Method and system for predicting rheological parameters of mortar prepared from hole slag

Abstract

The invention relates to the technical field of mortar, and provides a rheological parameter prediction method and system for mortar prepared from hole slag, and the method comprises the steps: obtaining the physical characteristics of hole slag aggregate, the basic data of a close packing test and the net slurry yield stress; according to the physical characteristics of the hole slag aggregate and the basic data of the close packing test, the close packing compactness of the subsea tunnel hole slag aggregate is obtained through calculation; predicting the yield stress of the mortar prepared from the hole slag aggregate under different mixing amounts based on the neat paste yield stress and the close packing compactness of the subsea tunnel hole slag aggregate; taking the predicted ratio of the yield stress of the mortar to the yield stress of the neat paste as the relative yield stress, and adjusting and optimizing the rheological property of the mortar according to the relative yield stress. By researching the relative yield stress of the mortar prepared from the hole slag aggregate, dynamic prediction and rapid optimization of the rheological property of the mortar prepared from the subsea tunnel hole slag aggregate are realized.

Description

Technical Field

[0001] The present invention relates to the technical field of mortar, and in particular to a method and system for predicting rheological parameters of mortar prepared by slag. Background Art

[0002] The statements in this section merely provide background information related to the present invention and do not necessarily constitute prior art.

[0003] With the development of infrastructure construction, the supply and demand contradictions of sand and gravel aggregates, as indispensable raw materials, have become increasingly prominent, and shortages have become frequent. However, undersea tunnel construction produces a large amount of tunnel slag, which can be processed into sand and gravel aggregate to prepare high-performance tunnel slag mortar.

[0004] However, in the existing technology, the research on slag aggregates mostly focuses on their mechanical properties (such as compressive strength and flexural strength) and basic durability (such as impermeability and frost resistance), while the research on their workability (such as fluidity, water retention, and segregation resistance) remains at the macro-experimental level. For example, fluidity is evaluated by indirect indicators such as slump and expansion, but there is a lack of quantitative analysis of the dynamic rheological behavior of mortar. In addition, when preparing mortar, traditional methods mostly rely on trial and error to adjust the ratio, and repeated laboratory trials are required to verify parameters such as aggregate dosage and water-cement ratio, which makes it difficult to adapt to the dynamic needs under complex working conditions.

[0005] Therefore, how to establish a theoretical model based on the physical properties of slag aggregate, such as apparent density, close packing density, and net slurry rheological parameters, to accurately predict the mortar yield stress and achieve rapid optimization of rheological properties has become a technical problem that needs to be solved. Summary of the Invention

[0006] To address the deficiencies of the prior art, the present invention provides a method and system for predicting the rheological parameters of mortar prepared with slag. Rheological parameters include multiple indicators such as yield stress, viscosity, and elastic modulus. The mortar rheological parameter prediction of the present invention refers to predicting the yield stress of the mortar. Therefore, based on key parameters such as the apparent density and close packing density of the slag aggregate, the present invention predicts the yield stress of mortars prepared with different slag aggregate dosages. The ratio of the predicted yield stress of the mortar to the yield stress of the neat slurry is used as the relative yield stress. The rheological properties of the cement paste are adjusted and optimized based on the obtained relative yield stress, thereby achieving efficient and accurate prediction of the rheological behavior of the mortar under complex working conditions.

[0007] In order to achieve the above object, the present invention adopts the following technical solutions:

[0008] A first aspect of the present invention provides a method for predicting rheological parameters of mortar prepared from slag.

[0009] A method for predicting rheological parameters of mortar prepared by slag slag, comprising:

[0010] Obtain the physical properties of slag aggregate, basic data of close packing test and the yield stress of net paste;

[0011] Based on the physical properties of tunnel slag aggregate and basic data from the compaction test, the compaction density of submarine tunnel slag aggregate is calculated. Based on the yield stress of the paste and the compaction density of submarine tunnel slag aggregate, the yield stress of mortar prepared with different slag aggregate dosages is predicted.

[0012] The ratio of the predicted yield stress of the mortar to the yield stress of the neat paste is taken as the relative yield stress, and the rheological properties of the mortar are adjusted and optimized according to the relative yield stress.

