Method and system for optimizing anti-freezing performance and intelligently proportioning compressed pouring concrete

Abstract

The invention provides an anti-freezing performance optimization and intelligent proportioning method and system for compressed pouring concrete, and belongs to the technical field of concrete member production. The method comprises the following steps: carrying out a compressive strength test on the basis of a group of mixing ratios for determining the concrete strength grade, and establishing a quantitative mapping relation of cement dosage-pressing stress-volume ratio before and after pressing; screening the concrete mix proportion meeting the target strength grade; preparing a test piece based on the screened concrete mix proportion, and carrying out a freeze-thaw cycle test to obtain concrete anti-freezing grades under different mix proportions; screening out the concrete mix proportion meeting the anti-freezing grade; and based on the quantitative mapping relation of the volume ratio and the anti-freezing grade data, a quantitative mapping model of the cement use amount-pressing stress-anti-freezing grade is constructed, and an optimal combination scheme is obtained through reverse solution. The limitation of traditional empirical ratio design is broken through, scientific prediction and optimization of the anti-freezing performance are achieved, scientific control over the material dosage is ensured, waste is reduced, and quality is ensured.

Description

TECHNICAL FIELD

[0001] The present application belongs to the technical field of concrete member production, and particularly relates to a method and system for optimizing and intelligently proportioning the frost resistance of compression-poured concrete. BACKGROUND

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

[0003] As one of the most widely used materials in modern construction, the durability of concrete directly affects the safety and service life of the structure. In cold regions or environments with frequent freeze-thaw cycles, concrete structures often suffer surface spalling, cracking and other damage due to freeze-thaw action, which severely shortens the service life of the project.

[0004] Currently, the main technical means to improve the frost resistance of concrete include the introduction of air entraining agents, the optimization of mix proportions, and the use of high-performance admixtures. Air entraining agents introduce uniformly distributed micro-bubbles into concrete to alleviate the expansion pressure during freeze-thaw processes, but their effectiveness is greatly influenced by bubble parameters such as spacing factor and pore size distribution, and may also reduce the mechanical properties of concrete. In addition, traditional mix proportion design methods are usually based on experience or standard specifications, and do not fully consider the quantitative relationship between material properties, environmental conditions and frost resistance, resulting in conservative or insufficient design results.

[0005] Compression-poured concrete technology is a pouring technology that applies a certain pressure during concrete pouring, thereby rapidly pouring and forming the member. It can significantly improve the mechanical properties and durability of concrete members, and is a new type of sustainable, high-performance and low-cost concrete member preparation technology. However, the mix proportion design rules for frost resistance design of this technology are not clear, and the concrete mix proportion under the target frost resistance grade cannot be determined, lacking a dynamic optimization mechanism, which easily causes material waste or performance not meeting the standards. SUMMARY

[0006] To overcome the shortcomings of the prior art, the present application provides a method and system for optimizing and intelligently proportioning the frost resistance of compression-poured concrete, which establishes a quantitative relationship between cement dosage, compression stress and frost resistance grade through experiments and machine learning, and realizes accurate prediction of frost resistance and optimized control of material dosage.

[0007] To achieve the above-mentioned purpose, one or more embodiments of the present application provide the following technical solutions: The first aspect of the present application provides a method for optimizing and intelligently proportioning the frost resistance of compression-poured concrete; A method for optimizing and intelligently proportioning the frost resistance of compression-poured concrete, comprising: Based on a set of mix proportions that determine the concrete strength grade, compressive strength tests were conducted to establish a quantitative mapping relationship between cement content, compressive stress, and volume ratio before and after compressive construction. Based on the compressive strength test data, select concrete mix proportions that meet the target strength grade; Specimens were prepared based on the selected concrete mix proportions, and freeze-thaw cycle tests were conducted to obtain the freeze-thaw resistance grade of concrete under different mix proportions. Based on the set target frost resistance level, the concrete mix proportions that meet the frost resistance level are selected; Based on the quantitative mapping relationship of volume ratio and frost resistance grade data, a quantitative mapping model of cement dosage-compression stress-frost resistance grade is constructed, and the optimal combination scheme of cement dosage and compression stress that meets the target performance is obtained by inverse solution.

