Tunnel disease evaluation method based on external environment and fire action

Abstract

The invention relates to a tunnel disease evaluation method, in particular to a tunnel disease evaluation method based on external environment and fire action, which can characterize and evaluate tunnel diseases, and comprises the following steps: step 1, establishing a tunnel disease database by collecting tunnel disease data; 2, determining a classic domain and a node domain of tunnel structure diseases; 3, determining to-be-evaluated matter elements in the disease evaluation model; 4, determining the weight of the evaluation index and checking the consistency of the evaluation index; 5, calculating the correlation degree of the evaluation indexes to the disease grades; step 6, determining a comprehensive correlation degree; and 7, determining the grade of the tunnel structure disease. The tunnel disease evaluation method has the advantages that the tunnel disease evaluation method is given according to the evaluation model formed by the eight tunnel disease evaluation indexes under the structural factors and the environmental factors in combination with the analytic hierarchy process, and the influence of human factors is reduced.

Description

Technical Field

[0001] The present invention relates to a method for evaluating tunnel diseases, and particularly to a method for evaluating tunnel diseases based on external environment and fire action, which can characterize and evaluate tunnel diseases. Background Technique

[0002] In tunnel engineering, not all problems can be quantitatively expressed by numbers, and the factors affecting the health of tunnel structures are also diverse. During the regular inspection of tunnels, the qualitative evaluation of various factors is usually random, with strong subjective factors and variable results, making it difficult to accurately evaluate the tunnel structure or its evaluation constituent factors. Taking the detection of carbonation depth in concrete strength detection as an example, different inspectors may obtain very different carbonation depth values for the same measuring area, resulting in inaccurate actual concrete strength detection values during the later processing, which affects the decision-making of managers and maintenance personnel.

[0003] In order to quantitatively express the typical diseases of tunnel structures, the extension theory is introduced to solve the problem of mutual contradiction among multiple disease sub-evaluation indicators in tunnel engineering. By establishing a scientific method of a multivariate evaluation model, incompatible problems can be made compatible, and the quantitative display of analysis results can be achieved. Therefore, the results are more reasonable and clear. Using the extension theory to evaluate the health status of tunnel structures, based on the analytic hierarchy process, the disease evaluation factors, matter-element models, and weights of each evaluation index of the tunnel structure are determined, and the health status of the tunnel structure is comprehensively evaluated. Summary of the Invention

[0004] In order to solve the above technical problems, the present invention provides a method for evaluating tunnel diseases based on external environment and fire action, aiming to provide a unified method for tunnel disease evaluation, reduce the influence of human factors, break through the limitations of traditional single-factor evaluation, and construct a hierarchical evaluation model to achieve refined diagnosis of tunnel damage after fire.

[0005] To achieve the above object, a method for evaluating tunnel diseases based on external environment and fire action of the present invention includes the following steps:

[0006] Step 1: Establish a tunnel disease database by collecting tunnel disease data;

[0007] Step 2: Determine the classical domain and joint domain of tunnel structure diseases: Combining the matter-element theory, determine the classical domain of the matter-elements in the disease evaluation system according to the types of tunnel disease evaluation indicators in the tunnel disease database; Determine the joint domain of the tunnel disease evaluation grade model according to the evaluation grade and the maximum and minimum values of the index classical domain.

[0008] Step 3: Determine the matter element to be evaluated in the disease evaluation model: Determine the matter element to be evaluated according to the disease evaluation indicators in the tunnel disease evaluation level model. Then, through the analytic hierarchy process combined with the Saaty scale method, make pairwise comparisons of the evaluation indicators in the tunnel disease evaluation level model, and finally form a judgment matrix. Then list the scales between the indicators in the judgment matrix;

