Method for calculating carbon emissions for reinforcing concrete bridges based on fiber-reinforced composites

Abstract

The application discloses a kind of concrete bridge reinforcement carbon emission calculation methods based on fiber reinforced composite material, comprising: obtaining the carbon emission data and material attribute data of fiber reinforced composite material (FRP) in the whole life cycle, and establishing FRP material database;According to the design model of concrete bridge and the bridge data collected, the bridge construction reinforcement information model is established, the static data driven open loop semi-physical simulation is carried out to bridge construction reinforcement information model, and the FRP material selected is continuously optimized and updated;Based on FRP material database, the carbon emission calculation model of bridge reinforcement engineering whole life cycle is constructed;Based on the carbon emission calculation model and the FRP material selected by optimization, the total carbon emission of concrete bridge reinforcement construction is determined.The application can accurately evaluate the carbon emission of FRP material in the process of bridge reinforcement, optimize material selection, reduce the impact on the environment, promote sustainable development, improve the scientific nature and accuracy of decision-making.

Description

Technical Field

[0001] This invention belongs to the field of engineering accounting technology, specifically relating to a method for calculating carbon emissions from the reinforcement of concrete bridges based on fiber-reinforced composite materials. Background Technology

[0002] With the acceleration of urbanization and the growth of transportation demands, the safety and durability of bridges, as crucial infrastructure, are receiving increasing attention. Fiber-reinforced polymer (FRP) materials, including carbon fiber reinforced polymer (CFRP) and glass fiber reinforced polymer (GFRP), have become commonly used materials for reinforcing concrete bridges due to their excellent properties such as lightweight, high strength, and corrosion resistance. FRP materials not only effectively improve the load-bearing capacity and durability of bridges but also reduce the increase in self-weight after reinforcement, significantly enhancing the long-term performance of bridge structures.

[0003] However, the selection and application of FRP materials are not without challenges. First, there are many types of FRP materials with varying properties, making the selection of the appropriate material based on specific engineering needs a crucial issue. Second, the use of FRP materials involves considerations of structural safety, construction feasibility, and economic costs, often presenting conflicts and trade-offs that require comprehensive evaluation and optimization. Furthermore, with increasing environmental awareness, the carbon emissions from the production and application of FRP materials are receiving growing attention. How to minimize environmental impact while ensuring reinforcement effectiveness has become an important issue in bridge reinforcement projects.

[0004] Traditional methods for selecting FRP materials often rely on empirical judgment or single-objective optimization, making it difficult to comprehensively consider the aforementioned factors. Therefore, it is necessary to develop a multi-objective optimization model that can optimize the selection of FRP materials by comprehensively considering material performance, economic costs, and environmental impact, while ensuring structural safety and construction feasibility. This model can not only help engineers select FRP materials more scientifically and rationally, but also improve the overall performance and economic benefits of reinforcement schemes, promoting the sustainable development of bridge reinforcement projects. Summary of the Invention

[0005] In view of this, the present invention proposes a carbon emission calculation method for the reinforcement of concrete bridges based on fiber-reinforced composite materials. The present invention can accurately assess the carbon emissions of FRP materials in the process of bridge reinforcement, optimize material selection, reduce environmental impact, promote sustainable development, and improve the scientificity and accuracy of decision-making.

[0006] To achieve the above objectives, the present invention provides the following technical solution:

[0007] This invention provides a method for calculating carbon emissions from the reinforcement of concrete bridges based on fiber-reinforced composite materials, comprising:

[0008] Acquire carbon emission data and material property data of fiber-reinforced composite materials (FRP) throughout their entire life cycle, and establish an FRP material database;

[0009] Based on the design model of the concrete bridge and the collected bridge data, a bridge reinforcement information model is established. Static data-driven open-loop semi-solid simulation is performed on the bridge reinforcement information model, and the selected FRP material is continuously optimized and updated.

