Prediction model for mechanical properties of standing timber and construction method and prediction method thereof

Abstract

The invention discloses a stumpage mechanical property prediction model and a construction method and a prediction method thereof, and belongs to material property detection. According to the method, the absolute content of cellulose in unit wood volume is adopted as an independent variable, a prediction model of the bending-resistant elastic modulus, the bending strength and the rift-grain compressive strength of the standing timber is constructed, and the correlation coefficient is obviously higher than that of a traditional model using wood density as a parameter. The three related mechanical property indexes of the standing tree are predicted by collecting the small sample and measuring two related parameters, so that the destructiveness that the tree needs to be cut down during traditional detection is avoided, the normal growth state of the tree is not influenced, only the small sample for detection is taken out due to small damage to the tree, the cost is reduced, and the efficiency is improved; the method is especially suitable for detecting and analyzing the mechanical properties of wood materials which cannot be excessively sampled, such as wood mechanical property comparison and evaluation of standing trees, and wood mechanical property evaluation of wood cultural relics and ancient buildings.

Description

Technical Field

[0001] This invention relates to the field of material performance testing technology, specifically to a predictive model for the mechanical properties of standing timber, its construction method, and its prediction method. Background Technology

[0002] In forestry cultivation and dominant tree species selection projects, wood properties are a key evaluation indicator, and low-cost and efficient evaluation methods are urgently needed in the industry. Wood properties are important parameters for evaluating wood quality, playing a crucial role in bridging forest cultivation and wood processing. Therefore, accurate prediction of wood properties directly affects the future processing and economic value of forest trees. The evaluation of wood properties in artificially bred trees mainly focuses on the analysis and assessment of their physical and chemical properties. Researchers use various scientific methods and techniques to conduct systematic wood property tests on artificially bred trees to ensure their reliability and stability in practical applications. The mechanical properties of wood evaluate its ability to resist external forces and are important indicators in the processing and utilization of structural and furniture materials. Relevant testing and evaluation indicators include wood density, modulus of elasticity in bending, bending strength, and compressive strength. Regarding density, the mass per unit volume of wood is typically measured to assess its weight and structural compactness. Tests for flexural modulus of elasticity, flexural strength, and compressive strength measure the ability of wood to withstand forces in different directions. These test results are crucial for determining the suitability of wood for construction, furniture, and other applications. They also help understand the processing and performance properties of wood, thus providing a scientific basis for artificial breeding of forest trees. The evaluation of wood properties in artificially bred forest trees is a multi-dimensional and multi-level comprehensive assessment process involving a wide range of scientific methods and technical means. In-depth research and evaluation of forest wood properties can better guide the direction of artificial breeding, cultivate high-quality tree varieties adapted to different needs, and thus promote the sustainable and healthy development of forestry.

[0003] However, conventional methods for testing the material properties of living timber are time-consuming, labor-intensive, costly, and damaging to trees. For example, measuring properties such as wood density, annual ring width, and microfibril angle requires core samples, while measuring properties like bending modulus of elasticity, bending strength, and compressive strength parallel to the grain requires harvesting the entire tree for analysis. Furthermore, the evaluation of material properties in artificially cultivated trees needs to be conducted periodically during tree growth. While large-scale harvesting for material property evaluation offers high accuracy in selecting tree strains, it consumes a significant amount of wood, hindering continuous evaluation of the same tree at different stages and impacting the preservation of superior strains. Therefore, non-destructive or minimally destructive methods are urgently needed for testing and evaluating the material properties of living timber to achieve low-cost, high-efficiency evaluation of artificially cultivated tree traits and selection of dominant strains. Summary of the Invention

[0004] To address the aforementioned shortcomings of existing technologies, the present invention aims to provide a predictive model for the mechanical properties of standing timber, along with its construction and prediction methods. This invention establishes a model for the mechanical properties of timber based on the absolute cellulose content parameter per unit volume of timber. It employs a minimally invasive method to sample from standing timber and predict its mechanical properties, providing an efficient method for the early screening and evaluation of the mechanical properties of timber used in structural applications.

