High-viscosity concrete fluidity prediction method based on particle flow program

Abstract

The invention relates to a high-viscosity concrete fluidity prediction method based on a particle flow program. A discrete element numerical model is constructed to replace a traditional physical experiment, and the problems that in the prior art, the experiment period is long, cost is high, and influences of mesoscopic parameters on fluidity are difficult to quantify are solved. The method specifically comprises the following steps: establishing an initial model containing container geometric parameters and grain composition; inputting a concrete mix proportion parameter and a silica fume viscosity mixing amount gradient; calibrating mesoscopic contact parameters; simulating and dynamically monitoring a particle diffusion form through a jumping table experiment, calculating an expansion radius, and comparing the expansion radius with an experimental value for calibration; and establishing a relation model of the viscosity mixing amount and the expansion radius, optimizing the concrete mix proportion, and constraining the cement dosage, the silica fume mixing amount and the water-binder ratio. According to the method, the prediction period is shortened from several weeks to several hours, the expansion degree prediction error is smaller than or equal to 5%, material waste and equipment investment are reduced, the research and development cost is reduced by 60%-70%, expandability is high, fluidity prediction of materials doped with mineral powder, fibers and the like is supported, and only particle gradation parameters need to be adjusted.

Description

Technical Field

[0001] The present invention belongs to the technical field of concrete performance evaluation, and in particular relates to a method for predicting the fluidity of high-viscosity concrete based on a particle flow program (PFC). Background Art

[0002] The workability of a concrete mixture is a core indicator for evaluating its construction performance, encompassing three key properties: fluidity, cohesion, and water retention. Fluidity directly determines the uniformity and density with which fresh concrete fills the formwork under its own weight or mechanical vibration, and is a key factor influencing construction efficiency and structural quality. While widely used, traditional fluidity testing methods have become increasingly limited with new concrete materials and complex working conditions, necessitating the development of more accurate and efficient assessment methods.

[0003] At present, the slump test is the mainstream method for fluidity testing. It quantifies fluidity by measuring the height change of concrete after it collapses from a slump cone. This method has the advantages of simple operation and intuitive indicators. It is particularly suitable for plastic concrete with aggregate particle size less than 40mm and slump greater than 10mm. However, its applicability to high-viscosity systems such as dry hard concrete and self-compacting concrete is significantly insufficient: when the slump is close to zero, the height change is difficult to capture, and the test results are easily affected by the operator's technical level and subjective judgment, resulting in large data discreteness. In addition, when the aggregate particle size is too large or the slump is too low, the slump method cannot accurately reflect the actual workability, which limits its application in modern concrete engineering.

[0004] The cement mortar fluidity test (jump table test) has become a complementary testing method for assessing the fluidity of high-viscosity concrete. This method uses a vibrating table to induce concrete flow and, combined with diffusion diameter measurement, indirectly characterizes rheological properties. It is particularly suitable for analyzing the correlation between water demand and viscosity. Its advantage lies in its ability to quantify fluidity under vibration and, through the correlation between fluidity and water demand, provide a basis for water-cement ratio control. However, the stringent operational requirements of the jump table test pose a significant bottleneck: according to the GB / T 2419 standard, the mortar must be added with water to the end of the measurement within 6 minutes, of which mixing takes 4 minutes, leaving less than 2 minutes for the actual mold installation, tamping, and jumping table operations. Under time pressure, operators must precisely control the tamping position, force, and mold filling uniformity. Any inaccuracy can result in specimen deformation (e.g., elliptical diffusion) or fluidity deviations caused by water evaporation. Furthermore, the test relies on specialized equipment (jump table, mixing drum, etc.), making the process complex and requiring high maintenance costs. Fluctuations in ambient temperature and humidity can also affect the reliability of the results. These limitations limit the application of the jump table test for rapid on-site testing and large-scale mix optimization.

[0005] Traditional concrete flow testing relies on physical jump-table experiments, which require multiple adjustments to mix proportions and repeated testing. This is time-consuming, costly, and difficult to quantify the effects of viscosity. Existing numerical simulation methods are mostly limited to macroscopic mechanical analysis and fail to effectively link microscopic particle parameters with macroscopic flow properties. Therefore, an intelligent prediction method is urgently needed that can accurately predict the effect of viscosity dosage on flow and reduce reliance on experimental methods.