[0010] A carbon emission calculation model for the entire life cycle of bridge reinforcement projects was constructed based on an FRP material database.

[0011] Based on the carbon emission calculation model and the optimized FRP material, the total carbon emissions of concrete bridge reinforcement construction are determined.

[0012] Preferably, the carbon emission data for FRP materials includes:

[0013] Life cycle carbon emission data: Carbon emission factors in the production of FRP materials, including resin synthesis, fiber manufacturing, transportation distance, vehicle type, energy consumption of construction and bonding processes, maintenance and disposal stages, are obtained in advance and calculated according to ISO 14040 standard using the life cycle assessment (LCA) method.

[0014] Material property data includes FRP type, mechanical properties, durability parameters, and geometric specifications. FRP types include carbon fiber CFRP, metal fiber, and glass fiber GFRP. Mechanical properties include tensile strength, elastic modulus, and interlaminar shear strength. Durability parameters include damp heat aging and fatigue life. Geometric specifications include sheet / fabric thickness and fiber weaving direction.

[0015] Preferably, the FRP material database uses an SQL relational database;

[0016] In the process of establishing the FRP material database, a material property table, a carbon emission factor table, and an environmental condition correlation table are created, and multi-dimensional data retrieval and dynamic update technology are provided.

[0017] Preferably, in the process of static data-driven open-loop semi-solid simulation of the bridge reinforcement information model, a multi-objective optimization model is established to optimize and adjust the selection of FRP materials in the reinforcement project, so as to achieve a balance between carbon emissions, structural stability and economic cost in the reinforcement project.

[0018] Preferably, a bridge structural reinforcement information model is established based on the design model of the concrete bridge and the collected bridge data. Static data-driven open-loop semi-solid simulation of the bridge structural reinforcement information model includes:

[0019] Obtain the design model and design parameters of the concrete bridge, and establish the BIM model of the bridge;

[0020] Based on the detection data of the bridge, the current elastic modulus of the concrete, the corrosion rate of the steel bars, and the structural damage data of the bridge are determined. Among them, the structural damage data includes crack distribution, crack parameters, and concrete deterioration.

[0021] The bridge's BIM model and survey data are imported into PointCloud software to generate a point cloud model, which is then converted into a solid geometric model using MeshLab. In ANSYS, the material's nonlinear constitutive relation is defined and matched with measured performance data.

[0022] The simulation results are obtained by simulating the solid geometric model and comparing them with the measured response of the bridge. When the error meets the preset range requirements, the solid geometric model is determined to be up to standard and proceed to the next step.

[0023] Extract any type of FRP material from the FRP material database and generate parameterized reinforcement schemes;

[0024] Use Python to write automated scripts and call ANSYS APDL or ABAQUS CAE interfaces to generate finite element models of different FRP material reinforcement schemes in batches based on solid geometric models.

[0025] Semi-solid simulations are performed on the finite element model, and key output parameters are automatically extracted for each simulation, including:

[0026] Structural performance parameters: Load-bearing capacity improvement rate R load Crack closure degree C crack Maximum stress of FRP

[0027] Carbon emission parameters: based on material usage Q FRP Based on process parameters, the carbon emission calculation model is called to output the carbon emission parameter C. CO2 ;

[0028] Economic cost: based on the unit price P of materials FRP With material usage Q FRP The material cost is determined, and the construction time is determined based on the reinforcement plan, thereby determining the labor cost and equipment usage cost, ultimately yielding the economic cost (Cost).

[0029] Preferably, the parameterized reinforcement schemes generated based on FRP materials include:

[0030] Define the material type of FRP material and predefine the material's EFRP, ultimate strain, and carbon emission factor properties;

[0031] Determine the parameters of the reinforcement scheme for FRP materials, including:

[0032] Geometric parameters: 1 to 3 FRP layers, thickness 0.2 to 1.0 mm, laying direction 0°, 45°, 90°;

[0033] Process parameters: Select epoxy resin or polyurethane as the adhesive type, and sandblasting or chemical etching as the interface treatment method.

