Construction method for concrete outer wall and green low-energy-consumption thermal insulation wall

Abstract

The invention relates to the technical field of green building construction, and discloses a concrete outer wall and green low-energy-consumption thermal insulation wall construction method which comprises the following steps that firstly, based on the heat conduction characteristic of a wall body, the thickness and the heat conduction coefficient of a thermal insulation layer are optimally designed through heat conduction analysis, so that the total heat loss of the wall body is reduced; 2, according to the mechanical equilibrium condition of the wall, stress distribution and a displacement field of the wall are determined through finite element analysis, and it is ensured that the structural strength of the constructed wall meets the design requirement; and 3, combining a multi-objective optimization theory, comprehensively considering the heat conduction performance, the mechanical stability and the construction cost of the wall body, and dynamically optimizing construction parameters. According to the construction parameter dynamic adjustment technology based on the multi-objective optimization theory, multiple constraint conditions such as heat loss, mechanical properties and construction cost are comprehensively considered, and the technical effect of achieving overall performance balance in the complex construction environment is achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of green building construction, and specifically to a construction method for a concrete exterior wall and a green low-energy consumption insulation wall. Background Art

[0002] In recent years, with the increasing requirements of the construction industry for energy conservation and environmental protection, green low-energy consumption buildings have gradually become the mainstream development trend. Among them, the concrete exterior wall and the insulation wall, as important components of green buildings, undertake the key tasks of improving the building's thermal efficiency, reducing energy consumption, and enhancing structural stability. Therefore, how to optimize the insulation performance and structural strength of the wall simultaneously during building construction has become a key area of research and practice.

[0003] In the prior art, for the construction methods of concrete exterior walls and insulation walls, empirical design or single optimization methods are usually adopted. For example, in the design of the insulation layer, insulation materials with lower thermal conductivity are usually selected according to the temperature difference conditions of the environment where the building is located, and a fixed thickness is determined; in the design of the concrete layer, the focus is mainly on its load-bearing capacity to ensure that the load conditions and the strength requirements for long-term use are met. However, these design methods rely mostly on fixed parameter settings or traditional experience in practical applications, and cannot take into account multiple performance requirements. Especially under complex construction conditions, it is often difficult to balance the contradictions among insulation performance, structural performance, and construction cost.

[0004] The deficiencies of the prior art lie in that the design and adjustment of construction parameters lack systematicness and dynamics. Specifically, the prior art mostly designs wall materials and construction processes in a fixed parameter manner, ignoring the dynamic changes of conditions such as climate and load in the actual construction environment. Since the parameters cannot be adjusted in real time, when the construction conditions change, the design scheme often fails to meet the actual requirements, resulting in problems such as a decline in wall performance and waste of construction resources. Therefore, the existing methods have obvious deficiencies in multi-objective optimization and dynamic adaptability. Summary of the Invention

[0005] Aiming at the deficiencies of the prior art, the present invention provides a construction method for a concrete exterior wall and a green low-energy consumption insulation wall, which solves the problem that the prior art mostly designs wall materials and construction processes in a fixed parameter manner, ignoring the dynamic changes of conditions such as climate and load in the actual construction environment.

[0006] To achieve the above object, the present invention is realized through the following technical solutions: A construction method for a concrete exterior wall and a green low-energy consumption insulation wall, comprising the following steps:

[0007] Step 1: Based on the heat conduction characteristics of the wall, optimize the design of the thickness and thermal conductivity of the insulation layer by using heat conduction analysis to reduce the total heat loss of the wall;

[0008] Step 2: According to the mechanical equilibrium conditions of the wall, determine the stress distribution and displacement field of the wall through finite element analysis to ensure that the structural strength of the wall after construction meets the design requirements;

[0009] Step 3: Combine the multi-objective optimization theory, comprehensively consider the heat conduction performance, mechanical stability and construction cost of the wall, and dynamically optimize the construction parameters;

[0010] Step 4: Through topology optimization, design the spatial distribution of the wall materials, and rationally configure the insulation layer and the concrete layer to maximize the material utilization rate;

[0011] Step 5: Use experimental verification and simulation analysis to verify the optimization results and feedback to adjust the optimization parameters.

[0012] Preferably, the heat conduction analysis in Step 1 is based on the unsteady heat conduction equation, considering the thermal diffusivity of the wall material, the initial temperature distribution, and the convective boundary conditions between the outer wall surface and the environment. The optimization objective is to reduce the heat loss power of the wall and keep the temperature of the inner wall surface stable.

