Method for predicting particle size distribution of improved soil and method for predicting particle size distribution of mixed soil

Abstract

To accurately predict the particle size distribution of improved soil when improving raw soil into improved soil. [Solution] A method for predicting the particle size distribution of improved soil, in which a computer performs the following processes: divides information on the mass of raw soil by particle size into multiple particle size ranges; calculates the average particle size of the raw soil and the standard deviation of particle size in each of the multiple particle size ranges; calculates the average particle size of the improved soil based on a first relationship showing the relationship between the crushing energy received by the raw soil and the rate of change of the average particle size of the raw soil after the crushing energy, and the crushing energy of the treatment device that improves the raw soil; calculates the standard deviation of particle size in each of the multiple particle size ranges of the improved soil based on a second relationship showing the relationship between the crushing energy received by the raw soil and the rate of change of the standard deviation of particle size of the raw soil after the crushing energy, and the crushing energy of the treatment device; and predicts the particle size distribution of the improved soil based on the calculated average particle size and standard deviation of the particle size of the improved soil.

Description

[Technical Field]

[0001] The present invention relates to a method for predicting the particle size distribution of improved soil and a method for predicting the particle size distribution of mixed soil. [Background technology]

[0002] BACKGROUND ART A rotary crushing (mixing) method for improving and effectively utilizing construction generated soil and the like, and an apparatus (rotary crushing apparatus) used in this method are known (see, for example, Patent Document 1, etc.).

[0003] Conventionally, the particle size distribution of crushed products (crushed sand) has been determined by taking samples from the crushed sand continuously discharged from a crusher, sieving the samples with a specified sieve, and then determining the particle size distribution from the weight of the crushed sand of each particle size. In contrast, Patent Document 2 discloses a method for predicting the particle size distribution of crushed products that makes it possible to predict the particle size distribution of crushed sand in a short time with little effort. [Prior art documents] [Patent documents]

[0004] [Patent Document 1] International Publication No. 2019 / 016859 [Patent Document 2] Japanese Patent Application Publication No. 9-29170 Summary of the Invention [Problem to be solved by the invention]

[0005] However, the particle size distribution prediction method of Patent Document 2 mentioned above has a problem in that the prediction accuracy is not very high.

[0006] Therefore, an object of the present invention is to provide a method for predicting the particle size distribution of improved soil, which can accurately predict the particle size distribution of improved soil when raw soil is improved into improved soil.Another object of the present invention is to provide a method for predicting the particle size distribution of mixed soil, which can accurately predict the particle size distribution of mixed soil when multiple types of raw soil are crushed and mixed in a processing device to produce mixed soil. [Means for solving the problem]

[0007] According to a first aspect, a method for predicting the particle size distribution of improved soil is a method for predicting the particle size distribution of improved soil when raw soil is improved into improved soil, which comprises dividing information on the mass of the raw soil by particle size into a plurality of particle size ranges, calculating the average particle size of the raw soil and the standard deviation of the particle size in each of the plurality of particle size ranges, and calculating the average particle size of the raw soil based on a first relationship showing the relationship between the crushing energy received by the raw soil and the rate of change of the average particle size of the raw soil subjected to the crushing energy, and the crushing energy of a treatment device that improves the raw soil. a second relationship showing the relationship between the crushing energy received by the raw soil and the rate of change of the standard deviation of the particle size of the raw soil subjected to the crushing energy, and the crushing energy of the processing device; and a method for predicting the particle size distribution of the improved soil based on the average particle size of the improved soil and the standard deviation of the particle size of the improved soil in each of the plurality of particle size ranges.

[0008] According to a second aspect, the method for predicting the particle size distribution of mixed soil is a method for predicting the particle size distribution of mixed soil when multiple types of raw soil are crushed and mixed in a processing device to produce mixed soil, and the method for predicting the particle size distribution of mixed soil uses the above-mentioned method for predicting the particle size distribution of improved soil to predict the particle size distribution when each of the multiple types of raw soil is improved in the processing device, and predicts the particle size distribution of the mixed soil based on the predicted particle size distribution when each of the multiple types of raw soil is improved and the mixing ratio of the multiple types of raw soil, in which processing is performed by a computer. [Effects of the Invention]

[0009] This allows for accurate prediction of the particle size distribution of improved soil when raw soil is improved into improved soil. It also allows for accurate prediction of the particle size distribution of mixed soil when multiple types of raw soil are crushed and mixed in a processing device to produce mixed soil. [Brief explanation of the drawings]

[0010] [Figure 1] FIG. 1 is a diagram schematically showing the configuration of a rotary crushing device according to the first embodiment. [Figure 2] FIG. 2 is a diagram illustrating a schematic hardware configuration of the information processing device. [Figure 3] Figure 3(a) shows the results of a sieving test of raw soil (sample), and Figure 3(b) shows the particle size accumulation curve (actual measured values) obtained by plotting the sieving test results of Figure 3(a) on a logarithmic graph. [Figure 4] Figure 4(a) is a diagram showing the curve (approximation) of equation (2) obtained by method A and the particle size accumulation curve (actual measurement values) of Figure 3(b), and Figure 4(b) is a diagram showing the curve (approximation) of equation (2) obtained by method B and the particle size accumulation curve (actual measurement values) of Figure 3(b). [Figure 5] FIG. 5(a) is a table showing particle sizes D10, D20, ..., D90 determined from the sieving test results of FIG. 3(a), and FIG. 5(b) is a diagram showing the curve (approximation) of equation (2) obtained by method C and the particle size accumulation curve (actual measurement values) of FIG. 3(b). [Figure 6] FIG. 6(a) is a diagram showing a curve (approximation) obtained by improvement plan 1 of method C, and FIG. 6(b) is a diagram showing a curve (approximation) obtained by improvement plan 2 of method C. [Figure 7] Figure 7(a) plots the data obtained from the experiment with the energy index KEn on the horizontal axis and the λ change rate (Δλ) on the vertical axis, and Figure 7(b) plots the data obtained from the experiment with the energy index KEn' on the horizontal axis and the λ change rate (Δλ) on the vertical axis. [Figure 8]FIG. 8 is a table showing the values ​​of the influence degree of the influencing factors for Δλ and Δζ. [Figure 9] Figure 9(a) is a graph plotting the relationship between Δλ and the energy index when two different values, "KEn" and "KEn'", are used as the energy index of Δλ, and Figure 9(b) is a graph plotting the relationship between Δλ and the energy index when two different values, "KEn' × (D50 / σc)" and "KEn' × (D50 / σc 0.25)", are used as the energy index of Δλ. [Figure 10] Figure 10(a) is a graph plotting the relationship between Δζ1 and the energy index when two different values, KEn and KEn', are used as the energy index of Δζ1, and Figure 10(b) is a graph plotting the relationship between Δζ1 and the energy index when two different values, KEn' × (D50 / σc) × (1 / Uc) and KEn' × (D50 / σc 0.25) × (1 / Uc), are used as the energy index of Δζ1. [Figure 11] FIG. 11 is a functional block diagram of the information processing device. [Figure 12] FIG. 12 is a flowchart showing the processing of the information processing device according to the first embodiment. [Figure 13] FIG. 13(a) shows the results of a sieving test of raw soil in the first embodiment, and FIG. 13(b) shows data in which the particle size (x) in FIG. 13(a) is multiplied by N (200 times). [Figure 14] FIG. 14(a) is a diagram showing a table created in step S14 of FIG. 12, and FIG. 14(b) is a diagram showing a table in which Dn in the table of FIG. 14(a) is converted to lnDn. [Figure 15] 15(a) and 15(b) are diagrams (part 1) for explaining the process of step S20 in FIG. [Figure 16] 16(a) and 16(b) are diagrams (part 2) for explaining the process of step S20 in FIG. [Figure 17] FIG. 17 is a graph showing a curve indicating the particle size distribution of the improved soil predicted in step S28 of FIG. 12 and the actually measured particle size distribution of the improved soil. [Figure 18] FIG. 18 is a flowchart showing the processing of the information processing device according to the second embodiment. DETAILED DESCRIPTION OF THE INVENTION

[0011] First Embodiment The first embodiment will be described in detail below with reference to FIGS.

