Low-carbon concrete mix proportion design method and concrete

Abstract

The invention discloses a low-carbon concrete mix proportion design method, which comprises the following steps of: constructing a multi-factor prediction model among a concrete workability index, a strength index, a durability index and a concrete mix proportion parameter, and setting the multi-factor prediction model as a constraint condition for concrete mix proportion optimization; taking economical efficiency and low-carbon property of the concrete mix proportion as double targets, and constructing a cost-carbon emission double-target optimization calculation model for optimizing the concrete mix proportion; and on the basis of meeting the constraint condition, solving a concrete mix proportion parameter combination which enables the target function value to be minimum. The invention further discloses the concrete. The method has the advantages that raw material actual cost accounting is used as a primary optimization target, the defect that economy is affected by unstable carbon price fluctuation in an existing comprehensive optimization method is overcome, and rigid economic benefits of the mix proportion are guaranteed.

Description

Technical Field

[0001] This invention relates to concrete preparation technology, specifically to a low-carbon concrete mix design method and concrete. Background Technology

[0002] Concrete, as the world's most widely used building material, is a major source of carbon dioxide emissions, particularly through its production process, especially cement manufacturing. Low-carbon concrete, made with more environmentally friendly raw materials and processes, has lower carbon emissions than ordinary concrete and offers superior environmental performance. Therefore, with the increasing severity of global climate change and the emergence of related requirements, the research and development of low-carbon concrete has become a core direction for sustainable development in the construction industry. Simultaneously, in the face of fierce market competition, strict cost control is also crucial for concrete design. Therefore, developing a concrete mix design method that simultaneously meets the requirements of low carbon emissions, cost-effectiveness, and engineering performance is of significant practical importance.

[0003] Currently, mainstream concrete mix design methods suffer from the following shortcomings: First, traditional performance-oriented design methods based on strength or strength-durability (such as patent CN103992076B) primarily focus on the mechanical and long-term performance of concrete. However, in the mix design process, they lack direct quantification and optimization of carbon emissions and economic costs, failing to meet current stringent low-carbon and economic requirements. Second, design methods based on optimization algorithms use strength, economic efficiency, and carbon emissions as objective functions. However, when establishing optimization models, these methods often neglect the quantitative constraints of durability indicators (such as resistance to chloride ion intrusion and freeze-thaw resistance) on mix parameters in order to simplify the model or data acquisition. This results in the designed mix proportions failing to meet long-term durability requirements in actual engineering environments. In recent years, some scholars have explored comprehensive optimization methods. For example, CN118332749A incorporates strength, durability, economy and low-carbon indicators into the optimization model. However, this patent couples raw material costs and carbon emission costs into a total cost objective function through unstable carbon dioxide emission unit price, and does not consider carbon emission indicators in the raw material transportation process. As a result, the economic benefits of the final optimized mix ratio lack stability and reliability, and are difficult to use as a rigid basis for cost control of engineering projects. Summary of the Invention

[0004] The purpose of this invention is to address the shortcomings of existing technologies by providing a low-carbon concrete mix design method and concrete, aiming to solve the technical problem of unstable economic benefits in existing technologies.

[0005] The technical solution adopted in this invention is: a low-carbon concrete mix design method, which includes the following steps: S1. Construct a database of concrete raw material parameters; S2. Construct a multi-factor prediction model between concrete workability, strength and durability indices and concrete mix proportion parameters, and set it as a constraint condition for concrete mix proportion optimization. S3. With the dual objectives of economic efficiency and low carbon emissions in concrete mix design, a cost-carbon emission dual-objective optimization calculation model is constructed for optimizing concrete mix design. S4. Based on satisfying the constraints, find the concrete mix proportion parameter combination that minimizes the objective function value in the bi-objective optimization calculation model.

[0006] According to the above scheme, in S2, the concrete slump S is used as the workability index, the initial chloride ion diffusion coefficient of concrete is used as the durability index, and the 28-day compressive strength f of concrete is used as the strength index.

[0007] According to the above scheme, the multi-factor prediction model based on the workability, strength, and durability indices of concrete is as follows: ; ; ; In the formula, S The concrete slump is expressed in mm. f The 28-day compressive strength of concrete is expressed in MPa. D The initial chloride ion diffusion coefficient of concrete is given in m² / s. m w 、m AE The figures represent water consumption per cubic meter and water-reducing agent dosage, both in kg. R i The amount of admixture is expressed in kg. S p The sand ratio of the concrete, expressed as a percentage. w / B The water-cement ratio of concrete; m wo This is based on empirical water consumption, expressed in kg. R i It refers to admixtures i Dosage, expressed as a percentage; S 0、 s w , m w0 、s AE , s i 、s p 、S p0The regression coefficients of the multi-factor prediction model for work performance are respectively the benchmark slump coefficient, empirical water consumption coefficient, water sensitivity coefficient, water-reducing agent efficiency coefficient, admixture morphology effect coefficient, sand ratio influence coefficient, and empirical sand ratio value coefficient. f c0 , k 0、 k i The coefficients to be determined in the multi-factor prediction model of compressive strength are the baseline virtual strength coefficient, the water-cement ratio attenuation coefficient, and the admixture strength contribution coefficient, respectively. Gc 0、 G 0、 G i , H i The coefficients are undetermined coefficients in the multi-factor prediction model for chloride ion diffusion coefficient, representing the baseline diffusion coefficient, the water-cement ratio influence coefficient, the linear influence coefficient of admixtures, and the interaction coefficient between admixtures and water-cement ratio, respectively; all coefficients are dimensionless.