[0013] Preferably, the mechanical equilibrium analysis in Step 2 is based on the nonlinear finite element method. By calculating the stress tensor and displacement field of the wall, optimize the thickness and reinforcement distribution of the concrete layer to ensure that the maximum stress value of the wall during construction load and long-term use does not exceed the material yield strength.

[0014] Preferably, in Step 3, by establishing a multi-objective optimization model, simultaneously optimize the heat loss power, maximum stress value and construction cost of the wall. The optimization variables include the thickness of the insulation layer, the thermal conductivity and the construction speed. The optimization results are calculated by the genetic algorithm to form a Pareto optimal solution set that meets different construction conditions.

[0015] Preferably, the topology optimization in Step 4 is based on design variables to represent the distribution ratio of the insulation layer and the concrete layer in the wall. The optimization objective is to minimize the weighted sum rate of material heat loss and stress concentration within the given wall thickness range.

[0016] Preferably, during the topology optimization process, set the geometric constraint conditions for the distribution of the wall materials, including the minimum area ratio of the insulation layer and the concrete layer and the total wall thickness range. The optimization results are used to adjust the input parameters in the mechanical analysis.

[0017] Preferably, in Step 3, adopt the method of dynamically optimizing the weights, adjust the weight parameters of the heat conduction performance, mechanical performance and construction cost according to the design objectives of the construction scenario, prioritize the optimization of the insulation performance in low-temperature environments, and prioritize the optimization of the construction cost and efficiency in high-temperature environments, and dynamically balance the construction efficiency and material utilization rate.

[0018] Preferably, the optimized wall construction method monitors the construction environment temperature, humidity and material bonding state in real time through sensors, feeds back and dynamically adjusts the input parameters in the optimization model, and realizes the adaptive adjustment of the construction process.

[0019] Preferably, the experimental verification includes preparing wall samples with different insulation layer thicknesses and thermal conductivities, testing their heat conduction performance, stress distribution and construction efficiency, verifying the heat loss and mechanical properties of the wall through simulation, checking the accuracy of the optimization results, and adjusting the design parameters of the topology optimization with experimental data.

[0020] Preferably, the construction method is realized through the following optimization steps:

[0021] Based on the wall heat conduction analysis, determine the optimal range of the insulation layer thickness, and dynamically adjust the thermal conductivity of the insulation material to reduce heat loss;

[0022] Through topology optimization, design the distribution of wall materials to maximize the thermal efficiency of the insulation layer and optimize the stress distribution;

[0023] During the construction process, collect the ambient temperature, humidity and wall bonding state in real time, and adjust the construction speed and concrete material ratio through sensor feedback, significantly improving the construction efficiency and reducing material waste.

[0024] The present invention provides a construction method for a concrete exterior wall and a green low-energy insulation wall. It has the following

[0025] Beneficial effects:

[0026] 1. Adopt the dynamic adjustment technology of construction parameters based on the multi-objective optimization theory, comprehensively consider multiple constraint conditions such as heat loss, mechanical properties and construction cost, and achieve the technical effect of realizing the overall performance balance in a complex construction environment. Compared with the construction methods in the prior art that only optimize a single performance, it solves the problem that the traditional scheme is difficult to take into account multiple performance indicators, resulting in low construction efficiency and resource waste.

[0027] 2. By introducing the topology optimization design method, the present invention realizes the efficient configuration of the material distribution of the insulation layer and the concrete layer, and achieves the effect of simultaneously meeting the thermal performance and structural safety under the condition of limited wall thickness. In the prior art, the material distribution design mostly relies on experience, and it is easy to have the deficiencies of performance overcapacity or material waste. The present invention effectively solves this bottleneck and improves the material utilization rate.

[0028] 3. Through the combination of experimental verification and multi-physics field simulation analysis, the optimization results were comprehensively verified, ensuring the feasibility of the construction plan in actual projects. Compared with the existing technologies, this solution overcomes the problem of the disconnection between experimental and simulation data in traditional design methods, realizes data-driven correction of construction parameters, and avoids engineering quality problems caused by model errors.

