Mix proportion design method of fiber shrinkage-compensating self-healing concrete

Abstract

The invention relates to the technical field of concrete preparation, in particular to a mix proportion design method of fiber-compensating shrinkage self-healing concrete, which comprises the following steps: S1, determining a concrete formula and forming a final experiment matrix; s2, preparing first concrete samples according to the final experiment matrix, testing the multiple first concrete samples one by one, and collecting test parameters to form a database; establishing a concrete mix proportion prediction model according to the database; s3, inputting the actual environment temperature and humidity and the actual production parameters into the concrete mix proportion prediction model, and searching an optimal mix proportion by using an optimization algorithm; s4, verifying whether the optimal mix proportion meets a target or not; through systematized experimental design and data analysis, accurate customization of the concrete mix proportion is realized, the performance stability and adaptability of the fiber compensation shrinkage self-healing concrete are remarkably improved, and the research and development cost and time are effectively reduced.

Description

Technical Field

[0001] The present invention relates to the technical field of concrete preparation, and particularly to a method for designing the mix proportion of fiber compensating shrinkage and self-healing concrete. Background Art

[0002] In modern concrete engineering, fiber compensating shrinkage and self-healing concrete, as an innovative material, is widely used in various infrastructure construction, repair projects, and structural construction in special environments. Especially in projects with strict requirements for durability and crack resistance, such as bridges, tunnel linings, and underground structures, the role of this kind of concrete is particularly crucial because it directly relates to the safety, service life, and service quality of the structure.

[0003] In the design of fiber compensating shrinkage and self-healing concrete, while meeting the compensating shrinkage performance, self-healing should also be achieved, that is, when cracks appear, the concrete cracks can automatically heal. The self-healing of concrete refers to the automatic closure phenomenon of cracks under certain factors. There are two main mechanisms for self-healing: (1) the rehydration of unhydrated cement; (2) the calcium carbonate precipitation caused by the chemical reaction between the calcium in the cement matrix and the carbon dioxide dissolved in the crack filling water.

[0004] Although self-healing also exists in ordinary concrete, due to its high water-cement ratio and large crack width, the self-healing efficiency is low. Therefore, by adding special components to traditional concrete materials, self-healing concrete materials can pre-sense and actively repair the cracks that appear in the concrete structure, and at the same time can restore or even improve the mechanical properties of the structure and extend the service life.

[0005] Regarding the mix proportion design of fiber compensating shrinkage and self-healing concrete, in traditional methods, it is usually based on the experience of engineers and current standards, and the selected raw materials are mixed and prepared in a certain proportion. However, in actual applications, in order to ensure that the prepared concrete can meet specific performance requirements, such as focusing on strength and durability requirements while taking into account workability, crack resistance, and economy, it is necessary to conduct multiple tests to adjust the proportions of each component. In this process, due to the lack of systematic scientific basis and data analysis support, it not only increases the R & D cost and time consumption, but also may cause the final product to fail to stably reach the expected performance indicators. In addition, traditional methods often fail to fully consider the influence of construction environment (such as temperature, humidity) on the concrete performance, which may affect the actual performance of the concrete and its long-term stability. Summary of the Invention

[0006] To solve the above technical problems, the present invention provides a mix proportion design method for fiber compensated shrinkage self-healing concrete. Through systematic experimental design and data analysis, precise customization of the concrete mix proportion is achieved, which not only significantly improves the performance stability and adaptability of fiber compensated shrinkage self-healing concrete, but also effectively reduces the R & D cost and time. In addition, this method fully considers the influence of construction environment factors, ensuring excellent performance and long-term stability of the concrete in practical applications.