[0009] Step 4: Determination and consistency test of the weights of evaluation indicators: After obtaining the judgment matrix in Step 3, use mathematical methods to calculate values such as the mean or square root of each row of judgment indicators. Subsequently, perform normalization processing on the corresponding values of each indicator to initially obtain the weight sizes corresponding to each indicator in the target problem, thus determining the weights of each matter element disease indicator in the tunnel disease evaluation level; Then, calculate the consistency ratio based on the maximum eigenvalue and the order of the matrix to determine whether the weights of the matrix meet the requirements;

[0010] Step 5: Calculation of the correlation degree of evaluation indicators with the disease level: Calculate the correlation degree of the tunnel disease level according to the evaluation indicators;

[0011] Step 6: Determination of the comprehensive correlation degree: After determining the proportion of each matter element indicator in the evaluation model, combine the correlation degree of the evaluation indicators with respect to the severity level of the engineering structure disease obtained from the formula in Step 5, and then obtain the comprehensive correlation degree of each disease indicator in the evaluation system with respect to the evaluation level of the tunnel to be evaluated:

[0012]

[0013] where a i is the proportion of each evaluation indicator in the matter element model, and k ji is the correlation degree of evaluation indicator i with respect to tunnel disease level j;

[0014] Step 7: Determination of the tunnel structure disease level: After all the parameters in Step 6 are determined, the disease level of the target tunnel structure can be determined:

[0015] k = max{k j | j = 1, 2... n}.

[0016] The classical domain of the matter element in Step 1 above is expressed as:

[0017]

[0018] where R ab represents the classical domain of the matter element of the tunnel disease evaluation system; N i represents the i-th system evaluation level, C im represents the m-th evaluation indicator in the i-th evaluation level of the evaluation system; and (μimmin, μimmax) represents the i-th evaluation level with respect to the evaluation indicator C mThe classical domain range of the values, where the value range of i corresponds to the value range of the system evaluation level.

[0019] The range domain of the matter-element of the tunnel disease evaluation level model in step 1 above is expressed as:

[0020]

[0021] In the formula, R P represents the range domain of the matter-element of the tunnel disease evaluation system; P represents the value range of various evaluation indicators corresponding to all evaluation levels; (μ mmin , μ mmax ) represents the value range of the classical domain of indicator m in all evaluation levels, that is, the range domain of the matter-element corresponding to indicator m.

[0022] The construction process of the above matter-element model to be evaluated is to obtain the formula of the matter-element model to be evaluated through the construction methods of the range domain and the classical domain:

[0023]

[0024] In the formula, μ m is the measured value taken by the tunnel to be evaluated with respect to the evaluation indicator C m .

[0025] The formula for multiplying and taking the square root of the row vectors corresponding to each evaluation indicator in step 3 above is:

[0026]

[0027] In the formula, A i is the value obtained by multiplying and taking the square root of the numerical values of the row vector corresponding to the i-th evaluation indicator in the judgment matrix; a ij is the element in the i-th row and j-th column of the judgment matrix;

[0028] Subsequently, normalize the square root values corresponding to each indicator, and the weight vector of each indicator in the target model can be obtained. The formula is:

[0029]

[0030] The calculation formula for the maximum eigenvalue of the matrix is:

[0031]

[0032] In the formula, R is the judgment matrix; A is the vector obtained after normalizing A i , that is, (α1α 2… α i… α n ); n is the number of evaluation indicators, that is, the order of the judgment matrix;

[0033] During the inspection process, the consistency index is CI, and the random consistency index is RI. The values of CI and RI are determined by the order of the matter-element judgment matrix. The calculation method of the consistency index CI is as follows:

[0034]

[0035] The calculation method formula of the consistency ratio is as follows:

[0036]

[0037] If the actually calculated CR value in the tunnel project is less than 0.1, it is determined that the matter-element judgment matrix meets the consistency requirement, and the weights determined by this matrix meet the requirements.

