Functional gradient concrete based on multi-source building solid waste recycled aggregate and design method thereof

Abstract

The present application relates to a kind of functional gradient concrete based on multi-source building solid waste recycled aggregate and its design method, method is used to prepare the functional gradient concrete using multi-source building solid waste recycled aggregate, comprising: S1, according to target performance determines strength grade, component and target structure;S2, based on the above parameters determine macroscopic effective performance and target distribution function, if macroscopic effective performance meets target performance then turn S5, otherwise execute S3;S3, according to target distribution function type selection preparation process and prepare sample, carry out performance test and sample data;S4, define design variable, based on sampling data to establish prediction proxy model and solve optimal design variable, update macroscopic effective performance and target distribution function;S5, according to target distribution function type selection preparation process, prepare functional gradient concrete.Compared with prior art, the present application solves the problem that functional gradient concrete gradient design relies on experience, and the performance predictability is poor.

Description

Technical Field

[0001] This invention relates to the fields of resource recycling technology and building material design, and in particular to a functional gradient concrete based on recycled aggregates from multi-source building solid waste and its design method. Background Technology

[0002] With the acceleration of urban renewal, the resource utilization of construction waste has become a major challenge for sustainable development. Traditionally, construction waste is crushed into recycled aggregates for the preparation of ordinary concrete. However, due to its inherent porosity, high water absorption, and low strength, these recycled materials are often considered to have "performance defects" and are mostly downgraded for use in non-load-bearing structures, resulting in low economic added value and failing to maximize the material's potential. At the same time, modern buildings have an increasing demand for functional materials, and functionally graded materials have attracted much attention because they can integrate multiple properties within a single component. Transforming the "porosity defect" of recycled aggregates from construction waste into a "functional advantage" for constructing graded structures, thereby preparing graded concrete with load-bearing, thermal insulation, and sound insulation properties, opens up a new path for the high-value utilization of construction waste.

[0003] However, current technological approaches have significant limitations: 1) Gradient design methods are crude, relying heavily on trial and error or empirical qualitative design, which is not conducive to performance control and secondary optimization. Specifically, for example, Chinese patent application CN117166664A discloses a method for preparing gradient components by controlling the vibration of semi-dry concrete to separate materials of different densities. This method utilizes precast concrete slabs with gradually increasing densities and cast-in-place concrete slabs to optimize the stress distribution and insulation effect of the floor slab. However, the operation is cumbersome and the material properties of the gradient parts are difficult to control. 2) In existing technologies, the design and manufacturing processes are relatively independent, making it difficult to achieve precise design of the internal functional layers of materials based on accurate gradient functions. For example, Chinese patent application CN116161907A, although it achieves the control of internal structures with different porosities, does not solve the problem of how to design functional layers based on quantitative gradient functions. And Chinese patent CN117401949A, which discloses a method for 3D printing functional gradient concrete, although it has functional features, it is not easy to achieve customized designs for more complex functions. 3) Existing technologies lack the ability to reverse-quantitatively design from macroscopic performance targets to microscopic structural parameters. For example, Chinese patent application CN118324474A discloses a method for preparing functionally graded ultra-high performance concrete, which improves cold joint connections through microscale continuous agents and layering agents, but still lacks reverse-quantitative design capabilities, resulting in unpredictable and imprecisely controllable performance of the prepared graded components.

[0004] In other words, the technical problems that need to be solved are how to avoid the problems of unpredictable and difficult-to-precise control of the performance of functional graded concrete components, such as the extensiveness of design methods, the disconnect between design and manufacturing, and the lack of reverse quantitative design capabilities from macroscopic performance to microstructure. Summary of the Invention

[0005] The purpose of this invention is to overcome the shortcomings of existing technologies, such as reliance on experience in the design of functional graded concrete, unpredictable performance, and disconnect between material graded design and preparation process selection, and to provide a functional graded concrete based on recycled aggregates from multi-source building solid waste and its design method.

[0006] The objective of this invention can be achieved through the following technical solutions: According to a first aspect of the present invention, a method for designing functionally graded concrete based on recycled aggregates from multi-source construction solid waste is provided, the method comprising: S1. Determine the target performance vector of functionally graded concrete according to the requirements, and determine the strength grade, composition and target structure of functionally graded concrete based on the target performance vector; the target performance vector includes at least one element, and each element represents a type of performance index. S2. Based on the intensity level, composition, and target structure, determine the macroscopic effective performance and target distribution function. If the macroscopic effective performance satisfies the target performance vector, execute S5; otherwise, execute S3. The target distribution function includes piecewise constant functions and continuous functions. The target distribution function satisfies that the performance change rate between adjacent gradient layers is less than a preset threshold. S3. Based on the type of the target distribution function, select the preparation process to prepare the sample, perform performance testing on the sample and sample data. S4. Define design variables, establish a predictive surrogate model from the design variables to the performance vector based on the sampled data, use the NSGA-II optimization algorithm to solve for the optimal design variables based on the predictive surrogate model, update the macroscopic effective performance based on the optimal design variables, and update the target distribution function based on the updated macroscopic effective performance. S5. Select the preparation process based on the type of the target distribution function to prepare functionally graded concrete.