[0013] Furthermore, the physical property of the tunnel slag aggregate is the apparent density of the submarine tunnel slag aggregate; the basic data of the tight packing test at least includes the cross-sectional area of ​​the container in the aggregate tight packing test, the mass of the sample to be tested, and the compaction height of the sample to be tested after the test.

[0014] Furthermore, the kerosene displacement method was used to obtain the apparent density of the submarine tunnel slag aggregate, and the vibration compaction method was used to obtain the compaction height of the test sample after the test. An Anton Paar rheometer was used to measure the rheological curve of the net slurry using a step shear system, and the Herschel-Bulkley Model was used to fit the data to obtain the net slurry yield stress.

[0015] Furthermore, the vibration compaction method is used to obtain the compaction height of the test sample after the test, specifically including:

[0016] Mix the set mass of the test sample evenly and pour it evenly into the container. Fix the container on the vibration table, and then place the compacted object on the surface of the test sample in the container. After vibrating for a preset time, measure the height of the test sample in the container. The height at this time is the compacted height of the test sample after the test.

[0017] Furthermore, the rheological curve of the pure slurry was measured using an Anton Paar rheometer using a step shear system, and the Herschel-Bulkley Model was used to fit the data to obtain the pure slurry yield stress, which specifically includes:

[0018] An Anton Paar rheometer was used to set a step-like shear system. The data of the descending section was taken to fit the net slurry yield stress. The model fitting was based on the Herschel-Bulkley Model, whose mathematical form is:

[0019]

[0020] Among them, τ (Pa) is the shear stress, τ0 (Pa) is the yield stress, K (Pa·s) is the consistency, is the shear rate and n is a dimensionless exponent.

[0021] Further, the compacted bulk density of the sample to be tested is calculated based on the cross-sectional area of ​​the container, the mass of the sample to be tested, and the compacted height of the sample to be tested after the test;

[0022] Compacted bulk density refers to the packing density of solid particles in the container after the test sample is compacted.

[0023]

[0024] Among them, ρ d is the compacted bulk density, M is the mass of the test sample, A0 is the cross-sectional area of ​​the container, and h is the compacted height of the test sample after the test.

[0025] Furthermore, the compaction density of the submarine tunnel slag aggregate is calculated based on the compacted bulk density of the sample to be tested and the apparent density of the submarine tunnel slag aggregate;

[0026]

[0027] in, is the compactness of the submarine tunnel slag aggregate, ρ d is the compacted bulk density, ρ s is the apparent density of submarine tunnel slag aggregate.

[0028] Furthermore, the Chateau-Ovarlaz model was used to predict the yield stress of mortars prepared with different dosages of slag aggregate.

[0029]

[0030] in, is the yield stress of the suspension containing rigid particles, is the yield stress of the yield stress fluid in the suspension, is the volume fraction of rigid particles in the suspension, is the ratio of the elastic modulus of the suspension to the yield stress fluid;

[0031]

[0032] in, is the critical packing fraction at which the yield stress diverges, is the maximum packing density, that is, the maximum density of the slag aggregate in a tightly packed state, ranging from 0.6 to 0.8, and η is the dispersion coefficient of the suspension performance, usually around 0.8.

[0033] Furthermore, the relative yield stress of the mortar prepared by slag aggregate changes with the volume fraction, and the aggregate dosage is dynamically adjusted or the particle size distribution is optimized. The yield stress of the mortar with the new formula is re-predicted by the Chateau-Ovarlaz model and confirmed by rheometer measurement.

[0034] A second aspect of the present invention provides a system for predicting rheological parameters of mortar prepared from slag.

[0035] A rheological parameter prediction system for mortar prepared by slag slag, comprising:

[0036] an acquisition module configured to acquire physical properties of slag aggregate, basic data of close packing test, and net paste yield stress;

[0037] A prediction module is configured to calculate the compactness of the subsea tunnel slag aggregate based on its physical properties and basic data from the compactness test; and to predict the yield stress of mortar prepared with different slag aggregate dosages based on the yield stress of the slurry and the compactness of the subsea tunnel slag aggregate.

[0038] The adjustment module is configured to use the ratio of the predicted yield stress of the mortar to the yield stress of the neat paste as the relative yield stress, and adjust and optimize the rheological properties of the mortar according to the relative yield stress.

[0039] A third aspect of the present invention provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps of the method described in the first aspect of the present invention.