[0005] The technical solution of the present invention to solve the above-mentioned technical problems is as follows: a prediction model for the mechanical properties of standing timber is provided. The prediction model uses the absolute cellulose content per unit volume of timber as the independent variable. The mechanical properties include the modulus of elasticity in bending (MOE), bending strength (MOR), and compressive strength parallel to the grain (CS). The prediction model for the flexural modulus of elasticity (MOE) is shown in equation (Ⅰ): MOE 预测 =a×C A +b(Ⅰ) The prediction model for flexural strength MOR is shown in equation (II): MOR 预测 =c×C A +d(Ⅱ) The prediction model for the compressive strength CS parallel to the grain is shown in equation (Ⅲ): CS 预测 =e×C A +f(Ⅲ) a, b, c, d, e, and f are coefficients, and C A The absolute cellulose content per unit volume of wood in the sample; Absolute cellulose content (C) per unit volume of wood A =ρ0×C R Where ρ0 is the oven-dry density of the sample, C R The value represents the relative cellulose content of the sample.

[0006] This invention provides a method for constructing the above-mentioned prediction model, comprising the following steps: (1) Samples were prepared from logs of the same tree species, and the corresponding wood bending modulus, bending strength and compressive strength along the grain were measured on the samples at the same location. (2) Take samples from the test specimens after mechanical properties testing to test the oven-dry density and relative cellulose content, ensuring that a set of data samples come from the same location in the logs; (3) Calculate the C of the corresponding sample based on the oven-dry density and relative cellulose content obtained in step (2). A value; (4) Combine the flexural modulus of elasticity, flexural strength, and compressive strength parallel to the grain from step (1) with the C from step (3). ASubstitute the values ​​into equations (Ⅰ)-(Ⅲ) above and perform linear regression to obtain the coefficients a, b, c, d, e, and f, and construct a predictive model for the bending modulus of elasticity, bending strength, and compressive strength parallel to the grain of wood.

[0007] This invention provides a minimally invasive method for predicting the mechanical properties of standing timber, comprising the following steps: ① Collect wood samples from the breast height of living trees; ②Dry the wood sample collected in step ①, measure its oven-dry weight and volume, and then calculate the oven-dry density ρ0 of the sample. ③ Crush the dried sample from step ② into wood flour, and then determine the relative cellulose content (C) of the wood sample. R ; ④ Based on ρ0 in step ② and C in step ③ R C is calculated using the above formula for calculating the absolute cellulose content per unit volume of wood. A value; ⑤ Take the C obtained in step ④ A Substitute the values ​​into equations (I)-(III) above to calculate the predicted values ​​of the bending modulus of elasticity, bending strength and compressive strength along the grain of the living timber.

[0008] Furthermore, in step ①, the breast height of the standing tree is 1.3m above the ground.

[0009] Furthermore, the absolute dry weight in step ② is 3g or more.

[0010] Furthermore, in step ③, the particle size of the wood flour is ≤200 mesh.

[0011] Furthermore, in step ③, the relative cellulose content C of the wood sample is detected. R Specifically, wet chemical methods or NREL methods are used for detection.

[0012] The present invention has the following beneficial effects: (1) This invention predicts three relevant mechanical properties of standing trees by collecting small samples and measuring two related parameters, avoiding the destructive nature of felling trees required in traditional testing. It does not affect the normal growth of trees, causing only minor trauma and minimal impact on the growth of standing trees. The sampling method is diverse, not limited to growth cones, but can also include small cubes (chiseld out) or small cylindrical samples, reducing testing costs. Furthermore, the testing uses absolutely dry wood samples, allowing for rapid sample processing and testing, and enabling the acquisition of key mechanical property data for wood, thus facilitating rapid prediction and evaluation of the mechanical properties of standing trees. This method can effectively monitor and evaluate the impact of artificial afforestation measures, soil and climate conditions on the mechanical properties of oriented cultivated trees, providing scientific guidance for the selection of trees and superior varieties in artificial forests.

