Method for evaluating comprehensive performance of fireproof coating based on AHP-TOPSIS

Abstract

The invention discloses a method for evaluating the comprehensive performance of a fireproof coating based on AHP-TOPSIS. The method comprises the following steps: constructing a hierarchical structure model for evaluating the comprehensive performance of the fireproof coating, and defining a target layer, a criterion layer and an index layer of the model; based on comparison and judgment results of experts on the evaluation indexes of the fireproof coating and performance characterization experimental data of the evaluation indexes, combined weights of the AHP-entropy weight method are calculated; and according to the AHP-entropy weight method combination weight, calculating the relative proximity of each fireproof coating and the ideal solution, and according to the relative proximity, sorting the comprehensive performance of the fireproof coating, thereby obtaining the good-bad relationship of the comprehensive performance of the fireproof coating. According to the method, the comprehensive performance of the fireproof coating can be comprehensively and objectively evaluated by establishing a scientific hierarchical structure model and combining experimental test data; not only is the optimal fireproof coating product selected favorably, but also the development and application of the fireproof coating technology can be promoted, and the fireproof safety of a building is improved.

Description

Technical Field

[0001] The invention relates to the technical field of comprehensive performance evaluation of fire retardant coatings, and in particular to a comprehensive performance evaluation method of fire retardant coatings based on AHP-TOPSIS. Background Art

[0002] With the development of the construction industry, steel structures are widely used due to their superior performance. However, steel structures expose significant safety hazards in fires, and their rapid loss of bearing capacity under high temperatures makes fire protection an indispensable measure. Fire retardant coatings are an important means of fire protection for steel structures, and their performance is directly related to the safety and durability of buildings in fires. In order to scientifically and comprehensively evaluate the comprehensive performance of fire retardant coatings, it is particularly important to choose a suitable evaluation method.

[0003] There are many types of fire retardant coatings on the market, and the quality varies. Traditional fire retardant coating performance evaluation is mostly focused on a single indicator, such as fire resistance time, adhesion, durability, etc. However, this single indicator evaluation method is difficult to fully reflect the comprehensive performance of fire retardant coatings and cannot meet the diverse needs in practical applications. At the same time, the performance of fire retardant coatings from different manufacturers varies greatly in different indicators. How to conduct a comprehensive evaluation objectively and impartially has become an urgent problem that the industry needs to solve.

[0004] Traditional subjective evaluation methods are easily affected by human factors, and the evaluation results have great uncertainty. The introduction of comprehensive performance evaluation methods based on mathematical models and statistical methods, such as entropy weight method and TOPSIS method, can reduce the interference of subjective factors and improve the objectivity and scientificity of evaluation results.

[0005] For example, Chinese patent document CN116205524A discloses a performance evaluation system and evaluation method for fireproof plugging system of cable shaft in high-rise buildings. The evaluation system is constructed by target layer, criterion layer and index layer. The target layer is the performance of fireproof plugging system of cable shaft in high-rise buildings; the criterion layer includes eight sub-capabilities, namely material performance, module performance, cable performance, plugging scheme, construction technology, engineering management, ultrasonic guided wave and environmental factors; the index layer includes 32 evaluation indicators. Through the constructed evaluation system, the indicators of fireproof plugging system of cable shaft in high-rise buildings are calculated to obtain a comprehensive evaluation score, and the fireproof plugging capacity is compared accordingly.

[0006] Chinese patent document CN114818387A provides a performance evaluation method for nonlinear conductive materials, including: S1) using the electrical properties, thermal properties and mechanical properties of the nonlinear conductive materials as performance evaluation indicators, and using the AHP method to obtain the weights of the performance evaluation indicators; S2) using the TOPSIS model to perform forward normalization on the measured data of the electrical properties, thermal properties and mechanical properties of the nonlinear conductive materials, and combining the weights of the performance evaluation indicators to obtain the distance between the nonlinear conductive materials and the optimal solution and the worst solution, perform a comprehensive distance score, and obtain a comprehensive performance evaluation result. The evaluation method comprehensively evaluates nonlinear conductive materials with different doping mass fractions of fillers while comprehensively considering electrical, thermal and mechanical properties.

[0007] However, there are no reports on the application of AHP and TOPSIS models in the comprehensive performance evaluation of fire retardant materials in the prior art. The present invention intends to provide a comprehensive performance evaluation method for fire retardant coatings based on AHP-TOPSIS, so as to reduce the interference of subjective evaluation factors, thereby objectively and comprehensively reflecting the advantages and disadvantages of fire retardant coatings, promoting manufacturers to continuously improve product technology and improve the overall quality level of fire retardant coatings. Summary of the invention

[0008] The technical problem to be solved by the present invention is to provide a comprehensive performance evaluation method for fire retardant coatings based on AHP-TOPSIS. Its purpose is to comprehensively and objectively evaluate the comprehensive performance of fire retardant coatings by establishing a scientific hierarchical evaluation model and combining experimental test data.