[0007] As a preferred technical solution, when the target distribution function is a piecewise constant function, we have: The macroscopic performance ,for: , Indicating the functionally graded concrete, the first The material properties of the layer; This indicates the total number of gradient layers in functionally graded concrete; Represents a mixture function; Indicating the functionally graded concrete, the first The thickness of the gradient layer, and , , Indicates the first The lower limit and the first limit of gradient layer material thickness The upper limit of the gradient layer material thickness; The target distribution function ,for: ; in, This indicates the material thickness value.

[0008] As a preferred technical solution, when the target distribution function is a continuous function, we have: The target distribution function It is a family of Sigmoid functions, calculated based on the upper and lower limits of the target performance vector, as well as the gradient change center point and gradient change rate of the functionally graded concrete; The macroscopic performance ,for: , Indicates the first The upper limit of the gradient layer material thickness, Indicates the material thickness value. This represents an integral function.

[0009] As a preferred technical solution, when the target distribution function is a piecewise constant function, the manufacturing process used is a layer-by-layer stacking process or a 3D printing process. If the target distribution function is a continuous function, the preparation process used is centrifugal molding.

[0010] As a preferred technical solution, the method further includes: In the process of solving for the optimal design variable, if each performance index in the performance vector corresponding to the current optimal design variable is greater than or equal to the corresponding performance index in the target performance vector, then the current optimal design variable is the final optimal design variable. If any performance index in the performance vector corresponding to the current optimal design variable is less than the corresponding performance index in the target performance vector, then a tradeoff weight is defined, the current optimal design variable is adjusted based on the tradeoff weight, and the corresponding performance vector is calculated using a predictive surrogate model based on the adjusted optimal design variable. The Euclidean distance between the performance vector and the target performance vector is also calculated. If the Euclidean distance is less than a preset value, then the adjusted optimal design variable is taken as the final optimal design variable; otherwise, the tradeoff weight is redefined.

[0011] As a preferred technical solution, the method for updating the aforementioned macroscopic effective performance is as follows: The material properties of each layer in the functionally graded concrete are updated based on the aforementioned optimal design variables, expressed as follows: , Indicating the functionally graded concrete, the first Optimal design variables for gradient layers; This indicates the updated functional graded concrete. Material properties of the gradient layer; Based on the updated material properties, obtain the updated macroscopic properties. , represented as: ; This indicates the total number of gradient layers in functionally graded concrete; This represents a mixed function.

[0012] According to a second aspect of the present invention, a functional gradient concrete based on recycled aggregate from multi-source construction solid waste is provided, characterized in that the functional gradient concrete is prepared by the above-described method, and the components of the functional gradient concrete include cement, water, fiber and pretreated recycled aggregate from multi-source construction solid waste; the pretreatment includes low-temperature crushing, particle shaping and porosity adjustment processes.

[0013] As a preferred technical solution, the low-temperature crushing is as follows: By sorting the construction solid waste blocks in The solid waste is subjected to freezing treatment in the environment to fully condense the pore water in the construction solid waste blocks, and then sent into a closed cryogenic crushing device to maintain [the process]. The following ambient temperatures are used for crushing, and the resulting recycled aggregate is then vibrated and screened according to the required particle size to obtain recycled aggregate of different particle sizes; the freezing treatment includes at least one of mechanical refrigeration, liquid nitrogen quick-freezing, and freezing liquid immersion.

[0014] As a preferred technical solution, the particle shaping includes at least one of vertical impact shaping, horizontal rotary shaping, airflow vortex shaping, and wet grinding shaping, to generate recycled aggregates with indicators that meet design requirements; the indicators include angularity, sphericity, and surface texture.

[0015] As a preferred technical solution, the porosity adjustment process is as follows: Recycled aggregate is soaked in alkaline solution and then cured in a carbon dioxide environment to generate carbonate deposits that fill the internal pores, thereby obtaining recycled aggregates with different preset porosity levels; wherein, the porosity level is related to the concentration of alkaline solution, soaking time, concentration of carbon dioxide, and curing time.

[0016] Compared with the prior art, the present invention has the following beneficial effects: 1) This invention introduces a target distribution function and strictly limits the rate of performance change between adjacent gradient layers to less than a preset threshold. This parameterized and quantitative gradient characterization method replaces traditional qualitative design and iterative trial and error, enabling the performance changes of gradient layers to be accurately described and controlled, laying a solid mathematical foundation for subsequent performance optimization and secondary development. Furthermore, the technical solution provided by this invention selects the optimal fabrication process based on the type of the target distribution function, ensuring that complex functional layers designed according to precise gradient functions can be accurately manufactured.

[0017] 2) After constructing the macroscopic performance and corresponding target distribution function in the forward direction, this invention also compares the macroscopic performance with the determined target performance vector, and introduces a reverse optimization process based on the comparison results. Specifically, by introducing design variables and constructing the relationship between the design variables and performance, the optimal design variables are solved based on the relationship, and the macroscopic performance is updated based on the optimal value. Based on the updated macroscopic performance, the optimal target distribution function is solved in reverse, and the macroscopic performance target is accurately associated with the microscopic performance parameters. This effectively avoids the impact of unpredictable performance on the preparation of functionally graded concrete, and achieves quantitative and accurate preparation. Attached Figure Description

[0018] Figure 1 This is a flowchart of the method of the present invention; Figure 2 This is a schematic diagram illustrating the preparation of functionally graded concrete according to the present invention; Figure 3 This is a graph of the piecewise constant function type target distribution function of the present invention; Figure 4 This is a schematic diagram of the gradient profile structure corresponding to the piecewise constant function type target distribution function of the present invention; Figure 5 This is a graph of the continuous function-type target distribution function of the present invention; Figure 6 This is a schematic diagram of the gradient profile structure corresponding to the continuous function-type target distribution function of the present invention; Figure 7 This is a schematic diagram illustrating the preparation of a continuous gradient using eccentric rotation according to the present invention. Figure 8 This is a schematic diagram illustrating the preparation of a continuous gradient using axial rotation according to the present invention. Detailed Implementation

[0019] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some, not all, of the embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort should fall within the scope of protection of the present invention.

