Evaluation method for green and low-carbon construction level of mountain building based on matter element extension

Abstract

The invention aims to provide a mountain building green low-carbon construction level evaluation method based on matter element extension, and belongs to the technical field of building green low-carbon construction evaluation. According to the method, an evaluation system including seven first-level indexes including environmental protection, green low-carbon construction comprehensive management, resource saving and utilization, social benefit, green low-carbon construction technology, human resource saving and protection, and carbon emission and carbon sink and 56 second-level indexes is constructed, and comprehensive evaluation is carried out for mountain building characteristics; and a G1 method and a C-OWA operator are combined to determine the weight of each index, and the green low-carbon construction level of the mountain building is scientifically evaluated based on a matter-element extension model. The method can objectively reflect the green low-carbon construction situation of the mountain building, fills the blank of evaluation of the low-carbon construction of the mountain building in the existing research, and provides an effective countermeasure for improving the construction level through the evaluation result, thereby promoting the green low-carbon high-quality development of the building industry and assisting the realization of the carbon emission reduction target.

Description

Technical Field

[0001] The present invention relates to the technical field of green and low-carbon construction evaluation of buildings, and particularly to a method for evaluating the green and low-carbon construction level of mountain buildings based on matter-element extension. Background Art

[0002] Since the Industrial Revolution, with the gradual increase in the global temperature, addressing climate change has become the consensus of all mankind. However, at present, China's per capita carbon emissions are higher than the world average level, and the pressure of carbon emission reduction is huge. The construction industry, as an important field of energy consumption and carbon emissions, its energy consumption and carbon emissions are close to 50% of the total national amount. Among them, the construction stage has become the stage with the highest carbon emission density due to its long duration, large resource consumption, large greenhouse gas emissions, and heavy environmental burden. Therefore, promoting green and low-carbon construction is one of the key ways to achieve carbon emission reduction in the construction industry.

[0003] China is a large mountainous country, with the mountainous area accounting for about 2 / 3 of the national land area, reaching 6.66 million square kilometers. With the in-depth development of urbanization, the flat land resources are becoming increasingly scarce, and construction activities are gradually expanding to the mountains. More and more plain cities are also starting to extend to the mountainous areas. Due to its unique geographical environment and technical requirements, mountain buildings have become an important development direction for the future construction industry. However, existing green construction and low-carbon construction evaluation standards, such as "Code for Green Construction of Building Engineering" (GB / T 50905-2014), "Evaluation Standard for Green Buildings" (GB / T 50378-2019), "Evaluation Standard for Green Construction of Building and Municipal Engineering" (GB / T 50640-2023), and "Evaluation Standard for Low-Carbon Buildings" (T / CSUS 60-2023), although they have guiding significance in the evaluation of green construction and low-carbon buildings, are all general specifications and do not fully consider the regional particularity and construction characteristics of mountain buildings. In addition, there is currently a lack of a special evaluation system for low-carbon construction in China. Although green construction and low-carbon construction can complement each other, they cannot completely replace each other, resulting in difficulty in fully considering both in actual projects and restricting the effective evaluation and improvement of the green and low-carbon level of mountain buildings.

[0004] Therefore, under the background of the dual-carbon goal, in view of the special needs of mountain buildings, exploring the development direction of green and low-carbon construction and constructing a set of scientific and objective evaluation systems for the green and low-carbon construction level of mountain buildings not only can fill the gaps in existing research, but also has important significance for guiding the green and low-carbon practice of mountain buildings and promoting the sustainable development of the construction industry. Summary of the Invention

[0005] The purpose of the present invention is to overcome the technical defects in the prior art of lacking a quantitative evaluation system for green and low-carbon construction of mountain buildings, a single weighting method resulting in significant weight deviation, and difficulty in integrating qualitative and quantitative indicators, and to provide a green and low-carbon construction level evaluation method for mountain buildings based on matter-element extension. By constructing a multidimensional evaluation system covering mountain characteristic indicators such as slope support technology and slag interception rate, integrating the subjective and objective dynamic balance mechanism of G1 method expert experience weighting and C-OWA operator data-driven weighting, and using matter-element extension theory to establish a unified quantitative model for qualitative / quantitative indicators, accurate classification of green and low-carbon construction levels and identification of weak links in mountain scenarios can be achieved, providing data support for construction optimization.

[0006] To achieve the above object, the present invention provides the following technical solutions:

[0007] S1. Construct an evaluation index system for the green and low-carbon construction level of mountain buildings, including seven first-level indicators: environmental protection, comprehensive management of green and low-carbon construction, resource conservation and utilization, social benefits, green and low-carbon construction technology, human resource conservation and protection, and carbon emissions and carbon sinks. Each first-level indicator has several second-level indicators.

[0008] S2. Use the G1 method to determine the weight of each evaluation index, including determining the order relationship between the primary and secondary indicators, calculating the weight coefficient and the weight under the expert group decision;

[0009] S3, using C-OWA operator to determine the weight of each evaluation index, including expert scoring, data descending order, calculation of weighted vectors based on the number of combinations, and determination of absolute weight and relative weight;

[0010] S4, weighting the weights of S2 and S3 by linear weighted synthesis method to obtain the final weight to form a matter-element extension model;

[0011] S5. Evaluate the green and low-carbon construction level of mountain buildings based on the object-element extension model, including constructing the object-element to be evaluated, determining the grade domain and the classic domain, and calculating the index correlation and evaluation grade;

[0012] S6. According to steps S1 to S5, a technical process for evaluating the green and low-carbon construction level of mountain buildings is formed, and an evaluation grade is output.

[0013] Furthermore, in step S2, when determining the order relationship, the relative importance ratio r between the indicators is obtained through an expert questionnaire. k , where r k It represents the importance ratio of the kth indicator to the k+1th indicator; the weight coefficient is obtained by multiplying the r between adjacent indicators. k Calculation; The weight under the expert group decision is calculated by the weighted average algorithm:

[0014]

[0015] Wherein: is the weight of the s-th expert for the index j, and m is the total number of experts.