[0013] (2) This invention uses the absolute cellulose content per unit volume of wood as the independent variable to construct a predictive model for the bending modulus of elasticity, bending strength, and compressive strength parallel to the grain of wood. The correlation coefficient is significantly higher than that of traditional models that use wood density as a parameter. When using the model to predict the mechanical strength of wood, the sample size required is small. A single sample only requires a minimum of 3-5g of oven-dry wood to meet the testing requirements. The test uses oven-dry density, and the oven-drying treatment of the sample can be completed within 24 hours. From sample preparation to obtaining the mechanical property results after testing, it can be completed in as little as 2-3 days. Compared with traditional testing methods, the technical solution of this invention can save a large amount of test materials and significantly improve testing efficiency. It is especially suitable for the testing and analysis of the mechanical properties of wood materials that cannot be sampled in large quantities, such as the comparison and evaluation of the mechanical properties of standing timber, as well as the evaluation of the mechanical properties of wood materials such as wooden cultural relics and timber used in ancient buildings, which are not suitable for large-scale sampling. Attached Figure Description

[0014] Figure 1 The graph shows the linear fit of the flexural modulus of elasticity, flexural strength, and compressive strength parallel to the grain in Example 1. Figure 2 The graph shows the linear fit of the flexural modulus of elasticity, flexural strength, and compressive strength parallel to the grain for Example 3. Detailed Implementation

[0015] The following examples are for illustrative purposes only and are not intended to limit the scope of the invention. Unless otherwise specified, conditions in the examples are performed under conventional conditions or as recommended by the manufacturer. Reagents or instruments whose manufacturers are not specified are all commercially available products.

[0016] Example 1: This embodiment uses the larch interspecific hybrid family A (Ri3 × Xing2) as the research object, and constructs predictive models for three mechanical properties of wood—flexural modulus of elasticity, flexural strength, and compressive strength parallel to the grain—with the absolute cellulose content per unit volume of wood as the independent variable. These predictive models are then used to predict and evaluate the mechanical properties of living larch hybrid trees. Specifically, the following steps are included: (1) Construction of the mechanical property model of wood: ① Sixteen larch trees (Rizhao 3×Xing 2) planted in 1980, 1993, 2009, and 2015 were collected. Logs with a height of approximately 1.3m were taken. Forty-six specimens for flexural strength / flexural modulus of elasticity and longitudinal compressive strength were prepared according to the national standard GB / T 1927.2-2021 "Test Methods for Physical and Mechanical Properties of Wood in Defect-Free Small Samples Part 2: Sampling Methods and General Requirements". The specimens for testing flexural modulus of elasticity were also used to test flexural strength (flexural modulus of elasticity was tested first, followed by flexural strength; that is, the same specimen was used to test both flexural modulus of elasticity and flexural strength). The same numbered specimens for longitudinal compressive strength and flexural strength were taken from the same location on the same piece of wood (thus, the three mechanical properties of the same numbered specimen have a one-to-one correspondence). The specimens were conditioned to air-dry state under the specified temperature and humidity conditions. ② Test the flexural modulus, flexural strength and compressive strength parallel to the grain of the samples from step ① according to the specifications of GB / T 1927.10-2021 "Test Methods for Physical and Mechanical Properties of Wood Without Defects - Part 10: Determination of Modulus of Elasticity in Bending", GB / T 1927.9-2021 "Test Methods for Physical and Mechanical Properties of Wood Without Defects - Part 9: Determination of Bending Strength" and GB / T 1927.11-2021 "Test Methods for Physical and Mechanical Properties of Wood Without Defects - Part 11: Determination of Compressive Strength Parallel to Grain" using a universal testing machine, and record the data. ③ Cut a 20mm long small sample from the sample in step ②, dry it, and then test the moisture content, oven-dry mass, and oven-dry volume of the sample. The moisture content of the sample is used for moisture content correction of the three mechanical properties; the oven-dry mass and volume are used to calculate the oven-dry density ρ0 of the sample. ④ Take the remaining sample from step ②, dry it, pulverize it, and sieve it to obtain wood flour smaller than 200 mesh. Analyze the relative cellulose content (C) of the wood sample using the NREL method (National Renewable Energy Laboratory Standard Method). R ; ⑤ Use the oven-dry density ρ0 and relative cellulose content C of the same numbered sample. R Calculate the absolute cellulose content C per unit volume of wood. A ; ⑥ List the corresponding values ​​of each sample's number, modulus of elasticity in bending, bending strength, compressive strength parallel to the grain, and absolute cellulose content per unit volume of wood. Use data processing software to perform linear fitting (e.g., ...). Figure 1 As shown in the figure, a prediction model with the absolute cellulose content per unit volume of wood as the key independent variable is obtained, as detailed below: MOE (Modulus of Flexural Elasticity) Prediction Model 预测 =44013×C A -3572.4 MOR (Modulation of Bending Strength) 预测 =392.9×C A -15.391 CS model for predicting compressive strength along the grain 预测 =169.11×C A +0.9354 The correlation coefficients of the established prediction models for the bending elastic modulus, bending strength, and compressive strength parallel to the grain of larch wood were 0.89, 0.96, and 0.90, respectively. The results show that the constructed prediction models for the mechanical properties of larch wood of variety A have high accuracy and can effectively predict the three main mechanical properties of larch wood.