[0006] 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 an admission or any form of suggestion that the information constitutes the prior art known to those skilled in the art. Summary of the Invention

[0007] To address the technical problems of existing technologies, such as strong experimental dependence, insufficient precision, limited applicability, and low efficiency, the present invention aims to provide a method for predicting the fluidity of high-viscosity concrete based on a particle flow code (PFC). By constructing a microscopic contact parameter calibration system and a viscosity-extension relationship model, the method achieves the following goals: ① Rapidly predict the extensibility of concrete at different viscosity content by replacing traditional experiments; ② Quantify the impact of interparticle interactions on fluidity through parametric modeling; ③ Automatically optimize the concrete mix ratio, reducing R&D costs and cycles.

[0008] To achieve the above objectives, the present invention provides a method for predicting the fluidity of high-viscosity concrete, which includes the following core steps: 1. Initial model construction (1) Parameter definition: Set the container geometry parameters (top diameter d1 = 160-180 mm, bottom diameter d2 = 100-120 mm, height h = 60-80 mm), oscillation times N ≥ 25 times, particle size range (D min =0.1mm, D max =5mm) and the diffusion radius calculation step Δr = 2mm.

[0009] (2) Particle placement: Based on the target mix ratio (the proportion of quartz sand and silica fume), a three-dimensional spherical particle model is generated in PFC, and particles are randomly placed in proportion and the boundary conditions are fixed.

[0010] 2. Microscopic parameter calibration (1) Parameter input: Initialize the microscopic contact parameters (friction coefficient μ = 0.2-0.6, normal stiffness k n =1×10 8 -5×10 8 N / m, tangential stiffness k s =0.5×10 8 -2.5×10 8 N / m), input silica fume viscosity dosage in a gradient of 0%, 2%, 4%, and 6%.

[0011] (2) Experimental simulation: A virtual table-hopping experiment was performed to monitor the morphological evolution of particles from spherical accumulation to pancake-shaped diffusion through a visualization module, and the dynamic rheological behavior during the oscillation stage was recorded.

[0012] (3) Parameter calibration: Calculate the simulation expansion radius R sim , and the measured value R exp In contrast, if the error |R sim -R exp If |≤5%, the current mesoscopic parameters are calibrated as the valid set; otherwise, the parameters are automatically adjusted and optimized iteratively.

[0013] 3. Viscosity-fluidity modeling Relationship establishment: Based on the calibration parameter set, the second-order polynomial regression model R(V)=aV²+bV+c is used to establish a quantitative relationship between the silica fume viscosity content V and the expansion radius R, where the coefficients a, b, and c are determined by least squares fitting.

[0014] 4. Mix ratio optimization (1) Intelligent iteration: Input the target expansion range. For example, the constraints include silica fume content ≤ 8% and water-binder ratio 0.25-0.45.

[0015] (2) Result output: If the simulation expansion meets the requirements, the final mix ratio scheme is output; otherwise, the parameters are adjusted and recalculated until convergence.

[0016] One or more technical solutions provided in the embodiments of this application have at least any of the following technical effects or advantages: 1. Efficient alternative experiment: shortens prediction cycle from weeks to hours, improving efficiency by over 80%; 2. High-precision prediction: The microscopic parameter calibration system makes the expansion prediction error ≤5%, which is better than the empirical formula method (error >10%); 3. Significant cost reduction: Reduce material waste and experimental equipment investment, and reduce R&D costs by 60%-70%; 4. Factor coupling analysis: By quantifying the synergistic effect of silica fume and water-binder ratio, scientific mix design can be guided; 5. Scalability: Supports fluidity prediction of materials such as mineral powder and fiber, requiring only adjustment of particle size distribution parameters. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 1 is a schematic flow chart of a method for predicting the fluidity of high-viscosity concrete based on a particle flow program in one embodiment of the present application.

[0018] Figure 2Schematic diagram of the evolution of particle diffusion morphology (spherical → fluid state → pancake shape) in one embodiment of the present application.

[0019] Figure 3 A simulated diagram of a particle-dense packing skeleton obtained by using a PFC-based high-viscosity concrete fluidity prediction method in one embodiment of the present application.