[0007] A mix proportion design method for fiber compensated shrinkage self-healing concrete of the present invention comprises the following steps: S1. Determine the concrete formula and form the final experimental matrix; the concrete formula includes concrete components, the content of each component, and temperature and humidity; Among them, the concrete components include water, cement, mineral powder, fly ash, crushed stone, sand, water reducing agent, expansive agent, polypropylene fiber, and cementitious capillary crystalline waterproofing agent; Water serves as the medium for the hydration reaction, causing the cement and other gelling materials to undergo hydration reactions to form a solid gel; cement is the main gelling material, and through hydration reactions, products such as calcium silicate gel are generated, endowing the concrete with strength and adhesion; Crushed stone, as the coarse aggregate, provides the skeletal structure of the concrete, increasing the volume stability and load-bearing capacity. The specifications of the crushed stone are that the particle size is 5 - 25 mm, the mud content ≤ 1.0%, the lump content ≤ 0.5%, the content of needle and flake particles ≤ 10%, and the crushing index value ≤ 12%; the crushed stone with a particle size in the range of 5 - 25 mm can provide an ideal skeletal structure, which helps to disperse and bear the load, improving the overall load-bearing capacity of the concrete. Controlling the mud content and lump content can avoid the negative impact of these impurities on the cement hydration reaction, prevent them from reducing the strength and durability of the concrete, and reduce the pore formation caused by impurities. Appropriate crushed stone particle size and shape (controlling the content of needle and flake particles) can ensure that the concrete mixture has good fluidity, is easy to pour and vibrate, thereby obtaining a uniform concrete structure. A suitable crushing index value means that the crushed stone has sufficient hardness and compressive capacity and is not easily crushed in the concrete, which helps to improve the compressive and flexural strength of the concrete; Sand, as fine aggregate, fills the voids between coarse aggregates, increasing the density and workability of concrete. The specifications of the sand are as follows: stone powder content ≤ 3.0%, mud lump content ≤ 1.0%, fineness modulus 2.3 - 3.0, methylene blue value ≤ 1.4 g / kg, and crushing index value ≤ 25%. Sands with a fineness modulus between 2.3 and 3.0 have an ideal particle size distribution, which can provide good fluidity and easy vibration, while ensuring the density of the concrete. Controlling the stone powder content and mud lump content can avoid the influence of these fine particles on the cement hydration reaction. Excessive stone powder or mud lumps may absorb water, reducing the effective water-cement ratio and affecting the strength development of the concrete. Limiting the methylene blue value and crushing index value means that there are fewer harmful substances in the sand, and the sand itself has a high hardness and compressive capacity, which helps to improve the compressive strength, flexural strength, and long-term durability of the concrete. Mineral admixtures are the components that are preferentially considered in the preparation of self-healing concrete. Currently, fly ash, slag powder, and silica fume are the most widely used. As an active mineral admixture, slag powder can react with the alkaline substances in cement to generate more hydration products, fill the pores, and improve the density and durability of the concrete. A large number of literature studies have shown that under the same water-cement ratio, fly ash can effectively reduce the autogenous shrinkage of concrete and improve the volume stability of the concrete structure. The higher the dosage, the better the effect. Water reducers can improve the strength and durability of concrete, reduce the risk of crack formation, and help to meet the design requirements of a low water-cement ratio. The main way to solve the shrinkage of concrete is to add an expansive agent, and use the expansive deformation under constrained conditions to compensate for the shrinkage of the concrete. Its expansion rate depends on the type and dosage of the expansive agent. Existing studies have shown that as the dosage of the expansive agent increases, the expansion rate of the concrete increases, but the strength will decrease to some extent. This requires selecting an appropriate type of expansive agent and controlling its dosage so that it can reach the designed expansion value without significantly affecting the strength. The specifications of the expansive agent used in this invention are as follows: the restricted expansion rate in water for 7 days ≥ 0.050%, specific surface area ≥ 200 m 2 / kg, residue on 1.18 mm square hole sieve ≤ 0.5%. Polypropylene fibers are dispersed in the concrete, playing the role of crack resistance and toughening. Especially in the early stage, they can effectively prevent the expansion of microcracks, and at the same time, they can also improve the impact resistance and toughness of the concrete, especially suitable for concrete that requires self-healing ability and crack resistance. Cement-based permeable crystalline waterproofing agent reacts with water and unhydrated cement particles, and through internal catalytic reactions, generates crystals, which play a good role in filling concrete cracks. Use the orthogonal experimental design to arrange the concrete components, the content of each component, and the temperature and humidity to form the final experimental matrix. The final experimental matrix contains multiple concrete preparation formulas, and the concrete formula includes concrete components, the content of each component, and the temperature and humidity. Through the above steps, the final experimental matrix contains multiple detailed concrete preparation formulas, clearly specifying the specific contents of each component and the temperature and humidity conditions, providing clear guidance for subsequent precise preparation and testing, and ensuring the reliability and repeatability of the results; S2. Prepare the first concrete sample according to the final experimental matrix, test each of the multiple cured first concrete samples one by one and collect the test parameters to form a database; establish a concrete mix proportion prediction model based on the database; By constructing a database and establishing a prediction model, accurate prediction of concrete performance can be achieved, greatly reducing the blindness and repetitive labor of subsequent tests, improving the R & D efficiency. In addition, the prediction model established based on actual test data is more reliable, can better adapt to different construction conditions and requirements, ensure that the finally selected mix proportion can stably meet the expected performance indicators in actual applications, and take into account economic benefits at the same time. This process enhances the scientificity and reliability of the results, making the entire mix proportion design process more efficient and accurate; S3. Input the actual environmental temperature and humidity and the actual production parameters corresponding to the test parameters into the concrete mix proportion prediction model, and use an optimization algorithm to search for the optimal mix proportion; In this step, inputting the actual environmental temperature and humidity and the actual production parameters into the concrete mix proportion prediction model can enable the model to fully consider the actual situation of the construction site, make the mix proportion more suitable for the actual construction conditions. Using an optimization algorithm to search for the optimal mix proportion can accurately locate the most suitable mix proportion among numerous possibilities, improving efficiency and accuracy. This can not only ensure that the concrete performance meets the construction requirements, but also reasonably control costs while ensuring quality, reduce material waste, and improve the economic benefits of construction, enhancing the reliability and stability of the entire project.

[0008] S4. Verify whether the optimal mix proportion meets the target; if it meets, use this mix proportion for on-site construction; if it does not meet, modify the concrete mix proportion prediction model until it can output a mix proportion that meets the target; In actual construction, the environment and production parameters are complex and variable. Through considering the actual temperature, humidity and production parameters, the designed concrete mix proportion of the present invention can perfectly adapt to different construction conditions, ensure stable performance in various environments. The data-based processing method, from sample testing to establishing a database and then constructing a prediction model, provides accurate data support for mix proportion design, effectively reducing the error of manual experience judgment, greatly improving the accuracy and reliability of the mix proportion. In addition, using an optimization algorithm to search for the optimal mix proportion can not only meet the engineering quality requirements, but also reasonably allocate materials, avoid material waste, reduce production costs, and help the project to be carried out efficiently and economically.

