Geopolymer concrete pumpability analysis method, device, computer equipment and medium

Abstract

The invention relates to the technical field of concrete working performance detection, and discloses a geopolymer concrete pumpability analysis method, device, computer equipment and medium. The method provided by the invention can calculate the pipeline pressure difference based on the viscosity coefficient and yield stress of the concrete sample to be analyzed by using a pre-built first target model when performing geopolymer concrete pumpability analysis, calculate the slump and limit injection thickness of the concrete sample to be analyzed based on the pipeline pressure difference and the pre-built first correlation model and second correlation model, and determine the pumpability analysis result based on the calculated slump and limit injection thickness. The pumpability analysis of geopolymer concrete can be realized without performing a concrete pumpability analysis test, thereby solving the problem of high cost and long time consumption in the related art of performing pumpability analysis through a geopolymer concrete injection test.

Description

Technical Field

[0001] The invention relates to the technical field of concrete working performance detection, and in particular to a method, device, computer equipment and medium for analyzing the pumpability of geopolymer concrete. Background Art

[0002] Geopolymer is a green cementitious material. It does not require sintering during its preparation process, consumes low energy, can significantly reduce the emission of waste gas, waste water, waste residue, etc., has low environmental pollution, and can turn waste into treasure. It is expected to become an ideal cementitious material to replace cement, and has gradually attracted widespread attention from a large number of scholars. Geopolymer concrete has the characteristics of high early strength compared to ordinary cement-based concrete, and is particularly suitable for shotcrete. It has a wide range of application prospects in the fields of lining tunnels, membrane structures, and repair of existing structures.

[0003] Although geopolymer concrete has broad application prospects, the current basic research on this material cannot meet the actual needs of engineering. Existing research on the pumpability of geopolymer concrete is based on the analysis of a large amount of geopolymer concrete spraying test data, and the test process requires a long specimen preparation process and curing period, which has the disadvantages of high cost and long time consumption. Summary of the invention

[0004] In view of this, the present invention provides a method, device, computer equipment and medium for analyzing the pumpability of geopolymer concrete to solve the problems of high cost and long time consumption in the existing geopolymer concrete pumpability analysis method.

[0005] In a first aspect, the present invention provides a method for analyzing the pumpability of geopolymer concrete, the method comprising: obtaining the viscosity coefficient and yield stress of a concrete sample to be analyzed; substituting the viscosity coefficient and yield stress of the concrete sample to be analyzed into a first target model, calculating the pipeline pressure difference of the concrete sample to be analyzed in a pumping and spraying system, the first target model being used to characterize the correlation between the viscosity coefficient and yield stress of concrete in the pumping and spraying system and the pipeline pressure difference; substituting the pipeline pressure difference corresponding to the concrete sample to be analyzed into a first correlation model, calculating the slump of the concrete sample to be analyzed, the first correlation model being used to characterize the correlation between the slump of the concrete sample and the pipeline pressure difference; substituting the pipeline pressure difference corresponding to the concrete sample to be analyzed into a second correlation model, calculating the limit spraying thickness of the concrete sample to be analyzed, the second correlation model being used to characterize the correlation between the limit spraying thickness of the concrete sample and the pipeline pressure difference; determining the pumpability analysis result of the concrete sample to be analyzed based on the slump of the concrete sample to be analyzed and the limit spraying thickness of the concrete sample to be analyzed.

[0006] The pumpability analysis method of geopolymer concrete provided by the present invention comprises the following steps: substituting the viscosity coefficient and yield stress of a concrete sample to be analyzed into a first target model, and calculating the pipeline pressure difference of the concrete sample to be analyzed in a pumping and spraying system; the first target model is used to characterize the correlation between the viscosity coefficient and yield stress of concrete in the pumping and spraying system and the pipeline pressure difference; substituting the pipeline pressure difference into a first correlation model and a second correlation model respectively, and calculating the limit spraying thickness and slump of the concrete sample to be analyzed; the first correlation model is used to characterize the correlation between the slump of the concrete sample and the pipeline pressure difference; and the second correlation model is used to characterize the correlation between the limit spraying thickness of the concrete sample and the pipeline pressure difference; and obtaining the pumpability analysis result of the concrete sample to be analyzed based on the limit spraying thickness and slump of the concrete sample to be analyzed. The method provided by the present invention can calculate the pipeline pressure difference based on the viscosity coefficient and yield stress of the concrete sample to be analyzed by using a pre-constructed first target model when conducting a pumpability analysis of geopolymer concrete, calculate the slump and the limit injection thickness of the concrete sample to be analyzed based on the pipeline pressure difference and the pre-constructed first correlation model and the second correlation model, and determine the pumpability analysis result based on the calculated slump and the limit injection thickness. The pumpability analysis of geopolymer concrete can be realized without conducting a concrete pumpability analysis test, thereby solving the problem of high cost and long time consumption in the related art of conducting a pumpability analysis through a geopolymer concrete injection test.

[0007] In an optional embodiment, the first correlation model is constructed by the following steps: obtaining the slump of multiple concrete samples and the viscosity coefficient and yield stress of each concrete sample, the concrete samples are obtained by mixing concrete of a target material ratio with water, each concrete sample has a different water content from other concrete samples, and the slump of the multiple concrete samples is obtained by a preset slump experiment; substituting the viscosity coefficient and yield stress of each concrete sample into the first target model, and calculating the pipeline pressure difference of the corresponding concrete sample in the pumping and injection system; based on the pipeline pressure difference and slump of each concrete sample, determining the first correlation model between the slump and pipeline pressure difference of the concrete of the target material ratio.

[0008] The method provided by this optional implementation manner uses the slump of multiple concrete samples and the pipeline pressure difference to construct a first correlation model, so as to facilitate the subsequent calculation of the slump of the concrete samples using the pipeline pressure difference of the concrete samples and the first correlation model.