[0016] Further, in the step S3, the weighted vector of the C-OWA operator is determined by the following combination formula:

[0017]

[0018] Wherein: n is the number of expert scoring data is the combination symbol, indicating taking i from

[0019] n - 1 elements, i is the data serial number after descending order, numbered from 0, and the constraint condition is

[0020] Further, in the step S3, the absolute weight value W a and the relative weight W r The calculation formulas are respectively:

[0021]

[0022] Wherein, v i is the i-th expert scoring data after descending order, m is the total number of indicators, and w i is the weighted vector determined by claim 3.

[0023] Further, in the step S4, the preference coefficients θ1 and θ2 of the linear weighted synthesis method are both taken as 0.5, and the final weight is:

[0024]

[0025] Wherein: is the weight of the index j obtained by the G1 method is the weight of the index j obtained by the C-OWA operator.

[0026] Further, in the step S5, the index correlation degree is calculated by the following formula:

[0027]

[0028] Wherein, ρ is the distance function, used to measure the distance relationship between the index value of the matter element to be evaluated and the index values of the classical domain and the section domain, and the distance reflected by calculating the distance function ρ is used to reflect the degree of closeness of the association between the matter element to be evaluated and each evaluation level; ρ(v 0i , v ji ) represents the i-th index value v 0i of the matter element to be evaluated and the i-th index value v ji under the j-th level in the classical domain; ρ(v 0i , v pi ) represents the distance between the value v 0i of the i-th indicator of the matter element to be evaluated and the value v pi of the i-th indicator in the section domain. The smaller the calculation result, the closer the value of the i-th indicator of the matter element to be evaluated is to the indicator range of the corresponding level, and the higher the correlation degree with this level. Otherwise, it is lower;

[0029] And

[0030] v ji = <a ji , b ji , v pi = <a pi , b pi , v 0i is the measured value of the i-th indicator of the matter element to be evaluated, v ji is the classical domain interval of the i-th indicator of the matter element to be evaluated for level j, a ji is the lower limit of the classical domain interval v ji , b ji is the upper limit of the classical domain interval v ji ; v pi is the section domain interval of the i-th indicator of the matter element to be evaluated, a pi is the lower limit of the section domain interval v pi , b pi is the upper limit of the section domain interval v pi ; The length of the classical domain interval |v ji | = b ji - a ji , and the length of the section domain interval |v pi | = b pi - a pi .

[0031] Further, in the step S5, the evaluation level is determined by the principle of maximum correlation degree, and the formula is:

[0032]

[0033] Among them, w i is the combined weight of the i-th indicator value of the matter element to be evaluated, n is the total number of indicators, K j (v i ) is the correlation degree of the i-th indicator value of the matter element to be evaluated for level j, and j0 is the finally determined evaluation level.

[0034] Further, in the step S6, the technical process includes:

[0035] Data collection and preprocessing: Obtain the measured values or expert scores of each secondary indicator;

[0036] Input index data: Input the data into the matter-element extension model.

[0037] Output evaluation level: Output the green and low-carbon construction level according to the maximum correlation degree.

[0038] Furthermore, the first-level social benefit indicators include 4 second-level indicators: underground resource protection, surrounding cultural environment protection, animal and plant protection, and the satisfaction of nearby residents with the construction; the first-level green and low-carbon construction technology indicators include 7 second-level indicators: the application of BIM information technology, the application of new green and low-carbon technologies and new processes, prefabricated construction technology, technology research and development or micro-innovation, slope support technology, semi-slope pile construction technology, and semi-basement construction technology.

[0039] Furthermore, in step S5, the grade domain is divided into 5 grades: low, relatively low, medium, relatively high, and high, and the corresponding intervals are [0, 20), [20, 40), [40, 60),

[0040] [60, 80), [80, 100];

[0041] The classical domain and the joint domain are determined by the natural breakpoint grading method or expert opinions.

[0042] Compared with the prior art, the beneficial effects achieved by the present invention are as follows:

[0043] (1) The present invention adopts a combined weighting technique that combines the G1 method and the C-OWA operator to scientifically determine the weights of evaluation indicators, effectively avoiding the deviation caused by subjective judgment in traditional single weighting methods. Compared with the commonly used expert scoring or simple weighting methods in the prior art, the weighting technique of the present invention combines expert knowledge and data characteristics, making the weight distribution more reasonable and significantly improving the objectivity and accuracy of the evaluation of the green and low-carbon construction level of mountain buildings.

[0044] (2) The present invention designs an evaluation system including 7 first-level indicators (environmental protection, comprehensive management of green and low-carbon construction, resource conservation and utilization, social benefits, green and low-carbon construction technology, human resource conservation and protection, carbon emissions and carbon sinks) and 56 second-level indicators, comprehensively covering the key aspects of the green and low-carbon construction of mountain buildings. Compared with the evaluation systems with strong generality in the prior art, the indicator system of the present invention is optimized for the special needs of mountain buildings and can more accurately reflect its construction characteristics and green and low-carbon level.

[0045] (3) The present invention fully considers the characteristics of complex terrain and ecological sensitivity of mountain architecture, combines the matter-element extension model and a customized evaluation system, and solves the problem of insufficient adaptability of the general evaluation method in the prior art to the mountain environment. Compared with the prior art, the present invention can more accurately evaluate the green and low-carbon construction level of mountain architecture and provide more targeted guidance for construction practice under complex terrain conditions.

[0046] (4) Based on the matter-element extension model, the present invention can effectively handle the uncertainties generated during the evaluation process due to the changing construction environment and fuzzy data. Compared with the statistical analysis or linear models commonly used in the prior art, the present invention has significant advantages in dealing with fuzzy information under complex and changing conditions, thereby improving the reliability and applicability of the evaluation results.

[0047] (5) The evaluation results of the present invention can provide specific and operable scientific basis for the green and low-carbon construction of mountain architecture, help construction enterprises identify the advantages and disadvantages in construction, and formulate improvement measures. Compared with the defect of relatively general evaluation results in the prior art, the present invention is more practical and helps to promote the improvement of the construction level of mountain architecture and the reduction of environmental impact.

