Green high-strength polymer pervious concrete mix proportion design method

Abstract

The present application relates to pervious concrete technical field, especially to a kind of green high-strength geopolymer pervious concrete mix design method, to solve the problem of lack of comprehensive and reasonable geopolymer pervious concrete mix design method in the prior art, the method comprises: selecting coarse aggregate particle size, water-binder ratio and the initial selection range of design porosity, select multiple water-binder ratio and design porosity in initial selection range, and then combined to form multiple set mix proportion;According to set mix proportion, prepare multiple test blocks and carry out performance test to obtain performance test data set;Utilize performance test data set to construct the regression model of water permeability and compressive strength;Utilize sparrow search algorithm and BP neural network to construct compressive strength prediction model, and determine optimal design porosity in combination with regression model;Under optimal design porosity, adjust coarse aggregate particle size and water-binder ratio, and re-produce test block to test to obtain optimal coarse aggregate particle size and optimal water-binder ratio, and then obtain optimal mix proportion.

Description

Technical Field

[0001] This invention relates to the field of permeable concrete technology, and in particular to a method for designing the mix proportions of green, high-strength, geopolymer permeable concrete. Background Technology

[0002] Permeable concrete is an eco-friendly type of concrete with numerous advantages such as permeability, water retention, noise reduction, and water purification, and has been widely used in sidewalks, parking lots, and various light-duty roads. Currently, ordinary Portland cement is commonly used as the cementitious material for permeable concrete, but the relatively low strength of permeable concrete prepared from it greatly restricts its application and development. Geopolymers prepared from industrial solid waste have better mechanical properties and erosion resistance than cement, and the materials are widely available and environmentally friendly. Red mud, an industrial solid waste, contains some free alkaline substances, which can act as alkali activators to enhance the strength of slag. Furthermore, red mud contains a large amount of oxides such as silicon and aluminum. Therefore, using red mud slag geopolymers as a cementitious material to replace cement in the preparation of permeable concrete can improve the overall performance of permeable concrete.

[0003] In the concrete production process, mix design is the most important and core aspect, directly affecting the performance and quality of the concrete. However, due to the lack of a unified understanding of the hydration and hardening mechanisms of geopolymers and the absence of matching chemical admixtures, it is impossible to achieve consistent paste workability by adding admixtures. Consequently, it is impossible to directly control the design porosity and water-cement ratio. Therefore, traditional concrete mix design methods are not applicable to geopolymer permeable concrete. Furthermore, geopolymers based on industrial solid waste have many components, and the mix design methods for geopolymer permeable concrete prepared from different industrial solid wastes also vary. Thus, even if existing permeable concrete mix design methods are reasonable, they may not be entirely suitable for application to red mud slag geopolymers. Current research on red mud slag geopolymer permeable concrete directly uses fixed design porosity and water-cement ratio, assuming no interaction between the design porosity and water-cement ratio. This is completely inconsistent with reality and is somewhat crude and unstandardized in terms of scientific research methodology. Furthermore, existing technologies focus primarily on water purification, with a main emphasis on the adsorption performance of heavy metals. However, they do not adequately consider the interplay between factors such as design porosity and water-cement ratio during mix design, failing to provide a comprehensive and reasonable mix design method for permeable concrete. Consequently, the theoretical methods for mix design of red mud slag geopolymer permeable concrete lag far behind engineering practice, thus limiting its widespread application and failing to meet the application requirements of new high-strength geopolymer permeable concrete in various scenarios. Summary of the Invention

[0004] To address the shortcomings of existing technologies, this invention provides a method for designing the mix proportions of green, high-strength, polymer-modified permeable concrete.

[0005] To achieve the above objectives, this invention provides a method for designing the mix proportion of green high-strength geopolymer permeable concrete. The method includes the following steps: selecting an initial range for coarse aggregate particle size, water-cement ratio, and design porosity; selecting multiple water-cement ratios and design porosities within the initial range, and then combining them to form multiple sets of predetermined mix proportions; calculating the mass of each component in a unit volume of geopolymer permeable concrete under each predetermined mix proportion, and then preparing multiple geopolymer permeable concrete specimens; conducting permeability and compressive strength tests on the geopolymer permeable concrete specimens to obtain a performance test dataset; constructing a regression model for the permeability and compressive strength of the geopolymer permeable concrete specimens using the performance test dataset; based on the mass of each component and the performance test dataset, constructing a compressive strength prediction model using a sparrow search algorithm and a backpropagation neural network, and then combining the regression model to determine the optimal design porosity; adjusting the coarse aggregate particle size and water-cement ratio under the optimal design porosity, and remaking the geopolymer permeable concrete specimens for performance testing to obtain the optimal coarse aggregate particle size and optimal water-cement ratio, thereby determining the optimal mix proportion.