[0008] As a further technical solution, the process of establishing a quantitative mapping relationship between cement dosage, compaction stress, and volume ratio before and after compaction is as follows: Based on a set of mix proportions that determine the concrete strength grade, compressive strength tests were conducted by gradually reducing the amount of cement and increasing the compressive stress. The variable in the mix proportions was the amount of cement, and the variable in the compressive stress was the compressive stress. The volume change of concrete before and after compression pouring was recorded, and a quantitative mapping relationship between cement content, compressive stress, and the volume ratio before and after compressive pouring was established based on the test data.

[0009] As a further technical solution, the process of preparing specimens based on the selected concrete mix proportions and conducting freeze-thaw cycle tests to obtain the freeze-thaw resistance grade of concrete under different mix proportions includes calculating the freeze-thaw resistance grade by measuring the mass loss rate and relative dynamic elastic modulus of the specimens in the freeze-thaw cycle test.

[0010] As a further technical solution, the formula for the mass loss rate of the specimen is as follows:

[0011]

[0012] In the formula: The mass loss rate of the specimen after n freeze-thaw cycles; The mass of the specimen before freeze-thaw; The mass of the specimen after n freeze-thaw cycles; The average mass loss rate of a group of specimens after n freeze-thaw cycles.

[0013] As a further technical solution, the relative dynamic elastic modulus of the specimen is:

[0014]

[0015] In the formula: Let be the relative dynamic elastic modulus of the i-th specimen after n freeze-thaw cycles; The transverse resonant frequency of the specimen after n freeze-thaw cycles; The transverse resonant frequency of the specimen before freeze-thaw. The average relative dynamic elastic modulus of a group of specimens after n freeze-thaw cycles.

[0016] As a further technical solution, the quantitative mapping model of cement dosage-compression stress-frost resistance grade is constructed using the XGBoost algorithm. The input parameters of the model are cement dosage and compression stress; the output of the model is the predicted frost resistance grade.

[0017] As a further technical solution, the process of obtaining the optimal combination of cement dosage and compaction stress that meets the target performance through reverse solution is as follows: input the target frost resistance level, calculate multiple combinations of cement dosage and compaction stress that meet the conditions through numerical inversion, and select the optimal solution with the minimum cement dosage and minimum compaction stress as the optimization objectives.

[0018] The second aspect of the present invention provides a system for optimizing the frost resistance and intelligent mix proportioning of compressed cast concrete.

[0019] A system for optimizing the frost resistance and intelligent mix proportioning of compression-cast concrete includes: The compressive strength test module is configured to: conduct compressive strength tests based on a set of mix proportions that determine the concrete strength grade, and establish a quantitative mapping relationship between cement content, compressive stress, and volume ratio before and after compressive construction; The first screening module is configured to: screen concrete mix proportions that meet the target strength grade based on compressive strength test data; The freeze-thaw cycle test module is configured to: prepare specimens based on the screened concrete mix proportions, conduct freeze-thaw cycle tests, and obtain the freeze-thaw resistance grade of concrete under different mix proportions; The second screening module is configured to: screen out concrete mix proportions that meet the set target frost resistance level; The modeling optimization and material calculation module is configured to: construct a quantitative mapping model of cement dosage-compression stress-compression stress based on the quantitative mapping relationship of volume ratio and frost resistance grade data, and obtain the optimal combination scheme of cement dosage and compression stress that meets the target performance through inverse solution.

[0020] A third aspect of the present invention provides a computer-readable storage medium having a program stored thereon, which, when executed by a processor, implements the steps of the method for optimizing the frost resistance and intelligent mix proportioning of compressed cast concrete as described in the first aspect of the present invention.

[0021] A fourth aspect of the present invention provides an electronic device, including a memory, a processor, and a program stored in the memory and executable on the processor, wherein the processor executes the program to implement the steps in the method for optimizing the frost resistance and intelligent mix proportioning of compressed cast concrete as described in the first aspect of the present invention.