[0029] 4. By dynamically collecting environmental data at the construction site and adjusting the optimization model in real time, the present invention achieves the technical effect of adapting to different climate and load conditions. Compared with the construction methods with fixed parameters in the existing technologies, the present invention solves the defect that construction parameters cannot be adjusted according to environmental changes, and greatly improves the adaptability and flexibility of the construction plan. BRIEF DESCRIPTION OF THE DRAWINGS

[0030] Figure 1 It is a schematic flow chart of the method of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0031] The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings of the present invention. Obviously, the described embodiments are only a part of the embodiments of the present invention, rather than all the embodiments. All other embodiments obtained by those of ordinary skill in the art based on the embodiments of the present invention without creative efforts shall fall within the protection scope of the present invention.

[0032] Please refer to the attached Figure 1 , the embodiments of the present invention provide a construction method for a concrete exterior wall and a green low-energy consumption insulation wall, including the following steps:

[0033] Step 1. Based on the heat conduction characteristics of the wall, optimize the design of the thickness and thermal conductivity of the insulation layer by using heat conduction analysis to reduce the total heat loss of the wall;

[0034] Step 2. According to the mechanical equilibrium conditions of the wall, determine the stress distribution and displacement field of the wall through finite element analysis to ensure that the structural strength of the wall after construction meets the design requirements;

[0035] Step 3. Combine the multi-objective optimization theory, comprehensively consider the heat conduction performance, mechanical stability and construction cost of the wall, and dynamically optimize the construction parameters;

[0036] Step 4. Through topological optimization, design the spatial distribution of the wall materials, and reasonably configure the insulation layer and the concrete layer to maximize the material utilization rate;

[0037] Step 5. Use experimental verification and simulation analysis to verify the optimization results and feedback to adjust the optimization parameters.

[0038] The heat conduction analysis in Step 1 is based on the unsteady heat conduction equation, considering the thermal diffusivity of the wall material, the initial temperature distribution, and the convective boundary condition between the outer wall and the environment. The optimization objective is to reduce the heat loss power of the wall and maintain the stability of the inner wall temperature.

[0039] The mechanical equilibrium analysis in Step 2 is based on the nonlinear finite element method. By calculating the stress tensor and displacement field of the wall, the thickness and reinforcement distribution of the concrete layer are optimized to ensure that the maximum stress value of the wall during construction load and long-term use does not exceed the material yield strength.

[0040] In Step 3, by establishing a multi-objective optimization model, the heat loss power, maximum stress value, and construction cost of the wall are optimized simultaneously. The optimization variables include the thickness of the insulation layer, thermal conductivity, and construction speed. The optimization results are calculated by the genetic algorithm to form a Pareto optimal solution set that meets different construction conditions.

[0041] The topology optimization in Step 4 is based on design variables to represent the distribution ratio of the insulation layer and concrete layer in the wall. The optimization objective is to minimize the weighted sum rate of material heat loss and stress concentration within the given wall thickness range.

[0042] During the topology optimization process, geometric constraint conditions for the distribution of wall materials are set, including the minimum area ratio of the insulation layer and concrete layer and the total wall thickness range. The optimization results are used to adjust the input parameters in the mechanical analysis.

[0043] In Step 3, the method of dynamically optimizing weights is adopted. According to the design objectives of the construction scenario, the weight parameters of heat conduction performance, mechanical performance, and construction cost are adjusted. Priority is given to optimizing the insulation performance in low-temperature environments, and priority is given to optimizing construction cost and efficiency in high-temperature environments to dynamically balance construction efficiency and material utilization rate.

[0044] The optimized wall construction method uses sensors to monitor the construction environment temperature, humidity, and material bonding state in real time, and feeds back to dynamically adjust the input parameters in the optimization model to achieve adaptive adjustment during the construction process.

[0045] Experimental verification includes preparing wall samples with different insulation layer thicknesses and thermal conductivities, testing their heat conduction performance, stress distribution, and construction efficiency, verifying the heat loss and mechanical performance of the wall through simulation, checking the accuracy of the optimization results, and adjusting the design parameters of the topology optimization with experimental data.

[0046] The construction method is achieved through the following optimization steps:

[0047] Based on the heat conduction analysis of the wall, determine the optimal range of the insulation layer thickness, and dynamically adjust the thermal conductivity of the insulation material to reduce heat loss;

[0048] Design the distribution of wall materials through topological optimization to maximize the thermal efficiency of the insulation layer and optimize the stress distribution;

[0049] During the construction process, collect the ambient temperature, humidity, and wall bonding status in real-time, and adjust the construction speed and concrete material ratio through sensor feedback, significantly improving the construction efficiency and reducing material waste.