[0006] Optionally, the step of calculating the mass of each component in a unit volume of geopolymer permeable concrete under the set mix proportion for each group, and then preparing multiple geopolymer permeable concrete test blocks, includes the following steps:

[0007] Calculate the amount of coarse aggregate per unit volume of polymer permeable concrete for each set of mix proportions;

[0008] The volume of the cementitious material slurry is calculated based on the designed porosity, and then the mass of each component of the cementitious material slurry per unit volume of polymer permeable concrete under each set mix ratio is determined.

[0009] Multiple geopolymer permeable concrete test blocks were prepared based on the amount of coarse aggregate and the mass of each component.

[0010] Optionally, the volume of the cementitious material slurry is the total volume of distilled water, alkali activator, and cementitious material slurry.

[0011] Optionally, the cementitious slurry includes red mud, slag, distilled water and water glass, and the mass ratio of red mud to slag is 3:7, and the mass of silica in the water glass accounts for 5% of the mass of the cementitious slurry.

[0012] Optionally, the alkaline activator is a mixed solution of water glass, sodium hydroxide, and distilled water.

[0013] Optionally, the step of preparing multiple geopolymer permeable concrete test blocks based on the amount of coarse aggregate and the mass of each component includes the following steps:

[0014] Based on the amount of coarse aggregate and the mass of each component, coarse aggregate, red mud, slag, distilled water and water glass are mixed and stirred thoroughly using the cement-coating method to obtain geopolymer permeable concrete slurry;

[0015] Based on the mass of the geopolymer permeable concrete slurry required for a single geopolymer permeable concrete specimen under different mix proportions, the corresponding mass of the geopolymer permeable concrete slurry is weighed and molded to prepare multiple geopolymer permeable concrete specimens.

[0016] Furthermore, controlling the quality of the geopolymer permeable concrete slurry during the molding process resulted in lower strength dispersion of the prepared geopolymer permeable concrete specimens, providing a foundation for obtaining accurate and reliable performance test datasets.

[0017] Optionally, the volume of the cementitious material slurry satisfies the following relationship:

[0018]

[0019] Among them, V p The volume of the cementitious material slurry is given by α, where α is a correction factor for the amount of coarse aggregate. ρ is the compact packing density of coarse aggregate. g R represents the apparent density of coarse aggregate. void M is the designed porosity. R M K M W and M j These represent the masses of red mud, slag, distilled water, and water glass in the cementitious slurry per unit volume of polymer permeable concrete, ρ. R For, ρ K For, ρ W and ρ j The apparent densities are red mud, slag, distilled water, and water glass, respectively.

[0020] Furthermore, sodium hydroxide was not considered when calculating the volume of the cementitious material slurry in order to simplify the calculation and improve calculation efficiency.

[0021] Optionally, the regression model satisfies the following relationship:

[0022]

[0023] Wherein, Str is the compressive strength of the geopolymer permeable concrete specimen, T is the permeability of the geopolymer permeable concrete specimen, and d, b, and c are coefficients.

[0024] Optionally, the step of constructing a compressive strength prediction model based on the mass of each component and the performance test dataset, using a sparrow search algorithm and a BP neural network, and then combining the regression model to determine the optimal design porosity includes the following steps:

[0025] The initial weights and thresholds of the BP neural network are used as inputs to the sparrow search algorithm to optimize the weights and thresholds of the BP neural network. The BP neural network is then trained using the optimization results and the performance test dataset to obtain the compressive strength prediction model.

[0026] The compressive strength of the geopolymer permeable concrete specimen is optimized using the aforementioned compressive strength prediction model, and the permeability is calculated using the regression model based on the optimization results, thereby obtaining the optimal design porosity.

[0027] Furthermore, the excellent global search capability of the sparrow search algorithm is used to optimize the weights and thresholds of the BP neural network, so as to solve the problems that the BP neural network is prone to getting trapped in local minima, the randomness of the output results and the slow convergence speed, thereby improving the accuracy and reliability of the compressive strength prediction model.

[0028] Optionally, the step of adjusting the coarse aggregate particle size and the water-cement ratio under the optimal design porosity, and remaking the geopolymer permeable concrete test blocks for performance testing to obtain the optimal coarse aggregate particle size and the optimal water-cement ratio, thereby determining the optimal mix proportion, includes the following steps:

[0029] While keeping the optimal design porosity constant, design the coarse aggregate particle size gradient and its corresponding water-cement ratio gradient to obtain multiple sets of verification mix proportions.