[0009] As a preferred embodiment of the present invention, by weight, the concrete components include 80 - 130 parts of water, 250 - 275 parts of cement, 0 - 45 parts of mineral powder, 40 - 130 parts of fly ash, 1030 - 1090 parts of crushed stone, 750 - 780 parts of sand, 8.5 - 9.5 parts of water reducing agent, 30 - 35 parts of expansive agent, 0.8 - 1.1 parts of polypropylene fiber, and 3 - 4 parts of cementitious capillary crystalline waterproofing agent; 80 - 130 parts of water: Ensures good workability and fluidity, facilitating construction operations, while avoiding increased porosity and reduced strength caused by excessive water; 250 - 275 parts of cement: The cement dosage within this range can provide sufficient early strength development without increasing the heat of hydration and cost due to excess; 0 - 45 parts of mineral powder: The mineral powder within this range can reduce the cement dosage, lower carbon emissions, and improve the long - term performance of concrete; 40 - 130 parts of fly ash: The fly ash dosage within this range can fully utilize its advantages without negatively affecting the early strength and other key properties of concrete, ensuring high - performance performance of concrete in practical applications; 1030 - 1090 parts of crushed stone: A reasonable amount of crushed stone can reduce the water and cement dosages, optimize the workability and strength of concrete, and ensure good mixing and pouring effects; 750 - 780 parts of sand: An appropriate amount of sand can improve the overall performance of concrete, ensure good mixing and pouring effects, and not affect the functions of other materials; 8.5 - 9.5 parts of water reducing agent: The water reducing agent dosage within this range can fully utilize its advantages without negatively affecting the workability and other key properties of concrete, ensuring high - performance performance and construction convenience of concrete in practical applications; 30 - 35 parts of expansive agent: A reasonable dosage of expansive agent can effectively prevent shrinkage cracks, improve impermeability and durability without significantly affecting strength; 0.8 - 1.1 parts of polypropylene fiber: The dosage within this range can fully utilize the advantages of polypropylene fiber without significantly increasing the material cost. Excessive use of fiber may lead to increased costs and may negatively affect the workability of concrete; 3 - 4 parts of cementitious capillary crystalline waterproofing agent: The dosage within this range can fully utilize the effect of the cementitious capillary crystalline waterproofing agent without significantly increasing the material cost.

[0010] As a preferred embodiment of the present invention, the water - binder ratio is 0.25 - 0.35, and the specific surface area of cement is 280 - 350m 2 / kg; A water-cement ratio of 0.25 - 0.35 is a relatively low one. At the same time, the content of 40 - 130 parts of fly ash is higher compared to the prior art. The lower water-cement ratio reduces the porosity inside the concrete, making the structure more dense, significantly improving the compressive strength, impermeability and freeze-thaw resistance. This is particularly important for long-life concrete because high density can extend the service life of the concrete. The low water-cement ratio helps reduce autogenous shrinkage and drying shrinkage caused by water evaporation, reducing the risk of crack formation, especially for projects that require long-term stability. The specific surface area of the cement is 280 - 350 m 2 / kg. Such cement particles are relatively coarse, and the initial hydration rate is moderate, avoiding the problem of temperature rise caused by excessive hydration. The coarse cement particles have a positive effect on the repair of concrete damage. When the concrete is damaged, the unhydrated coarse particles are exposed, and when they encounter water, they hydrate again, generating hydration products to fill the voids, making the structure more dense, thereby improving the self-repair ability and ultimate strength of the concrete.

[0011] As a preferred embodiment of the present invention, the slag powder selected is S95 grade slag powder, and the slag powder specification is: the slag powder selected is S95 grade slag powder; the fly ash used is F-class II fly ash; the water reducer is a polycarboxylate-based water reducer. The water reduction rate of the polycarboxylate-based water reducer is ≥25%, the solid content is 0.90 - 1.10%, the air content is ≤6.0%, and the pH value is ≤6. The advantage of selecting S95 grade slag powder is that it significantly improves the strength, workability, durability and self-repair ability of the concrete through its high specific surface area, high fluidity ratio and high activity index, while achieving low-carbon environmental protection and economic benefits.

[0012] The fly ash used is F-class II fly ash, and the fly ash specification is: fineness ≤30.0%, water demand ratio ≤105%, loss on ignition ≤5.0%; the advantage of selecting F-class II fly ash is that it significantly improves the microstructure, workability, strength, durability and self-repair ability of the concrete through strict control of fineness, water demand ratio and loss on ignition, while achieving low-carbon environmental protection and economic benefits; Selecting a polycarboxylate-based water reducer with a water reduction rate of ≥25% can effectively reduce the water consumption in the concrete mixture. On the premise of maintaining the same work performance, it can reduce the water-cement ratio, improve the strength and durability of the concrete, make the concrete more dense, and enhance the impermeability, frost resistance and other properties; The polycarboxylate-based water reducer with a solid content of 0.90 - 1.10% helps to ensure the performance stability and use effect of the water reducer. It not only ensures the effective ingredient content of the water reducer in the solution, but also facilitates the metering and preparation during storage, transportation and use, and can better play its role in improving the concrete performance; The polycarboxylate-based water reducer with an air content ≤ 6.0% can avoid problems such as the reduction of concrete strength caused by excessive air content. At the same time, it can improve the workability of concrete, making the concrete have good fluidity, cohesiveness and water retention during mixing, transportation and pouring, which is convenient for construction operations. The polycarboxylate-based water reducer with a pH value ≤ 6 indicates that it is weakly acidic or close to neutral, has good compatibility with other components in concrete, will not have an adverse impact on reactions such as cement hydration due to pH problems, can ensure the performance stability of concrete during preparation and use, and can also reduce adverse effects such as corrosion of construction equipment and the environment.