[0009] In order to solve the above technical problems, the present invention adopts the following technical solutions:

[0010] The present invention provides a fire retardant coating comprehensive performance evaluation method based on AHP-TOPSIS, which specifically comprises the following steps:

[0011] S1. Construct a hierarchical model for the comprehensive performance evaluation of fire retardant coatings: select evaluation indicators according to the types and characteristics of fire retardant coatings and the expected use environment, and define the target layer, criterion layer and indicator layer of the model, where the target layer is the comprehensive performance evaluation of fire retardant coatings; the criterion layer represents the first-level indicators of the comprehensive performance evaluation of fire retardant coatings; and the indicator layer represents the second-level indicators of the comprehensive performance evaluation of fire retardant coatings;

[0012] S2. Calculate the combined weight of the AHP-entropy weight method based on the expert's comparative judgment results of each evaluation index of the fire retardant coating and the performance characterization experimental data of each evaluation index;

[0013] S3. According to the combined weights of the AHP-entropy weight method, the relative closeness of each fire retardant coating to the ideal solution is calculated, and the comprehensive performance of the fire retardant coating is ranked according to the relative closeness, so as to obtain the relationship between the comprehensive performance of the fire retardant coating.

[0014] Furthermore, the calculation method of the AHP-entropy weight method combination weight in step S2 is specifically as follows:

[0015] S201, using the 9-scale method to judge the scale, obtaining a comparative judgment matrix based on the comparative judgment scale results of the evaluation indicators of the materials by the experts, and calculating the AHP weight values ​​of the evaluation indicators at different levels in turn;

[0016] S202, obtaining performance characterization experimental data of each evaluation index of the fire retardant coating through a performance characterization test, that is, original data of the evaluation index; listing a decision matrix based on the original data of the evaluation index, and calculating the entropy weight of each evaluation index using the decision matrix;

[0017] S203, according to the AHP weight value of each evaluation index to the target layer and the entropy weight value of each evaluation index, the AHP-entropy weight method combination weight is calculated.

[0018] Furthermore, the performance characterization experimental data of each evaluation index in step S202 is obtained through a performance characterization test. For example:

[0019] Fire resistance time, through standard fire resistance test, measures the protection time of fire retardant coating in high temperature environment.

[0020] High temperature resistance: Through high temperature exposure experiments, the stability of fire retardant coatings at high temperatures is evaluated.

[0021] Adhesion strength, the adhesion between the fire retardant coating and the substrate is tested through a tensile test.

[0022] Surface adhesion, through contact angle testing, evaluates the adhesion ability of fire retardant coatings on different surfaces.

[0023] Weathering resistance, through artificial weathering experiments, measures the durability of fire retardant coatings under ultraviolet rays, rain and temperature changes.

[0024] Resistance to moisture and heat: Through high temperature and high humidity environment testing, the performance changes of fire retardant coatings under moist and hot conditions are evaluated.

[0025] Acid and alkali resistance: The chemical corrosion resistance of fire retardant coatings is tested through acid and alkali solution immersion experiments.

[0026] VOC content, the volatile organic compound content in fire retardant coatings is measured by gas chromatography.

[0027] Heavy metal content, the heavy metal content in fire retardant coatings is measured by atomic absorption spectroscopy.

[0028] Construction technology: evaluates the difficulty of fire retardant coating construction, including the applicability of spraying, brushing and other methods.

[0029] Drying time, through actual testing, measures the time required for fire retardant coatings to completely dry from application.

[0030] Construction safety, evaluates the safety of fire retardant coatings to construction workers during the construction process, including factors such as toxicity and flammability.

[0031] More specifically, for quantitative indicators such as fire resistance time, high temperature resistance, adhesion strength, surface adhesion, VOC content, heavy metal content, drying time, etc., the original data can be the test data. For qualitative indicators such as weather resistance, moisture and heat resistance, acid and alkali resistance, construction technology, construction safety, etc., points are assigned on a scale of 1-10 according to the quality of the relevant performance of the coating. The higher the score, the better the performance.

[0032] Furthermore, the specific process of calculating the entropy weight of each evaluation index in step S202 is as follows:

[0033] (1) Using the original data of evaluation indicators, list the decision matrix;

[0034] (2) Performing linear normalization on the decision matrix to obtain a linear normalized decision matrix;

[0035] (3) Calculate the information entropy of each indicator based on the linear standardized decision matrix;

[0036] (4) Calculate the entropy weight of each evaluation index.

[0037] Furthermore, in step S3, the method for calculating the relative closeness of each fire retardant coating to the ideal solution according to the combined weights of the AHP-entropy weight method is as follows:

[0038] S301, performing modular-one normalization processing on the listed decision matrices to obtain modular-one normalized decision matrices;

[0039] S302, multiplying each element in the modular standardized decision matrix by the corresponding AHP-entropy weight method combination weight to obtain a weighted standardized decision matrix;

[0040] S303, obtaining a positive ideal solution and a negative ideal solution from a weighted standardized decision matrix;

[0041] S304, calculating the Euclidean distance between each evaluation object and the positive ideal solution and the negative ideal solution;

[0042] S305: Calculate the relative proximity of each evaluation object.

[0043] Furthermore, in step S3, each evaluation object is sorted according to the calculated relative proximity. The greater the relative proximity, the better the comprehensive performance of the fire retardant coating.

