Method and system for quickly estimating using amount of structural material

Abstract

The invention relates to a method and a system for quickly estimating the consumption of structural materials, and relates to the technical field of building construction. The quick estimation method for the structural material consumption comprises the following steps: selecting a building structure model according to project requirements, and calling and filling corresponding key control parameters in combination with a structure type; generating a plurality of building material combination tables according to a construction estimation rule base in combination with the key control parameters; all the building material combination tables are screened and optimized according to business constraint conditions, and the optimal material use amount is output; the component section and the material consumption of the building structure are quickly evaluated according to the construction estimation rule base in the initial stage of the project, so that various construction cost consultation requirements are quickly responded, and the method adapts to the fast market rhythm; under the condition that overall modeling and finite element calculation are conducted without consuming a large amount of time, the scale of the structural component is preliminarily estimated, multiple times of repeated trial calculation are avoided, and therefore the design efficiency is improved.

Description

Technical Field

[0001] This application relates to the field of building construction technology, and in particular to a method and system for rapidly estimating the amount of structural materials used. Background Technology

[0002] In the architectural design process, there needs to be a clear plan and budget for the amount of structural materials (such as carbon fiber cloth, steel bars, concrete, etc.) used, so as to be able to respond quickly to the client's cost consultation needs in the early stages of design.

[0003] However, in reality, the analysis and estimation of the amount of such structural materials used often rely on scattered and individualized engineering experience, which is often reflected in calculation formulas and empirical formulas scattered in different national standards or passed down by word of mouth among engineers.

[0004] Existing patents disclose a method for calculating the cost of reinforced concrete beams based on a target optimization algorithm, comprising the following steps: A) Inputting the parameters of the reinforced concrete beam to obtain initial beam parameters; B) Processing the initial beam parameters to generate a beam information database; C) Using a calculation module to calculate the information in the beam information database, outputting all solvable beam information database data and the BImin data of the beam with the most economical cost. The above invention enables parameterized calculation of the economic cost of reinforced concrete beams and efficient comparison and selection of beam design schemes.

[0005] The existing technical solutions mentioned above have the following drawbacks: 1. Existing discrete structural estimation rules that rely on personal experience have led to long-standing problems in the industry, such as strong reliance on experience, low efficiency, and poor consistency of results. Summary of the Invention

[0006] To address the shortcomings of existing technologies, the purpose of this application is to provide a method and system for rapid estimation of structural material usage. By constructing an estimation rule library, it helps engineers quickly assess the component cross-sections and material usage of building structures in the early stages of a project, enabling rapid response to various cost consulting needs and adapting to the fast pace of the market. It also helps engineers to preliminarily estimate the dimensions of structural components without spending a lot of time on overall modeling and finite element calculations, avoiding repeated trial calculations and thus improving design efficiency.

[0007] This was achieved using the following technical solutions: Firstly, this application provides a method for rapidly estimating the amount of structural materials used, including: Select the building structure model according to the project requirements, and call and fill in the corresponding key control parameters based on the structure type. Based on the constructed estimation rule base and the aforementioned key control parameters, several building material combination tables are generated; Based on business constraints, the entire list of building material combinations is filtered and optimized to output the optimal material usage.

[0008] By adopting the above technical solution, through the rule engine and parametric design method, key control parameters are automatically called according to the selected building structure model. Then, based on the construction rule base, multiple material combination tables are generated. Multi-objective optimization algorithms (such as genetic algorithms or linear programming) are used to screen and globally optimize under business constraints, and finally output the optimal material usage plan. This realizes full-process automation and data-driven approach from design to material decision-making, which significantly improves the scientific nature of the plan, decision-making efficiency and cost control accuracy.

[0009] This application further specifies that the steps of selecting a preset building structure model according to project requirements, and combining the structural type call and filling in the corresponding key control parameters include: Several different architectural structure models were obtained by performing 3D reconstruction on images of architectural structures from different angles. Based on project requirements, all the aforementioned building structure models were screened to obtain several target structure models; Based on the structure type, each target structure model is analyzed to obtain several structure parameter fields; Based on the historical usage frequency of parameters and the parameter calculation formula, the structural parameter fields are weighted and sorted in descending order to obtain the target parameter fields. The target parameter field is called and filled in in conjunction with the target structure parameters to obtain the key control parameters.

[0010] By adopting the above technical solution, through computer vision and multi-view... Figure 3 The 3D reconstruction algorithm generates building structure models from multi-angle images and filters them according to project requirements. Then, it uses structural analysis technology to extract parameter fields and calculates field weights based on historical frequency and weighted scoring formulas to determine key parameters, ultimately realizing the automatic calling and filling of model parameters. It achieves fully automatic, data-driven intelligent analysis from original images to key design parameters, significantly improving the efficiency, accuracy and knowledge reuse level of structural modeling.