[0050] This step plays a fundamental role in the overall construction method of the present invention and has a direct relevance to subsequent mechanical analysis, multi-objective optimization, and topological optimization design steps. By analyzing and optimizing the heat conduction characteristics of the wall at the initial stage, it can effectively guide the material selection, thickness design, and subsequent structural optimization work. This step particularly analyzes and optimizes the thickness and thermal conductivity of the insulation layer to significantly control heat loss and lay a thermal foundation for subsequent construction.

[0051] Generally, the heat conduction characteristics of the wall are affected by various factors, including ambient temperature differences, thermal diffusivity of the insulation material, thickness distribution of the construction layer, and temperature difference between the inner and outer walls. Based on these factors, the present invention combines the unsteady heat conduction equation to establish a heat conduction model to comprehensively evaluate and optimize the thermal performance of the materials.

[0052] In this embodiment, based on the heat conduction characteristics of the wall, an unsteady heat conduction mathematical model is constructed, and the equation is as follows:

[0053]

[0054] Specifically, the definitions of the parameters in the formula are as follows:

[0055] T(x, y, z, t): Represents the temperature distribution function inside the wall, with the unit of K;

[0056] t: Time, with the unit of s;

[0057] The thermal diffusivity of the wall material, with the unit of m 2 / s, where.

[0058] k: The thermal conductivity of the material, with the unit of W / (m·K).

[0059] ρ: The density of the material, with the unit of kg / m 3 .

[0060] c: The specific heat capacity of the material, with the unit of J / (kg·K).

[0061] The Laplace operator of the temperature field, used to describe the diffusion of temperature in three-dimensional space;

[0062] Q: Heat source term per unit volume, unit: W / m 3 .

[0063] As an option, in the construction environment, the exterior wall is continuously affected by the ambient temperature. In the present invention, the boundary condition of the exterior wall is set as a heat flux boundary:

[0064]

[0065] Where:

[0066] h: Convective heat transfer coefficient between the outer surface of the wall and the environment, unit:.

[0067] T env : External ambient temperature, unit: K;

[0068] n: Represents the normal vector direction of the outer surface of the wall.

[0069] On the inner wall, in order to keep the indoor temperature stable, the present invention sets a fixed target temperature boundary condition:

[0070] T(x,y,z,t) = T target

[0071] Where:

[0072] T target : Set indoor target temperature, unit: K.

[0073] In a possible implementation, the core objective of optimization is to reduce the total heat loss power of the wall while keeping the inner wall temperature stable. The heat loss power is calculated by the following formula:

[0074]

[0075] Where:

[0076] J: Represents the total heat loss power of the wall, unit: W.

[0077] Ω: Wall surface area;

[0078] Unit normal vector of the wall surface.

[0079] Specifically, in the construction design, the optimization parameters include the thickness d1 of the insulation layer and the thermal conductivity λ1 of the material. Generally, a lower thermal conductivity and appropriate thickness can significantly reduce heat loss, but the material cost and construction requirements need to be considered comprehensively. In some embodiments, a composite foaming material with a thermal conductivity of 0.02 - 0.04 W / (m·K) is selected as the insulation layer, and the thickness range is limited to 50 - 100 mm.

[0080] In an optimized implementation, the temperature field distributions of different materials and thicknesses are calculated through numerical simulation to evaluate the optimization effect. The simulation uses the finite difference method (FDM) to discretize the heat conduction equation, and the discrete format is as follows:

[0081]

[0082] Where:

[0083] The temperature of the i-th node at the n-th time step;

[0084] Δt: Time step;

[0085] Δx, Δy: Spatial step.

[0086] As an extended method, during construction, the changes in the ambient temperature are monitored in real time, and the temperature field data is fed back through a thermal sensor to dynamically adjust the thermal conductivity of the material and the thickness of the insulation layer. For example, in the case of a large external temperature difference, the thickness of the insulation layer can be preferentially increased; while in the case of cost constraints, a composite material with a lower thermal conductivity can be selected to replace the traditional insulation material.

[0087] Generally, the mechanical properties of the wall are jointly affected by mechanical characteristics and construction parameters. External loads, the self-weight of the wall during construction, and thermal stresses caused by temperature differences will all affect the wall structure. The present invention evaluates these influencing factors by establishing the mechanical equilibrium equation of the wall and determines the construction parameters through iterative optimization.