[0020] Figure 4 This is a trend diagram of the relationship between micro-contact parameters and expansion radius in one embodiment of the present application. DETAILED DESCRIPTION

[0021] In order to better understand the technical solution of the present application, the above technical solution will be described in detail below with reference to the accompanying drawings and specific implementation methods.

[0022] Example 1: Analysis of the influence of viscosity dosage on fluidity This example demonstrates the quantitative effect of silica fume viscosity on concrete expansion using a particle flow program (PFC). The specific steps are as follows (see Figure 1 ): Step 1: Model Initialization (1) Set the container parameters: top diameter d1 = 200 mm, bottom diameter d2 = 100 mm, height h = 100 mm; (2) Define particle size distribution: cement (particle size 0.5-2 mm), fly ash (1-3 mm), quartz sand (0.1-0.5 mm), silica fume (0.05-0.1 mm); (3) Input the initial mix ratio: cement 40%, fly ash 20%, quartz sand 35%, silica fume 5% (mass percentage).

[0023] Step 2: Viscosity dosage gradient setting (1) Set the silica fume viscosity gradient to 0%, 2%, 4%, and 6%; (2) Fixed other parameters: inter-particle friction coefficient μ = 0.4, normal stiffness k n =3×10 8 N / m, tangential stiffness k s =1.8×10 8 N / m.

[0024] Step 3: Table jumping experiment simulation (1) Add particles in descending order of viscosity (6% → 4% → 2% → 0%); (2) Perform oscillation simulation (N=25 times) and monitor the particle diffusion process in real time ( Figure 2 ): ① Initial stage: particles accumulate in a spherical shape; ② Oscillation stage: particles form radial flow and the interface gradually flattens; ③ Stable stage: a round pancake-shaped final state is formed, and the diffusion radius tends to be stable ( Figure 3 ).

[0025] Step 4: Calculation and verification of expansion (1) Using radial distribution function to calculate the simulated expansion radius R sim ( Figure 4 ): ① 0% dosage: R sim =2.48 (unit: normalized radius); ③ 2% dosage: R sim =2.40; ③ 4% ​​dosage: R sim =2.65; ④ 6% dosage: R sim =2.78.

[0026] (2) Comparing the results of the physical table jumping experiment (error ≤ 4.2%), the accuracy of the model was verified.

[0027] Technical effects: ① The viscosity dosage is positively correlated with the expansion radius (R²=0.98). For every 1% increase in silica fume dosage, the expansion radius increases by an average of 0.09. ② A single simulation takes about 3.5 hours, which is 95% more efficient than traditional experiments (5-7 days).

[0028] Example 2: Mix ratio optimization based on calibration parameters After calibrating the micro-contact parameters in Example 1, this example demonstrates the optimization process of the concrete mix ratio: Step 1: Enter the calibration parameter set (1) Microscopic parameters: μ = 0.4, k n =3×10 8 N / m, k s =1.8×10 8 N / m; (2) Target extension: normalized radius R target =2.6±0.1.

[0029] Step 2: Iterative optimization of mix ratio (1) Initial input mix ratio A: cement 45%, fly ash 45%, quartz sand 64%, silica fume 68% (mass percentage); (2) Execute table jump simulation and output R sim =2.55 (not up to standard); (3) Adjust mix ratio B: reduce silica fume to 62%, increase cement to 48%, and re-simulate R sim =2.61 (meets the requirements); (4) Optimization constraints: water-binder ratio = 0.35, silica fume content ≤ 65%.

[0030] As can be seen from Examples 1 and 2, the method of the present invention has the following advantages: ① A microscopic parameter calibration system was constructed: the inter-particle friction coefficient, contact stiffness and other parameters were calibrated using a discrete element model to establish a quantitative relationship between microscopic properties and macroscopic fluidity; ② A viscosity-extension model was established: a second-order polynomial regression model was used to accurately predict the expansion radius of concrete at different viscosity contents with an error of ≤5%; ③Optimization algorithm: automatically iteratively optimizes the mix ratio based on constraint conditions (silica fume content, water-binder ratio), reducing reliance on experiments.

[0031] 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.

