A method and system for low-carbon intelligent construction of carbon fiber cloth reinforced concrete

Abstract

The application provides a kind of carbon fiber cloth reinforced concrete low-carbon intelligent construction method and system, by collecting the structural parameters of reinforced concrete structure to be reinforced, environmental data and alternative construction process library, relying on work decomposition structure integration model input characteristics to form nested level feature group, build the objective function with carbon emission minimization as target, the function includes normalized carbon emission loss term and structured sparse regularization term, the structure safety reserve coefficient of construction option is obtained by finite element model, the carbon emission weight is determined by combining carbon emission factor, considering the structure safety requirement;According to the setting of corresponding norm penalty value according to the feature cooperation and mutual exclusion relationship, the hierarchical weight is obtained by weighting from the bottom weight, the regularization term construction is completed, the sparse feature coefficient is obtained by solving the objective function, the construction option corresponding to the non-zero coefficient is selected, and the intelligent reinforcement construction scheme considering structure safety and low-carbon emission reduction is quickly generated.

Description

Technical Field

[0001] This application belongs to the field of intelligent construction, and in particular relates to a method and system for low-carbon intelligent construction of carbon fiber reinforced concrete. Background Technology

[0002] In the field of existing building maintenance and renovation, concrete structure reinforcement projects are large-scale, and the construction process is usually accompanied by high energy consumption and carbon emissions. Carbon fiber reinforcement technology has advantages such as lightweight, high strength, corrosion resistance, and convenient construction, and has become one of the important processes for improving the load-bearing capacity of concrete structures. However, carbon fiber fabric and its supporting materials have a high implicit carbon footprint during production and use. Traditional reinforcement construction schemes often rely on the experience of engineers and usually focus on meeting structural mechanical performance requirements and shortening the construction period, lacking refined control and overall optimization of carbon emissions throughout the entire construction process. Therefore, how to scientifically combine and select construction schemes with lower carbon emissions from the alternative construction technology library and multiple cross-processes while meeting the safety requirements of the reinforced structure has become an urgent technical problem to be solved in the field of reinforcement engineering.

[0003] By abstracting construction processes into hierarchical feature groups according to the work decomposition structure, and constructing a mathematical optimization model that aims to minimize carbon emissions and incorporates structured sparse regularization terms, the algorithm can be driven to filter among a large number of alternative construction options. However, existing models typically separate structural safety attributes from carbon emission attributes during penalty weight calculation, making it difficult to establish a mapping relationship between carbon emission weights and the finite element safety reserve coefficient of concrete structures. This can lead to the model selecting construction options with insufficient safety redundancy during iterative solutions due to an overemphasis on low-carbon objectives. Furthermore, existing structured regularization methods struggle to accurately identify cooperative and mutually exclusive relationships within nested hierarchical features, and are also unable to reasonably assign penalty weights to each level of feature group based on specific carbon emission levels. This can easily lead to process conflicts or incomplete connections between preceding and subsequent processes in the sparse feature coefficient solution, making it difficult to implement the generated low-carbon construction scheme and achieve reliable optimization of carbon emissions throughout the entire construction process while ensuring structural mechanical safety. Summary of the Invention

[0004] To achieve the coordinated development of safety compliance and low-carbon emission reduction in concrete structure reinforcement projects, one or more embodiments of the present invention provide a method for low-carbon intelligent construction of carbon fiber reinforced concrete, including the following steps: Obtain the structural parameters, environmental data, and alternative construction technology library of the concrete structure to be reinforced. Organize the model input features associated with the construction options into nested hierarchical feature groups according to the work breakdown structure. Construct an objective function with the goal of minimizing carbon emissions. The objective function consists of a carbon emission loss term with unified dimensions after normalization and a structured sparse regularization term. The carbon emission loss term is the sum of the feature coefficients of the construction options, the unit carbon emission, and the corresponding engineering quantity. For construction options in the bottom feature group, the structural safety reserve coefficient is calculated using a finite element model, and the carbon emission weight is calculated in combination with the carbon emission factor. The carbon emission weight is proportional to the unit carbon emission and has a monotonically increasing relationship with the excess redundancy degree after the structural safety reserve coefficient exceeds the safety threshold. This amplifies the weight of construction options that exceed the safety threshold and whose difference exceeds a preset range, and reduces the weight of construction options that are close to the safety threshold and not lower than the safety threshold. The penalty value of each level feature group is calculated based on the feature synergy relationship and the feature mutual exclusion relationship. The penalty value of the mutual exclusion group is the first norm of the feature coefficient vector, and the penalty value of the synergy group is the second norm of the feature coefficient vector. The weight of the level feature group is obtained by weighted summation of the carbon emission weights corresponding to the construction options in the lower feature group. This completes the construction of the structured sparse regularization term. Solve the objective function to obtain the sparse characteristic coefficient solution, and generate the low-carbon construction scheme based on the construction options corresponding to the non-zero characteristic coefficients.

[0005] Furthermore, the acquisition of structural parameters, environmental data, and a library of alternative construction techniques for the concrete structure to be reinforced includes: Analyze the construction drawings, extract the cross-sectional dimensions, reinforcement ratio and material strength parameters of the concrete components, and construct the structural geometry and material property matrix; A network of temperature and humidity sensors deployed at the construction site collects ambient temperature and humidity data in real time. Send a retrieval request to the cloud-based process database to obtain data on material types, construction machinery, and construction methods suitable for carbon fiber fabric reinforcement, and parse and construct a tree-like library of alternative construction processes.

[0006] Furthermore, the model input features associated with construction options are organized into nested hierarchical feature groups according to the work decomposition structure, including: Using the construction flow section as the parent node, a top-level index for the feature group is established, and the overall process requirements of each flow section are used as the top-level feature object. Extract the names of materials, equipment and construction methods corresponding to each construction process, as well as the corresponding mechanical and geometric parameters, from the alternative construction process library to generate a child-level feature matrix, and map the matrix to the corresponding parent node as the bottom-level leaf node. Establish a one-way mapping relationship chain between parent nodes and bottom leaf nodes, and construct a multi-branch nested hierarchical feature data structure.