[0017] (2) Prediction of mechanical properties of standing timber: ① Collect samples from living trees. At breast height (1.3m above the ground, to maintain comparability with traditional mechanical properties), starting from the bark, use a hole cutter to extract cylindrical wood blocks with a diameter of 20mm and a length of 25mm (xylem length). ② The wood sample from step ① was dried to absolute dryness using an oven set at 103±2℃, and its mass and volume were measured. The calculated absolute dry density ρ0 of the sample was 0.52 g / cm³. 3 ; ③ The oven-dried sample was pulverized into wood flour below 200 mesh, and then the relative cellulose content (C) of the wood sample was determined using the NREL method. R It is 38.35%; ④ Calculate the absolute cellulose content (C) per unit volume of wood. A = ρ0×C R = 0.52 × 38.35% = 0.199 g / cm³ 3 ; ⑤ Calculate the absolute cellulose content C per unit volume of wood. A Substituting these values ​​into the constructed models for the modulus of elasticity in bending, bending strength, and compressive strength parallel to the grain of timber, the predicted values ​​for the modulus of elasticity in bending, bending strength, and compressive strength parallel to the grain of standing timber are calculated as follows: Predicted flexural modulus of elasticity: MOE 预测 =44013×CA-3572.4=5204.67MPa Predicted flexural strength: MOR 预测 =392.9×CA-15.391=62.96MPa Predicted compressive strength parallel to grain: CS 预测 =169.11×CA+0.9354=34.66MPa Example 2: The tree species used in this embodiment is the same as in Example 1, still using larch interspecific hybrid family A (Ri3 × Xing2) as the research object. Using a constructed model with the absolute cellulose content per unit volume of wood as the independent variable, the model considers three mechanical properties: wood bending modulus of elasticity, bending strength, and compressive strength parallel to the grain. The model compares the changes in wood mechanical properties at different radial positions of the same tree. Specifically, the following steps are included: (1) Construction of the mechanical property model of wood The construction of the wood mechanical property model is the same as step (1) in Example 1, and is applied on the prediction model obtained in Example 1.