[0012] Figure 1 is a diagram showing the schematic configuration of a rotary crushing device 100 used when improving raw soil to improve soil in the first embodiment. For convenience of illustration, Fig. 1 shows a partial cross section. For convenience of explanation, Fig. 1 shows the vertical direction as the Z-axis direction, and two orthogonal axes in a horizontal plane as the X-axis direction and the Y-axis direction.

[0013] The rotary crusher 100 is a device used to improve and effectively utilize raw soil such as construction waste soil. The rotary crusher 100 crushes and refines the raw soil, creating a smooth particle size distribution. Additives (lime-based solidification materials such as quicklime and slaked lime, cement-based solidification materials such as ordinary cement and blast furnace cement, soil improvement materials made from polymeric materials, natural fibers, etc.) are also added to the rotary crusher 100 as needed. When additives are added, the rotary crusher 100 mixes the raw soil with the additives to produce improved soil, thereby adjusting the properties and strength of the improved soil.

[0014] As shown in FIG. 1, the rotary crusher 100 includes a stand 10, a fixed drum 12, a rotating drum 14, a rotation mechanism 16, a belt conveyor 122, and the like.

[0015] The stand 10 supports the various components of the rotary crushing device 100 and has a top plate 10a and legs 10b. The top plate 10a is, for example, a plate-like member made of iron, and functions as a lid that closes the upper opening of the fixed drum 12 fixed to the underside (the surface on the -Z side). An inlet member 20 is provided inside the fixed drum 12 for charging raw soil and additives. The raw soil is transported to the inlet member 20 by a belt conveyor 122.

[0016] Fixed drum 12 is a cylindrical container, and is fixed to the underside (-Z side) of top plate 10a. Raw soil and additives are charged into fixed drum 12 through inlet member 20, and the raw soil and additives are introduced into rotating drum 14, which is provided below fixed drum 12 (-Z side).

[0017] The rotating drum 14 is a cylindrical container that rotates (spins) around the central axis of the cylinder (around the Z axis) by a rotating drum drive motor (not shown). The rotating drum 14 is supported by the frame 10 via a plurality of support rollers 24, and therefore receives the rotational force of the rotating drum drive motor 154 to rotate smoothly. The rotation direction of the rotating drum 14 and the rotation direction of the impact member 34 may be the same or opposite.

[0018] One or more scraping bars (scrapers) (not shown) are provided inside the rotating drum 14. The scraping bars are in contact with the inner circumferential surface of the rotating drum 14 and are fixed relative to the fixed drum 12. Therefore, as the rotating drum 14 rotates, the scraping bars move relatively along the inner circumferential surface of the rotating drum 14. As a result, even if raw soil or additives adhere to the inner circumferential surface of the rotating drum 14, the raw soil or additives are scraped off by the scraping bars as the rotating drum 14 rotates. In other words, the scraping bars and the rotating drum 14, which moves relative to the scraping bars, function as a scraping unit that scrapes off the raw soil or additives adhering to the inner circumferential surface of the rotating drum 14.

[0019] 1, the rotation mechanism 16 includes a rotating shaft 30 extending in the vertical direction (Z-axis direction) and disposed at the center of the fixed drum 12 and the rotating drum 14, a pulley 32 provided at the upper end of the rotating shaft 30, and a plurality of impact members 34 provided in multiple tiers (two tiers in FIG. 1) above and below near the lower end of the rotating shaft 30. The impact members 34 are provided at equal intervals around the rotating shaft 30, and each impact member 34 includes a metal chain 40 and a steel thick plate 42. However, the configuration is not limited to this, and the impact members 34 may be formed from a universal joint and a thick plate 42, or various other configurations may be used.

[0020] The rotating shaft 30 is a cylindrical member that penetrates the top panel 10a of the gantry 10 and is rotatably supported by the top panel 10a via two ball bearings 36a, 36b provided on the upper surface of the top panel 10a. A spacer 38 is provided between the two ball bearings 36a, 36b, leaving a predetermined gap between the ball bearings 36a, 36b. The lower end of the rotating shaft 30 is located inside the rotating drum 14 and serves as a free end. In other words, the rotating shaft 30 is cantilevered. The rotating shaft 30 may be rotatably supported not only by the top panel 10a but also near the lower end via a ball bearing.

[0021] The pulley 32 is connected to a motor (not shown) via a belt. When the motor (not shown) rotates, the pulley 32 and the rotary shaft 30 rotate. The impact member 34 rotates centrifugally due to the rotation of the rotary shaft 30, and the thick plates 42 move at high speed near the inner surface of the rotary drum 14, crushing the raw soil and mixing the raw soil with additives. For this reason, the rotary crushing device 100 can also be called a rotary crushing and mixing device. The number of chains 40 and thick plates 42 of the impact member 34 can be adjusted depending on the type and properties of the raw soil, the processing amount, the type and amount of additives, the target quality of the improved soil, etc.

[0022] The belt conveyor 122 transports the raw soil to the inlet member 20. In this embodiment, the belt conveyor 122 transports the raw soil in the Y and Z directions. In the Y direction, the belt conveyor 122 transports the raw soil from the back of the paper to the front of the paper. In the Z direction, the belt conveyor 122 transports the raw soil from below to above. The additives are transported to the inlet member 20 by a transport mechanism not shown.

[0023] According to the rotary crushing device 100 of this embodiment, raw soil and additives are fed into the fixed drum 12 through the inlet member 20, crushed and mixed by the impact member 34 inside the rotating drum 14, and then discharged below the rotating drum 14.

[0024] (Information processing device 200) In the first embodiment, when raw soil is improved to improved soil using the rotary crushing apparatus 100, the particle size distribution of the improved soil is predicted using an information processing device 200 (see FIG. 2). More specifically, the information processing device 200 executes a process of predicting the particle size distribution of the improved soil based on information on the raw soil obtained by the user of the rotary crushing apparatus 100, information on the specifications of the rotary crushing apparatus 100, etc.

[0025] (Method for predicting particle size distribution of improved soil) The basic principles of the method for predicting the particle size distribution of improved soil will be explained in detail below.

[0026] First, the inventors investigated a method for accurately determining a curve (function) that approximates the particle size distribution obtained from the results of a sieving test of soil and sand (raw soil and improved soil).

[0027] As a premise, in soil mechanics, it is said that the particle size distribution of general soil and sand can be approximated by a cumulative function that follows a log-normal distribution. Therefore, when the particle size is x, the particle size distribution follows the probability density function shown in the following equation (1). Here, μ is the average value of particle size x, and σ is the standard deviation of particle size x.