[0044] Furthermore, the first-level indicators for comprehensive performance evaluation of fire retardant coatings of the standard layer include the following indicators: fire resistance, adhesion, durability, environmental protection and construction convenience;

[0045] The secondary indicators of the comprehensive performance evaluation of fire retardant coatings in the index layer include the following indicators: fire resistance time, high temperature resistance, adhesion strength, surface adhesion, weather resistance, moisture and heat resistance, acid and alkali resistance, VOC content, heavy metal content, construction technology, drying time, and construction safety.

[0046] Among these evaluation indicators, the larger the extremely large evaluation indicator, the better the performance of the coating, and the smaller the extremely small evaluation indicator, the better the performance of the coating. Specifically, among the quantitative indicators, fire resistance time, high temperature resistance, adhesion strength, surface adhesion and all qualitative indicators belong to extremely large evaluation indicators; quantitative indicators such as VOC content, heavy metal content, drying time, etc. belong to extremely small evaluation indicators. Extremely large evaluation indicators and extremely small evaluation indicators need to be treated differently when they are standardized.

[0047] Furthermore, the method of calculating the AHP weight value of each evaluation index in step S201 is as follows:

[0048] (1) The 9-scale method is used to judge the scale, and the comparison judgment matrix A is obtained based on the expert's comparative judgment of the indicators:

[0049]

[0050] In the formula, n is the number of criteria under the target layer or the number of indicators under a certain criterion layer;

[0051] (2) Normalize the columns of the obtained comparison judgment matrix A according to the following formula:

[0052]

[0053] The normalized judgment matrix

[0054]

[0055] (3) Find the sum of the elements in each row The solution formula is as follows:

[0056]

[0057] (4) Sum the elements of each row according to the following formula Do normalization:

[0058]

[0059] Then the weight vector ω can be obtained as follows:

[0060]

[0061] The weight vector represents the weight of each criterion in the target layer or the weight of each indicator in the corresponding target layer;

[0062] (5) Calculate the maximum eigenvalue λ max :

[0063] Aω=λ max ω

[0064] In the formula, A represents the comparison judgment matrix, ω represents the weight vector;

[0065] (6) Perform consistency check:

[0066]

[0067] CR=CI / RI

[0068] If CR is less than 0.1, the matrix meets the consistency requirements; otherwise, the comparison judgment matrix A is adjusted until CR is less than 0.1, where n is the number of criteria at the target layer or the number of indicators at a certain criterion layer; where CI represents the consistency index; RI represents the average random consistency index; and CR represents the consistency ratio.

[0069] (7) According to the weight vector, the weight of each criterion in the target layer and the weight of each indicator in the corresponding target layer are calculated; then the weight value of the relative importance of each level to the highest level is calculated, and the calculation is performed from the highest level to the lowest level to obtain the AHP weight value u of each evaluation indicator to the target layer. j .

[0070] Furthermore, the specific calculation method of step S202 is as follows:

[0071] (1) Using the original data of evaluation indicators, the decision matrix R is listed; assuming there are m evaluation objects and n evaluation indicators, the decision matrix R is expressed as:

[0072]

[0073] (2) The decision matrix R is linearly normalized by the following formula to obtain a linear normalized decision matrix:

[0074] For extremely large evaluation indicators, the standardized formula is:

[0075]

[0076] For the very small evaluation index, the standardized formula is:

[0077]

[0078] Among them, r ij is the jth index value of the ith evaluation object; x ij is the standardized value, max(r ij ) and min(r ij ) are the maximum and minimum values ​​of the jth index of all evaluation objects respectively;

[0079] The linear normalization decision matrix X obtained after linear normalization is expressed as:

[0080]

[0081] (3) Calculate the information entropy of each indicator based on the linear standardized decision matrix X; the calculation formula is as follows:

[0082]

[0083] Among them, e j represents the information entropy of the jth indicator; m is the number of evaluation objects; p ij The jth data in the ith row after the linear normalization decision matrix is ​​normalized; when p ij =0, lnp ij =0;

[0084] (4) Calculate the entropy weight v of each evaluation index j , the calculation formula is as follows:

[0085]

[0086] Furthermore, in step 203, the AHP-entropy weight combination weight w j The calculation method is as follows:

[0087]

[0088] In the formula, w j represents the combined weight of AHP-entropy weight method, v j Represents the entropy weight of each evaluation index, u j It represents the AHP weight value of each evaluation index to the target layer.