[0018] (2) Compare the mechanical properties of wood in different radial positions, such as young wood, transition zone, mature wood and sapwood, of larch A strain (Ri 3 × Xing 2) planted in 1993; ① Collect samples from living trees. At breast height (1.3m above the ground, to maintain comparability with traditional mechanical properties), use a growth cone with a diameter of 15mm to drill to the pith of the tree and take out a cylindrical growth cone strip of the entire radius length. ②The entire growth cone was cut into four segments according to the young wood, transition zone, mature wood, and sapwood. The collected wood samples were dried to absolute dryness using an oven at a set temperature of 103±2℃, and their mass and volume were measured. The absolute dry density ρ0 of the four sample segments was calculated (Table 1). ③ Each oven-dried sample was pulverized into wood flour below 200 mesh, and then the relative cellulose content (C) of the wood sample was determined using the NREL method. R (Table 1); ④ Calculate the absolute cellulose content (C) per unit volume of wood. A ; ⑤ Calculate the absolute cellulose content C per unit volume of wood. A Substituting these values ​​into the constructed model for the modulus of elasticity in bending, bending strength, and compressive strength parallel to the grain of timber, the predicted values ​​of the modulus of elasticity in bending, bending strength, and compressive strength parallel to the grain of larch standing timber from the center to the sapwood at the diameter at breast height were calculated (see Table 1 for specific results).

[0019] Table 1. Mechanical properties of larch standing timber at diameter at breast height (DBH) from center (young wood) to sapwood.

[0020] Example 3: This embodiment uses larch interspecific hybrid family E (Ri 5 × Xing 9) as the research object. First, a model is constructed with the absolute cellulose content per unit volume of wood as the independent variable to determine the wood's bending modulus of elasticity, bending strength, and compressive strength parallel to the grain. Then, using this model, the bending modulus of elasticity, bending strength, and compressive strength parallel to the grain of wood from the sapwood location of trees planted in different years are compared. Specifically, the following steps are included: (1) Construction of the mechanical property model of wood The process of constructing the wood mechanical property model is the same as step (1) in Example 1. A list is created showing the corresponding numbers, bending modulus of elasticity, bending strength, compressive strength parallel to the grain, and absolute cellulose content per unit volume of each E-strain specimen. Linear fitting is then performed using data processing software (e.g., ...). Figure 2 As shown in the figure, a prediction model with the absolute cellulose content per unit volume of wood as the independent variable is obtained, as follows: MOE (Modulus of Flexural Elasticity) Prediction Model 预测 =72239×C A -7847.6 MOR (Modulation of Bending Strength) 预测 =728.03×C A -59.003 Predicted compressive strength parallel to grain: CS 预测 = 292.22×C A -12.762 The correlation coefficients of the established prediction models for the bending modulus of elasticity, bending strength, and compressive strength parallel to the grain of larch wood were 0.89, 0.88, and 0.85, respectively. This indicates that the constructed prediction models for the mechanical properties of larch wood of variety E have high accuracy and can be used effectively to predict the bending modulus of elasticity, bending strength, and compressive strength parallel to the grain of larch wood.

[0021] (2) Compare the sapwood of larch E strain (Ri5×Xing9) trees planted in 1980 and 1993, respectively, in terms of bending modulus, bending strength and compressive strength along the grain.

[0022] ① Collect samples from living trees. At breast height (1.3m above the ground) of two living trees of different ages, use a 20mm diameter hole cutter to extract cylindrical wood blocks with a radial length of 25mm (xylem length) on the south side and label them. ② The collected wood samples were dried to absolute dryness using an oven with a set temperature of 103±2℃, and their mass and volume were measured. The absolute dry density ρ0 of the two types of samples was calculated (Table 2). ③ Each oven-dried sample was pulverized into wood flour below 200 mesh, and then the relative cellulose content (C) of the wood sample was determined using the NREL method.R (Table 2); ④ Calculate the absolute cellulose content (C) per unit volume of wood. A ; ⑤ Calculate the absolute cellulose content C per unit volume of wood. A Substituting the values ​​into the constructed models for the modulus of elasticity in bending, bending strength, and compressive strength parallel to the grain of timber, the predicted values ​​of the modulus of elasticity in bending, bending strength, and compressive strength parallel to the grain of living trees were calculated (Table 2). The results show that the modulus of elasticity in bending, bending strength, and compressive strength parallel to the grain of larch sapwood planted in 1980 are all higher than the relevant mechanical properties of sapwood planted in 1993.