[0028]

number

[0029] When this formula (1) is expanded and the normal distribution formula is converted into a log-normal distribution formula, the following formula (2) is obtained.

[0030]

number

[0031] In the above formula (2), ln is the natural logarithm, λ is the average value of lnx, and ζ is the standard deviation of lnx (i.e., the slope component of the particle size distribution). Note that λ is expressed by the following formula (3).

[0032]

number

[0033] Also, ζ 2 is expressed by the following equation (4).

[0034]

number

[0035] The inventors conducted a "sieving test" in which raw soil (sample) was sieved using several types of sieves to determine the passing mass percentage for each particle size x (sieve opening). As a result, an example of the sieving test results shown in Figure 3(a) was obtained. When the sieving test results (measured values) in Figure 3(a) were plotted on a logarithmic graph, the particle size accumulation curve (measured values) shown in Figure 3(b) was obtained. The inventors investigated how to best determine λ and ζ in the above equation (2) so that the particle size accumulation curve in Figure 3(b) would approximate the curve expressed by the above equation (2).

[0036] The following three patterns (Method A to Method C) were considered as methods for determining λ and ζ.

[0037] (Method A) Method A is a method in which the sieving test results (actual data) in Figure 3(a) are used as is to determine the average (μ) and standard deviation (σ) of particle size x, and then the determined μ and σ are substituted into the above equations (3) and (4) to calculate λ and ζ.

[0038] Fig. 4(a) shows the curve (approximation) of formula (2) obtained by method A and the particle size accumulation curve (actual measurement values) of Fig. 3(b). As shown in Fig. 4(a), the slope of the particle size accumulation curve (actual measurement values) changes when the passing mass percentage is around 40% (around D40), but it can be seen that the curve (approximation) of formula (2) cannot follow this change in slope.

[0039] (Method B) Method B is a method for calculating λ (average value of lnx) and ζ (standard deviation of lnx) using the sieving test results (actual data) in Figure 3(a) as they are.

[0040] Figure 4(b) shows the curve (approximation) of Equation (2) obtained by Method B and the particle size accumulation curve (measured values) of Figure 3(b). Comparing Figure 4(b) with Figure 4(a) reveals that Figure 4(b) (Method B) more closely approximates the curve (approximation) of Equation (2) to the particle size accumulation curve (measured values) than Figure 4(a) (Method A). However, Figure 4(b) shows that the divergence between the curve (approximation) of Equation (2) and the particle size accumulation curve (measured values) is large when the passing mass percentage is near 50% (near D50). The divergence is also large when the passing mass percentage is 20% or less (D20 or less) and 80% or more (D80 or more).

[0041] (Method C) Method C is a method in which a table (see FIG. 5(a)) is created from the sieving test results (actual data) of FIG. 3(a) to determine particle sizes D10, D20, ..., D90, and λ and ζ are calculated from the table in FIG. 5(a).

[0042] Here, the particle sizes D10, D20, ..., D90 refer to the particle sizes corresponding to passing mass percentages of 10%, 20%, ..., 90% on the particle size accumulation curve (actual measured values) in Figure 3(b).

[0043] Fig. 5(b) is a diagram showing the curve (approximation) of formula (2) obtained by method C and the particle size accumulation curve (measured values) of Fig. 3(b). Comparing Fig. 5(b) with Fig. 4(a) and Fig. 4(b) shows that Fig. 5(b) (method C) is able to more closely approximate the curve (approximation) of formula (2) to the particle size accumulation curve (measured values) than Fig. 4(a) and Fig. 4(b) (method A and method B), particularly at D20 or less and D80 or more.

[0044] Therefore, the inventors decided to adopt Method C as the method for calculating λ and ζ. Note that another method involves creating a table (see FIG. 5(a)) of particle sizes D10, D20, ..., D90 from the sieving test results (actual data), calculating μ and σ from the table in FIG. 5(a), and then calculating λ and ζ from the calculated μ and σ. However, this method was unable to obtain an approximation curve with higher accuracy than Method C.

[0045] (Improvement 1 of Method C) Even with Method C, as shown in Figure 5(b), (i) the divergence between the two curves becomes large near D50, and (ii) it can be seen that the curve (approximate value) of Equation (2) is unable to follow the change in the slope of the particle size accumulation curve (actual value) near D40.

[0046] In order to improve the above (i), the inventors decided to change the definition of λ from "the average value of lnx" to "lnD50." In addition, in order to improve the above (ii), the inventors decided to divide the range into a first range (D50 or less) and a second range (D50 or more), using D50 as the boundary, and to calculate ζ (ζ1, ζ2) for each range.

[0047] As a result, the curves (approximation values) shown in Figure 6(a) were obtained. As can be seen by comparing with Figure 5(b), the approximation accuracy of the two curves was improved overall by adopting Improvement Plan 1. However, it can be seen that the bending of the approximation curve at D50 (the boundary between the first and second ranges) became larger.

[0048] (Improvement 2 of Method C) In order to eliminate the bending of the approximation curve at D50, which is a problem in Improvement Plan 1, the inventor adjusted ζ (ζ1, ζ2) in each range using the least squares method so that the sum of the differences between the particle sizes at D10, D20, ..., D90 on the approximation curve and the particle sizes (measured values) at D10, D20, ..., D90 in the table of Figure 5(a) would be minimized.

[0049] As a result, the curve (approximation) shown in Figure 6(b) was obtained. As can be seen by comparing Figure 6(b) with Figure 6(a), the bending of the approximation curve at D50 was reduced.

[0050] (Summary of methods for finding approximate curves) From the above explanation, it is considered preferable to adopt the following method when curve approximating the particle size distribution of raw soil. (1) Create a table (see Figure 5(a)) that calculates particle sizes D10, D20, ..., D90 from the sieving test results (actual data). (2) Obtain D50 (particle size) from the table in Figure 5(a), calculate lnD50, and set it as λ. (3) Divide into a first range below D50 and a second range above D50, and calculate ζ(ζ1, ζ2) for each range. (4) Adjust ζ1 and ζ2 so that the total difference between the actual measured value and the approximated value becomes small in each of the first and second ranges. (5) Using λ and the adjusted ζ1 and ζ2, approximate curves for the first and second ranges are calculated.

[0051] (Regarding the energy given to the raw soil by the rotary crusher 100 and the changes in λ and ζ due to the energy) The inventors have investigated the relationship between the energy index of the rotary crusher 100 and the λ change rate between the raw soil and the improved soil, and the relationship between the energy index of the rotary crusher 100 and the ζ change rate.

[0052] Here, the total value of the effective device rotational energy in the rotary crusher 100 is defined as the energy index KEn (initial index). The effective device rotational energy is the total value of the rotational kinetic energy of the impact member 34 multiplied by the impact probability Ki, and is expressed by the following equation (5). Even for the same impact probability, the value of the effective device rotational energy increases as the length and mass of the impact member 34 increases.

[0053]

number

[0054] In the above formula (5), m is the mass of the impact member 34, r is the radius of rotation of the impact member 34, ω is the angular velocity of the impact member 34, n is the number of impact members 34 per stage, and Ns is the number of stages of the impact members 34. Note that i ranges from 1 to Ns.

[0055] Furthermore, the total (ΣKi) of the hit probabilities Ki in the above equation (5) is expressed by the following equation (6).

