Building material carbon emission index prediction method

Abstract

The invention provides a building material carbon emission index prediction method. The method comprises the following steps: establishing an engineering case building material carbon emission database; the engineering foundation sub-information at least comprises seismic fortification intensity, a building structure form, an underground space area ratio and an overground building layer number; performing saliency judgment by taking the engineering basic sub-information as independent variables and taking the building material carbon emission per unit building area of the sample number as dependent variables, and dynamically perfecting the sample library until the four independent variables all meet a saliency judgment standard; the building structure form is used as a classification mode, the seismic fortification intensity, the underground space area ratio and the overground building layer number are used as fitting independent variables, the building material carbon emission per unit building area of an engineering case is used as a fitting dependent variable, and a building material carbon emission prediction model is fitted by adopting a multiple linear regression method. The method can solve the problem of building material carbon emission index estimation in the absence of building material consumption data in the early stage of design, and provides support for scheme planning of zero-carbon buildings in the whole process.

Description

Technical Field

[0001] The present invention relates to the technical field of carbon emission calculation, and particularly relates to a method for predicting carbon emission indicators of building materials. Background Art

[0002] The construction field is an important source of carbon emissions in the whole society. Relevant research shows that the carbon emissions during the operation stage of the construction field in China account for more than 20% of the total social carbon emissions. If the carbon emissions throughout the whole process such as building material production and construction are considered, the proportion will exceed 40%. Therefore, energy conservation and emission reduction in the construction field are important guarantees for realizing the national strategy of "carbon peak - carbon neutrality", and the calculation of building carbon emissions is one of the bases for promoting building carbon reduction work.

[0003] According to the national standard "Standard for Calculating Building Carbon Emissions" GB / T51366 - 2019, the calculation scope of building carbon emissions includes greenhouse gas emissions related to activities such as building material production and transportation, construction and demolition, and operation of buildings. Among them, the calculation of carbon emissions in the building material production stage is calculated by multiplying the building material consumption by the building material carbon emission factor, so it depends on the acquisition of building material usage data. However, in the early stages such as building scheme design and preliminary design, there is usually no detailed building material usage data, and it is impossible to calculate the carbon emissions of building materials through the method in this standard, which is the main difficulty in calculating the carbon emissions of building materials.

[0004] With the proposal of the concept of zero - carbon buildings throughout the whole process, it is of great significance to plan zero - carbon building schemes in the early design stage. Since zero - carbon buildings throughout the whole process need to offset the embodied carbon emissions of buildings through renewable energy, reasonable prediction of building material carbon emissions is one of the important contents of zero - carbon building scheme planning throughout the whole process, and it is necessary to solve the problem of how to calculate the carbon emission indicators of building materials when there is a lack of building material usage data. Summary of the Invention

[0005] The purpose of the present invention is to provide a method for predicting carbon emission indicators of building materials, an electronic device, and a readable storage medium, which can solve the problem of reasonable estimation of carbon emissions of building materials in the early design stage when there is a lack of building material usage data.

[0006] To achieve the above object, the present invention provides a method for predicting the carbon emission index of building materials, comprising the following steps: establishing a carbon emission database of engineering cases according to cases of multiple past construction projects, wherein the carbon emission database of engineering cases includes several kinds of basic engineering sub-information of each case and the carbon emission of building materials per unit building area of each case, and the several kinds of basic engineering sub-information at least include seismic fortification intensity, building structure form, underground space area ratio, and number of above-ground building floors; selecting any one of the basic engineering sub-information, taking the basic engineering sub-information of this kind with a sample quantity as an independent variable, taking the carbon emission of building materials per unit building area of the case as a dependent variable, and making a significance determination of the effect of the independent variable on the dependent variable based on a statistical method; taking the basic engineering sub-information including the seismic fortification intensity, the underground space area ratio, and the number of above-ground building floors that passes the significance determination as a fitting independent variable, taking the carbon emission of building materials per unit building area of the case corresponding to the basic engineering sub-information as a fitting dependent variable, performing data fitting, and generating a fitting prediction model; and using the fitting prediction model to predict the carbon emission of building materials of a target construction project.

[0007] Optionally, the basic engineering sub-information includes quantitative information, and the significance determination of the effect of the independent variable on the dependent variable based on a statistical method specifically includes: for the quantitative information, making a significance determination of the effect of the independent variable on the dependent variable based on correlation analysis.