[0022] The above one or more technical solutions have the following beneficial effects: (1) This invention conducts freeze-thaw cycle tests on compressed concrete specimens with different cement dosages and compressive stresses under a set compressive strength grade through systematic experiments. It combines machine learning algorithms to train and predict the data, establishes a mapping relationship model of cement dosage-compressive stress-freeze grade, breaks through the limitations of traditional empirical mix design, and realizes scientific prediction and optimization of freeze-thaw performance.

[0023] (2) The model constructed by the present invention can be reverse-engineered to meet specific engineering requirements (such as the requirement of F100 frost resistance for C30 strength grade concrete structures). Through numerical inversion, multiple sets of optimized combinations of cement dosage and compressive stress that meet the target performance can be obtained.

[0024] (3) By recording the changes in concrete volume before and after compression pouring under various cement dosages and compressive stress, this invention establishes a quantitative mapping relationship of “cement dosage - compressive stress - volume ratio before and after compressive pouring” based on experimental data, thereby achieving accurate calculation of the amount of concrete to be poured, ensuring scientific control of material usage during production, reducing waste and ensuring the stability of component quality.

[0025] Advantages of additional aspects of the invention will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. Attached Figure Description

[0026] The accompanying drawings, which form part of this invention, are used to provide a further understanding of the invention. The illustrative embodiments of the invention and their descriptions are used to explain the invention and do not constitute an improper limitation of the invention.

[0027] Figure 1 This is a flowchart of the method in the first embodiment.

[0028] Figure 2 This is a schematic diagram of the model structure in the first embodiment.

[0029] Figure 3 The diagram shows the results of the compressive strength test in the first embodiment.

[0030] Figure 4 This is a system structure diagram of the second embodiment. Detailed Implementation

[0031] It should be noted that the following detailed descriptions are exemplary and intended to provide further illustration of the invention. Unless otherwise specified, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention pertains.

[0032] It should be noted that the terminology used herein is for the purpose of describing particular implementations only and is not intended to limit the exemplary implementations of the present invention.

[0033] Where there is no conflict, the embodiments and features in the embodiments of the present invention can be combined with each other.

[0034] Example 1 This embodiment discloses a method for optimizing the frost resistance and intelligent mix proportioning of compression-cast concrete; like Figure 1 As shown, a method for optimizing the frost resistance and intelligent mix proportioning of compression-cast concrete includes: A method for optimizing the frost resistance and intelligent mix proportioning of compression-cast concrete includes: Step S1: Based on a set of mix proportions that determine the concrete strength grade, conduct compressive strength tests and establish a quantitative mapping relationship between cement content, compressive stress, and volume ratio before and after compressive construction. Step S2: Based on the compressive strength test data, select the concrete mix proportion that meets the target strength grade; Step S3: Prepare specimens based on the selected concrete mix proportions, conduct freeze-thaw cycle tests, and obtain the freeze-thaw resistance grade of concrete under different mix proportions; Step S4: Based on the set target frost resistance level, select concrete mix proportions that meet the frost resistance level; Step S5: Based on the quantitative mapping relationship of volume ratio and frost resistance grade data, construct a quantitative mapping model of cement dosage-compression stress-frost resistance grade, and obtain the optimal combination scheme of cement dosage and compression stress that meets the target performance by inverse solution.

[0035] Specifically, it includes the following: In step S1, a compressive strength test is conducted by reducing the cement content and increasing the compressive stress based on a set of mix proportion gradients for a given concrete strength grade (such as C30, C40, C50, etc.). The variable in the mix proportion is the cement content, and the variable in the compressive pouring technique is the compressive stress. The volume change of concrete before and after compression pouring is recorded. Based on the test data, a quantitative mapping relationship between cement content, compressive stress, and the volume ratio before and after compressive pouring is established. The established mapping relationship will provide training data for the subsequent model construction.

[0036] In step S2, based on the data from the compressive strength test, a first screening is conducted to select the corresponding concrete mix proportions that meet the compressive strength grade for use in the frost resistance test.