[0056]

number

[0057] In the above formula (6), ni is the number of impact members 34 in the i-th stage, hi is the height of the raw soil falling to the top surface of the i-th impact member 34, B is the width of the impact member 34 (thick plate 42), and g is the acceleration due to gravity. Furthermore, Rs is the effective rotation area ratio, which is expressed by the following formula (7). Rs = π × (L 2 -(φ / 2+L1) 2 ) / S …(7)

[0058] Here, S means the area of ​​the portion of the XY cross section of the rotating drum 14 excluding the rotating shaft 30. If the inner diameter of the rotating drum 14 is D and the diameter of the rotating shaft 30 is φ, then S=π×(D 2 -φ 2 ) / 4. In the above formula (7), L is the distance from the center of the rotary shaft 30 to the tip of the thick plate 42 of the impact member 34, and L1 is the distance from the tip of the impact member 34 to the inner surface of the rotary drum 14 (i.e., the dimension of the gap between the impact member 34 and the rotary drum 14).

[0059] The inventor conducted an experiment to confirm the relationship between the energy index KEn and the λ change rate (Δλ) for a rotary crusher 100 having a rotary drum 14 with an inner diameter of 500 mm and a rotary crusher 100 having a rotary drum 14 with an inner diameter of 1000 mm. The λ change rate (Δλ) is calculated as follows, where λ is the average particle size of the raw soil fed into the rotary crusher 10, and λ' is the average particle size of the improved soil discharged from the rotary crusher 10: Δλ=(λ-λ') / λ …(8) Figure 7(a) plots the data obtained from the experiment, with the horizontal axis representing the energy index KEn and the vertical axis representing the rate of change of λ (Δλ).

[0060] As can be seen from Figure 7(a), when the inner diameter of the rotary crusher 100 is different, the points showing the relationship between the energy index KEn and the λ change rate are distributed in different regions. Therefore, the inventors decided to standardize the relationship between the energy index KEn and the λ change rate so that it would be approximately constant regardless of the model. As a standardization method, the energy index KEn is divided by the upper space volume of the rotary crusher 100 (the volume from the top plate 10a to the position where the uppermost impact member 34 is located), and the resulting value KEn' is used as a new energy index.

[0061] Figure 7(b) plots the data obtained from the experiment, with the energy index KEn' on the horizontal axis and the λ change rate (Δλ) on the vertical axis. As shown in Figure 7(b), it was found that the points showing the relationship between the energy index KEn' and the λ change rate are distributed in almost the same region. Therefore, it was found that the above standardization is effective.

[0062] Furthermore, the inventors conducted research into what influential factors should be taken into consideration when calculating energy indices in order to more accurately calculate Δλ (λ change rate), Δζ1, and Δζ2 (ζ change rate) from the energy indices.

[0063] Specifically, the inventors performed a sensitivity analysis of Δλ and Δζ with each of the influential factors shown in FIG. 8 using the extended quantification method type 1. As a result, the influence degree values ​​shown in FIG. 8 were obtained. Then, among the influential factors with large influence degree values ​​(absolute values), at least one of the influential factors not considered in the energy index KEn' (influence factors indicated by gray-filled boxes in FIG. 8) was reflected in the energy index. That is, "KEn' x α" was used as a new energy index, and α was expressed using the influential factors. Note that influence factors with positive influence degree values ​​were included in the numerator of α, and influence factors with negative influence degree values ​​were included in the denominator of α.

[0064] FIG. 9(a) is a graph plotting the relationship between Δλ and the energy index when two types of values, "KEn" and "KEn'", are used as the energy index of Δλ. FIG. 9(b) is a graph plotting the relationship between Δλ and the energy index when "KEn' × (D50 / σ c )," "KEn'×(D50 / σ c 0.25 9(a) and 9(b) are graphs plotting the relationship between Δλ and energy index when two types of values ​​are used: Δλ and σ. In addition, in Fig. 9(a) and Fig. 9(b), approximate curves obtained by fitting each point with a hyperbolic approximation are also shown. Note that D50 means the particle size of the raw soil D50 (the passing mass percentage is 50%), and σ cmeans the single particle strength of D50 particles (hardness of raw soil).

[0065] As can be seen from Figure 9(a) and Figure 9(b), the coefficients of determination for the fitted curves of the four energy indices are KEn <KEn’<KEn’×(D50 / σ c ) <KEn’×(D50 / σ c 0.25 ) Therefore, from Fig. 9(a) and Fig. 9(b), the energy index of the rotary crusher 100 is KEn' × (D50 / σ c 0.25 ) is considered to be able to accurately determine the value of the λ change rate (Δλ) from the value of the energy index.

[0066] The energy index of Δλ does not reflect the influence of the standard deviation of the raw soil shown in FIG. 8 (expressed as the particle size variation Uc (=D60 particle size / D10 particle size)). However, this is not limited to this, and for example, KEn' × (D50 / σ c 0.25 )×(1 / Uc) has a larger coefficient of determination, then KEn'×(D50 / σ c 0.25 )×(1 / Uc) may also be used.

[0067] FIG. 10(a) is a graph plotting the relationship between Δζ1 and the energy index when two values, KEn and KEn', are used as the energy index of Δζ1. FIG. 10(b) is a graph plotting the relationship between Δζ1 and the energy index when KEn'×(D50 / σ c )×(1 / Uc), KEn'×(D50 / σ c 0.25 ) × (1 / Uc). 10(a) and 10(b) also show approximate curves obtained by fitting each point with a hyperbolic approximation. Here, the rate of change of ζ1 (Δζ1) is given by: ζ1 is the standard deviation of particle size of D50 or less of the raw soil fed into the rotary crusher 10, and ζ1' is the standard deviation of particle size of D50 or less of the improved soil discharged from the rotary crusher 10. Δζ1=(ζ1'-ζ1) / ζ1…(9) It is expressed as:

[0068] As can be seen from Figure 10(a) and Figure 10(b), the coefficients of determination for the fitted curves of the four energy indices are KEn <KEn’<KEn’×(D50 / σ c )×(1 / Uc) <KEn’×(D50 / σ c 0.25 )×(1 / Uc). Therefore, from Fig. 10(a) and Fig. 10(b), the energy index of the rotary crusher 100 is KEn'×(D50 / σ c 0.25 )×(1 / Uc), it is believed that the value of the ζ1 change rate (Δζ1) can be accurately determined from the value of the energy index.

[0069] Although not shown in the figure, the inventors have used KEn, KEn', and KEn'×(D50 / σ c )×(1 / Uc), KEn'×(D50 / σ c 0.25 ) × (1 / Uc), the relationship between Δζ2 and the energy index was also examined in the same way as in Figures 10(a) and 10(b). Here, the ζ2 change rate (Δζ2) is given by: where ζ2 is the standard deviation of the particle size of the raw soil of D50 or larger fed into the rotary crusher 10, and ζ2' is the standard deviation of the particle size of the improved soil of D50 or larger discharged from the rotary crusher 10. Δζ2=(ζ2'-ζ2) / ζ2…(10) It is expressed as:

[0070] As a result of the above study, the coefficient of determination of the approximation curve when the energy index is KEn is 0.049, and when KEn' is used, the coefficient of determination of the approximation curve is 0.347, KEn' × (D50 / σ c ) × (1 / Uc), the coefficient of determination of the fitted curve is 0.747, KEn' × (D50 / σ c 0.25 ) × (1 / Uc), the coefficient of determination of the approximation curve was 0.818. From this result, it was determined that the energy index of the rotary crusher 100 was KEn' × (D50 / σ c0.25 ) × (1 / Uc), it is believed that the value of the ζ2 change rate (Δζ1) can be accurately determined from the value of the energy index.