[0007] Furthermore, the calculation of the structural safety reserve factor using the finite element model includes: An initial finite element model of the concrete member to be reinforced is established based on the structural geometry and material property matrix. The mechanical and geometric parameters corresponding to the carbon fiber cloth reinforcement material in the bottom feature group are input into the initial finite element model. Dead load and live load are applied, and the external load is gradually increased until the model reaches the critical failure state. The ultimate bearing capacity at this time is extracted. Calculate the ratio of the ultimate bearing capacity to the structural requirement bearing capacity, and set the redundancy obtained by subtracting 1 from the ratio as the structural safety reserve factor.

[0008] Furthermore, the step of amplifying the weight of construction options that exceed the safety threshold and whose difference exceeds a preset range, and reducing the weight of construction options that are close to the safety threshold and not lower than the safety threshold, includes: A weighted adjustment mapping model is constructed using an exponential function or a variant of the sigmoid function that monotonically increases with the degree of excess redundancy. When the structural safety reserve coefficient is greater than the set safety threshold and the difference exceeds the preset range, the weight adjustment mapping model outputs an amplification factor greater than 1 to increase the carbon emission weight of the construction option in the structured sparse regularization term, so that the excessively safe and redundant construction option is more compressed in the minimization solution process. When the structural safety reserve coefficient is not lower than the set safety threshold and the difference between it and the safety threshold is within a preset range, the weight adjustment mapping model outputs a reduction factor of less than 1 to reduce the carbon emission weight of the construction option in the structured sparse regularization term, so that the construction option that meets the safety requirements and has appropriate safety redundancy is more likely to be retained in the minimization solution process.

[0009] Further, the step of solving the objective function to obtain sparse characteristic coefficient solutions, and generating the low-carbon construction scheme based on the construction options corresponding to the non-zero characteristic coefficients, includes: The objective function is solved iteratively using the proximal gradient descent algorithm, and the feature coefficients are updated using a soft threshold operator in each iteration. When the objective function converges or reaches the maximum number of iterations, the iteration stops and the continuous solution of the characteristic coefficients is obtained; Set a truncation threshold, set the feature coefficients whose absolute value is less than the truncation threshold to zero, and retain the non-zero feature coefficients whose absolute value is greater than or equal to the truncation threshold; The non-zero characteristic coefficients are matched with the construction options in the alternative construction process library to output the low-carbon construction plan that includes the selected materials, equipment and construction methods.

[0010] One or more embodiments of the present invention also provide a system for low-carbon intelligent construction of carbon fiber reinforced concrete, comprising the following modules: The construction module is used to acquire the structural parameters, environmental data and alternative construction technology library of the concrete structure to be reinforced. According to the work breakdown structure, the model input features associated with the construction options are organized into nested hierarchical feature groups. An objective function with the goal of minimizing carbon emissions is constructed. The objective function consists of a carbon emission loss term with unified dimensions after normalization and a structured sparse regularization term. The carbon emission loss term is the sum of the characteristic coefficient of the construction option, the unit carbon emission, and the corresponding engineering quantity. The calculation module is used to calculate the structural safety reserve coefficient of the construction options of the bottom feature group through the finite element model, and calculate the carbon emission weight in combination with the carbon emission factor. The carbon emission weight is proportional to the unit carbon emission and has a monotonically increasing relationship with the excess redundancy degree after the structural safety reserve coefficient exceeds the safety threshold. The weight of construction options that exceed the safety threshold and the difference exceeds the preset range is amplified, while the weight of construction options that are close to the safety threshold and not lower than the safety threshold is reduced. The penalty value of each level feature group is calculated according to the feature synergy relationship and the feature mutual exclusion relationship. The penalty value of the mutual exclusion group is the first norm of the feature coefficient vector, and the penalty value of the synergy group is the second norm of the feature coefficient vector. The weight of the level feature group is obtained by weighted summation of the carbon emission weights corresponding to the construction options of the lower bottom feature group. In this way, the construction of the structured sparse regularization term is completed. The generation module is used to solve the objective function to obtain the sparse characteristic coefficient solution, and generate the low-carbon construction plan based on the construction options corresponding to the non-zero characteristic coefficients.

[0011] Preferably, the acquisition of structural parameters, environmental data, and a library of alternative construction techniques for the concrete structure to be reinforced includes: Analyze the construction drawings, extract the cross-sectional dimensions, reinforcement ratio and material strength parameters of the concrete components, and construct the structural geometry and material property matrix; A network of temperature and humidity sensors deployed at the construction site collects ambient temperature and humidity data in real time. Send a retrieval request to the cloud-based process database to obtain data on material types, construction machinery, and construction methods suitable for carbon fiber fabric reinforcement, and parse and construct a tree-like library of alternative construction processes.

[0012] Preferably, the step of organizing the model input features associated with construction options into nested hierarchical feature groups according to the work breakdown structure includes: Using the construction flow section as the parent node, a top-level index for the feature group is established, and the overall process requirements of each flow section are used as the top-level feature object. Extract the names of materials, equipment and construction methods corresponding to each construction process, as well as the corresponding mechanical and geometric parameters, from the alternative construction process library to generate a child-level feature matrix, and map the matrix to the corresponding parent node as the bottom-level leaf node. Establish a one-way mapping relationship chain between parent nodes and bottom leaf nodes, and construct a multi-branch nested hierarchical feature data structure.

[0013] Preferably, the calculation of the structural safety reserve factor through the finite element model includes: An initial finite element model of the concrete member to be reinforced is established based on the structural geometry and material property matrix. The mechanical and geometric parameters corresponding to the carbon fiber cloth reinforcement material in the bottom feature group are input into the initial finite element model. Dead load and live load are applied, and the external load is gradually increased until the model reaches the critical failure state. The ultimate bearing capacity at this time is extracted. Calculate the ratio of the ultimate bearing capacity to the structural requirement bearing capacity, and set the redundancy obtained by subtracting 1 from the ratio as the structural safety reserve factor.

[0014] Preferably, the step of amplifying the weight of construction options that exceed the safety threshold and whose difference exceeds a preset range, and reducing the weight of construction options that are close to the safety threshold and not lower than the safety threshold, includes: A weighted adjustment mapping model is constructed using an exponential function or a variant of the sigmoid function that monotonically increases with the degree of excess redundancy. When the structural safety reserve coefficient is greater than the set safety threshold and the difference exceeds the preset range, the weight adjustment mapping model outputs an amplification factor greater than 1 to increase the carbon emission weight of the construction option in the structured sparse regularization term, so that the excessively safe and redundant construction option is more compressed in the minimization solution process. When the structural safety reserve coefficient is not lower than the set safety threshold and the difference between it and the safety threshold is within a preset range, the weight adjustment mapping model outputs a reduction factor of less than 1 to reduce the carbon emission weight of the construction option in the structured sparse regularization term, so that the construction option that meets the safety requirements and has appropriate safety redundancy is more likely to be retained in the minimization solution process.