[0040] The fourth aspect of the present invention provides an electronic device, comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor implements the steps of the method described in the first aspect of the present invention when executing the program.

[0041] Compared with the prior art, the present invention has the following beneficial effects:

[0042] 1) By obtaining the apparent density, close packing density, and neat slurry yield stress of tunnel slag aggregate and combining it with the Chateau-Ovarlaz model, this method dynamically predicts and rapidly optimizes the rheological properties of mortar prepared from submarine tunnel slag aggregate, rather than relying on empirical trial and error. Based on the obtained relative yield stress, the aggregate dosage, water-cement ratio, or particle size distribution can be dynamically adjusted to ensure that the mortar has appropriate rheological properties (such as fluidity and segregation resistance) during pumping, pouring, or spraying, thereby reducing defects such as pipe blockage and honeycombing and improving structural density.

[0043] 2) The present invention studies the yield stress of the mortar, optimizes the relative yield stress of the mortar, predicts and optimizes the rheological properties of the mortar prepared with slag aggregate, and uses a model to predict the yield stress at different aggregate dosages, thereby quickly determining the maximum aggregate dosage that meets construction requirements, reducing the number of laboratory test mixes and shortening the mix optimization cycle (saving approximately 30% to 50% of time and material costs). BRIEF DESCRIPTION OF THE DRAWINGS

[0044] The accompanying drawings, which constitute a part of the present invention, are used to provide a further understanding of the present invention. The exemplary embodiments of the present invention and their descriptions are used to explain the present invention and do not constitute improper limitations on the present invention.

[0045] Figure 1 This is an overall block diagram of a method for predicting rheological parameters of mortar prepared from slag according to Example 1 of the present invention.

[0046] Figure 2 Schematic diagram of an aggregate vibration compaction test device according to a first embodiment of the present invention;

[0047] Figure 3 This is a comparison chart of the relative yield stress of mortars with different aggregate types as a function of volume fraction according to Example 1 of the present invention;

[0048] Figure 4 This is a comparison chart of the change in relative yield stress of the mortar with the relative volume content of sand in Example 1 of the present invention. DETAILED DESCRIPTION

[0049] The present invention will be further described below with reference to the accompanying drawings and embodiments.

[0050] It should be noted that the following detailed descriptions are exemplary and intended to provide further explanation of the present invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by those skilled in the art to which the present invention belongs.

[0051] Example 1

[0052] like Figure 1As shown, this embodiment provides a method for predicting the rheological parameters of mortar prepared from tunnel slag. Based on the calculation of the yield stress of the neat mortar at the corresponding water-cement ratio and the compactness of the densely packed aggregate of submarine tunnel slag, the relative yield stress of the mortar prepared from tunnel slag is optimized. By studying the change in the relative yield stress of the mortar prepared from tunnel slag aggregate as a function of volume fraction, a theoretical method is provided for predicting and optimizing the rheological properties of mortar prepared from submarine tunnel slag aggregate. The method specifically includes the following steps:

[0053] Step 1: Obtain the physical properties of the tunnel slag aggregate, basic data from the close packing test, and the yield stress of the slurry. The physical properties of the tunnel slag aggregate are the apparent density of the submarine tunnel slag aggregate. The basic data from the close packing test include at least the cross-sectional area of ​​the container used in the close packing test, the mass of the test sample, and the compaction height of the test sample after the test.

[0054] Furthermore, the apparent density of submarine tunnel slag aggregate was obtained by using the kerosene drainage method. The specific implementation steps refer to GB / T 14684-2011 "Construction Sand".

[0055] Furthermore, the vibration compaction method is used to obtain the compaction height of the test sample after the test, including:

[0056] Mix the set mass of the test sample evenly and pour it evenly into the container. Fix the container on the vibration table, and then place the compacted object on the surface of the test sample in the container. After vibrating for a preset time, measure the height of the test sample in the container. The height at this time is the compacted height of the test sample after the test.

[0057] Specifically, if Figure 2 The aggregate vibration compaction test device shown in the figure first mixes 7.5 kg of the test sample and evenly pours it into a rigid container with a diameter of 16 cm and a height of 32 cm, which is then fixed on a vibration table with a vibration frequency of 150 Hz. Then, a compactor with a diameter slightly smaller than 16 cm that can generate 1 kPa is placed on the sample inside the container. After vibrating for 60 seconds, the height of the sample inside the container is measured.