[0038] The correlation degree calculation formula of the evaluation index i with respect to the tunnel disease level j in step 5 above is as follows:

[0039]

[0040] In the formula, k ji is the correlation degree of the evaluation index i with respect to the evaluation level j in the model; μ ji is the value range of the classical domain, and μ pi is the value range of the section domain; ρ(μ i , μ ji ) is the distance from the point μ i to the interval (μ jimin , μ jimax ), and ρ(μ i , μ pi ) is the distance from the point μ i to the interval (μ pimin , μ pimax );

[0041] The types of tunnel disease evaluation indicators in step 1 above are lining cracking, water leakage, material strength deterioration, lining delamination and peeling, effective lining thickness, freeze-thaw, bias pressure, and fire.

[0042] Advantages and effects of the present invention: The present invention provides an evaluation method for tunnel diseases considering the action of fire by combining an evaluation model composed of 8 tunnel disease evaluation indicators under structural factors and environmental factors with the analytic hierarchy process, reducing the influence of human factors. Based on three dimensions of structural damage degree, environmental erosion degree, and fire-specific damage, a hierarchical evaluation model containing 8 quantitative indicators is constructed to break through the limitations of traditional single-factor evaluation and achieve refined diagnosis of tunnel damage after fire. Description of the Drawings

[0043] Figure 1 is the evaluation flow chart of the tunnel structure disease of the present invention. Detailed Embodiments

[0044] The technical solutions in the specific embodiments of the present invention will be clearly and completely described below in conjunction with the accompanying drawings. Apparently, the specific embodiments described are only a part of the specific embodiments of the present invention, rather than all of them. All other specific embodiments obtained by those of ordinary skill in the art based on the specific embodiments of the present invention without creative efforts belong to the scope of protection of the present invention.

[0045] As Figure 1 shown, a tunnel disease evaluation method based on the external environment and fire action includes the following steps:

[0046] Step 1: Establish a tunnel disease database by collecting tunnel disease data; the types of tunnel disease evaluation indicators are lining cracking, leakage, material strength deterioration, lining delamination and peeling, lining effective thickness, freeze-thaw, bias pressure, and fire.

[0047] Step 2: Determine the classical domain and the joint domain of tunnel structure diseases: Combining the matter-element theory, determine the classical domain of the matter-elements in the disease evaluation system according to the types of tunnel disease evaluation indicators in the tunnel disease database; determine the joint domain of the tunnel disease evaluation grade model according to the evaluation grade and the maximum and minimum values of the classical domain of the indicators.

[0048] The classical domain of the matter-element is expressed as:

[0049]

[0050] In the formula, R ab represents the classical domain of the matter-element of the tunnel disease evaluation system; N i represents the i-th system evaluation grade, C im represents the m-th evaluation indicator in the i-th evaluation grade of the evaluation system; and (μ immin , μ immax ) represents the classical domain range of the i-th evaluation grade with respect to the evaluation indicator C m , where the value range of i corresponds to the value range of the system evaluation grade.

[0051] The joint domain of the matter-element of the tunnel disease evaluation grade model is expressed as:

[0052]

[0053] In the formula, R P represents the joint domain of the matter-element of the tunnel disease evaluation system; P represents the value range of various evaluation indicators corresponding to all evaluation grades; (μ mmin , μ mmax ) represents the value range of the classical domain of the indicator m in all evaluation grades, that is, the joint domain of the matter-element corresponding to the indicator m;

[0054] Taking a tunnel project as an example, for a certain evaluation level N in the evaluation of structural disease levels, this evaluation level may be composed of m disease indicators, such as cracks, water seepage and leakage, insufficient lining thickness, etc. Then these m evaluation indicators are the matter elements in the evaluation model. And the value range corresponding to each evaluation indicator for this evaluation level is the classical domain of this matter element (indicator). For example, when the damage state of the lining structure is grade two, the evaluation range of the matter element of cracks in its disease evaluation indicators is: the length is not greater than 5m, and the width is not greater than 0.2mm. Then when the structural damage in this evaluation model is grade two, the classical domain of the matter element is 0 < L < 5m, 0 < W < 0.2mm.