[0048] In summary, through a scientific weight allocation method, a comprehensive index system, an optimized design for the particularity of mountain architecture, and an effective handling of uncertainties, the present invention significantly improves the accuracy, objectivity, and practicality of the evaluation of the green and low-carbon construction level of mountain architecture. Compared with the prior art, the present invention not only fills the gap in the green and low-carbon evaluation method in the field of mountain architecture, but also provides a powerful tool for industry practice and policy implementation, having significant technical advantages and application prospects. BRIEF DESCRIPTION OF THE DRAWINGS

[0049] The drawings forming a part of the specification depict embodiments of the present invention and, together with the specification, are used to explain the principles of the present invention.

[0050] Referring to the drawings, the present invention can be more clearly understood according to the following detailed description, wherein:

[0051] Figure 1 is a flowchart of the method for evaluating the green and low-carbon construction level of mountain architecture based on the matter-element extension provided by an embodiment of the present invention. DETAILED DESCRIPTION OF THE EMBODIMENTS

[0052] The technical solution of the present invention is described in detail below through the accompanying drawings and specific embodiments. It should be understood that the embodiments of the present application and the specific features in the embodiments are detailed descriptions of the technical solution of the present application, rather than limitations on the technical solution of the present application. In the absence of conflict, the embodiments of the present application and the technical features in the embodiments can be combined with each other. The following embodiments are only used to more clearly illustrate the technical solution of the present invention, and cannot be used to limit the scope of protection of the present invention.

[0053] The term "and / or" in this article is only a description of the association relationship of the associated objects, indicating that there can be three relationships. For example, A and / or B can mean: A exists alone, A and B exist at the same time, and B exists alone. In addition, the character " / " in this article generally indicates that the associated objects before and after are in an "or" relationship.

[0054] Figure 1 This is a flow chart of the method for evaluating the green and low-carbon construction level of mountain buildings based on matter-element extension in Example 1 of the present invention. This flow chart only shows the logical sequence of the method described in this embodiment. Under the premise of no conflict, in other possible embodiments of the present invention, different Figure 1 The steps shown or described are accomplished in the order shown.

[0055] This embodiment is a typical implementation of the present invention, and provides a method for evaluating the green and low-carbon construction level of mountain buildings based on matter-element extension. Figure 1 As shown, the method of this embodiment specifically includes the following steps:

[0056] S1. Construct an evaluation index system for the green and low-carbon construction level of mountain buildings, including seven first-level indicators: environmental protection, comprehensive management of green and low-carbon construction, resource conservation and utilization, social benefits, green and low-carbon construction technology, human resource conservation and protection, and carbon emissions and carbon sinks. Each first-level indicator has several second-level indicators.

[0057] S2. Use the G1 method to determine the weight of each evaluation index, including determining the order relationship between the primary and secondary indicators, calculating the weight coefficient and the weight under the expert group decision;

[0058] S3, using C-OWA operator to determine the weight of each evaluation index, including expert scoring, data descending order, calculation of weighted vectors based on the number of combinations, and determination of absolute weight and relative weight;

[0059] S4, weighting the weights of S2 and S3 by linear weighted synthesis method to obtain the final weight to form a matter-element extension model;

[0060] S5. Evaluate the green and low-carbon construction level of mountain buildings based on the matter-element extension model, including constructing the matter-elements to be evaluated, determining the grade domain and classical domain, calculating the index correlation degree, and evaluating the grade;

[0061] S6. Form the evaluation technical process of the green and low-carbon construction level of mountain buildings according to steps S1 to S5, and output the evaluation grade.

[0062] Specifically, in step S1, the first-level index of environmental protection includes 12 second-level indexes: dust control, waste gas emission control, noise control, construction waste emissions per unit area, construction waste recycling rate, domestic waste treatment, control of toxic and harmful waste, sewage discharge control, light pollution control, vegetation restoration rate, slag retention rate, and soil environmental impact; the first-level index of resource conservation and utilization includes 17 second-level indexes: main material conservation measures, material localization rate, green and low-carbon material usage rate, recyclable material usage rate, repeated utilization rate of turnover materials, material loss reduction rate, water use plan, water conservation rate, non-conventional water utilization rate, water-saving equipment usage rate, effective utilization rate of temporary facility floor area, waste residue utilization rate, new energy mechanical equipment usage rate, energy conservation rate, clean and renewable energy usage rate, electric energy and fuel conservation measures, and energy-saving lighting fixture usage rate.

[0063] Furthermore, in step S2, when determining the order relationship, obtain the relative importance ratio r k of the indexes through an expert questionnaire k , where r k represents the importance ratio of the kth index to the k + 1th index; the weight coefficient is calculated by multiplying the r k between adjacent indexes; the weight under the group decision-making of the expert group is calculated by the weighted average algorithm:

[0064]

[0065] where: is the weight of the sth expert for the index j, and m is the total number of experts.

[0066] Specifically, in step S3, the weighted vector of the C-OWA operator is determined by the following combination formula:

[0067]

[0068] where: n is the number of expert scoring data is the combination symbol, indicating the combination of taking

[0069] i from n - 1 elements, i is the data serial number after descending order, numbered from 0, and the constraint condition is

[0070] Furthermore, in step S3, the absolute weight value W aand the relative weight W r The calculation formulas are as follows:

[0071]

[0072] where v i is the i-th expert scoring data after being sorted in descending order, m is the total number of indicators, and w i is the weighted vector determined by claim 3.

[0073] Specifically, in step S4, both the preference coefficients θ1 and θ2 of the linear weighted synthesis method are taken as 0.5, and the final weight is:

[0074]

[0075] where: is the weight of indicator j obtained by the G1 method is the weight of indicator j obtained by the C-OWA operator.