[0009] In an optional embodiment, the second correlation model is constructed by the following steps, including: obtaining the limit spraying thickness of multiple concrete samples, the limit spraying thickness of multiple concrete samples is obtained by a preset concrete spraying experiment; determining a third correlation model based on the slump and limit spraying thickness of each concrete sample, the third correlation model is used to characterize the correlation between the slump and limit spraying thickness of the target material mix concrete; determining the second correlation model based on the first correlation model and the third correlation model.

[0010] The method provided in this optional embodiment establishes a third correlation model based on the ultimate shotcrete thickness and slump of multiple concrete samples, and determines the second correlation model based on the third correlation model and the first correlation model, so as to facilitate the subsequent calculation of the ultimate shotcrete thickness of the concrete samples using the second correlation model and the pipeline pressure difference of the concrete samples.

[0011] In an optional implementation, the first target model is constructed by the following steps: obtaining internal parameter information of a concrete pipe of a pumping and spraying system and a Bingham fluid model; and constructing the first target model based on the Bingham fluid model and the internal parameter information of the concrete pipe.

[0012] The method provided in this optional implementation manner constructs a first target model, thereby facilitating subsequent calculation of the pipeline pressure difference of the concrete sample using the yield stress and viscosity coefficient of the concrete sample.

[0013] In an optional embodiment, the step of constructing a first target model based on a Bingham fluid model and internal parameter information of a concrete pipe includes: constructing a second target model using the Bingham fluid model and internal parameter information of the concrete pipe, the second target model being used to characterize the correlation between the yield stress and viscosity coefficient of the concrete sample and the flow velocity of the concrete sample in the concrete pipe; and constructing the first target model based on the second target model and the internal parameter information of the concrete pipe.

[0014] The method provided by this optional implementation mode,

[0015] In an optional implementation, the second target model is:

[0016]

[0017] in, represents the average flow velocity of the concrete sample in the concrete pipe, τ B represents the shear stress inside the concrete pipe, D represents the diameter of the concrete pipe, μ represents the viscosity coefficient of the concrete sample, τ 0 represents the yield stress of the concrete sample.

[0018] In an optional implementation, the first target model is:

[0019]

[0020] Among them, ΔP represents the pipeline pressure difference of the concrete pipe in the pumping spraying system, L represents the pipeline length of the concrete pipe, τ 0 represents the yield stress of the concrete sample, μ represents the viscosity coefficient of the concrete sample, It represents the average flow velocity of concrete sample in the concrete pipe.

[0021] In a second aspect, the present invention provides a device for analyzing the pumpability of geopolymer concrete, the device comprising: a first acquisition module, used to acquire the viscosity coefficient and yield stress of a concrete sample to be analyzed; a first calculation module, used to substitute the viscosity coefficient and yield stress of the concrete sample to be analyzed into a first target model, and calculate the pipeline pressure difference of the concrete sample to be analyzed in a pumping and spraying system, the first target model is used to characterize the correlation between the viscosity coefficient and yield stress of concrete in the pumping and spraying system and the pipeline pressure difference; a second calculation module, used to substitute the pipeline pressure difference corresponding to the concrete sample to be analyzed into a first correlation model, and calculate the slump of the concrete sample to be analyzed, the first correlation model is used to characterize the correlation between the slump of the concrete sample and the pipeline pressure difference; a third calculation module, used to substitute the pipeline pressure difference corresponding to the concrete sample to be analyzed into a second correlation model, and calculate the limit injection thickness of the concrete sample to be analyzed, the second correlation model is used to characterize the correlation between the limit injection thickness of the concrete sample and the pipeline pressure difference; a first determination module, used to determine the pumpability analysis result of the concrete sample to be analyzed based on the slump of the concrete sample to be analyzed and the limit injection thickness of the concrete sample to be analyzed.

[0022] In an optional embodiment, the first correlation model is constructed by the following steps: obtaining the slump of multiple concrete samples and the viscosity coefficient and yield stress of each concrete sample, the concrete samples are obtained by mixing concrete of a target material ratio with water, each concrete sample has a different water content from other concrete samples, and the slump of the multiple concrete samples is obtained by a preset slump experiment; substituting the viscosity coefficient and yield stress of each concrete sample into the first target model, and calculating the pipeline pressure difference of the corresponding concrete sample in the pumping and injection system; based on the pipeline pressure difference and slump of each concrete sample, determining the first correlation model between the slump and pipeline pressure difference of the concrete of the target material ratio.

[0023] In an optional embodiment, the second correlation model is constructed by the following steps, including: obtaining the limit spraying thickness of multiple concrete samples, the limit spraying thickness of multiple concrete samples is obtained by a preset concrete spraying experiment; determining a third correlation model based on the slump and limit spraying thickness of each concrete sample, the third correlation model is used to characterize the correlation between the slump and limit spraying thickness of the target material mix concrete; determining the second correlation model based on the first correlation model and the third correlation model.

[0024] In an optional implementation, the first target model is constructed by the following steps: obtaining internal parameter information of a concrete pipe of a pumping and spraying system and a Bingham fluid model; and constructing the first target model based on the Bingham fluid model and the internal parameter information of the concrete pipe.

[0025] In an optional embodiment, the step of constructing a first target model based on a Bingham fluid model and internal parameter information of a concrete pipe includes: constructing a second target model using the Bingham fluid model and internal parameter information of the concrete pipe, the second target model being used to characterize the correlation between the yield stress and viscosity coefficient of the concrete sample and the flow velocity of the concrete sample in the concrete pipe; and constructing the first target model based on the second target model and the internal parameter information of the concrete pipe.

[0026] In an optional implementation, the second target model is:

[0027]

[0028] in, represents the average flow velocity of the concrete sample in the concrete pipe, τ B represents the shear stress inside the concrete pipe, D represents the diameter of the concrete pipe, μ represents the viscosity coefficient of the concrete sample, τ 0 represents the yield stress of the concrete sample.