[0034] Preferably, establishing a multi-objective optimization model to optimize and adjust the selection of FRP materials in reinforcement projects includes:

[0035] Based on the selection criteria for FRP materials, the following optimization objective function is formulated:

[0036]

[0037] Where ω1, ω2, and ω3 are dynamically adjustable weighting coefficients. Indicates the ultimate tensile strength of FRP material;

[0038] Redefine the objective function:

[0039]

[0040] Among them, C CO2 (x) represents the total carbon emissions under the selected decision variable x, R0 load (x) represents the rate of improvement in the bearing capacity of the bridge structure after reinforcement under the selected decision variable x, and Cost(x) represents the total economic cost under the selected decision variable x;

[0041] x = [FRP type, number of construction layers, construction thickness, construction process, maintenance strategy]

[0042] Establish constraints:

[0043]

[0044] Where g1(x) represents structural safety constraints, g2(x) represents construction feasibility constraints, and g3(x) represents economic constraints;

[0045] The objective function is normalized to eliminate dimensional differences;

[0046] The NSGA-II algorithm is used to solve the objective function, obtain the optimal decision variable x, and determine its corresponding reinforcement scheme.

[0047] The present invention has achieved at least the following beneficial effects:

[0048] 1. This invention can accurately assess the carbon emissions of FRP materials in the process of bridge reinforcement, optimize material selection, reduce environmental impact, promote sustainable development, and improve the scientific nature and accuracy of decision-making.

[0049] Other advantages, objectives, and features of the invention will be set forth in the following description and will be apparent to those skilled in the art in some respects, or may be learned by practice of the invention. The objectives and other advantages of the invention can be realized and obtained through the following description. Attached Figure Description

[0050] To make the objectives, technical solutions, and beneficial effects of this invention clearer, the following figures are provided for illustration:

[0051] Figure 1 This is a flowchart illustrating the steps of the carbon emission calculation method for reinforced concrete bridges based on fiber-reinforced composite materials in an embodiment of the present invention.

[0052] Figure 2 This is a flowchart illustrating the steps of bridge reinforcement modeling and semi-solid simulation in an embodiment of the present invention. Detailed Implementation

[0053] The preferred embodiments of the present invention will be described below with reference to the accompanying drawings. It should be understood that the preferred embodiments described herein are for illustration and explanation only and are not intended to limit the present invention.

[0054] This invention provides a method for calculating carbon emissions from the reinforcement of concrete bridges based on fiber-reinforced composite materials, referring to... Figure 1 ,include:

[0055] Step S1: Obtain carbon emission data and material property data of fiber-reinforced composite materials (FRP) throughout their entire life cycle, and establish an FRP material database;

[0056] Step S2: Based on the design model of the concrete bridge and the collected bridge data, establish a bridge structural reinforcement information model, perform static data-driven open-loop semi-solid simulation on the bridge structural reinforcement information model, and continuously optimize and update the selected FRP material.

[0057] Step S3: Construct a carbon emission calculation model for the entire life cycle of bridge reinforcement projects based on the FRP material database;

[0058] Step S4: Based on the carbon emission calculation model and the optimized FRP material, determine the total carbon emissions of the concrete bridge reinforcement construction.