[0008] Optionally, the basic engineering sub-information includes classification information, and the significance determination of the effect of the independent variable on the dependent variable based on a statistical method specifically includes: for the classification information, making a significance determination of the effect of the independent variable on the dependent variable based on variance analysis.

[0009] Optionally, the significance determination of the effect of the independent variable on the dependent variable based on a statistical method specifically includes: if the result shown by the significance index is no significant effect, increasing the sample quantity and performing the significance determination again until the result shown by the significance index is a significant effect.

[0010] Optionally, the generation of the fitting prediction model specifically includes: based on the multiple linear regression method, establishing prediction formulas corresponding one by one to several kinds of building structure forms according to the building structure form of the case, and taking the prediction formulas as the fitting prediction model.

[0011] Optionally, the generation of the fitting prediction model specifically further includes: using the coefficient of determination R-squared value to evaluate and test the prediction formula.

[0012] Optionally, the generation of the fitting prediction model specifically further includes: using the F-test method to evaluate and test the prediction formula.

[0013] Optionally, if the evaluation test fails, increase the sample size and perform the significance determination and / or data fitting again until the evaluation test passes.

[0014] The present invention also provides an electronic device, which includes: a memory storing a computer program; a processor communicatively connected to the memory and executing the building material carbon emission index prediction method according to any one of the above claims when calling the computer program; a display communicatively connected to the processor and the memory for displaying a GUI interaction interface related to the building material carbon emission index prediction method.

[0015] To achieve the above object, the present invention also provides a readable storage medium storing a computer program, which realizes the building material carbon emission index prediction method according to any one of the above when executed by a processor.

[0016] To achieve the above object, the present invention also provides a computer program product, which realizes the building material carbon emission index prediction method according to any one of the above when executed by a processor.

[0017] The building material carbon emission index prediction method provided by the present invention has the following beneficial effects:

[0018] The present invention provides a method for predicting the carbon emission index of building materials, which includes the following steps: establishing a database of carbon emissions of building materials for engineering cases based on multiple previous construction projects. The database of carbon emissions of building materials for engineering cases includes several types of basic engineering sub-information for each case and the carbon emissions of building materials per unit building area for each case. The several types of basic engineering sub-information at least include the seismic fortification intensity, building structure type, underground space area ratio, and number of above-ground building floors; selecting any one of the types of basic engineering sub-information, using the sample quantity of this type of basic engineering sub-information as the independent variable, and using the carbon emissions of building materials per unit building area of the case corresponding to this type of basic engineering sub-information as the dependent variable, and making a significance determination of the effect of the independent variable on the dependent variable based on statistical methods; using the basic engineering sub-information including the seismic fortification intensity, the underground space area ratio, and the number of above-ground building floors that passes the significance determination as the fitting independent variable, and using the carbon emissions of building materials per unit building area of the case corresponding to the basic engineering sub-information as the fitting dependent variable, performing data fitting to generate a fitting prediction model; using the fitting prediction model to predict the carbon emissions of building materials for a target construction project. Essentially, the present invention provides a specific machine learning method based on an engineering case database, providing a new idea for solving the calculation of carbon emissions of building materials in the early design stage. When a sufficient dataset of building material usage and carbon emissions for engineering cases is collected, by mining the influencing factors and variation law characteristics of the data, a prediction model can be established using machine learning methods, so that the carbon emission index of building materials can be quickly predicted according to the characteristic parameters. The present invention provides a method for predicting the carbon emission index of building materials for buildings that does not rely on building material usage data, which can solve the problem of estimating the carbon emission index of building materials when there is a lack of building material usage data in the early design stage, and provide strong support for the scheme planning of a whole-process zero-carbon building.

[0019] The present invention also provides an electronic device, since the electronic device is used to execute any one of the above-mentioned methods for predicting the carbon emission index of building materials for buildings. Therefore, the electronic device can provide a method for predicting the carbon emission index of building materials for buildings that does not rely on building material usage data, which can solve the problem of estimating the carbon emission index of building materials when there is a lack of building material usage data in the early design stage, and provide strong support for the scheme planning of a whole-process zero-carbon building.