[0015] Preferably, the step of solving the objective function to obtain the sparse characteristic coefficient solution, and generating the low-carbon construction scheme based on the construction options corresponding to the non-zero characteristic coefficients, includes: The objective function is solved iteratively using the proximal gradient descent algorithm, and the feature coefficients are updated using a soft threshold operator in each iteration. When the objective function converges or reaches the maximum number of iterations, the iteration stops and the continuous solution of the characteristic coefficients is obtained; Set a truncation threshold, set the feature coefficients whose absolute value is less than the truncation threshold to zero, and retain the non-zero feature coefficients whose absolute value is greater than or equal to the truncation threshold; The non-zero characteristic coefficients are matched with the construction options in the alternative construction process library to output the low-carbon construction plan that includes the selected materials, equipment and construction methods.

[0016] This invention organizes construction options into nested hierarchical feature groups and calculates penalty values ​​using different norms based on the mutual exclusion and synergistic relationships of the features. Simultaneously, it incorporates non-negativity constraints, mutual exclusion constraints, and safety lower limit constraints to eliminate construction options that do not meet safety requirements or have process conflicts, ensuring the engineering feasibility of the generated scheme. Carbon emission weights are set by combining structural safety reserve coefficients and carbon emission factors, and the weights of construction options with different safety margins are directionally scaled. By solving the objective function to obtain sparse feature coefficient solutions, high-energy-consuming and redundant construction steps are reduced while ensuring the safety threshold of the reinforced concrete structure. The optimal construction combination corresponding to non-zero features is output, reducing the total carbon emissions of the overall reinforcement construction process. Attached Figure Description

[0017] Figure 1 A flowchart of a low-carbon intelligent construction method for carbon fiber reinforced concrete; Figure 2 This is a schematic diagram of the convergence curve; Figure 3 This is a schematic diagram of the load-displacement curve; Figure 4 This is a diagram illustrating the overall effect comparison. Detailed Implementation

[0018] The features and exemplary embodiments of various aspects of this application will be described in detail below. To make the objectives, technical solutions, and advantages of this application clearer, the application will be further described in detail below with reference to the accompanying drawings and specific embodiments. It should be understood that the specific embodiments described herein are only intended to explain this application and not to limit it. For those skilled in the art, this application can be implemented without some of these specific details. The following description of the embodiments is merely to provide a better understanding of this application by illustrating examples.

[0019] It should be noted that, in this document, relational terms such as "first" and "second" are used merely to distinguish one entity or operation from another, and do not necessarily require or imply any such actual relationship or order between these entities or operations. Furthermore, the terms "comprising," "including," or any other variations thereof are intended to cover non-exclusive inclusion, such that a process, method, article, or apparatus that comprises a list of elements includes not only those elements but also other elements not expressly listed, or elements inherent to such a process, method, article, or apparatus. Without further limitations, an element defined by the phrase "comprising..." does not exclude the presence of additional identical elements in the process, method, article, or apparatus that includes said element.

[0020] Example 1: In Embodiment 1 of the present invention, as Figure 1 As shown, a method for low-carbon intelligent construction of carbon fiber reinforced concrete includes the following steps: S1. Obtain the structural parameters, environmental data, and alternative construction technology library of the concrete structure to be reinforced. Organize the model input features associated with the construction options into nested hierarchical feature groups according to the work breakdown structure. Construct an objective function with the goal of minimizing carbon emissions. The objective function consists of a carbon emission loss term with unified dimensions after normalization and a structured sparse regularization term. The carbon emission loss term is the sum of the characteristic coefficients of the construction options, the product of the unit carbon emission and the corresponding engineering quantity.

[0021] The system reads the structural parameters, environmental data, and alternative construction techniques of the concrete structure to be reinforced. A directed acyclic graph (DAG) is constructed to implement the work decomposition structure. The features of each construction option are encoded as nodes in the DAG, and these nodes are categorized into nested hierarchical feature groups based on their hierarchical relationships: main processes, sub-processes, and specific construction methods. Carbon emission-related data are normalized to unify dimensions. The feature coefficient vector is initialized, and the element-wise product (Hadamard product) of the corresponding quantity vector and the unit carbon emission vector is calculated. The dot product of this product vector and the feature coefficient vector is then calculated as the carbon emission loss term. The normalized carbon emission loss term is only used for dimensionless comparisons in the optimization solution; when outputting actual carbon emission indicators, the actual carbon emissions should be recalculated based on the original quantities and original unit carbon emission factors of the selected construction options.

[0022] As an optional implementation, the acquisition of structural parameters, environmental data, and a library of alternative construction techniques for the concrete structure to be reinforced includes: Analyze the construction drawings, extract the cross-sectional dimensions, reinforcement ratio and material strength parameters of the concrete components, and construct the structural geometry and material property matrix; A network of temperature and humidity sensors deployed at the construction site collects ambient temperature and humidity data in real time. Send a retrieval request to the cloud-based process database to obtain data on material types, construction machinery, and construction methods suitable for carbon fiber fabric reinforcement, and parse and construct a tree-like library of alternative construction processes.

[0023] During the analysis of construction drawings, key parameters are extracted through an optical character recognition (OCR) network model and a BIM model data interface. The OCR network model comprises a deep network structure consisting of convolutional neural networks and recurrent neural networks. Specifically, its structure includes a residual feature extraction layer, a bidirectional long short-term memory layer, and a connection-time classification layer. The input to this OCR network model is the pixel matrix feature tensor of the scanned construction drawing, where the pixel matrix feature tensor is set as... Where H is the image height, W is the image width, and C is the number of color channels. This input is processed by a residual feature extraction layer to output a local visual feature map, then by a bidirectional long short-term memory layer to detect sequence context dependencies. A sequential classification layer calculates the sequence alignment probability and outputs a text sequence of key parameters from the construction drawings. From this, cross-sectional dimension parameters are extracted, such as beam width b (200-500mm), beam height h (400-1000mm), and reinforcement ratio. Such as 0.5%~2.5%, and material strength parameters, such as the standard value of axial compressive strength of concrete. The tensile strength is 20~50MPa, for example, 20.1MPa for C30 grade steel; The pressure ranges from 300 to 500 MPa, for example, 360 MPa for HRB400. The above parameters are used to construct a structural geometry and material property matrix of dimension N×M according to the spatial coordinate mapping relationship, where N is the total number of components, for example, N=150, and M is the dimension of the extracted attribute features, for example, M=12.

