated, re-lay out the measuring points so that two measuring points on one of the diameters pass through the defect center, and re-detect the attenuation coefficient on this diameter δ , and then substitute it into the mathematical model in Step 3 to calculate the corrected defect area y .

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