[0008] According to the above scheme, the dual-objective optimization calculation model is as follows:

[0009]

[0010] In the above formula, J is the objective function, which is dimensionless; w 1. w 2 represents the target weight, where, w 1 represents the target weight related to economic efficiency. w 1 represents the target weight related to low carbon emissions, satisfying... w 1+ w 2 = 1; C i Raw materials for concrete i The unit cost is expressed in yuan / kg; E i Raw materials for concrete i The unit carbon emission factor, expressed in kg·CO2 / kg; α i Raw materials for concrete i The carbon emission factor per unit of transportation, expressed in kg·CO2 / (kg·km); d i Raw materials for concrete i The transport distance, in km; m i Raw materials for concrete i The dosage per unit, in kg / m³ 3 ; C refFor reference cost, the unit is yuan; E ref The reference carbon emissions, in kg·CO2, are used for normalization of the objective function. S l , S u Concrete slump S The lower and upper limits, in mm; f l , f u These are the 28-day compressive strengths of concrete. f The lower and upper limits, in MPa; D l , D u These are concrete durability indicators. D The lower and upper limits, in meters. 2 / s; m i,min , m i,max Raw materials for concrete i The lower and upper limits of dosage are both in kg. ρ i Raw materials for concrete i Density, expressed in kg / m³; V a This represents the air volume in the concrete, expressed in m³. n This represents the total number of different types of concrete raw materials.

[0011] According to the above scheme, the method for finding the concrete mix proportion parameter combination that minimizes the objective function J value is as follows: Determine the decision variables: The unit dosage of each component in the concrete mix design is used as the independent decision variables for the optimization algorithm, specifically including: cement dosage. m 1. Fly ash dosage m 2. Mineral powder dosage m 3. Fine aggregate dosage m 4. Amount of coarse aggregate m 5. Water consumption per cubic meter m 6. Dosage of water-reducing agent m 7; Algorithm selection and settings; Solution process: 1) Population initialization: Based on the set upper and lower limits of the raw material usage constraints. m i,min , m i,maxAn initial population is randomly generated within the search space, and each individual in the population corresponds to a complete set of concrete mix proportion parameters. m 1,……, m 7); 2) Fitness Assessment and Constraint Treatment: The matching ratio parameters of each individual in the population are substituted into the multi-factor prediction model to calculate the predicted slump, predicted strength, and predicted durability indices. If an individual does not satisfy any of the rigid constraints, its objective function is adjusted accordingly. J A penalty value is applied to decrease the fitness of the individual, ensuring its elimination in subsequent iterations; if an individual satisfies all constraints, its objective function value is directly calculated. J As a basis for fitness determination; 3) Evolutionary Iteration Operation: Sort individuals according to their fitness. Based on the fitness ranking, use tournament selection to select individuals with low cost, low carbon emissions, and that meet performance requirements from the current population to enter the next generation. Perform arithmetic crossover on the selected individuals to generate new combination ratios to expand the solution space. Perturb the raw material usage of some individuals to prevent the algorithm from getting stuck in local optima and ensure that the global optimum is obtained. 4) Iteration termination judgment: Repeat the fitness evaluation and evolution operation until the algorithm reaches the preset maximum number of iterations, or the objective function J value of the best individual in the population converges within multiple consecutive generations, then terminate the iteration. Output the optimal result: After the algorithm terminates, the individual with the highest fitness in the population is extracted first, and all decision variable values ​​m1~m7 corresponding to it are output, which are the optimal concrete mix proportion parameters.

[0012] According to the above scheme, all regression coefficients in the multi-factor prediction model are determined by solving the multivariate nonlinear regression analysis method.

[0013] According to the above scheme, the core variable parameters for concrete mix design also include w / B , R i , S p , m w 、m AE In the stage of establishing the prediction model, historical production data or laboratory test data are collected from the raw material database; in the optimization solution stage, the optimal concrete mix proportion parameters are determined.

[0014] According to the above plan, w 1. w 2. Determining the priority based on the project's focus on economic efficiency and low carbon emissions: When the project requires optimal rigid cost control, the weight... w1. The value range is 0.7 < w 1 < 0.9.