[0024] For environmental data acquisition, temperature sensors with an accuracy of ±0.3 degrees Celsius and humidity sensors with a relative humidity of ±2% are deployed in a grid pattern along the construction area, transmitting data to the control terminal in real time at 10-minute sampling intervals. During the process data retrieval phase, a search request with structural type and operating condition tags is sent to the cloud-based process database via an application programming interface. The cloud returns alternative construction data in a standard format, including material types such as 200g / m³. 2 Or 300g / m 2The process involves unidirectional carbon fiber cloth and epoxy resin impregnation adhesive. Construction machinery includes equipment such as grinders and vacuum pumps. The construction process consists of seven standardized steps, including surface treatment, primer application, and carbon fiber cloth bonding. After receiving the data, a tree-structured alternative construction process library with a depth of 3 to 4 layers is constructed according to the rule of "process type - procedure - material or machinery option". Each level node is accompanied by carbon emission factors and construction cost unit price attributes, ensuring that the bottom leaf nodes can exhaustively cover all feasible options under the current engineering conditions. The environmental temperature and humidity data are used to filter out process options that do not meet the environmental requirements for carbon fiber cloth adhesive construction, or to adjust the construction efficiency and carbon emission estimation parameters of the corresponding construction process.

[0025] As an optional implementation, the method of organizing the model input features associated with construction options into nested hierarchical feature groups according to the work breakdown structure includes: Using the construction flow section as the parent node, a top-level index for the feature group is established, and the overall process requirements of each flow section are used as the top-level feature object. Extract the names of materials, equipment and construction methods corresponding to each construction process, as well as the corresponding mechanical and geometric parameters, from the alternative construction process library to generate a child-level feature matrix, and map the matrix to the corresponding parent node as the bottom-level leaf node. Establish a one-way mapping relationship chain between parent nodes and bottom leaf nodes, and construct a multi-branch nested hierarchical feature data structure.

[0026] The construction of the work breakdown structure begins with dividing the entire reinforcement project into spatially continuous and logically independent construction flow segments. Each flow segment serves as a parent node, assigned a globally unique identity index, and bound to the overall process requirements of that flow segment, such as specifying that the total area of ​​carbon fiber cloth pasted must be greater than 50 square meters and the construction period must be limited to 15 days. The tree structure of the alternative construction process library is traversed, and for each specific process, the corresponding options are extracted to construct a child-level feature matrix X. This is a real number matrix, where k represents the number of specific options, such as k = 5 different specifications of carbon fiber cloth, and d represents the mechanical and geometric parameters associated with that option, such as the standard value of tensile strength. ≥3400MPa, elastic modulus ≥230000MPa, single layer thickness The thickness is 0.111~0.167mm. After generating the child-level feature matrix, the feature matrix is ​​used as the bottom-level leaf node through a pointer mechanism or hash table and mapped to the parent node of the corresponding construction flow section.

[0027] This process establishes an irreversible unidirectional mapping chain, ensuring that top-level process requirements can be accurately issued from top to bottom and decomposed into a set of representable parameters. The resulting multi-branch nested hierarchical feature data structure presents a tree-like topological form. For example, the root node is the overall project, pointing to the child node of the first-level main beam flow section, then to the process node of carbon fiber cloth pasting, and finally to the leaf node of the 300g / m² first-level carbon fiber cloth option. This hierarchical organization ensures that in the subsequent construction of regularization terms, feature options within the same group exhibit group sparsity during iterative updates because they belong to the same parent node. Mutually exclusive options are further ensured through mutual exclusion constraints and post-discretization rules to retain at most one option within the same mutually exclusive group.

[0028] S2, for the construction options of the bottom feature group, the structural safety reserve coefficient is calculated using a finite element model, and the carbon emission weight is calculated in combination with the carbon emission factor. The carbon emission weight is proportional to the unit carbon emission and has a monotonically increasing relationship with the excess redundancy degree after the structural safety reserve coefficient exceeds the safety threshold. The weight of construction options that exceed the safety threshold and whose difference exceeds a preset range is amplified, while the weight of construction options that are close to the safety threshold and not lower than the safety threshold is reduced. The penalty value of each level feature group is calculated according to the feature synergy relationship and the feature mutual exclusion relationship. The penalty value of the mutual exclusion group is the first norm of the feature coefficient vector, and the penalty value of the synergy group is the second norm of the feature coefficient vector. The weight of the level feature group is obtained by weighted summation of the carbon emission weights corresponding to the construction options of the lower bottom feature group, thereby completing the construction of the structured sparse regularization term.

[0029] For construction options in the bottom-level feature group, a finite element model of a carbon fiber reinforced concrete structure is established. Dead and live loads are applied, and external loads are gradually increased to perform nonlinear static analysis to obtain the ultimate bearing capacity. The ratio of the ultimate bearing capacity to the structural requirement bearing capacity is calculated, and the redundancy obtained by subtracting 1 from this ratio is used as the structural safety reserve coefficient. For construction options with a structural safety reserve coefficient lower than a preset safety threshold, they are directly marked as infeasible options that do not meet the lower safety limit and are not included in subsequent weight mapping and optimization solutions; or the feature coefficient of the construction option is set to always be 0 in the optimization model. A weight adjustment mapping model with monotonically increasing excess redundancy is constructed, and the carbon emission weight is obtained by multiplying the unit carbon emission by the weight adjustment factor. For hierarchical feature groups with mutually exclusive features, the first norm of the calculated feature coefficient vector is specified as the penalty value for the mutually exclusive group; for hierarchical feature groups with synergistic features, the second norm of the calculated feature coefficient vector is specified as the penalty value for the synergistic group. The directed acyclic graph is traversed to calculate the sum of carbon emission weights corresponding to all construction options in the lower-level feature groups contained in each level feature group. This sum is used as the weight of that level feature group. Each level feature group weight is then multiplied by its corresponding mutual exclusion group penalty value and cooperative group penalty value, and these are accumulated to complete the definition of the structured sparse regularization term. Simultaneously, during the objective function solution, each feature coefficient... Apply Non-negative boundary constraints; for mutually exclusive construction option group G, if the process must select a construction option, then set... If this process can be left unselected, then set ∑. .