[0076] Furthermore, in the said step S5, the index correlation degree is calculated by the following formula:

[0077]

[0078] where ρ is the distance function, which is used to measure the distance relationship between the index value of the matter element to be evaluated and the index values of the classical domain and the section domain. The correlation tightness degree between the matter element to be evaluated and each evaluation level is reflected by calculating the distance reflected by the distance function ρ; ρ(v 0i , v ji ) represents the distance between the i-th index value v 0i of the matter element to be evaluated and the i-th index value v ji under the j-th level in the classical domain; ρ(v 0i , v pi ) represents the distance between the i-th index value v 0i of the matter element to be evaluated and the i-th index value v pi in the section domain. The smaller the calculation result, the closer the index value of the matter element to be evaluated is to the index range of the corresponding level, and the higher the correlation degree with this level, otherwise it is lower;

[0079] and

[0080] v ji = <a ji , b ji >, v pi = <a pi , b pi >, v 0i is the measured value of the i-th index value of the matter element to be evaluated, v jiis the classical domain interval of the i-th index value of the matter element to be evaluated for level j, a ji is the classical domain interval v ji 's lower limit, b ji is the classical domain interval v ji 's upper limit; v pi is the section domain interval of the i-th index value of the matter element to be evaluated, a pi is the section domain interval v pi 's lower limit, b pi is the section domain interval v pi 's upper limit; the length of the classical domain interval |v ji | = b ji - a ji , the length of the section domain interval |v pi | = b pi - a pi .

[0081] Furthermore, in the step S5, the evaluation level is determined by the principle of maximum correlation degree, and the formula is:

[0082]

[0083] where w i is the combined weight of the i-th index value of the matter element to be evaluated, n is the total number of indicators, K j (v i ) is the correlation degree of the i-th index value of the matter element to be evaluated for level j, and j0 is the finally determined evaluation level.

[0084] Specifically, in step S6, the technical process includes:

[0085] Data collection and preprocessing: Obtain the measured values or expert scores of each secondary indicator;

[0086] Input index data: Input the data into the matter element extension model;

[0087] Output evaluation level: Output the green and low-carbon construction level according to the maximum correlation degree.

[0088] Specifically, the first-level indicator of social benefits includes 4 secondary indicators: underground resource protection, surrounding cultural environment protection, animal and plant protection, and the satisfaction of nearby residents with the construction; the first-level indicator of green and low-carbon construction technology includes 7 secondary indicators: the application of BIM information technology, the application of new green and low-carbon technologies and new processes, prefabricated construction technology, technology R & D or micro-innovation, slope support technology, semi-slope pile construction technology, and semi-basement construction technology.

[0089] Further, in step S5, the grade field is divided into five grades: low, relatively low, medium, relatively high, and high, and the corresponding intervals are [0, 20), [20, 40), [40, 60), [60, 80), and [80, 100]; the classical field and the section field are determined by the natural breakpoint grading method or expert opinions.

[0090] The purpose of the present invention is to overcome the deficiencies in the prior art and provide a method for evaluating the green and low-carbon construction level of mountain buildings based on matter-element extension, so as to realize the comprehensive evaluation of the green and low-carbon construction level in the special scenarios of mountain buildings from multiple dimensions and dynamically quantify it. The present invention mainly solves the following technical problems:

[0091] One is to construct an evaluation index system suitable for the characteristics of mountain buildings. By integrating the particularity of construction technology (such as semi-basement construction technology E7), the sensitivity of ecological protection (such as the slag retention rate A11), and the requirements for carbon emission control (such as the carbon emission per unit area G1), it solves the problem of insufficient pertinence of traditional general evaluation systems to the complex terrain and construction technology of mountains; the other is to establish a dynamic weight assignment model that combines subjective and objective factors. The G1 method is used to quantify the weights of expert experience, and the C-OWA operator is used to calculate the data-driven weights. Through the linear weighted synthesis formula:

[0092]

[0093] It realizes the balance between subjective judgment and objective data and eliminates the weight deviation caused by a single weight assignment method; the third is to propose a unified quantification method for qualitative and quantitative indicators based on matter-element extension. By constructing a multi-level correlation function, the expert scores of qualitative indicators (such as light pollution control A9) and the measured data of quantitative indicators (such as the water-saving rate C8) are mapped to the same extension space, solving the problem of multi-dimensional heterogeneous data fusion.

[0094] Embodiment 1

[0095] As a specific embodiment, the steps included in the method for evaluating the green and low-carbon construction level of mountain buildings based on matter-element extension proposed by the present invention are as follows:

[0096] Step S1: Construct an evaluation index system for the green and low-carbon construction level of mountain buildings. An evaluation index system for this study is constructed from the dimensions of environmental protection, comprehensive management of green and low-carbon construction, resource conservation and utilization, etc., and then secondary indicators are set.

[0097] Step S2: Apply the G1 method to determine the weights of evaluation indicators;

[0098] Step S3: Apply the C-OWA operator to determine the weights of evaluation indicators;

[0099] Step S4: Apply the linear weighted synthesis method to combine the G1-C-OWA combined weights to determine the final weights;

[0100] Step S5: Evaluate the green and low-carbon construction level of mountain buildings based on the matter-element extension model;

[0101] Step S6: Propose a technical process for evaluating the green and low-carbon construction level of mountain buildings based on the above basic operations.

[0102] Preferably, the evaluation index system for the green and low-carbon construction level of mountain buildings in Step S1 is determined based on national and group-level specification standards, relevant domestic and foreign literature, and expert interviews. The evaluation indexes for the green and low-carbon construction level of mountain buildings include seven first-level indexes: environmental protection, comprehensive management of green and low-carbon construction, resource conservation and utilization, social benefits, green and low-carbon construction technology, human resource conservation and protection, and carbon emissions and carbon sinks;

[0103] Environmental protection includes twelve second-level indexes: dust control, waste gas emission control, noise control, construction waste emissions per unit area, construction waste recycling rate, domestic waste treatment, control of toxic and harmful waste, sewage discharge control, light pollution control, vegetation restoration rate, slag retention rate, and soil environmental impact;