[0058] The reason for mixing the test sample thoroughly and then pouring it evenly into the container is that even pouring ensures a more uniform initial distribution of the sample within the container, avoiding areas of overcrowding or oversparseness. This allows for more uniform vibration across the entire sample during subsequent operations such as vibration compaction, ensuring a representative compaction state and more accurate and reliable data such as measured sample height, calculated volume, and ultimately, maximum bulk density.

[0059] Furthermore, the rheological curve of the pure slurry was measured using an Anton Paar rheometer through a step shear system, and the Herschel-Bulkley Model was used to fit the data to obtain the pure slurry yield stress.

[0060] The main reason for using the Anton Paar rheometer is that it is suitable for the precise measurement of complex fluids (such as mortars and pastes). For example, the Anton Paar rheometer uses an advanced torque measurement system and temperature control technology to accurately capture tiny stress changes at low shear rates, making it suitable for measuring key rheological parameters such as yield stress. In addition, materials such as pastes exhibit shear-thinning or shear-thickening behavior. The Anton Paar rheometer can cover the full range from low shear (near static) to high shear, ensuring data integrity. Furthermore, it supports oscillation mode and steady-state shear mode, which can distinguish between the elasticity, viscosity, and yield behavior of the material, avoiding errors caused by a single test method.

[0061] Specifically, an Anton Paar rheometer was used to set a stepped shear system. The descending section data was used to fit the slurry yield stress. The Herschel-Bulkley Model was used for model fitting. The Herschel-Bulkley Model is a model that describes the rheological properties of non-Newtonian fluids (such as mortar and slurry). Its mathematical form is:

[0062]

[0063] Among them, τ (Pa) is the shear stress, τ0 (Pa) is the yield stress, K (Pa·s) is the consistency, is the shear rate, and n is a dimensionless exponent. Here, τ0 represents the yield stress of the mortar or paste sample in this formula.

[0064] By fitting the descending segment of the shear stress-shear rate curve, we can avoid errors caused by material structural damage and directly reflect the static yield characteristics of the slurry. Specifically, during the ascending segment of a step-like shear regime (gradually increasing shear rate), the material structure may not be completely destroyed, and the measured yield stress may be too high. However, during the descending segment (gradually decreasing shear rate), the material is in a relatively stable state after structural damage due to high shear, and the measurement results at this stage better reflect the yield stress at the onset of actual flow.

[0065] Step 2: Based on the physical properties of the tunnel slag aggregate and the basic data from the tight packing test, the tight packing density of the submarine tunnel slag aggregate is calculated; based on the yield stress of the neat paste and the tight packing density of the submarine tunnel slag aggregate, the yield stress of the mortar prepared with different slag aggregate dosages is predicted.

[0066] Specifically, the compacted bulk density of the sample to be tested is calculated according to the cross-sectional area of ​​the container, the mass of the sample to be tested, and the compacted height of the sample to be tested after the test.

[0067] The compacted bulk density refers to the packing density of the solid particles in the container after the sample to be tested is compacted. The calculation formula is as follows:

[0068]

[0069] Among them, ρ d is the compacted bulk density (kg / cm 3 ), M is the mass of the sample to be tested (kg), A0 is the cross-sectional area of ​​the cylindrical container (cm 2 ), h is the compaction height of the test sample after the test (cm).

[0070] The compacted bulk density of the submarine tunnel slag aggregate is calculated based on the compacted bulk density of the sample to be tested and the apparent density of the submarine tunnel slag aggregate.

[0071]

[0072] in, is the compactness of the submarine tunnel slag aggregate, ρ d is the compacted bulk density, ρ s is the apparent density of submarine tunnel slag aggregate.

[0073] Step 3: Based on the yield stress of the slurry and the compactness of the subsea tunnel slag aggregate, predict the relative yield stress of the mortar prepared with different slag aggregate dosages. Adjust and optimize the rheological properties of the mortar based on the relative yield stress to complete the prediction of the rheological parameters of the mortar prepared with slag.