[0011] This application further specifies that the steps for calculating the weights and sorting the structural parameter fields in descending order based on the historical usage frequency of the parameters and the parameter calculation formula to obtain the target parameter fields include: The historical data dataset is parsed and classified according to the structure name to obtain several structure parameter operation blocks; Based on the parameter log, the structural parameter fields of the structural parameter operation block are extracted and normalized to obtain structural parameter symbols, and the call timestamp is recorded. The frequency of all the structural parameter symbols is counted, the absolute frequency of use of the parameters is recorded, and the relative weight of use is calculated. The parameter calculation formula is measured based on business indicators and structural functions, and the parameter influence and importance are calculated. The parameter's overall weight is obtained by weighting the parameter's influence, importance, and relative usage weight based on the call timestamp. All the parameters are sorted in descending order by their combined weights and then filtered based on their absolute usage frequency to obtain the target parameter field.

[0012] By adopting the above technical solutions, historical parameter records are symbolically parsed and frequency statistically analyzed based on natural language processing technology. Combined with time decay factors and multi-index weighted fusion algorithms (such as entropy weight method or AHP hierarchical analysis method), the comprehensive weight is calculated by quantifying the business impact, functional importance and usage frequency of parameters, and finally the intelligent sorting and filtering of parameter fields are realized. A data-driven parameter evaluation system is established, which can adaptively identify key design parameters and significantly improve the accuracy of structural design process and the efficiency of knowledge reuse.

[0013] This application further specifies that the specific steps for generating several building material combination tables based on the constructed estimation rule base and the key control parameters include: The construction estimation rule base is parsed to obtain structural technical specifications, material compatibility relationships, and construction hierarchy logic; The target structure is simulated and deduced based on key control parameters to obtain a three-dimensional structural model. The three-dimensional structural model is deconstructed according to the constructed hierarchical logic to obtain several structural hierarchical components; Based on the structural technical specifications and the building environment, the structural hierarchical components are assigned functions to obtain component functional constraints. Based on the functional constraints of the components, the materials for manufacturing the components are screened, and a preliminary determination is made in combination with the material compatibility relationship to obtain several candidate material combinations for the components; Based on structural performance indicators, the candidate material combinations for the components are aggregated, quantified, and constrained for filtering, resulting in several building material combination tables.

[0014] By adopting the above technical solution, based on parametric generation technology and the constraint satisfaction problem (CSP) solution framework, a three-dimensional model is constructed by parsing the rule base and deconstructing it into hierarchical components. Then, by using rule reasoning and multi-objective optimization algorithms, the functional constraints of the components are transformed into material compatibility conditions and combined and screened to finally generate a multi-material scheme that meets the structural performance index. This achieves fully automatic intelligent conversion from design parameters to material configuration, significantly improving the efficiency of scheme generation and the accuracy of multi-disciplinary collaboration.

[0015] This application further specifies that the construction of the estimation rule base includes: Based on the data type, the structural design standard documents are identified and extracted to obtain the structural design content, and structural parameter symbols are marked. The structural design content is clustered and regularized according to the structural type to obtain design specifications and rules for various types of structures. Based on the rule transformation logic, the senior design judgment experience of various structures is quantified and transformed, and the structural parameter symbols are replaced to obtain senior experience rules; The design specification rules and the senior experience rules are correlated and filtered to construct design correlation judgment rules; Based on the target optimization rules, the design association judgment rules are iteratively calculated and updated to obtain the construction estimation rule library.

[0016] By adopting the above technical solutions, parameterized rules are extracted from structural design standards based on natural language processing and clustering algorithms. Association rule mining technology is used to transform the experience judgments of senior designers into quantifiable experience rules. Through multi-source rule fusion and optimization algorithms, a self-improving structural estimation knowledge base is constructed. This achieves the systematic integration and dynamic optimization of design specifications and expert experience, significantly improving the intelligence level and scientific decision-making of structural design.

[0017] This application further specifies that the specific steps for filtering and optimizing all the building material combination tables according to business constraints and outputting the optimal material usage include: Based on the time constraints of the business constraints, the supply time of each building material in the complete building material combination table is verified and screened to obtain the timely material combination table. Based on the budget constraints and the allowance range of the business constraints, the total cost of the timely material combination table is determined to obtain the compliant material combination table. Based on the parameter structure formula of the constructed estimation rule base and the component dimensions, the compliant material combination table is cut and calculated to obtain the simulated material usage of several components. Based on the empirical estimation formulas of the constructed estimation rule base, the simulated material usage of each component is corrected, and the optimal material usage of each structure is output.

[0018] By adopting the above technical solution, using constraint satisfaction problem solving and combinatorial optimization algorithms, the initial screening of material combinations is achieved through time constraint screening and budget range verification. Then, the usage of compliant materials is simulated and optimized using a parametric cutting model and an empirical rule-based correction algorithm. Finally, the optimal material usage that satisfies multiple business constraints is output. By transforming the traditional experience-based material estimation into a data-driven, precise decision-making process, the accuracy of procurement and cost control capabilities are significantly improved.