[0029] In a third aspect, the present invention provides a computer device, comprising: a memory and a processor, the memory and the processor being communicatively connected to each other, the memory storing computer instructions, and the processor executing the geopolymer concrete pumpability analysis method of the first aspect or any corresponding embodiment thereof by executing the computer instructions.

[0030] In a fourth aspect, the present invention provides a computer-readable storage medium having computer instructions stored thereon, the computer instructions being used to enable a computer to execute the geopolymer concrete pumpability analysis method of the first aspect or any corresponding embodiment thereof. BRIEF DESCRIPTION OF THE DRAWINGS

[0031] In order to more clearly illustrate the specific implementation methods of the present invention or the technical solutions in the prior art, the drawings required for use in the specific implementation methods or the description of the prior art will be briefly introduced below. Obviously, the drawings described below are some implementation methods of the present invention. For ordinary technicians in this field, other drawings can be obtained based on these drawings without paying creative work.

[0032] Figure 1 is a schematic flow chart of a method for analyzing pumpability of geopolymer concrete according to an embodiment of the present invention;

[0033] Figure 2 is a graph showing the flow velocity and shear stress distribution of geopolymer concrete in a pipe in a pumping and spraying system according to an embodiment of the present invention;

[0034] Figure 3 A schematic diagram of a specific example of a method for analyzing pumpability of geopolymer concrete according to an embodiment of the present invention;

[0035] Figure 4 is a structural block diagram of a geopolymer concrete pumpability analysis device according to an embodiment of the present invention;

[0036] Figure 5 It is a schematic diagram of the hardware structure of a computer device according to an embodiment of the present invention. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solution and advantages of the embodiments of the present invention clearer, the technical solution in the embodiments of the present invention will be clearly and completely described below in conjunction with the drawings in the embodiments of the present invention. Obviously, the described embodiments are part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative work are within the scope of protection of the present invention.

[0038] In the related art, the pumpability of concrete samples is generally analyzed through slump test and shotcrete test. However, this method requires a long specimen preparation process and curing period during the experiment, and has the disadvantages of high cost and long time consumption.

[0039] The embodiment of the present invention provides a geopolymer concrete pumpability analysis method, which can be applied to a processor to implement the pumpability analysis of geopolymer concrete. The method provided by the embodiment of the present application, when performing the pumpability analysis of geopolymer concrete, uses a pre-constructed first target model, calculates the pipeline pressure difference based on the viscosity coefficient and yield stress of the concrete sample to be analyzed, calculates the slump and the limit injection thickness of the concrete sample to be analyzed based on the pipeline pressure difference and the pre-constructed first correlation model and the second correlation model, and determines the pumpability analysis result based on the calculated slump and the limit injection thickness. The pumpability analysis of geopolymer concrete can be implemented without performing a concrete pumpability analysis test, which solves the problem of high cost and long time consumption in the related art of performing pumpability analysis through geopolymer concrete injection test.

[0040] According to an embodiment of the present invention, an embodiment of a method for analyzing the pumpability of geopolymer concrete is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer executable instructions, and although a logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that shown here.

[0041] In this embodiment, a method for analyzing the pumpability of geopolymer concrete is provided, which can be used in the above-mentioned processor. Figure 1 FIG. 4 is a flow chart of a method for analyzing pumpability of geopolymer concrete according to an embodiment of the present invention. Figure 1 As shown, the process includes the following steps:

[0042] Step S101, obtaining the viscosity coefficient and yield stress of the concrete sample to be analyzed.

[0043] Exemplarily, the concrete sample to be analyzed can be a concrete sample obtained by mixing concrete of a target material ratio with water. The concrete of the target material ratio can be concrete of any material ratio. The material ratio refers to the raw material ratio of the concrete. The raw materials include fly ash, slag, sand, etc. In the embodiment of the present application, the concrete of the target material ratio can include but is not limited to ordinary strength geopolymer concrete and high strength geopolymer concrete. The viscosity coefficient of the concrete to be analyzed can be measured by a viscosity coefficient measuring instrument, and the yield stress of the concrete to be analyzed can be measured by a yield strength testing machine.

[0044] Step S102, the viscosity coefficient and yield stress of the concrete sample to be analyzed are substituted into the first target model, and the pipeline pressure difference of the concrete sample to be analyzed in the pumping and spraying system is calculated. The first target model is used to characterize the correlation between the viscosity coefficient and yield stress of the concrete in the pumping and spraying system and the pipeline pressure difference.

[0045] For example, in the embodiment of the present application, the pressure of concrete in the pumping injection system can be called the pipeline pressure difference, and the concrete samples with different viscosity coefficients and yield stresses have different corresponding pipeline pressure differences. The first target model is used to characterize the correlation between the yield stress and viscosity coefficient of the concrete sample and its corresponding pipeline pressure difference. Substituting the yield stress and viscosity coefficient of the concrete sample to be analyzed into the first target model, the pipeline pressure difference of the concrete sample to be analyzed can be calculated.

[0046] Step S103, substituting the pipeline pressure difference corresponding to the concrete sample to be analyzed into the first correlation model to calculate the slump of the concrete sample to be analyzed. The first correlation model is used to characterize the correlation between the slump of the concrete sample and the pipeline pressure difference.

[0047] Exemplarily, in the embodiment of the present application, the first correlation model can be used to characterize the correlation between the pipeline pressure difference and the slump of different concrete samples with target material ratios, and the different concrete samples have the same material ratio and different water content. Substituting the pipeline pressure difference of the concrete sample to be analyzed into the first correlation model, the slump of the concrete sample to be analyzed can be calculated.