[0074] Specifically, the Chateau-Ovarlaz model is used to predict the yield stress of mortars prepared with different slag aggregate dosages. That is, the yield stress of the neat slurry, the close packing density, and the aggregate volume fraction are substituted into the Chateau-Ovarlaz model to calculate the yield stress of the mortar:

[0075]

[0076] in, is the yield stress (Pa) of the suspension containing rigid particles (mortar or concrete), is the yield stress of the yield stress fluid (cement paste) in the suspension (Pa), is the volume fraction of rigid particles (aggregate) in the suspension (mortar or concrete), is the ratio of the elastic modulus of the suspension to the yield stress fluid, The mathematical expression of is:

[0077]

[0078] in, is the critical packing fraction at which the yield stress diverges, is the maximum packing density, that is, the maximum density of the slag aggregate in a tightly packed state, ranging from 0.6 to 0.8, and η is the dispersion coefficient of the suspension performance, usually around 0.8.

[0079] The present invention can not only quickly calculate the yield stress of mortar under different aggregate dosages through the Chateau-Ovarlaz model, thereby achieving the prediction purpose, but also connect the neat paste and mortar through the model.

[0080] After determining the yield stress of the mortar using the Chateau-Ovarlaz model, the relationship between the mortar yield stress and parameters such as particle volume fraction and bulk density can be quantitatively analyzed, thereby guiding formulation design and process optimization. Specifically, the relative yield stress is calculated by dividing the mortar yield stress by the neat mortar yield stress. After calculating the relative yield stress of the mortar at different aggregate content, the yield stress of the newly formulated mortar is measured using a Viskomat PC rheometer and compared with the model prediction. To reduce the yield stress of pumped mortar, adjustments can be made to optimize aggregate grading, reduce the fines content (lowering the volume fraction), and so on. Finally, the yield stress predicted by the model for the new formulation is verified and confirmed by actual rheometer measurements.

[0081] The reason why the present invention uses relative yield stress is to eliminate interference from other factors and only focus on the influence of aggregate on rheology. Combined with construction requirements (such as pumping fluidity and anti-segregation), the optimal aggregate content is determined, and the water-cement ratio or aggregate gradation is directly optimized. For example, if the predicted yield stress is too high, it will cause pipe blockage or construction difficulties, and the aggregate content needs to be reduced or the water-cement ratio needs to be adjusted; if the predicted yield stress is too low, it will cause water seepage or segregation, and the aggregate content needs to be increased or the particle gradation needs to be optimized. The Viskomat PC rheometer is used to accurately measure the rheological parameters of mortar with high particle concentration to meet the testing requirements under complex working conditions; and the Chateau-Ovarlaz model is used to couple the net slurry yield stress with the aggregate stacking state, directly reflecting the influence mechanism of aggregate content on the mortar yield stress, and realizing rapid and accurate prediction of the mortar yield stress. In addition, the model introduces the suspension performance divergence coefficient (usually taking a value of about 0.8) to correct the deviation caused by factors such as particle shape and surface roughness, thereby improving the prediction accuracy.

[0082] The following is a detailed introduction taking the pure slurry as an example; the mortar mix ratio used is shown in Table 1.

[0083] Table 1 Mortar mix ratio for slag preparation

[0084] Volume fraction of aggregate (%) Cement (g) Water (g) Slag (g) River sand (g) 30 194.58 77.83 153.06 156.9 35 180.68 72.27 178.57 183.05 40 166.79 66.71 204.08 209.2 42 161.23 64.49 214.28 219.66 45 152.89 61.15 229.59 235.35 47 147.33 58.93 239.79 245.81

[0085] like Figure 3As shown in Figure 2, the relative yield stress of mortars with different aggregate types varies with volume fraction. Figure 3 It can be seen from the changes in the relative yield stress of the mortar prepared with cave slag and the mortar prepared with river sand at different aggregate dosages obtained through the experiment that the relative yield stress of the cave slag mortar is greater than that of the river sand mortar. The reason is that the morphology and gradation of the cave slag are more irregular, which affects the bulk density, and thus increases the yield stress compared with the river sand mortar.

[0086] like Figure 4 As shown in the figure, the change of relative yield stress of mortar with the relative volume content of sand (the ratio of sand volume content to its maximum bulk density) is compared. Figure 4 The middle curve is the prediction result of Chateau-Ovarlez model. Figure 4 It can be seen that the two different types of aggregate mortars, cavity slag mortar and river sand mortar, both showed a high degree of fit after Chateau-Ovarlaz model fitting, which shows that the model described in this application is suitable for calculating the yield stress of cavity slag mortar.

