Concrete mix proportion determination method for designing compact filler skeleton based on particle flow program

Abstract

The invention relates to a concrete mix proportion determination method for designing a compact filler skeleton based on a particle flow program, and aims to solve the problems that the existing modified Anresenamp; and the technical problem that the minimum porosity is difficult to predict and obtain by an Androsen model is solved. The method comprises the following steps: obtaining a grading curve of each solid particle of concrete through a screening test, inputting particle grading parameters into a particle flow program, constructing a model boundary, determining a particle feeding proportion, sequentially feeding particles from large particles to small particles, and calculating to obtain the minimum porosity, so as to determine the particle size of each component in the concrete and obtain a dense filler skeleton; and finally, determining the dosage ratio of each component according to the apparent density and the particle size of each particle component, and obtaining an optimal mix proportion and a mixed material distribution curve at the same time. By means of the method, the concrete mix proportion and the optimal proportion of all the aggregate components can be obtained, the optimal using amount of all the components is determined, and therefore the densest accumulation of the concrete is achieved from the minimum porosity.

Description

Technical Field

[0001] The invention relates to the technical field of building material preparation, and in particular to a method for determining a concrete mix ratio of a dense filler skeleton based on a particle flow program design. Background Art

[0002] The main principles of concrete mix design are to reduce porosity, improve microstructure, enhance uniformity and increase toughness, obtain a dense filler skeleton, and achieve the most compact packing, which has an important impact on the performance of concrete. The applicant has found that in the existing concrete mix design methods, the Fuller model is only applicable to mixtures with large differences in particle size; the linear packing density model is applicable to spherical particles that deviate from the actual situation; the compressible packing model assumes that the particles are specific and the packing density is determined by the compaction process; the modified Andreasen & Andreasen model achieves the minimum pores between the various components of concrete through the compaction of aggregates, which can optimize the particle size distribution of all applied granular materials in concrete, but there is still a lot of room for optimization in the control of the amount of concrete cementitious materials, and the porosity needs to be obtained through tedious experiments.

[0003] The information disclosed in this background technology section is only used to deepen the understanding of the background technology of the present disclosure, and should not be regarded as acknowledging or suggesting in any form that the information constitutes the prior art known to those skilled in the art. Summary of the invention

[0004] In order to solve the technical problem that the existing modified Andresen & Andresen model is difficult to predict the minimum porosity, the present application adopts the following technical ideas: based on the particle flow code (Particle Flow Code, PFC), a particle dense filler skeleton is designed, and the spherical particle material is represented by Ball. The grading curves of each solid particle used in the concrete are obtained by screening test, and then the particle grading parameters are input into the particle flow program design, a certain space is given, the model boundary is constructed, the particle placement ratio is determined, random particles are input, and the particles are placed in sequence from large particles to small particles until the optimal placement is completed to obtain a dense filler skeleton, thereby determining the diameter of each component in the concrete based on the most dense stacking state, and determining the volume ratio based on the known apparent density of each particle component, and at the same time obtaining the optimal mix ratio and mixed material distribution curve, which is consistent with the modified Andresen & Andre The mixed material distribution curve, target curve, and mix ratio obtained by the Andresen & Andresen model are compared and verified. The results show that the raw material particle size distribution curves of the two methods are close and close to the target curve, achieving the densest packing, which can solve the shortcomings of the existing mix design method and achieve the purpose of the densest packing and optimal distribution of concrete. Since the modified Andresen & Andresen model fails to predict the porosity through calculation, the dense filler skeleton is obtained through the particle flow program (PFC), and the minimum porosity can be predicted. Specifically, the present disclosure provides a method for determining the concrete mix ratio based on the particle flow program design of a dense filler skeleton, comprising the following steps: (1) Conduct screening tests on each solid particle of concrete to determine the grading curve of each solid particle of concrete; (2) Use the particle flow program (PFC) to construct the model boundary; (3) Input the frequency distribution of each solid particle component; (4) Determine the particle release ratio; (5) Particle placement is completed from large particles to small particles, and the minimum porosity value is obtained by screening; (6) Output the calculated particle size of each particle; (7) Output the dense packing model of concrete solid particles; (8) Calculate the corresponding dosage based on the apparent density of each solid particle; (9) Output the dosage ratio of each solid particle that meets the minimum porosity target; (10) Output the optimal mixture curve.

[0005] In some embodiments of the present disclosure, in step (1), the solid particles of the concrete include at least one of fly ash, cement, river sand, silica fume, and slag.

[0006] In some embodiments of the present disclosure, in the step (3), the frequency distribution is determined by a laser particle size analyzer and a sieve shaker.