[0037] During the screening process, the compressive strength data of each test combination were checked one by one, with a focus on analyzing the influence of the two variables of cement content and compressive stress on strength. For combinations with different cement contents, compressive strength was tested under compressive stress to select combinations that fully meet the strength requirements of different types of concrete. Through this targeted screening, test combinations that did not meet the compressive strength standards were eliminated, ultimately forming multiple sets of benchmark mix proportions that combine strength reliability and parameter representativeness. This provides accurate and effective test subjects for subsequent freeze-thaw cycle tests, ensuring the scientific rigor and relevance of the freeze-thaw performance research.

[0038] In step S3, based on the determined concrete mix proportions that meet the compressive strength grade, prism specimens are cast according to relevant standards. At least three specimens are prepared for each mix proportion to ensure data reliability. After specimen molding, they are cured for 28 days under standard curing conditions to ensure the concrete reaches its design strength. After curing, a rapid freeze-thaw cycle test is conducted. After every 25 freeze-thaw cycles, the specimen is removed to determine its mass loss rate and transverse fundamental frequency, calculate the relative dynamic modulus of elasticity, and observe and record surface spalling. When the mass loss rate of the specimen reaches 5% or the relative dynamic modulus of elasticity drops to 60%, the test is terminated, the number of cycles is recorded, and the concrete freeze-thaw resistance grade is evaluated according to relevant standards. In this embodiment, the formula for the mass loss rate of the specimen is as follows:

[0039]

[0040] In the formula: The mass loss rate of the specimen after n freeze-thaw cycles; The mass of the specimen before freeze-thaw; The mass of the specimen after n freeze-thaw cycles; The average mass loss rate of a group of specimens after n freeze-thaw cycles.

[0041] The relative dynamic elastic modulus of the specimen is:

[0042]

[0043] In the formula: Let be the relative dynamic elastic modulus of the i-th specimen after n freeze-thaw cycles; The transverse resonant frequency of the specimen after n freeze-thaw cycles; The transverse resonant frequency of the specimen before freeze-thaw. The average relative dynamic elastic modulus of a group of specimens after n freeze-thaw cycles.

[0044] In step S4, the range of frost resistance grades is set according to the engineering requirements. The concrete mix proportions that have passed the frost resistance test are screened a second time to select the concrete mix proportions that meet the set frost resistance grades.

[0045] Combination Figure 2 In step S5, based on the established quantitative mapping relationship between cement dosage, compaction stress, and volume ratio before and after compaction, and the frost resistance grade data, the XGBoost algorithm is used to construct a quantitative mapping model of cement dosage, compaction stress, and frost resistance grade. The input parameters of the model are cement dosage and compaction stress; the model output is the predicted frost resistance grade. XGBoost is a gradient boosting ensemble learning algorithm that constructs a strong prediction model by iteratively training multiple weak learners (i.e., decision trees).

[0046] Furthermore, the model construction process includes: (1) Establish the original dataset of “cement dosage - compaction stress - frost resistance grade” after secondary screening, where cement dosage and compaction stress are input variables and frost resistance grade is output variable.

[0047] (2) Iteratively train each tree, where each tree refers to the decision tree in the XGBoost algorithm. Calculate the gradient of the current prediction. and second derivative Specifically, for the predicted value of the current model after the (k-1)th iteration, calculate the gradient of the loss function (usually the mean squared error, MSE) for each sample i with respect to the target value. and second derivative .

[0048] A greedy tree structure is generated based on the structure score; specifically, the gradients of all samples are used. and second derivative Based on the principle of maximizing the fractional gain of the tree structure, a greedy algorithm is used to recursively determine the current tree. The optimal splitting characteristics (cement dosage or compaction stress) and splitting points are determined. The gain calculation formula is usually based on an improvement of the objective functions (including regularization terms) of the left and right child nodes after splitting.

[0049] In the XGBoost tree construction, a greedy algorithm is used to recursively select the optimal splitting features and splitting points to maximize the gain of the objective function. The process is based on calculating the gradient for each sample. and second derivative And evaluate the gain of all possible splits. The specific process is as follows: For the current node, first calculate the sum of the gradients of the current node: and ; For each feature, sort the sample values ​​under that feature, and then iterate through all possible split points (usually the midpoint between the feature values ​​of adjacent samples). For each candidate split point, the samples are divided into a left subset (eigenvalues ​​less than or equal to the split point) and a right subset (eigenvalues ​​greater than the split point). Then, the sum of the gradients of the left subset is calculated separately. The sum of the second derivatives , and the sum of the gradients of the right subset The sum of the second derivatives .