[0059] The working principle and beneficial effects of the above technical solution are as follows: This invention provides a method for calculating carbon emissions from concrete bridge reinforcement based on fiber-reinforced polymer (FRP) composite materials. This method first involves acquiring carbon emission data and material property data of FRP materials throughout their entire life cycle, and establishing an FRP material database accordingly, laying the foundation for subsequent accurate calculations. Next, a bridge reinforcement information model is established based on the design model of the concrete bridge and the collected bridge data. Static data-driven open-loop semi-solid simulation is performed on the bridge reinforcement information model, continuously optimizing and updating the selected FRP materials during this process. This step, through simulation-optimized material selection, helps to select FRP materials with lower carbon emissions, thereby reducing the environmental impact of bridge reinforcement projects. Then, a carbon emission calculation model for the entire life cycle of the bridge reinforcement project is constructed based on the FRP material database. This model can accurately calculate the carbon emissions generated throughout the entire process from material production to reinforcement construction completion, ensuring the accuracy of the carbon emission assessment. Finally, based on the carbon emission calculation model and the optimized FRP materials, the total carbon emissions of the concrete bridge reinforcement construction are determined, providing a scientific basis for the environmental impact assessment of bridge reinforcement projects. Through this series of working principles, the beneficial effects of this invention are reflected in its ability to accurately assess the carbon emissions of FRP materials during bridge reinforcement, optimize material selection, reduce environmental impact, promote sustainable development, and improve the scientific rigor and accuracy of decision-making. Overall, this method not only supports the concepts of green building and sustainable development, helping to drive the construction industry towards a more environmentally friendly and sustainable direction, but also enhances the competitiveness of engineering projects by adopting low-carbon reinforcement solutions, meeting market demands for green buildings. Ultimately, the beneficial effect is a significant improvement in the environmental performance and market competitiveness of bridge reinforcement projects, providing strong support for green building.

[0060] In a preferred embodiment, the carbon emission data of the FRP material includes:

[0061] Life cycle carbon emission data: Carbon emission factors in the production of FRP materials, including resin synthesis, fiber manufacturing, transportation distance, vehicle type, energy consumption of construction and bonding processes, maintenance and disposal stages, are obtained in advance and calculated according to ISO 14040 standard using the life cycle assessment (LCA) method.

[0062] Material property data includes FRP type, mechanical properties, durability parameters, and geometric specifications. FRP types include carbon fiber CFRP, metal fiber, and glass fiber GFRP. Mechanical properties include tensile strength, elastic modulus, and interlaminar shear strength. Durability parameters include damp heat aging and fatigue life. Geometric specifications include sheet / fabric thickness and fiber weaving direction.

[0063] The working principle and beneficial effects of the above technical solution are as follows: By collecting carbon emission data and material property data of fiber-reinforced polymer (FRP) throughout its entire life cycle, accurate calculation of carbon emissions during the reinforcement of concrete bridges can be achieved. The working principle first involves pre-acquiring carbon emission factors at each stage of FRP material production, including resin synthesis, fiber manufacturing, transportation, energy consumption during construction and bonding processes, maintenance, and disposal. This data is calculated using the Life Cycle Assessment (LCA) method and in accordance with the ISO 14040 standard. The beneficial effect of this step is that it provides a comprehensive and accurate environmental impact assessment for FRP materials, thus providing a scientific basis for selecting low-carbon emission FRP materials. Next, the collected material property data includes the type of FRP, mechanical properties, durability parameters, and geometric specifications. This data helps to evaluate the material's performance and suitability, ensuring that the reinforcement material meets the specific needs of the project. The beneficial effects of this step are that it optimizes material selection, improves material utilization, enhances design adaptability, and also promotes technological innovation.

[0064] In a preferred embodiment, the FRP material database uses an SQL relational database;

[0065] In the process of establishing the FRP material database, a material property table, a carbon emission factor table, and an environmental condition correlation table are created, and multi-dimensional data retrieval and dynamic update technology are provided.

[0066] In a preferred embodiment, during the static data-driven open-loop semi-solid simulation of the bridge reinforcement information model, a multi-objective optimization model is established to optimize and adjust the selection of FRP materials in the reinforcement project, so as to achieve a balance between carbon emissions, structural stability and economic costs in the reinforcement project.