[0013] As a preferred embodiment of the present invention, the method for creating the final experimental matrix in step S1 includes the following steps: The method for creating the final experimental matrix in step S1 includes the following steps: S11. List the influencing factors of concrete performance, including component types and inherent parameters; S12. For each influencing factor, select two extreme levels; S13. Generate a transitional experimental matrix containing the influencing factors and their combinations of extreme levels; S14. Prepare the different formulations in the transitional experimental matrix under unified preparation conditions; test the samples after curing and collect the test parameters; S15. Evaluate the influence of each influencing factor on the test parameters, detect the interaction between the influencing factors, and evaluate the importance of the interaction; S16. Mark the factors that have a significant impact on the concrete test parameters as key factors; for non-key factors, select their typical values as fixed settings; S17. Based on the key factors and non-key factors, construct the final experimental matrix; Through the above steps, an experimental matrix is generated using statistical software, and the influence of each factor and their interaction is evaluated in combination with the multi-factor analysis of variance model, enabling accurate screening of the key factors that have a significant impact on concrete performance, workability and economy. For non-key factors, typical values can be selected for fixed settings to further simplify the experimental design; significantly reducing the experimental scale and time cost, improving the R & D efficiency and economic benefits. This method not only optimizes the mix design process but also enhances the stability and adaptability of the final product, ensuring that it can stably meet the expected performance indicators in practical applications.

[0014] As a preferred embodiment of the present invention, the test parameters include performance data, work data and economic data. The performance data includes compressive strength, splitting tensile strength, shrinkage rate, self-healing performance and durability; the work data includes concrete slump, initial setting time and final setting time; the economic data includes material cost per unit volume and production cost. Compressive strength is the core index for evaluating the load-bearing capacity of concrete. High compressive strength ensures the safety and stability of the structure, especially when bearing large loads; splitting tensile strength reflects the tensile performance of concrete, which is crucial for resisting crack propagation and improving the integrity of the structure, especially in structures such as bridges and roads that need to bear tensile stress; the shrinkage rate is very important for preventing cracking and maintaining volume stability. A low shrinkage rate can reduce the crack risk caused by water evaporation or temperature changes; the self-healing performance evaluates the ability of concrete to self-repair after microcracks appear, which helps to extend the service life of the structure and reduce maintenance requirements; durability includes impermeability, freeze-thaw resistance, corrosion resistance, etc. These characteristics determine the stability and reliability of concrete during long-term use, ensuring that it can resist the influence of various environmental factors. The slump of concrete measures the workability and fluidity of concrete, ensuring that it is easy to operate during mixing, transportation and pouring, and can fully fill the formwork, avoiding problems such as voids and non-compactness; initial setting time and final setting time: These two parameters determine the construction window period of concrete, that is, the time range from mixing to final hardening. Reasonable setting time can ensure the smooth progress of the construction and avoid problems caused by premature or late setting. Material cost: It is directly related to the total cost of the project. Selecting the appropriate material ratio can minimize the cost and improve economic efficiency on the premise of ensuring performance; production cost: It includes expenses such as labor, equipment use, and transportation. Optimizing the production process can reduce unnecessary expenses, improve the overall economic efficiency, and ensure that the project is completed within the budget.

[0015] As a preferred solution of the present invention, in step S3, in the actual production parameters, obtain the required concrete performance data according to the construction location; determine the temperature, humidity, working data and economic data according to the construction site; Among them, the items included in the required concrete performance data are the same as the test parameters, which are all compressive strength, splitting tensile strength, shrinkage rate, self-healing performance and durability; the items included in the working data are the same as the test parameters, which are all concrete slump, initial setting time and final setting time; the items included in the economic data are the same as the test parameters, the material cost and production cost per unit volume; From the perspective of performance adaptation, different construction locations have different requirements for concrete performance; for example, the building foundation part may emphasize compressive strength more to bear the weight of the upper structure, while hydraulic structures have extremely high requirements for the impermeability of concrete; determining the performance data according to the construction location can ensure that the concrete performance is accurately adapted to the specific construction needs and guarantee the project quality and safety. The temperature and humidity have a significant impact on the performance of concrete. In a high-temperature environment, the water in the concrete evaporates quickly, which may lead to rapid setting, reduced strength, and crack generation. Low temperature may delay the cement hydration reaction and affect the normal hardening of concrete. The temperature and humidity conditions at the construction site are complex and variable. Adjusting the mix ratio according to the actual temperature and humidity can effectively avoid these problems and ensure that the concrete can stably perform its properties in different environments. The working data is closely related to the construction process. The operating conditions and construction techniques at the construction site determine the requirements for working data such as the slump, initial setting time, and final setting time of concrete. For example, pumping construction requires the concrete to have good fluidity and an appropriate setting time to ensure smooth pumping and construction progress. Determining the working data based on the construction site can make the construction performance of the concrete fit the construction method and improve the construction efficiency and quality. The economic data is related to project cost control. The material prices, labor costs, and rental fees of construction equipment in different regions vary greatly. Determining the economic data based on the construction site can fully consider cost factors in the mix ratio design, reasonably select materials, and optimize the production process to avoid unnecessary expenses. On the premise of ensuring the project quality, maximize the economic benefits and ensure the smooth completion of the project within the budget.