[0050] Calculate the gain (Gain) at the split point using the following formula:

[0051] in: This is the L2 regularization coefficient (the penalty term for leaf weights); The complexity cost of splitting (i.e., the threshold, which will not split if the gain is less than this value).

[0052] The split point with the highest gain (and a gain greater than 0 or a threshold) is selected as the optimal split point for this feature. Compare the optimal split points of all features, and select the feature and split point with the largest gain as the splitting scheme for the current node.

[0053] Furthermore, after determining the tree structure, the optimal weight for each leaf node j is calculated. To minimize the objective function of the samples at that leaf node, the new tree... The prediction results are scaled by the learning rate η and then added to the cumulative prediction value of the current model to complete this round of model update.

[0054] (3) Output the final addition model .

[0055] Furthermore, based on this model, reverse engineering can be performed to solve specific engineering requirements (such as a C30 strength grade concrete structure requiring a frost resistance grade of F100). Specifically, by inputting the target frost resistance grade, multiple combinations of cement dosage and compaction stress that meet the conditions are calculated through numerical inversion, and the optimal solution is selected with the minimum cement dosage and minimum compaction stress as the optimization objectives.

[0056] Furthermore, in this embodiment, the method used in this invention is verified using the mix proportion of C30 concrete with verified strength grade as an example.

[0057] The concrete mix proportion for C30 strength grade (480g cement) was verified. Water 245 Sand 606 1078 stones Based on this, the cement usage is set at 480. 420 360 330 Four gradients were used, while maintaining consistency in water-cement ratio, aggregate gradation, and other factors to ensure comparison of a single variable. The main variable in the compaction process was the compaction stress; considering equipment economy, a stress range of 0-10 MPa was adopted. Three standard cubic or cylindrical specimens were prepared for each mix design. After 28 days of standard curing (the curing time for compacted concrete can be reduced, consistent with ordinary concrete), a pressure machine was used for loading, the peak load was recorded, and the compressive strength was calculated. The test results are shown in […]. Figure 3 Record the changes in concrete volume before and after compression pouring. Based on the experimental data, establish a quantitative mapping relationship of "cement dosage - compression stress - volume ratio before and after compression pouring". Detailed data of each parameter and result are shown in Table 1.

[0058]

[0059] In the formula: y is the volume ratio before and after compaction. This refers to the amount of cement used. This refers to the pressure stress during the pressure build-up process.

[0060] Table 1: Concrete Mix Proportion and Compression Stress

[0061] Based on compressive strength test data, several benchmark mix proportions that meet the requirements of C30 compressive strength grade were selected, namely: cement content 480 Compressed stress 0-10MPa; Cement dosage 420g The compressive stress is 2-10 MPa; the cement dosage is 360 kcal / kg. The compressive stress is 6-10 MPa; the cement dosage is 330 kcal / kg. The compressive stress is 8-10 MPa. See Table 2 for specific data.

[0062] Table 2: Proportions after the first screening

[0063] Furthermore, 100mm×100mm×400mm prism specimens were cast, with at least three specimens prepared for each mix design to ensure data reliability. After specimen molding, they were cured under standard curing conditions for 28 days to ensure the concrete reached its design strength. After curing, a rapid freeze-thaw cycle test was conducted. After every 25 freeze-thaw cycles, the specimens were removed to determine their mass loss rate and transverse fundamental frequency, calculate the relative dynamic modulus of elasticity, and observe and record surface spalling. When the mass loss rate of the specimen reached 5% or the relative dynamic modulus of elasticity dropped to 60%, the test was terminated and the number of cycles was recorded. The concrete's freeze-thaw resistance grade was evaluated according to the prescribed standards. Specific data are shown in Table 3.

[0064] Table 3: Freeze-thaw resistance grade of concrete

[0065] The range of frost resistance grades was set to ≥F100, and the optimal mix proportions that meet the durability requirements were selected, as shown in Table 4. By constructing a quantitative mapping model of cement content, compaction stress, and frost resistance grade, the optimal combination of cement content and compaction stress that meets the target performance was obtained through inverse solving.