[0071] In addition, the energy indexes Δζ1 and Δζ2 are intended to reflect the influence of the particle size variation Uc of the raw soil, but this is not limited to this. For example, the energy index may be expressed as KEn' × (D50 / σ c 0.25 )×(1 / Uc), rather than KEn'×(D50 / σ c 0.25 ) gives a larger coefficient of determination, then use KEn' × (D50 / σ c 0.25 ) may also be used.

[0072] (Configuration of information processing device 200) FIG. 2 is a diagram schematically illustrating the hardware configuration of an information processing device 200. As illustrated in FIG. 2, the information processing device 200 includes a central processing unit (CPU) 190, a read-only memory (ROM) 192, a random access memory (RAM) 194, storage (such as a solid-state drive (SSD) or a hard disk drive (HDD)) 196, a network interface 197, a display unit 193, an input unit 195, and a portable storage medium drive 199. The display unit 193 includes a liquid crystal display or the like, and the input unit 195 includes a keyboard, a mouse, a touch panel, and the like. These components of the information processing device 200 are connected to a bus 198. In the information processing device 200, the CPU 190 executes a program stored in the ROM 192 or the storage 196, or a program read by the portable storage medium drive 199 from the portable storage medium 191, thereby realizing the functions of the components illustrated in FIG. 11. The functions of the units in FIG. 11 may be realized by an integrated circuit such as an ASIC (Application Specific Integrated Circuit) or an FPGA (Field Programmable Gate Array).

[0073] Fig. 11 shows a functional block diagram of the information processing device 200. In the information processing device 200, the CPU 190 executes a program to function as a raw soil information acquisition unit 102, an apparatus information acquisition unit 104, a target particle-size distribution information acquisition unit 106, a raw soil index calculation unit 108, an energy index calculation unit 110, an improved soil index calculation unit 112, an improved soil particle-size distribution prediction unit 114, a comparison unit 116, an output unit 118, and an apparatus information change unit 120, as shown in Fig. 11.

[0074] The raw soil information acquisition unit 102 acquires information on the sieving test results of the raw soil (information on the passing mass percentage for each particle size of the raw soil) as information on the raw soil input from the outside. In addition, the raw soil information acquisition unit 102 acquires information on the raw soil, such as σ c The information on the raw soil is input to the information processing device 200 by, for example, the user of the rotary crushing device 100 (the worker who improves the raw soil to make improved soil).

[0075] The device information acquisition unit 104 acquires information about the rotary crushing device 100 input from an external device. The information about the rotary crushing device 100 includes the mass m of the impact members 34, the rotation radius r of the impact members 34, the angular velocity ω of the impact members 34, the number n of impact members 34 per stage, the number Ns of stages of impact members 34, the drop height hi of the raw soil to the top surface of the i-th stage of impact members 34, the width B of the impact members 34 (thick plates 42), the inner diameter D of the rotating drum 14, the diameter φ of the rotating shaft 30, and the distance L from the center of the rotating shaft 30 to the tip of the thick plate 42 of the impact member 34. The information about the rotary crushing device 100 may be input to the information processing device 200 by a user of the rotary crushing device 100, or may be acquired by the information processing device 200 from the rotary crushing device 100 or a management device for the rotary crushing device 100 via a communication line or the like.

[0076] The target particle size distribution information acquisition unit 106 acquires information on the target particle size distribution of the improved soil input from outside. The user of the rotary crushing device 100 inputs information on the desired particle size distribution (for example, the particle size distribution curve that is considered ideal at the site) as information on the target particle size distribution of the improved soil.

[0077] The raw soil index calculation unit 108 uses the information on the raw soil acquired by the raw soil information acquisition unit 102 to calculate the indexes of the raw soil (λ, ζ1, ζ2 described above).

[0078] The energy index calculation unit 110 calculates the energy index of the rotary crusher 100 based on the information of the rotary crusher 100 acquired by the device information acquisition unit 104.

[0079] The improved soil index calculation unit 112 calculates the λ change rate (Δλ), ζ1 change rate (Δζ1), and ζ2 change rate (Δζ2) between the raw soil and the improved soil based on the energy index calculated by the energy index calculation unit 110. In addition, the improved soil index calculation unit 112 calculates the improved soil indexes (λ', ζ1', ζ2') based on the calculated λ change rate (Δλ), ζ1 change rate (Δζ1), and ζ2 change rate (Δζ2) and the raw soil indexes (λ, ζ1, ζ2) calculated by the raw soil index calculation unit 108.

[0080] The improved soil particle size distribution prediction unit 114 predicts the particle size distribution of the improved soil based on the indexes (λ′, ζ1′, ζ2′) of the improved soil calculated by the improved soil index calculation unit 112.

[0081] The comparison unit 116 compares the particle size distribution of the improved soil predicted by the improved soil particle size distribution prediction unit 114 with the target particle size distribution of the improved soil acquired by the target particle size distribution information acquisition unit 106, and determines whether the difference between the predicted particle size distribution of the improved soil and the target particle size distribution is within an acceptable range.

[0082] The output unit 118 outputs the determination result of the comparison unit 116. The output unit 118 also outputs the particle size distribution of the improved soil predicted by the improved soil particle size distribution prediction unit 114.

[0083] If the comparison unit 116 determines that the difference between the predicted particle size distribution of the improved soil and the target particle size distribution is not within the allowable range, the device information change unit 120 changes the changeable information of the rotary crushing device 100 (such as the mass m of the impact members 34, the angular velocity ω of the impact members 34, the number n of impact members 34 per stage, the number Ns of stages of impact members 34, and the width B of the impact members 34 (thick plates 42)). More specifically, the device information change unit 120 changes the information of the rotary crushing device 100 based on, for example, predetermined rules so as to reduce the difference between the predicted particle size distribution of the improved soil and the target particle size distribution. For example, the predetermined rules include information indicating which information can be changed and information indicating how the information can be changed.

[0084] The device information change unit 120 transmits the changed information of the rotary crushing device 100 to the energy index calculation unit 110. When the energy index calculation unit 110 acquires the changed information of the rotary crushing device 100 from the device information change unit 120, it recalculates the energy index using the acquired changed information and transmits the recalculation result to the improved soil index calculation unit 112.

[0085] (Specific processing by the information processing device 200) Next, specific processing by the information processing device 200 will be described in detail with reference to the flowchart in Fig. 12 and other drawings as appropriate. The processing in Fig. 12 starts when various information is input by the user of the rotary crusher 100.

[0086] When the process of FIG. 12 is started, in step S10, the raw soil information acquisition unit 102 acquires raw soil information (sieving test results, σ c ) is acquired. The sieving test results are assumed to be, for example, data such as that shown in Fig. 13(a). Furthermore, the device information acquisition unit 104 acquires information on the rotary crushing device 100, and the target particle size distribution information acquisition unit 106 acquires information on the target particle size distribution of the improved soil.

[0087] Next, in step S12, the raw soil index calculation unit 108 multiplies each particle size (x) in the sieving test results acquired by the raw soil information acquisition unit 102 by N so that the particle size becomes a positive value when converted to a logarithm. In this embodiment, as an example, N is set to 200. Figure 13(b) shows the data obtained by multiplying the particle size (x) in Figure 13(a) by N (200 times).