[0089] Furthermore, the relative proximity of each evaluation object in step S3 is calculated as follows:

[0090] S301, performing modular-one normalization processing on the listed decision matrix R according to the following formula to obtain a modular-one normalized decision matrix:

[0091] For extremely large evaluation indicators, the modulus-one standardization processing formula is as follows:

[0092]

[0093] For the extremely small evaluation index, the modulus-one normalization processing formula is as follows:

[0094]

[0095] The processed modulo-normalized decision matrix Y is expressed as:

[0096]

[0097] S302, multiply each element in the modular standardized decision matrix Y by the corresponding AHP-entropy weight method combined weight w j , we get the weighted standardized decision matrix Z, which is expressed as follows:

[0098]

[0099] S303, obtain the positive ideal solution Z from the weighted standardized decision matrix Z through the following formula + and negative ideal solution Z - :

[0100] Z + =(max(z 1j ),max(z 2j ),…max(z nj ));

[0101] Z - =(min(z 1j ),min(z 2j ),…min(z nj ));

[0102] S304, calculate the difference between each evaluation object and the positive ideal solution Z by the following formula + and negative ideal solution Z - The corresponding Euclidean distance

[0103]

[0104]

[0105] S305: Calculate the relative proximity D of each evaluation object by the following formula i :

[0106]

[0107] In the solution of the present invention, AHP, namely the analytic hierarchy process, is a multi-criteria decision-making method used to deal with complex decision-making problems. AHP decomposes the problem into multiple levels, then compares and assigns weights to each level, and finally obtains an overall weight ranking to help decision makers make reasonable decisions.

[0108] Entropy Weight Method (EWM) is an objective weighting method used to determine the weight of each indicator in multi-indicator evaluation. This method is based on the concept of information entropy and determines its weight in comprehensive evaluation by measuring the "information amount" of each indicator. Information entropy reflects the uncertainty and dispersion of data. The larger the entropy value, the more dispersed the information of the indicator, the smaller its contribution to the comprehensive evaluation, and the lower its weight; conversely, the smaller the entropy value, the more concentrated the information of the indicator, the greater its contribution to the comprehensive evaluation, and the higher its weight.

[0109] TOPSIS, or the Technique for Order Preference by Similarity to Ideal Solution, is also known as the superior and inferior solution distance method in China. Its full English name is “Technique for Order Preference by Similarity to Ideal Solution”. It is a comprehensive evaluation method that can make full use of the information of the original data, and its results can accurately reflect the gap between the evaluation schemes.

[0110] The present invention has the following beneficial effects:

[0111] The AHP-TOPSIS fire retardant coating comprehensive performance evaluation method provided by the present invention can comprehensively and objectively evaluate the comprehensive performance of fire retardant coatings by establishing a scientific hierarchical model, according to the type and use environment of fire retardant coatings, combined with performance characterization test data. This not only helps to select the best fire retardant coating products, but also promotes the development and application of fire retardant coating technology and improves the fire safety of buildings. At the same time, through an open and transparent evaluation system, fair competition within the industry can be promoted and the overall technical level of the industry can be improved. BRIEF DESCRIPTION OF THE DRAWINGS

[0112] Figure 1 A flow chart of the comprehensive performance evaluation method of fire retardant coatings based on AHP-TOPSIS provided by the present invention.

[0113] Figure 2 Schematic diagram of the hierarchical model for comprehensive performance evaluation of fire retardant coatings provided by the present invention.

[0114] Figure 3 Schematic diagram of the hierarchical model for comprehensive performance evaluation of steel structure fire retardant coatings provided in Example 1. DETAILED DESCRIPTION

[0115] In order to more clearly understand the above-mentioned objects, features and advantages of the present invention, the present invention is further described in detail below with reference to the accompanying drawings and specific embodiments.

[0116] Many specific details are described in the following description to facilitate a full understanding of the present invention, but the present invention can also be implemented in other ways different from those described herein, and therefore, the protection scope of the present invention is not limited by the specific embodiments disclosed below. Any modification, replacement or improvement made without departing from the spirit and principle of the present invention shall fall within the scope of protection claimed by the present invention.

[0117] Embodiment 1:

[0118] The climate conditions in a certain place are high temperature, high humidity and high ultraviolet radiation. In order to verify the feasibility of the present invention, the comprehensive performance of three environmentally friendly steel structure fire retardant coatings (all epoxy resin-based intumescent steel structure fire retardant coatings with ammonium polyphosphate, melamine and pentaerythritol as the main flame retardant system, numbered A, B and C) conventionally used in the local area is evaluated using the performance evaluation method provided by the present invention. The specific process of the evaluation method is as follows:

[0119] S1. Construct a hierarchical model for the comprehensive performance evaluation of fire retardant coatings: select evaluation indicators based on the types and characteristics of fire retardant coatings and the expected use environment, and define the target layer, criterion layer and indicator layer of the model. Among them, the target layer is the comprehensive performance evaluation of fire retardant coatings; the criterion layer represents the first-level indicators of the comprehensive performance evaluation of fire retardant coatings; the indicator layer represents the second-level indicators of the comprehensive performance evaluation of fire retardant coatings.

[0120] The coating type in this embodiment is steel structure fire retardant coating, and the application conditions are high temperature, high humidity and high ultraviolet radiation. The hierarchical structure model for comprehensive performance evaluation of steel structure fire retardant coating constructed in this embodiment is as follows: Figure 3 :

[0121] The target layer is the comprehensive performance evaluation of fire retardant coatings.

[0122] The criterion layer represents the first-level indicators for the comprehensive performance evaluation of fire retardant coatings, including fire resistance, adhesion, durability, and ease of construction.

