Non-steam-curing low-carbon ultra-high performance concrete mix proportion design method

Abstract

The invention relates to the technical field of concrete mix proportion design, in particular to a mix proportion design method of steam-curing-free low-carbon ultra-high performance concrete. A multi-objective optimization algorithm model with the water-binder ratio, the mineral admixture activity index and the machine-made sand grading stacking compactness as input variables is constructed, and the weight ratio range of all the components is calculated; a dynamic feedback adjustment algorithm is adopted, real-time aggregate water content and admixture activity detection data are combined, and the actual construction ratio is optimized; the multi-objective optimization algorithm model is a hybrid model of improved particle swarm optimization (MPSO) and adaptive genetic algorithm (AGA). According to the designed concrete mix proportion, the compressive strength of 28 days can be larger than or equal to 150 MPa under the steam-curing-free condition, the carbon emission of the concrete in unit volume is smaller than or equal to 300, the concrete performance is effectively improved, the carbon emission is reduced, the production cost is reduced, and a green and efficient concrete mix proportion design scheme is provided for the building industry.

Description

Technical Field

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

[0002] In the field of concrete mix design technology, traditional concrete mix design has many limitations and cannot meet the current construction industry's demand for high performance, environmental protection and sustainable development, which has prompted the emergence of a steam-free, low-carbon ultra-high performance concrete mix design method. Specifically, it is reflected in:

[0003] High-performance requirements are difficult to meet: With the development of the construction industry, the requirements for the mechanical properties of concrete are increasing. For example, high-rise, large-span buildings and special engineering structures require concrete to have higher compressive and flexural strengths. However, concrete under traditional mix design has limited performance in terms of strength and durability, and it is difficult to meet these high standards, limiting 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 its performance. This process consumes a lot of energy, which not only increases production costs, but also puts great pressure on the environment. At the same time, the steam curing equipment requires large investment, is complex to operate, and has low production efficiency. It is not suitable for large-scale, fast-construction project requirements, which restricts the application of concrete in some projects.

[0005] The carbon emission problem is severe: cement production is the main source of carbon emissions from concrete. In the context of global response to climate change and advocacy of low-carbon development, the traditional concrete mix uses a large amount of cement, resulting in high carbon emissions per unit volume of concrete, which does not conform to the concept of green building and sustainable development. The construction industry is in urgent need of technologies and methods to reduce carbon emissions from concrete;

[0006] Unreasonable use of aggregate grading and mineral admixtures: Traditional design methods do not pay enough attention to aggregate grading. The particle size distribution of aggregates such as machine-made sand is unscientific, which affects the density and working performance of concrete. In addition, the selection and use of mineral admixtures lacks systematicity, fails to give full play to their activity and advantages, and cannot achieve a balanced optimization of concrete performance and environmental benefits.

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

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

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

[0010] A method for designing a mix ratio of a steam-free low-carbon ultra-high performance concrete comprises the following steps:

[0011] 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 input variables, output the weight ratio of each component that meets the steam-free curing conditions;

[0012] Step S2: Calculate the following weight ratio range using a multi-objective optimization algorithm model:

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

[0014] 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;

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

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

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

[0018] Water-to-binder ratio 0.18-0.22;

[0019] 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.

[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: ,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.

[0021] As a preferred solution, 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; It is the global optimal solution.

[0022] As a preferred solution, the crossover probability of the adaptive genetic algorithm AGA is 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 larger fitness value among the crossover individuals; ;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.

[0023] As a preferred solution, the grading parameters of machine-made sand are obtained by laser particle size analyzer, and the particle size distribution ratio of 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.

[0024] Activity index of mineral admixture 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.

[0025] As a preferred solution, the dynamic feedback adjustment algorithm includes the following steps:

[0026] Real-time collection of aggregate moisture content using humidity sensors and X-ray fluorescence spectrometers and admixture activity index ;

[0027] 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;

[0028] The adjusted ratio must meet the constraints: ; ;

[0029] 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.

[0030] As a preferred solution, 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.

