A Mix Proportion Design Method for Steam-Curing-Free Low-Carbon Ultra-High Performance Concrete

Abstract

The present invention relates to the technical field of concrete mix design, and in particular to a mix design method for steam-curing-free low-carbon ultra-high performance concrete. Based on target mechanical properties, carbon emission constraints and aggregate gradation parameters, a multi-objective optimization algorithm model with water-binder ratio, activity index of mineral admixture, and packing density of manufactured sand gradation as input variables is constructed to calculate the weight ratio range of each component. A dynamic feedback adjustment algorithm is adopted, combined with real-time aggregate moisture content and admixture activity detection data, to optimize the actual construction mix ratio. The multi-objective optimization algorithm model is a hybrid model of improved particle swarm optimization (MPSO) and adaptive genetic algorithm (AGA). The concrete mix ratio designed by the present invention can achieve a 28-day compressive strength ≥ 150 MPa and a carbon emission per unit volume of concrete ≤ 300 under steam-curing-free conditions, effectively improving the performance of concrete, reducing carbon emissions, and reducing production costs, providing a green and efficient concrete mix design scheme for the construction industry.

Description

Technical Field

[0001] The present invention relates to the technical field of concrete mix design, and specifically provides a mix design method for steam-curing-free and low-carbon ultra-high-performance concrete. Background Art

[0002] In the technical field of concrete mix design, traditional concrete mix design has many limitations and is difficult to meet the current requirements of the construction industry for high performance, environmental protection, and sustainable development. This has led to the emergence of a mix design method for steam-curing-free and low-carbon ultra-high-performance concrete. Specifically, it is manifested as follows:

[0003] Difficulty in meeting high-performance requirements: With the development of the construction industry, the requirements for the mechanical properties of concrete are increasing day by day. For high-rise, long-span buildings, and special engineering structures, concrete is required to have higher compressive and flexural strengths, etc. However, the concrete under traditional mix design has limited performance in terms of strength, durability, etc., and it is difficult to meet these high standards, which limits the safety and service life of building structures.

[0004] Disadvantages of the steam-curing process: Traditional ultra-high-performance concrete often relies on the steam-curing process to improve performance. This process consumes a large amount of energy, not only increasing production costs but also causing greater pressure on the environment. At the same time, the investment in steam-curing equipment is large, the operation is complex, and the production efficiency is low, which is not suitable for the requirements of large-scale and rapid construction projects, restricting the application of concrete in some projects.

[0005] Severe carbon emission problem: Cement production is the main source of concrete carbon emissions. In the context of the global response to climate change and the advocacy of low-carbon development, the large amount of cement used in traditional concrete mixes results in a high carbon emission per unit volume of concrete, which does not conform to the concept of green buildings and sustainable development. The construction industry urgently needs technologies and methods to reduce concrete carbon emissions.

[0006] Irrational aggregate gradation and utilization of mineral admixtures: Traditional design methods do not pay enough attention to aggregate gradation. The particle size distribution of aggregates such as manufactured sand is not scientific, affecting the compactness and workability of concrete. Moreover, the selection and use of mineral admixtures lack systematicness, and their activity and advantages cannot be fully utilized, making it impossible to achieve a balanced optimization of concrete performance and environmental benefits.

[0007] Therefore, a mix design method for steam-curing-free and low-carbon ultra-high-performance concrete is proposed to address the above problems. Summary of the Invention

[0008] The purpose of the present invention is to provide a mix design method for steam-curing-free and low-carbon ultra-high-performance concrete to solve the problems raised in the above background art.

[0009] To achieve the above purpose, the present invention provides the following technical solutions:

[0010] A mix proportion design method for steam-curing-free low-carbon ultra-high performance concrete includes the following steps:

[0011] Step S1: Based on the target mechanical properties, carbon emission constraints, and aggregate gradation parameters, construct a multi-objective optimization algorithm model. The multi-objective optimization algorithm model uses the water-binder ratio , the activity index of mineral admixture , the packing density of manufactured sand gradation as input variables and outputs the weight ratios of each component that meet the steam-curing-free conditions;

[0012] Step S2: Calculate the following weight ratio ranges through the multi-objective optimization algorithm model:

[0013] Ordinary Portland cement: 650 - 900 parts;

[0014] Mineral admixtures: metakaolin 80 - 100 parts, silica fume 50 - 120 parts, slag powder 150 - 250 parts, microsphere ultrafine powder 20 - 50 parts;

[0015] Manufactured sand aggregate: 400 - 500 parts of 10 - 35 mesh, 280 - 400 parts of 35 - 80 mesh, 180 - 260 parts of 80 - 120 mesh;

[0016] Admixtures: water reducer 5 - 15 parts, defoamer 1 - 3 parts, shrinkage reducing agent 1 - 3 parts;

[0017] Steel fiber: volume fraction 1% - 2%;

[0018] Water-binder ratio 0.18 - 0.22;

[0019] Step S3: Based on the ratio range in Step S2, adopt a dynamic feedback adjustment algorithm, and combine the real-time aggregate moisture content and the test data of the activity of admixtures to optimize the actual construction mix proportion.

[0020] As a preferred solution, the multi-objective optimization algorithm model is a hybrid model based on the improved particle swarm optimization MPSO and the adaptive genetic algorithm AGA, and its objective function is as follows:

[0021] , where is the 28-day compressive strength, and the target value ≥ 150 MPa; is the carbon emission of unit volume of concrete, and the constraint condition ≤ 300; is the 7-day shrinkage value; , , , is the weight coefficient, with a value range of 0.2 - 0.4 and satisfying ; represents the packing density of the manufactured sand gradation.

[0022] As a preferred solution, the velocity update formula of the improved particle swarm optimization MPSO is:

[0023] , where is the velocity of the th particle at the th iteration; is the inertia weight, dynamically adjusted to , where , , is the maximum number of iterations; , , are the learning factors, taking 1.5, 1.5, and 0.5 respectively; , , are random numbers in the range of [0, 1]; is the gradient of the objective function at ; is the historical optimal solution of particle ; is the global optimal solution.

[0024] As a preferred solution, the crossover probability and the mutation probability of the adaptive genetic algorithm AGA are dynamically adjusted as follows:

[0025] , where represents the maximum value of the crossover probability , , represents the minimum value of the crossover probability , ; is the average fitness of the population; is the maximum fitness of the population; is the larger fitness value among the crossover individuals;

[0026] ; where represents the maximum value of the mutation probability , , represents the minimum value of the mutation probability , ; is the fitness value of the mutated individual.

[0027] As a preferred solution, the gradation parameters of manufactured sand are obtained by a laser particle size analyzer, and the particle size distribution ratio of three grades of manufactured sand is optimized based on the modified Fuller curve theory. Its gradation formula is:

[0028] , where is the cumulative sieve residue percentage with a particle size less than ; is the maximum particle size; is the main gradation index; is the correction coefficient; is the secondary gradation index.

[0029] Activity index of mineral admixture is calculated by the following formula:

[0030] , where is the weight coefficient of the admixture , and ; is the specific surface area of the admixture ; is the time decay factor, taking 0.01 - 0.05; is the curing time; is the mass percentage of the active oxides and in the admixture .

[0031] As a preferred solution, the dynamic feedback adjustment algorithm includes the following steps:

[0032] The water content of the aggregate and the activity index of the admixture are collected in real time by a humidity sensor and an X-ray fluorescence spectrometer;

[0033] The water content of the aggregate and the activity index of the admixture are input into the multi-objective optimization model to recalculate the water-binder ratio and the dosage of the admixture;

[0034] The adjusted mix ratio needs to meet the constraint conditions:

[0035] ;

[0036] ;

[0037] where represents the new water-binder ratio recalculated by the dynamic feedback adjustment algorithm, It is the initial water-binder ratio preliminarily calculated by the multi-objective optimization algorithm model.

[0038] As a preferred solution, let the volume fraction of steel fiber be , which is dynamically adjusted through the fracture toughness model, and its formula is:

[0039] , where is the designed flexural strength; is the fracture toughness of the matrix material; is the fiber-matrix interface bonding efficiency coefficient, taking 0.6 - 0.8; is the elastic modulus of steel fiber; is the density of the matrix material.