[0016] As a preferred embodiment of the present invention, in S2, the method for establishing a concrete mix ratio prediction model based on a database includes the following steps: S21. Preprocess the original data in the database and create interaction features based on the preprocessed original data. S22. Use the original data and interaction features to form a feature database, and divide the data in the feature database into a training set and a test set. Specifically, in order to ensure the quality of the data and improve the stability of the model, the data in the database can be preprocessed, including data cleaning, data standardization, and normalization. To improve the training efficiency of the subsequent model, the data is normalized to [0,1] or standardized to zero mean and unit variance. Data cleaning can check and clean the outliers or missing values in the database. If some features have missing values, they can be filled using the mean, median, or interpolation method, or the features with too many missing values can be directly removed. For the treatment of outliers, box plots or standard deviation methods can be used to identify outliers and remove or replace them as appropriate, such as replacing them with adjacent values. The performance of the concrete mix ratio depends on the interaction of multiple factors. Therefore, in order to enhance the expressiveness of the model, interaction features can be created, including feature combinations, polynomial features, etc. The created interaction features and the original data in the database are merged into a new feature database to provide more comprehensive information for the model. S23. Select a machine learning model to construct a concrete mix ratio prediction model and design the architecture of the machine learning model. S24. Train the machine learning model using the training set, evaluate the machine learning model using the test set, deploy the trained model to the actual application, make predictions on new data, and obtain the prediction results; In this step, a machine learning model is selected as the concrete mix ratio prediction model, including recurrent neural network, long short-term memory network, random forest, etc. Input variables for the concrete mix ratio prediction model are selected, including temperature and humidity, required concrete performance data, working data, economic data, etc. The output variable of the concrete mix ratio prediction model is determined to be the mix ratio of each component. Since the machine learning model has a complex structure, architecture design is required, including the structure and number of neurons in the input layer, hidden layer, and output layer. After designing the model architecture, the model can be trained, the number of neurons in the hidden layer and the activation function can be adjusted to find the optimal configuration. After training, an independent test set can be used to evaluate the performance of the model, evaluate the performance of the trained model on unseen data, and optimize the model according to the evaluation results. The optimization methods include adjusting the model architecture, modifying the loss function, and adjusting hyperparameters; S25. Introduce confidence filtering to screen the quality of the prediction results of new data; Based on the above embodiments, in order to improve the reliability of the prediction results and avoid engineering decision-making mistakes caused by low-confidence predictions, confidence filtering can be introduced to calculate the confidence of the prediction results of new data, and the prediction results are screened based on the confidence scores. Among them, high-confidence results are used as valid outputs, and low-confidence results are marked or further processed, so that the quality of the model prediction results can be screened to ensure that the prediction results in actual applications have high reliability.

[0017] In the above steps, systematic data preprocessing ensures the quality and consistency of the data, improves the reliability and accuracy of model training; selecting a variety of advanced machine learning models as the basic architecture can capture complex non-linear relationships and improve the prediction accuracy; through strict training and testing processes, ensure the stability and generalization ability of the model in actual applications; the optimization process further enhances the performance of the model, enabling the finally deployed model to provide accurate and reliable predictions in actual applications, helping engineers quickly find the optimal concrete mix ratio, reducing trial-and-error costs and time consumption, and improving R & D efficiency and economic benefits.

[0018] As a preferred solution of the present invention, the method for verifying whether the optimal mix ratio meets the target includes the following steps: S41. According to the optimal mix ratio output by the concrete mix ratio prediction model, accurately weigh each component, and prepare concrete sample two under unified mixing, vibrating, forming, and curing conditions; S42. Test the performance data of the second concrete sample after curing, calculate the working data and economic data, and determine whether the performance data, working data, and economic data meet the requirements; This step provides a systematic and scientific verification process, ensuring the feasibility and stability of the optimal mix ratio in practical applications, reducing unnecessary experiments and adjustments, improving the R & D efficiency and economic benefits, and providing a reliable concrete mix ratio solution for engineering projects.

[0019] As a preferred embodiment of the present invention, if the performance data, working data, and economic data do not meet the requirements, the following adjustments are made: S421. Analyze the deviations between the performance data, working data, and economic data of the second concrete sample and the design objectives. For the performance indicators that do not meet the requirements, determine the key factors related to them (e.g., insufficient compressive strength may be related to the water-binder ratio and fly ash content); S422. Based on the correlation between the performance indicators and the key factors (such as the relationship between the water-binder ratio and compressive strength), adjust the parameters of the key factors. The parameters of the key components include the type and addition amount of the key components, etc. For example, if the slump is insufficient, it can be corrected by adjusting the water reducer or the sand and gravel ratio; for the economic indicators (such as high material cost), optimize the content of non-key components or replace them with low-cost materials; By accurately adjusting the parameters of the key factors and optimizing the non-key components, the improvement of the performance indicators and economic indicators is initially achieved. However, these adjustments are only based on the current sample data and empirical judgment. In order to further improve the accuracy and adaptability of the prediction model and enable it to more comprehensively and deeply reflect the complex relationship between concrete performance and various factors, new experimental results need to be incorporated into the data system, and the model is retrained and optimized with more abundant data; regularly prepare the second concrete sample using the adjusted concrete formula, use the real data of the second concrete sample as a new sample, add it to the database, retrain the prediction model with the extended database, focus on optimizing the fitting ability of the model for the new data, assign higher weights to the key components affecting the target in the model, enhance its sensitivity to the target, and based on the corrected model, re-predict and output a new optimal mix ratio, and repeat the verification process of S41 - S42 until the output mix ratio meets the performance, working, and economic objectives; In the above steps, starting from data analysis, through precise adjustment and model optimization until the verification of the new optimal mix ratio is passed, a closed-loop feedback mechanism is formed, which greatly improves the iteration speed and quality of the R & D process, ensures that the final product not only meets the expected performance indicators but also has good economic benefits, and at the same time enhances the adaptability to on-site construction conditions and reduces project risks. This optimization method provides strong support for the R & D and application of high-performance concrete, promoting the technological progress and sustainable development of the construction industry.