[0019] This application further specifies that the specific steps for performing a trimming calculation on the compliant material combination table based on the parameter structure formula of the constructed estimation rule base and the component dimensions to obtain the simulated material usage of several components include: The compliant material combination table is analyzed to obtain the material name, specifications, and physical properties. Based on the parameter structure formula of the constructed estimation rule base and the component size, the material is simulated and estimated to obtain the preliminary material usage. Based on the material type, specifications, and component dimensions, the materials are comprehensively laid out to generate a material cutting plan; Based on the physical properties of the material and the cutting loss rate, the initial material usage is optimized to obtain the corrected material usage; Based on the material cutting scheme and the application of the minimum usage rule, the modified material usage is planned to obtain the component material cutting amount; Based on the material names, the material cutting quantities and cutting schemes for the components are summarized to generate several simulated material usage quantities for the components.

[0020] By adopting the above technical solution, based on the two-dimensional rectangular layout optimization algorithm and linear programming method, the component size is converted into a material cutting scheme through parameterized structural formula, and the loss rate coefficient and minimum usage rule are introduced to correct the usage, finally generating accurate component-level material simulation usage; global optimization of material cutting scheme and accurate usage prediction are achieved, significantly improving material utilization and effectively reducing construction costs and waste.

[0021] Secondly, this application also provides a rapid estimation system for structural material usage, employing the following technical solution: A system for rapidly estimating the amount of structural materials used includes: The parameter filtering module is used to select the building structure model according to project requirements, and call and fill the corresponding key control parameters based on the structure type. The rule building module is used to identify and extract design specification rules from structural design standard documents, and to quantify and transform the senior design judgment experience of various structures according to the rule transformation logic, generate senior experience rules, and combine them with target optimization rules for comprehensive correlation and filtering to generate a construction estimation rule library; The material combination module is used to generate several building material combination tables based on the construction estimation rule base and the key control parameters. The simulated cutting module is used to simulate the cutting operation of the building material combination table based on the parameter structure formula of the constructed estimation rule base and the component size, so as to obtain the simulated material usage of several components. The usage estimation module is used to filter and optimize the simulated usage of component materials in all the building material combination tables according to business constraints, and output the optimal material usage.

[0022] By adopting the above technical solutions, a complete algorithm chain is constructed, including parameter screening, rule knowledge graph, material combination optimization, simulated cutting, and multi-objective decision-making. Material combinations are generated using rule engines and constraint satisfaction problem-solving techniques. Cutting schemes are calculated using two-dimensional optimization layout algorithms. Finally, multi-objective optimization is performed based on genetic algorithms, realizing intelligent decision-making throughout the entire process from design parameters to optimal material usage, which significantly improves the economy and construction feasibility of architectural design.

[0023] Thirdly, this application also provides an electronic device, comprising: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement any of the methods described in the above scheme.

[0024] Fourthly, this application also provides a storage medium storing at least one instruction, at least one program, code set, or instruction set, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to implement the rapid estimation method for structural material usage as described above.

[0025] In summary, the beneficial technical effects of this application are as follows: By quickly assessing the component cross-sections and material usage of building structures based on the construction estimation rule library at the early stage of a project, we can respond quickly to various cost consulting needs and adapt to the fast pace of the market. By preliminarily estimating the dimensions of structural components without spending a lot of time on overall modeling and finite element calculations, design efficiency can be improved by avoiding repeated trial calculations. Attached Figure Description

[0026] Figure 1 This is a flowchart illustrating the rapid estimation method for structural material usage in this application; Figure 2 This is a flowchart illustrating step S14 in the rapid estimation method for structural material usage in this application. Figure 3 This is a schematic diagram of the construction process for building the estimation rule base in this application; Figure 4 This is a schematic diagram of the structure of the rapid estimation system for structural material usage in this application. Detailed Implementation

[0027] The present application will be further described in detail below with reference to the accompanying drawings.

[0028] Reference Figure 1 This application discloses a method for rapidly estimating the amount of structural materials used, comprising: S1: Select the building structure model according to the project requirements, and call and fill in the corresponding key control parameters based on the structure type; S2: Based on the construction estimation rule base and the key control parameters, generate several building material combination tables; S3: Based on business constraints, filter and optimize all the building material combination tables and output the optimal material usage.

[0029] Step S1 includes: S11: Perform three-dimensional reconstruction on several building structure images from different angles to obtain several different building structure models; S12: Based on project requirements, all the aforementioned building structure models are screened to obtain several target structure models; S13: Analyze each target structural model according to the structural type to obtain several structural parameter fields; S14: Based on the historical usage frequency of the parameters and the parameter calculation formula, the structural parameter fields are weighted and sorted in descending order to obtain the target parameter fields; S15: Call the target parameter field and fill it in with the target structure parameters to obtain the key control parameters.