[0040] It can be seen from the technical solution provided by the present invention above that a mix proportion design method for steam-curing-free low-carbon ultra-high performance concrete provided by the present invention has the beneficial effects as follows:

[0041] Performance optimization: By constructing a multi-objective optimization algorithm model with the water-binder ratio, activity index of mineral admixture, and packing density of manufactured sand gradation as input variables, and comprehensively considering indicators such as 28-day compressive strength, carbon emission per unit volume of concrete, and 7-day shrinkage value, the mix proportion can be accurately designed, enabling the target value of 28-day compressive strength of concrete to be ≥150 MPa. While ensuring high strength, the shrinkage value can be effectively controlled, the comprehensive performance of concrete can be improved, and the requirements of various high-standard projects can be met;

[0042] Low-carbon environmental protection: Taking the carbon emission per unit volume of concrete as a constraint condition (≤300) into the model, optimizing the ratio of cement to mineral admixture, reducing the cement dosage, thereby reducing carbon emissions during the concrete production process, and promoting the green and sustainable development of the construction industry;

[0043] Cost saving: Reasonably using a variety of mineral admixtures (metakaolin, silica fume, slag powder, microbead ultrafine powder) to replace part of the cement, reducing the raw material cost on the premise of ensuring performance; at the same time, the steam-curing-free feature avoids energy consumption and equipment investment in the steam-curing link, further saving production costs;

[0044] Convenient construction: Adopting a dynamic feedback adjustment algorithm, optimizing the actual construction mix proportion in combination with the real-time aggregate moisture content and activity detection data of admixtures, ensuring the stable quality of concrete at the construction site; and the volume fraction of steel fiber is dynamically adjusted through the fracture toughness model, enhancing the toughness of concrete, adapting to different construction conditions and engineering requirements, and improving construction efficiency and quality. Brief Description of the Drawings

[0045] Figure 1 It is a schematic flow chart of the steps of a mix proportion design method for steam-curing-free low-carbon ultra-high performance concrete of the present invention. Detailed implementation manners

[0046] In order to make the objectives, technical solutions and advantages of the present invention clearer and more understandable, the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are only used to explain the present invention and are not used to limit the present invention.

[0047] In order to better understand the above technical solutions, the above technical solutions will be described in detail below in conjunction with the accompanying drawings of the specification and specific implementation manners.

[0048] As Figure 1 shown, an embodiment of the present invention provides a mix proportion design method for steam-curing-free low-carbon ultra-high performance concrete, including the following steps:

[0049] Step S1: Based on the target mechanical properties, carbon emission constraints and aggregate gradation parameters, construct a multi-objective optimization algorithm model. The multi-objective optimization algorithm model uses the water-binder ratio , the activity index of mineral admixture , the packing density of the manufactured sand gradation as input variables, and outputs the weight ratios of each component that meet the steam-curing-free conditions;

[0050] Step S2: Calculate the following weight ratio ranges through the multi-objective optimization algorithm model:

[0051] Ordinary Portland cement: 650-900 parts;

[0052] Mineral admixture: 80-100 parts of metakaolin, 50-120 parts of silica fume, 150-250 parts of slag powder, 20-50 parts of microsphere ultrafine powder;

[0053] Manufactured sand aggregate: 400-500 parts of 10-35 mesh, 280-400 parts of 35-80 mesh, 180-260 parts of 80-120 mesh;

[0054] Admixture: 5-15 parts of water reducer, 1-3 parts of defoamer, 1-3 parts of shrinkage reducing agent;

[0055] Steel fiber: volume fraction 1%-2%;

[0056] Water-binder ratio 0.18-0.22;

[0057] Step S3: Based on the ratio range in Step S2, adopt a dynamic feedback adjustment algorithm, and combine the real-time aggregate moisture content and the test data of the activity of the admixture to optimize the actual construction ratio. The dynamic feedback adjustment algorithm includes the following steps:

[0058] Real-time collect the aggregate moisture content through a humidity sensor and an X-ray fluorescence spectrometer and the activity index of admixtures ;

[0059] Input the water content of aggregates and the activity index of admixtures into the multi-objective optimization model to recalculate the water-binder ratio and the dosage of admixtures;

[0060] The adjusted mix ratio shall meet the constraint conditions:

[0061] ;

[0062] ;

[0063] Among them, represents the new water-binder ratio recalculated by the dynamic feedback adjustment algorithm, and

[0064] is the initial water-binder ratio initially calculated by the multi-objective optimization algorithm model. In this embodiment, step S1 is to construct a multi-objective optimization algorithm model based on the target mechanical properties, carbon emission constraints, and aggregate gradation parameters. This model takes the water-binder ratio , the activity index of mineral admixtures , and the compactness of the graded accumulation of manufactured sand as input variables, and outputs the weight mix ratio of each component that meets the condition of non-steam curing. To describe this step more clearly, it can be further refined into the following steps:

[0065] Step S1-1: Determine the target mechanical property indexes:

[0066] According to the actual engineering requirements, clarify the mechanical property targets that the concrete needs to achieve, such as compressive strength, flexural strength, etc.; in the present invention, the 28-day compressive strength target value is specifically set to be ≥150 MPa; this index is a key parameter for measuring the quality and applicability of concrete, and is directly related to the bearing capacity and stability of the concrete structure; for example, when building the foundation of a high-rise building or the pier of a large bridge, it is necessary to ensure that the concrete has a sufficiently high compressive strength to withstand huge loads;

[0067] Step S1-2: Clarify the carbon emission constraint conditions:

[0068] Considering the environmental protection requirements and sustainable development goals, determine the carbon emission constraint value per unit volume of concrete; in the present invention, it is stipulated that the carbon emission per unit volume of concrete ≤ 300; this constraint aims to reduce the negative impact on the environment during the concrete production process and promote the development of the construction industry towards a low-carbon direction; in actual production, the production of cement is one of the main sources of concrete carbon emissions. By controlling the cement dosage and optimizing the proportion of mineral admixtures, the carbon emissions of concrete can be effectively reduced;

[0069] Step S1-3: Obtain aggregate gradation parameters:

[0070] Use a laser particle size analyzer to detect the manufactured sand and obtain its gradation parameters; based on the modified Fuller curve theory, through the gradation formula Optimize the particle size distribution ratio of three grades of manufactured sand (10-35 mesh, 35-80 mesh, 80-120 mesh), where is the cumulative sieve residue percentage with a particle size less than ; is the maximum particle size; is the main gradation index; is the correction coefficient; is the secondary gradation index; A reasonable aggregate gradation can improve the compactness of concrete, enhance its mechanical properties, while reducing the cement consumption, lowering the cost and carbon emissions;

[0071] Step S1-4: Construct a multi-objective optimization algorithm model:

[0072] Adopt a hybrid model based on the improved particle swarm optimization MPSO and the adaptive genetic algorithm AGA as the multi-objective optimization algorithm model; the objective function of this model is

[0073] where is the 28-day compressive strength, and the target value ≥ 150 MPa; is the carbon emission of concrete per unit volume, and the constraint condition ≤ 300; is the 7-day shrinkage value; , , , are the weight coefficients, and the value range is 0.2-0.4, and satisfies ; represents the packing compactness of the manufactured sand gradation; these weight coefficients are used to balance the importance between different objectives and are adjusted according to the specific engineering requirements and emphases; for example, in a project with higher strength requirements, the weight coefficient related to the compressive strength can be appropriately increased;

[0074] The velocity update formula of the improved particle swarm optimization MPSO is:

[0075] where is the velocity of the th particle in the th iteration; is the inertia weight, which is dynamically adjusted to where , , is the maximum number of iterations; , , are learning factors, taking 1.5, 1.5, and 0.5 respectively; , , are random numbers within the range of [0, 1]; is the gradient of the objective function at ; is the historical best solution of particle ; is the global best solution; Through this dynamically adjusted velocity update formula, the particles can better search for the optimal solution in the solution space, improving the convergence speed and accuracy of the algorithm;

[0076] The crossover probability and mutation probability of the adaptive genetic algorithm AGA are dynamically adjusted according to the following formula:

[0077] , where represents the maximum value of the crossover probability , , represents the minimum value of the crossover probability , ; is the average fitness of the population; is the maximum fitness of the population; is the larger fitness value among the crossover individuals;