[0023] Table 2. Modulus of elasticity, bending strength, and compressive strength parallel to grain of sapwood from larch live timbers of different planting years.

[0024] The above three embodiments demonstrate that the prediction model and prediction method of the present invention can compare the mechanical properties of living trees cultivated under different cultivation measures, at different ages, and in different planting environments. This provides a reliable analysis and evaluation of the changes in mechanical properties of structural trees during their growth process, and offers technical support for the targeted cultivation and screening of dominant tree species and varieties for structural trees.

[0025] The above description is only a preferred embodiment of the present invention and is not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of the present invention should be included within the protection scope of the present invention.

Claims

1. A predictive model for the mechanical properties of standing timber, characterized in that, The prediction model uses the absolute cellulose content per unit volume of wood as the independent variable, and the mechanical properties include the wood's modulus of elasticity in bending (MOE), bending strength (MOR), and compressive strength parallel to the grain (CS). The prediction model for the flexural modulus of elasticity (MOE) is shown in equation (Ⅰ): TIRED 预测 =a×C A +b(Ⅰ) The prediction model for the bending strength MOR is shown in Equation (II): TENDER 预测 =c×C A +d(Ⅱ) The prediction model for the parallel compressive strength CS is shown in Equation (Ⅲ): CS 预测 =e×C A +f(Ⅲ) a, b, c, d, e, and f are coefficients, and C is... A The absolute cellulose content per unit volume of wood in the sample; The absolute cellulose content (C) per unit volume of wood A =ρ0×C R Where ρ0 is the oven-dry density of the sample, C R The value represents the relative cellulose content of the sample.

2. The method for constructing the prediction model according to claim 1, characterized in that, Includes the following steps: (1) Samples were prepared from logs of the same tree species, and the corresponding wood bending modulus, bending strength and compressive strength along the grain were measured on the samples at the same location. (2) Take samples from the test specimens after mechanical properties testing to test the oven-dry density and relative cellulose content, ensuring that a set of data samples come from the same location in the logs; (3) Calculate the C of the corresponding sample based on the oven-dry density and relative cellulose content obtained in step (2). A value; (4) Combine the flexural modulus of elasticity, flexural strength, and compressive strength parallel to the grain from step (1) with the C from step (3). A Substitute the values ​​into equations (I)-(III) of claim 1 and perform linear regression to obtain the coefficients a, b, c, d, e, and f, and construct a predictive model for the bending modulus of elasticity, bending strength, and compressive strength parallel to the grain of wood.

3. A method for predicting the mechanical properties of living timber using minimally invasive techniques, characterized in that, Includes the following steps: ① Collect wood samples from the breast height of living trees; ②Dry the wood sample collected in step ①, measure its oven-dry weight and volume, and then calculate the oven-dry density ρ0 of the sample. ③ Crush the dried sample from step ② into wood flour, and then determine the relative cellulose content (C) of the wood sample. R ; ④ Based on ρ0 in step ② and C in step ③ R C is calculated using the formula for calculating the absolute cellulose content per unit volume of wood as described in claim 1. A value; ⑤ Take the C obtained in step ④ A Substitute the values ​​into equations (I)-(III) of claim 1 to calculate the predicted values ​​of the bending modulus of elasticity, bending strength and compressive strength along the grain of the standing timber.

4. The prediction method according to claim 3, characterized in that, The breast height of the standing tree mentioned in step ① is 1.3m above the ground.

5. The prediction method according to claim 3, characterized in that, The absolute dry weight mentioned in step ② is 3g or more.

6. The prediction method according to claim 3, characterized in that, The particle size of the wood flour mentioned in step ③ is ≤200 mesh.

7. The prediction method according to claim 3, characterized in that, Step ③ describes the detection of the relative cellulose content C of the wood sample. R Specifically, wet chemical methods or NREL methods are used for detection.

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