[0020] The present invention also provides a readable storage medium, since the readable storage medium is used to execute any one of the above-mentioned methods for predicting the carbon emission index of building materials for buildings. Therefore, the readable storage medium can provide a method for predicting the carbon emission index of building materials for buildings that does not rely on building material usage data, which can solve the problem of estimating the carbon emission index of building materials when there is a lack of building material usage data in the early design stage, and provide strong support for the scheme planning of a whole-process zero-carbon building. Description of the Drawings

[0021] Figure 1 Schematic flowchart of the method for predicting the carbon emission index of building materials provided by an embodiment of the present invention;

[0022] Figure 2 Schematic block diagram of an electronic device provided by an embodiment of the present invention;

[0023] Figure 3 Schematic flowchart of the method for predicting the carbon emission index of building materials provided by another embodiment of the present invention;

[0024] Wherein the reference numerals are:

[0025] 101 - Processor; 102 - Communication interface; 103 - Memory; 104 - Communication bus; 105 - Display. Detailed Embodiments

[0026] To make the objectives, advantages and features of the present invention clearer, the present invention will be further described in detail below with reference to the drawings and specific embodiments. It should be noted that the drawings are in very simplified forms and are not drawn to scale, and are only used to facilitate and clearly assist in explaining the objectives of the embodiments of the present invention. In addition, the structures shown in the drawings are often part of the actual structures. In particular, the focus to be shown in each drawing is different, and sometimes different scales are used.

[0027] It should be understood that when an element or layer is referred to as "on", "connected to" another element or layer, it can be directly on the other element or layer, connected to the other element or layer, or there may be intervening elements or layers. In contrast, when an element is referred to as "directly on", "directly connected to" another element or layer, there are no intervening elements or layers. Although the terms first, second, third, etc. may be used to describe various elements, components, regions, layers, and / or parts, these elements, components, regions, layers, and / or parts should not be limited by these terms. These terms are only used to distinguish one element, component, region, layer, or part from another element, component, region, layer, or part. Thus, without departing from the teachings of the present invention, the first element, component, region, layer, or part discussed below may be referred to as the second element, component, region, layer, or part. Spatial relationship terms such as "under", "below", "lower", "above", "upper", etc. may be used herein for convenience in describing the relationship of one element or feature shown in the figures to other elements or features. It should be understood that, in addition to the orientation shown in the figures, spatial relationship terms are intended to also include different orientations of the device during use and operation. For example, if the device in the figures is flipped, then elements or features described as "under", "below", "lower" will be oriented "on" other elements or features. The device may be otherwise oriented (rotated 90 degrees or other orientations) and the spatial descriptors used herein are to be interpreted accordingly. The purpose of the terms used herein is only to describe specific embodiments and not to limit the present invention. As used herein, the singular forms "a", "an", and "the" are also intended to include the plural forms, unless the context clearly dictates otherwise. It should also be understood that the term "comprising" is used to identify the presence of features, steps, operations, elements, and / or components, but does not preclude the presence or addition of one or more other features, steps, operations, elements, components, and / or groups. As used herein, the term "and / or" includes any and all combinations of the associated listed items.

[0028] The object of the present invention is to provide a method for predicting the carbon emission index of building materials, an electronic device, and a readable storage medium. It can solve the problem of reasonable estimation of the carbon emissions of building materials when there is a lack of building material consumption data in the early stage of design.

[0029] To achieve the above object, the present invention provides a method for predicting the carbon emission index of building materials, comprising the following steps: establishing a carbon emission database of engineering cases based on previous cases of multiple construction projects, wherein the carbon emission database of engineering cases includes several types of basic engineering sub-information of each case and the carbon emission of building materials per unit building area of each case, and the several types of basic engineering sub-information at least include seismic fortification intensity, building structure type, underground space area ratio, and number of above-ground building floors; selecting any one of the basic engineering sub-information, taking the sample quantity of this type of basic engineering sub-information as the independent variable, and taking the carbon emission of building materials per unit building area of the case corresponding to this type of basic engineering sub-information as the dependent variable, and making a significance determination of the effect of the independent variable on the dependent variable based on statistical methods; taking the basic engineering sub-information including the seismic fortification intensity, the underground space area ratio, and the number of above-ground building floors that pass the significance determination as the fitting independent variable, and taking the carbon emission of building materials per unit building area of the case corresponding to the basic engineering sub-information as the fitting dependent variable, and performing data fitting to generate a fitting prediction model; using the fitting prediction model to predict the carbon emission of building materials of the target construction project.