[0078] ; where represents the maximum value of the mutation probability , , represents the minimum value of the mutation probability , ; is the fitness value of the mutated individual; Dynamically adjusting the crossover probability and mutation probability can enable the algorithm to better balance the global search and local search capabilities during the search process, avoiding the algorithm from prematurely falling into the local optimal solution;

[0079] Step S1-5: Input variables and output results:

[0080] Substitute the water-binder ratio , the activity index of mineral admixture , and the compactness of the graded aggregate of manufactured sand as input variables into the constructed multi-objective optimization algorithm model; Among them, the activity index of mineral admixture is calculated by the formula Calculated, calculated, where ( , corresponding to different types of mineral admixtures, such as metakaolin, silica fume, blast furnace slag powder, and ultrafine microspheres) are admixtures is the weight coefficient, and ; is the admixture specific surface area; is the time decay factor, taking values from 0.01 to 0.05; is the curing time; is the admixture active oxides in and mass percentage;

[0081] The model outputs the weight ratios of each component of the concrete that meet the conditions of non-steam curing through calculation, including ordinary Portland cement, mineral admixtures (metakaolin, silica fume, blast furnace slag powder, ultrafine microspheres), manufactured sand aggregates (different mesh numbers), admixtures (water reducing agent, defoaming agent, shrinkage reducing agent), and the volume fraction of steel fibers, etc., providing basic data for the subsequent preparation of concrete.

[0082] In this embodiment, step S2 is to conduct a trial mix of concrete according to the weight ratios of each component obtained in step S1, and modify the parameters of the multi-objective optimization algorithm model based on the trial mix results; the following is a detailed description of this step:

[0083] Step S2-1: Preparation for concrete trial mix:

[0084] Raw material preparation: According to the weight ratios of each component output in step S1, accurately weigh the required raw materials, including ordinary Portland cement, mineral admixtures (metakaolin, silica fume, blast furnace slag powder, ultrafine microspheres), manufactured sand aggregates (different mesh numbers), admixtures (water reducing agent, defoaming agent, shrinkage reducing agent), and steel fibers; ensure that the quality of the raw materials meets relevant standards and requirements, such as the strength grade of cement and the activity index of mineral admixtures;

[0085] Trial mix equipment preparation: Prepare concrete mixing equipment, such as a forced mixer, to ensure its good performance and ability to uniformly mix concrete; at the same time, prepare equipment such as molds and curing boxes for forming and curing test blocks to ensure that the forming and curing conditions of the test blocks meet the standard regulations;

[0086] Step S2-2: Concrete trial mix process:

[0087] Mixing process: Add the weighed raw materials into the mixer in a certain order for mixing. Generally, first dry-mix the manufactured sand aggregate and cement for a period of time to make them fully and evenly mixed, and then add mineral admixtures and continue dry-mixing. Next, add an appropriate amount of water and admixtures for wet-mixing. The mixing time should be determined according to the performance of the mixer and the concrete mix ratio to ensure the uniformity of the concrete.

[0088] Forming process: Pour the mixed concrete into the test block mold and vibrate it by an appropriate method, such as an inserted vibrator or a plate vibrator, to make the concrete dense and remove the air in it. After vibration, use a trowel to level the surface of the test block to make its surface flat.

[0089] Curing process: Put the formed test blocks into the curing box for curing, and the curing conditions should meet the standard requirements. For the steam-curing-free concrete of the present invention, generally standard curing conditions are adopted, that is, the temperature is (20±2)°C and the relative humidity is not less than 95%. The curing time is determined according to the performance indexes to be tested, such as 7 days, 28 days, etc.

[0090] Step S2-3: Performance testing:

[0091] Mechanical property testing: At the specified curing age, conduct mechanical property testing on the test blocks, mainly including compressive strength and flexural strength testing. Apply pressure to the test blocks using a compression testing machine until the test blocks are damaged, record the failure load, and calculate the compressive strength and flexural strength. Compare the test results with the target mechanical property indexes set in step S1 to evaluate whether the mechanical properties of the trial-mixed concrete meet the requirements.

[0092] Shrinkage property testing: Measure the shrinkage value of the test blocks using a shrinkage meter and record the shrinkage data at different ages. The present invention focuses on the 7-day shrinkage value, compare the obtained 7-day shrinkage value with the relevant indexes in the model, and analyze whether the shrinkage property meets the expectation.