[0030] As an optional implementation, the calculation of the structural safety reserve factor through the finite element model includes: An initial finite element model of the concrete member to be reinforced is established based on the structural geometry and material property matrix. The mechanical and geometric parameters corresponding to the carbon fiber cloth reinforcement material in the bottom feature group are input into the initial finite element model. Dead load and live load are applied, and the external load is gradually increased until the model reaches the critical failure state. The ultimate bearing capacity at this time is extracted. Calculate the ratio of the ultimate bearing capacity to the structural requirement bearing capacity, and set the redundancy obtained by subtracting 1 from the ratio as the structural safety reserve factor.

[0031] During the construction of the finite element model, the extracted structural geometry and material property matrices are called. In OpenSeesPy, Concrete02, Concrete04, or equivalent uniaxial constitutive materials are used to simulate the compressive softening and tensile cracking behavior of concrete. Material parameters recognizable by OpenSeesPy, such as concrete compressive strength, peak strain, ultimate strain, tensile strength, and softening slope, are input, instead of directly inputting Abaqus concrete plastic damage model parameters such as expansion angle and eccentricity. Truss elements are used to simulate the reinforcing steel, and a bilinear elastoplastic constitutive model is applied. For carbon fiber fabric, shell elements are used for modeling, and the bonding layer between the carbon fiber fabric and the concrete surface is simulated by establishing interface cohesive elements. The interface normal and tangential stiffness can be set to... = = =100000N / mm 3 The failure displacement is controlled to be around 0.2 mm. The material parameters in the bottom feature group are precisely input, such as the properties of carbon fiber cloth with a thickness of 0.167 mm and a tensile strength of 3400 MPa. During the load application and analysis phase, a dead load of 25 kN / m (including the structure's self-weight) and a live load of 10 kN / m are applied to the model for static initialization. An incremental nonlinear analysis step is initiated, gradually increasing the applied concentrated load or uniformly distributed load at a loading rate of 0.01 mm per step, controlled by displacement.

[0032] By tracking failure criteria such as yielding of the main reinforcement in the tension zone, fiber optic cable breakage, or concrete crushing, the model is determined to have reached the critical failure state, and the ultimate bearing capacity corresponding to the base reaction force at this point is extracted. If the current project's structural requirements specify load-bearing capacity... If the load is 150kN, then calculate the bearing capacity ratio. for and The merchant, that is =1.233. Subtracting 1 from this ratio, the resulting value SR = 0.233 is set as the structural safety reserve factor. This factor represents the additional safety margin that the current construction options can provide beyond meeting basic mechanical requirements; the range of this factor for conventional reinforcement schemes is generally between 0.1 and 0.5.

[0033] As an optional implementation, the step of amplifying the weight of construction options that exceed the safety threshold and whose difference exceeds a preset range, and reducing the weight of construction options that are close to the safety threshold and not lower than the safety threshold, includes: A weighted adjustment mapping model is constructed using an exponential function or a variant of the sigmoid function that monotonically increases with the degree of excess redundancy. When the structural safety reserve coefficient is greater than the set safety threshold and the difference exceeds the preset range, the weight adjustment mapping model outputs an amplification factor greater than 1 to increase the carbon emission weight of the construction option in the structured sparse regularization term, so that the excessively safe and redundant construction option is more compressed in the minimization solution process. When the structural safety reserve coefficient is not lower than the set safety threshold and the difference between it and the safety threshold is within a preset range, the weight adjustment mapping model outputs a reduction factor of less than 1 to reduce the carbon emission weight of the construction option in the structured sparse regularization term, so that the construction option that meets the safety requirements and has appropriate safety redundancy is more likely to be retained in the minimization solution process.

[0034] The construction of the weighted adjustment mapping model relies on a predefined nonlinear variant function. Preferably, the structural safety reserve coefficient is SR, and the safety threshold is... The allowable difference is Δ, which represents excess redundancy. If d < 0, the construction option is below the safety threshold and is marked as infeasible; if 0 ≤ d ≤ Δ, then reduction mapping is used. ,in This ensures that construction options that are close to the safety threshold and meet safety requirements receive a reduction factor of less than 1; if d > Δ, then a monotonically increasing amplification mapping is used. ,in Furthermore, α > 0, allowing for an amplification factor greater than 1 for overly safe and redundant construction options. The final carbon emission weight is... ,in In the optimization solution, the normalized unit carbon emission is used; when outputting the actual carbon emission index, the original unit carbon emission factor, original engineering quantity, and discretized selection variables from the construction options are used to recalculate the actual carbon emission. A safety threshold is set for the target structure. The value is 0.15, the preset allowable difference Δ is 0.05, and the lower limit is reduced. The maximum magnification is 0.35. The nonlinear adjustment slope coefficient α is set to 15. When the structural safety reserve factor SR calculated by finite element analysis is greater than the safety threshold and exceeds the allowable difference, it indicates that the currently selected materials such as carbon cloth lead to over-construction of the structure, accompanied by unnecessary carbon emission waste. For example, SR and The difference is 0.25, exceeding the preset range of 0.05. The mapping model enters the amplification range, outputting an amplification factor greater than 1 and close to the upper limit, for example, φ≈1.76, which can be approximated as 1.8. This amplification factor, multiplied by the corresponding unit carbon emission, increases the carbon emission weight of this highly redundant, high-carbon-emission option in the structured sparse regularization term, making it more susceptible to compression in subsequent L1-norm or L2-norm regularization penalties, and more easily compressed to zero and eliminated.

[0035] When the calculated structural safety reserve factor SR is in the critical fit zone, it is related to the safety threshold. The difference is 0.03, falling precisely within the preset buffer range of 0 to 0.05. This indicates that the construction option has high material utilization efficiency while meeting safety requirements. After the mapping model determines that the condition is met, it enters the reduction interval and outputs a value less than 1 and not less than... A reduction factor, for example, φ≈0.74, is used. This factor, applied to the carbon emission weight of the corresponding option, reduces the penalty intensity of that option in the structured sparse regularization term, making it easier to retain construction options that meet safety requirements and have appropriate safety redundancy during the minimization solution process. Through this factor regulation mechanism ranging from 0.1 to 3.0, the structural safety margin at the engineering level is converted into a descent gradient guiding force in the mathematical model, which helps to generate construction schemes that are closer to the safety threshold and have lower carbon emissions.