[0030] The content of each component in the unit volume of the geopolymer permeable concrete of each group of the verification mix proportions was calculated, and then multiple geopolymer permeable concrete test blocks were re-prepared.

[0031] The permeability and compressive strength of the remade geopolymer permeable concrete specimens were tested, and the coarse aggregate particle size and water-cement ratio corresponding to the remade geopolymer permeable concrete specimen with the best comprehensive performance were selected as the optimal coarse aggregate particle size and optimal water-cement ratio.

[0032] The optimal water-cement ratio, the optimal design porosity, and the optimal coarse aggregate particle size are determined as the optimal mix proportion.

[0033] Furthermore, by designing gradient coarse aggregate particle sizes and corresponding gradient water-cement ratios, geopolymer permeable concretes with different coarse aggregate particle sizes can achieve optimal mixing states under the same designed porosity, providing a foundation for obtaining accurate and reliable optimal coarse aggregate particle size and optimal water-cement ratio. Attached Figure Description

[0034] To more clearly illustrate the technical solutions of the embodiments of this application, the accompanying drawings used in the embodiments will be briefly introduced below. It should be understood that the following drawings only show some embodiments of this application and should not be regarded as a limitation of the scope. For those skilled in the art, other related drawings can be obtained based on these drawings without creative effort.

[0035] Figure 1 This is a flowchart illustrating a green, high-strength geopolymer permeable concrete mix design method according to an embodiment of the present invention. Detailed Implementation

[0036] Specific embodiments of the present invention will now be described in detail. It should be noted that the embodiments described herein are for illustrative purposes only and are not intended to limit the invention. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the invention. However, it will be apparent to those skilled in the art that these specific details are not necessary to practice the invention. In other instances, well-known circuits, software, or methods have not been specifically described to avoid obscuring the invention.

[0037] Throughout this specification, references to "an embodiment," "an embodiment," "an example," or "an example" mean that a particular feature, structure, or characteristic described in connection with that embodiment or example is included in at least one embodiment of the invention. Therefore, the phrases "in an embodiment," "in an embodiment," "an example," or "an example" appearing in various places throughout the specification do not necessarily refer to the same embodiment or example. Furthermore, specific features, structures, or characteristics can be combined in one or more embodiments or examples in any suitable combination and / or sub-combination. Moreover, those skilled in the art will understand that the illustrations provided herein are for illustrative purposes and are not necessarily drawn to scale.

[0038] It should be noted in advance that, in one alternative embodiment, except for independent descriptions, the same symbols or letters appearing in all formulas have the same meaning and value.

[0039] In one optional embodiment, please refer to Figure 1 This invention provides a method for designing the mix proportion of green, high-strength, polymer-modified permeable concrete, the method comprising the following steps:

[0040] S1. Select an initial range for coarse aggregate particle size, water-cement ratio, and design porosity, and select multiple water-cement ratios and design porosities within the initial range to form multiple sets of set mix proportions.

[0041] Specifically, in this embodiment, the performance requirements for the geopolymer permeable concrete at the construction site are set as follows: compressive strength ≥ 15 MPa and permeability coefficient ≥ 0.5 mm / s. The permeability coefficient is equivalent to the permeability. Bayer red mud and S95 grade slag are selected as cementing materials. The main elemental compositions of Bayer red mud and S95 grade slag are shown in Tables 1 and 2, respectively.

[0042]

[0043] Table 1 Chemical composition of Bayer process red mud

[0044]

[0045] Table 2 Chemical composition of slag

[0046] Furthermore, the content of needle-like and flaky particles in the coarse aggregate is strictly controlled to be less than or equal to 15%. Referring to the "Technical Specification for Permeable Cement Concrete Pavement (CJJT135-2009)" and the "Technical Specification for Permeable Concrete Pile Composite Foundation (DB37 / T 5124-2018)," the initial selection of coarse aggregate particle size is 4.75mm to 9.5mm, and the design porosity range is A-10%, B-15%, C-20%, and D-25%. Five water-cement ratio gradients are set: a-0.32, b-0.34, c-0.36, d-0.38, and e-0.40. These combinations form 20 sets of predetermined mix proportions, namely Aa, Ab, Ac, Ad, Ae, Ba, Bb, Bc, Bd, Be, Ca, Cb, Cc, Cd, Ce, Da, Db, Dc, Dd, and De.

[0047] Furthermore, in other alternative embodiments, the performance requirements of the geopolymer permeable concrete at the construction site are based on actual needs.