[0093] Carbon emission calculation: Calculate the carbon emission per unit volume of concrete according to the types and dosages of raw materials used in the trial-mixing process. Compare it with the carbon emission constraint value set in step S1 to judge whether the carbon emission of the trial-mixed concrete is within the allowable range.

[0094] Step S2-4: Parameter correction:

[0095] Analyze the trial-mixing results: According to the performance test results, analyze the differences between the performance indexes of the trial-mixed concrete and the target values. If the mechanical properties do not meet the requirements, it may be that parameters such as the water-binder ratio, activity index of mineral admixtures, or packing density of the manufactured sand gradation are inappropriate. If the carbon emission exceeds the constraint value, it may be necessary to adjust the dosage ratio of cement and mineral admixtures. If the shrinkage value is too large, it may be necessary to adjust the type or dosage of the admixture.

[0096] Modify the model parameters: Based on the analysis of the trial mixing results, modify the parameters in the multi-objective optimization algorithm model. For example, if the 28-day compressive strength is lower than the target value, the water-binder ratio can be appropriately reduced or the activity index of mineral admixtures can be increased; if the carbon emissions per unit volume of concrete exceed the constraint value, the dosage of mineral admixtures can be increased and the dosage of cement can be reduced. At the same time, adjust the weight coefficient 、 、 、 value to balance the relationship between different objectives;

[0097] Verify by trial mixing again: Substitute the modified parameters into the multi-objective optimization algorithm model, recalculate the weight ratio of each component, and then conduct concrete trial mixing and performance testing again according to the above trial mixing process. Repeat the above steps until all performance indicators of the trial-mixed concrete meet the target requirements and constraint conditions;

[0098] Through the above steps, the mix proportion of concrete can be continuously optimized, the performance of concrete can be improved, and the requirements of environmental protection and sustainable development can be met at the same time.

[0099] In this embodiment, step S3 is to determine the final weight ratio of each component according to the modified multi-objective optimization algorithm model and conduct large-scale production. The following is a detailed description of this step:

[0100] Step S3-1: Determine the final weight ratio of each component:

[0101] Comprehensively evaluate the modification results:

[0102] Comprehensively evaluate the multi-objective optimization algorithm model obtained after multiple trial mixings and parameter modifications in step S2. Carefully analyze the performance indicators of the concrete obtained from each trial mixing, including compressive strength, flexural strength, shrinkage value, and carbon emissions, etc., to ensure that these performance indicators can stably and satisfactorily meet the requirements of the preset target mechanical properties, carbon emission constraints, etc.

[0103] For example, the compressive strength should be continuously stable at the 28-day compressive strength target value ≥ 150 MPa, and the carbon emissions per unit volume of concrete should always be ≤ 300, etc.;

[0104] Determine the final mixing ratio parameters:

[0105] According to the comprehensive evaluation results, determine the optimal values of the input variables (water-binder ratio 、activity index of mineral admixtures 、graded packing density of manufactured sand ) in the model;

[0106] Based on these optimal input variables, the final weight ratios of each component of the concrete (such as ordinary Portland cement, mineral admixtures (metakaolin, silica fume, fly ash, ultrafine microspheres), manufactured sand aggregates (different mesh sizes), admixtures (water reducers, defoamers, shrinkage reducing agents), and the volume fraction of steel fibers, etc.) are calculated through the modified multi-objective optimization algorithm model;

[0107] Step S3-2: Preparation and quality control of raw materials:

[0108] Procurement and inspection of raw materials:

[0109] According to the final determined weight ratios of each component, calculate the quantities of various raw materials required for large-scale production and conduct procurement;

[0110] When the raw materials enter the site, strictly conduct quality inspections on them in accordance with relevant standards and specifications; for example, for cement, inspect its strength grade, soundness and other indicators; for mineral admixtures, detect their activity index, fineness, etc.; for manufactured sand, check its gradation, mud content, etc.; only the raw materials that pass the inspection can be put into use;

[0111] Storage and management of raw materials:

[0112] Provide suitable storage conditions for different raw materials; cement should be stored in a dry and ventilated warehouse to prevent moisture and caking; mineral admixtures should be stored in a sealed manner to avoid affecting their activity due to long-term contact with air; manufactured sand should be stacked separately to prevent mixing of sands with different particle sizes;

[0113] Establish a complete raw material management ledger to record information such as the incoming date, quantity, inspection status of raw materials, etc., for quality traceability and management;

[0114] Step S3-3: Preparation and commissioning of large-scale production equipment:

[0115] Selection and installation of production equipment:

[0116] According to the production scale and process requirements, select suitable concrete production equipment, such as large concrete mixers, batching plants, conveying equipment, etc.; ensure that the production capacity of the equipment can meet the requirements of large-scale production;

[0117] According to the requirements of the equipment installation manual, carry out the installation and commissioning of the equipment to ensure the normal operation of the equipment; check and maintain the key components of the equipment, such as the mixing blades of the mixer, the metering devices of the batching plant, etc., to ensure their accuracy and reliability;

[0118] Setting of production process parameters:

[0119] Set reasonable production process parameters according to the final weight ratio of each component and the characteristics of the production equipment; for example, determine the mixing time and speed of the mixer, the batching accuracy and batching sequence of the batching machine, etc.;

[0120] Monitor and control environmental parameters such as temperature and humidity during the production process to ensure that the production environment meets the requirements of concrete production;

[0121] Step S3-4: Large-scale production process control:

[0122] Batching process control:

[0123] Use high-precision batching equipment and batch according to the final determined weight ratio of each component; during the batching process, monitor the weight of the batching in real time to ensure the accuracy of batching; for example, accurately weigh various raw materials through metering equipment such as electronic scales, and control the error within the specified range;

[0124] Calibrate and maintain the batching equipment regularly to ensure the stability of its metering accuracy;

[0125] Mixing process control:

[0126] Mix the concrete according to the set mixing time and speed to ensure the uniformity of the concrete; during the mixing process, observe the state of the concrete, such as fluidity, cohesiveness, etc., and adjust the mixing parameters in a timely manner;

[0127] Regularly detect the mixing effect of the mixer, such as analyzing the homogeneity of the concrete by sampling;

[0128] Transportation and pouring process control:

[0129] Select appropriate concrete transportation equipment, such as concrete mixer trucks, to ensure that the concrete does not segregate or bleed during transportation; control the transportation time to avoid the loss of fluidity of the concrete during transportation;

[0130] During the pouring process, adopt appropriate pouring methods and vibration methods to ensure the compactness of the concrete; monitor and control the environmental conditions at the pouring site, such as temperature, humidity, etc., to avoid affecting the quality of the concrete due to environmental factors;

[0131] Step S3-5: Product quality inspection and monitoring:

[0132] Sampling inspection:

[0133] During large-scale production, sample and inspect the produced concrete at a certain frequency; the inspection items include mechanical properties (compressive strength, flexural strength, etc.), workability (fluidity, cohesiveness, water retention, etc.), shrinkage properties, and carbon emissions, etc.;

[0134] Compare the sampling inspection results with the pre-set target performance indicators to determine whether the product quality is qualified;

[0135] Quality monitoring and adjustment:

[0136] Establish a quality monitoring system to monitor various quality indicators in the production process in real time; if abnormal fluctuations in quality indicators are found, analyze the reasons in a timely manner and take corresponding adjustment measures; for example, if the compressive strength is lower than the target value, it may be necessary to adjust the raw material ratio or production process parameters;

[0137] Record and analyze the quality monitoring data, summarize the experiences and lessons in the production process, and continuously optimize the production process and product quality.

[0138] In this embodiment, let the volume fraction of steel fibers be and dynamically adjust it through the fracture toughness model, and its formula is:

[0139] , where is the designed flexural strength; is the fracture toughness of the matrix material; is the fiber-matrix interface bonding efficiency coefficient, taking 0.6 - 0.8; is the elastic modulus of steel fibers; is the density of the matrix material.