[0031] It can be seen from the technical solution provided by the present invention that the method for designing the mix proportion of a steam-free low-carbon ultra-high performance concrete provided by the present invention has the following beneficial effects:

[0032] Performance optimization: By constructing a multi-objective optimization algorithm model with water-binder ratio, mineral admixture activity index, and machine-made sand grading packing density as input variables, and comprehensively considering indicators such as 28-day compressive strength, carbon emissions per unit volume of concrete, and 7-day shrinkage value, the mix ratio can be accurately designed to make the target value of concrete's 28-day compressive strength ≥150MPa. While ensuring high strength, the shrinkage value is effectively controlled, the comprehensive performance of concrete is improved, and the needs of various high-standard projects are met;

[0033] Low-carbon and environmentally friendly: The carbon emissions per unit volume of concrete are incorporated into the model as a constraint (≤300), the ratio of cement to mineral admixtures is optimized, and the amount of cement is reduced, thereby reducing carbon emissions in the concrete production process and promoting green and sustainable development of the construction industry;

[0034] Cost saving: Rationally use a variety of mineral admixtures (metakaolin, silica fume, mineral powder, micro-bead ultrafine powder) to replace part of the cement, reducing the cost of raw materials while ensuring performance; at the same time, the feature of steam-free curing avoids energy consumption and equipment investment in the steam-curing process, further saving production costs;

[0035] Convenient construction: The dynamic feedback adjustment algorithm is used to optimize the actual construction ratio in combination with the real-time aggregate moisture content and admixture activity detection data to ensure the stability of concrete quality at the construction site; and the steel fiber volume dosage is dynamically adjusted through the fracture toughness model to enhance the toughness of concrete, adapt to different construction conditions and engineering requirements, and improve construction efficiency and quality. BRIEF DESCRIPTION OF THE DRAWINGS

[0036] Figure 1 The present invention is a schematic flow chart of the steps of a method for designing a mix ratio of a steam-curing-free low-carbon ultra-high performance concrete. DETAILED DESCRIPTION

[0037] In order to make the purpose, technical solution and advantages of the present invention more clearly understood, the present invention is further described in detail below in conjunction with 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 intended to limit the present invention.

[0038] In order to better understand the above technical solution, the above technical solution will be described in detail below in conjunction with the accompanying drawings and specific implementation methods.

[0039] like Figure 1 As shown, the embodiment of the present invention provides a method for designing a mix ratio of a steam-free low-carbon ultra-high performance concrete, comprising the following steps:

[0040] 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 input variables, output the weight ratio of each component that meets the steam-free curing conditions;

[0041] Step S2: Calculate the following weight ratio range using a multi-objective optimization algorithm model:

[0042] Ordinary Portland cement: 650-900 parts;

[0043] 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;

[0044] Machine-made sand aggregate: 10-35 mesh 400-500 parts, 35-80 mesh 280-400 parts, 80-120 mesh 180-260 parts;

[0045] Admixtures: 5-15 parts of water reducing agent, 1-3 parts of defoaming agent, 1-3 parts of shrinkage reducing agent;

[0046] Steel fiber: volume dosage 1%-2%;

[0047] Water-to-binder ratio 0.18-0.22;

[0048] 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 ,To optimize the actual construction ratio, the dynamic feedback adjustment algorithm includes the following steps:

[0049] Real-time collection of aggregate moisture content using humidity sensors and X-ray fluorescence spectrometers and admixture activity index ;

[0050] 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;

[0051] The adjusted ratio must meet the constraints: ; ;

[0052] 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.

[0053] 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 grading parameters. , mineral admixture activity index , Machine-made sand grading and packing density As input variables, the weight ratio of each component that meets the steam-free curing conditions is output. To explain this step more clearly, it can be further refined into the following steps:

[0054] Step S1-1: Determine the target mechanical performance index:

[0055] According to the actual needs of the project, the mechanical performance targets that the concrete needs to achieve, such as compressive strength, flexural strength, etc., are clearly defined; in the present invention, a 28-day compressive strength target value is specifically set to be ≥150MPa; this indicator 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 constructing the foundation of a high-rise building or the piers of a large bridge, it is necessary to ensure that the concrete has a sufficiently high compressive strength to withstand huge loads;

[0056] Step S1-2: Clarify carbon emission constraints:

[0057] Taking into account environmental protection requirements and sustainable development goals, the carbon emission constraint value per unit volume of concrete is determined; the present invention stipulates that the carbon emission per unit volume of concrete is ≤300; this constraint is intended to reduce the negative impact on the environment during concrete production and promote the construction industry to develop in a low-carbon direction; in actual production, cement production is one of the main sources of carbon emissions from concrete. By controlling the amount of cement, optimizing the proportion of mineral admixtures, etc., the carbon emissions of concrete can be effectively reduced;