[0030] Reference Figure 2 Step S14 includes: S141: Parse and classify the historical data dataset according to the structure name to obtain several structure parameter operation blocks; S142: Based on the parameter recording log, extract and normalize the symbols of the structural parameter fields of the structural parameter operation block to obtain structural parameter symbols and record the call timestamp; S143: Perform a frequency count on all the structural parameter symbols, record the absolute frequency of use of the parameters, and calculate the relative usage weight; S144: Measure the parameter calculation formula based on business indicators and structural functions, and calculate the parameter influence and importance; S145: The parameter's influence, importance, and relative usage weight are weighted according to the call timestamp to obtain the parameter's comprehensive weight; S146: Sort all the parameters in descending order by their combined weights and filter them by their absolute usage frequency to obtain the target parameter field.

[0031] Step 2 includes: The construction estimation rule base is parsed to obtain structural technical specifications, material compatibility relationships, and construction hierarchy logic; The target structure is simulated and deduced based on key control parameters to obtain a three-dimensional structural model. The three-dimensional structural model is deconstructed according to the constructed hierarchical logic to obtain several structural hierarchical components; Based on the structural technical specifications and the building environment, the structural hierarchical components are assigned functions to obtain component functional constraints. Based on the functional constraints of the components, the materials for manufacturing the components are screened, and a preliminary determination is made in combination with the material compatibility relationship to obtain several candidate material combinations for the components; Based on structural performance indicators, the candidate material combinations for the components are aggregated, quantified, and constrained for filtering, resulting in several building material combination tables.

[0032] Reference Figure 3 The construction of the estimation rule base includes: A: Identify and extract structural design standard documents based on data type to obtain structural design content and mark structural parameter symbols; B: Cluster and regularize the structural design content according to the structural type to obtain the design specifications and rules for various types of structures; C: Based on the rule transformation logic, the senior design judgment experience of various structures is quantified and transformed, and the structural parameter symbols are replaced to obtain senior experience rules; D: Perform correlation filtering on the design specification rules and the senior experience rules to construct design correlation judgment rules; E: Based on the target optimization rules, iterative calculations and updates are performed on the design association judgment rules to obtain the construction estimation rule library.

[0033] The implementation principle of this embodiment is as follows: by constructing a knowledge graph that integrates design specifications and expert experience, using parametric modeling technology to generate a three-dimensional structure and deconstruct it into hierarchical components, using a constraint solver to screen for material compatibility and verify performance, and finally outputting the optimal solution through a multi-objective optimization algorithm, intelligent decision-making is achieved throughout the entire process from design parameters to material configuration, which significantly improves design efficiency and resource utilization.

[0034] Step S3 includes: S31: Based on the time constraints of the business constraints, verify and screen the supply time of each building material in the complete building material combination table to obtain the timely material combination table; S32: Based on the budget constraints and the allowance range of the business constraints, determine the total cost of the timely material combination table to obtain the compliant material combination table; S33: Based on the parameter structure formula of the constructed estimation rule base and the component size, perform a cutting calculation on the compliant material combination table to obtain the simulated material usage of several components; S34: Based on the empirical estimation formula of the constructed estimation rule base, the simulated material usage of each component is corrected, and the optimal material usage of each structure is output.

[0035] Step S33 includes: The compliant material combination table is analyzed to obtain the material name, specifications, and physical properties. Based on the parameter structure formula of the constructed estimation rule base and the component size, the material is simulated and estimated to obtain the preliminary material usage. Based on the material type, specifications, and component dimensions, the materials are comprehensively laid out to generate a material cutting plan; Based on the physical properties of the material and the cutting loss rate, the initial material usage is optimized to obtain the corrected material usage; Based on the material cutting scheme and the application of the minimum usage rule, the modified material usage is planned to obtain the component material cutting amount; Based on the material names, the material cutting quantities and cutting schemes for the components are summarized to generate several simulated material usage quantities for the components. Example

[0036] Building structure image sequences were acquired through multi-angle photogrammetry, and several different frame structure models (such as reinforced concrete frames and steel frames) were constructed based on point cloud data extraction technology. After selecting target structural models that meet the specifications based on project requirements (such as seismic resistance level and building height), their structural types (frame columns, beams, floor slabs, etc.) were analyzed to extract structural parameter fields (such as column cross-sectional dimensions, beam spans, floor slab thicknesses, etc.). Furthermore, the historical design database was accessed to statistically analyze the absolute frequency of use of each parameter and calculate its relative weight. Simultaneously, the influence and importance of parameters were quantified based on structural functions (such as load-bearing capacity and fire resistance), and a comprehensive parameter weight was generated through timestamp-weighted calculations. Finally, high-weight target parameter fields (such as column cross-sectional dimensions, which have the highest priority) were selected and filled as key control parameters (such as C40 concrete strength grade and Q345 steel type).