[0032] Obviously, those skilled in the art may make various changes and modifications to this application without departing from the spirit and scope of the inventive concept. Thus, if such changes and modifications fall within the scope of the claims of this application and their equivalents, this application is intended to include such changes and modifications.

Claims

1. A method for predicting the fluidity of high-viscosity concrete based on a particle flow program, characterized in that: The following steps are involved: (a) Establishing the initial model parameter set, including the container geometry, particle size distribution range, oscillation number N, and diffusion radius calculation step Δr; (b) Input the target concrete mix parameters, including the proportions of cement, fly ash, quartz sand, and silica fume, and the viscosity gradient; (c) Construct a three-dimensional discrete element model based on a particle flow program, place the component particles in proportion, and fix the boundary conditions; (d) Initialize the mesoscopic contact parameter set, perform table-hopping simulation, and monitor the evolution of particle diffusion morphology in real time; (e) Calculate the simulation expansion radius R sim and the experimental value R exp By comparison, if the error ≤ threshold ε, the current mesoscopic contact parameters are calibrated as the valid parameter set; (f) Based on the calibration parameter set, a quantitative relationship model between viscosity dosage and expansion radius is established; (g) Determine the concrete mix ratio that meets the target fluidity through an iterative optimization algorithm.

2. The method for predicting fluidity of high-viscosity concrete according to claim 1, wherein: In step (a), the container geometric parameters satisfy: The top diameter d1 is 160-200 mm; the bottom diameter d2 is 100-120 mm; the container height h is 60 to 100 mm; Oscillation times N≥25 times; Particle size range is 0.1mm≤ D min ≤0.5mm, 0.5mm≤ D max ≤5mm; The diffusion radius calculation step Δr is 1-3 mm.

3. The method for predicting fluidity of high-viscosity concrete according to claim 1, wherein: In step (d), the microscopic contact parameters include: Interparticle friction coefficient μ∈[0.2-0.6], normal contact stiffness k n ∈[1×10 8 -5×10 8 N / m], tangential contact stiffness k s ∈[0.5×10 8 -2.5×10 8 N / m].

4. The method for predicting fluidity of high-viscosity concrete according to claim 1, wherein: In the step (d), the particle diffusion morphology evolution is monitored in real time by a visualization module, including: (1) Maintaining a spherical stacking morphology in the initial stage; (2) During the oscillation phase, radial diffusion flow is formed and the interface flattens; (3) In the stable stage, a pancake-shaped final state is formed, and the expansion radius tends to be stable.

5. The method for predicting fluidity of high viscosity concrete according to claim 1, characterized in that: In the step (e), the simulated expansion radius R sim The calculation method is: R sim = max{Δr·n | Σθ(r=Δr·n) ≥ 95% total particle amount}; Where n is the step number and θ is the azimuth distribution function.

6. The method for predicting fluidity of high viscosity concrete according to claim 1, characterized in that: In step (e), the threshold ε is set to ±5% of the experimental measurement value. When the error exceeds the limit, the microscopic contact parameters are automatically adjusted and the process returns to step (d).

7. The method for predicting fluidity of high-viscosity concrete according to claim 1, wherein: In the step (f), a second-order polynomial regression model is established: R(V) = a·V² + b·V + c; Where V is the viscosity dosage, and a, b, and c are material constants determined through calibration tests.

8. The method for predicting fluidity of high-viscosity concrete according to claim 1, wherein: In step (g), the iterative optimization algorithm uses the expansion radius as the objective function, and the constraints include: ①Silica fume content ≤8%; ②Water-binder ratio∈[0.25,0.45].

9. The method for predicting fluidity of high-viscosity concrete according to claim 1, wherein: It also includes establishing a concrete mix ratio-viscosity parameter relationship map.

10. A computer-readable storage medium storing program instructions for executing the method according to any one of claims 1 to 9, characterized in that: The procedures include: Parameter input module, used to receive container parameters, mix ratio parameters and micro-contact parameters; Particle modeling module, used to generate three-dimensional discrete element models; Experimental simulation module, used to perform table jumping experiment simulation and dynamic visualization; The optimization analysis module is used to calculate the expansion radius, calibrate parameters and output the optimized mix ratio.

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