[0015] According to the above scheme, the concrete mix proportion parameters include the unit dosage of cement, admixtures, fine aggregate, coarse aggregate, water, and water-reducing agent.

[0016] The present invention also employs a type of concrete, which uses the low-carbon concrete mix design method described above to design the mix proportion parameters.

[0017] The beneficial effects of this invention are as follows: 1. Cost accounting-driven, stable economic benefits: This invention takes the actual cost accounting of raw materials as the primary optimization target, which overcomes the defect of the economic benefits of existing comprehensive optimization methods that are affected by unstable carbon price fluctuations, and ensures the rigid economic benefits of the mix proportion.

[0018] 2. Rigid guarantee of performance and low carbon emissions: This invention incorporates the strength, durability and carbon emissions of concrete as rigid constraints into the model, ensuring that while pursuing the lowest cost, all engineering performance indicators and low carbon targets can strictly meet the design requirements.

[0019] 3. High model practicality: This invention adopts adjustable cost-carbon dual objective weights, which can be flexibly adjusted according to the engineering emphasis on economic or environmental needs, thus enhancing the engineering applicability and flexibility of the method. Attached Figure Description

[0020] Figure 1 This is a flowchart of the method of the present invention. Detailed Implementation

[0021] To make the objectives, technical solutions, and advantages of this application clearer, the technical solutions of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0022] In the description of the embodiments of this application, it should be noted that the terms "center," "longitudinal," "lateral," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," and "outer," etc., indicate the orientation or positional relationship based on the orientation or positional relationship shown in the accompanying drawings. They are only for the convenience of describing the embodiments of this application and simplifying the description, and do not indicate or imply that the device or element referred to must have a specific orientation, or be constructed and operated in a specific orientation. Therefore, they should not be construed as limitations on the embodiments of this application. In addition, the terms "first," "second," and "third" are used for descriptive purposes only and should not be construed as indicating or implying relative importance.

[0023] In the description of the embodiments of this application, it should be noted that, unless otherwise explicitly specified and limited, the terms "connected" and "linked" should be interpreted broadly. For example, they can refer to a fixed connection, a detachable connection, or an integral connection; they can refer to a mechanical connection or an electrical connection; they can refer to a direct connection or an indirect connection through an intermediate medium. Those skilled in the art can understand the specific meaning of the above terms in the embodiments of this application based on the specific circumstances.

[0024] In the embodiments of this application, unless otherwise expressly specified and limited, "above" or "below" the second feature can mean that the first feature is in direct contact with the second feature, or that the first feature is in indirect contact with the second feature through an intermediate medium. Furthermore, "above," "on top of," and "over" the second feature can mean that the first feature is directly above or diagonally above the second feature, or simply that the first feature is at a higher horizontal level than the second feature. "Below," "below," and "under" the second feature can mean that the first feature is directly below or diagonally below the second feature, or simply that the first feature is at a lower horizontal level than the second feature.

[0025] In the description of this specification, the references to terms such as "one embodiment," "some embodiments," "example," "specific example," or "some examples," etc., refer to specific features, structures, materials, or characteristics described in connection with that embodiment or example, which are included in at least one embodiment or example of the embodiments of this application. In this specification, the illustrative expressions of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the specific features, structures, materials, or characteristics described may be combined in any suitable manner in one or more embodiments or examples. Moreover, those skilled in the art can combine and integrate the different embodiments or examples described in this specification, as well as the features of different embodiments or examples, without contradiction. Additionally, the term "a plurality of" indicates two or more.

[0026] The concrete mix proportion parameters in this invention are the amounts of the main components of the concrete, such as cement, admixtures (fly ash, mineral powder, etc.), fine aggregate, coarse aggregate, water, and water-reducing agent.

[0027] like Figure 1 The method for designing the mix proportions of low-carbon concrete shown is specifically a cost-based method for designing the mix proportions of low-carbon concrete, which includes the following steps: S1. Construct a concrete raw material parameter database: collect data on various concrete raw materials. i The attribute parameters, including unit cost C i Unit carbon emissions E i Raw material transportation distance d i Carbon emission factor per unit of transportation α i .

[0028] S2. Constructing Engineering Performance Constraints: Guided by the design requirements of concrete structures and combined with actual production data of concrete mix proportions, a multi-factor prediction model is constructed based on regression analysis to connect concrete workability, strength, and durability indices with concrete mix proportion parameters. This prediction model is then set as a rigid constraint for optimizing concrete mix proportions.

[0029] In this invention, the concrete slump S As a workability indicator, the initial chloride ion diffusion coefficient of concrete is used as a durability indicator, and the 28-day compressive strength of concrete is used as a durability indicator. f As an indicator of strength.