[0123] The index layer represents the secondary indicators for the comprehensive performance evaluation of fire retardant coatings, including: fire resistance time and high temperature resistance; adhesion strength; moisture and heat resistance, UV resistance; construction technology, drying time and construction safety, etc.

[0124] S2. Calculate the combined weight of the AHP-entropy weight method using the expert's comparative judgment results on each evaluation index of the fire retardant coating and the performance characterization experimental data of each evaluation index. The details are as follows:

[0125] S201. Expert consultation is conducted. Experts make comparative judgments on the indicators based on past cases and relevant literature reports. The 9-scale method is used to judge the scale (as shown in Table 1 below). Based on the comparative judgment results of the experts on each evaluation indicator, a comparative judgment matrix is ​​obtained, and the AHP weight values ​​of the evaluation indicators at different levels are obtained in turn. The details are as follows:

[0126] Table 1 9-scale method judgment scale

[0127] Scale meaning 1 The two factors are equally important. 3 When comparing two factors, one is slightly more important than the other 5 When comparing two factors, one factor is significantly more important than the other 7 When comparing two factors, one factor is much more important than the other 9 When comparing two factors, one factor is extremely more important than the other 2，4，6，8 Indicates the middle value of adjacent judgments

[0128] (1) The scaling results of criterion layer B are shown in Table 2.

[0129] Table 2 Scaling results of criterion layer B

[0130]

[0131]

[0132] Get the comparison judgment matrix:

[0133]

[0134] After obtaining the comparison judgment matrix, the comparison judgment matrix is ​​normalized. The normalization method is as follows:

[0135]

[0136] The normalized comparison judgment matrix is

[0137] Then find the sum of the elements in each row

[0138]

[0139] Then the sum of the elements in each row Normalization:

[0140]

[0141] Then the weight vector ω can be obtained as follows:

[0142]

[0143] After that, the maximum eigenvalue λ is obtained max The calculation formula is as follows:

[0144] Aω=λ max ω;

[0145] The maximum eigenvalue λ is calculated by the above formula: max=4.240, weight vector ω=(0.55, 0.27, 0.13, 0.05).

[0146] Finally, perform a consistency check:

[0147]

[0148] CR=CI / RI.

[0149] In the formula, CI represents the consistency index; RI represents the average random consistency index; CR represents the consistency ratio. The RI values ​​are shown in Table 2.

[0150] Table 2. Average random consistency index value table

[0151] Degree 1 2 3 4 5 6 7 8 9 RI 0 0 0.52 0.89 1.12 1.26 1.36 1.41 1.46

[0152] It is generally believed that the matrix has satisfactory consistency only when CR < 0.1, otherwise the judgment matrix needs to be adjusted until CR < 0.1 is satisfied.

[0153] CR=0.09<0.1, which meets the conditions.

[0154] Referring to the above method, the weight values ​​of the relative importance of each level to the highest level are calculated in the same way, and the calculations are performed from the highest level to the lowest level to obtain the AHP weight values ​​u of the evaluation indicators at different levels. j .

[0155] (2) The scale of indicator layer C under criterion layer B1 is shown in Table 4:

[0156] Table 4 Scaling of indicator layer C under criterion layer B1

[0157] Standard layer fire resistance performance B1 Fire resistance time C1 High temperature resistance C2 Fire resistance time C1 1 5 High temperature resistance C2 1 / 5 1

[0158] Then the judgment matrix

[0159] The same calculation formula is used to calculate: The weight vector and the maximum eigenvalue are obtained:

[0160] Weight vector ω = (0.83, 0.17), λ max =2.

[0161] CR=0<0.1, which meets the condition.

[0162] (3) The scale of indicator layer C under criterion layer B2 is shown in Table 5:

[0163] Table 5 Scaling of indicator layer C under criterion layer B2

[0164] Standard layer durability B2 Moisture and heat resistance C3 UV resistance C4 Moisture and heat resistance C3 1 1 / 3 UV resistance C4 3 1

[0165] Then the judgment matrix The weight vector and the maximum eigenvalue are obtained as follows:

[0166] Weight vector ω = (0.25, 0.75)λ max =2.

[0167] CR=0<0.1, which meets the condition.

[0168] (4) The scale of indicator layer C under criterion layer B4 is shown in Table 6:

[0169] Table 6 Scaling of indicator layer C under criterion layer B4

[0170] Standard layer construction convenience B4 Construction technology C6 Drying time C7 Construction safety C8 Construction technology C6 1 3 1 Drying time C7 1 / 3 1 1 / 3 Construction safety C8 1 3 1

[0171] Then the judgment matrix The weight vector and the maximum eigenvalue are obtained as follows:

[0172] Weight vector ω=(1,1 / 3,1)λ max =3

[0173] CR=0<0.1, which meets the condition.

[0174] In summary, the AHP weight values ​​u of different levels of evaluation indicators j As shown in Table 7:

[0175] Table 7 Weights of evaluation indicators at different levels

[0176]

[0177]

[0178] Calculation example: Using the weight vector, such as the weight value of the fire resistance time C1, its weight value for the fire resistance performance B1 is 0.83, and the weight of the fire resistance performance B1 to the comprehensive performance A of the coating is 0.55. Then the weight of the fire resistance time C1 to the comprehensive performance A is 0.83*0.55=0.458.