[0067] In a preferred embodiment, refer to Figure 2 Based on the design model of the concrete bridge and the collected bridge data, a bridge structural reinforcement information model is established. Static data-driven open-loop semi-solid simulation of the bridge structural reinforcement information model includes:

[0068] Obtain the design model and design parameters of the concrete bridge, and establish the BIM model of the bridge;

[0069] Based on the detection data of the bridge, the current elastic modulus of the concrete, the corrosion rate of the steel bars, and the structural damage data of the bridge are determined. Among them, the structural damage data includes crack distribution, crack parameters, and concrete deterioration.

[0070] The bridge's BIM model and survey data are imported into PointCloud software to generate a point cloud model, which is then converted into a solid geometric model using MeshLab. In ANSYS, the material's nonlinear constitutive relation is defined and matched with measured performance data.

[0071] The simulation results are obtained by simulating the solid geometric model and comparing them with the measured response of the bridge. When the error meets the preset range requirements, the solid geometric model is determined to be up to standard and proceed to the next step.

[0072] Extract any type of FRP material from the FRP material database and generate parameterized reinforcement schemes;

[0073] Use Python to write automated scripts and call ANSYS APDL or ABAQUS CAE interfaces to generate finite element models of different FRP material reinforcement schemes in batches based on solid geometric models.

[0074] Semi-solid simulations are performed on the finite element model, and key output parameters are automatically extracted for each simulation, including:

[0075] Structural performance parameters: Load-bearing capacity improvement rate R load Crack closure degree C crack Maximum stress of FRP

[0076] Carbon emission parameters: based on material usage Q FRP Based on process parameters, the carbon emission calculation model is called to output the carbon emission parameter C. CO2 ;

[0077] Economic cost: based on the unit price P of materials FRP With material usage Q FRP The material cost is determined, and the construction time is determined based on the reinforcement plan, thereby determining the labor cost and equipment usage cost, ultimately yielding the economic cost (Cost).

[0078] The working principle and beneficial effects of the above technical solution are as follows: First, by acquiring the design model and design parameters of the concrete bridge and establishing a BIM model of the bridge, a digital three-dimensional model foundation is provided for bridge reinforcement. Next, based on the bridge's detection data, the current elastic modulus of the concrete, the corrosion rate of the reinforcing steel, and the structural damage data of the bridge, including crack distribution, crack parameters, and concrete deterioration, are determined. This data provides necessary structural information for bridge reinforcement. Then, the bridge's BIM model and detection data are imported into PointCloud software to generate a point cloud model, which is then converted into a solid geometric model using MeshLab. The nonlinear constitutive relation of the material is defined in ANSYS and matched with measured performance data; this step ensures the accuracy and reliability of the model. Simulation of the solid geometric model yields simulation results, which are compared with the measured response of the bridge. When the error meets the preset range requirements, the solid geometric model is deemed satisfactory, and the next step is initiated; this process ensures the accuracy of the simulation results. Finally, arbitrary types of FRP materials are extracted from the FRP material database, and parameterized reinforcement schemes are generated; this step provides a variety of possible material choices for bridge reinforcement. Using Python to write automated scripts that call ANSYS APDL or ABAQUS CAE interfaces, finite element models of different FRP material reinforcement schemes are generated in batches based on solid geometric models. This process greatly improves simulation efficiency. Semi-solid simulations are performed on the finite element models, automatically extracting key output parameters for each simulation, including structural performance parameters, carbon emission parameters, and economic costs. These parameters provide a comprehensive evaluation basis for bridge reinforcement. By establishing a bridge reinforcement information model and performing semi-solid simulations, the structural performance, carbon emissions, and economic costs of different FRP material reinforcement schemes can be accurately evaluated, providing a scientific basis for bridge reinforcement decisions. This method can optimize reinforcement schemes, selecting FRP materials with optimal performance, lowest carbon emissions, and most reasonable economic costs, thereby improving the efficiency and quality of bridge reinforcement, reducing environmental impact, and achieving the goals of green building. Simultaneously, the use of automated scripts greatly improves simulation efficiency, saves time and labor costs, and provides an efficient, economical, and environmentally friendly solution for bridge reinforcement.