[0088] Next, in step S14, the raw soil index calculation unit 108 calculates the particle sizes of D5, D10, ..., D90, and D95 ​​based on the data in FIG. 13(b), and creates a table as shown in FIG. 14(a).

[0089] For example, the method for calculating the particle size of D10 is as follows. (1) In Figure 13(b), the passing mass percentage is identified as the value closest to 10% that is smaller than 10%, and the value of "particle size x 200 (mm)" corresponding to that value. Also, the passing mass percentage is identified as the value closest to 10% that is larger than 10%, and the value of "particle size x 200 (mm)" corresponding to that value. In the example of Figure 13(b), the passing mass percentage is identified as 7.7%, "particle size x 200 (mm)" = 21.2 mm, and the passing mass percentage is identified as 13.9%, "particle size x 200 (mm)" = 50 mm. (2)log 10 In a graph with the horizontal axis being (particle size x 200) and the vertical axis being the passing mass percentage, the coordinate (log 10 (21.2), 7.7) and the coordinates (log 10 (50),13.9). (3) Using the formula obtained in (2) above, the logarithm when the passing mass percentage is 10% 10 The value of (particle size x 200) is obtained, and the particle size is calculated from this value.

[0090] The particle sizes of other Dn are calculated in the same way. Note that in the case of the data in Figure 13(b), there is no data with a passing mass percentage less than 5%, so D5 cannot be calculated. Therefore, D5 is not displayed in Figure 14(a).

[0091] Next, in step S16, the raw soil index calculation unit 108 calculates λ (= lnD50). Figure 14(b) shows a table in which Dn in the table of Figure 14(a) is converted to lnDn. From Figure 14(b), λ is lnD50 = 7.964.

[0092] Next, in step S18, the raw soil index calculation unit 108 divides the range below D50 into a first range and the range above D50 into a second range, using D50 as the boundary, and calculates ζ1 and ζ2. ζ1 is calculated using the following formula (11), and ζ2 is calculated using the following formula (12). ζ1=[(lnD10-lnD50) 2 +(lnD15-lnD50) 2 +(lnD20-lnD50) 2 +(lnD25-lnD50) 2 +(lnD30-lnD50) 2 +(lnD35-lnD50) 2 +(lnD40-lnD50) 2 +(lnD45-lnD50) 2 ] 1 / 2 / 8 …(11) ζ2=[(lnD55-lnD50) 2 +(lnD60-lnD50) 2 +(lnD65-lnD50) 2 +(lnD70-lnD50) 2 +(lnD75-lnD50) 2 +(lnD80-lnD50) 2 +(lnD85-lnD50) 2 +(lnD90-lnD50) 2 +(ln95-lnD50) 2 ] 1 / 2 / 9 …(12)

[0093] In the example of FIG. 14(b), ζ1=2.4758 and ζ2=0.6450.

[0094] Next, in step S20, the raw soil index calculation unit 108 adjusts ζ1 and ζ2. Specifically, as shown in Figure 15(a), for the first range (the range below D50), the value of lnDn in Figure 14(b) (measured lnDn value) is set to A, the value of lnDn calculated using λ and ζ1 calculated in steps S16 and S18 (calculated lnDn value) is set to B, and for each Dn, (B-A) 2 15(b), for the second range (range of D50 or more), the value of lnDn in FIG. 14(b) (measured lnDn value) is A, the value of lnDn calculated using λ and ζ2 calculated in steps S16 and S18 (calculated lnDn value) is B, and for each Dn, (B-A) 2 Here, the "calculated lnDn value" can be calculated by, for example, entering n% for Dn in the "probability" of the spreadsheet function NORM.INV (probability, mean, standard deviation), entering λ in the "mean", and entering ζ (ζ1 or ζ2) in the "standard deviation". Then, (BA) in Figure 15(a) 2 The value of ζ1 is adjusted using the least squares method so that the sum of (BA) in Figure 15(b) is minimized. 2 The value of ζ2 is adjusted using the least squares method so that the sum of

[0095] In the first embodiment, when ζ1=3.302 and ζ2=0.703, (BA) in FIG. 15(a) 2 The sum of (BA) in Figure 15(b) 2 The sum of the following is minimized. Figure 16(a) shows the relationship between the ln calculated value calculated using the adjusted ζ1 (=3.302) and the (B'-A) 2 16(b) is a table showing the values ​​of (B'-A) ​​when the ln calculated value calculated using the adjusted ζ2 (=0.703) is B'. 2 15(a) is a table showing the values ​​of (BA) in FIG. 2 The total was 4.240, while (BA) in Figure 16(a) 2 The total was 1.249. Also, (BA) in Figure 15(b) 2 The sum of (BA) in Figure 16(b) was 0.261. 2The total was 0.237.

[0096] From the above, in this embodiment, ζ1=3.302 is used as adjusted ζ1, and ζ2=0.703 is used as adjusted ζ2.

[0097] Next, in step S22, the energy index calculation unit 110 calculates the information of the rotary crushing device 10 and σ c Specifically, the energy index for Δλ is calculated by using the above-mentioned KEn'×(D50 / σ c 0.25 ) is calculated. In addition, as the energy index for calculating Δζ1 and Δζ2, the above-mentioned KEn' × (D50 / σ c 0.25 ) × (1 / Uc).

[0098] As an example, when KEn'=14294 is obtained from the information of the rotary crusher 10, the single particle intensity σ c If the value is 7.83MPa, D50 is 14.377mm, and Uc is 139, then KEn'×(D50 / σ c 0.25 ), KEn'×(D50 / σ c 0.25 )×(1 / Uc) is as follows: KEn'×(D50 / σ c 0.25 )=14294×(14.377 / 7.83 0.25 )=122852 KEn'×(D50 / σ c 0.25 ) × (1 / Uc) = 122852 / 139 ≒ 883.8

[0099] Next, in step S24, the improved soil index calculation unit 112 calculates Δλ, Δζ1, and Δζ2 from the calculated energy index. Specifically, in the thick solid curve in FIG. 9(b) (the curve showing the relationship between Δλ and the energy index (first relationship)), KEn' × (D50 / σ c 0.25) = 122852, and Δλ corresponding to this is found. In this case, Δλ ≒ 0.0944. In addition, in the thick solid curve in FIG. 10(b) (the curve showing the relationship between Δζ1 and the energy index (second relationship)), KEn' × (D50 / σ c 0.25 )×(1 / Uc)≒883.8, and find the corresponding Δζ1. In this case, Δζ1≒0.533. Similarly, find Δζ2 and KEn'×(D50 / σ c 0.25 ) × (1 / Uc) (the curve showing the relationship between Δζ2 and the energy index (second relationship)). In this case, Δζ2 is assumed to be 0.157.