[0055] If there are a total of four levels for the tunnel lining damage level in this evaluation model, and the classical domain of the matter element evaluation indicator of cracks in the fourth level is 10m < L < 15m, 0.3mm < W < 0.4mm, then the range of variation of the matter element evaluation indicator of one of the lining cracks in this evaluation model is 0 < L < 15m, 0 < W < 0.4mm;

[0056] Step 3: Determine the matter element to be evaluated in the disease evaluation model: Determine the matter element to be evaluated according to the disease evaluation indicators in the tunnel disease evaluation level model. Then, through the analytic hierarchy process combined with the Saaty scale method, compare the evaluation indicators of the tunnel disease evaluation level model pairwise, finally form a judgment matrix, and list the scales between the indicators in the judgment matrix;

[0057] Among them, the construction process of the matter element model to be evaluated is to obtain the formula of the matter element model to be evaluated through the construction methods of the range of variation and the classical domain:

[0058]

[0059] In the formula, μ m is the measured value taken by the tunnel to be evaluated with respect to the evaluation indicator C m ;

[0060] Combined with the evaluation criteria for the technical conditions of different types of diseases contained in the maintenance specifications, the evaluation criteria for the measured values of the five items of structural cracking, water seepage and leakage, voids behind the lining, insufficient lining thickness, and erosion and deterioration in the secondary indicators are listed in Tables 1 - 5.

[0061] Table 1 Evaluation criteria when cracks exist and develop

[0062]

[0063] Table 2 Evaluation criteria when it is impossible to determine whether cracks exist and develop

[0064]

[0065] Table 3 Evaluation criteria for lining section deterioration, delamination and spalling

[0066]

[0067] Table 4 Evaluation criteria for tunnel lining leakage

[0068]

[0069] Table 5 Evaluation criteria for technical conditions of insufficient lining thickness and back voids

[0070]

[0071] As for the external environmental factors such as freeze-thaw and eccentric pressure in the secondary evaluation indicators, the technical condition assessment standards for tunnel lining freeze-thaw are listed in Table 6, the technical condition assessment standards under eccentric pressure are listed in Table 7, and the technical condition assessment standards under fire are listed in Table 8.

[0072] Table 6 Technical status assessment standard for freeze-thaw action of lining

[0073]

[0074] Table 7 Technical status assessment standard for tunnel bias pressure

[0075]

[0076] Table 8 Fire action technical status assessment standards

[0077]

[0078] The above is a description of the status of all secondary evaluation indicators in the evaluation model. The evaluation indicators are scored in combination with the inspection work and the technical condition assessment standards, and then the tunnel disease level is evaluated through the above hierarchical extension theory. The evaluation levels are divided into four technical condition levels: intact, slightly damaged, moderately damaged, and severely damaged. In the process of evaluating the condition value, there will be a situation where the technical condition description is vague and the evaluation value selection is ambiguous; for example, cracks have appeared in the lining structure, but it is not certain whether the crack will continue to expand. At this time, the condition value of the evaluation indicator of lining cracking cannot be determined. In the process of highway tunnel inspection, staff usually use quantitative descriptions to describe certain tunnel structure diseases. For example, the width and length of tunnel cracks, the area of lining concrete delamination and spalling, and the proportion of lining thickness after deterioration to the design thickness are clearly defined. Therefore, in the case of vague technical condition description, the condition value of a single evaluation indicator can be evaluated based on the quantitative parameters of the evaluation indicator and combined with the relevant provisions in the maintenance specification, and then the above hierarchical extension model is used to determine the disease level of the tunnel structure.