[0079] In a preferred embodiment, generating a parameterized reinforcement scheme based on the FRP material includes:

[0080] Define the material type of FRP material and predefine the material's EFRP, ultimate strain, and carbon emission factor properties;

[0081] Determine the parameters of the reinforcement scheme for FRP materials, including:

[0082] Geometric parameters: 1 to 3 FRP layers, thickness 0.2 to 1.0 mm, laying direction 0°, 45°, 90°;

[0083] Process parameters: Select epoxy resin or polyurethane as the adhesive type, and sandblasting or chemical etching as the interface treatment method.

[0084] The working principle and beneficial effects of the above technical solution are as follows: First, a detailed definition of the FRP material is provided, covering attributes such as material type, ultimate strain, and carbon emission factor. This step ensures that the key performance indicators and environmental impact of the material are considered in the early design stages of the reinforcement scheme. The corresponding benefit is that it provides a scientific and precise basis for material selection in bridge reinforcement, thereby optimizing the performance of reinforcement materials and reducing environmental impact at the source. Next, we determine the reinforcement scheme parameters of the FRP material, including geometric parameters such as the number of FRP layers, thickness, and laying direction, as well as process parameters such as adhesive type and interface treatment method. Geometric parameters directly affect the material distribution and reinforcement effect, while process parameters relate to the durability and bond strength of the reinforcement. By accurately setting these parameters, the beneficial effect is that it improves the adaptability and reinforcement effect of the reinforcement scheme, ensuring that the reinforced bridge structure meets both load-bearing capacity requirements and has good durability. Furthermore, through parametric methods, multiple reinforcement schemes can be quickly generated to adapt to different engineering needs and environmental conditions. This improves the adaptability of the reinforcement scheme and helps optimize material use and reduce material waste. Clearly defined process parameters can guide the construction process, improve construction efficiency and quality, and reduce construction time and costs. Selecting environmentally friendly materials based on their carbon emission factors helps reduce the environmental impact of reinforcement projects and achieve green building goals. Overall, this embodiment, through parametric definition of FRP materials and reinforcement schemes, not only improves the design efficiency and material utilization efficiency of reinforcement schemes but also enhances the reinforcement effect, reduces environmental impact, and improves construction efficiency. Through this method, we can achieve comprehensive optimization of bridge reinforcement projects, thereby improving the economic and environmental benefits of reinforcement projects and providing an efficient, economical, and environmentally friendly solution for bridge reinforcement.

[0085] In a preferred embodiment, establishing a multi-objective optimization model to optimize the selection of FRP materials in the reinforcement project includes:

[0086] Based on the selection criteria for FRP materials, the following optimization objective function is formulated:

[0087]

[0088] Where ω1, ω2, and ω3 are dynamically adjustable weighting coefficients. Indicates the ultimate tensile strength of FRP material;

[0089] Redefine the objective function:

[0090]

[0091] Among them, C CO2 (x) represents the total carbon emissions under the selected decision variable x, R0 load (x) represents the rate of improvement in the bearing capacity of the bridge structure after reinforcement under the selected decision variable x, and Cost(x) represents the total economic cost under the selected decision variable x;

[0092] x = [FRP type, number of construction layers, construction thickness, construction process, maintenance strategy]

[0093] Establish constraints:

[0094]

[0095] Where g1(x) represents structural safety constraints, g2(x) represents construction feasibility constraints, and g3(x) represents economic constraints;

[0096] The objective function is normalized to eliminate dimensional differences;

[0097] The NSGA-II algorithm is used to solve the objective function, obtain the optimal decision variable x, and determine its corresponding reinforcement scheme.