[0048] S2. Calculate the mass of each component in the unit volume of geopolymer permeable concrete under the set mix ratio for each group, and then prepare multiple geopolymer permeable concrete test blocks.

[0049] Specifically, S2 includes the following steps:

[0050] S21. Calculate the amount of coarse aggregate per unit volume of polymer permeable concrete under the set mix proportions for each group.

[0051] Specifically, in this embodiment, the amount of coarse aggregate used satisfies the following relationship:

[0052]

[0053] Where, m g The mass of coarse aggregate in one cubic meter of permeable concrete is expressed in kg. This refers to the compacted density of coarse aggregate, expressed in kg / m³. 3 The compacted density of the coarse aggregate was measured according to the standard "Crushed Stone and Gravel for Construction" (GB / 8077-2012); α is the correction factor for coarse aggregate dosage, taken as 0.98; the initial selection of coarse aggregate particle size is 4.75mm~9.5mm. It is 1614 kg / m 3 We can obtain m g It weighs 1581.72 kg;

[0054] S22. Calculate the volume of the cementitious material slurry based on the designed porosity, and then determine the mass of each component of the cementitious material slurry per unit volume of polymer permeable concrete under each set mix ratio.

[0055] Specifically, in this embodiment, the mass of each component in the cementitious slurry per unit volume of polymer permeable concrete includes the mass of red mud, slag, distilled water, and water glass in the cementitious slurry, and the volume of the cementitious slurry satisfies the following relationship:

[0056]

[0057] Among them, V p Let α be the volume of the cementitious slurry, and α be the correction factor for the amount of coarse aggregate. ρ is the compact packing density of coarse aggregate. g R represents the apparent density of coarse aggregate. void To design porosity, M R M K M W and M j M represents the mass of red mud, slag, distilled water, and water glass in the cementitious slurry per unit volume of polymer permeable concrete. R M K M W and M j The unit is kg, ρ R For, ρ K For, ρ W and ρ j These are the apparent densities of red mud, slag, distilled water, and water glass, respectively. The mass of red mud, slag, distilled water, and water glass in the cementitious slurry can be calculated using this formula.

[0058] Furthermore, the volume of the cementitious slurry is the total volume of distilled water, alkali activator, and the cementitious slurry itself. The cementitious slurry includes red mud, slag, distilled water, and water glass, with a mass ratio of red mud to slag of 3:7. The water glass contains 5% silica by mass of the cementitious slurry. The modulus of the water glass is 1.6, meaning the ratio of silica to sodium oxide in the water glass is 1.6. The original modulus of the water glass is 2, and sodium hydroxide must be added to adjust the modulus to 1.6. The mass of sodium hydroxide in the water glass is denoted by M. N M indicates N The unit is kg / m 3 The alkali activator is a mixed solution of water glass, sodium hydroxide, and distilled water. Sodium hydroxide was not considered when calculating the volume of the cementitious material slurry to simplify the calculation and improve efficiency.

[0059] Furthermore, the calculated and recorded mass of each component in the 20 set mix proportions is shown in Table 3:

[0060]

[0061]

[0062] Table 3 shows the content of each component in the specified mix proportion.

[0063] The percentages in parentheses in the first column of Table 3 represent the design porosity, and the decimals represent the water-cement ratio.

[0064] S23. Based on the amount of coarse aggregate and the mass of each component, prepare multiple geopolymer permeable concrete test blocks.

[0065] S23 includes the following steps:

[0066] S231. Based on the amount of coarse aggregate and the mass of each component, the coarse aggregate, red mud, slag, distilled water and water glass are mixed and stirred thoroughly using the cement-coating method to obtain geopolymer permeable concrete slurry.

[0067] Specifically, in this embodiment, according to the data in Table 3, coarse aggregate, red mud, slag, distilled water and water glass are mixed and stirred thoroughly using the cement-coating method to obtain 20 sets of 20 parts of geopolymer permeable concrete slurry corresponding to the set mix proportions.

[0068] S232. Based on the mass of the geopolymer permeable concrete slurry required for a single geopolymer permeable concrete test block under different mix proportions, weigh the corresponding mass of the geopolymer permeable concrete slurry and fill it into a mold to prepare multiple geopolymer permeable concrete test blocks.

[0069] Specifically, in this embodiment, since there are 20 portions of geopolymer permeable concrete slurry, 20 geopolymer permeable concrete test blocks need to be made. Before making the geopolymer permeable concrete test blocks corresponding to each set of mix proportions, the mass of geopolymer permeable concrete slurry required for each geopolymer permeable concrete test block needs to be designed and recorded in advance. Then, the corresponding mass of geopolymer permeable concrete slurry is weighed from the 20 portions of geopolymer permeable concrete slurry to make the geopolymer permeable concrete test blocks.