[0030] S3. With the dual objectives of economic efficiency (cost) and low carbon emissions (carbon emissions) of concrete mix proportions, a cost-carbon emission dual-objective optimization calculation model for concrete mix proportion optimization is constructed: With the dual objectives of economic efficiency (cost) and low carbon emissions (carbon emissions) of concrete mix proportions, the reasonable value range of low-carbon concrete workability index, strength index, and durability index determined by the engineering performance constraint model constructed based on S2 is used as the core rigid constraint condition; at the same time, the boundary constraints of concrete component dosage and volume constraints are used as constraint conditions to form a dual-objective optimization calculation model.

[0031] S4. Based on the constraints, use an optimization algorithm to find the concrete mix proportion parameter combination that minimizes the objective function J value (i.e., optimizes overall cost and carbon emissions) in the bi-objective optimization calculation model.

[0032] In S2 of this invention, the multi-factor prediction model based on the workability, strength, and durability indices of low-carbon concrete is as follows: ; ; ; In the formula, S The concrete slump is expressed in mm. f The 28-day compressive strength of concrete is expressed in MPa. D The initial chloride ion diffusion coefficient of concrete is given in m² / s. m w 、m AE The figures represent water consumption per cubic meter and water-reducing agent dosage, both in kg. R i The dosage of admixtures (fly ash, mineral powder, silica fume, etc.) is expressed in kg. S p The sand ratio of the concrete is expressed as a percentage (%). w / B The water-cement ratio of concrete; S 0、 s w , m w0 、s AE , s i 、s p 、S p0 The regression coefficients are those of the multi-factor prediction model for work performance. f c0 , k 0、 k i These are the undetermined coefficients for the multi-factor prediction model of compressive strength; Gc 0、 G 0、 G i , H i These are the undetermined coefficients in the multi-factor prediction model for chloride ion diffusion coefficient; m wo This is the empirical water consumption, expressed in kg, and is set based on the average level of engineering experience or historical data. R i It refers to admixtures i The dosage is expressed as a percentage. All coefficients are dimensionless.

[0033] In this invention, the core variable parameters for concrete mix design include: w / B , R i , S p , m w 、mAE In the stage of establishing the prediction model (step S2), the above-mentioned variable parameters are taken from historical production data or laboratory trial verification data collected from the raw material database; in the optimization solution stage (step S4), the above-mentioned variable parameters are used as decision variables of the optimization algorithm and are determined by the algorithm through iterative search within the preset constraint boundary range.

[0034] In this invention, S 0、 s w , m w0 、s AE , s i 、s p 、S p0 All of these are regression coefficients of the multi-factor prediction model for work performance, specifically representing the benchmark slump coefficient, empirical water consumption coefficient, water sensitivity coefficient, water-reducing agent efficiency coefficient, admixture morphology effect coefficient, sand ratio influence coefficient, and empirical sand ratio value coefficient, respectively. f c0 , k 0、 k i The coefficients to be determined in the multi-factor prediction model of compressive strength are, respectively, the baseline virtual strength coefficient, the water-cement ratio attenuation coefficient, and the admixture strength contribution coefficient. G c0 , G 0、 G i , H i All are undetermined coefficients of the multi-factor prediction model of chloride ion diffusion coefficient, specifically representing the baseline diffusion coefficient, water-cement ratio influence coefficient, admixture linear influence coefficient, and admixture-water-cement ratio interaction coefficient respectively; all coefficients are dimensionless. The regression coefficients and undetermined coefficients in the above model are determined by existing multivariate nonlinear regression analysis methods. The specific methods are as follows: (1) Data collection: Collect sufficient laboratory system test data and engineering historical production data containing the core parameters of concrete mix proportion (input variables) and corresponding measured performance indicators (output variables: slump S, 28d compressive strength f, initial chloride ion diffusion coefficient D) to ensure that the sample covers different mix proportion combinations and working conditions; (2) Fitting solution: Based on the principle of multivariate nonlinear regression analysis, statistical fitting algorithms (such as least squares method) are used to iteratively fit and solve the mathematical expressions of each performance prediction model; (3) Coefficient verification and determination: After verifying the reliability of the model through the analysis of fitting results, the specific values ​​of each coefficient are determined.

[0035] In this invention, the coefficient solution in the concrete performance prediction model constructed using regression analysis is a mature technique in the fields of statistics and concrete materials, widely used in various concrete mix design software and systems. This technique has been widely adopted in the industry and is considered a standard method in published literature and patents, and therefore does not constitute the innovative point of this invention.

[0036] In this invention, the above-mentioned solution method is a mature processing method, and will not be described in detail here.