[0036] S3, Solve the objective function to obtain the sparse characteristic coefficient solution, and generate the low-carbon construction scheme based on the construction options corresponding to the non-zero characteristic coefficients.

[0037] The objective function is transformed into a convex optimization problem, and the alternating direction multiplier method is used to solve it. During the iterative update process, a soft thresholding shrinkage operator is used to handle the penalty terms of the mutually exclusive groups formed by the first norm, and a block soft thresholding operator is used to handle the penalty terms of the cooperative groups formed by the second norm. When the objective function value converges, the iteration stops and the sparse feature coefficient solution is output. Non-zero feature coefficients with values ​​greater than a preset minimum value are selected. Based on the index of the non-zero feature coefficients in the feature mapping table, the corresponding specific construction technology, material usage, and machinery type are matched. The matched construction options are then concatenated according to the sequential logic of the work breakdown structure to generate a low-carbon construction plan.

[0038] As an optional implementation, the step of solving the objective function to obtain sparse characteristic coefficient solutions, and generating the low-carbon construction scheme based on the construction options corresponding to the non-zero characteristic coefficients, includes: The objective function is solved iteratively using the proximal gradient descent algorithm, and the feature coefficients are updated using a soft threshold operator in each iteration. When the objective function converges or reaches the maximum number of iterations, the iteration stops and the continuous solution of the characteristic coefficients is obtained; Set a truncation threshold, set the feature coefficients whose absolute value is less than the truncation threshold to zero, and retain the non-zero feature coefficients whose absolute value is greater than or equal to the truncation threshold; The non-zero characteristic coefficients are matched with the construction options in the alternative construction process library to output the low-carbon construction plan that includes the selected materials, equipment and construction methods.

[0039] For a composite objective function consisting of a smooth carbon emission loss term and a non-smooth structured sparse regularization term, a proximal gradient descent algorithm is initiated to perform fully automated optimization. In the iterative loop, the carbon emission loss term is calculated with respect to the current feature coefficient vector. The partial derivative gradient g is obtained by using a fixed step size. Perform the gradient descent forward update operation, calculated as follows: The algorithm utilizes proximal operators to process non-smooth regularization terms during backward projection. Considering that the regularization term includes intra-group cooperative L2 norm and inter-group mutually exclusive L1 norm, the algorithm applies a block coordinate soft thresholding operator for shrinkage. For each feature dimension within mutually exclusive groups, if the absolute value of the coefficient is less than the shrinkage threshold... For example, setting If the product threshold is 0.005, the coefficient value is shifted and truncated to 0; otherwise, the corresponding magnitude is contracted, applying a group soft threshold contraction operation based on the overall L2 norm to the cooperative group. This iterative update loop continues until the change in the objective function value between two consecutive iterations is reached. The iteration may terminate when the maximum number of iterations is reached, outputting the current stable and convergent continuous solution with characteristic coefficients. Since the carbon emission loss term in the objective function has been normalized to its minimum and maximum values, the objective function value in the convergence curve is dimensionless and not directly equivalent to kgCO2e / m³. 2 The actual carbon emissions. For the true carbon emissions per unit area of ​​the output scheme, according to... Calculate the actual total carbon emissions, of which Select variables for the discretized construction options. To correspond to the amount of work, The original unit carbon emission factor for this construction option; then according to This is converted to carbon emissions per unit area, where A represents the reinforcement construction area. For example... Figure 2 As shown, the normalized objective function value of the complete experimental group decreased faster and tended to stabilize after about 4000 iterations; the ablation control group converged after about 8000 iterations. Figure 2 The objective function value is a dimensionless optimization index and is not directly equivalent to the actual carbon emissions. After mapping the convergence result to discrete construction schemes and recalculating according to the original carbon emission factors, the carbon emissions per unit area of ​​the complete experimental group are 12.4 kgCO2e / m². 2 It was lower than the 15.6 kg CO2e / m³ of the ablation control group. 2 This indicates that the safety-carbon emission coupling weight adjustment mechanism can improve optimization efficiency and carbon reduction effect.

[0040] Based on the numerical distribution of continuous solutions for all characteristic coefficients, a cutoff threshold is set. Typically, the values ​​are taken as the first 85th percentile after the absolute values ​​of the coefficients are sorted in descending order, or fixed as a tiny quantity, such as... =0.05. Set all feature coefficients whose absolute value is less than τ to zero to ensure high sparsity of the scheme. Retain non-zero feature coefficients whose absolute value is greater than or equal to τ. Then, perform discretization projection according to mutual exclusion group constraints, necessary process selection constraints, and safety lower limit constraints. Activate the retained terms that satisfy the constraints to a Boolean value of 1, and set the remaining conflicting terms to 0. Follow the index path bound to the non-zero coefficients in the nested hierarchical feature data structure for source matching, and retrieve entity representation information from the cloud-based alternative construction process library. Generate and output a list of highly practical low-carbon construction schemes. For example, the output result shows that the base surface treatment of the S01 section of the flow line is performed using a grinding machine, and a 0.2 kg / m² coating is applied. 2 Apply a single layer of base adhesive, 0.111mm thick, with a g / m² thickness. 2 Primary carbon fiber cloth.

[0041] The experiment used a carbon fiber reinforcement project for the main beam of a first-floor building as the test object. Structural parameters, environmental data, and process parameters collected on-site were input, and a library of alternative construction processes containing different materials, machinery, and construction methods was constructed. The structural load-bearing capacity requirement for this project was set at 150kN, and the target structural safety threshold was set at 0.15. Three sets of algorithms were configured for comparative experiments: the baseline control group used conventional static carbon emission evaluation combined with exhaustive search for scheme selection; the ablation control group used nested hierarchical feature data structures and near-end gradient descent algorithm for optimization, but did not introduce a weight adjustment mapping model; the complete experimental group added finite element safety reserve feedback and weight adjustment mechanisms to complete the optimization of the low-carbon construction scheme.

[0042] The ultimate structural bearing capacity of the baseline control group's generated scheme is 195 kN, the structural safety reserve factor is 0.30, and the carbon emission per unit area during construction is 18.5 kg CO2e / m². 2 The calculation took 55 minutes. The ultimate bearing capacity of the ablation control group was 183 kN, the structural safety margin was 0.22, and the carbon emissions per unit area were reduced to 15.6 kg CO2e / m². 2 The calculation time was reduced to 12 minutes. The ultimate bearing capacity of the complete experimental group was 174 kN, the structural safety reserve factor was 0.16, and the carbon emissions per unit area were further reduced to 12.4 kg CO2e / m². 2 The calculation took 15 minutes. For example... Figure 3 As shown, the load-displacement curve of the baseline control group has the highest peak value, corresponding to a larger bearing capacity redundancy; the curve peak value of the complete experimental group is lower, but still meets the safety threshold requirements, and its bearing capacity redundancy is closer to the preset safety threshold boundary.