[0037] The system analyzes and constructs a rule base for estimation, extracting structural technical specifications (such as the "Code for Design of Concrete Structures" GB50010), material compatibility relationships (such as the bond performance between concrete and steel reinforcement), and structural hierarchy logic (such as the hierarchical dependency between floor slabs, beams, and columns). Based on key control parameters, a three-dimensional frame model is simulated and generated, and then deconstructed into hierarchical components such as columns, beams, and floor slabs. Subsequently, functional constraints are assigned to the components according to the code rules (such as columns needing to meet axial compression ratio limits), and candidate material combinations are selected based on material compatibility relationships (such as candidate column materials being C40 / C50 concrete or steel-concrete composite). Finally, performance indicators (such as bearing capacity and durability) are used for quantitative verification, and several building material combination tables are output (e.g., Scheme A: all-concrete frame; Scheme B: hybrid structural frame).

[0038] The system first verifies the material supply cycle based on time constraints (e.g., C50 concrete requires 7 days of advance preparation) and filters out suitable material combinations. Next, combining budget constraints (e.g., a total cost limit of 120 million yuan) and a grace period (±5%), it generates compliant material combinations. Then, using parametric structural formulas from the construction rule base (e.g., formulas for calculating beam cross-section dimensions and span) combined with component dimensions (e.g., a beam span of 8 meters), it simulates and estimates the usage of each material (e.g., Scheme A requires a total of 20,000 cubic meters of concrete), and optimizes the cutting scheme through comprehensive layout (e.g., reducing formwork splicing losses). Finally, based on empirical estimation formulas (e.g., considering a 3% construction loss rate), it corrects the usage and outputs the optimal material usage (e.g., after correction, Scheme A requires 20,600 cubic meters of concrete, reducing costs by 15%).

[0039] The implementation principle of this embodiment is as follows: Multiple framework structure models are constructed using photogrammetry and point cloud 3D reconstruction technology. After selecting the target model based on project requirements, high-weight structural parameters are extracted from the historical database using data mining and multi-index weighted algorithms as key control parameters. Subsequently, an estimation rule base is constructed based on the integration of standard specifications and expert experience. A 3D structure is generated and deconstructed into hierarchical components using parametric modeling and constraint satisfaction problem-solving techniques. Candidate material combinations are selected based on functional constraints and material compatibility relationships. Finally, a multi-objective optimization method is adopted to output accurate material usage under the dual constraints of time and budget. This is achieved through a parametric cutting model and empirical correction formulas, realizing intelligent decision-making throughout the entire process from raw data to optimal material usage, significantly improving design efficiency, resource utilization, and cost control accuracy.

[0040] Reference Figure 4 A rapid estimation system for structural material usage, applied to the aforementioned fault detection method, includes: The parameter filtering module is used to select the building structure model according to project requirements, and call and fill the corresponding key control parameters based on the structure type. The rule building module is used to identify and extract design specification rules from structural design standard documents, and to quantify and transform the senior design judgment experience of various structures according to the rule transformation logic, generate senior experience rules, and combine them with target optimization rules for comprehensive correlation and filtering to generate a construction estimation rule library; The material combination module is used to generate several building material combination tables based on the construction estimation rule base and the key control parameters. The simulated cutting module is used to simulate the cutting operation of the building material combination table based on the parameter structure formula of the constructed estimation rule base and the component size, so as to obtain the simulated material usage of several components. The usage estimation module is used to filter and optimize the simulated usage of component materials in all the building material combination tables according to business constraints, and output the optimal material usage.

[0041] Example 2: The parameter input interface is a simplified interface (think of it as an Excel spreadsheet or a simple web page). It's important to note that only key control parameters are entered. For example: for beam reinforcement: input the cross-sectional width (b), height (h), calculated span (L), and design bending moment (M); for node bolts: input the node design tensile force (N), shear force (V), and bolt grade; for concrete members: input the axial force (N), bending moment (M), shear force (V), and other control internal forces.

[0042] The estimation rule base includes normative rules, empirical rules, and optimization rules.

[0043] Standard rules refer to embedding the structural requirements and minimum limits from the specifications into the algorithm. For example, the "Code for Design of Strengthening Concrete Structures" specifies the requirements for the spacing and width of carbon fiber cloth bonding.