[0087] The present invention uses a Viskomat PC rheometer, which uses a motor to drive a stirring paddle (blade) to rotate in the sample, and measures the torque (N·m) and speed (rpm) during the rotation process. The yield stress of mortars prepared with different amounts of slag aggregate is obtained by fitting the intercept of the Herschel-Bulkley Model. The relationship between yield stress and volume fraction is fitted, and the Chateau-Ovarlaz model is verified to be applicable to the yield stress of mortars prepared with slag aggregate. This model is used to fit mortars with different amounts of slag and river sand to determine the parameter R. 2 The values ​​of the two coefficients are all greater than 0.9, which shows a good fitting result. Therefore, the Chateau-Ovarlaz model can be used to predict the rheological properties of mortar prepared with slag.

[0088] Rheometer tests were performed to determine the yield stress of mortars with varying aggregate content, and model fitting revealed a high degree of goodness of fit (R² > 0.9). Based on these predictions, the maximum amount of slag aggregate required to meet construction requirements can be quickly determined, reducing the number of laboratory trials (saving approximately 30% to 50% in time and material costs), and promoting the use of slag aggregate as a substitute for natural sand and gravel.

[0089] This embodiment uses a model to fit the yield stress of mortar prepared from slag and predicts it, providing scientific and efficient technical support for actual projects. This method quantifies the relationship between the compact packing effect of slag aggregate and the rheological properties of the slurry, establishing a predictive framework that can adapt to complex material properties, significantly reducing the time and resource consumption of traditional trial-and-error methods. Based on the model, engineers can quickly determine the optimal mix ratio for different aggregate dosages, avoiding problems such as pump blockage, spray rebound, or pouring segregation caused by uncontrolled rheological properties, directly improving construction quality and efficiency.

[0090] Example 2

[0091] This embodiment provides a system for predicting rheological parameters of mortar prepared by slag slag, including:

[0092] an acquisition module configured to acquire physical properties of slag aggregate, basic data of close packing test, and net paste yield stress;

[0093] A prediction module is configured to calculate the compactness of the subsea tunnel slag aggregate based on its physical properties and basic data from the compactness test; and to predict the yield stress of mortar prepared with different slag aggregate dosages based on the yield stress of the slurry and the compactness of the subsea tunnel slag aggregate.

[0094] The adjustment module is configured to use the ratio of the predicted yield stress of the mortar to the yield stress of the neat paste as the relative yield stress, and adjust and optimize the rheological properties of the mortar according to the relative yield stress.

[0095] The examples and application scenarios implemented by the above modules and corresponding steps are the same, but are not limited to the contents disclosed in the above embodiment 1. It should be noted that the above modules as part of the system can be executed in a computer system such as a set of computer executable instructions.

[0096] The descriptions of the various embodiments in the above embodiments have different focuses. For parts not described in detail in a certain embodiment, reference can be made to the relevant descriptions of other embodiments.

[0097] The proposed system can be implemented in other ways. For example, the system embodiment described above is merely illustrative. For example, the above module division is only a logical function division. In actual implementation, other division methods may be used. For example, multiple modules can be combined or integrated into another system, or some features can be ignored or not implemented.

[0098] Example 3

[0099] The purpose of this embodiment is to provide a computer-readable storage medium on which a computer program is stored. When the program is executed by a processor, the steps in the method described in the above embodiment 1 are implemented.

[0100] Example 4

[0101] The purpose of this embodiment is to provide an electronic device, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein when the processor executes the program, the steps in the method described in the first embodiment are implemented.

[0102] The steps involved in the systems of Examples 2, 3, and 4 above correspond to those of Example 1. For detailed implementation, please refer to the relevant description of Example 1. The term "computer-readable storage medium" should be understood to mean a single medium or multiple media that includes one or more instruction sets; it should also be understood to include any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and causing the processor to perform any of the methods of the present invention.

[0103] Those skilled in the art will appreciate that the modules or steps of the present invention described above can be implemented using a general-purpose computer device. Alternatively, they can be implemented using program code executable by a computing device, which can then be stored in a storage device and executed by the computing device. Alternatively, they can be fabricated into separate integrated circuit modules, or multiple modules or steps can be fabricated into a single integrated circuit module for implementation. The present invention is not limited to any specific combination of hardware and software.