[0100] In step S26, the improved soil index calculation unit 112 calculates λ', ζ1', and ζ2' from Δλ, Δζ1, and Δζ2 calculated in step S24 and λ, ζ1, and ζ2 of the raw soil. Since Δλ is expressed by the above formula (8), 0.0944=(7.946-λ') / 7.946 And λ'=7.946-(0.0944×7.946)≒7.2 This becomes:

[0101] In addition, since Δζ1 is expressed by the above equation (9), 0.533=(ζ1'-3.302) / 3.302 And ζ1'=3.302+(0.533×3.302)≒5.06 This becomes:

[0102] In addition, since Δζ2 is expressed by the above equation (10), 0.157=(ζ2'-0.703) / 0.703 And ζ1'=0.703+(0.157×0.703)≒0.81 This becomes:

[0103] Next, in step S28, the improved soil particle size distribution prediction unit 114 predicts the particle size distribution of the improved soil from λ', ζ1', and ζ2' calculated in step S26. In this case, for the first range (the range below D50), λ' and ζ1' are substituted into the above formula (2) to obtain a curve (prediction curve) showing the particle size distribution of the improved soil. For the second range (the range above D50), λ' and ζ2' are substituted into the above formula (2) to obtain a curve (prediction curve) showing the particle size distribution of the improved soil. The predicted curve in this embodiment is shown by the dashed line in Figure 17. When raw soil having the particle size distribution shown in Figure 13(a) was actually improved using the rotary crushing device 100, the measured particle size distribution of the improved soil was plotted as shown by the black circles (●) in Figure 17. Thus, it can be seen that the particle size distribution of the improved soil can be predicted with high accuracy by predicting the particle size distribution using the method of this embodiment.

[0104] Next, in step S30, the comparison unit 116 compares the predicted particle size distribution curve of the improved soil with the target particle size distribution curve.

[0105] Next, in step S32, the comparison unit 116 determines whether the difference between the two curves is within an allowable range. If the determination in step S32 is positive, that is, if the difference is within an allowable range, the process proceeds to step S34.

[0106] When the process proceeds to step S34, the output unit 118 outputs (for example, displays on the display unit 193) a message indicating that the distribution is close to the target particle size distribution and the particle size distribution of the improved soil predicted by the improved soil particle size distribution prediction unit 114. After step S34 is performed, all the processing in FIG. 12 ends.

[0107] On the other hand, if the determination in step S32 is negative, i.e., if the difference is not within the allowable range, the process proceeds to step S36, where the device information change unit 120 changes the information about the rotary crushing device. More specifically, the device information change unit 120 changes the information about the rotary crushing device 100, for example, based on a predetermined rule, so as to reduce the difference between the predicted particle size distribution of the improved soil and the target particle size distribution. For example, the device information change unit 120 increases or decreases the number of stages of the impact members 34 of the rotary crushing device 100, the number of impact members 34 per stage, or the angular velocity of the impact members 34. The device information change unit 120 passes the changed information about the rotary crushing device 100 to the energy index calculation unit 110. Thereafter, the process returns to step S22.

[0108] When the process returns to step S22, the energy index calculation unit 110 recalculates the energy index using the information on the changed rotary crushing apparatus 100. Thereafter, the processes of steps S24 to S30 are executed using the recalculated energy index, and if the determination in step S32 is affirmative, the process proceeds to step S34. If the difference between the predicted particle size distribution curve of the improved soil and the target particle size distribution curve can be brought within the tolerance range by using the information on the changed rotary crushing apparatus 100 in this way, in step S34 the output unit 118 also outputs the information on the changed rotary crushing apparatus 100. This allows the user to confirm how to adjust the rotary crushing apparatus 100 to bring the particle size distribution of the improved soil closer to the target particle size distribution.

[0109] As described above, in the first embodiment, by inputting the results of the sieving test of the raw soil, the single particle strength, and information on the rotary crusher 100 into the information processing device 200, it is possible to accurately predict the particle size distribution of the improved soil obtained as a result of improving the raw soil with the rotary crusher 100. Furthermore, by inputting the target particle size distribution, it is possible to confirm how the rotary crusher 100 should be adjusted in order to bring the particle size distribution of the improved soil closer to the target particle size distribution.

[0110] As described in detail above, according to the first embodiment, the information processing device 200 divides the information on the mass of raw soil for each particle size into multiple particle size ranges (first range, second range) (S18), and calculates the average particle size (λ) of the raw soil and the standard deviation of particle size (ζ1, ζ2) in each of the multiple particle size ranges (S18). The information processing device 200 also calculates the average particle size (λ') of the improved soil based on the relationship (first relationship) (Figure 9(b)) between the energy index of the crushing energy received by the raw soil and the rate of change (Δλ) of the average particle size of the raw soil subjected to the crushing energy, and the energy index of the crushing energy of the rotary crushing device 100 that improves the raw soil (S24, S26), and calculates the standard deviations (ζ1', ζ2') of particle size in the first and second ranges of the improved soil based on the relationship (second relationship) (Figure 10(b)) between the energy index of the crushing energy received by the raw soil and the rate of change (Δζ1, Δζ2) of the standard deviation of particle size of the raw soil subjected to the crushing energy, and the energy index of the crushing energy of the rotary crushing device 100 (S24, S26). The information processing device 200 then substitutes the average particle size (λ') of the improved soil and the standard deviations of particle size in the first and second ranges of the improved soil (ζ1', ζ2') into the above equation (2) to predict the particle size distribution of the improved soil (S28, Figure 17). As a result, in this first embodiment, when raw soil is improved using the rotary crushing device 100, the particle size distribution of the improved soil can be predicted with high accuracy using a method that takes prediction accuracy into consideration (a method of dividing the soil into multiple particle size ranges) based on the particle size distribution of the raw soil and information from the rotary crushing device 100.

[0111] Furthermore, in the first embodiment, the information processing device 200 predicts the particle size distribution of the improved soil by substituting the average particle size (λ') of the improved soil and the standard deviation of the particle size (ζ1' or ζ2') of the improved soil into a cumulative density function (Equation (2)) that follows a log-normal distribution for each of the first and second ranges. As a result, the particle size distribution is predicted using Equation (2) for each of the first and second ranges, and it is therefore possible to approximate the predicted particle size distribution to the actual measured value, as explained using Figure 6(a).

[0112] In addition, in this first embodiment, the particle size range is divided into multiple ranges so as to minimize the difference between the first curve (FIG. 3(b)) obtained from the information on the mass of each particle size in a specific raw soil (sample) and the second curve (approximation curve) obtained by approximating the information on the mass of each particle size to a cumulative density function that follows a log-normal distribution. In this embodiment, as an example, the range is divided into two ranges: above D50 and below D50. This allows the particle size range to be appropriately divided (so as to improve prediction accuracy) based on the results of experiments using samples.

[0113] In the first embodiment, the average particle size (λ) of the raw soil is set to the natural logarithm (lnD50) of the particle size of D50, which improves the approximation accuracy of the approximation curve for the measured particle size, as shown in Figure 6(a).

[0114] In the first embodiment, the standard deviations (ζ1, ζ2) of the particle size of the raw soil in each of the first and second ranges are calculated, and then the calculated standard deviations (ζ1, ζ2) are corrected using information on the mass of each particle size of the raw soil (S20, Figures 15 and 16). This allows the standard deviations (ζ1, ζ2) of the particle size of the raw soil to be corrected to appropriate values.

[0115] In the first embodiment, the energy index of the crushing energy is calculated based on the hardness of the raw soil (single particle strength σ c ), average particle size (D50), and particle size variation (Uc). This makes it possible to accurately express the relationships between the energy index and Δλ, Δζ1, and Δζ2 (first relationship and second relationship) compared to when the total effective device rotational energy (KEn) of the rotary crusher 100 is used as the energy index.

[0116] In the first embodiment, the particle size range is divided into D50 or more and D50 or less, but this is not limiting. The particle size range may be divided into other particle sizes based on the relationship between the measured particle size of the raw soil (sample) and the approximation curve (FIG. 5(b)). According to the inventor's research, it is preferable to divide the particle size range between D30 and D60.