[0058] Step S1-3: Obtain aggregate gradation parameters:

[0059] The machine-made sand was tested using a laser particle size analyzer to obtain its gradation parameters. Based on the modified Fuller curve theory, the gradation formula Optimize the particle size distribution ratio of three-grade machine-made sand (10-35 mesh, 35-80 mesh, 80-120 mesh), among which, For particles smaller than The cumulative percentage of residues; is the maximum particle size; is the main grading index; is the correction factor; is the secondary grading index; reasonable aggregate grading can improve the density of concrete, enhance its mechanical properties, and reduce cement consumption, cost and carbon emissions;

[0060] Step S1-4: Construct a multi-objective optimization algorithm model:

[0061] A hybrid model based on improved particle swarm optimization MPSO and adaptive genetic algorithm AGA is used as the multi-objective optimization algorithm model; the objective function of this model is ,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 the manufactured sand grading; these weight coefficients are used to balance the importance of different objectives and are adjusted according to specific project needs and priorities; for example, in projects with higher strength requirements, the weight coefficient related to compressive strength can be appropriately increased The value of

[0062] 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; through this dynamically adjusted speed update formula, particles can better search for the optimal solution in the solution space, improving the convergence speed and accuracy of the algorithm;

[0063] Crossover Probability of 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 larger fitness value among the crossover individuals; ;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; 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, and avoid the algorithm falling into the local optimal solution too early;

[0064] Step S1-5: Input variables and output results:

[0065] The water-to-binder ratio , mineral admixture activity index , Machine-made sand grading and packing density Substituted as input variables into the constructed multi-objective optimization algorithm model; among them, the mineral admixture activity index By formula Calculate and calculate, where ( , corresponding to different types of mineral admixtures, such as kaolin, silica fume, mineral powder, micro-bead ultrafine powder) as admixtures 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 of

[0066] The model calculates and outputs the weight ratio of each component of concrete that meets the steam-free curing conditions, including ordinary Portland cement, mineral admixtures (metakaolin, silica fume, mineral powder, ultrafine microbead powder), machine-made sand aggregate (different mesh sizes), admixtures (water reducer, defoamer, shrinkage reducer) and volumetric dosage of steel fiber, etc., providing basic data for subsequent concrete preparation.

[0067] In this embodiment, step S2 is to conduct a concrete trial mix according to the weight ratio of each component obtained in step S1, and to modify the parameters of the multi-objective optimization algorithm model based on the trial mix result; the following is a detailed description of this step:

[0068] Step S2-1: Concrete trial preparation:

[0069] Raw material preparation: according to the weight ratio of each component output in step S1, accurately weigh the required raw materials, including ordinary Portland cement, mineral admixtures (metakaolin, silica fume, mineral powder, micro-bead ultrafine powder), machine-made sand aggregate (different mesh sizes), admixtures (water reducer, defoamer, shrinkage reducer) and steel fiber; ensure that the quality of the raw materials meets the relevant standards and requirements, such as the strength grade of cement, the activity index of mineral admixtures, etc.;

[0070] Preparation of trial equipment: prepare concrete mixing equipment, such as a forced mixer, to ensure that it has good performance and can mix concrete evenly; at the same time, prepare molds, curing boxes and other equipment for forming and curing test blocks to ensure that the forming and curing conditions of the test blocks meet the standard requirements;

[0071] Step S2-2: Concrete trial mixing process:

[0072] Mixing process: Add the weighed raw materials into the mixer in a certain order for mixing; generally, dry mix the machine-made sand aggregate and cement for a period of time to make them fully mixed, then add the mineral admixtures and continue dry mixing; then, 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 mix ratio of concrete to ensure the uniformity of the concrete;

[0073] Forming process: Pour the mixed concrete into the test block mold and vibrate it using appropriate methods, such as an inserted vibrator or a flat vibrator, to make the concrete dense and remove the air in it; after the vibration is completed, use a spatula to smooth the surface of the test block to make it smooth;