[0088] In this embodiment, the mechanical equilibrium relationship of the wall is described by the following equation:

[0089]

[0090] Specifically:

[0091] σ: Stress tensor, with the unit of Pa, used to describe the stress distribution within the wall;

[0092] f: Body force vector, with the unit of N / m, including the self-weight and external loads of the wall;

[0093] Represents the divergence of the stress tensor, reflecting the stress equilibrium state within the wall.

[0094] Generally, the mechanical properties of the wall material can be described by a linear elastic model, and the following constitutive relationship is adopted in the present invention:

[0095] σ = Dε

[0096] Where:

[0097] ε: Strain tensor, dimensionless, representing the deformation of the wall under load.

[0098] D: Elastic modulus matrix, with the unit of Pa, related to the mechanical properties of the wall material.

[0099] As an option, under construction conditions, the maximum stress value of the wall should not exceed the yield strength of the material. By setting the constraint condition:

[0100] maxσ ≤ σ yield

[0101] Where:

[0102] σ yield : Yield strength of the material, with the unit of Pa.

[0103] Specifically, to ensure the overall stability of the wall, the present invention discretizes the wall based on the finite element method and divides the wall into finite elements. The stress, strain, and displacement of each element are calculated through the following discrete equations:

[0104] K·U = F

[0105] Where:

[0106] K: Stiffness matrix, with the unit of N / m, determined by the geometric shape and material properties of the wall;

[0107] U: Node displacement vector, with the unit of m;

[0108] F: Node force vector, with the unit of N, including the external force and body force of the wall.

[0109] In a possible implementation, for the specific construction requirements of the wall, the thickness d2 of the concrete layer and the reinforcement distribution are optimized. For example, when the wall bears a large horizontal load, the steel bar density can be increased in the high-stress area to improve the tensile strength of the wall. In some embodiments, the thickness of the concrete layer is limited to 100 - 150 mm, the reinforcement ratio is 0.5% - 1.0%, and it is adjusted according to the specific construction load.

[0110] In another possible implementation, the present invention analyzes the thermal stress of the wall in combination with the temperature difference effect. The thermal stress under the temperature difference effect can be calculated by the following formula:

[0111] σ t = E·α t ·ΔT

[0112] Where:

[0113] σ t : Thermal stress, with the unit of Pa;

[0114] E: Elastic modulus of the material, with the unit of Pa;

[0115] α t : Coefficient of thermal expansion of the material, with the unit of 1 / K;

[0116] ΔT: Temperature change of the wall, with the unit of K.

[0117] In an expansion mode, the present invention conducts a simulation analysis on the long-term use stability of the wall. By setting different load combinations (such as wind load and seismic load), the dynamic response and fatigue life of the wall are calculated. For example, in some embodiments, the wind load is taken as 0.5 kPa, and the seismic load simulates the seismic fortification condition of degree VII. The simulation results show that by optimizing the concrete thickness and steel bar distribution, the maximum displacement of the wall during long-term use is reduced by about 10%.

[0118] Generally, the optimization objectives of wall construction can be divided into the following three aspects: one is to minimize the heat loss power of the wall; the second is to maintain the structural safety of the wall and avoid stress concentration; the third is to control the construction cost and time and improve the construction efficiency. The present invention generates a set of Pareto optimal solution sets by constructing a multi-objective optimization model and comprehensively weighing these objectives, providing a feasible optimization scheme for specific construction.

[0119] In this embodiment, the objective function of the multi-objective optimization model includes the following three parts:

[0120] minJ = [J1, J2, J3]

[0121] Specifically:

[0122] J1: Heat loss power of the wall, with the unit of W. This part of the objective is determined by the heat conduction analysis in step one;

[0123] J2: Maximum stress value of the wall, with the unit of Pa. This part of the objective is obtained from the mechanical equilibrium analysis in step two;

[0124] J3: Construction cost and time. This part of the objective comprehensively considers material cost, construction process complexity and time factors.

[0125] The optimization variables include:

[0126] d1: Thickness of the insulation layer, with the unit of m;

[0127] λ1: Thermal conductivity of the insulation layer material, with the unit of W / (m·K);

[0128] d2: Thickness of the concrete layer, with the unit of m.

[0129] v: Construction speed, with the unit of m / s.

[0130] The constraint conditions determined in the foregoing steps are directly used to limit the range of optimization variables. For example:

[0131] The thermal conductivity of the insulation material shall not exceed the established design target;

[0132] The maximum stress value of the wall shall not exceed the yield strength of the material.