[0066] Table 4: Final Screening Ratio

[0067] Example 2 This embodiment discloses an intelligent mix proportioning system for optimizing the frost resistance of compression-cast concrete; like Figure 4 As shown, a system for optimizing the frost resistance and intelligent mix proportioning of compressed cast concrete includes: The compressive strength test module is configured to: conduct compressive strength tests based on a set of mix proportions that determine the concrete strength grade, and establish a quantitative mapping relationship between cement content, compressive stress, and volume ratio before and after compressive construction; The first screening module is configured to: screen concrete mix proportions that meet the target strength grade based on compressive strength test data; The freeze-thaw cycle test module is configured to: prepare specimens based on the screened concrete mix proportions, conduct freeze-thaw cycle tests, and obtain the freeze-thaw resistance grade of concrete under different mix proportions; The second screening module is configured to: screen out concrete mix proportions that meet the set target frost resistance level; The modeling optimization and material calculation module is configured to: construct a quantitative mapping model of cement dosage-compression stress-compression stress based on the quantitative mapping relationship of volume ratio and frost resistance grade data, and obtain the optimal combination scheme of cement dosage and compression stress that meets the target performance through inverse solution.

[0068] Example 3 The purpose of this embodiment is to provide a computer-readable storage medium.

[0069] A computer-readable storage medium having a computer program stored thereon, which, when executed by a processor, implements the steps in the method for optimizing the frost resistance and intelligent mix proportioning of compressed cast concrete as described in Example 1.

[0070] Example 4 The purpose of this embodiment is to provide an electronic device.

[0071] An electronic device includes a memory, a processor, and a program stored in the memory and executable on the processor. When the processor executes the program, it implements the steps in the method for optimizing the frost resistance and intelligent mix proportioning of compressed cast concrete as described in Example 1.

[0072] The steps and methods involved in the apparatuses of Embodiments 2, 3, and 4 above correspond to those in Embodiment 1. For specific implementation details, please refer to the relevant description section of Embodiment 1. The term "computer-readable storage medium" should be understood as a single medium or multiple media including one or more instruction sets; it should also be understood as including any medium capable of storing, encoding, or carrying an instruction set for execution by a processor and enabling the processor to perform any of the methods in this invention.

[0073] Those skilled in the art will understand that the modules or steps of the present invention described above can be implemented using general-purpose computer devices. Optionally, they can be implemented using computer-executable program code, thereby allowing them to be stored in a storage device for execution by a computer device, or they can be fabricated as separate integrated circuit modules, or multiple modules or steps can be fabricated as a single integrated circuit module. The present invention is not limited to any particular combination of hardware and software.

[0074] While the specific embodiments of the present invention have been described above in conjunction with the accompanying drawings, this is not intended to limit the scope of protection of the present invention. Those skilled in the art should understand that various modifications or variations that can be made by those skilled in the art without creative effort based on the technical solutions of the present invention are still within the scope of protection of the present invention.

Claims

1. A method for optimizing the frost resistance and intelligent mix proportioning of compression-cast concrete, characterized in that, include: Based on a set of mix proportions that determine the concrete strength grade, compressive strength tests were conducted to establish a quantitative mapping relationship between cement content, compressive stress, and volume ratio before and after compressive construction. Based on the compressive strength test data, select concrete mix proportions that meet the target strength grade; Specimens were prepared based on the selected concrete mix proportions, and freeze-thaw cycle tests were conducted to obtain the freeze-thaw resistance grade of concrete under different mix proportions. Based on the set target frost resistance level, the concrete mix proportions that meet the frost resistance level are selected; Based on the quantitative mapping relationship of volume ratio and frost resistance grade data, a quantitative mapping model of cement dosage-compression stress-frost resistance grade is constructed, and the optimal combination scheme of cement dosage and compression stress that meets the target performance is obtained by inverse solution.