[0074] Curing process: the formed test blocks are placed in a curing box for curing, and the curing conditions should meet the standard requirements; for the steam-free concrete of the present invention, standard curing conditions are generally adopted, that is, the temperature is (20±2)°C, the relative humidity is not less than 95%, and the curing time is determined according to the performance indicators to be tested, such as 7 days, 28 days, etc.;

[0075] Step S2-3: Performance test:

[0076] Mechanical property test: at the prescribed curing age, the test block is subjected to mechanical property test, mainly including compressive strength and flexural strength test; a pressure tester is used to apply pressure to the test block until the test block is destroyed, the failure load is recorded, and the compressive strength and flexural strength are calculated; the test results are compared with the target mechanical property index set in step S1 to evaluate whether the mechanical properties of the trial-mixed concrete meet the requirements;

[0077] Contraction performance test: The contraction value of the test block is measured using a contraction instrument, and the contraction data of different ages are recorded; the present invention focuses on the 7-day contraction value, and the 7-day contraction value obtained by the test is compared with the relevant indicators in the model to analyze whether the contraction performance meets expectations;

[0078] Carbon emission calculation: Calculate the carbon emission per unit volume of concrete based on the types and amounts of raw materials used in the trial mix; compare it with the carbon emission constraint value set in step S1 to determine whether the carbon emission of the trial mix concrete is within the allowable range;

[0079] Step S2-4: Parameter modification:

[0080] Analyze the trial mix results: Analyze the differences between the performance indicators of the trial mix concrete and the target values ​​based on the performance test results; if the mechanical properties do not meet the requirements, it may be that the parameters such as the water-cement ratio, the activity index of the mineral admixture, or the packing density of the machine-made sand grading are not appropriate; if the carbon emissions exceed 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 admixtures;

[0081] Modify model parameters: Based on the analysis of the trial mix 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-cement ratio can be appropriately reduced or the activity index of the mineral admixture can be increased; if the carbon emissions per unit volume of concrete exceed the constraint value, the amount of mineral admixture can be increased and the amount of cement can be reduced; at the same time, adjust the weight coefficient according to the actual situation , , , to balance the relationship between different goals;

[0082] Re-test and verify: Substitute the corrected 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 the performance indicators of the trial mixed concrete meet the target requirements and constraints;

[0083] Through the above steps, the mix ratio of concrete can be continuously optimized and the performance of concrete can be improved, while meeting the requirements of environmental protection and sustainable development.

[0084] 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 carry out large-scale production; the following is a detailed description of this step:

[0085] Step S3-1: Determine the final weight ratio of each component:

[0086] Comprehensive assessment and correction results:

[0087] Comprehensively evaluate the multi-objective optimization algorithm model obtained after multiple trial mixes and parameter corrections in step S2; carefully analyze the various performance indicators of concrete obtained from each trial mix, including compressive strength, flexural strength, shrinkage value, and carbon emissions, to ensure that these performance indicators can stably and well meet the pre-set target mechanical properties, carbon emission constraints, and other requirements;

[0088] For example, the compressive strength must be continuously stable at the 28-day compressive strength target value ≥ 150MPa, and the carbon emissions per unit volume of concrete must always be ≤ 300, etc.;

[0089] Determine the final ratio parameters:

[0090] According to the comprehensive evaluation results, the input variables (water-binder ratio , mineral admixture activity index , Machine-made sand grading and packing density )’s optimal value;

[0091] Based on these optimal input variables, the final weight ratio of each component of concrete (ordinary Portland cement, mineral admixtures (metakaolin, silica fume, mineral powder, micro-bead ultrafine powder), machine-made sand aggregate (different mesh sizes), admixtures (water reducer, defoamer, shrinkage reducer) and volumetric dosage of steel fiber, etc.) is calculated through the modified multi-objective optimization algorithm model;

[0092] Step S3-2: Raw material preparation and quality control:

[0093] Raw materials procurement and inspection:

[0094] According to the final determined weight ratio of each component, calculate the quantity of various raw materials required for large-scale production and purchase them;

[0095] When raw materials are delivered to the site, they are strictly inspected for quality in accordance with relevant standards and specifications. For example, cement must be inspected for strength grade, stability and other indicators; mineral admixtures must be tested for activity index, fineness, etc.; artificial sand must be checked for grading, mud content, etc.; only raw materials that pass the inspection can be put into use;