[0020] In the following examples: the cement is ordinary Portland cement (PO42.5); the particle size of the recycled aggregate from multi-source construction solid waste is 0.15-1.18 mm, the moisture content is 0.6%, and the water absorption rate is 13.5%; the thickener is nano-purified attapulgite clay powder for concrete; the water-reducing agent is a polycarboxylate-based high-efficiency water-reducing agent; the retarder is sodium gluconate; the water-retaining agent is hydroxypropyl methylcellulose; and the water is tap water. The proportions of each component are: 1000 parts cement, 10 parts fiber, 1000 parts fine aggregate, 400 parts water, 1.28 parts cellulose, 1.1 parts water-reducing agent, 0.7 parts sodium gluconate, and 6 parts nano-clay.

[0021] Example 1 To address the problems existing in current technologies, this paper proposes a functionally graded concrete design method based on recycled aggregates from multi-source construction solid waste. This method effectively coordinates multiple performance objectives, including mechanical and thermal properties, through parametric modeling and multi-objective optimization algorithms. It transforms the previously experience-dependent and fragmented design and manufacturing process into a calculable and optimizable digital workflow with performance objective functions as input and gradient design parameters as output. Based on the mathematical characteristics of the gradient distribution, it intelligently matches the optimal preparation process, achieving integrated preparation from design to manufacturing. This ensures that the optimal design blueprint can be manufactured with the highest fidelity using processes such as centrifugation, printing, or stacking. This elevates recycled aggregates from construction solid waste into programmable and customizable functional material units, maximizing the value of resource utilization.

[0022] In detail, the method flow provided by this invention is as follows: Figure 1 As shown, including as Figure 2 The two processes shown, forward design and reverse optimization, include the following steps: S1. Determine the target performance vector of functionally graded concrete according to the requirements, and determine the strength grade, composition and target structure of functionally graded concrete based on the target performance vector.

[0023] It should be clarified that functionally graded concrete has one or more different functional characteristics in its components, including concrete strength grade, water-cement ratio, recycled aggregates from multi-source construction solid waste, and fibers, etc. The specific adjustments are made according to the service environment required by the design to determine the target performance vector of the functionally graded concrete to be prepared. The target performance vector includes at least one element, and each element represents a type of performance index, including compressive strength, flexural strength, density, thermal conductivity, and acoustic impedance, etc.

[0024] After determining the target performance vector, the concrete strength grade, water-cement ratio, aggregate, and fiber composition of the functionally graded concrete are determined in conjunction with the actual application environment, and its target structure is determined. For example, for components whose main stress state is bending, a high-strength gradient layer with high strength grade and high fiber content needs to be configured in the area of ​​maximum bending moment; for components whose main service environment is impact resistance or corrosion resistance, a high-strength, high-corrosion-resistant high-performance gradient layer is configured on the damaged surface; for components whose main functional requirements are thermal insulation and sound insulation, the amount of lightweight aggregate is increased and the concrete strength grade is reduced in order to save costs and reduce carbon emissions.

[0025] S2. Based on the strength level, composition and target structure, determine the macroscopic effective performance and target distribution function. If the macroscopic effective performance meets the target performance vector, execute S5; otherwise, execute S3.

[0026] It should be noted that the target distribution function provided by the present invention includes piecewise constant functions and continuous functions, and the target distribution function satisfies that the performance change rate between adjacent gradient layers is less than a preset threshold. The preset threshold is generally set to 20% to ensure that the performance change rate between adjacent gradient layers is not too large, so as to avoid microcracks or phase separation caused by abrupt changes in composition.

[0027] 1) When the target distribution function is a piecewise constant function, we have: Macro performance Estimating the performance of each layer in functionally graded concrete using a series or parallel model can be expressed as follows: , Indicating the functionally graded concrete, the first The material properties of the layer; This indicates the total number of gradient layers in functionally graded concrete; The mixing function is represented by a mixing law formula, the form of which is selected based on the type of performance, such as mechanical properties, density, thermal conductivity, and acoustic impedance. Indicating the functionally graded concrete, the first The thickness of the gradient layer, and , , Indicates the first The lower limit and the first limit of gradient layer material thickness The upper limit of the thickness of the gradient layer material.

[0028] Target distribution function ,for: ; in, This indicates the material thickness value.

[0029] Can be constructed as Figure 3 The two piecewise constant functions shown are as follows: the left side of the attached figure represents the monolinear type, and the right side represents the bilinear type. The corresponding cross-sections of the constructed functionally graded concrete are shown in the figure below. Figure 4 As shown, the left side corresponds to a single-line type, and the right side corresponds to a double-line type.