[0043] Compared to the baseline control group, the ablation control group showed improvements in both solution efficiency and basic carbon reduction, primarily due to the optimization capabilities of the hierarchical group sparse regularization term and the proximal gradient algorithm. The complete experimental group further introduced a weight adjustment mapping model, amplifying the penalty weights of over-configured construction options through finite element feedback and reducing the penalty weights of options that critically meet safety requirements, guiding the model to prioritize construction options that meet safety thresholds and have lower carbon emissions. As a result, the load-bearing capacity redundancy of the complete scheme is closer to the safety threshold boundary, reducing ineffective material consumption and achieving a further 20.5% reduction in carbon emissions compared to the ablation control group. Figure 4 As shown, the baseline control group had high carbon emissions and safety margin redundancy, while the complete experimental group showed a lower carbon emission level while meeting safety requirements, reflecting the balancing role of the weighting adjustment mechanism between safety margin and carbon reduction target.

[0044] Example 2: Embodiment 2 of the present invention proposes a low-carbon intelligent construction system for carbon fiber reinforced concrete, comprising the following modules: The construction module is used to acquire the structural parameters, environmental data and alternative construction technology library of the concrete structure to be reinforced. According to the work breakdown structure, the model input features associated with the construction options are organized into nested hierarchical feature groups. An objective function with the goal of minimizing carbon emissions is constructed. The objective function consists of a carbon emission loss term with unified dimensions after normalization and a structured sparse regularization term. The carbon emission loss term is the sum of the characteristic coefficient of the construction option, the unit carbon emission, and the corresponding engineering quantity. The calculation module is used to calculate the structural safety reserve coefficient of the construction options of the bottom feature group through the finite element model, and calculate the carbon emission weight in combination with the carbon emission factor. The carbon emission weight is proportional to the unit carbon emission and has a monotonically increasing relationship with the excess redundancy degree after the structural safety reserve coefficient exceeds the safety threshold. The weight of construction options that exceed the safety threshold and the difference exceeds the preset range is amplified, while the weight of construction options that are close to the safety threshold and not lower than the safety threshold is reduced. The penalty value of each level feature group is calculated according to the feature synergy relationship and the feature mutual exclusion relationship. The penalty value of the mutual exclusion group is the first norm of the feature coefficient vector, and the penalty value of the synergy group is the second norm of the feature coefficient vector. The weight of the level feature group is obtained by weighted summation of the carbon emission weights corresponding to the construction options of the lower bottom feature group. In this way, the construction of the structured sparse regularization term is completed. The generation module is used to solve the objective function to obtain the sparse characteristic coefficient solution, and generate the low-carbon construction plan based on the construction options corresponding to the non-zero characteristic coefficients.

[0045] It should be clarified that this application is not limited to the specific configurations and processes described above and shown in the figures. For the sake of brevity, detailed descriptions of known methods are omitted here. In the above embodiments, several specific steps are described and shown as examples. However, the method process of this application is not limited to the specific steps described and shown. Those skilled in the art can make various changes, modifications, and additions, or change the order of steps, after understanding the spirit of this application.

[0046] The above description is merely a specific implementation of this application. Those skilled in the art will clearly understand that, for the sake of convenience and brevity, the specific working processes of the systems, modules, and units described above can be referred to the corresponding processes in the foregoing method embodiments, and will not be repeated here. It should be understood that the protection scope of this application 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 this application, and these modifications or substitutions should all be covered within the protection scope of this application.

Claims

1. A method for low-carbon intelligent construction of carbon fiber reinforced concrete, characterized in that, Includes the following steps: Obtain the structural parameters, environmental data, and alternative construction technology library of the concrete structure to be reinforced. Organize the model input features associated with the construction options into nested hierarchical feature groups according to the work breakdown structure. Construct an objective function with the goal of minimizing carbon emissions. The objective function consists of a carbon emission loss term with unified dimensions after normalization and a structured sparse regularization term. The carbon emission loss term is the sum of the feature coefficients of the construction options, the unit carbon emission, and the corresponding engineering quantity. For construction options in the bottom feature group, the structural safety reserve coefficient is calculated using a finite element model, and the carbon emission weight is calculated in combination with the carbon emission factor. The carbon emission weight is proportional to the unit carbon emission and has a monotonically increasing relationship with the excess redundancy degree after the structural safety reserve coefficient exceeds the safety threshold. This amplifies the weight of construction options that exceed the safety threshold and whose difference exceeds a preset range, and reduces the weight of construction options that are close to the safety threshold and not lower than the safety threshold. The penalty value of each level feature group is calculated based on the feature synergy relationship and the feature mutual exclusion relationship. The penalty value of the mutual exclusion group is the first norm of the feature coefficient vector, and the penalty value of the synergy group is the second norm of the feature coefficient vector. The weight of the level feature group is obtained by weighted summation of the carbon emission weights corresponding to the construction options in the lower feature group. This completes the construction of the structured sparse regularization term. Solve the objective function to obtain the sparse characteristic coefficient solution, and generate the low-carbon construction scheme based on the construction options corresponding to the non-zero characteristic coefficients.

2. The method according to claim 1, characterized in that, The acquisition of structural parameters, environmental data, and a library of alternative construction techniques for the concrete structure to be reinforced includes: Analyze the construction drawings, extract the cross-sectional dimensions, reinforcement ratio and material strength parameters of the concrete components, and construct the structural geometry and material property matrix; A network of temperature and humidity sensors deployed at the construction site collects ambient temperature and humidity data in real time. Send a retrieval request to the cloud-based process database to obtain data on material types, construction machinery, and construction methods suitable for carbon fiber fabric reinforcement, and parse and construct a tree-like library of alternative construction processes.