[0104] Comprehensive management of green and low-carbon construction includes twelve second-level indexes: layout of the general construction plan, green and low-carbon construction organization design, management of green and low-carbon construction plans, the enterprise's own environmental management system, green and low-carbon publicity and training, construction site appearance and enclosure management, energy conservation and emission reduction statistics and monitoring, green and low-carbon construction awareness, green and low-carbon evaluation and assessment management, protection of finished and semi-finished products, planning of the access route for large machinery, and planning of temporary land occupation for waste residue;

[0105] Resource conservation and utilization includes seventeen second-level indexes: main material conservation measures, material localization rate, usage rate of green and low-carbon materials, usage rate of recyclable materials, repeated utilization rate of turnover materials, material loss reduction rate, water use plan, water conservation rate, non-traditional water utilization rate, usage rate of water-saving equipment, effective utilization rate of the floor area of temporary facilities, waste residue utilization rate, usage rate of new energy construction machinery and equipment, energy conservation rate, usage rate of clean and renewable energy, electricity and fuel conservation measures, and usage rate of energy-saving lighting fixtures;

[0106] Social benefits includes four second-level indexes: protection of underground resources, protection of surrounding cultural environment, protection of animals and plants, and satisfaction of nearby residents with the construction;

[0107] Green and low-carbon construction technology includes seven second-level indexes: application of information technologies such as BIM, application of new green and low-carbon technologies and new processes, prefabricated construction technology, technology research and development or micro-innovation, slope support technology, semi-slope pile construction technology, and semi-basement construction technology;

[0108] Human resource conservation and protection includes two second-level indexes: human resource conservation rate and employee health check-up;

[0109] Carbon emissions and carbon sinks include two secondary indicators: carbon emissions per unit area and greening management at the construction site;

[0110] Preferably, the determination of the weight of each indicator in step S2 applies the G1 method, including the following steps:

[0111] Step S2.1: Determine the order relationship between the primary indicators and the secondary evaluation indicators;

[0112] Step S2.2: Give the judgment on the relative importance between each indicator, denoted by r k indicates;

[0113] Step S2.3: Calculate the weight coefficient of the evaluation indicator s is the serial number of the expert

[0114] Step S2.4: Determine the weight under the group decision-making situation of the expert group

[0115] Preferably, in steps S2.1 and S2.2, the determination of the order relationship of the evaluation indicators and the judgment of the relative importance of the indicators are obtained through the data of the expert questionnaire;

[0116] Preferably, in step S2.3, the calculation of the weight coefficient of the indicator is obtained by multiplying the relative importance ratios between adjacent indicators to obtain the weight of each indicator;

[0117] Preferably, in step S2.4, the determination of the weight under the group decision-making situation of the expert group is obtained by using the weighted average algorithm, so as to obtain the final weight coefficient, including the following steps:

[0118] 1) Calculate the weight coefficient of each expert for a certain evaluation indicator through steps S2.1, S2.2, and S2.3;

[0119] Specifically, select the most important one of the n indicators from the evaluation indicator set {X1, X2, X3…X n}, marked as Select the most important one of the remaining n - 1 indicators, marked as And so on, mark the last selected evaluation indicator as Thus, the order relationship of each evaluation indicator is determined, denoted as: After that, determine the evaluation indicators and The ratio of importance r k .

[0120] 2) Use the weighted average algorithm, (s = 1, 2, 3,…, m) to obtain the final weight coefficient m represents the number of experts;

[0121] Preferably, the determination of the weights of each index in step S3 applies the C-OWA operator method, including the following steps:

[0122] Step S3.1: Invite experts in the relevant field to score the importance degree of the first-level index and the second-level index (using a ten-point system, the higher the score, the greater the importance degree), and obtain the initial decision data set A = (a1, a2, a3, ……, a n );

[0123] Step S3.2: Process the initial decision data. Following the descending order principle, rearrange the initial decision data from largest to smallest to obtain a new data set B = (b0, b1, b2, ……, b n-1 ), where b0 ≥ b1 ≥ b2 ≥ … ≥ b n-1 ;

[0124] Step S3.3: Calculate the data weights and determine the weighted vector w i+1 ;

[0125] Step S3.4: Determine the weights of the evaluation indexes based on the C-OWA operator

[0126] Preferably, in step S3.3, the combination number is used to calculate the weights of the scoring data and determine the weighted vector w i+1 , where In the formula, w i+1 represents the weight of the (i + 1)-th value in the data set B, is the combination number of selecting the i-th from n - 1 data, i = 0, 1, 2, ···, n - 1, and

[0127] Preferably, the determination of the weights of the evaluation indexes in step S3.4 includes the following steps:

[0128] 1) Calculate the absolute weight value of the index In the formula, j ∈ [1, k], i ∈ [0, n - 1], where k represents the number of indexes; n represents the number of experts participating in the scoring;

[0129] 2) Calculate the relative weight of the index

[0130] Preferably, in step S4, the final weight W is determined by using the linear weighted synthesis method to combine the G1-C-OWA weights; θ1 and θ2 are the preference coefficients of the two weights respectively. Usually, θ1 and θ2 can both be taken as 0.5.

[0131] Preferably, in step S5, the green and low-carbon construction level of mountainous buildings is evaluated using the matter-element extension model, which includes the following steps:

[0132] Step S5.1: Construct the matter-element R to be evaluated for the green and low-carbon construction level;

[0133] Step S5.2: Determine the grade domain for the evaluation of the green and low-carbon construction level;

[0134] Step S5.3: Determine the classical domain R j and the joint domain R p , where In the formula, Nj (j = 1, 2,..., m) represents that the evaluated object N has j grades; ci (i = 1, 2,..., n) is the characteristic of the green and low-carbon construction evaluation grade N j and represents the constructed i-th evaluation index; v ji <aji, bji> represents the value range of the i-th index at grade j, that is, the classical domain, where a ji represents the upper limit of the value, and b ji represents the lower limit of the value; the joint domain In the formula, N p represents the overall effect grade, ci represents the i-th evaluation index, and v pi is the value range specified for the effect grade N p with respect to c i .

[0135] Step S5.4: Determine the matter-element matrix R0 to be evaluated; In the formula, N0 represents the evaluation grade of the construction level of the object to be evaluated, and v 0i is the value of N0 with respect to c i .