[0030] 2) When the target distribution function is a continuous function, we have: Target distribution function As a member of the Sigmoid function family, it is calculated based on the upper and lower limits of the target performance vector, as well as the gradient change center point and gradient change rate of functionally graded concrete, and can be expressed as: ; in, This represents the lower limit of the target performance vector for functionally graded concrete. This represents the upper limit of the target performance vector for functionally graded concrete; Indicates the center point of gradient change; The gradient rate of change.

[0031] Macro performance ,for: , Indicates the first The upper limit of the gradient layer material thickness, Indicates the material thickness value. This represents an integral function.

[0032] Can be constructed as Figure 5 The two continuity functions shown in the figure are monolinear on the left and bilinear on the right, corresponding to the cross-sections of the constructed functionally graded concrete as follows. Figure 6 As shown, the left side corresponds to a single-line type, and the right side corresponds to a double-line type.

[0033] S3. Based on the type of the target distribution function, select the preparation process to prepare the sample, perform performance testing on the sample, and collect data.

[0034] Specifically, when the target distribution function is a piecewise constant function, the fabrication process used is a layer-by-layer stacking process or a 3D printing process; if the target distribution function is a continuous function, the fabrication process used is centrifugal molding.

[0035] S4. Define design variables, establish a predictive surrogate model from design variables to performance vectors based on sampled data, use the NSGA-II optimization algorithm to solve for the optimal design variables based on the predictive surrogate model, update the macroscopic effective performance based on the optimal design variables, and update the objective distribution function based on the updated macroscopic effective performance.

[0036] S41. Construction of the predictive agent model.

[0037] Based on the sampled data, establish the design variables To performance vector Predictive agent model Its form can be Kriging model, radial basis function model, BP neural network model, response surface model, or support vector regression model, etc. Taking Kriging model and radial basis function model as examples, the general expressions for both can be constructed as follows: Kriging model: ; Indicates the overall trend across the region; Represents a performance vector; This is the correlation vector between the sample points and the optimization points; This represents the sample correlation matrix.

[0038] Radial basis function model: , Indicates the first The weights of the gradient layer The parameter representing the width of the radial basis functions; This indicates the total number of functionally graded concrete layers; and Representing design variables and the first Design variables for gradient layers.

[0039] S42, Solving for optimal design variables.

[0040] With the goal of maximizing the performance vector, design variables To optimize the variables, the NSGA-II optimization algorithm is used to obtain a Pareto optimal solution set, from which the optimal design variables are selected. .

[0041] It should be noted that: 1) In the process of solving for the optimal design variable, if each performance index in the performance vector corresponding to the current optimal design variable is greater than or equal to the corresponding performance index in the target performance vector, then the current optimal design variable is the final optimal design variable.

[0042] 2) If any performance index in the performance vector corresponding to the current optimal design variable is less than the corresponding performance index in the target performance vector, then a tradeoff weight is defined, the current optimal design variable is adjusted based on the tradeoff weight, and the corresponding performance vector is calculated using the predictive surrogate model based on the adjusted optimal design variable. The Euclidean distance between this performance vector and the target performance vector is also calculated. If the Euclidean distance is less than a preset value, then the adjusted optimal design variable is taken as the final optimal design variable; otherwise, the tradeoff weight is redefined, and the current optimal design variable is readjusted based on the redefined tradeoff weight.

[0043] S43. Update the macroscopic effective performance and the objective distribution function.

[0044] S431. Update the material properties of each layer in the functionally graded concrete based on the optimal design variables, expressed as: , Indicating the functionally graded concrete, the first Optimal design variables for gradient layers; This indicates the updated functional graded concrete. Material properties of gradient layers.

[0045] S432. Based on the updated material properties, obtain the updated macroscopic properties. , represented as: ; This indicates the total number of gradient layers in functionally graded concrete; This represents a mixed function.

[0046] S433. Substitute the updated macroscopic performance into the corresponding target distribution function to update the target distribution function.

[0047] S5. Select the preparation process based on the type of the target distribution function to prepare functionally graded concrete.

[0048] Example 2 This embodiment provides a functionally graded concrete based on recycled aggregates from multi-source construction solid waste. This functionally graded concrete is prepared using the method described above. Specifically, its spatial structure is determined by the aforementioned method, with key microstructural parameters along at least one spatial dimension. According to the target distribution function Changes, thus affecting its macroscopic performance The preset requirements are met.

[0049] The components of the aforementioned functionally graded concrete include cement, water, fiber, cellulose, water-reducing agent, sodium gluconate, nano-clay, and fine aggregate. The cement is preferably ordinary Portland cement, grade 425; the fiber is one or more of polyethylene fiber, steel fiber, nylon fiber, carbon fiber, and basalt fiber; the cellulose is hydroxypropyl methylcellulose with a viscosity of 200,000; the water-reducing agent is a polycarboxylate-based high-performance water-reducing agent with a water reduction rate of 15-30%; the nano-clay is attapulgite clay powder; the water is tap water; and the fine aggregate includes natural aggregate and pre-treated multi-source recycled construction waste aggregate with a particle size less than or equal to 1.18 mm. The multi-source recycled construction waste aggregate includes demolition concrete, brick blocks, and decoration demolition waste, and its pre-treatment includes low-temperature crushing, particle shaping, and porosity adjustment processes.