[0179] S202, by performing performance characterization tests on each fire retardant coating under the same test conditions, obtain performance characterization experimental data of each evaluation index of the fire retardant coating, that is, the original data of the evaluation index; list a decision matrix based on the original data of the evaluation index, and use the decision matrix to calculate the entropy weight of each evaluation index. The details are as follows:

[0180] First, the three types of steel structure fire retardant coatings (A, B, C) were tested for performance characterization as follows. The test methods for each performance index are as follows:

[0181] Fire resistance time: Through standard fire resistance test, measure the time it takes for the equilibrium temperature on the back of the steel structure fire retardant coating to reach 300°C.

[0182] High temperature resistance: Through thermogravimetric test analysis, the peak decomposition temperature of the steel structure fire retardant coating is used as an evaluation index for high temperature resistance.

[0183] Adhesion strength: The adhesion between the fire retardant coating and the steel plate substrate is tested through a tensile test.

[0184] Resistance to moisture and heat: Through high temperature and high humidity environment testing, the moisture and heat resistance is scored according to the severity of peeling, cracking, blistering and powdering of the steel structure fire retardant coating. After 10 days of moisture and heat aging, both steel structure fire retardant coatings A and B showed swelling and cracking, and the aging phenomenon of steel structure fire retardant coating A was more obvious. After 15 days of moisture and heat aging, the aging phenomenon of steel structure fire retardant coatings A and B was more obvious, and steel structure fire retardant coating C began to show swelling and cracking. Therefore, the moisture and heat resistance scores of the three steel structure fire retardant coatings were 5, 6, and 8 points respectively.

[0185] UV resistance: Through artificial UV aging experiments, the moisture and heat resistance is scored according to the severity of peeling, cracking, blistering and powdering of the steel structure fire retardant coating. In the UV aging test, there was no obvious change in the samples in the first 5 days. After 10 days of aging test, cracking appeared in steel structure fire retardant coating A, wrinkling appeared around steel structure fire retardant coating B, and serious cracking and partial detachment occurred in steel structure fire retardant coating C. Therefore, the UV resistance scores of the three steel structure fire retardant coatings were 6, 7, and 4 points respectively.

[0186] Construction technology: The construction technology of fire retardant coatings is scored based on the difficulty of construction, including the applicability of spraying, brushing, etc. All three types of fire retardant coatings must be applied by brushing, so the construction technology of the three types of coatings is scored 8 points each.

[0187] Drying time: Through actual testing, the time required for the fire retardant coating to completely dry from application was measured. The time required for the three types of fire retardant coating to completely dry was 22h, 24h and 26h respectively.

[0188] Construction safety: The fire retardant coating construction safety is scored based on the safety of construction workers during the construction process, including factors such as toxicity and flammability. All three types of fire retardant coatings do not contain toxic substances and are scored 8 points.

[0189] The original data of the performance characterization test of each evaluation index are shown in Table 8:

[0190] Table 8 Performance characterization test raw data

[0191]

[0192]

[0193] Then, the original data of the measured evaluation indicators are used to list the decision matrix, and the entropy weight of each evaluation indicator is calculated according to the decision matrix. The details are as follows:

[0194] (1) Using the original data of each evaluation index, the decision matrix R is listed:

[0195]

[0196] (2) Perform linear normalization on the decision matrix R. The normalization method is as follows:

[0197] For very large metrics:

[0198]

[0199] For very small indicators:

[0200]

[0201] Among them, x ij is the standardized value, max(r ij ) and min(r ij ) are the maximum and minimum values ​​of the j-th indicator respectively.

[0202] Get the linear standardized decision matrix X:

[0203]

[0204] (3) Calculate the information entropy of each indicator based on the standardized decision matrix:

[0205]

[0206] Among them, when p ij =0, lnp ij =0.

[0207] (4) Calculate the entropy weight v of each evaluation index based on the information entropy of each index j :

[0208]

[0209] S203: Calculate the AHP-entropy weight method combined weight according to the AHP weight value of each evaluation index for the target layer and the entropy weight value of each evaluation index. j The calculation method is as follows:

[0210]

[0211] The entropy weights of the evaluation indicators and the combined weights of the AHP-entropy weight method calculated in this embodiment are shown in Table 9:

[0212] Table 9 Combination weights of evaluation indicators

[0213]

[0214]

[0215] S3. Calculate the relative closeness between each fire retardant coating and the ideal solution, and sort the comprehensive performance of the fire retardant coating according to the relative closeness, so as to obtain the relationship between the comprehensive performance of the fire retardant coating. The details are as follows:

[0216] S301, perform modular normalization processing on the decision matrix R to obtain a modular normalized decision matrix. The method of modular normalization processing is as follows:

[0217] For very large metrics:

[0218]

[0219] For very small indicators:

[0220]

[0221] The modulus-one normalized decision matrix Y obtained after modulus-one normalization is expressed as:

[0222]

[0223] The modulo-normalized decision matrix Y obtained by calculation in this embodiment is as follows:

[0224]