[0020] The beneficial effects of the present invention compared with the prior art are as follows: (1) By constructing a database and establishing a concrete mix ratio prediction model, the accurate prediction of concrete performance can be achieved. This method not only reduces the blindness and repetitive labor of subsequent tests, improves the R & D efficiency, but also ensures that the finally selected mix ratio can stably meet the expected performance indicators in actual applications, while taking into account economic benefits; (2) Adopting the compound admixture technology of "two lows and one high", the lower water-binder ratio and higher fly ash dosage reduce the porosity, make the structure more dense, significantly improve the compressive strength, impermeability and freeze-thaw resistance. The low cement specific surface area enables the coarse-grained cement to rehydrate when damaged, generating hydration products to fill the voids, enhancing the self-healing ability and final strength; (3) Using statistical software to generate an experimental matrix and combining with a multi-factor variance analysis model to evaluate the effects of various factors and their interactions, accurately screening out the key factors that have significant effects on concrete performance, workability and economy; for non-key factors, selecting their typical values as fixed settings further simplifies the experimental design, reducing unnecessary experimental times and resource consumption. Brief Description of the Drawings

[0021] Figure 1 It is a flow schematic diagram of the present invention. Detailed Embodiments

[0022] In order to make the above objects, features and advantages of the present invention more obvious and understandable, the following detailed description of the specific embodiments of the present invention will be given in conjunction with the drawings in the specification.

[0023] In the following description, many specific details are set forth in order to fully understand the present invention. However, the present invention can also be implemented in other ways different from those described herein. Those skilled in the art can make similar extensions without departing from the connotation of the present invention. Therefore, the present invention is not limited by the specific embodiments disclosed below.

[0024] Secondly, the "embodiment" mentioned herein refers to specific features, structures or characteristics that may be included in at least one implementation manner of the present invention. The phrase "in one embodiment" that appears in different places in this specification does not necessarily refer to the same embodiment, nor is it an individual or alternative embodiment that is mutually exclusive with other embodiments.

[0025] Embodiment A mix proportion design method for fiber compensating shrinkage and self-healing concrete includes the following steps: S1. Determine the concrete formula and form a final experimental matrix; the concrete formula includes concrete components, the content of each component, and temperature and humidity; Among them, the concrete components include water, cement, mineral powder, fly ash, crushed stone, sand, water reducer, expansion agent, polypropylene fiber and cement-based penetrating crystalline waterproofing agent; Among them, P.O 42.5 cement is selected as the cement; S95 grade mineral powder is selected as the mineral powder; F-class II fly ash is selected as the fly ash; The particle size of the crushed stone is 5 - 25 mm; Medium sand is selected as the sand; JY-PS-1 polycarboxylic acid high-performance water reducer of Beijing Jinyu Energy Saving Technology Co., Ltd. is selected as the water reducer; HCSA-II type of Tianjin Baoming Co., Ltd. is selected as the expansion agent; Polypropylene fiber: the length is 6 - 19 mm, the diameter is 18 μm to 30 μm, and the elastic modulus is 3.5 GPa - 4.5 GPa; Cement-based penetrating crystalline waterproofing agent of Canadian Ketton International Co., Ltd. is selected as the cement-based penetrating crystalline; By weight, 80 - 130 parts of water, 250 - 275 parts of cement, 0 - 45 parts of mineral powder, 90 - 130 parts of fly ash, 1030 - 1090 parts of crushed stone, 750 - 780 parts of sand, 8.5 - 9.5 parts of water reducer, 30 - 35 parts of expansion agent, 0.8 - 1.1 parts of polypropylene fiber and 3 - 4 parts of cement-based penetrating crystalline waterproofing agent; the water-binder ratio is 0.25 - 0.35, and the specific surface area of the cement is 280 - 350 m 2 / kg; The method for creating the final experimental matrix includes the following steps; S11. List all the factors that may affect the concrete performance, including the types and inherent parameters of cement, mineral powder, fly ash, crushed stone, sand, water reducer, expansion agent, polypropylene fiber and cement-based penetrating crystalline waterproofing agent; S12. For each influencing factor, select two extreme levels; S13. Use statistical software to generate a transitional experimental matrix containing the selected factors and their combinations of extreme levels; S14. Prepare different formulations in the transitional experimental matrix under unified preparation conditions; test the samples after curing and collect the test parameters; S15. Use a multi - factor analysis of variance model to evaluate the influence of each factor on performance data, working data, and economic data; use a multi - factor ANOVA model to detect the interaction between factors and evaluate the importance of the interaction; S16. Based on the above evaluations, retain the key factors that have a significant impact on concrete performance data, working data, and economic data. The key factors include: water - binder ratio, cement dosage, fly ash dosage, type and dosage of water - reducing agent, expansion agent dosage, polypropylene fiber dosage, and cement - based penetrating crystalline waterproofing agent dosage, etc.; for non - key factors, select their typical values as fixed settings. The non - key factors include fineness modulus of sand, maximum particle size of crushed stone, dosage of mineral powder, and temperature and humidity, etc.; S17. Re - construct the final experimental matrix based on the key factors and non - key factors; S2. Prepare the first concrete sample according to the final experimental matrix, test each of the cured concrete samples one by one and collect the test parameters to form a database; establish a concrete mix proportion prediction model based on the database; S3. Obtain the required concrete performance data according to the construction location, and determine the temperature, humidity, working data, and economic data according to the construction site. Among them, the required concrete performance data: compressive strength ≥ 25 MPa, splitting tensile strength ≥ 3.0 MPa, shrinkage rate ≤ 0.03%, self - healing performance (through crack width test), impermeability is P8, and freeze - thaw resistance should reach F200; The temperature, humidity, working data, and economic data determined at the construction site: Temperature: 15°C - 30°C; Humidity: 40% - 80%; Slump: 180 mm ± 20 mm; Initial setting time: 8 ± 3 hours; Final setting time: 10 ± 3 hours; Material cost per unit volume: ≤ 500 yuan; Production cost per unit volume: ≤ 300 yuan; Input the obtained required concrete performance data, working data, and economic data into the concrete mix proportion prediction model, and use an optimization algorithm to search for the optimal mix proportion; The optimal mix proportion is: 120 parts of water, 260 parts of cement, 30 parts of mineral powder, 110 parts of fly ash, 1060 parts of crushed stone, 765 parts of sand, 9 parts of water - reducing agent, 32 parts of expansion agent, 1 part of polypropylene fiber, and 3.6 parts of cement - based penetrating crystalline waterproofing agent; the water - binder ratio is 0.25 - 0.35, and the specific surface area of cement is 280 - 350 m2 / kg, and the fly ash is 90 - 130 parts; among which the water - binder ratio is 0.3; S4. According to the above - mentioned optimal mix ratio, accurately weigh each component, and prepare concrete sample two under unified mixing, vibrating, forming and curing conditions; test the performance data of the cured concrete sample two, calculate the working data and economic data, and judge whether the performance data, working data and economic data meet the requirements; Test results: 7 - day compressive strength: 29.3 MPa; Splitting tensile strength: 3.2 MPa; Hydrostatic pressure resistance: 1.4 MPa; Shrinkage rate: 0.028%; Self - healing performance: the crack width is reduced from 0.6 mm to 0.05 mm; Impermeability: P8; Freeze - thaw resistance: F200; Chloride ion content: 0.08%; Non - toxic certification: non - toxic; Sulfate resistance: reaching KS150; Slump: 185 mm; Initial setting time: 2.5 hours; Final setting time: 11.5 hours; Unit volume material cost: meeting the budget requirements; Unit volume production cost: meeting the budget requirements.