[0133] In a possible implementation, the present invention uses a genetic algorithm (GA) to solve the above multi-objective optimization model. The genetic algorithm realizes the search for the global optimal solution by simulating the biological evolution process. The main steps include:

[0134] In the initialization stage, a set of construction parameter combinations is generated as the initial population;

[0135] By selecting parameter combinations with higher fitness, crossover operations are performed to generate the next generation of parameters;

[0136] Random mutations are introduced into the new parameter combinations to increase the diversity of solutions;

[0137] According to the weight function of multi-objective optimization, the parameter combinations are screened until convergence to the Pareto optimal solution set.

[0138] As an option, the weight values of the optimization objectives are dynamically adjusted in the fitness function of the present invention. For example, in a low-temperature environment, the weight of J1 is increased to preferentially optimize the thermal insulation performance; while in a construction project with limited budget, the weight of J3 is increased to preferentially reduce the construction cost.

[0139] Specifically, the optimization results can be selected according to the actual construction requirements. For example:

[0140] In the construction of a green building that emphasizes thermal performance, it is preferable that the thickness d1 of the insulation layer is 0.12 m and the thermal conductivity λ1 is 0.02 W / (m·K);

[0141] In a commercial project that requires rapid construction, it is possible to select to reduce the thickness d1 of the insulation layer to 0.08 m and appropriately reduce the construction speed v to reduce errors.

[0142] In an extended mode, the present invention combines sensor technology to monitor the environmental parameters at the construction site in real time, such as data on the temperature difference between the exterior walls, humidity, and material bonding state. The real-time data fed back by the sensors is directly input into the optimization model for dynamically updating the optimization results. For example, when the sensor detects a sudden increase in the external temperature difference, the optimization model will automatically adjust the thickness of the insulation layer and the concrete reinforcement ratio to ensure the wall performance.

[0143] In general, the distribution design of wall materials needs to balance the contradiction between thermal performance and mechanical performance. Insulation materials are mainly used to reduce heat loss, while concrete materials bear the function of structural stress. The traditional design method cannot flexibly adjust the spatial distribution of the two materials, resulting in material waste or insufficient performance. Through topology optimization technology, the present invention transforms the material distribution design into a mathematical optimization problem to seek the global optimal solution.

[0144] In the embodiment, the design variable ρ(x) of the topology optimization problem represents the material distribution ratio at a certain point in the wall. Specifically:

[0145] ρ(x) = 1 indicates that this point is concrete;

[0146] ρ(x) = 0 indicates that this point is the insulation layer;

[0147] ρ(x) ∈ [0, 1] indicates the mixed area, reflecting the ratio of the insulation layer to concrete.

[0148] In general, the optimization goal is to minimize the weighted sum of the heat loss power and stress concentration of the materials within the given wall thickness range. The optimization objective function is:

[0149]

[0150] Specifically:

[0151] J(ρ):

[0152] α, β: weighting factors, controlling the priorities of thermal performance and mechanical performance;

[0153] Ω: wall design area;

[0154] λ(ρ): material thermal conductivity, related to the value of ρ;

[0155] σ(ρ): material stress, related to the value of ρ;

[0156] Represents the square of the temperature field gradient, used to measure the heat conduction efficiency.

[0157] As an option, geometric constraint conditions are set for the wall material distribution in this embodiment. For example:

[0158] The total wall thickness is limited within 200 mm;

[0159] The minimum area ratios of the insulation layer and the concrete layer are 30% and 70% respectively;

[0160] Concrete is preferentially configured in specific areas (such as high stress areas).

[0161] In a possible implementation, the optimization problem is discretized by the finite element method and solved using the density method. The density method is a topology optimization technique based on the continuousization of design variables, with ρ(x) as the continuous variable. The optimization process is as follows:

[0162] In the initialization stage, the wall area is evenly distributed as a mixed area, i.e., ρ(x) = 0.5;

[0163] In each iteration, the value of ρ(x) is updated according to the gradient of the objective function;

[0164] After the iteration converges, the value of ρ(x) is approximated to 0 or 1 to generate the final material distribution.

[0165] Specifically, the optimization constraints include:

[0166] The total amount of material used does not exceed the established upper limit;

[0167] The maximum stress in each area of the wall shall not exceed the material yield strength.

[0168] In some embodiments, to avoid construction difficulties caused by discontinuous material distribution, the present invention introduces a filtering technique to smooth the gradient of ρ(x).