[0098] The working principle and beneficial effects of the above technical solution are as follows: In this preferred embodiment, a multi-objective optimization model is established to optimize the selection of FRP materials in the reinforcement project. The working principle of this model first involves formulating an optimization objective function based on the selection criteria for FRP materials, which includes dynamically adjustable weight coefficients to reflect the relative importance of different objectives. This step ensures that the reinforcement scheme meets structural performance requirements while also considering environmental impact and economic costs, achieving a balance among multiple objectives. Next, the objective function is redefined, explicitly representing the total carbon emissions, the increase in the load-bearing capacity of the reinforced bridge structure, and the total economic cost under the selected decision variables. This helps to more clearly identify and evaluate the comprehensive performance of the reinforcement scheme. This step improves the adaptability and reinforcement effect of the scheme, ensuring that the reinforced bridge structure meets both load-bearing capacity requirements and has good durability. Subsequently, constraints are established, including structural safety constraints, construction feasibility constraints, and economic constraints. These constraints ensure that the scheme obtained during the optimization process is not only theoretically optimal but also feasible in practical applications. This step ensures the safety, feasibility, and economy of the reinforcement scheme. Normalizing the objective function eliminates dimensional differences and ensures comparability between different objectives, which helps improve the efficiency and accuracy of the optimization algorithm. The NSGA-II algorithm, a commonly used multi-objective optimization algorithm, is used to solve the objective function. This algorithm effectively finds a set of Pareto optimal solutions, i.e., a balanced solution set among multiple objectives. This step provides multiple feasible reinforcement schemes, enhancing their adaptability and flexibility. Overall, this embodiment optimizes the selection of FRP materials in reinforcement projects by establishing a multi-objective optimization model. This not only improves the overall performance of the reinforcement scheme but also promotes the sustainable development of bridge reinforcement projects. This method optimizes reinforcement schemes by comprehensively considering structural performance, environmental impact, and economic costs, improving the scientific nature and accuracy of decision-making, reducing environmental impact, achieving green building goals, and simultaneously improving construction efficiency and economic benefits. It provides an efficient, economical, and environmentally friendly solution for bridge reinforcement.

[0099] Finally, it should be noted that the above preferred embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit it. Although the present invention has been described in detail through the above preferred embodiments, those skilled in the art should understand that various changes can be made to it in form and detail without departing from the scope defined by the claims of the present invention.

Claims

1. A method for calculating carbon emissions from the reinforcement of concrete bridges based on fiber-reinforced composite materials, characterized in that, include: Acquire carbon emission data and material property data of fiber-reinforced composite materials (FRP) throughout their entire life cycle, and establish an FRP material database; Based on the design model of the concrete bridge and the collected bridge data, a bridge reinforcement information model is established. Static data-driven open-loop semi-solid simulation is performed on the bridge reinforcement information model, and the selected FRP material is continuously optimized and updated. A carbon emission calculation model for the entire life cycle of bridge reinforcement projects was constructed based on an FRP material database. Based on the carbon emission calculation model and the optimized FRP material, the total carbon emissions of concrete bridge reinforcement construction are determined. Establishing a multi-objective optimization model to optimize and adjust the selection of FRP materials in reinforcement projects includes: Based on the selection criteria for FRP materials, the following optimization objective function is formulated: in, Indicates the rate of increase in load-bearing capacity. Indicates carbon emission parameters, Indicates economic cost, Indicates the maximum stress in FRP material. , , These are dynamically adjustable weighting coefficients. Indicates the ultimate tensile strength of FRP material. Indicates the degree of crack closure; Redefine the objective function: in, Indicates the selection of decision variables Total carbon emissions, Indicates the selection of decision variables The increase in the load-bearing capacity of the bridge structure after reinforcement Indicates the selection of decision variables The total economic cost; Establish constraints: in, Indicates structural safety constraints. This indicates a constraint on construction feasibility. Indicates economic constraints; The objective function is normalized to eliminate dimensional differences; The NSGA-II algorithm is used to solve the objective function to obtain the optimal decision variables. And determine the corresponding reinforcement scheme.