[0007] One or more technical solutions provided in the embodiments of the present application have at least any of the following technical effects or advantages: 1. This application obtains the grading curve of each solid particle through screening test, inputs the obtained particle grading parameters into the design program, outputs the diameter distribution of each component in the particle flow program (PFC), obtains the dense filling skeleton of concrete, and obtains the volume ratio according to the known apparent density, thereby achieving the densest packing of concrete, that is, the minimum porosity, successfully optimizing the concrete aggregate grading and obtaining the optimal mix ratio, which can reduce the uncertainty and repeatability of the test.

[0008] 2. This application designs a particle dense filler skeleton based on the particle flow program (PFC) to obtain the concrete mix ratio and the optimal ratio of each aggregate component, thereby determining the optimal amount of each component and achieving the best mix ratio design of concrete, which can achieve the densest packing of concrete from the minimum porosity.

[0009] 3. The present application method can accurately control the porosity of the material through precise calculation and adjustment through an iterative process to achieve the required performance standards. Through precise particle placement ratio and particle size optimization, it can reduce material waste in the actual construction process, while improving the strength, durability and impermeability of the material. In addition, the particle mix ratio can be flexibly adjusted according to different material properties and engineering requirements to adapt to different application scenarios. BRIEF DESCRIPTION OF THE DRAWINGS

[0010] Figure 1 This is a flow chart of a method for determining a concrete mix ratio for designing a dense filler skeleton based on a particle flow program (PFC) in one embodiment of the present application.

[0011] Figure 2 It is a particle dense filler skeleton obtained by a method for determining a concrete mix ratio for designing a dense filler skeleton based on a particle flow program (PFC) in one embodiment of the present application.

[0012] Figure 3 This is an enlarged view of the internal particle dense filler skeleton obtained by the concrete mix ratio determination method for designing a dense filler skeleton based on the particle flow program (PFC) in one embodiment of the present application.

[0013] Figure 4The target curve and mixture curve in one embodiment of the present application; wherein, a: target curve and mixture curve of concrete mix ratio based on particle flow program (PFC); b: target curve and mixture curve obtained by modifying Andreasen & Andreasen model; c: comparative verification of modified Andreasen & Andreasen model and concrete mix ratio determination method based on particle flow program (PFC); d: particle size distribution of concrete solid particles and target curve diagram.

[0014] Figure 5 It is a particle dense filler skeleton obtained by a method for determining a concrete mix ratio for designing a dense filler skeleton based on a particle flow program (PFC) in one embodiment of the present application.

[0015] Figure 6 This is an enlarged view of the internal particle dense filler skeleton obtained by the concrete mix ratio determination method for designing a dense filler skeleton based on the particle flow program (PFC) in one embodiment of the present application.

[0016] Figure 7 The target curve and mixture curve in one embodiment of the present application; wherein, a: target curve and mixture curve of intelligent concrete mix proportion based on particle flow program (PFC); b: target curve and mixture curve obtained by modifying Andreasen & Andreasen model; c: comparative verification of modified Andreasen & Andreasen model and concrete mix proportion determination method based on particle flow program (PFC); d: raw material particle size distribution of concrete and target curve diagram. DETAILED DESCRIPTION

[0017] The sources of materials used in the following examples are as follows: silica fume is provided by Chengdu Southeast Star Technology Development Co., Ltd.; fly ash is Class I fly ash produced by Guangzhou Yunhong Fly Ash Comprehensive Development Co., Ltd.; cement is China Resources Runfeng brand PII52.5 cement produced by China Resources Cement Investment Co., Ltd.; clean river sand with a particle size range of 0-0.06 mm is used as fine aggregate.

[0018] In order to better understand the technical solution of the present application, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0019] Example 1 This example provides a method for determining the concrete mix ratio for designing a dense fill skeleton based on the particle flow program (PFC). Figure 1 As shown, the following steps are included: (1) Conduct screening tests using a laser particle size analyzer and a sieve shaker to determine the grading curve of each solid particle in the concrete; (2) Based on the particle flow program (PFC), establish the sample space and construct the model boundary; (3) Input the frequency distribution (gradation) of each solid particle component; (4) Determine the particle release ratio (∑ V 固体组份 =1); (5) Randomly input particles into the model to simulate the actual particle distribution; (6) The particles are placed in order from large particles to small particles. During the placement process, each level of particles needs to find the vacant positions left by the particles of the previous level to ensure that the particles can be densely packed and reduce the pores. Once the particles find a suitable vacant position in the model and are placed, the placement can be stopped; (7) After the release is stopped, the porosity and its mean are calculated. If the porosity mean reaches a convergence state, the minimum porosity is output; if the porosity mean does not reach a convergence state, return to step (5) and release particles again; (8) After the porosity mean reaches a convergence state, if the output porosity is equal to or lower than the preset minimum porosity value, it means that the close packing of the particles has reached an ideal state, then the calculated particle size is output and the optimal mix ratio is determined; if the output porosity is higher than the preset minimum value, then return to step (4) to redetermine the particle placement ratio; (9) After determining the optimal mix ratio, output the dense packing model of concrete solid particles; (10) Calculate the particle size and the apparent density of each solid particle to calculate the corresponding dosage; (11) Outputting the optimal dosage ratio of each solid particle that meets the minimum porosity target; (12) Output the optimal mixture target gradation curve.