2. The method for optimizing the frost resistance and intelligent mix proportioning of compressed cast concrete as described in claim 1, characterized in that, The process of establishing a quantitative mapping relationship between cement dosage, compaction stress, and volume ratio before and after compaction is as follows: Based on a set of mix proportions that determine the concrete strength grade, compressive strength tests were conducted by gradually reducing the amount of cement and increasing the compressive stress. The variable in the mix proportions was the amount of cement, and the variable in the compressive stress was the compressive stress. The volume change of concrete before and after compression pouring was recorded, and a quantitative mapping relationship between cement content, compressive stress, and the volume ratio before and after compressive pouring was established based on the test data.

3. The method for optimizing the frost resistance and intelligent mix proportioning of compressed cast concrete as described in claim 1, characterized in that, The process of preparing specimens based on the selected concrete mix proportions and conducting freeze-thaw cycle tests to obtain the freeze-thaw resistance grade of concrete under different mix proportions includes calculating the freeze-thaw resistance grade by measuring the mass loss rate and relative dynamic elastic modulus of the specimens in the freeze-thaw cycle test.

4. The method for optimizing the frost resistance and intelligent mix proportioning of compressed cast concrete as described in claim 3, characterized in that, The formula for the mass loss rate of the specimen is as follows: In the formula: The mass loss rate of the specimen after n freeze-thaw cycles; The mass of the specimen before freeze-thaw; The mass of the specimen after n freeze-thaw cycles; The average mass loss rate of a group of specimens after n freeze-thaw cycles.

5. The method for optimizing the frost resistance and intelligent mix proportioning of compressed cast concrete as described in claim 3, characterized in that, The relative dynamic elastic modulus of the specimen is: In the formula: Let be the relative dynamic elastic modulus of the i-th specimen after n freeze-thaw cycles; The transverse resonant frequency of the specimen after n freeze-thaw cycles; The transverse resonant frequency of the specimen before freeze-thaw. The average relative dynamic elastic modulus of a group of specimens after n freeze-thaw cycles.

6. The method for optimizing the frost resistance and intelligent mix proportioning of compressed cast concrete as described in claim 1, characterized in that, The quantitative mapping model of cement dosage-compression stress-frost resistance grade is constructed using the XGBoost algorithm. The input parameters of the model are cement dosage and compression stress; the output of the model is the predicted frost resistance grade.

7. The method for optimizing the frost resistance and intelligent mix proportioning of compressed cast concrete as described in claim 1, characterized in that, The process of obtaining the optimal combination of cement dosage and compaction stress that meets the target performance through reverse solving is as follows: input the target frost resistance level, calculate multiple combinations of cement dosage and compaction stress that meet the conditions through numerical inversion, and select the optimal solution with the minimum cement dosage and minimum compaction stress as the optimization objectives.

8. A system for optimizing the frost resistance and intelligent mix proportioning of compressed cast concrete, characterized in that, include: The compressive strength test module is configured to: conduct compressive strength tests based on a set of mix proportions that determine the concrete strength grade, and establish a quantitative mapping relationship between cement content, compressive stress, and volume ratio before and after compressive construction; The first screening module is configured to: screen concrete mix proportions that meet the target strength grade based on compressive strength test data; The freeze-thaw cycle test module is configured to: prepare specimens based on the screened concrete mix proportions, conduct freeze-thaw cycle tests, and obtain the freeze-thaw resistance grade of concrete under different mix proportions; The second screening module is configured to: screen out concrete mix proportions that meet the set target frost resistance level; The modeling optimization and material calculation module is configured to: construct a quantitative mapping model of cement dosage-compression stress-compression stress based on the quantitative mapping relationship of volume ratio and frost resistance grade data, and obtain the optimal combination scheme of cement dosage and compression stress that meets the target performance through inverse solution.

9. A computer-readable storage medium having a program stored thereon, characterized in that, When executed by the processor, the program implements the steps in the method for optimizing the frost resistance and intelligent mix proportioning of compressed cast concrete as described in any one of claims 1-7.

10. An electronic device comprising a memory, a processor, and a program stored in the memory and executable on the processor, characterized in that, When the processor executes the program, it implements the steps in the method for optimizing the frost resistance and intelligent mix proportioning of compressed cast concrete as described in any one of claims 1-7.

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