[0136] Step S5.5: Calculate the index correlation degree K j (v i );

[0137] Step S5.6: Determine the evaluation grade K j ;

[0138] Preferably, in step S5.2, the evaluation grades of the green and low-carbon construction level of mountainous buildings are divided into 5 grades: "low, relatively low, medium, relatively high, high";

[0139] Preferably, the specific methods for determining the classical domain and the section domain in step S5.3 are as follows: For quantitative indicators, generally, the grading evaluation criteria of the indicators are determined by referring to relevant literature, consulting experts, etc. For qualitative indicators, according to the data of the actual project, they are quantitatively evaluated by expert scoring, and the grading criteria are determined by equal division using the natural breakpoint grading method;

[0140] Preferably, the calculation of the correlation degree in step S5.5 includes the following steps:

[0141] 1) Select the correlation function K j (v i );

[0142]

[0143] v ji =<a ji ,b ji >,v pi =<a pi ,b pi >,v ji represents the length of the classical domain interval of the indicator i at level j, v ji =b ji -a ji ,v pi represents the effect level N p with respect to the specified value range of the evaluation indicator c i , that is, the value range of the i-th evaluation indicator in the section domain, v pi =b pi -a pi .

[0144] Among them, ρ is the distance function, which is used to measure the distance relationship between the index value of the matter element to be evaluated and the index values of the classical domain and the section domain. The closeness of the association between the matter element to be evaluated and each evaluation level is reflected by calculating this distance. ρ(v 0i ,v ji ) in formula (2) represents the distance between the i-th index value v 0i of the matter element to be evaluated and the i-th index value v ji at the j-th level in the classical domain; ρ(v 0i ,v pi ) in formula (3) represents the distance between the i-th index value v 0i of the matter element to be evaluated and the i-th index value v pi in the section domain. The smaller the distance calculation result, the closer the index value of the matter element to be evaluated is to the index range of the corresponding level, and the higher the correlation degree with this level, and vice versa.

[0145] 2) Calculate the correlation degree K of each secondary indicator by combining equations (1), (2), and (3) j (vi ); The calculation process of dust control A1 is as follows:

[0146] ρ(v 01 ,v 11 ) = |v1 - (a 11 + b 11 ) / 2| - (b 11 - a 11 ) = |75.8 - (0 + 20) / 2| - (20 - 0) / 2 = 55.8

[0147] ρ(v 01 ,v p1 ) = |v1 - (a p1 + b p1 ) / 2| - (b p1 - a p1 ) = |75.8 - (0 + 100) / 2| - (100 - 0) / 2 = -24.2

[0148] K1(v1) = ρ(v 01 ,v 11 ) / [ρ(v 01 ,v p1 ) - ρ(v 01 ,v 11 )] = 55.8 / (-24.2 - 55.8) = -0.6975

[0149] ρ(v 01 ,v 21 ) = |v1 - (a 21 + b 21 ) / 2| - (b 21 - a 21 ) = |75.8 - (20 + 40) / 2| - (40 - 20) / 2 = 35.8

[0150] K2(v1) = ρ(v 01 ,v 21 ) / [ρ(v 01 ,v p1 ) - ρ(v 01 ,v 21 )] = 35.8 / (-24.2 - 35.8) = -0.5967

[0151] ρ(v 01 ,v 31 ) = |v1 - (a 31 + b 31 ) / 2| - (b 31 - a 31 ) = |75.8 - (40 + 60) / 2| - (60 - 40) / 2 = 15.8

[0152] K3(v1) = ρ(v 01 , v 31 ) / [ρ(v 01 , v p1 ) - ρ(v 01 , v 31 )] = 15.8 / (-24.2 - 15.8) = -0.3950

[0153] ρ(v 01 , v 41 ) = |v1 - (a 41 + b 41 ) / 2| - (b 41 - a 41 ) = |75.8 - (60 + 80) / 2| - (80 - 60) / 2 = -4.2

[0154] K4(v1) = -ρ(v 01 , v 41 ) / |b 41 - a 41 | = 4.2 / (80 - 60) = 0.2100

[0155] ρ(v 01 , v 51 ) = |v1 - (a 51 + b 51 ) / 2| - (b 51 - a 51 ) = |75.8 - (80 + 100) / 2| - (100 - 80) / 2 = 4.2

[0156] K5(v1) = ρ(v 01 , v 51 ) / [ρ(v 01 , v p1 ) - ρ(v 01 , v 51 )] = 4.2 / (-24.2 - 4.2) = -0.1479

[0157] Therefore, the correlation degree of A1 can be expressed as K(A1) = (-0.6975, -0.5967, -0.3950, 0.2100, -0.1479). According to the principle of maximum correlation degree, since the value of K4(A1) is the largest, the evaluation level of index A1 is at a relatively high level. Similarly, the correlation degree levels of other secondary indicators and their corresponding evaluation levels can be calculated.

[0158] Combined with the comprehensive weight, calculate the comprehensive correlation degree K j (N0), where w iis the corresponding comprehensive weight; N0 represents the evaluation grade of the construction level of the subject to be evaluated; taking the solution of K1(N0) as an example:

[0159]

[0160] Preferably, the evaluation grade K j in step S5.6 is determined according to the principle of the largest correlation degree, and K j = max K j (N0);

[0161] Preferably, the technical process for evaluating the green and low-carbon construction level of mountain buildings proposed in step S6 based on the above basic operations includes the following steps:

[0162] Step S6.1, data collection and preprocessing of evaluation indicators for the green and low-carbon construction level of mountain buildings;

[0163] Step S6.2, inputting the data of evaluation indicators for the green and low-carbon construction level of mountain buildings;

[0164] Step S6.3, based on step S6, determining the evaluation model for the green and low-carbon construction level of mountain buildings and outputting the green and low-carbon construction level grade of mountain buildings.