[0026] Comparative example 1: According to the construction location, obtain the required concrete performance data, and determine the temperature, humidity, working data and economic data according to the construction site; among them, the required concrete performance data: compressive strength ≥ 25 MPa, splitting tensile strength ≥ 3.0 MPa, shrinkage rate ≤ 0.03%, self - healing performance (tested by crack width), impermeability is P8, freeze - thaw resistance reaches F200; The temperature, humidity, working data and economic data determined by the construction site: Temperature: 15℃ - 30℃; Humidity: 40% - 80%; Slump: 180 mm ± 20 mm; Initial setting time: 8 ± 3 hours; Final setting time: 10 ± 3 hours; Unit volume material cost: ≤ 500 yuan; Unit volume production cost: ≤ 300 yuan; According to the empirical values, the mix ratio is as follows: 200 parts of water, 250 parts of cement, 25 parts of mineral powder, 40 parts of fly ash, 1080 parts of crushed stone, 750 parts of sand, 8.9 parts of water reducer, 30 parts of expansion agent, 0.9 part of polypropylene fiber, and 3.4 parts of cement-based penetrating crystalline waterproofing agent; Test results: Compressive strength at 7 days: 26.4 MPa; Splitting tensile strength: 2.6 MPa; Hydrostatic pressure resistance: 1.13 MPa; Shrinkage rate: 0.42%; Self-healing performance: crack width reduced from 0.6 mm to 0.3 mm; Impermeability: P8; Freeze-thaw resistance: F200; Chloride ion content: 0.08%; Non-toxic certification: non-toxic; Sulfate resistance: KS150; Slump: 210 mm; Initial setting time: 3.5 hours; Final setting time: 13 hours; Material cost per unit volume: does not meet the budget requirements; Production cost per unit volume: does not meet the budget requirements.

[0027] From the results of the examples and comparative examples, through the systematic experimental matrix and multi-factor analysis of variance (ANOVA), this method can accurately identify the key factors that have a significant impact on the performance of concrete and optimize them. The finally obtained optimal mix ratio not only meets the strict performance requirements such as compressive strength, splitting tensile strength, shrinkage rate, self-healing performance, impermeability, and freeze-thaw resistance, but also performs excellently in actual tests; at the same time, it also takes into account the specific conditions at the construction site, such as temperature and humidity, slump, initial setting time, and final setting time, etc., ensuring the workability and operation convenience of concrete in actual construction: by accurately calculating and optimizing the proportions of each component, this method effectively controls the material cost and production cost per unit volume, achieving the maximization of economic benefits; the "two lows and one high" compound admixture technology adopted in the examples, with a lower water-binder ratio and a higher fly ash dosage, reduces the porosity, makes the structure more dense, significantly improves the compressive strength, impermeability, and freeze-thaw resistance, and the low cement specific surface area enables the coarse-grained cement to rehydrate when damaged, generating hydration products to fill the voids, enhancing the self-repair ability and ultimate strength.