[0225] S302, multiply each element in the modular standardized decision matrix Y by the corresponding AHP-entropy weight combination weight to obtain a weighted standardized decision matrix Z:

[0226]

[0227] S303, obtain the positive ideal solution Z from the weighted standardized decision matrix Z + and negative ideal solution Z - , Z + and negative ideal solution Z - The calculation formula is as follows:

[0228] Z + =(max(z 1j ),max(z 2j ),…max(z nj ));

[0229] Z - =(min(z 1j ),min(z 2j ),…min(z nj ));

[0230] In this embodiment, it is calculated that:

[0231] Z + =(0.297,0.072,0.086,0.118,0.086,0.006,0.044,0.006);

[0232] Z - =(0.123,0.048,0.054,0.067,0.072,0.006,0.030,0.006);

[0233] S304, calculating the Euclidean distance between each evaluation object and the positive ideal solution and the negative ideal solution The calculation formula is as follows:

[0234]

[0235] In this embodiment, the Euclidean distances of fire retardant coatings A, B, and C from the positive and negative ideal solutions are:

[0236]

[0237] S304, calculate the relative proximity D of each evaluation object i :

[0238]

[0239] In this embodiment, the relative closeness of fire retardant coatings A, B, and C to the ideal solution is:

[0240] D 甲 =0.229,D 乙 =0.839,D 丙 =0.081.

[0241] Sort each evaluation object according to its relative proximity, D i The larger it is, the better the overall performance of the fire retardant coating.

[0242] In this embodiment, D 乙 >D 甲 >D 丙 In summary, fire retardant coating B has the best comprehensive performance among the three steel structure fire retardant coatings.

[0243] The above descriptions are only some preferred embodiments of the present invention and are not intended to limit the present invention. Any modifications, equivalent substitutions, improvements, etc. made within the spirit and principles of the present invention should be included in the protection scope of the present invention.

Claims

1. A fire retardant coating comprehensive performance evaluation method based on AHP-TOPSIS, characterized in that: The specific steps include: S1. Construct a hierarchical model for the comprehensive performance evaluation of fire retardant coatings: select evaluation indicators according to the types and characteristics of fire retardant coatings and the expected use environment, and define the target layer, criterion layer and indicator layer of the model, where the target layer is the comprehensive performance evaluation of fire retardant coatings; the criterion layer represents the first-level indicator of the comprehensive performance evaluation of fire retardant coatings; and the indicator layer represents the second-level indicator of the comprehensive performance evaluation of fire retardant coatings; S2. Calculate the combined weight of the AHP-entropy weight method based on the expert's comparative judgment results of each evaluation index of the fire retardant coating and the performance characterization experimental data of each evaluation index; S3. According to the combined weights of the AHP-entropy weight method, the relative closeness of each fire retardant coating to the ideal solution is calculated, and the comprehensive performance of the fire retardant coating is ranked according to the relative closeness, so as to obtain the relationship between the comprehensive performance of the fire retardant coating.

2. The fire retardant coating comprehensive performance evaluation method based on AHP-TOPSIS according to claim 1, characterized in that: The calculation method of the AHP-entropy weight combination weight in step S2 is specifically as follows: S201, using the 9-scale method to judge the scale, obtaining a comparative judgment matrix based on the comparative judgment scale results of the evaluation indicators of the materials by the experts, and calculating the AHP weight values ​​of the evaluation indicators at different levels in turn; S202, obtaining performance characterization experimental data of each evaluation index of the fire retardant coating through a performance characterization test, that is, original data of the evaluation index; listing a decision matrix based on the original data of the evaluation index, and calculating the entropy weight of each evaluation index using the decision matrix; S203, according to the AHP weight value of each evaluation index to the target layer and the entropy weight value of each evaluation index, the AHP-entropy weight method combination weight is calculated.

3. The fire retardant coating comprehensive performance evaluation method based on AHP-TOPSIS according to claim 2, characterized in that: The specific process of calculating the entropy weight of each evaluation index in step S202 is as follows: (1) Using the original data of evaluation indicators, list the decision matrix; (2) Performing linear normalization on the decision matrix to obtain a linear normalized decision matrix; (3) Calculate the information entropy of each indicator based on the linear standardized decision matrix; (4) Calculate the entropy weight of each evaluation index.

4. The fire retardant coating comprehensive performance evaluation method based on AHP-TOPSIS according to claim 2, characterized in that: In step S3, the method for calculating the relative closeness of each fire retardant coating to the ideal solution according to the combined weights of the AHP-entropy weight method is as follows: S301, performing modular-one normalization processing on the listed decision matrices to obtain modular-one normalized decision matrices; S302, multiplying each element in the modular standardized decision matrix by the corresponding AHP-entropy weight method combination weight to obtain a weighted standardized decision matrix; S303, obtaining a positive ideal solution and a negative ideal solution from a weighted standardized decision matrix; S304, calculating the Euclidean distance between each evaluation object and the positive ideal solution and the negative ideal solution; S305: Calculate the relative proximity of each evaluation object.