[0165] Embodiment 2

[0166] As an embodiment, the method for evaluating the green and low-carbon construction level of mountain buildings based on matter-element extension systematically identifies the evaluation indicators for the green and low-carbon construction level of mountain buildings from seven dimensions: environmental protection, comprehensive management of green and low-carbon construction, resource conservation and utilization, social benefits, green and low-carbon construction technology, human resource conservation and protection, and carbon emissions and carbon sinks, and selects the G1-C-OWA combined weighting and matter-element extension model as the method for evaluating the green and low-carbon construction level of mountain buildings, fully combining sample data with human experience and knowledge to avoid subjective biases, and thus objectively evaluating the process of the green and low-carbon construction level grade of mountain buildings, including the following steps:

[0167] Step S1: Construct an evaluation index system for the green and low-carbon construction level of mountain buildings, establish an index system from seven dimensions: environmental protection, comprehensive management of green and low-carbon construction, resource conservation and utilization, social benefits, green and low-carbon construction technology, human resource conservation and protection, and carbon emissions and carbon sinks, and set up secondary indicators.

[0168] Specifically, according to national and group-level specification standards, relevant domestic and foreign literature, and expert interviews, the primary and secondary indicators for evaluating the green and low-carbon construction level of mountain buildings are determined, as shown in Table 1.

[0169] Table 1 is the evaluation index system for the green and low-carbon construction level of mountain buildings:

[0170]

[0171]

[0172] Step S2: Determine the weight of each evaluation index by applying the G1 method based on expert knowledge;

[0173] In step S2, according to the professional knowledge and work practice experience of experts or scholars in the field of mountain architecture, the G1 method is used to rank the importance of the first-level and second-level evaluation indicators respectively. The most important indicator is selected one by one from the evaluation index set to determine the order relationship between each evaluation index. Then, the importance of the first-level and second-level indicators is assigned values from 1.0 to 1.8. The weight of each indicator is obtained by multiplying the relative importance ratios between adjacent indicators. To ensure the objectivity and representativeness of the weights, the weight coefficient of each expert for a certain evaluation index is calculated; the weighted average algorithm is used to obtain the final weight coefficient. Among them, the importance assignment refers to the ratio of importance r k Assignment table, as shown in Table 2.

[0174] Table 2 is the r k Assignment table:

[0175]

[0176] Step S3: Determine the weight of the evaluation index by using the C-OWA operator based on expert knowledge;

[0177] In step S3, according to the professional knowledge and work practice experience of experts or scholars in the field of mountain architecture, the C-OWA operator is used to score the importance of the first-level and second-level evaluation indicators from 1 to 10. The score description is as follows: very unimportant (1-2), relatively unimportant (3-4), generally important (5-6), relatively important (7-8), very important (9-10). Following the descending order principle, the initial decision data is rearranged in the order from large to small; the combination number is used to calculate the weight of the scoring data to determine the weighted vector w i+1 , and finally the absolute weight value and relative weight of each index are calculated;

[0178] In step S4, the final weight is determined by using the linear weighted synthesis method for G1-C-OWA combined weighting;

[0179] In step S4, the final weight In the formula, represents the weight calculated by the G1 method, represents the weight calculated by the C-OWA operator; θ1 and θ2 are the preference coefficients of the two weights respectively. Usually, θ1 and θ2 can both be taken as 0.5. The calculation results of the combined weight based on G1-C-OWA are shown in Table 3.

[0180] Table 3 shows the index combination weights based on G1-C-OWA:

[0181]

[0182]

[0183] Step S5: Evaluate the green and low-carbon construction level of mountain buildings based on the matter-element extension model;

[0184] In step S5, the matter-element extension model is used to evaluate the green and low-carbon construction level of mountain buildings. First, determine the grade domain. The evaluation grades of the green and low-carbon construction level of mountain buildings are divided into 5 grades: "low, relatively low, medium, relatively high, high". According to this grade, set the range domain as [0, 100], and use the natural breakpoint grading method to equally divide [0, 100]. The value ranges of the evaluation grades of the green and low-carbon construction level of each mountain building are shown in Table 4. Determine the classical domain and the range domain. For quantitative indicators, generally determine the grading evaluation criteria of the indicators by referring to relevant literature and consulting expert opinions. For qualitative indicators, according to the data of the actual project, conduct quantitative evaluation through expert scoring. The classical domain and range domain of each indicator are shown in Table 5. Finally, determine the matter-element matrix to be evaluated.

[0185] Table 4 is the table of value ranges for the evaluation grades of the green and low-carbon construction level of mountain buildings:

[0186]

[0187] Table 5 is the statistical table of the classical domain and range domain of each indicator:

[0188]

[0189]

[0190] Step S6: Propose the evaluation technical process for the green and low-carbon construction level of mountain buildings based on the above basic operations.

[0191] In step S6, the evaluation index data of the green and low-carbon construction level of Dengkefu (Phase I project) in Chongqing is selected as the input data and input into the evaluation model for the green and low-carbon construction level of mountain buildings constructed above. The actual values of each evaluation index of this project are shown in Table 6. Combining the correlation function formula and the characteristic values of the evaluation indexes, calculate the correlation level of each secondary index. The correlation level of the secondary index and its evaluation grade are shown in Table 7. Combining the comprehensive weight, calculate the comprehensive correlation of each grade.

[0192]

[0193] maxK j (N0) = K4(N0) = -0.0457. According to the principle of maximum correlation degree, the corresponding evaluation level is obtained, and the green and low-carbon construction level of this project is grade four (relatively high level).

[0194] Table 6 is the table of actual values of each evaluation index:

[0195]

[0196] Table 7 Correlation degree level of secondary indicators

[0197]

[0198]

[0199]

[0200] The evaluation research on the green and low-carbon construction level of mountain buildings is carried out by using the combination weighting and matter-element extension model. The evaluation scheme and technical process of the green and low-carbon construction level of mountain buildings are constructed, and the green and low-carbon construction level grade of Dengkefu (Phase I project) in Chongqing is given. The purpose is to more actively improve the green and low-carbon construction level of mountain buildings, better support the green, low-carbon and high-quality development of the construction industry, better help the construction industry achieve green and low-carbon transformation and carbon emission reduction, so as to relieve the carbon emission reduction pressure in China.