[0070] Furthermore, in the process of preparing geopolymer permeable concrete specimens by molding geopolymer permeable concrete slurry, the strength dispersion of the prepared geopolymer permeable concrete specimens can be reduced by controlling the quality of the geopolymer permeable concrete slurry during the molding process, thereby providing a basis for obtaining accurate and reliable performance test datasets.

[0071] S3. The permeability and compressive strength of the geopolymer permeable concrete specimens are tested to obtain a performance test dataset.

[0072] Specifically, in this embodiment, the water-cement ratio mainly affects the compressive strength and permeability of the geopolymer permeable concrete by influencing the mixing effect of the slurry. When the water-cement ratio is small, the cementitious slurry agglomerates and cannot fully coat the coarse aggregate; when the water-cement ratio is large, the cementitious slurry easily settles to the bottom, causing the geopolymer permeable concrete to lose its permeability. Different design porosities correspond to an optimal water-cement ratio, which allows the geopolymer permeable concrete slurry at that design porosity to achieve the best mixing effect. At this water-cement ratio, the strength of the corresponding design porosity reaches its highest level, and the permeability is the best. Therefore, under the premise of consistent mixing effect of the geopolymer permeable concrete slurry, the influence of different design porosities and their corresponding optimal water-cement ratio combinations on the comprehensive performance of geopolymer permeable concrete can be studied.

[0073] Furthermore, permeability and compressive strength tests were conducted on 20 geopolymer permeable concrete specimens corresponding to each set of mix proportions. The resulting performance test dataset is shown in Table 4.

[0074]

[0075]

[0076] Table 4. Permeability and compressive strength of permeable concrete specimens made from different polymers.

[0077] S4. Using the performance test dataset, construct a regression model for the permeability and compressive strength of the geopolymer permeable concrete specimen.

[0078] Specifically, in this embodiment, the regression model satisfies the following relationship:

[0079]

[0080] Wherein, Str is the compressive strength of the geopolymer permeable concrete specimen, T is the permeability of the geopolymer permeable concrete specimen, and d, b, and c are coefficients.

[0081] Furthermore, by fitting the permeability and compressive strength of the geopolymer permeable concrete specimens in Table 4 using matlb, this regression model can be obtained.

[0082] S5. Based on the quality of each component and the performance test dataset, a compressive strength prediction model is constructed using the sparrow search algorithm and the BP neural network, and then the optimal design porosity is determined by combining the regression model.

[0083] Specifically, S5 includes the following steps:

[0084] S51. The initial weights and initial thresholds of the BP neural network, along with the mass of each component, the performance test dataset, and the BP neural network are used as inputs to the sparrow search algorithm to optimize the weights and thresholds of the BP neural network. The BP neural network is then trained using the optimization results and the performance test dataset to obtain the compressive strength prediction model.

[0085] Specifically, in this embodiment, the sparrow search algorithm is first initialized with the following parameters: the maximum number of iterations is set to 50, the number of sparrows is set to 30, the upper limit of the optimization parameter target is set to [20, 20], the lower limit of the optimization parameter target is set to [0.1, 0.1], and the number of optimization parameters is set to 2. Then, a BP neural network model is established, and m... g M R M K M W and M j As input to the BP neural network model, the design porosity, water-cement ratio, and compressive strength are used as outputs of the BP neural network model, and eight nodes are set in the hidden layer of the BP neural network model.

[0086] Furthermore, the weights and thresholds of the BP neural network model are initialized to obtain the initial weights and thresholds of the BP neural network model. Then, the initial weights and thresholds, the mass of each component in the 20 sets of set mix proportions in Table 3, and the permeability and compressive strength of the geopolymer permeable concrete test blocks made according to these 20 sets of set mix proportions in Table 4 are used as inputs to the sparrow search algorithm to optimize the weights and thresholds of the BP neural network model. The optimization result is the output of the sparrow search algorithm. For the specific steps and principles of initializing the weights and thresholds of the BP neural network model and optimizing the parameters by the sparrow search algorithm, please refer to existing technologies. For simplicity, they will not be described in detail here.

[0087] Furthermore, after obtaining the optimal weights and optimal thresholds of the BP neural network model using the sparrow search algorithm, the first 15 sets of data in Tables 3 and 4 are used as the training set, and the last 5 sets of data are used as the test set to train and test the BP neural network model, thus obtaining the compressive strength prediction model. This is existing technology and will not be described in detail here.