[0096] Raw materials storage and management:

[0097] Provide appropriate storage conditions for different raw materials; cement should be stored in a dry, ventilated warehouse to prevent moisture and agglomeration; mineral admixtures should be sealed to avoid long-term contact with air that affects their activity; machine-made sand should be stacked in different categories to prevent sand of different particle sizes from mixing;

[0098] Establish a complete raw material management ledger to record the purchase date, quantity, inspection status and other information of raw materials for quality traceability and management;

[0099] Step S3-3: Preparation and commissioning of large-scale production equipment:

[0100] Production equipment selection and installation:

[0101] According to the production scale and process requirements, select appropriate concrete production equipment, such as large concrete mixers, batching machines, conveying equipment, etc.; ensure that the production capacity of the equipment can meet the needs of large-scale production;

[0102] Install and debug the equipment according to the requirements of the equipment installation manual to ensure the normal operation of the equipment; inspect and maintain the key components of the equipment, such as the mixing blades of the mixer and the metering device of the batching machine, to ensure their accuracy and reliability;

[0103] Production process parameter setting:

[0104] According to the final weight ratio of each component and the characteristics of the production equipment, set reasonable production process parameters; for example, determine the mixing time and mixing speed of the mixer, the batching accuracy and batching sequence of the batching machine, etc.;

[0105] 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;

[0106] Step S3-4: Large-scale production process control:

[0107] Batching process control:

[0108] Use high-precision batching equipment and batch the ingredients strictly according to the final weight ratio of each component; during the batching process, monitor the weight of the ingredients in real time to ensure the accuracy of the ingredients; for example, use electronic scales and other measuring equipment to accurately weigh various raw materials, and control the error within the specified range;

[0109] Regularly calibrate and maintain batching equipment to ensure the stability of its measurement accuracy;

[0110] Mixing process control:

[0111] Mix concrete according to the set mixing time and mixing speed to ensure the uniformity of concrete; during the mixing process, observe the state of concrete, such as fluidity and cohesion, and adjust the mixing parameters in time;

[0112] Regularly test the mixing effect of the mixer, such as by sampling and analyzing the homogeneity of concrete;

[0113] Transportation and pouring process control:

[0114] Choose 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 prevent the concrete from losing fluidity during transportation;

[0115] During the pouring process, appropriate pouring methods and vibration methods should be used to ensure the compactness of the concrete; the environmental conditions at the pouring site, such as temperature and humidity, should be monitored and controlled to avoid environmental factors affecting the quality of the concrete;

[0116] Step S3-5: Product quality inspection and monitoring:

[0117] Sampling inspection:

[0118] During the mass production process, the concrete produced is sampled and tested at a certain frequency; the test items include mechanical properties (compressive strength, flexural strength, etc.), workability (fluidity, cohesion, water retention, etc.), shrinkage performance and carbon emissions, etc.;

[0119] Compare the sampling inspection results with the pre-set target performance indicators to determine whether the product quality is qualified;

[0120] Quality monitoring and adjustment:

[0121] 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 causes 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;

[0122] Record and analyze quality monitoring data, summarize lessons learned from the production process, and continuously optimize production processes and product quality.

[0123] In this embodiment, 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.

[0124] Although embodiments of the present invention have been shown and described, it will be appreciated by those skilled in the art that various changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the present invention, and that 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 input variables, output the weight ratio of each component that meets the steam-free curing conditions; 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.

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 multi-objective optimization algorithm model is a hybrid model based on improved particle swarm optimization MPSO and adaptive genetic algorithm AGA, and 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.

3. The method for designing the mix proportion of the non-steamed low-carbon ultra-high performance concrete according to claim 2, characterized in that: 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; It is the global optimal solution.

4. The method for designing the mix proportion of the non-steamed low-carbon ultra-high performance concrete according to claim 3, characterized in that: 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 larger fitness value among the crossover individuals; ;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.

5. The method for designing the mix proportion of the 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.

6. The method for designing the mix proportion of the non-steamed low-carbon ultra-high performance concrete according to claim 1, characterized in that: 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.

7. The method for designing the mix proportion of the non-steamed low-carbon ultra-high performance concrete according to claim 1, characterized in that: The dynamic feedback adjustment algorithm comprises 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.

8. 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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