[0201] The above is only the preferred implementation mode of the present invention. It should be noted that for those of ordinary skill in the art of this technology, without departing from the technical principle of the present invention, several improvements and deformations can still be made, and these improvements and deformations should also be regarded as the protection scope of the present invention.

Claims

1. A green and low-carbon construction level evaluation method for mountain buildings based on matter-element extension is characterized by: The following steps are involved: S1. Construct an evaluation index system for the green and low-carbon construction level of mountain buildings, including seven first-level indicators: environmental protection, comprehensive management of green and low-carbon construction, resource conservation and utilization, social benefits, green and low-carbon construction technology, human resource conservation and protection, and carbon emissions and carbon sinks. Each first-level indicator has several second-level indicators. S2. Use the G1 method to determine the weight of each evaluation index, including determining the order relationship between the primary and secondary indicators, calculating the weight coefficient and the weight under the expert group decision; S3, using C-OWA operator to determine the weight of each evaluation index, including expert scoring, data descending order, calculation of weighted vectors based on the number of combinations, and determination of absolute weight and relative weight; S4, weighting the weights of S2 and S3 by linear weighted synthesis method to obtain the final weight to form a matter-element extension model; S5. Evaluate the green and low-carbon construction level of mountain buildings based on the object-element extension model, including constructing the object-element to be evaluated, determining the grade domain and the classic domain, and calculating the index correlation and evaluation grade; S6. According to steps S1 to S5, a technical process for evaluating the green and low-carbon construction level of mountain buildings is formed, and an evaluation grade is output.

2. The method for evaluating the green and low-carbon construction level of mountain buildings based on matter-element extension according to claim 1 is characterized in that: In step S2, when determining the order relationship, the relative importance ratio r between the indicators is obtained through an expert questionnaire. k , where r k It represents the importance ratio of the kth indicator to the k+1th indicator; the weight coefficient is obtained by multiplying the r between adjacent indicators. k Calculation; The weight under the expert group decision is calculated by the weighted average algorithm: in: is the weight of the sth expert on indicator j and m is the total number of experts.

3. The method for evaluating the green and low-carbon construction level of mountain buildings based on matter-element extension according to claim 1 is characterized in that: In step S3, the weight vector of the C-OWA operator is determined by the following combination number formula: Where: n is the number of expert scoring data Is the symbol of the combination number, which means taking from n-1 elements The number of combinations i is the data sequence number after descending order, starting from 0, and the constraints are 4. The method according to claim 3, characterized in that In step S3, the absolute weight value W a and the relative weight W r The calculation formulas are: Among them, v i is the i-th expert rating data after descending order, m is the total number of indicators, w i is the weighting vector determined by claim 3.

5. The method for evaluating the green and low-carbon construction level of mountain buildings based on matter-element extension according to claim 1 is characterized in that: In step S4, the preference coefficients θ1 and θ2 of the linear weighted synthesis method are both 0.5, and the final weight is: in: is the weight of index j obtained by G1 method is the weight of index j obtained by the C-OWA operator.

6. The method according to claim 1, characterized in that In step S5, the index correlation is calculated by the following formula: Among them, ρ is the distance function, which is used to measure the distance relationship between the index value of the object-element to be evaluated and the index value of the classical domain and the section domain. The degree of correlation between the object-element to be evaluated and each evaluation level is reflected by calculating the distance reflected by the distance function ρ; ρ(v 0i ,v ji ) represents the i-th index value v of the object to be evaluated 0i and the i-th index value v at the j-th level in the classical domain ji The distance between 0i ,v pi ) represents the i-th index value v of the object to be evaluated 0i and the i-th index value v in the section domain pi The smaller the calculated result is, the closer the index value of the object-element to be evaluated is to the index range of the corresponding level, and the higher the correlation with the level, and vice versa; and v ji = ji ,b ji >,v pi = pi ,b pi >, v 0i is the measured value of the i-th index value of the object to be evaluated, v ji is the classical domain interval of the i-th indicator value of the object-to-be-evaluated object for level j, a ji For the classical domain interval v ji The lower limit of b ji For the classical domain interval v ji The upper limit of v pi is the section interval of the i-th index value of the object element to be evaluated, a pi is the section interval v pi The lower limit of b pi is the section interval v pi The upper limit of the classical domain interval length |v ji |=b ji -a ji , section interval length |v pi |=b pi -a pi .​​ 7. The method according to claim 6, characterized in that In step S5, the evaluation level is determined by the maximum correlation principle, and the formula is: Among them, w i is the combined weight of the i-th indicator value of the object to be evaluated, n is the total number of indicators, K j (v i ) is the correlation between the ith indicator value of the object to be evaluated and the level j, and j0 is the final evaluation level.

8. The method for evaluating the green and low-carbon construction level of mountain buildings based on matter-element extension according to claim 1 is characterized in that: In step S6, the technical process includes: Data collection and preprocessing: obtain the measured values ​​or expert scores of each secondary indicator; Input indicator data: input data into the matter-element extension model; Output evaluation level: Output the green and low-carbon construction level based on the maximum correlation value.

9. The method for evaluating the green and low-carbon construction level of mountain buildings based on matter-element extension according to claim 2 is characterized in that: The first-level indicators of social benefits include four second-level indicators: underground resource protection, surrounding cultural environment protection, animal and plant protection, and nearby residents' satisfaction with the construction; the first-level indicators of green and low-carbon construction technology include the application of BIM information technology, the application of green and low-carbon new technologies and processes, prefabricated construction technology, technology research and development or micro-innovation, slope support technology, semi-slope pile construction technology, and semi-basement construction technology, a total of seven second-level indicators.

10. The method for evaluating the green and low-carbon construction level of mountain buildings based on matter-element extension according to claim 1 is characterized in that: In step S5, the grade domain is divided into five grades: low, lower, medium, higher, and high, and the corresponding intervals are [0, 20), [20, 40), [40, 60), [60, 80), and [80, 100]; the classic domain and the section domain are determined by the natural break point grading method or expert opinion.

Images