[0088] Backpropagation (BP) neural networks possess superior nonlinear processing capabilities, enabling them to achieve m... g M R M K M W and M j The nonlinear mapping between these five input parameters and the three output parameters—design porosity, water-cement ratio, and compressive strength—enables prediction of these parameters. Furthermore, by utilizing the excellent global search capability of the sparrow search algorithm to optimize the weights and thresholds of the BP neural network, problems such as the BP neural network's tendency to get trapped in local minima, randomness in output results, and slow convergence speed can be solved, thereby improving the accuracy and reliability of the compressive strength prediction model.

[0089] S52. The compressive strength of the geopolymer permeable concrete specimen is optimized using the compressive strength prediction model, and the permeability is calculated using the regression model based on the optimization results, thereby obtaining the optimal design porosity.

[0090] Specifically, in this embodiment, the compressive strength and permeability of geopolymer permeable concrete are mutually exclusive. In fact, as shown in Table 4, while increasing the designed porosity significantly improves the permeability of geopolymer permeable concrete, it leads to a decrease in its compressive strength. Therefore, it is necessary to comprehensively consider both the compressive strength and permeability of geopolymer permeable concrete to achieve an optimal balance between the two. Thus, it is necessary to continuously adjust m... g M R M K M W and Mj The compressive strength prediction model then outputs multiple sets of design porosity, water-cement ratio, and compressive strength. Based on the compressive strength output by the compressive strength prediction model, a regression model is used to calculate the corresponding permeability.

[0091] Furthermore, the output layer of the compressive strength prediction model does not directly output a prediction of permeability. Instead, it uses a regression model to calculate the corresponding permeability after outputting the compressive strength. This is to reduce the complexity of the BP neural network and thus improve the prediction efficiency of the compressive strength prediction model. Ultimately, it was determined that when the design porosity is 15% and the water-cement ratio is 0.36, the compressive strength and permeability of the geopolymer permeable concrete are 18.2 and 2.96, respectively. At this point, both the compressive strength and permeability of the geopolymer permeable concrete are in a relatively optimal state, therefore the optimal design porosity is 15%.

[0092] S6. Adjust the coarse aggregate particle size and the water-cement ratio under the optimal design porosity, and remake the geopolymer permeable concrete test block for performance testing to obtain the optimal coarse aggregate particle size and the optimal water-cement ratio, thereby determining the optimal mix proportion.

[0093] Specifically, S6 includes the following steps:

[0094] S61. While keeping the optimal design porosity unchanged, design the coarse aggregate particle size gradient and its corresponding water-cement ratio gradient, and then obtain multiple sets of verification mix proportions.

[0095] To verify whether the selection of coarse aggregate particle size in step S1 is optimal, a coarse aggregate particle size gradient needs to be designed while maintaining the optimal design porosity. Furthermore, as the coarse aggregate particle size changes, its surface area changes, leading to alterations in the encapsulation performance of the cementitious paste on the coarse aggregate. This, in turn, changes the optimal water-cement ratio obtained in S5. Therefore, different water-cement ratio gradients need to be designed simultaneously to ensure that permeable concrete with different aggregate particle sizes achieves optimal mixing under the same design porosity, providing a foundation for obtaining accurate and reliable optimal coarse aggregate particle size and optimal water-cement ratio.

[0096] Furthermore, two coarse aggregate size gradients are added above the coarse aggregate size given in S1. These two coarse aggregate size gradients are E (9.5mm~13.2mm) and F (13.2mm~16mm) from smallest to largest. The water-cement ratio gradients are set to a-0.32, b-0.34, c-0.36, and d-0.38 as set in step S1. Then, eight sets of verification mix proportions are set using the same method as setting the mix proportions in step S1, namely Ea, Eb, Ec, Ed, Fa, Fb, Fc, and Fd.

[0097] Furthermore, in other alternative embodiments, verification mix ratios of other numbers can also be set.

[0098] S62. Calculate the content of each component in the unit volume of the geopolymer permeable concrete of each group of the verification mix proportion, and then re-prepare multiple geopolymer permeable concrete test blocks.

[0099] Specifically, in this embodiment, the content performed by S62 is the same as that of S22 to S23, so it will not be described in detail here. For ease of explanation, the newly prepared geopolymer permeable concrete test block is named the test block.

[0100] Furthermore, the calculated and recorded content of each component in the unit volume of polymer permeable concrete in the eight verification mix proportions is shown in Table 5:

[0101]

[0102] Table 5 shows the content of each component in the verification mix proportion.

[0103] Furthermore, eight test blocks were made in sequence based on the eight sets of verification mix ratios.