2. The method for calculating carbon emissions from the reinforcement of concrete bridges based on fiber-reinforced composite materials according to claim 1, characterized in that, Carbon emission data for FRP materials include: Life cycle carbon emission data: Carbon emission factors in the production of FRP materials, including resin synthesis, fiber manufacturing, transportation distance, vehicle type, energy consumption of construction and bonding processes, maintenance and disposal stages, are obtained in advance and calculated according to ISO 14040 standard using the life cycle assessment (LCA) method. Material property data includes FRP type, mechanical properties, durability parameters, and geometric specifications. FRP types include carbon fiber (CFRP), metal fiber, and glass fiber (GFRP). Mechanical properties include tensile strength, elastic modulus, and interlaminar shear strength. Durability parameters include damp heat aging coefficient and fatigue life. Geometric specifications include sheet / fabric thickness and fiber weaving direction.

3. The method for calculating carbon emissions from the reinforcement of concrete bridges based on fiber-reinforced composite materials according to claim 2, characterized in that, The FRP material database uses an SQL relational database; In the process of establishing the FRP material database, a material property table, a carbon emission factor table, and an environmental condition correlation table are created, and multi-dimensional data retrieval and dynamic update technology are provided.

4. The method for calculating carbon emissions from the reinforcement of concrete bridges based on fiber-reinforced composite materials according to claim 1, characterized in that, In the process of static data-driven open-loop semi-solid simulation of bridge reinforcement information model, a multi-objective optimization model is established to optimize and adjust the selection of FRP material in reinforcement project, so as to achieve a balance between carbon emissions, structural stability and economic cost of reinforcement project.

5. The method for calculating carbon emissions from the reinforcement of concrete bridges based on fiber-reinforced composite materials according to claim 4, characterized in that, Based on the design model of the concrete bridge and the collected bridge data, a bridge structural reinforcement information model is established. Static data-driven open-loop semi-solid simulation of the bridge structural reinforcement information model includes: Obtain the design model and design parameters of the concrete bridge, and establish the BIM model of the bridge; Based on the detection data of the bridge, the current elastic modulus of the concrete, the corrosion rate of the steel bars, and the structural damage data of the bridge are determined. Among them, the structural damage data includes crack distribution, crack parameters, and concrete deterioration. The bridge's BIM model and survey data are imported into PointCloud software to generate a point cloud model, which is then converted into a solid geometric model using MeshLab. In ANSYS, the material's nonlinear constitutive relation is defined and matched with measured performance data. The simulation results are obtained by simulating the solid geometric model and comparing them with the measured response of the bridge. When the error meets the preset range requirements, the solid geometric model is determined to be up to standard and proceed to the next step. Extract any type of FRP material from the FRP material database and generate parameterized reinforcement schemes; Use Python to write automated scripts and call ANSYS APDL or ABAQUS CAE interfaces to generate finite element models of different FRP material reinforcement schemes in batches based on solid geometric models. Semi-solid simulations are performed on the finite element model, and key output parameters are automatically extracted for each simulation, including: Structural performance parameters: Load-bearing capacity improvement rate Crack closure Maximum stress of FRP ; Carbon emission parameters: based on material usage Based on process parameters, call the carbon emission calculation model to output carbon emission parameters. ; Economic cost: based on material unit price With material usage By determining material costs, construction hours are calculated based on the reinforcement plan, thereby determining labor costs and equipment usage costs, ultimately yielding the economic cost. .

6. The method for calculating carbon emissions from the reinforcement of concrete bridges based on fiber-reinforced composite materials according to claim 5, characterized in that, The parameterized reinforcement schemes based on FRP materials include: Define the material type of FRP material and predefine the material's EFRP, ultimate strain, and carbon emission factor properties; Determine the parameters of the reinforcement scheme for FRP materials, including: Geometric parameters: 1-3 FRP layers, thickness 0.2-1.0mm, laying direction 0°, 45°, 90°; Process parameters: Select epoxy resin or polyurethane as the adhesive type, and sandblasting or chemical etching as the interface treatment method.

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