[0169] In an extended manner, the present invention dynamically adjusts the weight factors of topology optimization in combination with the multi-objective optimization results of step three. For example:

[0170] In a low-temperature environment, increase the weight of α to make the optimization result more biased towards thermal performance;

[0171] Under high load conditions, increase the weight of β to give priority to meeting the mechanical performance requirements.

[0172] In addition, the present invention also conducts finite element verification on the optimization results. The verification contents include:

[0173] Temperature field distribution: Ensure that the area covered by the insulation layer has the lowest heat loss;

[0174] Stress distribution: Ensure that the stress concentration factor in the concrete distribution area is the lowest.

[0175] Generally, in the optimization design, the thickness of the insulation layer, the thickness of the concrete, and the material distribution parameters need to be adjusted in experimental verification to adapt to different construction environments and load conditions. At the same time, multi-physical field simulation provides accurate temperature field and stress field distribution results, providing a theoretical basis for verification and adjustment.

[0176] Generally, the parameters optimized in the construction need to be verified through experiments, such as the thermal conductivity, thickness of the insulation layer, and reinforcement ratio of the concrete, etc. During the experiment, different environmental conditions and load states should be simulated to ensure that the optimized design can meet the requirements of various construction scenarios.

[0177] Prepare multiple groups of test samples, and the parameters of each group of samples are taken from the optimization results of Step 3 and Step 4. For example, the thickness d1 and thermal conductivity λ1 of the insulation layer are set according to the optimization results of the heat conduction performance, and the thickness d2 and reinforcement ratio of the concrete layer are adjusted according to the results of the mechanical analysis. During the experiment, these samples are placed in an environmental control laboratory, and the following test methods are used for performance verification:

[0178] Thermal performance test:

[0179] Through infrared thermal imaging technology, the temperature distribution of the specimen is monitored in real time, and the heat flux density between the outer wall and the inner wall is measured. At the same time, the total heat loss power of the sample under the condition of constant temperature difference is calculated to verify whether its thermal efficiency meets the design target.

[0180] Mechanical performance test:

[0181] Load different combinations of loads on the test samples, including the superposition of simulated self-weight, wind load, and seismic load. Measure the overall deformation of the specimen through displacement sensors and measure the local stress distribution through strain gauges to verify whether the maximum stress value is less than the yield strength σ yield .

[0182] In a possible implementation manner, the present invention combines multi-physical field simulation analysis to verify the feasibility of the optimized design in a virtual construction environment. Specifically:

[0183] Input the material distribution parameter ρ(x) of the topology optimization design in Step 4 to perform finite element discretization on the wall. By setting the boundary conditions (such as temperature difference, external load) and material properties under actual construction conditions, the temperature field and stress field of the wall are simulated.

[0184] During the simulation analysis process, the calculation of the temperature field is based on the heat conduction model in Step 1, and the calculation of the mechanical properties is based on the structural optimization results obtained in Step 2. Through multi-field coupling simulation, the influence of material distribution on thermal performance and mechanical properties can be verified simultaneously.

[0185] Specifically, the simulation analysis of the present invention focuses on the following contents:

[0186] The heat flux density distribution of the insulation layer to ensure the optimal material utilization rate;

[0187] The stress distribution of the concrete layer to avoid local stress concentration.

[0188] In an extended mode, the present invention further optimizes construction parameters by comparing and analyzing experimental and simulation results. For example, when the experimental results show that the thermal conductivity of a certain group of materials deviates from the design value, the thickness d1 of the thermal insulation layer or the thermal conductivity λ1 can be readjusted in combination with the simulation results. In some embodiments, through a real-time feedback mechanism, experimental data is directly input into the simulation model to update the optimization target, thereby realizing the dynamic adjustment of parameters.

[0189] In addition, the present invention also combines data visualization technology to present experimental and simulation results in an intuitive graphical form. For example:

[0190] Generate a temperature distribution map of the wall through thermal imaging technology to show local differences in thermal insulation performance;

[0191] Output a stress distribution nephogram, mark high stress areas and potential failure points, and provide guidance for further optimization.

[0192] Although the embodiments of the present invention have been shown and described, for those of ordinary skill in the art, it can be understood that various changes, modifications, substitutions, and variations can be made to these embodiments without departing from the principles and spirit of the present invention. The scope of the present invention is defined by the appended claims and their equivalents.