[0028] It should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention rather than to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that the technical solutions of the present invention can be modified or equivalently replaced without departing from the spirit and scope of the technical solutions of the present invention, and they should all be covered by the scope of the claims of the present invention.

Claims

1. A mix design method for fiber-compensated shrinkage self-healing concrete, characterized in that: The following steps are involved: S1. Determine the concrete formula and form a final experimental matrix; the concrete formula includes concrete components, content of each component, and temperature and humidity; S2. Prepare concrete sample 1 according to the final experimental matrix, test multiple concrete samples 1 after curing one by one and collect test parameters to form a database; establish a concrete mix ratio prediction model according to the database; S3, inputting the actual environmental temperature and humidity and the actual production parameters corresponding to the test parameters into the concrete mix ratio prediction model, and using an optimization algorithm to search for the optimal mix ratio; S4. Verify whether the optimal mix ratio meets the target; if so, use the mix ratio for on-site construction; if not, modify the concrete mix ratio prediction model to output a mix ratio that meets the target.

2. The mix design method of fiber-compensated shrinkage self-healing concrete according to claim 1, characterized in that: In parts by weight, the concrete components include 80-130 parts of water, 250-275 parts of cement, 0-45 parts of mineral powder, 40-130 parts of fly ash, 1030-1090 parts of crushed stone, 750-780 parts of sand, 8.5-9.5 parts of water reducer, 30-35 parts of expansion agent, 0.8-1.1 parts of polypropylene fiber and 3-4 parts of cement-based penetrating crystallization waterproofing agent.

3. The mix design method of fiber-compensated shrinkage self-healing concrete according to claim 2, characterized in that: The water-cement ratio of the concrete component is 0.25-0.35, and the specific surface area of ​​cement is 280-350m 2 / kg.

4. The mix design method of fiber-compensated shrinkage self-healing concrete according to claim 2, characterized in that: The mineral powder is S95 grade mineral powder; the fly ash is F class II fly ash; the water reducer is a polycarboxylic acid water reducer, the water reduction rate of the polycarboxylic acid water reducer is ≥25%, the solid content is 0.90-1.10%, the gas content is ≤6.0%, and the pH value is ≤6.

5. The mix design method of fiber-compensated shrinkage self-healing concrete according to claim 1, characterized in that: The method for creating the final experimental matrix in step S1 comprises the following steps: S11. List the factors that affect the performance of concrete, including the types of components and inherent parameters; S12. For each of the influencing factors, select two extreme levels; S13, generating a transition experiment matrix including the influencing factors and their extreme level combinations; S14, preparing different formulations in the transition experiment matrix using uniform preparation conditions; testing the samples after curing and collecting test parameters; S15. Evaluate the impact of each influencing factor on the test parameters, detect the interaction between the influencing factors, and evaluate the importance of the interaction; S16. Record the factors that have a significant impact on the concrete test parameters as key factors; for non-key factors, select their typical values ​​as fixed settings; S17. Construct the final experimental matrix based on key factors and non-key factors.

6. The mix design method of fiber-compensated shrinkage self-healing concrete according to claim 5, characterized in that: The test parameters include performance data, working data and economic data. The performance data include compressive strength, splitting tensile strength, shrinkage, self-healing performance and durability; the working data include concrete slump, initial setting time and final setting time; the economic data include material cost and production cost per unit volume.

7. The mix design method of fiber-compensated shrinkage self-healing concrete according to claim 1, characterized in that: In step S3, among the actual production parameters, the required concrete performance data is obtained according to the construction location; and the temperature and humidity, working data and economic data are determined according to the construction site.

8. The mix design method of fiber-compensated shrinkage self-healing concrete according to claim 1, characterized in that: In S2, a method for establishing a concrete mix ratio prediction model according to the database comprises the following steps: S21, preprocessing the original data in the database, and creating interactive features according to the preprocessed original data; S22, using the original data and the interactive features to form a feature database, and dividing the data in the feature database into a training set and a test set; S23. Select a machine learning model to build a concrete mix ratio prediction model and design the architecture of the machine learning model; S24. Use the training set to train the machine learning model, use the test set to evaluate the machine learning model, deploy the trained model to actual applications, predict new data, and obtain prediction results; S25. Introduce confidence filtering to perform quality screening on the prediction results of new data.

9. The mix design method of fiber-compensated shrinkage self-healing concrete according to claim 6, characterized in that: The method for verifying whether the optimal mix ratio meets the target comprises the following steps: S41, preparing concrete sample 2 according to the optimal mix ratio output by the concrete mix ratio prediction model and in accordance with uniform preparation conditions; S42. Test the performance data of the concrete sample 2 after curing, calculate the working data and economic data, and determine whether the performance data, working data and economic data meet the requirements.

10. The mix design method of fiber-compensated shrinkage self-healing concrete according to claim 9, characterized in that: If the performance data, working data and economic data do not meet the requirements, make the following adjustments: S421. Analyze the deviation between the performance data, working data and economic data of concrete sample 2 and the design target, and determine the key factors related to the performance indicators that do not meet the requirements; S422. Adjust the key factors based on the correlation between the performance indicators and the key factors.

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