[0050] 1) Low-temperature crushing.

[0051] By sorting the construction solid waste blocks in The solid waste is subjected to freezing treatment in the environment to fully condense the pore water in the construction solid waste blocks, and then sent into a closed cryogenic crushing device to maintain [the process]. The following ambient temperatures are used for crushing, and the resulting recycled aggregate is then vibrated and screened according to the required particle size to obtain recycled aggregate of different particle sizes.

[0052] Freezing treatment includes at least one of the following methods: mechanical refrigeration, liquid nitrogen quick-freezing, and freezing liquid immersion. Depending on the quality of the aggregate, a single freezing or multiple freeze-thaw cycles are used to fully freeze the recycled aggregate and remove residual mortar from the surface of the aggregate.

[0053] In this embodiment, mechanical refrigeration is preferred as the optimal cryogenic crushing process, and the material is frozen through a cryogenic tunnel. The material is... In the environment, the material passes through the tunnel at a speed of 0.5-2 meters per minute and stays for 10-16 hours to ensure that the material is fully brittle.

[0054] 2) Particle shaping.

[0055] Particle shaping includes at least one of vertical impact shaping, horizontal rotary shaping, airflow vortex shaping, and wet grinding shaping, to produce recycled aggregates with indicators that meet design requirements; the indicators include angularity, sphericity, and surface texture.

[0056] Preferably, in this embodiment, the particle shaping process is a combination of vertical impact shaping and airflow vortex shaping. A vertical impact shaping machine is used for primary shaping of the coarse aggregate. By controlling the impeller speed within the range of 50-55 m / s, the material is circulated for 3-5 minutes to obtain aggregate with rounded edges and a clean surface. Subsequently, the fine aggregate can be treated with an airflow vortex shaping device at an airflow speed of 60-80 m / s for 1-2 minutes to further remove surface powder and obtain a uniform particle shape.

[0057] 3) Pore adjustment process.

[0058] After soaking the recycled aggregate in an alkaline solution, it is cured in a carbon dioxide environment to generate carbonate deposits that fill the internal pores, thereby obtaining recycled aggregates with different preset porosity levels (including high, medium and low). The porosity level is related to the concentration of the alkaline solution, the soaking time, the concentration of carbon dioxide, and the curing time.

[0059] Example 3 In this embodiment, functionally graded concrete is designed according to the method provided in Embodiment 1 to prepare the concrete disclosed in Embodiment 2. It should be noted that the target distribution function constructed in this embodiment is a piecewise constant function of a single linear form, which is prepared by layer stacking process and 3D printing process respectively.

[0060] In this embodiment, the components are as follows: 1000 parts cement, 10 parts fiber, 1000 parts fine aggregate, 400 parts water, 1.28 parts cellulose, 1.1 parts water-reducing agent, 0.7 parts sodium gluconate, and 6 parts nano-clay. The gradient layer composition of the functionally graded concrete material is as follows: the fiber content decreases from region 1 to region 2, at 2%, 1.5%, 1.0%, 0.5%, and 0% respectively. The recycled aggregate replacement rate increases from region 1 to region 2, at 0%, 25%, 50%, 75%, and 100% respectively.

[0061] When using a layered stacking process, the compressive strength of the prepared functionally graded concrete samples was tested according to the standard "Standard for Test Methods of Physical and Mechanical Properties of Concrete" (GB / T50081-2019), and the compressive strength was measured to be 37.2 MPa. The thermal conductivity of the functionally graded concrete samples prepared using the steady-state hot plate method was measured according to the standard "Determination of Steady-State Thermal Resistance and Related Properties of Thermal Insulation Materials - Protective Hot Plate Method" (GB / T 10294-2008), and the thermal conductivity was measured to be 0.61 W / (m·K).

[0062] When using 3D printing technology, the compressive strength of functionally graded concrete samples prepared according to the standard "Standard for Test Methods of Physical and Mechanical Properties of Concrete" (GB / T50081-2019) was tested. The optimal (X-direction) compressive strength was found to be 41.3 MPa, and the minimum (Z-direction) compressive strength was found to be 31.1 MPa. The thermal conductivity of functionally graded concrete samples prepared using the steady-state hot plate method according to the standard "Determination of Steady-State Thermal Resistance and Related Properties of Thermal Insulation Materials - Protective Hot Plate Method" (GB / T 10294-2008) was measured. The thermal conductivity was found to be 0.55 W / (m·K).

[0063] It is evident that 3D printing technology can provide more precise and convenient gradient arrangements.

[0064] Example 4 In this embodiment, functionally graded concrete is designed according to the method provided in Embodiment 1 to prepare the concrete disclosed in Embodiment 2. It should be noted that the target distribution function constructed in this embodiment is a bilinear piecewise constant function, which is prepared by layer stacking process and 3D printing process respectively.

[0065] In this embodiment, the components are as follows: 1000 parts cement, 10 parts fiber, 1000 parts fine aggregate, 400 parts water, 1.28 parts cellulose, 1.1 parts water-reducing agent, 0.7 parts sodium gluconate, and 6 parts nano-clay. The gradient layer composition of the functionally graded concrete material is as follows: the fiber content decreases and then increases from region 1 to region 2, at 2%, 1.0%, 0%, 1.0%, and 2% respectively. The recycled aggregate replacement rate increases and then decreases from region 1 to region 2, at 0%, 50%, 100%, 50%, and 0% respectively.