[0104] S63. The permeability and compressive strength of the re-made geopolymer permeable concrete specimens are tested, and the coarse aggregate particle size and water-cement ratio corresponding to the re-made geopolymer permeable concrete specimen with the best comprehensive performance are selected as the optimal coarse aggregate particle size and optimal water-cement ratio.

[0105] Specifically, in this embodiment, the permeability and compressive strength of the test blocks were tested. The permeability and compressive strength corresponding to each set of verification mix proportions are shown in Table 6:

[0106] Ea(9.5mm-13.2mm, 0.32) 11.3 3.3 Eb(9.5mm-13.2mm, 0.34) 14.3 4.5 Ec(9.5mm-13.2mm, 0.36) 13.5 2.8 Ea (9.5mm-13.2mm, 0.38) 12.1 1.9 Fa (13.2mm-16mm, 0.32) 10.2 3.7 Fb(13.2mm-16mm, 0.34) 8.3 2.1 Fc(13.2mm-16mm, 0.36) 11.3 3.3 Fd(13.2mm-16mm, 0.38) 10.1 1.3

[0107] Table 6. Permeability and compressive strength of different test blocks

[0108] As can be seen from Table 6, as the coarse aggregate particle size increases, the compressive strength of the geopolymer permeable concrete gradually decreases, and although the permeability improves, it is not significantly enhanced. Among them, the permeability and compressive strength of the test blocks corresponding to groups Eb and Fa are at relatively optimal values, meaning that the overall performance of the test blocks is good at this point. Comparing the permeability and compressive strength of groups Eb and Fa with the compressive strength and permeability obtained through the compressive strength prediction model and regression model in step S52, it can be concluded that the geopolymer permeable concrete has the best overall performance when the coarse aggregate particle size and water-cement ratio are 4.75-9.5 mm and 0.36, respectively. That is, the optimal coarse aggregate particle size is 4.75-9.5 mm, and the optimal water-cement ratio is 0.36.

[0109] S64. The optimal water-cement ratio, the optimal design porosity, and the optimal coarse aggregate particle size are determined as the optimal mix proportion.

[0110] Specifically, in this embodiment, when the required performance of the geopolymer permeable concrete is a compressive strength ≥15MPa and a permeability coefficient ≥0.5mm / s, the coarse aggregate particle size of the geopolymer permeable concrete should be selected as 4.75-9.5mm, the porosity as 15%, and the water-cement ratio as 0.36.

[0111] Furthermore, given the optimal coarse aggregate particle size, optimal design porosity, and optimal water-cement ratio, the mass of each component of the geopolymer permeable concrete can also be calculated using the method in step S22. Then, the geopolymer permeable concrete can be produced based on the calculated results.

[0112] It should be noted that in some cases, the actions described in the specification can be performed in different orders and still achieve the desired results. In this embodiment, the order of steps is given only to make the embodiment clearer and easier to explain, and not to limit it.

[0113] In summary, this invention designs multiple sets of predetermined mix proportions, and then, based on the calculated mass of each component of the geopolymer permeable concrete under each predetermined mix proportion, produces multiple geopolymer permeable concrete specimens. Based on the measured data of the permeability and compressive strength of the geopolymer permeable concrete specimens, a regression model and a compressive strength prediction model are constructed to obtain the optimal design porosity. Finally, based on the optimal design porosity, a coarse aggregate particle size gradient and its corresponding water-cement ratio gradient are designed for experimentation, yielding the optimal coarse aggregate particle size and the optimal water-cement ratio, thus obtaining the optimal mix proportion of the geopolymer permeable concrete. In obtaining the optimal mix proportion, this invention reduces the strength dispersion of the prepared permeable concrete specimens by controlling the quality of the geopolymer slurry during the molding process, thereby making the obtained data more reliable. The method of constructing a compressive strength prediction model using a sparrow search algorithm and a BP neural network, combined with a regression model to determine the optimal design porosity, improves the speed and efficiency of obtaining the optimal mix proportion. This is beneficial for further optimizing the compressive strength and permeability of permeable concrete to meet the construction needs of different application scenarios, providing new ideas for the application and development of permeable concrete. By adjusting the water-cement ratio, permeable concrete with different mix proportions can achieve good mixing effects, thus simplifying the mix proportion design method and reducing the need for admixtures such as water-reducing agents in the mix proportion design process. This provides a scientific basis for the application of new cementitious materials such as geopolymers in permeable concrete.

[0114] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention, and not to limit them. Although the present invention has been described in detail with reference to the foregoing embodiments, those skilled in the art should understand that modifications can still be made to the technical solutions described in the foregoing embodiments, or equivalent substitutions can be made to some or all of the technical features therein. Such modifications or substitutions do not cause the essence of the corresponding technical solutions to deviate from the scope of the technical solutions of the embodiments of the present invention, and they should all be covered within the scope of the claims and specification of the present invention.