[0037] In S3 of this invention, the cost-carbon emission dual-objective optimization calculation model is as follows:

[0038]

[0039] In the above formula, J is the objective function, which is dimensionless; w 1. w 2 represents the target weight, where, w 1 represents the target weight related to economic efficiency. w 1 represents the target weight related to low carbon emissions, satisfying... w 1+ w 2 = 1; C i Raw materials for concrete i The unit cost is expressed in yuan / kg; E i Raw materials for concrete i The unit carbon emission factor, expressed in kg·CO2 / kg; α i Raw materials for concrete i The carbon emission factor per unit of transportation, expressed in kg·CO2 / (kg·km); d i Raw materials for concrete i The transport distance, in km; m i Raw materials for concrete i The dosage per unit, in kg / m³ 3 ; C ref For reference cost, the unit is yuan; E ref The reference carbon emissions, in kg·CO2, are used for normalization of the objective function. S l , S u Concrete slump S The lower and upper limits, in mm; f l , fu These are the 28-day compressive strengths of concrete. f The lower and upper limits, in MPa; D l , D u These are concrete durability indicators. D The lower and upper limits, in meters. 2 / s; m i,min , m i,max Raw materials for concrete i The lower and upper limits of dosage are both in kg. ρ i Raw materials for concrete i Density, expressed in kg / m³; V a This represents the air volume in the concrete, expressed in m³. n This represents the total number of different types of concrete raw materials.

[0040] In this invention, w 1. w 2. The weight is determined based on the project's emphasis on economic efficiency and low carbon emissions, where: when the project requires optimal rigid cost control, the weight is... w 1. The value range is 0.7 < w 1 < 0.9, to ensure that cost accounting plays a dominant role in optimization; when engineering projects require high low-carbon environmental benefits, w 2 takes values ​​in the range of 0.3 < w 2 < 0.5, to balance cost and carbon emissions.

[0041] In this invention, m i The value is obtained through subsequent optimization algorithms and ranges from [ m i,min , m i,max ]between, m i,min , m i,max Determined based on specifications or engineering experience; C i For market research and procurement data; E i , α i Available from existing Life Cycle Assessment (LCA) databases; d i Obtained from raw material logistics records; ρ i For laboratory testing data;V a It can be obtained through standard table lookup or engineering requirements; S l , S u , f l , f u , D l , D u It can be determined according to the requirements of the building structure design.

[0042] In S4 of this invention, a multi-objective optimization algorithm is used to solve for the mix proportion parameters of low-carbon concrete that simultaneously meet the requirements of workability, strength, durability, and economy. The concrete mix proportion parameters include the dosage of each raw material of concrete (cement, admixtures (fly ash, mineral powder, etc.), fine aggregate, coarse aggregate, water, and water-reducing agent).

[0043] This step uses a multi-objective optimization algorithm to optimize the mix proportion parameters of low-carbon concrete while simultaneously meeting the requirements of workability, strength, durability, and economy. It aims to obtain the optimal combination of raw material dosages for concrete that balances carbon emissions and overall cost. The mix proportion parameters include the unit dosage of cement, fly ash / mineral powder admixtures, fine aggregate, coarse aggregate, water, and water-reducing agent.

[0044] In this invention, the specific optimization process uses the rigid constraints such as performance indicators, raw material usage, and volume parameters constructed in step S3 as boundaries, and takes the minimization of the objective function J (comprehensive cost and carbon emissions) as the optimization direction to complete the iterative screening of the combination of proportion parameters. The specific method is as follows: 1. Determine decision variables: The unit dosage of each component in the low-carbon concrete mix proportion is used as the independent decision variables for the optimization algorithm, specifically including: cement dosage. m 1. Fly ash dosage m 2. Mineral powder dosage m 3. Fine aggregate dosage m 4. Amount of coarse aggregate m 5. Water consumption per cubic meter m 6. Dosage of water-reducing agent m 7.

[0045] 2. Algorithm selection and settings: Genetic algorithm is used for solving the problem. Key parameters of the algorithm, such as population size, maximum number of iterations, crossover probability, and mutation probability, are set.

[0046] 3. Solution process: 1) Population initialization: Based on the upper and lower limits of the raw material usage constraints set in step S3. m i,min , mi,max An initial population is randomly generated within the search space, and each individual in the population corresponds to a complete set of concrete mix proportion parameters. m 1, ……, m 7); 2) Fitness assessment and constraint treatment: Substitute the matching ratio parameter of each individual in the population into the multi-factor prediction model established in step S2, and calculate its predicted collapse degree respectively. S pred ), predicted intensity ( f pred ) and predicted durability indicators ( D pred If an individual does not satisfy any of the rigid constraints in step S3, then its objective function... J A penalty value is applied to decrease the fitness of the individual, ensuring its elimination in subsequent iterations; if an individual satisfies all constraints, its objective function value is directly calculated. J As a basis for fitness determination ( J The smaller the value, the higher the individual fitness. 3) Evolutionary Iteration Operation: Sort individuals according to their fitness. Based on the fitness ranking, use tournament selection to select individuals with low cost, low carbon emissions, and that meet performance requirements from the current population to enter the next generation. Perform arithmetic crossover on the selected individuals to generate new combination ratios to expand the solution space. Make small perturbations (mutations) on the raw material usage of some individuals to prevent the algorithm from getting stuck in local optima and ensure that the global optimum is obtained. 4) Iteration termination judgment: Repeat steps 2) and 3) fitness evaluation and evolution operation until the algorithm reaches the preset maximum number of iterations, or the objective function J value of the best individual in the population does not fluctuate significantly over multiple generations (achieving convergence), then the iteration is terminated.