[0066] When using a layered stacking process, the compressive strength of the prepared functionally graded concrete samples was tested according to the standard "Standard for Test Methods of Physical and Mechanical Properties of Concrete" (GB / T50081-2019), and the compressive strength was measured to be 39.4 MPa. The thermal conductivity of the functionally graded concrete samples prepared using the steady-state hot plate method was measured according to the standard "Determination of Steady-State Thermal Resistance and Related Properties of Thermal Insulation Materials - Protective Hot Plate Method" (GB / T 10294-2008), and the thermal conductivity was measured to be 0.72 W / (m·K).

[0067] When using 3D printing technology, the compressive strength of functionally graded concrete samples prepared according to the standard "Standard for Test Methods of Physical and Mechanical Properties of Concrete" (GB / T50081-2019) was tested. The optimal (X-direction) compressive strength was found to be 44.7 MPa, and the minimum (Z-direction) compressive strength was found to be 29.6 MPa. The thermal conductivity of functionally graded concrete samples prepared using the steady-state hot plate method according to the standard "Determination of Steady-State Thermal Resistance and Related Properties of Thermal Insulation Materials - Protective Hot Plate Method" (GB / T 10294-2008) was measured. The thermal conductivity was found to be 0.69 W / (m·K).

[0068] It is evident that 3D printing technology can provide more precise and convenient gradient arrangements.

[0069] Example 5 In this embodiment, functionally graded concrete is designed according to the method provided in Embodiment 1 to prepare the concrete disclosed in Embodiment 2. It should be noted that the target distribution function constructed in this embodiment is a linear continuous function and is prepared by centrifugation.

[0070] In this embodiment, the components are: 1000 parts cement, 10 parts fiber, 1000 parts fine aggregate, 400 parts water, 1.28 parts cellulose, 1.1 parts water-reducing agent, 0.7 parts sodium gluconate, and 6 parts nano-clay. The functionally graded concrete material has two ends: one end is fiber-reinforced concrete containing 2% fiber, and the other end is recycled aggregate concrete with a 100% replacement rate.

[0071] Place the above components into the mold respectively, such as Figure 7 The sample was placed in a centrifuge and fixed in place. The centrifuge used a stepped rotation speed (gradually increasing from 200 rpm to 450 rpm) to utilize the sedimentation and distribution patterns of materials with different densities under centrifugal force to form a linear density and porosity gradient in the radial direction of rotation that matches the design function. After centrifugation and molding, the sample was cured in a mold and then water-cured for 28 days after demolding to obtain functionally graded concrete.

[0072] The compressive strength of the prepared functionally graded concrete sample was tested according to the standard "Standard for Test Methods of Physical and Mechanical Properties of Concrete" (GB / T50081-2019), and its radial compressive strength was measured to be 39.8 MPa. The thermal conductivity of the prepared functionally graded concrete sample was determined using the steady-state hot plate method according to the standard "Determination of Steady-State Thermal Resistance and Related Properties of Thermal Insulation Materials - Protective Hot Plate Method" (GB / T 10294-2008), and its thermal conductivity was measured to be 0.67 W / (m·K).

[0073] Example 6 In this embodiment, functionally graded concrete is designed according to the method provided in Example 1 to prepare the concrete disclosed in Example 2. It should be noted that the target distribution function constructed in this embodiment is a bilinear continuous function and is prepared by centrifugation.

[0074] In this embodiment, the components are: 1000 parts cement, 10 parts fiber, 1000 parts fine aggregate, 400 parts water, 1.28 parts cellulose, 1.1 parts water-reducing agent, 0.7 parts sodium gluconate, and 6 parts nano-clay. The functionally graded concrete material consists of fiber-reinforced concrete with 2% fiber in the outer ring and recycled aggregate concrete with 100% replacement rate in the central part.

[0075] The aforementioned functionally graded concrete materials are placed in molds, such as... Figure 8 The sample was placed in a centrifuge and fixed in place. The centrifuge used a stepped rotation speed (gradually increasing from 200 rpm to 450 rpm) to utilize the sedimentation and distribution patterns of materials with different densities under centrifugal force to form a linear density and porosity gradient in the radial direction of rotation that matches the design function. After centrifugation and molding, the sample was cured in a mold and then water-cured for 28 days after demolding to obtain functionally graded concrete.

[0076] The compressive strength of the prepared functionally graded concrete sample was tested according to the standard "Standard for Test Methods of Physical and Mechanical Properties of Concrete" (GB / T50081-2019), and its radial compressive strength was found to be 42.5 MPa. The thermal conductivity of the prepared functionally graded concrete sample was determined using the steady-state hot plate method according to the standard "Determination of Steady-State Thermal Resistance and Related Properties of Thermal Insulation Materials - Protective Hot Plate Method" (GB / T 10294-2008), and its thermal conductivity was found to be 0.81 W / (m·K).

[0077] Example 7 To verify the superiority of the method provided by this invention, the following five comparative examples were constructed: 1) Comparative Example 1.

[0078] The same material ratio as in Example 3 is used, but the method provided in Example 1 is not used. The difference from Example 3 is that this comparative example uses homogeneous materials. The concrete, by weight, includes the following components: 1000 parts cement, 10 parts fiber, 1000 parts fine aggregate, 400 parts water, 1.28 parts cellulose, 1.1 parts water-reducing agent, 0.7 parts sodium gluconate, and 6 parts nano clay.