[0030] With such a setting, the present invention essentially provides a specific machine learning method based on an engineering case database, providing a new idea for solving the calculation of the carbon emission of building materials in the early design stage. When a sufficient dataset of the building material usage and carbon emissions of engineering cases is collected, by mining the influencing factors and changing rule characteristics of the data, a prediction model can be established using machine learning methods, so that the carbon emission index of building materials can be quickly predicted according to the characteristic parameters. The present invention provides a method for predicting the carbon emission index of building materials of a building that does not depend on the building material usage data, which can solve the problem of estimating the carbon emission index of building materials when there is a lack of building material usage data in the early design stage, and provide strong support for the scheme planning of a whole-process zero-carbon building.

[0031] In an exemplary embodiment, the basic engineering sub-information includes building function type, seismic fortification intensity, building structure type, building area (including above-ground building area and underground building area), and number of above-ground building floors, but is not limited thereto. Among them, the building structure type is classified according to reinforced concrete structure, steel structure, and mixed structure. The carbon emission of building materials per unit building area can be calculated using the building material usage data and building material emission factors of each case. The building material usage data should at least include the following main building material types: steel bars, steel sections, concrete, blocks, mortar, and exterior windows; the spatial scope of building material statistics should include above-ground projects, underground projects, and pile foundation projects, and the accounting scope of pile foundations includes load-bearing piles and does not include retaining piles. The building material carbon emission factors should adopt the data in the current national standard "Building Carbon Emission Calculation Standard" GB / T 51366. The calculation formula can be as follows:

[0032]

[0033] Among them, C iM is the carbon emission index of building materials per unit construction area for the i-th engineering case sample, M j is the consumption of the j-th main building material, F j is the carbon emission factor of the j-th main building material, and S is the total construction area of the i-th engineering case.

[0034] For the engineering case building material carbon emission database, building function is used as the main classification method. The prediction of the building material carbon emission index is differentiated by function. The number of case samples for each building function is generally not less than 50.

[0035] By the characteristics of different engineering basic sub-information, we will find that the engineering basic sub-information includes quantitative information and non-quantitative information (classification information). The quantitative information includes, for example, the ratio of underground space area, the number of above-ground building floors, etc. The classification information includes, for example, the seismic fortification intensity, the building structure form, etc. It should be specially noted here that although the seismic fortification intensity itself has quantitative data, its essence is also used for classification. Therefore, it is more appropriate as classification information, but it does not exclude it from being used as quantitative information.

[0036] Based on this, when studying the quantitative information, the present invention provides the following technical solution: The engineering basic sub-information includes quantitative information, and the significance determination of the effect of the independent variable on the dependent variable based on the statistical method specifically includes: for the quantitative information, based on the correlation analysis, the significance determination of the effect of the independent variable on the dependent variable is carried out.

[0037] When studying the classification information, the present invention provides the following technical solution: The engineering basic sub-information includes classification information, and the significance determination of the effect of the independent variable on the dependent variable based on the statistical method specifically includes: for the classification information, based on the variance analysis, the significance determination of the effect of the independent variable on the dependent variable is carried out.

[0038] Both the correlation analysis and the variance analysis are branches of the statistical method. The correlation analysis includes, but is not limited to, the Pearson analysis. The variance analysis (Analysis of Variance, abbreviated as ANOVA), also known as "analysis of variance", is used for the significance test of the difference in the means of two or more samples.

[0039] Further, the significance determination of the influence of independent variables on the dependent variable based on statistical methods specifically includes: if the result shown by the significance index is no significant influence, increase the sample size and conduct the significance determination again until the result shown by the significance index is significant influence. Such a setting is because, according to the inventor's research, some engineering basic sub-information will necessarily have a significant influence on the carbon emissions of building materials, such as seismic fortification intensity, underground space area ratio, and number of above-ground building floors. However, sometimes these engineering basic sub-information cannot pass the significance determination due to a small sample size. In this case, the sample size should be increased. In an exemplary embodiment, the determination criterion for the significance determination is that if the significance index p < 0.05, it indicates that the characteristic factor has a significant influence on the carbon emissions of building materials; if the significance index p ≥ 0.05, it indicates that the sample data is insufficient, and the sample size should be continuously increased and the determination should be repeated until the significance index p < 0.05.