[0047] 4. Output the optimal result: After the algorithm terminates, the individual with the highest fitness in the population is extracted first, and all decision variable values ​​(m1~m7) corresponding to it are output. This set of variables is the final optimal low-carbon concrete mix proportion parameters.

[0048] In this invention, based on the optimal low-carbon concrete mix proportion parameters, the core variable parameters for concrete mix proportion design can be obtained, including... w / B , R i , S p , m w 、m AE The value of .

[0049] Step S4 involves determining the mix proportion parameters for low-carbon concrete that simultaneously meet the requirements of workability, strength, durability, and economy, wherein the cement content is 100~480 kg / m³. 3 The amount of fly ash used is 0~180 kg / m³ 3 The amount of mineral powder used is 0~220kg / m³ 3 Fine aggregate dosage is 450~1200 kg / m³ 3 The amount of coarse aggregate used is 700~1500 kg / m³. 3 Water consumption is 100~220 kg / m³ 3 The dosage of water-reducing agent is 0~15 kg / m³. 3 .

[0050] Example 1 This embodiment is an application example, taking the mix design of a C40 marine concrete as an example. It compares and analyzes the method with traditional strength-based concrete mix design methods to verify the superiority of this method, including the following steps: S1: Construct a database of concrete raw material parameters. Collect data on the raw materials used in concrete. i unit cost C i Unit carbon emissions E i Raw material transportation distance d i Carbon emission factor per unit of transportation α i The parameters are shown in Table 1 for details.

[0051] Table 1 Concrete Raw Material Database

[0052] S2. Constructing Engineering Performance Constraints: Guided by the design requirements of concrete structures and combined with actual production data of concrete mix proportions, a multi-factor prediction model is constructed based on regression analysis to connect concrete workability, strength, and durability indices with concrete mix proportion parameters. This prediction model is then set as a rigid constraint for optimizing concrete mix proportions.

[0053] This example uses a C40 marine concrete structure located in the marine atmosphere, exposed to corrosive chloride ions. The design service life is 100 years, and the initial chloride ion diffusion coefficient is selected as the durability index. D Based on the structural load-bearing capacity and durability design, the limit range of the compressive strength at 28 days is determined to be 45MPa~50MPa, and the initial chloride ion diffusion coefficient is... D The limit range is (3.5×10). 12 ~4.0×10 12 )m 2 / s, using pumping construction, determine the workable slump. S The tolerance range is 180mm~220mm. Based on local raw material availability, ordinary Portland cement with grade PO 42.5 was selected, with crushed stone as the coarse aggregate, a particle size range of 5~31.5mm, and mineral admixtures consisting of a mixture of Grade II fly ash and S95 grade mineral powder. A polycarboxylate superplasticizer with a water reduction rate of 23% was used. According to structural design requirements, test data on concrete workability, 28-day compressive strength, and initial chloride ion diffusion coefficient were collected. Multi-factor prediction models for concrete workability, strength, and initial chloride ion diffusion coefficient were established through regression analysis.

[0054]

[0055]

[0056]

[0057] in, S The concrete slump is expressed in mm. f The 28-day compressive strength of concrete is expressed in MPa. D The initial chloride ion diffusion coefficient of concrete is given in m² / s. m w 、m AE These are the unit water consumption and water-reducing agent dosage, both in kg. R FA , R SL These are the dosages of fly ash and mineral powder, respectively, in kg. w / B This refers to the water-to-binder ratio.

[0058] S3: Construct a cost-carbon emission dual-objective optimization calculation model. The cost and carbon emissions of concrete are used as the dual objective function J. The range of values ​​for the workability, strength, and durability of low-carbon concrete are used as rigid constraints. The influence of constraints on concrete component dosage and volume is also considered to construct an optimization model.

[0059]

[0060]

[0061] In the formula, w 1, w 2 is the target weight, satisfying w 1+ w 2 = 1, in this example w 1 is 0.85.w 2 is 0.18; C ref , E ref For reference to cost and carbon emissions, and for normalization of the objective function, in this example... C ref Take 350 yuan / m 3 , E ref Take 300.0 kg of CO 2 / m 3 ; S l , S u Concrete slump S The lower and upper limits are set to 180mm and 220mm respectively in this example; f l , f u 28-day compressive strength of concrete f The lower and upper limits are set to 45MPa and 50MPa respectively in this example; D l , D u Concrete durability index D The lower and upper limits are set to 3.5 × 10 in this example. 12 m 2 / s and 4.0×10 12 m 2 / s; m i,min , m i,max Concrete raw materials i The lower and upper limits of dosage are shown in Table 2; ρ i Raw materials for concrete i The density is shown in Table 1; V a This represents the air volume in the concrete; in this example, it is taken as 1.0%. The units for each parameter are as described above.