[0140] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions and variations can be made to these embodiments without departing from the principles and spirit of the present invention, and the scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for designing the mix proportion of a steam-free low-carbon ultra-high performance concrete, characterized in that: The following steps are involved: Step S1: Based on the target mechanical properties, carbon emission constraints and aggregate grading parameters, a multi-objective optimization algorithm model is constructed. The multi-objective optimization algorithm model is based on the water-binder ratio , mineral admixture activity index , Machine-made sand grading and packing density As the input variable, the weight ratio of each component that meets the steam-free curing condition is output. The multi-objective optimization algorithm model is a hybrid model based on the improved particle swarm optimization MPSO and the adaptive genetic algorithm AGA. Its objective function is as follows: ,in, The 28-day compressive strength, target value ≥150MPa; is the carbon emission per unit volume of concrete, with the constraint condition ≤300; It is the 7-day contraction value; , , , is the weight coefficient, ranging from 0.2 to 0.4, and satisfies ; Represents the packing density of machine-made sand grading; The speed update formula of the improved particle swarm optimization MPSO is: ,in, For the The particle in The speed of iterations; is the inertia weight, dynamically adjusted to ,in, , , is the maximum number of iterations; , , is the learning factor, which is 1.5, 1.5, and 0.5 respectively; , , is a random number in the interval [0,1]; The objective function is The gradient at For particles The historical optimal solution; is the global optimal solution; The crossover probability of the adaptive genetic algorithm AGA and mutation probability Dynamic adjustment by pressing the formula: ,in, Represents the crossover probability The maximum value of , Represents the crossover probability The minimum value of ; is the average fitness of the population; is the maximum fitness of the population; is the fitness value of the crossover individual; ;in, Represents the probability of mutation The maximum value of , Represents the probability of mutation The minimum value of ; is the fitness value of the mutant individual; The mineral admixture activity index Calculated by the following formula: ,in, For admixture The weight coefficient of ; For admixture Specific surface area; is the time attenuation factor, ranging from 0.01 to 0.05; For maintenance time; For admixture Medium active oxygen and Mass percentage; Step S2: Calculate the following weight ratio range using the multi-objective optimization algorithm model: Ordinary Portland cement: 650-900 parts; Mineral admixtures: 80-100 parts of metakaolin, 50-120 parts of silica fume, 150-250 parts of mineral powder, 20-50 parts of micro-bead ultrafine powder; Machine-made sand aggregate: 10-35 mesh 400-500 parts, 35-80 mesh 280-400 parts, 80-120 mesh 180-260 parts; Admixtures: 5-15 parts of water reducing agent, 1-3 parts of defoaming agent, 1-3 parts of shrinkage reducing agent; Steel fiber: volume dosage 1%-2%; Water-to-binder ratio 0.18-0.22; Step S3: Based on the mix ratio range of step S2, a dynamic feedback adjustment algorithm is used, combined with the real-time aggregate moisture content. And admixture activity test data , optimize the actual construction ratio, the dynamic feedback adjustment algorithm includes the following steps: Real-time collection of aggregate moisture content using humidity sensors and X-ray fluorescence spectrometers and admixture activity index ; The moisture content of aggregate and admixture activity index Input to the multi-objective optimization model and recalculate the water-binder ratio and dosage of admixtures; The adjusted ratio must meet the constraints: ； ； in, It represents the new water-binder ratio recalculated by the dynamic feedback adjustment algorithm. This is the initial water-binder ratio calculated preliminarily by the multi-objective optimization algorithm model.

2. The method for designing the mix proportion of a non-steamed low-carbon ultra-high performance concrete according to claim 1, characterized in that: The grading parameters of the machine-made sand are obtained by a laser particle size analyzer, and the particle size distribution ratio of the three-grade machine-made sand is optimized based on the modified Fuller curve theory. The grading formula is: ,in, For particles smaller than The cumulative percentage of residues; is the maximum particle size; is the main grading index; is the correction factor; It is the secondary matching index.

3. The method for designing the mix proportion of the non-steamed low-carbon ultra-high performance concrete according to claim 1, characterized in that: Assume that the volume content of steel fiber is , dynamically adjusted through the fracture toughness model, the formula is: ,in, Design flexural strength; is the fracture toughness of the matrix material; is the fiber-matrix interface bonding efficiency coefficient, which is 0.6-0.8; is the elastic modulus of steel fiber; is the density of the base material.

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