[0040] Further, the generation of the fitting prediction model specifically includes: based on the multiple linear regression method, according to the building structure form of the case, establish prediction formulas corresponding to several building structure forms one by one, and use the prediction formulas as the fitting prediction model. In an exemplary embodiment, take "seismic fortification intensity, underground space area ratio, number of above-ground building floors" as independent variables and take "carbon emissions index of building materials per unit building area" as the dependent variable, and use the multiple linear regression method to establish a prediction model. According to the building structure form of the case, distinguish according to the building structure form category, and respectively use the multiple linear regression method to fit the prediction formula. Its basic prediction formula is as follows:

[0041] C M =b 0 +b 1 x 1 +b 2 x 2 +b 3 x 3

[0042] Where C M is the carbon emissions index of building materials per unit building area, x 1 ~x 3 respectively represent three independent variables of seismic fortification intensity, underground space area ratio, and number of above-ground building floors, and b 0 ~b 3 are regression coefficients, and this coefficient is obtained according to the multiple linear regression analysis of the sample.

[0043] Further, the prediction formula should be evaluated and tested. The goodness-of-fit R-squared value can be used to evaluate and test the prediction formula. In an exemplary embodiment, if the R-squared value ≥ 0.5, it means that the model has a fitting degree of more than 50%, and the fitting degree of the model can be accepted.

[0044] The F-test method can also be used to evaluate and test the prediction formula. If the F-test index p < 0.05, it indicates that the model is meaningful.

[0045] Similarly, if the evaluation and test fails, increase the sample size and perform the significance determination and / or data fitting again until the evaluation and test passes.

[0046] To achieve the above object, the present invention also provides an electronic device. Please refer to Figure 2 , Figure 2 which is a block structure diagram of the electronic device provided in an embodiment of the present invention. As Figure 2 shown, the electronic device includes:

[0047] A memory 103 storing a computer program;

[0048] A processor 101 communicatively connected to the memory, and when calling the computer program, executes the building material carbon emission index prediction method described in any one of the above;

[0049] A display 105 communicatively connected to the processor and the memory, and is used to display a GUI interaction interface related to the building material carbon emission index prediction method.

[0050] As Figure 2 shown, the electronic device further includes a communication interface 102 and a communication bus 104. Among them, the processor 101, the communication interface 102, and the memory 103 complete mutual communication through the communication bus 104. The communication bus 104 can be a Peripheral Component Interconnect (PCI) bus or an Extended Industry Standard Architecture (EISA) bus, etc. This communication bus 104 can be divided into an address bus, a data bus, a control bus, etc. For the sake of convenience of representation, only a thick line is used in the figure, but it does not mean that there is only one bus or one type of bus. The communication interface 102 is used for communication between the above electronic device and other devices.

[0051] The processor 101 mentioned in the present invention may be a central processing unit (CPU), or may also be other general-purpose processors, digital signal processors (DSPs), application specific integrated circuits (ASICs), field-programmable gate arrays (FPGAs), or other programmable logic devices, discrete gate or transistor logic devices, discrete hardware components, etc. The general-purpose processor may be a microprocessor or the processor may also be any conventional processor, etc. The processor 101 is the control center of the electronic device, connecting various parts of the entire electronic device through various interfaces and lines.

[0052] The memory 103 can be used to store the computer program. The processor 101 realizes various functions of the electronic device by running or executing the computer program stored in the memory 103 and calling the data stored in the memory 103.

[0053] The memory 103 may include non-volatile and / or volatile memory. The non-volatile memory may include read-only memory (ROM), programmable ROM (PROM), electrically programmable ROM (EPROM), electrically erasable programmable ROM (EEPROM), or flash memory. The volatile memory may include random access memory (RAM) or external cache memory. By way of illustration and not limitation, RAM is available in various forms, such as static RAM (SRAM), dynamic RAM (DRAM), synchronous DRAM (SDRAM), double data rate SDRAM (DDR SDRAM), enhanced SDRAM (ESDRAM), synchronous link DRAM (SLDRAM), Rambus direct RAM (RDRAM), direct memory bus dynamic RAM (DRDRAM), and Rambus dynamic RAM (RDRAM), etc.

[0054] Since the electronic device is used to execute any of the above-mentioned building material carbon emission index prediction methods. Therefore, the electronic device can provide a building material carbon emission index prediction method that does not depend on building material consumption data, which can solve the problem of estimating the building material carbon emission index when there is a lack of building material consumption data in the early stage of design, and provide strong support for the scheme planning of a whole-process zero-carbon building.