[0062] Table 2 Density, Dosage Range, and Proportional Parameter Range of Raw Materials for Concrete per Unit Volume

[0063] S4: Solving for the optimal mix proportion of low-carbon concrete. Determine the mix proportion parameters for low-carbon concrete that simultaneously meet the requirements of workability, strength, durability, and economy. A multi-objective optimization algorithm is used to solve for the low-carbon concrete mix proportion, including the amounts of cement, admixtures (fly ash, mineral powder, etc.), fine aggregate, coarse aggregate, water, and water-reducing agent. This embodiment uses a multi-objective genetic algorithm to solve for the low-carbon concrete mix proportion, and the results are shown in Table 3, compared with the mix proportion results designed using the traditional strength-based concrete mix proportion method.

[0064] Table 3 Comparison and Analysis of Mix Proportion Parameters

[0065] As shown in Table 3, the total cost of the optimized scheme described in this invention in this embodiment is 282.3 yuan / m². 3 Compared to the conventional mix ratio of 292.3 yuan / m³ 3 Reduced by 10 yuan / m 3 Total carbon emissions: 223.0 kg CO 2 / m 3 Compared to the conventional formula of 261.7 kg CO 2 / m 3 Reduced CO by 38.7 kg 2 / m 3 This achieves significant environmental benefits. Therefore, the method of this invention not only simultaneously meets the requirements of strength and durability, but also reduces total cost and carbon emissions.

[0066] Example 2 A type of concrete, wherein the mix proportion parameters are designed using the low-carbon concrete mix proportion design method described above.

[0067] The contents not described in detail in this specification are existing technologies known to those skilled in the art.

[0068] Finally, it should be noted that the above are merely preferred embodiments of this application and are not intended to limit this application. Although this application has been described in detail with reference to the embodiments, those skilled in the art can still modify the technical solutions described in the foregoing embodiments or make equivalent substitutions for some of the technical features. However, any modifications, equivalent substitutions, improvements, etc., made within the spirit and principles of this application should be included within the protection scope of this application.

Claims

1. A method for designing the mix proportions of low-carbon concrete, characterized in that, The method includes the following steps: A multi-factor prediction model was constructed to predict the relationship between concrete workability, strength, and durability indices and concrete mix proportion parameters, and this model was set as a constraint condition for concrete mix proportion optimization. With the dual objectives of economic efficiency and low carbon emissions in concrete mix design, a cost-carbon emission dual-objective optimization calculation model for optimizing concrete mix design is constructed. Based on the constraints, find the concrete mix proportion parameter combination that minimizes the objective function value in the bi-objective optimization calculation model.

2. The low-carbon concrete mix design method as described in claim 1, characterized in that, The concrete slump S was used as the workability index, the initial chloride ion diffusion coefficient of the concrete was used as the durability index, and the 28-day compressive strength f of the concrete was used as the strength index.

3. The low-carbon concrete mix design method as described in claim 2, characterized in that, The multi-factor prediction model based on the workability, strength, and durability indices of concrete is as follows: ； ； ； In the formula, S The concrete slump is expressed in mm. f The 28-day compressive strength of concrete is expressed in MPa. D The initial chloride ion diffusion coefficient of concrete is given in m² / s. m w 、m AE The figures represent water consumption per cubic meter and water-reducing agent dosage, both in kg. R i The dosage of the admixture is expressed in kg. S p The sand ratio of the concrete, expressed as a percentage. w / B The water-cement ratio of concrete; m wo Water consumption is based on experience, and the unit is kg. R i It refers to admixtures i Dosage, expressed as a percentage; S 0、 s w , m w0 、s AE , s i 、s p 、S p0 The regression coefficients of the multi-factor prediction model for work performance are respectively the benchmark slump coefficient, empirical water consumption coefficient, water sensitivity coefficient, water-reducing agent efficiency coefficient, admixture morphology effect coefficient, sand ratio influence coefficient, and empirical sand ratio value coefficient. f c0 , k 0、 k i The coefficients to be determined in the multi-factor prediction model of compressive strength are the baseline virtual strength coefficient, the water-cement ratio attenuation coefficient, and the admixture strength contribution coefficient, respectively. Gc 0、 G 0、 G i , H i The coefficients to be determined in the multi-factor prediction model of chloride ion diffusion coefficient are the baseline diffusion coefficient, the water-cement ratio influence coefficient, the linear influence coefficient of admixture, and the interaction coefficient between admixture and water-cement ratio, respectively. All coefficients are dimensionless.