[0079] 2) Comparative Example 2.

[0080] Using the same material proportions as in Example 3, only steps S1-S2 and S5 of Example 1 were performed. The concrete, by weight, comprises the following components: 1000 parts cement, 10 parts fiber, 1000 parts fine aggregate, 400 parts water, 1.28 parts cellulose, 1.1 parts water-reducing agent, 0.7 parts sodium gluconate, and 6 parts nano-clay. The gradient layer composition of the functionally graded concrete material follows a piecewise constant function of a singlet type, as follows: the fiber content decreases from region 1 to region 2, at 2%, 1.5%, 1.0%, 0.5%, and 0% respectively. The recycled aggregate replacement rate increases from region 1 to region 2, at 0%, 25%, 50%, 75%, and 100% respectively.

[0081] 3) Comparative Example 3.

[0082] The same material proportions as in Example 3 were used, and the complete method provided in Example 1 was employed, but the recycled aggregate used was untreated multi-source construction solid waste recycled aggregate. The concrete, by weight, comprised the following components: 1000 parts cement, 10 parts fiber, 1000 parts fine aggregate, 400 parts water, 1.28 parts cellulose, 1.1 parts water-reducing agent, 0.7 parts sodium gluconate, and 6 parts nano-clay. The gradient layer composition of the functionally graded concrete material follows a piecewise constant function of a singlet type, as follows: the fiber content decreases from region 1 to region 2, at 2%, 1.5%, 1.0%, 0.5%, and 0% respectively. The recycled aggregate replacement rate increases from region 1 to region 2, at 0%, 25%, 50%, 75%, and 100% respectively.

[0083] 4) Comparative Example 4.

[0084] Using the same material proportions as in Example 4, only steps S1-S2 and S5 of Example 1 were performed. The concrete, by weight, comprises the following components: 1000 parts cement, 10 parts fiber, 1000 parts fine aggregate, 400 parts water, 1.28 parts cellulose, 1.1 parts water-reducing agent, 0.7 parts sodium gluconate, and 6 parts nano-clay. The gradient layer composition of the functionally graded concrete material follows a bilinear piecewise constant function, as follows: the fiber content decreases and then increases from region 1 to region 2, at 2%, 1.0%, 0%, 1.0%, and 2% respectively. The recycled aggregate replacement rate increases and then decreases from region 1 to region 2, at 0%, 50%, 100%, 50%, and 0% respectively.

[0085] 5) Comparative Example 5.

[0086] The same material proportions as in Example 4 were used, and the complete method provided in Example 1 was employed, but the recycled aggregate used was untreated multi-source construction solid waste recycled aggregate. The concrete, by weight, comprised the following components: 1000 parts cement, 10 parts fiber, 1000 parts fine aggregate, 400 parts water, 1.28 parts cellulose, 1.1 parts water-reducing agent, 0.7 parts sodium gluconate, and 6 parts nano-clay. The gradient layer composition of the functionally graded concrete material followed a bilinear piecewise constant function, as follows: the fiber content decreased and then increased from region 1 to region 2, at 2%, 1.0%, 0%, 1.0%, and 2% respectively. The recycled aggregate replacement rate increased and then decreased from region 1 to region 2, at 0%, 50%, 100%, 50%, and 0% respectively.

[0087] The compressive strength and thermal conductivity of the functional graded concrete samples prepared in the above five comparative examples were tested, and the results are shown in Table 1. As shown in the table, the compressive strength was improved to varying degrees by adopting the method in Example 1, while the thermal conductivity was reduced. Comparison revealed that, except for Comparative Example 3, which showed some deterioration in compressive strength due to the use of untreated recycled aggregate, the compressive strength of the other comparative examples was significantly improved compared to Comparative Example 1. Regarding thermal conductivity, the method provided in Example 1 resulted in a decrease in thermal conductivity. The use of pretreated multi-source recycled building waste aggregate further reduced the thermal conductivity, indicating that porosity adjustment can further optimize thermal conductivity and improve the thermal insulation performance of functionally graded concrete. The sample in Example 4, produced using 3D printing technology, showed better performance in both compressive strength and thermal conductivity compared to the samples produced in Comparative Examples 1-5, demonstrating the practical engineering value of the functionally graded concrete design and optimization method based on multi-source recycled building waste aggregate provided by this invention.

[0088] The above description is merely a specific embodiment of the present invention, but the scope of protection of the present invention is not limited thereto. Any person skilled in the art can easily conceive of various equivalent modifications or substitutions within the technical scope disclosed in the present invention, and these modifications or substitutions should all be covered within the scope of protection of the present invention. Therefore, the scope of protection of the present invention should be determined by the scope of the claims.