[0048] Step S104, the pipeline pressure difference corresponding to the concrete sample to be analyzed is substituted into the second correlation model to calculate the limit spraying thickness of the concrete sample to be analyzed. The second correlation model is used to characterize the correlation between the limit spraying thickness of the concrete sample and the pipeline pressure difference.

[0049] Exemplarily, in an embodiment of the present application, the second correlation model can be used to characterize the correlation between the pipeline pressure difference and slump of concrete samples with different target material ratios. By substituting the pipeline pressure difference of the concrete sample to be analyzed into the second correlation model, the limiting spraying thickness of the concrete sample to be analyzed can be calculated.

[0050] Step S105 , determining a pumpability analysis result of the concrete sample to be analyzed based on the slump of the concrete sample to be analyzed and the limit injection thickness of the concrete sample to be analyzed.

[0051] Exemplarily, the pumpability analysis is mainly to determine whether the concrete sample to be analyzed is easy to use during the construction process. The slump and limit shotcrete thickness of the concrete sample are parameters that can reflect whether the concrete sample is easy to use during the construction process. The pumpability analysis result can be determined based on the slump and limit shotcrete thickness of the concrete sample to be analyzed.

[0052] The geopolymer concrete pumpability analysis method provided in the present embodiment uses a pre-constructed first target model to calculate the pipeline pressure difference based on the viscosity coefficient and yield stress of the concrete sample to be analyzed when conducting the geopolymer concrete pumpability analysis. The slump and the ultimate shotcrete thickness of the concrete sample to be analyzed are calculated based on the pipeline pressure difference and the pre-constructed first correlation model and the second correlation model. The pumpability analysis result is determined based on the calculated slump and ultimate shotcrete thickness. The pumpability analysis of the geopolymer concrete can be achieved without conducting a concrete pumpability analysis test, thereby solving the problem of high cost and long time consumption in the related art of conducting pumpability analysis through geopolymer concrete shotcrete test.

[0053] In some optional implementations, the first correlation model is constructed by the following steps:

[0054] Step a1, obtaining the slump of multiple concrete samples and the viscosity coefficient and yield stress of each concrete sample. The concrete samples are obtained by mixing concrete with a target material ratio with water. The water content of each concrete sample is different from that of other concrete samples. The slump of the multiple concrete samples is obtained through a preset slump experiment.

[0055] Exemplarily, the target material ratio can be concrete of any material ratio, and multiple concrete samples can be obtained by mixing concrete of the target material ratio with water, and the water content of different concrete samples is different; the slump of the concrete sample can be measured by a preset slump test, and the preset slump test is an experiment to measure the slump of the concrete sample by a slump bucket. In the embodiment of the present application, 12 groups of slump tests can be performed using concrete samples of 12 target material ratios, and the slump of each concrete sample is determined based on the experimental results.

[0056] Step a2: Substitute the viscosity coefficient and yield stress of each concrete sample into the first target model to calculate the pipeline pressure difference of the corresponding concrete sample in the pumping and injection system.

[0057] Exemplarily, the viscosity coefficient and yield stress of each concrete sample are substituted into the first target model, and the pipeline pressure difference of the corresponding concrete sample can be calculated.

[0058] Step a3: determining a first correlation model between the slump of concrete with a target material mix and the pipeline pressure difference based on the pipeline pressure difference and the slump of each concrete sample.

[0059] Exemplarily, based on the pipeline pressure difference and slump of each concrete sample, the correlation between the pipeline pressure difference and the slump of the concrete sample of the target ratio is determined. In the embodiment of the present application, the correlation between the slump and the pipeline pressure difference of the concrete of the target material ratio is a linear correlation.

[0060] In some optional implementations, the second correlation model is constructed by the following steps, including:

[0061] Step b1, obtaining the limit spraying thickness of multiple concrete samples, wherein the limit spraying thickness of multiple concrete samples is obtained through a preset concrete spraying experiment.

[0062] Exemplarily, the limit spraying thickness of multiple concrete samples can be obtained through a preset concrete spraying experiment, in which the concrete samples are sent to the nozzle of a pumping spraying system, sprayed to a vertical formwork board by high-pressure air, and the limit cumulative construction thickness data is recorded, and the limit spraying thickness of each concrete sample is determined based on the recorded data. In the embodiment of the present application, 12 groups of spraying tests can be performed based on concrete samples with 12 target material ratios, and the limit spraying thickness of each concrete sample is determined based on the test results.

[0063] Step b2: determining a third correlation model based on the slump and the limit shotcrete thickness of each concrete sample, wherein the third correlation model is used to characterize the correlation between the slump and the limit shotcrete thickness of the target material mix.

[0064] Exemplarily, there is a certain correlation between the slump and the limit shotcrete thickness of the concrete paste. Based on the slump and the limit shotcrete thickness of each concrete sample and the slump, a third correlation model is determined to characterize the correlation between the slump and the limit shotcrete thickness of the target material ratio. In an embodiment of the present application, the correlation between the slump and the limit shotcrete thickness of the target material ratio is a linear correlation.

[0065] Step b3: determining a second correlation model based on the first correlation model and the third correlation model.

[0066] Exemplarily, the first correlation model represents the correlation between the slump of a concrete sample of a target material ratio and the pipeline pressure difference, the third correlation model represents the correlation between the limit shotcrete thickness of a concrete sample of a target material ratio and the slump, the correlation between the limit shotcrete thickness of a concrete sample of a target material ratio and the pipeline pressure difference can be determined based on the first correlation model and the third correlation model, and the second correlation model can be determined based on the correlation between the limit shotcrete thickness and the pipeline pressure difference.

[0067] In some optional implementations, the first target model is constructed by the following steps:

[0068] Step c1, obtaining internal parameter information of the concrete pipe of the pumping and spraying system and the Bingham fluid model.