Claims

1. A method for constructing a concrete exterior wall and a green low-energy thermal insulation wall, characterized in that: The following steps are involved: Step 1: Based on the thermal conductivity characteristics of the wall, use thermal conduction analysis to optimize the thickness and thermal conductivity of the insulation layer to reduce the total heat loss of the wall; Step 2: According to the mechanical equilibrium conditions of the wall, the stress distribution and displacement field of the wall are determined through finite element analysis to ensure that the strength of the wall structure after construction meets the design requirements; Step 3: Combine multi-objective optimization theory, comprehensively consider the thermal conductivity, mechanical stability and construction cost of the wall, and dynamically optimize the construction parameters; Step 4: Design the spatial distribution of wall materials through topological optimization, and rationally configure the insulation layer and concrete layer to maximize material utilization; Step 5: Use experimental verification and simulation analysis to verify the optimization results, and provide feedback to adjust the optimization parameters.

2. A method for constructing a concrete exterior wall and a green low-energy thermal insulation wall according to claim 1, characterized in that: The heat conduction analysis in step 1 is based on the non-steady-state heat conduction equation, taking into account the thermal diffusivity of the wall material, the initial temperature distribution and the convection boundary conditions between the outer wall and the environment. The optimization goal is to reduce the heat loss power of the wall and keep the inner wall temperature stable.

3. A method for constructing a concrete exterior wall and a green low-energy thermal insulation wall according to claim 1, characterized in that: The mechanical equilibrium analysis in step 2 is based on the nonlinear finite element method. By calculating the stress tensor and displacement field of the wall, the thickness of the concrete layer and the distribution of reinforcement are optimized to ensure that the maximum stress value of the wall under construction load and long-term use does not exceed the yield strength of the material.

4. A method for constructing a concrete exterior wall and a green low-energy thermal insulation wall according to claim 1, characterized in that: In the step three, a multi-objective optimization model is established to simultaneously optimize the heat loss power, maximum stress value and construction cost of the wall. The optimization variables include the thickness of the insulation layer, thermal conductivity and construction speed. The optimization results are calculated by a genetic algorithm to form a Pareto optimal solution set that meets different construction conditions.

5. A method for constructing a concrete exterior wall and a green low-energy thermal insulation wall according to claim 1, characterized in that: The topology optimization in step 4 is based on design variables to represent the distribution ratio of the insulation layer and the concrete layer in the wall, and the optimization goal is to minimize the weighted sum ratio of material heat loss and stress concentration within a given wall thickness range.

6. A method for constructing a concrete exterior wall and a green low-energy thermal insulation wall according to claim 1, characterized in that: In the topology optimization process, geometric constraints for the distribution of wall materials are set, including the minimum area ratio of the insulation layer to the concrete layer and the total thickness range of the wall. The optimization results are used to adjust the input parameters in the mechanical analysis.

7. A method for constructing a concrete exterior wall and a green low-energy thermal insulation wall according to claim 1, characterized in that: In the step three, a dynamic optimization weight method is adopted to adjust the weight parameters of thermal conductivity, mechanical properties and construction cost according to the design objectives of the construction scenario, prioritize the optimization of thermal insulation performance in low-temperature environments, prioritize the optimization of construction costs and efficiency in high-temperature environments, and dynamically balance the construction efficiency and material utilization rate.

8. The method for constructing a concrete exterior wall and a green low-energy thermal insulation wall according to claim 1, characterized in that: The optimized wall construction method monitors the temperature, humidity and material bonding status of the construction environment in real time through sensors, and dynamically adjusts the input parameters in the optimization model through feedback to achieve adaptive adjustment of the construction process.

9. The method for constructing a concrete exterior wall and a green low-energy thermal insulation wall according to claim 1, characterized in that: The experimental verification includes preparing wall samples with different insulation layer thicknesses and thermal conductivity coefficients, testing their thermal conductivity, stress distribution and construction efficiency, verifying the heat loss and mechanical properties of the wall through simulation, checking the accuracy of the optimization results, and adjusting the design parameters of the topology optimization based on experimental data.

10. A method for constructing a concrete exterior wall and a green low-energy thermal insulation wall according to claim 1, characterized in that: The construction method is achieved through the following optimization steps: Determine the optimal range of insulation thickness based on wall heat conduction analysis, and dynamically adjust the thermal conductivity of the insulation material to reduce heat loss; Design wall material distribution through topology optimization to maximize the thermal efficiency of the insulation layer and optimize stress distribution; During the construction process, the ambient temperature, humidity and wall bonding status are collected in real time, and the construction speed and concrete material ratio are adjusted through sensor feedback, which significantly improves construction efficiency and reduces material waste.

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