Claims

1. A method for designing the mix proportions of green, high-strength, polymer-modified permeable concrete, characterized in that, Includes the following steps: A preliminary range of coarse aggregate particle size, water-cement ratio, and design porosity is selected, and multiple water-cement ratios and design porosities are selected within the preliminary range, and then combined to form multiple sets of set mix proportions; Calculate the amount of coarse aggregate per unit volume of polymer permeable concrete for each set of mix proportions; The volume of the cementitious material slurry is calculated based on the designed porosity, and then the mass of each component of the cementitious material slurry per unit volume of polymer permeable concrete under each set mix ratio is determined. The volume of the cementitious material slurry is the total volume of distilled water, alkali activator, and cementitious material slurry; The cementitious material slurry includes red mud, slag, distilled water and water glass, and the mass ratio of red mud to slag is 3:

7. The mass of silica in the water glass accounts for 5% of the mass of the cementitious material slurry. The alkali activator is a mixed solution of water glass, sodium hydroxide and distilled water. The modulus of the water glass is 1.6, that is, the ratio of silica to sodium oxide in the water glass is 1.

6. The original modulus of the water glass is 2. Multiple geopolymer permeable concrete test blocks were prepared based on the amount of coarse aggregate and the mass of each component. The permeability and compressive strength of the geopolymer permeable concrete specimens were tested to obtain a performance test dataset. A regression model for the permeability and compressive strength of the geopolymer permeable concrete specimen was constructed using the performance test dataset. The initial weights and thresholds of the BP neural network are used as inputs to the sparrow search algorithm to optimize the weights and thresholds of the BP neural network. The BP neural network is then trained using the optimization results and the performance test dataset to obtain a compressive strength prediction model. The mass of coarse aggregate, red mud, slag, distilled water, and water glass in the cementitious slurry of the polymer permeable concrete per unit volume is used as inputs to the BP neural network, and the design porosity, water-cement ratio, and compressive strength are used as outputs of the BP neural network. The compressive strength prediction model is used to optimize the compressive strength of the geopolymer permeable concrete specimen, and the permeability is calculated using the regression model based on the optimization results, thereby obtaining the optimal design porosity. While keeping the optimal design porosity constant, design the coarse aggregate particle size gradient and its corresponding water-cement ratio gradient to obtain multiple sets of verification mix proportions. The content of each component in the unit volume of the geopolymer permeable concrete of each group of the verification mix proportions was calculated, and then multiple geopolymer permeable concrete test blocks were re-prepared. The permeability and compressive strength of the remade geopolymer permeable concrete specimens were tested, and the coarse aggregate particle size and water-cement ratio corresponding to the remade geopolymer permeable concrete specimen with the best comprehensive performance were selected as the optimal coarse aggregate particle size and optimal water-cement ratio. The optimal water-cement ratio, the optimal design porosity, and the optimal coarse aggregate particle size are determined as the optimal mix proportion.

2. The mix design method for green high-strength geopolymer permeable concrete according to claim 1, characterized in that, The preparation of multiple geopolymer permeable concrete test blocks based on the amount of coarse aggregate and the mass of each component includes the following steps: Based on the amount of coarse aggregate and the mass of each component, coarse aggregate, red mud, slag, distilled water and water glass are mixed and stirred thoroughly using the cement-coating method to obtain geopolymer permeable concrete slurry; Based on the mass of the geopolymer permeable concrete slurry required for a single geopolymer permeable concrete specimen under different mix proportions, the corresponding mass of the geopolymer permeable concrete slurry is weighed and molded to prepare multiple geopolymer permeable concrete specimens.

3. The mix design method for green high-strength geopolymer permeable concrete according to claim 2, characterized in that, The volume of the cementitious material slurry satisfies the following relationship: in, The volume of the cementitious material slurry. This is a correction factor for coarse aggregate usage. This refers to the compact packing density of coarse aggregate. The apparent density of coarse aggregate, The designed porosity, , , and These represent the masses of red mud, slag, distilled water, and water glass in the cementitious slurry per unit volume of polymer permeable concrete. for, for, and The apparent densities are red mud, slag, distilled water, and water glass, respectively.

4. The mix design method for green high-strength geopolymer permeable concrete according to claim 3, characterized in that, The regression model satisfies the following relationship: in, T represents the compressive strength of the geopolymer permeable concrete specimen, and T represents the permeability of the geopolymer permeable concrete specimen. a b and c are coefficients.

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