[0069] For example, in the embodiment of the present application, the internal parameter information of the concrete pipe of the pumping spraying system may include but is not limited to the pipe length, pipe radius, and radius where the shear stress reaches the yield stress. The Bingham fluid model may be shown as follows (1):

[0070]

[0071] Where, τ represents shear stress (MPa); τ 0 represents yield stress (MPa); μ represents viscosity coefficient (MPa·s); du / dy represents velocity shear rate. Yield stress and viscosity coefficient are closely related to material ratio and setting time. The yield stress and viscosity coefficient of the same material ratio have a corresponding quantitative relationship with slump.

[0072] Step c2: constructing a first target model based on the Bingham fluid model and internal parameter information of the concrete pipe.

[0073] Exemplarily, based on the Bingham fluid model and the internal parameter information of the concrete pipe, the correlation between the pipeline pressure difference in the concrete pipe when the concrete sample is sprayed in the pumping injection system and the yield stress and viscosity coefficient of the concrete sample is determined, and the first target model is determined based on the correlation.

[0074] In some optional implementations, the above step c2 includes:

[0075] Step c21, constructing a second target model using the Bingham fluid model and the internal parameter information of the concrete pipe, wherein the second target model is used to characterize the correlation between the yield stress and viscosity coefficient of the concrete sample and the flow velocity of the concrete sample in the concrete pipe.

[0076] For example, in the embodiment of the present application, the distribution of the flow velocity and shear stress of the concrete sample in the geopolymer Bingham fluid concrete pipe is as follows: Figure 3 As shown, Figure 2 In the equation, r represents the distance from the center of the pipe, u represents the flow rate of the fluid when the distance from the center of the pipe is r, and R 0 represents the radius where the shear stress reaches the yield stress, R represents the pipe radius, τ represents the shear stress, τ 0 represents the yield stress, τ B represents the wall shear stress. The force balance equation of the cylindrical fluid microunit is shown in the following equation (2):

[0077] πr 2 ·ΔP=2πrL·τ (2)

[0078] Among them, r represents the distance from the center of the pipeline, ΔP is the pipeline pressure difference, τ represents the shear stress, and L represents the pipeline length.

[0079] From the above formula (2), we can know that the shear stress Wall shear stress Yield stress L represents the length of the pipeline, and R is the radius where the shear stress reaches the yield stress, that is,

[0080] Combining equations (1) and (2) yields the following relationships:

[0081]

[0082]

[0083] The velocity distribution of geopolymer Bingham pipe is shown in the following formula (5):

[0084]

[0085] The flow rate in the pipeline can be obtained by integrating the velocity distribution Then we can get the relationship shown in the following formula (6):

[0086]

[0087] Where Q represents the pipeline flow rate, and the meanings of the other parameters refer to the above equations (1) and (2).

[0088] According to the wall shear stress Then the above formula (6) can be transformed into the following formula (7):

[0089]

[0090] The average flow rate in the pipeline Substituting into the above formula (7), the second target model that characterizes the correlation between the yield stress and viscosity coefficient of the concrete sample and the flow velocity of the concrete sample in the concrete pipe can be obtained.

[0091] Step c22: constructing the first target model based on the second target model and the internal parameter information of the concrete pipe.

[0092] For example, the flow rate of the concrete sample in the concrete pipe may be determined based on the second target model, and then the first target model may be determined.

[0093] In some optional implementations, the second target model is:

[0094]

[0095] in, represents the average flow velocity of the concrete sample in the concrete pipe, τ Brepresents the shear stress inside the concrete pipe, D represents the diameter of the concrete pipe, μ represents the viscosity coefficient of the concrete sample, τ 0 represents the yield stress of the concrete sample.

[0096] In some optional implementations, the first target model is:

[0097]

[0098] Among them, ΔP represents the pipeline pressure difference of the concrete pipe in the pumping spraying system, L represents the pipeline length of the concrete pipe, τ 0 represents the yield stress of the concrete sample, μ represents the viscosity coefficient of the concrete sample, It represents the average flow velocity of concrete sample in the concrete pipe.

[0099] The geopolymer concrete pumpability analysis method provided in the present embodiment can calculate the pipeline pressure difference based on the viscosity coefficient and yield stress of the concrete sample to be analyzed by using the pre-constructed first target model when conducting the geopolymer concrete pumpability analysis. The slump and the ultimate shotcrete thickness of the concrete sample to be analyzed are calculated based on the pipeline pressure difference and the pre-constructed first correlation model and the second correlation model. The pumpability analysis result is determined based on the calculated slump and ultimate shotcrete thickness. The pumpability analysis of the geopolymer concrete can be realized without conducting a concrete pumpability analysis test, thereby solving the problem of high cost and long time consumption in the related art of conducting pumpability analysis through geopolymer concrete shotcrete test.

[0100] The specific contents of the oligomer concrete pumpability analysis method provided by the present invention are described below through a specific embodiment.

[0101] Example:

[0102] The applicability of the method provided by the embodiment of the present invention can be demonstrated by 12 groups of spraying tests with different slumps carried out on geopolymer concrete with two material ratios, respectively. The geopolymer concrete with two material ratios is ordinary strength geopolymer concrete and high strength geopolymer concrete. The spraying test steps are: (1) Fly ash, slag, sodium metasilicate pentahydrate powder, zero water sodium metasilicate particles and river sand are fully mixed and stirred in a mixer for 3 minutes; (2) Water is slowly added to the mixed dry materials and stirred for 5 minutes; (3) According to the specification ASTM C1437-2015, a slump test is carried out on the fresh slurry; (4) The geopolymer concrete slurry is pumped to the nozzle and sprayed to the vertical wooden board by high-pressure air; (5) The pump pressure at the pumping hose and the ultimate cumulative construction thickness are recorded. Through 12 groups of spraying tests with different slumps carried out on geopolymer concrete with two material ratios, the slump S and the ultimate spraying thickness of the geopolymer concrete with the two material ratios are obtained, as shown in Table 1 and Table 2.