5. The fire retardant coating comprehensive performance evaluation method based on AHP-TOPSIS according to claim 4, characterized in that: Step S3, sorting each evaluation object according to the calculated relative proximity. The greater the relative proximity, the better the comprehensive performance of the fire retardant coating.

6. The fire retardant coating comprehensive performance evaluation method based on AHP-TOPSIS according to claim 2, characterized in that: The first-level indicators for comprehensive performance evaluation of fire retardant coatings at the standard layer include the following indicators: fire resistance, adhesion, durability, environmental protection and construction convenience; The secondary indicators of the comprehensive performance evaluation of fire retardant coatings in the index layer include the following indicators: fire resistance time, high temperature resistance, adhesion strength, surface adhesion, weather resistance, moisture and heat resistance, acid and alkali resistance, VOC content, heavy metal content, construction technology, drying time, and construction safety.

7. The fire retardant coating comprehensive performance evaluation method based on AHP-TOPSIS according to claim 5, characterized in that: The method of calculating the AHP weight value of each evaluation index in step S201 is as follows: (1) The 9-scale method is used to judge the scale, and the comparison judgment matrix A is obtained based on the expert's comparative judgment of the indicators: In the formula, n is the number of criteria under the target layer or the number of indicators under a certain criterion layer; (2) Normalize the columns of the obtained comparison judgment matrix A according to the following formula: The normalized judgment matrix (3) Find the sum of the elements in each row The solution formula is as follows: (4) Sum the elements of each row according to the following formula Do normalization: Then the weight vector ω can be obtained as follows: The weight vector ω represents the weight of each criterion in the target layer or the weight of each indicator in the corresponding target layer; (5) Calculate the maximum eigenvalue λ max : Aω=λ max oh; In the formula, A represents the comparison judgment matrix, ω represents the weight vector; (6) Perform consistency check: (n is the matrix order) CR=CI / RI In the formula, CI represents the consistency index; RI represents the average random consistency index; CR represents the consistency ratio, and n is the number of criteria under the target layer or the number of indicators under a certain criterion layer; if CR < 0.1, the matrix meets the consistency requirements; otherwise, the comparison judgment matrix A is adjusted until CR < 0.1 is met; (7) According to the weight vector ω, the weight of each criterion in the target layer and the weight of each indicator in the corresponding target layer are calculated; then the weight value of the relative importance of each level to the highest level is calculated, and the calculation is performed from the highest level to the lowest level to obtain the AHP weight value u of each evaluation indicator to the target layer. j .

8. The fire retardant coating comprehensive performance evaluation method based on AHP-TOPSIS according to claim 7, characterized in that: The specific calculation method of step S202 is as follows: (1) Using the original data of evaluation indicators, the decision matrix R is listed; assuming there are m evaluation objects and n evaluation indicators, the decision matrix R is expressed as: (2) The decision matrix R is linearly normalized by the following formula to obtain a linear normalized decision matrix: For extremely large evaluation indicators, the standardized formula is: For the very small evaluation index, the standardized formula is: Among them, r ij is the jth index value of the ith evaluation object; x ij is the standardized value, max(r ij ) and min(r ij ) represent the maximum and minimum values ​​of the jth index of all evaluation objects respectively; The linear normalization decision matrix X obtained after linear normalization is expressed as: (3) Calculate the information entropy e of each indicator based on the linear standardized decision matrix X j ; The calculation formula is as follows: Among them, e j represents the information entropy of the jth indicator; m is the number of evaluation objects; p ij The jth data in the ith row after the linear normalization decision matrix is ​​normalized; when p ij =0, lnp ij =0; (4) Calculate the entropy weight v of each evaluation index j , the calculation formula is as follows:

9. The fire retardant coating comprehensive performance evaluation method based on AHP-TOPSIS according to claim 8, characterized in that: In step 203, the AHP-entropy weight method combined weight w j The calculation method is as follows: In the formula, w j represents the combined weight of AHP-entropy weight method, v j Represents the entropy weight of each evaluation index, u j It represents the AHP weight value of each evaluation index to the target layer.

10. The fire retardant coating comprehensive performance evaluation method based on AHP-TOPSIS according to claim 9, characterized in that: The calculation method of the relative proximity of each evaluation object in step S3 is as follows: S301, performing modular-one normalization processing on the listed decision matrix R according to the following formula to obtain a modular-one normalized decision matrix: For extremely large evaluation indicators, the modulus-one standardization processing formula is as follows: For the extremely small evaluation index, the modulus-one normalization processing formula is as follows: The processed modulo-normalized decision matrix Y is expressed as: S302, multiply each element in the modular standardized decision matrix Y by the corresponding AHP-entropy weight method combined weight w j , we get the weighted standardized decision matrix Z, which is expressed as follows: S303, obtain the positive ideal solution Z from the weighted standardized decision matrix Z through the following formula + and negative ideal solution Z - : WITH + =(max(of 1j ),max(of 2j ),…max(of nj )); WITH - =(min(of 1j ),min(of 2j ),…min(of nj )); S304, calculate the difference between each evaluation object and the positive ideal solution Z by the following formula + and negative ideal solution Z - The corresponding Euclidean distance S305: Calculate the relative proximity D of each evaluation object by the following formula i :

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