[0104] Although the above describes the specific embodiments of the present invention in conjunction with the accompanying drawings, it is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art on the basis of the technical solution of the present invention without any creative work are still within the scope of protection of the present invention.

Claims

1. A method for predicting rheological parameters of mortar prepared by slag, characterized in that: include: Obtain the physical properties of slag aggregate, basic data of close packing test and the yield stress of net paste; Based on the physical properties of tunnel slag aggregate and basic data from the compaction test, the compaction density of submarine tunnel slag aggregate is calculated. Based on the yield stress of the paste and the compaction density of submarine tunnel slag aggregate, the yield stress of mortar prepared with different slag aggregate dosages is predicted. The ratio of the predicted yield stress of the mortar to the yield stress of the neat paste is taken as the relative yield stress, and the rheological properties of the mortar are adjusted and optimized according to the relative yield stress.

2. The method for predicting rheological parameters of mortar prepared by slag according to claim 1, characterized in that: The physical property of the tunnel slag aggregate is the apparent density of the submarine tunnel slag aggregate; the basic data of the close packing test at least includes the cross-sectional area of ​​the container in the aggregate close packing test, the mass of the sample to be tested, and the compaction height of the sample to be tested after the test.

3. The method for predicting rheological parameters of mortar prepared by slag according to claim 2, characterized in that: The apparent density of submarine tunnel slag aggregate was obtained using the kerosene displacement method, and the compaction height of the test sample after the test was obtained using the vibration compaction method. An Anton Paar rheometer was used to measure the rheological curve of the net slurry using a step shear system, and the Herschel-Bulkley model was used to fit the data to obtain the net slurry yield stress.

4. The method for predicting rheological parameters of mortar prepared by slag according to claim 2, characterized in that: Calculate the compacted bulk density of the sample to be tested based on the cross-sectional area of ​​the container, the mass of the sample to be tested, and the compacted height of the sample to be tested after the test; Among them, ρ d is the compacted bulk density, M is the mass of the test sample, A0 is the cross-sectional area of ​​the container, and h is the compacted height of the test sample after the test.

5. The method for predicting rheological parameters of mortar prepared by slag according to claim 4, characterized in that: Calculate the compaction density of submarine tunnel slag aggregate based on the compacted bulk density of the sample to be tested and the apparent density of the submarine tunnel slag aggregate; in, is the compactness of the submarine tunnel slag aggregate, ρ d is the compacted bulk density, ρ s is the apparent density of submarine tunnel slag aggregate.

6. The method for predicting rheological parameters of mortar prepared by slag according to claim 5, characterized in that: The Chateau-Ovarlaz model was used to predict the yield stress of mortars prepared with different dosages of slag aggregate. in, is the yield stress of the suspension containing rigid particles, is the yield stress of the yield stress fluid in the suspension, is the volume fraction of rigid particles in the suspension, is the ratio of the elastic modulus of the suspension to that of the yield stress fluid.

7. The method for predicting rheological parameters of mortar prepared by slag according to claim 1, characterized in that: The relative yield stress of the mortar prepared with slag aggregate changes with the volume fraction. The aggregate dosage is dynamically adjusted or the particle size distribution is optimized. The yield stress of the mortar with the new formula is re-predicted using the Chateau-Ovarlaz model and confirmed by actual measurement using a rheometer.

8. A system for predicting rheological parameters of mortar prepared from slag, characterized in that: include: an acquisition module configured to acquire physical properties of slag aggregate, basic data of close packing test, and net paste yield stress; A prediction module is configured to calculate the compactness of the subsea tunnel slag aggregate based on its physical properties and basic data from the compactness test; and to predict the yield stress of mortar prepared with different slag aggregate dosages based on the yield stress of the slurry and the compactness of the subsea tunnel slag aggregate. The adjustment module is configured to use the ratio of the predicted yield stress of the mortar to the yield stress of the neat paste as the relative yield stress, and adjust and optimize the rheological properties of the mortar according to the relative yield stress.

9. A computer-readable storage medium having a program stored thereon, characterized in that: When the program is executed by a processor, the steps in the method according to any one of claims 1 to 7 are implemented.

10. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, wherein: When the processor executes the program, the steps in the method according to any one of claims 1 to 7 are implemented.

Images