[0103] Table 1: 12 test parameters of normal strength geopolymer concrete

[0104]

[0105] Table 2: 12 test parameters of high-strength geopolymer concrete

[0106]

[0107] According to the values ​​of yield stress and viscosity coefficient of geopolymer concrete with two material ratios, the correlation between pipeline pressure difference and slump of geopolymer concrete with the same material ratio can be fitted.

[0108] According to the values ​​of yield stress and viscosity coefficient of geopolymer concrete with two material ratios, the correlation between the two can be obtained. The correlation can be expressed as Figure 3 As shown in the figure, with the increase of viscosity coefficient, the yield stress tends to increase.

[0109] Based on the correlation between the pipeline pressure difference and slump of geopolymer concrete with the same material ratio and the correlation between the yield stress and viscosity coefficient of geopolymer concrete with the corresponding material ratio, the correlation between the pipeline pressure difference and the ultimate spraying thickness can be obtained.

[0110] In this embodiment, a device for analyzing the pumpability of geopolymer concrete is also provided, which is used to implement the above-mentioned embodiments and preferred embodiments, and the descriptions that have been made will not be repeated. As used below, the term "module" can be a combination of software and / or hardware that implements a predetermined function. Although the device described in the following embodiments is preferably implemented in software, the implementation of hardware, or a combination of software and hardware, is also possible and conceivable.

[0111] This embodiment provides a device for analyzing the pumpability of geopolymer concrete. Figure 4 As shown, including:

[0112] The first acquisition module 401 is used to obtain the viscosity coefficient and yield stress of the concrete sample to be analyzed;

[0113] A first calculation module 402 is used to substitute the viscosity coefficient and yield stress of the concrete sample to be analyzed into the first target model to calculate the pipeline pressure difference of the concrete sample to be analyzed in the pumping and spraying system. The first target model is used to characterize the correlation between the viscosity coefficient and yield stress of the concrete in the pumping and spraying system and the pipeline pressure difference;

[0114] The second calculation module 403 is used to substitute the pipeline pressure difference corresponding to the concrete sample to be analyzed into the first correlation model to calculate the slump of the concrete sample to be analyzed. The first correlation model is used to characterize the correlation between the slump of the concrete sample and the pipeline pressure difference;

[0115] The third calculation module 404 is used to substitute the pipeline pressure difference corresponding to the concrete sample to be analyzed into the second correlation model to calculate the limit injection thickness of the concrete sample to be analyzed, and the second correlation model is used to characterize the correlation between the limit injection thickness of the concrete sample and the pipeline pressure difference;

[0116] The first determination module 405 is used to determine the pumpability analysis result of the concrete sample to be analyzed based on the slump of the concrete sample to be analyzed and the limit injection thickness of the concrete sample to be analyzed.

[0117] In some optional implementations, the first correlation model is constructed by the following steps:

[0118] Obtaining the slump of multiple concrete samples and the viscosity coefficient and yield stress of each concrete sample, the concrete samples are obtained by mixing concrete of a target material ratio with water, each concrete sample has a different water content from other concrete samples, and the slump of the multiple concrete samples is obtained through a preset slump experiment;

[0119] Substituting the viscosity coefficient and yield stress of each concrete sample into the first target model, the pipeline pressure difference of the corresponding concrete sample in the pumping and spraying system is calculated;

[0120] Based on the pipe pressure difference and the slump of each concrete sample, a first correlation model between the slump of concrete of the target material mix ratio and the pipe pressure difference is determined.

[0121] In some optional implementations, the second correlation model is constructed by the following steps, including:

[0122] Obtaining the limit spraying thickness of multiple concrete samples, wherein the limit spraying thickness of multiple concrete samples is obtained through a preset concrete spraying experiment;

[0123] A third correlation model is determined based on the slump and the limit shotcrete thickness of each concrete sample, and the third correlation model is used to characterize the correlation between the slump and the limit shotcrete thickness of the target material mix;

[0124] A second correlation model is determined based on the first correlation model and the third correlation model.

[0125] In some optional implementations, the first target model is constructed by the following steps:

[0126] Obtain the internal parameter information of the concrete pipe of the pumping and spraying system and the Bingham fluid model;

[0127] The first target model is constructed based on the Bingham fluid model and the internal parameter information of the concrete pipe.

[0128] In some optional implementations, the step of constructing the first target model based on the Bingham fluid model and the internal parameter information of the concrete pipe includes:

[0129] The second target model is constructed by using the Bingham fluid model and the internal parameter information of the concrete pipe. The second target model is used to characterize the correlation between the yield stress and viscosity coefficient of the concrete sample and the flow velocity of the concrete sample in the concrete pipe.

[0130] The first target model is constructed based on the second target model and the internal parameter information of the concrete pipe.

[0131] In some optional implementations, the second target model is:

[0132]

[0133] in, represents the average flow velocity of the concrete sample in the concrete pipe, τ B represents the shear stress inside the concrete pipe, D represents the diameter of the concrete pipe, μ represents the viscosity coefficient of the concrete sample, τ 0 represents the yield stress of the concrete sample.

[0134] In some optional implementations, the first target model is:

[0135]

[0136] Among them, ΔP represents the pipeline pressure difference of the concrete pipe in the pumping spraying system, L represents the pipeline length of the concrete pipe, τ 0 represents the yield stress of the concrete sample, μ represents the viscosity coefficient of the concrete sample, It represents the average flow velocity of concrete sample in the concrete pipe.

[0137] The further functional description of each of the above modules and units is the same as that of the above corresponding embodiments and will not be repeated here.