3. The method according to claim 2, characterized in that, The model input features associated with construction options are organized into nested hierarchical feature groups according to the work breakdown structure, including: Using the construction flow section as the parent node, a top-level index for the feature group is established, and the overall process requirements of each flow section are used as the top-level feature object. Extract the names of materials, equipment and construction methods corresponding to each construction process, as well as the corresponding mechanical and geometric parameters, from the alternative construction process library to generate a child-level feature matrix, and map the matrix to the corresponding parent node as the bottom-level leaf node. Establish a one-way mapping relationship chain between parent nodes and bottom leaf nodes, and construct a multi-branch nested hierarchical feature data structure.

4. The method according to claim 1, characterized in that, The calculation of the structural safety reserve factor using the finite element model includes: An initial finite element model of the concrete member to be reinforced is established based on the structural geometry and material property matrix. The mechanical and geometric parameters corresponding to the carbon fiber cloth reinforcement material in the bottom feature group are input into the initial finite element model. Dead load and live load are applied, and the external load is gradually increased until the model reaches the critical failure state. The ultimate bearing capacity at this time is extracted. Calculate the ratio of the ultimate bearing capacity to the structural requirement bearing capacity, and set the redundancy obtained by subtracting 1 from the ratio as the structural safety reserve factor.

5. The method according to claim 1 or 2, characterized in that, The step of increasing the weight of construction options that exceed the safety threshold and whose difference exceeds a preset range, and decreasing the weight of construction options that are close to the safety threshold and not lower than the safety threshold, includes: A weighted adjustment mapping model is constructed using an exponential function or a variant of the sigmoid function that monotonically increases with the degree of excess redundancy. When the structural safety reserve coefficient is greater than the set safety threshold and the difference exceeds the preset range, the weight adjustment mapping model outputs an amplification factor greater than 1 to increase the carbon emission weight of the construction option in the structured sparse regularization term, so that the excessively safe and redundant construction option is more compressed in the minimization solution process. When the structural safety reserve coefficient is not lower than the set safety threshold and the difference between it and the safety threshold is within a preset range, the weight adjustment mapping model outputs a reduction factor of less than 1 to reduce the carbon emission weight of the construction option in the structured sparse regularization term, so that the construction option that meets the safety requirements and has appropriate safety redundancy is more likely to be retained in the minimization solution process.

6. The method according to claim 1, characterized in that, The process of solving the objective function to obtain sparse characteristic coefficient solutions, and generating the low-carbon construction scheme based on the construction options corresponding to the non-zero characteristic coefficients, includes: The objective function is solved iteratively using the proximal gradient descent algorithm, and the feature coefficients are updated using a soft threshold operator in each iteration. When the objective function converges or reaches the maximum number of iterations, the iteration stops and the continuous solution of the characteristic coefficients is obtained; Set a truncation threshold, set the feature coefficients whose absolute value is less than the truncation threshold to zero, and retain the non-zero feature coefficients whose absolute value is greater than or equal to the truncation threshold; The non-zero characteristic coefficients are matched with the construction options in the alternative construction process library to output the low-carbon construction plan that includes the selected materials, equipment and construction methods.

7. A system for low-carbon intelligent construction of carbon fiber reinforced concrete, characterized in that, Includes the following modules: The construction module is used to acquire the structural parameters, environmental data and alternative construction technology library of the concrete structure to be reinforced. According to the work breakdown structure, the model input features associated with the construction options are organized into nested hierarchical feature groups. An objective function with the goal of minimizing carbon emissions is constructed. The objective function consists of a carbon emission loss term with unified dimensions after normalization and a structured sparse regularization term. The carbon emission loss term is the sum of the characteristic coefficient of the construction option, the unit carbon emission, and the corresponding engineering quantity. The calculation module is used to calculate the structural safety reserve coefficient of the construction options of the bottom feature group through the finite element model, and calculate the carbon emission weight in combination with the carbon emission factor. The carbon emission weight is proportional to the unit carbon emission and has a monotonically increasing relationship with the excess redundancy degree after the structural safety reserve coefficient exceeds the safety threshold. The weight of construction options that exceed the safety threshold and the difference exceeds the preset range is amplified, while the weight of construction options that are close to the safety threshold and not lower than the safety threshold is reduced. The penalty value of each level feature group is calculated according to the feature synergy relationship and the feature mutual exclusion relationship. The penalty value of the mutual exclusion group is the first norm of the feature coefficient vector, and the penalty value of the synergy group is the second norm of the feature coefficient vector. The weight of the level feature group is obtained by weighted summation of the carbon emission weights corresponding to the construction options of the lower bottom feature group. In this way, the construction of the structured sparse regularization term is completed. The generation module is used to solve the objective function to obtain the sparse characteristic coefficient solution, and generate the low-carbon construction plan based on the construction options corresponding to the non-zero characteristic coefficients.

8. The system according to claim 7, characterized in that, The acquisition of structural parameters, environmental data, and a library of alternative construction techniques for the concrete structure to be reinforced includes: Analyze the construction drawings, extract the cross-sectional dimensions, reinforcement ratio and material strength parameters of the concrete components, and construct the structural geometry and material property matrix; A network of temperature and humidity sensors deployed at the construction site collects ambient temperature and humidity data in real time. Send a retrieval request to the cloud-based process database to obtain data on material types, construction machinery, and construction methods suitable for carbon fiber fabric reinforcement, and parse and construct a tree-like library of alternative construction processes.

9. The system according to claim 7, characterized in that, The model input features associated with construction options are organized into nested hierarchical feature groups according to the work breakdown structure, including: Using the construction flow section as the parent node, a top-level index for the feature group is established, and the overall process requirements of each flow section are used as the top-level feature object. Extract the names of materials, equipment and construction methods corresponding to each construction process, as well as the corresponding mechanical and geometric parameters, from the alternative construction process library to generate a child-level feature matrix, and map the matrix to the corresponding parent node as the bottom-level leaf node. Establish a one-way mapping relationship chain between parent nodes and bottom leaf nodes, and construct a multi-branch nested hierarchical feature data structure.

10. The system according to claim 7, characterized in that, The calculation of the structural safety reserve factor using the finite element model includes: An initial finite element model of the concrete member to be reinforced is established based on the structural geometry and material property matrix. The mechanical and geometric parameters corresponding to the carbon fiber cloth reinforcement material in the bottom feature group are input into the initial finite element model. Dead load and live load are applied, and the external load is gradually increased until the model reaches the critical failure state. The ultimate bearing capacity at this time is extracted. Calculate the ratio of the ultimate bearing capacity to the structural requirement bearing capacity, and set the redundancy obtained by subtracting 1 from the ratio as the structural safety reserve factor.

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