[0044] Empirical rules refer to converting the "experience values" and "quick judgment logic" of senior engineers into algorithms. For example: "When the bending moment at the bottom of the beam exceeds XXX, it is assumed that 3 layers of carbon fiber cloth need to be pasted" and "For bolts of this grade, under shear conditions, after quickly estimating the area, the M20 diameter bolt is selected by default, and then the number is calculated in reverse"; optimization rules refer to setting optimization objectives, such as "Under the premise of meeting the bearing capacity, the single-layer carbon fiber cloth and the increased width scheme are selected by default because their construction efficiency is higher than that of multi-layer pasting"; the algorithm library uses equivalent methods, lookup table methods, or interpolation methods to replace complex iterative calculations.

[0045] Example 1 (Steel Plate Bonding Reinforcement): The algorithm has a built-in "steel plate resistance database". Based on the input bending moment M, it quickly matches the required thickness and width of the steel plate and automatically meets the requirements of the standard regarding the anchorage length, directly outputting the steel plate size and quantity; Example 2 (embedded parts): Based on the input tensile force F, directly apply the empirical formula As>F / (0.8 * fy) (and round it off) to quickly provide a combination suggestion for the diameter and number of steel bars.

[0046] The output port can be customized according to user needs, and may include estimated material usage (e.g., XX square meters of carbon fiber cloth, XX sets of M20 high-strength bolts); key calculation assumptions and simplification conditions (e.g., "Based on the input bending moment M=150kN·m, the estimated result is 3 layers of 300mm wide carbon fiber cloth, and the anchorage length is XXX according to the specification.").

[0047] For the content of the estimation rule base, the following empirical or standardized formulas are provided: 1) Quickly estimate the area of ​​carbon fiber cloth required for reinforcing the normal section of a concrete member. Formula: Af=0.172C Parameter explanation: Af—Cross-sectional area of ​​carbon fiber cloth required for positive section reinforcement, in mm. 2 C—The area of ​​additional steel reinforcement required for strengthening the normal section, in mm. 2 ; For example, to reinforce a beam, an increase of 505 mm is required. 2 For Grade III steel rebar, if carbon fiber reinforced cloth is used for reinforcement, the required cross-sectional area of ​​the carbon fiber reinforced cloth can be quickly estimated as Af = 0.172C = 0.172 * 505 = 87 mm². 2 .

[0048] 2) Quickly estimate the area of ​​carbon fiber cloth required for reinforcing the oblique section of a concrete member. Formula: Afv=△Asv Parameter explanation: Afv—Cross-sectional area of ​​carbon fiber cloth required for oblique section reinforcement, in mm. 2 ; △Asv—Area of ​​additional Grade III steel stirrups required for diagonal section reinforcement, in mm 2 ; For example, to reinforce the inclined section of a beam, an increase of 66 mm is required. 2 For grade III steel stirrups, if U-shaped carbon fiber cloth is used for reinforcement, the required width and area of ​​the carbon fiber cloth to be bonded can be quickly estimated as Afv = △Asv = 66 mm. 2 That is, in the preliminary design, the required cross-sectional area of ​​the U-shaped carbon cloth can be considered equal to the area of ​​the stirrups required for reinforcement (within every 100mm spacing).

[0049] 3) Quickly estimate the required cross-sectional area of ​​reinforcing steel for shear-loaded embedded parts: Formula: As=Vd Parameter explanation: As—The area of ​​reinforcing steel required for the embedded part, in mm. 2 Vd—Design value of shear force on the embedded part, in kN; For example, if the design shear force Vd on an embedded part is estimated to be 1200 kN, then the required shear reinforcement area can be quickly estimated as As = Vd = 1200 mm². 2 If four anchor bars are used, then 1200 / 4 = 300 mm 2 Therefore, this embedded part uses a diameter of 20 (single cross-sectional area of ​​314mm). 2 >300 mm 2 There are 4 steel bars.

[0050] 4) Quickly estimate the amount of concrete required for the construction drawings: Formula: Vc = 1.1Vp Parameter description: Vc—Concrete usage required for construction drawings, unit: m³ 3 Vp—Concrete usage statistically obtained from Yingjianke modeling, unit: m³ 3 ; For example, the concrete volume Vp = 3000 m³ obtained from the modeling and statistical analysis of Yingjianke. 3 Based on empirical formulas, the required concrete volume Vc in the actual construction drawings can be quickly calculated to be 1.1 x 3000 = 3300 m³. 3 This result can guide the design process and help determine its economic viability.

[0051] 5) Quickly estimate the amount of steel reinforcement required for the construction drawings: Formula: ms = 1.15mp Parameter description: ms—the amount of steel reinforcement required for the construction drawings, in kg / m 2 ;mp—Reinforcing steel usage statistically obtained from Yingjianke's modeling data, unit: kg / m 2 ; For example, the modeling statistics obtained by Yingjianke show that the amount of steel reinforcement mp = 40 kg / m 2 Based on empirical formulas, the required steel reinforcement in the actual construction drawings can be quickly calculated as Vc = 1.15 x 40 = 46m. 3 This result can guide the design process and help determine its economic viability.