[0138] The geopolymer concrete pumpability analysis device in this embodiment is presented in the form of a functional unit, where the unit refers to an ASIC (Application Specific Integrated Circuit) circuit, a processor and memory that executes one or more software or fixed programs, and / or other devices that can provide the above functions.

[0139] The embodiment of the present invention also provides a computer device having the above Figure 4 The pumpability analysis device of geopolymer concrete is shown.

[0140] See also Figure 5 , Figure 5 is a schematic diagram of the structure of a computer device provided by an optional embodiment of the present invention, such as Figure 5 As shown, the computer device includes: one or more processors 10, a memory 20, and interfaces for connecting various components, including high-speed interfaces and low-speed interfaces. Various components are connected to each other using different buses for communication, and can be installed on a common mainboard or installed in other ways as needed. The processor can process the instructions executed in the computer device, including instructions stored in or on the memory to display the graphical information of the GUI on an external input / output device (such as, a display device coupled to the interface). In some optional embodiments, if necessary, multiple processors and / or multiple buses can be used together with multiple memories and multiple memories. Similarly, multiple computer devices can be connected, and each device provides some necessary operations (for example, as a server array, a group of blade servers, or a multi-processor system). Figure 5 A processor 10 is taken as an example.

[0141] The processor 10 may be a central processing unit, a network processor or a combination thereof. The processor 10 may further include a hardware chip. The hardware chip may be a dedicated integrated circuit, a programmable logic device or a combination thereof. The programmable logic device may be a complex programmable logic device, a field programmable gate array, a general purpose array logic or any combination thereof.

[0142] The memory 20 stores instructions executable by at least one processor 10, so that at least one processor 10 executes the method shown in the above embodiment.

[0143] The memory 20 may include a program storage area and a data storage area, wherein the program storage area may store an operating system, an application required for at least one function; the data storage area may store data created according to the use of the computer device, etc. In addition, the memory 20 may include a high-speed random access memory, and may also include a non-transient memory, such as at least one disk storage device, a flash memory device, or other non-transient solid-state storage device. In some optional embodiments, the memory 20 may optionally include a memory remotely arranged relative to the processor 10, and these remote memories may be connected to the computer device via a network. Examples of the above-mentioned network include, but are not limited to, the Internet, an intranet, a local area network, a mobile communication network, and combinations thereof.

[0144] The memory 20 may include a volatile memory, such as a random access memory; the memory may also include a non-volatile memory, such as a flash memory, a hard disk or a solid state drive; the memory 20 may also include a combination of the above types of memory.

[0145] The computer device further comprises a communication interface 30 for the computer device to communicate with other devices or a communication network.

[0146] The embodiment of the present invention also provides a computer-readable storage medium. The method according to the embodiment of the present invention can be implemented in hardware, firmware, or can be implemented as a computer code that can be recorded in a storage medium, or can be implemented as a computer code that is originally stored in a remote storage medium or a non-temporary machine-readable storage medium and will be stored in a local storage medium through a network download, so that the method described herein can be stored in such software processing on a storage medium using a general-purpose computer, a dedicated processor, or programmable or dedicated hardware. Among them, the storage medium can be a magnetic disk, an optical disk, a read-only storage memory, a random access memory, a flash memory, a hard disk or a solid-state hard disk, etc.; further, the storage medium can also include a combination of the above types of memories. It can be understood that a computer, a processor, a microprocessor controller, or programmable hardware includes a storage component that can store or receive software or computer code. When the software or computer code is accessed and executed by a computer, a processor, or hardware, the method shown in the above embodiment is implemented.

[0147] Although the embodiments of the present invention have been described in conjunction with the accompanying drawings, those skilled in the art may make various modifications and variations without departing from the spirit and scope of the present invention, and such modifications and variations are all within the scope defined by the appended claims.

Claims

1. A method for analyzing the pumpability of geopolymer concrete, characterized in that: The method comprises: Obtain the viscosity coefficient and yield stress of the concrete sample to be analyzed; Substituting the viscosity coefficient and yield stress of the concrete sample to be analyzed into the first target model, and calculating the pipeline pressure difference of the concrete sample to be analyzed in the pumping and spraying system, wherein the first target model is used to characterize the correlation between the viscosity coefficient and yield stress of the concrete in the pumping and spraying system and the pipeline pressure difference; Substituting the pipeline pressure difference corresponding to the concrete sample to be analyzed into a first correlation model to calculate the slump of the concrete sample to be analyzed, wherein the first correlation model is used to characterize the correlation between the slump of the concrete sample and the pipeline pressure difference; Substituting the pipeline pressure difference corresponding to the concrete sample to be analyzed into a second correlation model to calculate the limit injection thickness of the concrete sample to be analyzed, wherein the second correlation model is used to characterize the correlation between the limit injection thickness of the concrete sample and the pipeline pressure difference; Determine a pumpability analysis result of the concrete sample to be analyzed based on the slump of the concrete sample to be analyzed and the limit injection thickness of the concrete sample to be analyzed; The first target model is constructed by the following steps: Obtain the internal parameter information of the concrete pipe of the pumping and spraying system and the Bingham fluid model; Constructing the first target model based on the Bingham fluid model and the internal parameter information of the concrete pipe; The step of constructing the first target model based on the Bingham fluid model and the internal parameter information of the concrete pipe includes: A second target model is constructed using the Bingham fluid model and the internal parameter information of the concrete pipe, wherein the second target model is used to characterize the correlation between the yield stress and viscosity coefficient of the concrete sample and the flow velocity of the concrete sample in the concrete pipe; constructing the first target model based on the second target model and the internal parameter information of the concrete pipe; The second target model is: in, represents the average flow velocity of the concrete sample in the concrete pipe, τ B represents the shear stress inside the concrete pipe, D represents the diameter of the concrete pipe, μ represents the viscosity coefficient of the concrete sample, and τ0 represents the yield stress of the concrete sample; The first target model is: Wherein, ΔP represents the pipeline pressure difference of the concrete pipe in the pumping injection system, L represents the pipeline length of the concrete pipe, τ0 represents the yield stress of the concrete sample, D represents the diameter of the concrete pipe, μ represents the viscosity coefficient of the concrete sample, It represents the average flow velocity of concrete sample in the concrete pipe.