[0117] In the first embodiment, the case where the range of granularity is divided into two ranges has been described, but the present invention is not limited to this and the range may be divided into three or more ranges.

[0118] In the first embodiment, the average particle size (λ) of the raw soil is set to lnD50, but this is not limited to this. The average particle size (λ) of the raw soil may also be set to the average value of the natural logarithm (lnx) of the particle size x.

[0119] Second Embodiment Next, a second embodiment will be described. The second embodiment is characterized in that when multiple types of raw soil are crushed and mixed in the rotary crushing device 100 to obtain improved soil (called mixed soil), the particle size distribution of the mixed soil is predicted.

[0120] Fig. 18 is a flowchart showing the processing of the information processing device 200 in the second embodiment. In Fig. 18, parts that differ from the first embodiment (Fig. 12) are indicated by thick lines.

[0121] When the process of FIG. 18 is started, first, in step S10′, the raw soil information acquisition unit 102 acquires the mixing ratio of the multiple types of raw soil to be mixed, information on each of the multiple types of raw soil (sieving test results, σ c ) is acquired. The device information acquisition unit 104 acquires information on the rotary crushing device 100, and the target particle size distribution information acquisition unit 106 acquires information on the target particle size distribution of the mixed soil.

[0122] Next, the information processing device 200 (raw soil index calculation unit 108, improved soil index calculation unit 112) executes the processes of steps S12 to S20. In the second embodiment, the processes of steps S12 to S20 are executed for each of the multiple types of raw soil to obtain λ, ζ1, and ζ2 for each raw soil.

[0123] Next, the energy index calculation unit 110 executes the process of step S22 in the same manner as in the first embodiment. In the next step S24, the improved soil index calculation unit 112 calculates Δλ, Δζ1, and Δζ2 from the calculated energy indices. In this second embodiment, the improved soil index calculation unit 112 stores, for each type of raw soil, the relationship between the energy index and Δλ (first relationship) in FIG. 9(b) and the relationship between the energy index and Δζ1 (or Δζ2) (second relationship) in FIG. 10(b). Therefore, the improved soil index calculation unit 112 calculates Δλ, Δζ1, and Δζ2 from the calculated energy indices for each of the multiple types of raw soil.

[0124] Next, in step S26', the improved soil index calculation unit 112 calculates λ', ζ1', ζ2' for each raw soil after improvement (crushing) from the calculated Δλ, Δζ1, Δζ2 of each raw soil and the λ, ζ1, ζ2 of each raw soil.

[0125] Next, in step S28', the improved soil particle size distribution prediction unit 114 predicts the particle size distribution of the mixed soil using the λ', ζ1', and ζ2' of each raw soil after improvement (after crushing) and the mixing ratio obtained in step S10'. Specifically, using the λ', ζ1', and ζ2' of each raw soil after improvement (after crushing), the particle size distribution of each raw soil after improvement (after crushing) is predicted using the above equation (2), and the particle size distribution of the mixed soil is predicted based on the predicted particle size distribution (prediction curve) and the mixing ratio. Here, if the mixing ratio obtained in step S10' is a wet weight ratio, the wet weight ratio is converted to a dry weight ratio using the moisture content of each raw soil.

[0126] The subsequent steps S30 to S36 are the same as those in the first embodiment.

[0127] That is, if the difference between the predicted particle size distribution of the mixed soil and the target particle size distribution of the mixed soil is within the allowable range (S32: Yes), that fact and the predicted particle size distribution of the mixed soil are output (S34). On the other hand, if the difference is not within the allowable range, the information on the rotary crusher 100 is changed (S36), and the process of predicting the particle size distribution of the mixed soil is repeated, and when the difference becomes within the allowable range (S32: Yes), that fact, the predicted particle size distribution of the mixed soil, and information on the rotary crusher 100 that can bring the difference within the allowable range are output (S34).

[0128] As described above, according to the second embodiment, even when a plurality of types of raw soil are mixed, the particle size distribution of the mixed soil can be predicted with high accuracy.

[0129] The above-described embodiment is a preferred example of the present invention, but the present invention is not limited to this and can be modified in various ways without departing from the spirit of the present invention. [Explanation of symbols]

[0130] 200 Information processing device 12 Fixed drum 14 Rotating Drum 30 Rotation axis 34 Impact member 100 Rotary crusher (processing equipment)

Claims

1. A method for predicting the particle size distribution of improved soil when improving raw soil into improved soil, comprising: Dividing the mass information of the raw soil by particle size into a plurality of particle size ranges; Calculating the average particle size of the raw soil and the standard deviation of the particle size in each of the plurality of particle size ranges; Calculate the average particle size of the improved soil based on a first relationship showing the relationship between the crushing energy received by the raw soil and the rate of change of the average particle size of the raw soil subjected to the crushing energy, and the crushing energy of a treatment device that improves the raw soil; Calculate the standard deviation of particle size of the improved soil in each of the plurality of particle size ranges based on a second relationship showing the relationship between the crushing energy received by the raw soil and the rate of change of the standard deviation of particle size of the raw soil subjected to the crushing energy, and the crushing energy of the treatment device; predicting the particle size distribution of the improved soil based on the average particle size of the improved soil and the standard deviation of particle size in each of the plurality of particle size ranges of the improved soil; A method for predicting the particle size distribution of improved soil.

2. 2. A method for predicting the particle size distribution of improved soil as described in claim 1, wherein, when predicting the particle size distribution of the improved soil, the average particle size of the improved soil and the standard deviation of the particle size of the improved soil are substituted into a cumulative density function that follows a log-normal distribution for each of the plurality of particle size ranges to predict the particle size distribution of the improved soil.

3. The method for predicting the particle size distribution of improved soil described in claim 1, wherein the dividing process divides the particle size range into multiple parts so as to reduce the difference between a first curve obtained from information on the mass of each particle size in a specific raw soil and a second curve obtained by approximating the information on the mass of each particle size to a cumulative density function that follows a log-normal distribution.

4. 2. The method for predicting particle size distribution of improved soil according to claim 1, wherein in the dividing process, the particle size range is divided between D30 and D60.

5. 2. The method for predicting the particle size distribution of improved soil according to claim 1, wherein the average particle size of the raw soil is the natural logarithm (lnD50) of the D50 particle size.

6. A method for predicting the particle size distribution of improved soil as described in claim 1, wherein after calculating the standard deviation of the particle size in each of the plurality of particle size ranges of the raw soil, the calculated standard deviation is corrected using information on the mass of each particle size in the raw soil.

7. 2. The method for predicting particle size distribution of improved soil according to claim 1, wherein the crushing energy of the treatment device is calculated using at least one of the hardness, average particle size, and particle size variation of the raw soil.

8. A method for predicting the particle size distribution of mixed soil when a plurality of types of raw soil are crushed and mixed in a processing device to produce mixed soil, comprising: Using the improved soil particle size distribution prediction method according to any one of claims 1 to 7, predict the particle size distribution when each of the plurality of types of raw soil is improved by the treatment device; Predicting the particle size distribution of the mixed soil based on the predicted particle size distribution when each of the plurality of types of raw soil is improved and the mixing ratio of the plurality of types of raw soil. A method for predicting the particle size distribution of mixed soil, characterized in that processing is carried out by a computer.

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