[0052] 6) Quickly estimate the required steel reinforcement cross-sectional area for tension concrete members: Formula: As=0.003N Parameter description: As—Required cross-sectional area of ​​reinforcing steel bars for tension members such as concrete hanging columns, in mm. 2 N—Design tensile force borne by the component, in N; For example, if the design tensile force of a certain hanging column is N=300kN, the required steel reinforcement area can be quickly estimated as As=0.003*300*1000=900 mm². 2 The actual configuration includes four 18mm diameter Grade III steel bars (total area 4 x 254 = 1016 mm). 2 ).

[0053] 7) Empirical formula for rapid estimation of concrete beam cross-section: Formula: h = L0 / 12 Parameter description: h—height of the concrete frame beam section, in mm; L0—calculated span of the concrete frame beam, in mm; For example, if a frame beam has a span of 6000mm, its cross-sectional height can be quickly estimated as 6000 / 12=500mm, and the preliminary design can take the cross-section as 250x500.

[0054] 8) Empirical formula for rapid estimation of steel beam cross-section: Formula: h = L0 / 18 Parameter description: h—height of steel frame beam section, in mm; L0—calculated span of steel frame beam, in mm; For example, if the span of a steel frame beam is 6000mm, its cross-sectional height can be quickly estimated as 6000 / 18=333mm, and the preliminary design can take the cross-section as 200x400.

[0055] 9) Quickly estimate the cross-sectional area of ​​concrete columns based on the axial compression ratio limit: Formula: Ac = 1.5N / [uN] / fc Parameter explanation: Ac—Cross-sectional area of ​​concrete column, unit mm 2 N—Design value of axial compressive strength of concrete column, in N; [uN]—Limit of axial compression ratio; fc—Design value of concrete compressive strength, in N / mm² 2 ; For example, a concrete column in a frame structure with a seismic resistance level of 2 is estimated to bear a pressure of N=5000kN based on its load area. Using C30 concrete, fc=14.3N / mm² 2 Therefore, its cross-sectional area can be estimated as Ac = 1.5 x 5000 x 1000 / 0.75 / 14.3 = 699300 mm² 2 A square column with a side length of 850mm (cross-section of 722500mm) is used. 2 >699300mm 2 ).

[0056] The implementation principle of this embodiment is as follows: key control parameters are received through a parameterized input interface, and the building structure is quickly analyzed and material estimated using the constructed estimation rule library (integrating standard rules, empirical rules and optimization rules) and algorithm library (using equivalent methods, table lookup methods and other methods to simplify calculations). Finally, the optimized material usage that meets business constraints is output, realizing intelligent and rapid decision-making throughout the entire process from design parameters to economically reasonable construction schemes, which significantly improves design efficiency and cost control accuracy.

[0057] An electronic device, comprising: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement any of the methods described in the above scheme.

[0058] A storage medium storing at least one instruction, at least one program, a code set, or an instruction set, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to implement the rapid estimation method for structural material usage as described above. The embodiments described in this specific implementation are preferred embodiments of this application and are not intended to limit the scope of protection of this application. Therefore, all equivalent changes made in accordance with the structure, shape and principle of this application should be covered within the scope of protection of this application.

Claims

1. A method for rapidly estimating the amount of structural materials used, characterized in that, include: Select the building structure model according to the project requirements, and call and fill in the corresponding key control parameters based on the structure type. Based on the constructed estimation rule base and the aforementioned key control parameters, several building material combination tables are generated; Based on business constraints, the entire list of building material combinations is filtered and optimized to output the optimal material usage.

2. The method for rapid estimation of structural material usage according to claim 1, characterized in that, The specific steps for selecting a preset building structure model based on project requirements, and combining the structure type with the corresponding key control parameters include: Several different architectural structure models were obtained by performing 3D reconstruction on images of architectural structures from different angles. Based on project requirements, all the aforementioned building structure models were screened to obtain several target structure models; Based on the structure type, each target structure model is analyzed to obtain several structure parameter fields; Based on the historical usage frequency of parameters and the parameter calculation formula, the structural parameter fields are weighted and sorted in descending order to obtain the target parameter fields. The target parameter field is called and filled in in conjunction with the target structure parameters to obtain the key control parameters.

3. The method for rapid estimation of structural material usage according to claim 2, characterized in that, The specific steps for calculating the weights and sorting the structural parameter fields in descending order based on the historical usage frequency of the parameters and the parameter calculation formula to obtain the target parameter fields include: The historical data dataset is parsed and classified according to the structure name to obtain several structure parameter operation blocks; Based on the parameter log, the structural parameter fields of the structural parameter operation block are extracted and normalized to obtain structural parameter symbols, and the call timestamp is recorded. The frequency of all the structural parameter symbols is counted, the absolute frequency of use of the parameters is recorded, and the relative weight of use is calculated. The parameter calculation formula is measured based on business indicators and structural functions, and the parameter influence and importance are calculated. The parameter's overall weight is obtained by weighting the parameter's influence, importance, and relative usage weight based on the call timestamp. All the parameters are sorted in descending order by their combined weights and then filtered based on their absolute usage frequency to obtain the target parameter field.