[0055] Since the electronic device provided by the present invention and the building material carbon emission index prediction method described above belong to the same inventive concept, the electronic device provided by the present invention has all the advantages of the building material carbon emission index prediction method described above. Therefore, the beneficial effects of the electronic device provided by the present invention will not be elaborated one by one here.

[0056] To achieve the above object, the present invention provides a readable storage medium storing a computer program, which when executed by a processor implements the building material carbon emission index prediction method as described in any one of the above. Since the readable storage medium is used to execute the building material carbon emission index prediction method described in any one of the above. Therefore, the readable storage medium can provide a building material carbon emission index prediction method that does not depend on building material consumption data, can solve the problem of estimating the building material carbon emission index when there is a lack of building material consumption data in the early stage of design, and provides strong support for the scheme planning of a whole-process zero-carbon building.

[0057] Since the readable storage medium provided by the present invention and the building material carbon emission index prediction method described above belong to the same inventive concept, the readable storage medium provided by the present invention has all the advantages of the building material carbon emission index prediction method described above. Therefore, the beneficial effects of the readable storage medium provided by the present invention will not be elaborated one by one here.

[0058] The readable storage medium according to the embodiment of the present invention can adopt any combination of one or more computer-readable media. The readable medium can be a computer-readable signal medium or a computer-readable storage medium. The computer-readable storage medium can be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the above. More specific examples (non-exhaustive list) of the computer-readable storage medium include: an electrical connection having one or more wires, a portable computer hard disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the above. In this article, the computer-readable storage medium can be any tangible medium that contains or stores a program, which can be used by or in combination with an instruction execution system, apparatus, or device.

[0059] A computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, which carries computer-readable program code. Such a propagated data signal may take various forms, including but not limited to electromagnetic signals, optical signals, or any suitable combination of the above. The computer-readable signal medium may also be any computer-readable medium other than a computer-readable storage medium, which can send, propagate, or transmit a program for use by or in conjunction with an instruction execution system, apparatus, or device. The program code contained on the computer-readable medium may be transmitted using any appropriate medium, including but not limited to wireless, wire, optical fiber, RF, etc., or any suitable combination of the above.

[0060] The present invention also provides a computer program product, which, when executed by a processor, implements the method for predicting the carbon emission index of building materials described in any one of the above.

[0061] The present invention also provides a computer program product, since the computer program product is used to execute the method for predicting the carbon emission index of building materials described in any one of the above. Therefore, the computer program product can provide a method for predicting the carbon emission index of building materials that does not depend on building material usage data, can solve the problem of estimating the carbon emission index of building materials when there is a lack of building material usage data in the early stage of design, and provides strong support for the scheme planning of a whole-process zero-carbon building.

[0062] Since the computer program product provided by the present invention and the method for predicting the carbon emission index of building materials described above belong to the same inventive concept, the computer program product provided by the present invention has all the advantages of the method for predicting the carbon emission index of building materials described above. Therefore, the beneficial effects of the computer program product provided by the present invention will not be elaborated one by one here.

[0063] The technical solution of the present invention will be described below in conjunction with a specific embodiment.

[0064] Please refer to Figure 3 , Figure 3 which is a schematic flow chart of the method for predicting the carbon emission index of building materials provided by another embodiment of the present invention. As Figure 3 shown, in an exemplary embodiment, the present invention includes the following steps:

[0065] Step 1, establish a database of carbon emissions of building materials for engineering cases. The database needs to include the basic information of the construction project, the building material usage data, and the calculated carbon emissions of building materials per unit building area.

[0066] The basic information of construction projects in the building materials carbon emission database of engineering cases should at least include the building function type, seismic fortification intensity, building area (including above-ground building area and underground building area), and the number of above-ground building floors. Among them, the building structure forms are classified into reinforced concrete structures, steel structures, and mixed structures.

[0067] The building materials consumption data in the building materials carbon emission database of engineering cases should at least include the following main building material types: steel bars, steel sections, concrete, blocks, mortar, and exterior windows; the spatial scope of building materials statistics should include above-ground projects, underground projects, and pile foundation projects, and the accounting scope of pile foundations includes load-bearing piles and does not include retaining piles.