2. The method according to claim 1, characterized in that The first correlation model is constructed by the following steps: Obtaining the slump of multiple concrete samples and the viscosity coefficient and yield stress of each concrete sample, wherein the concrete samples are obtained by mixing concrete of a target material ratio with water, each concrete sample has a different water content from other concrete samples, and the slump of the multiple concrete samples is obtained through a preset slump experiment; Substituting the viscosity coefficient and yield stress of each concrete sample into the first target model, and calculating the pipeline pressure difference of the corresponding concrete sample in the pumping and spraying system; Based on the pipe pressure difference and the slump of each concrete sample, a first correlation model between the slump of concrete of the target material mix ratio and the pipe pressure difference is determined.

3. The method according to claim 2, characterized in that The second correlation model is constructed by the following steps, including: Obtaining the limit spraying thickness of the plurality of concrete samples, wherein the limit spraying thickness of the plurality of concrete samples is obtained through a preset concrete spraying experiment; Determine a third correlation model based on the slump and the limit shotcrete thickness of each concrete sample, wherein the third correlation model is used to characterize the correlation between the slump and the limit shotcrete thickness of the target material mix; The second correlation model is determined based on the first correlation model and the third correlation model.

4. A geopolymer concrete pumpability analysis device, characterized in that: The device comprises: A first acquisition module is used to obtain the viscosity coefficient and yield stress of the concrete sample to be analyzed; a first calculation module, for substituting the viscosity coefficient and yield stress of the concrete sample to be analyzed into a first target model, and calculating the pipeline pressure difference of the concrete sample to be analyzed in the pumping and spraying system, wherein the first target model is used to characterize the correlation between the viscosity coefficient and yield stress of the concrete in the pumping and spraying system and the pipeline pressure difference; A second calculation module is used to substitute the pipeline pressure difference corresponding to the concrete sample to be analyzed into the first correlation model to calculate the slump of the concrete sample to be analyzed, wherein the first correlation model is used to characterize the correlation between the slump of the concrete sample and the pipeline pressure difference; a third calculation module, for substituting the pipeline pressure difference corresponding to the concrete sample to be analyzed into a second correlation model to calculate the limit spraying thickness of the concrete sample to be analyzed, wherein the second correlation model is used to characterize the correlation between the limit spraying thickness of the concrete sample and the pipeline pressure difference; A first determination module, configured to determine a pumpability analysis result of the concrete sample to be analyzed based on the slump of the concrete sample to be analyzed and the limit injection thickness of the concrete sample to be analyzed; The first target model is constructed by the following steps: Obtain the internal parameter information of the concrete pipe of the pumping and spraying system and the Bingham fluid model; Constructing the first target model based on the Bingham fluid model and the internal parameter information of the concrete pipe; The step of constructing the first target model based on the Bingham fluid model and the internal parameter information of the concrete pipe includes: A second target model is constructed using the Bingham fluid model and the internal parameter information of the concrete pipe, wherein the second target model is used to characterize the correlation between the yield stress and viscosity coefficient of the concrete sample and the flow velocity of the concrete sample in the concrete pipe; constructing the first target model based on the second target model and the internal parameter information of the concrete pipe; The second target model is: in, represents the average flow velocity of the concrete sample in the concrete pipe, τ B represents the shear stress inside the concrete pipe, D represents the diameter of the concrete pipe, μ represents the viscosity coefficient of the concrete sample, and τ0 represents the yield stress of the concrete sample; The first target model is: Wherein, ΔP represents the pipeline pressure difference of the concrete pipe in the pumping injection system, L represents the pipeline length of the concrete pipe, τ0 represents the yield stress of the concrete sample, D represents the diameter of the concrete pipe, μ represents the viscosity coefficient of the concrete sample, It represents the average flow velocity of concrete sample in the concrete pipe.

5. The device according to claim 4, characterized in that The first correlation model is constructed by the following steps: Obtaining the slump of multiple concrete samples and the viscosity coefficient and yield stress of each concrete sample, wherein the concrete samples are obtained by mixing concrete of a target material ratio with water, each concrete sample has a different water content from other concrete samples, and the slump of the multiple concrete samples is obtained through a preset slump experiment; Substituting the viscosity coefficient and yield stress of each concrete sample into the first target model, and calculating the pipeline pressure difference of the corresponding concrete sample in the pumping and spraying system; Based on the pipe pressure difference and the slump of each concrete sample, a first correlation model between the slump of concrete of the target material mix ratio and the pipe pressure difference is determined.

6. The device according to claim 5, characterized in that The second correlation model is constructed by the following steps, including: Obtaining the limit spraying thickness of the plurality of concrete samples, wherein the limit spraying thickness of the plurality of concrete samples is obtained through a preset concrete spraying experiment; Determine a third correlation model based on the slump and the limit shotcrete thickness of each concrete sample, wherein the third correlation model is used to characterize the correlation between the slump and the limit shotcrete thickness of the target material mix; The second correlation model is determined based on the first correlation model and the third correlation model.

7. A computer device, characterized in that: include: A memory and a processor, wherein the memory and the processor are communicatively connected to each other, the memory stores computer instructions, and the processor executes the geopolymer concrete pumpability analysis method according to any one of claims 1 to 3 by executing the computer instructions.

8. A computer-readable storage medium, characterized in that: The computer-readable storage medium stores computer instructions, and the computer instructions are used to enable a computer to execute the geopolymer concrete pumpability analysis method according to any one of claims 1 to 3.

Images