4. The method for rapid estimation of structural material usage according to claim 1, characterized in that, The specific steps for generating several building material combination tables based on the constructed estimation rule base and the key control parameters include: The construction estimation rule base is parsed to obtain structural technical specifications, material compatibility relationships, and construction hierarchy logic; The target structure is simulated and deduced based on key control parameters to obtain a three-dimensional structural model. The three-dimensional structural model is deconstructed according to the constructed hierarchical logic to obtain several structural hierarchical components; Based on the structural technical specifications and the building environment, the structural hierarchical components are assigned functions to obtain component functional constraints. Based on the functional constraints of the components, the materials for manufacturing the components are screened, and a preliminary determination is made in combination with the material compatibility relationship to obtain several candidate material combinations for the components; Based on structural performance indicators, the candidate material combinations for the components are aggregated, quantified, and constrained for filtering, resulting in several building material combination tables.

5. The method for rapid estimation of structural material usage according to claim 1, characterized in that, The constructed estimation rule base includes: Based on the data type, the structural design standard documents are identified and extracted to obtain the structural design content, and structural parameter symbols are marked. The structural design content is clustered and regularized according to the structural type to obtain design specifications and rules for various types of structures. Based on the rule transformation logic, the senior design judgment experience of various structures is quantified and transformed, and the structural parameter symbols are replaced to obtain senior experience rules; The design specification rules and the senior experience rules are correlated and filtered to construct design correlation judgment rules; Based on the target optimization rules, the design association judgment rules are iteratively calculated and updated to obtain the construction estimation rule library.

6. The method for rapid estimation of structural material usage according to claim 1, characterized in that, The specific steps for filtering and optimizing all the building material combination tables based on business constraints and outputting the optimal material usage include: Based on the time constraints of the business constraints, the supply time of each building material in the complete building material combination table is verified and screened to obtain the timely material combination table. Based on the budget constraints and the allowance range of the business constraints, the total cost of the timely material combination table is determined to obtain the compliant material combination table. Based on the parameter structure formula of the constructed estimation rule base and the component dimensions, the compliant material combination table is cut and calculated to obtain the simulated material usage of several components. Based on the empirical estimation formulas of the constructed estimation rule base, the simulated material usage of each component is corrected, and the optimal material usage of each structure is output.

7. The method for rapid estimation of structural material usage according to claim 6, characterized in that, The specific steps for performing a trimming calculation on the compliant material combination table based on the parameter structure formula of the constructed estimation rule base and the component dimensions to obtain the simulated material usage of several components include: The compliant material combination table is analyzed to obtain the material name, specifications, and physical properties. Based on the parameter structure formula of the constructed estimation rule base and the component size, the material is simulated and estimated to obtain the preliminary material usage. Based on the material type, specifications, and component dimensions, the materials are comprehensively laid out to generate a material cutting plan; Based on the physical properties of the material and the cutting loss rate, the initial material usage is optimized to obtain the corrected material usage; Based on the material cutting scheme and the application of the minimum usage rule, the modified material usage is planned to obtain the component material cutting amount; Based on the material names, the material cutting quantities and cutting schemes for the components are summarized to generate several simulated material usage quantities for the components.

8. A rapid estimation system for structural material usage applied to the method of any one of claims 1 to 7, characterized in that, include: The parameter filtering module is used to select the building structure model according to project requirements, and call and fill in the corresponding key control parameters based on the structure type. The rule building module is used to identify and extract design specification rules from structural design standard documents, and to quantify and transform the senior design judgment experience of various structures according to the rule transformation logic, generate senior experience rules, and combine them with target optimization rules for comprehensive correlation and filtering to generate a construction estimation rule library; The material combination module is used to generate several building material combination tables based on the construction estimation rule base and the key control parameters. The simulated cutting module is used to simulate the cutting operation of the building material combination table based on the parameter structure formula of the constructed estimation rule base and the component size, so as to obtain the simulated material usage of several components. The usage estimation module is used to filter and optimize the simulated usage of component materials in all the building material combination tables according to business constraints, and output the optimal material usage.

9. An electronic device, characterized in that, include: One or more processors; Memory, used to store one or more programs; When the one or more programs are executed by the one or more processors, the one or more processors implement the method as described in any one of claims 1-7.

10. A storage medium storing at least one instruction, at least one program, a code set, or an instruction set, wherein the at least one instruction, the at least one program, the code set, or the instruction set is loaded and executed by a processor to implement the rapid estimation method for structural material usage as described in any one of claims 1 to 7.

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