[0068] The carbon emission of building materials per unit building area in the building materials carbon emission database of engineering cases should be calculated based on the building materials consumption data and building materials carbon emission factors, and its calculation formula is as follows:

[0069]

[0070] Among them, C iM is the carbon emission index of building materials per unit building area of the i-th engineering sample, M j is the consumption of the j-th main building material, F j is the carbon emission factor of the j-th main building material, and S is the total building area of the i-th engineering case.

[0071] Step 2: Analyze the characteristic factors affecting the carbon emissions of building materials. The characteristic factors included in the alternatives should at least include seismic fortification intensity, underground space area ratio, number of above-ground building floors, and building structure form.

[0072] Based on the sample database established in Step 1, significance determination should be carried out for factors such as seismic fortification intensity, building structure form, underground space area ratio, and number of above-ground building floors. Specifically, the alternative characteristic factors are used as independent variables, and the carbon emission index of building materials per unit building area is used as the dependent variable, and the significance of the effect of the independent variable on the dependent variable is determined through statistical methods.

[0073] Seismic fortification intensity and building structure form belong to categorical variables, and analysis of variance is used for significance determination. If the significance index p < 0.05, it indicates that this characteristic factor has a significant impact on the carbon emissions of building materials; if the significance index p ≥ 0.05, it indicates that the sample data in Step 1 is insufficient, and the sample quantity should be continued to be increased, and the characteristic factor determination in Step 2 should be repeated until the significance index p < 0.05. The building structure form participates in the significance determination, but in the fitting stage, it can be used as a classification basis to form multiple prediction formulas, or it can also be used as one of the fitting independent variables. In this embodiment, the building structure form is used as the classification basis to form multiple prediction formulas.

[0074] The underground space area ratio and the number of above-ground building floors are continuous variables, and correlation analysis is used to determine significant variables. If the significance index p < 0.05, it indicates that this characteristic factor has a significant impact on the carbon emissions of building materials; if the significance index p ≥ 0.05, it suggests that the sample data in Step 1 is insufficient, and the sample size should be continuously increased, and the determination of characteristic factors in Step 2 should be repeated until the significance index p < 0.05.

[0075] Step 3: According to the classification of building structure forms, the multiple linear regression method is used to fit the prediction formula. Taking "seismic fortification intensity, underground space area ratio, and number of above-ground building floors" as independent variables and "carbon emissions index of building materials per unit building area" as the dependent variable, a prediction model is established using the multiple linear regression method. The basic prediction formula is as follows:

[0076] C M =b 0 +b 1 x 1 +b 2 x 2 +b 3 x 3

[0077] Among them, C M is the carbon emissions index of building materials per unit building area, and x 1 ~x 3 represent the three independent variables of seismic fortification intensity, underground space area ratio, and number of above-ground building floors respectively, and b 0 ~b 3 are regression coefficients, which are obtained from the multiple linear regression analysis of the samples.

[0078] Evaluate and test the multiple regression model. The goodness of fit R-squared value and the F-test are used for evaluation.

[0079] Observe the R-squared value of the prediction model and analyze the fitting situation of the model. If the R-squared value ≥ 0.5, it means that the model has a fitting degree of more than 50%, and the fitting degree of the model can be accepted.

[0080] Conduct an F-test on the prediction model. If the test index p < 0.05, it indicates that the model is meaningful.

[0081] If the goodness of fit R-squared value and the F-test result do not meet the above standards, the sample size in Step 1 should be continuously increased, and the work in Step 2 and Step 3 should be repeated until the multiple regression model passes the evaluation and test.

[0082] It should be noted that the various embodiments in this specification are described in a progressive manner. The key point of any one embodiment is to illustrate the differences from other embodiments. For the same or similar parts among the embodiments, reference can be made to each other. For the systems disclosed in the embodiments, since they correspond to the methods disclosed in the embodiments, the description is relatively simple. For the relevant parts, reference can be made to the description in the method section.

[0083] It should also be noted that although the present invention has been disclosed above with preferred embodiments, the above embodiments are not intended to limit the present invention. For any person skilled in the art, without departing from the scope of the technical solution of the present invention, many possible changes and modifications can be made to the technical solution of the present invention by using the technical content disclosed above, or it can be modified into equivalent embodiments with equivalent changes. Therefore, any simple modification, equivalent change and modification made to the above embodiments according to the technical essence of the present invention without departing from the content of the technical solution of the present invention still fall within the scope of the protection of the technical solution of the present invention.