[0020] This method designs a particle dense filler skeleton based on the particle flow program (PFC), optimizes the particle size distribution, makes each particle component densely packed, achieves the optimal ratio, forms a dense stacking structure skeleton, and makes each component densely packed and non-overlappingly distributed; small particles fill the pores between large particles, reduce the pore volume, and increase the packing density of solid components, thereby achieving the purpose of reducing and optimizing the amount of cementitious materials in concrete. The mixture design can be regarded as a multi-objective optimization process. Verified and compared with the modified Andreasen&Andreasen model, the method of this application can reduce the uncertainty factors brought by the test and achieve the purpose of minimum porosity.

[0021] Example 2 This example is based on the concrete mix ratio determination method for designing a dense filler skeleton based on the particle flow program (PFC) provided in Example 1, wherein the concrete solid particles used are fly ash, cement, river sand, and silica fume. The particle size and frequency distribution percentage of the concrete solid particles used are shown in Table 1. The results are as follows Figure 2 , Figure 3 , Figure 4 As shown, the mixture curve obtained in this example is basically consistent with the mixture curve obtained by the improved Andreasen&Andreasen model, with a small error, and the mixture curve of the concrete mix proportion determination method based on the particle flow program (PFC) is made as close to the target curve as possible, indicating that the dense packed concrete prepared by the method of this application has high accuracy and can be applied to concrete mix design.

[0022] Table 1 Particle size and frequency distribution percentage of concrete solid particles .

[0023] Example 3 This example is based on the concrete mix ratio determination method for designing a dense filler skeleton based on the particle flow program (PFC) provided in Example 1, wherein the concrete solid particles used are cement, slag, and silica fume, and the particle size and frequency percentage distribution of the concrete solid particles are shown in Table 2. The results are as follows Figure 5 , Figure 6 , Figure 7 As shown, Figure 7 A comparison chart of the mixture curve obtained in this example and the mixture curve obtained by the improved Andreasen&Andreasen model is shown, and compared with the target curve. It can be clearly seen that the mixture curve obtained in this example is basically consistent with the mixture curve obtained by the improved Andreasen&Andreasen model, with a small error, and the mixture curve obtained in this example is as close to the target curve as possible, indicating that the method of this application has a high accuracy in preparing densely packed concrete, and it can be applied to concrete mix design.

[0024] Table 2 Raw material particle size and frequency distribution percentage .

[0025] It can be seen from the above embodiments that the method of the present application can optimize the particle size distribution of concrete components, so that each particle component can be densely packed and achieve the optimal ratio, thereby forming a dense stacking structure skeleton, so that the components can be most densely stacked and distributed without overlapping, thereby achieving the purpose of reducing and optimizing the amount of cementitious materials and the minimum porosity in concrete.

[0026] Although some preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications that fall within the scope of the present application.

[0027] Obviously, those skilled in the art can make various changes and modifications to the present application without departing from the spirit and scope of the inventive concept. Thus, if these modifications and variations of the present application fall within the scope of the claims of the present application and their equivalents, the present application is also intended to include these modifications and variations.

Claims

1. A method for determining the concrete mix ratio of a dense filler skeleton based on particle flow programming, It is characterized in that The following steps are involved: (1) Conduct screening tests on each solid particle of concrete to determine the grading curve of each solid particle of concrete; (2) Use the particle flow program PFC to construct the model boundary; (3) Input the frequency distribution of each solid particle component; (4) Determine the particle release ratio; (5) Particle placement is completed from large particles to small particles, and the minimum porosity value is obtained by screening; (6) Output the calculated particle size of each particle; (7) Output the dense packing model of concrete solid particles; (8) Calculate the corresponding dosage based on the apparent density of each solid particle; (9) Output the dosage ratio of each solid particle to meet the minimum porosity target; (10) Output the optimal mixture curve.

2. The method for determining the concrete mix ratio according to claim 1, It is characterized in that In step (1), the solid particles of the concrete include at least one of fly ash, cement, river sand, silica fume and slag.

3. The method for determining the concrete mix ratio according to claim 1, It is characterized in that In the step (3), the frequency distribution is determined by a laser particle size analyzer and a sieve shaker.

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