4. The low-carbon concrete mix design method as described in claim 3, characterized in that, The dual-objective optimization calculation model for cost and carbon emissions is as follows: In the above formula, J is the objective function, which is dimensionless; w 1. w 2 represents the target weight, where, w 1 represents the target weight related to economic efficiency. w 1 represents the target weight related to low carbon emissions, satisfying... w 1+ w 2 = 1; C i Raw materials for concrete i The unit cost is expressed in yuan / kg; E i Raw materials for concrete i The carbon emission factor per unit, expressed in kg·CO2 / kg; α i Raw materials for concrete i The carbon emission factor per unit of transportation, expressed in kg·CO2 / (kg·km); d i Raw materials for concrete i The transport distance, in km; m i Raw materials for concrete i The dosage per unit, in kg / m³ 3 ; C ref For reference cost, the unit is yuan; E ref The reference carbon emissions, in kg·CO2, are used for normalization of the objective function. S l , S u Concrete slump S The lower and upper limits, in mm; f l , f u These are the 28-day compressive strengths of concrete. f The lower and upper limits, in MPa; D l , D u These are concrete durability indicators. D The lower and upper limits, in meters. 2 / s; m i,min , m i,max Raw materials for concrete i The lower and upper limits of dosage are both in kg. ρ i Raw materials for concrete i Density, expressed in kg / m³; V a This represents the air volume in the concrete, expressed in m³. n This represents the total number of different types of concrete raw materials.

5. The low-carbon concrete mix design method as described in claim 4, characterized in that, The method for finding the concrete mix proportion parameter combination that minimizes the objective function J is as follows: Determine the decision variables: The unit dosage of each component in the concrete mix design is used as the independent decision variables for the optimization algorithm, specifically including: cement dosage. m 1. Fly ash dosage m 2. Mineral powder dosage m 3. Fine aggregate dosage m 4. Amount of coarse aggregate m 5. Water consumption per cubic meter m 6. Water-reducing agent dosage m 7; Algorithm selection and settings; Solution process: 1) Population initialization: Based on the set upper and lower limits of the raw material usage constraints. m i,min , m i,max An initial population is randomly generated within the search space, and each individual in the population corresponds to a complete set of concrete mix proportion parameters. m 1, ……, m 7); 2) Fitness Assessment and Constraint Treatment: The matching ratio parameters of each individual in the population are substituted into the multi-factor prediction model to calculate the predicted slump, predicted strength, and predicted durability indices. If an individual does not satisfy any of the rigid constraints, its objective function is adjusted accordingly. J A penalty value is applied to decrease the fitness of the individual, ensuring its elimination in subsequent iterations; if an individual satisfies all constraints, its objective function value is directly calculated. J As a basis for fitness determination; 3) Evolutionary Iteration Operation: Sort individuals according to their fitness. Based on the fitness ranking, use tournament selection to select individuals with low cost, low carbon emissions, and that meet performance requirements from the current population to enter the next generation. Perform arithmetic crossover on the selected individuals to generate new combination ratios to expand the solution space. Perturb the raw material usage of some individuals to prevent the algorithm from getting stuck in local optima and ensure that the global optimum is obtained. 4) Iteration termination judgment: Repeat the fitness evaluation and evolution operation until the algorithm reaches the preset maximum number of iterations, or the objective function J value of the best individual in the population converges within multiple consecutive generations, then terminate the iteration. Output the optimal result: After the algorithm terminates, the individual with the highest fitness in the population is extracted first, and all decision variable values ​​m1~m7 corresponding to it are output, which are the optimal concrete mix proportion parameters.

6. The low-carbon concrete mix design method as described in claim 3, characterized in that, All regression coefficients in the multifactor prediction model are determined by solving the multivariate nonlinear regression analysis method.

7. The low-carbon concrete mix design method as described in claim 3, characterized in that, The core variable parameters for concrete mix design also include w / B , R i , S p , m w 、m AE In the stage of establishing the prediction model, historical production data or laboratory test data are collected from the raw material database; in the optimization solution stage, the optimal concrete mix proportion parameters are determined.

8. The low-carbon concrete mix design method as described in claim 4, characterized in that, w 1. w 2. Determining the priority based on the project's focus on economic efficiency and low carbon emissions: When the project requires optimal rigid cost control, the weight... w 1. The value range is 0.7 < w 1 < 0.

9.

9. The low-carbon concrete mix design method as described in claim 1, characterized in that, Concrete mix proportion parameters include the unit dosage of cement, admixtures, fine aggregate, coarse aggregate, water, and water-reducing agent.

10. A type of concrete, characterized in that, The mix proportion parameters are designed using the low-carbon concrete mix proportion design method as described in claims 1 to 9.

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