[0084] It should also be understood that unless otherwise specified or indicated, the terms "first", "second", "third", etc. in the specification are only used to distinguish the various components, elements, steps, etc. in the specification, rather than to represent the logical relationship or sequential relationship, etc. between the various components, elements, steps.

[0085] In addition, it should be recognized that the terms described herein are only used to describe specific embodiments and are not used to limit the scope of the present invention. It must be noted that the singular forms "a" and "an" used herein and in the appended claims include plural references unless the context clearly dictates otherwise. For example, the reference to "a step" or "a device" means a reference to one or more steps or devices and may include sub-steps and sub-devices. All conjunctions used should be understood in their broadest sense. Also, the word "or" should be understood to have the definition of logical "or", rather than the definition of logical "exclusive or", unless the context clearly dictates otherwise. In addition, the implementation of the embodiments of the present invention may include performing the selected tasks manually, automatically, or in combination.

Claims

1. A method for predicting carbon emission index of building materials, characterized in that: The following steps are involved: Based on the cases of multiple construction projects in the past, a carbon emission database of engineering cases is established, wherein the carbon emission database of engineering cases includes several kinds of engineering basic sub-information of each case and the carbon emission of building materials per unit building area of ​​each case, wherein the several kinds of engineering basic sub-information at least include seismic fortification intensity, building structure form, underground space area ratio, and number of above-ground building floors; Taking the engineering basic sub-information of the sample quantity as the independent variable, and the carbon emission per unit building area of ​​the case corresponding to the engineering basic sub-information as the dependent variable, the significance of the effect of the independent variable on the dependent variable is determined based on statistical methods; The engineering foundation sub-information including the seismic fortification intensity, the underground space area ratio, and the number of above-ground building floors that have passed the significance judgment is used as a fitting independent variable, and the carbon emission per unit building area of ​​the building materials of the case corresponding to the engineering foundation sub-information is used as a fitting dependent variable, and data fitting is performed to generate a fitting prediction model; The fitting prediction model is used to predict the carbon emissions of building materials for the target construction project.

2. The method for predicting carbon emission index of building materials according to claim 1, characterized in that: The engineering basic sub-information includes quantitative information, and the significance determination of the effect of the independent variable on the dependent variable based on the statistical method specifically includes: Based on the correlation analysis, the significance of the effect of the independent variable on the dependent variable is determined for the quantitative information.

3. The method for predicting carbon emission index of building materials according to claim 1, characterized in that: The engineering basic sub-information includes classification information, and the significance determination of the effect of the independent variable on the dependent variable based on the statistical method specifically includes: For the classification information, the significance of the effect of the independent variable on the dependent variable is determined based on variance analysis.

4. The method for predicting carbon emission index of building materials according to claim 1, characterized in that: The significance determination of the effect of the independent variable on the dependent variable based on statistical methods specifically includes: If the significance index shows that the result is not significantly affected, the sample size is increased and the significance determination is performed again until the significance index shows that the result is significantly affected.

5. The method for predicting carbon emission index of building materials according to claim 1, characterized in that: The generating of the fitting prediction model specifically includes: Based on the multiple linear regression method, according to the architectural structure form of the case, a prediction formula corresponding to several kinds of the architectural structure forms is established, and the prediction formula is used as the fitting prediction model.

6. The method for predicting carbon emission index of building materials according to claim 5, characterized in that: The generating of the fitting prediction model specifically further includes: The prediction formula was evaluated and tested using the goodness of fit R-square value.

7. The method for predicting carbon emission index of building materials according to claim 5, characterized in that: The generating of the fitting prediction model specifically further includes: The prediction formula is evaluated and tested using the F test method.

8. The method for predicting carbon emission index of building materials according to claim 6 or 7, characterized in that: If the evaluation test fails, the sample size is increased, and the significance determination and / or data fitting are performed again until the evaluation test passes.

9. An electronic device, characterized in that: The electronic device comprises: a memory storing a computer program; A processor, communicatively connected to the memory, and configured to execute the method for predicting carbon emission index of building materials according to any one of claims 1 to 8 when calling the computer program; A display is communicatively connected with the processor and the memory, and is used to display a GUI interactive interface related to the building material carbon emission index prediction method.

10. A readable storage medium storing a computer program, characterized in that: When the computer program is executed by a processor, the method for predicting carbon emission index of building materials according to any one of claims 1 to 8 is implemented.

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