Claims

1. A functional graded concrete design method based on recycled aggregates from multi-source construction solid waste, characterized in that, The method includes: S1. Determine the target performance vector of functionally graded concrete according to the requirements, and determine the strength grade, composition and target structure of functionally graded concrete based on the target performance vector; the target performance vector includes at least one element, and each element represents a type of performance index. S2. Based on the intensity level, composition, and target structure, determine the macroscopic effective performance and target distribution function. If the macroscopic effective performance satisfies the target performance vector, execute S5; otherwise, execute S3. The target distribution function includes piecewise constant functions and continuous functions. The target distribution function satisfies that the performance change rate between adjacent gradient layers is less than a preset threshold. S3. Based on the type of the target distribution function, select the preparation process to prepare the sample, perform performance testing on the sample and sample data. S4. Define design variables, establish a predictive surrogate model from the design variables to the performance vector based on the sampled data, use the NSGA-II optimization algorithm to solve for the optimal design variables based on the predictive surrogate model, update the macroscopic effective performance based on the optimal design variables, and update the target distribution function based on the updated macroscopic effective performance. S5. Functionally graded concrete is prepared by selecting the preparation process based on the type of the target distribution function.

2. The functional graded concrete design method based on multi-source recycled aggregate from building solid waste according to claim 1, characterized in that, When the target distribution function is a piecewise constant function, we have: The macroscopic performance ,for: , Indicating the functionally graded concrete, the first The material properties of the layer; This indicates the total number of gradient layers in functionally graded concrete; Represents a mixture function; Indicating the functionally graded concrete, the first The thickness of the gradient layer, and , , Indicates the first The lower limit and the first limit of gradient layer material thickness The upper limit of the gradient layer material thickness; The target distribution function ,for: ； in, This indicates the material thickness value.

3. The functional graded concrete design method based on multi-source recycled aggregate from building solid waste according to claim 1, characterized in that, When the target distribution function is a continuous function, we have: The target distribution function It is a family of Sigmoid functions, calculated based on the upper and lower limits of the target performance vector, as well as the gradient change center point and gradient change rate of the functionally graded concrete; The macroscopic performance ,for: , Indicates the first The upper limit of the gradient layer material thickness, Indicates the material thickness value. This represents an integral function.

4. The functional gradient concrete design method based on multi-source recycled aggregate from building solid waste according to claim 1, characterized in that, When the target distribution function is a piecewise constant function, the fabrication process used is a layer-by-layer stacking process or a 3D printing process. If the target distribution function is a continuous function, the preparation process used is centrifugal molding.

5. The functional graded concrete design method based on multi-source recycled aggregate from building solid waste according to claim 1, characterized in that, The method further includes: In the process of solving for the optimal design variable, if each performance index in the performance vector corresponding to the current optimal design variable is greater than or equal to the corresponding performance index in the target performance vector, then the current optimal design variable is the final optimal design variable. If any performance index in the performance vector corresponding to the current optimal design variable is less than the corresponding performance index in the target performance vector, then a tradeoff weight is defined, the current optimal design variable is adjusted based on the tradeoff weight, and the corresponding performance vector is calculated using a predictive surrogate model based on the adjusted optimal design variable. The Euclidean distance between the performance vector and the target performance vector is also calculated. If the Euclidean distance is less than a preset value, then the adjusted optimal design variable is taken as the final optimal design variable; otherwise, the tradeoff weight is redefined.

6. The functional graded concrete design method based on multi-source recycled aggregate from building solid waste according to claim 1, characterized in that, The method for updating the aforementioned macroscopic effective performance is as follows: The material properties of each layer in the functionally graded concrete are updated based on the aforementioned optimal design variables, expressed as follows: , Indicating the functionally graded concrete, the first Optimal design variables for gradient layers; This indicates the updated functional graded concrete. Material properties of the gradient layer; Based on the updated material properties, the updated macroscopic properties are obtained and represented as follows: ； This indicates the total number of gradient layers in functionally graded concrete; This represents a mixed function.

7. A functionally graded concrete based on recycled aggregates from multi-source construction solid waste, characterized in that, The functionally graded concrete is prepared using the method described in any one of claims 1 to 6, and the components of the functionally graded concrete include cement, water, fiber, and pretreated multi-source recycled aggregate from building solid waste; the pretreatment includes low-temperature crushing, particle shaping, and porosity adjustment processes.

8. A functional graded concrete based on recycled aggregates from multi-source construction solid waste according to claim 7, characterized in that, The aforementioned low-temperature crushing is: By sorting the construction solid waste blocks in The solid waste is subjected to freezing treatment in the environment to fully condense the pore water in the construction solid waste blocks, and then sent into a closed cryogenic crushing device to maintain [the process]. The following ambient temperatures are used for crushing, and the resulting recycled aggregate is then vibrated and screened according to the required particle size to obtain recycled aggregate of different particle sizes; the freezing treatment includes at least one of mechanical refrigeration, liquid nitrogen quick-freezing, and freezing liquid immersion.

9. A functional graded concrete based on recycled aggregates from multi-source construction solid waste according to claim 7, characterized in that, The particle shaping process includes at least one of vertical impact shaping, horizontal rotary shaping, airflow vortex shaping, and wet grinding shaping, to generate recycled aggregates with indicators that meet design requirements; the indicators include angularity, sphericity, and surface texture.

10. A functionally graded concrete based on recycled aggregates from multi-source construction solid waste according to claim 7, characterized in that, The porosity adjustment process is as follows: Recycled aggregate is soaked in alkaline solution and then cured in a carbon dioxide environment to generate carbonate deposits that fill the internal pores, thereby obtaining recycled aggregates with different preset porosity levels; wherein, the porosity level is related to the concentration of alkaline solution, soaking time, concentration of carbon dioxide, and curing time.

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