[0079] After determining the above-mentioned matter-elements to be evaluated, the analytic hierarchy process combined with the Saaty scale method can be used to compare the evaluation indicators of the tunnel disease evaluation model pairwise according to Table 1, and finally form a judgment matrix, as shown in Table 9. For example, if the evaluation model includes several indicators such as cracks, leakage of water and infiltration, and insufficient lining thickness, and it is considered that cracks are slightly more important than leakage of water and infiltration, and significantly more important than insufficient lining thickness, then the scale of cracks relative to leakage of water is 3, and the scale relative to insufficient lining thickness is 5. The scales of leakage of water and insufficient thickness relative to the indicator of cracks are 1 / 3 and 1 / 5 respectively, and so on. Finally, the scales between the indicators are listed in the judgment matrix in Table 10;

[0080] Table 9 Saaty Scale Method and Its Definition Description

[0081]

[0082]

[0083] Table 10 General Form of Judgment Matrix

[0084] R <![CDATA[C1]]> <![CDATA[C2]]> .... <![CDATA[C n > <![CDATA[C1]]> 1 <![CDATA[a 12 > .... <![CDATA[a 1n > <![CDATA[C2]]> <![CDATA[1 / a 12 > 1 .... <![CDATA[a 2n > .... .... .... .... .... <![CDATA[C n > <![CDATA[1 / a 1n > <![CDATA[1 / a 2n > .... 1

[0085] Select the appropriate scales through the scales between the indicators in Table 10;

[0086] Step 4: Determination and Consistency Test of Evaluation Index Weights: After obtaining the judgment matrix in Step 3, use mathematical methods to calculate values such as the mean or square root of each row of judgment indicators, and then normalize the corresponding values of each indicator to initially obtain the weight sizes corresponding to each indicator in the target problem, so as to determine the weights of each matter-element disease indicator in the tunnel disease evaluation level; then calculate the consistency ratio according to the maximum eigenvalue and matrix order of the matrix to determine whether the weights of the matrix meet the requirements;

[0087] Multiply and take the square root of the row vectors corresponding to each evaluation indicator. The formula is:

[0088]

[0089] In the formula, A i is the value obtained by multiplying and taking the square root of the row vector values corresponding to the i-th evaluation indicator in the judgment matrix; a ij is the element in the i-th row and j-th column of the judgment matrix.

[0090] Subsequently, normalize the square root values corresponding to each indicator to obtain the proportion vector of each indicator in the target model. The formula is:

[0091]

[0092] So far, the weights of the matter-element disease indicators in the tunnel disease evaluation level have been basically determined. Subsequently, only the consistency test of the model is required. Due to the complexity of the objective world and the diversity of people's views on specific issues, there is no fixed reference standard when multiple elements in the matter-element model are compared pairwise. This may result in results that violate people's cognitive common sense in the importance comparison stage. For example, in the tunnel lining disease evaluation model, it is determined that the importance of lining cracking is stronger than that of lining leakage, and at the same time, the importance of leakage disease is stronger than that of insufficient lining thickness. However, when comparing lining cracking with insufficient thickness, it is considered that the importance of the latter is stronger than that of the former, which is obviously unreasonable. The consistency test is to avoid similar problems in the evaluation model. Therefore, it is allowed that the judgment matrix is not completely consistent. But how to ensure that the matrix has general consistency requires that the matrix must be subjected to a consistency test;

[0093] First, calculate the maximum eigenvalue of the judgment matrix. The formula is:

[0094]

[0095] In the formula, \(R\) is the judgment matrix; \(A\) is the vector obtained after normalization, that is, \((α_1α_2…α i …α i …α n ); \(n\) is the number of evaluation indicators, that is, the order of the judgment matrix;

[0096] During the inspection process, the consistency index is \(CI\), and the random consistency index is \(RI\). Its value is determined by the order of the matter-element judgment matrix. The values of \(RI\) for matrices of order 7 and below are shown in Table 11:

[0097] Table 11 Values of Random Consistency Index (\(RI\))

[0098] Order 1 2 3 4 5 6 7 8 RI 0 0 0.52 0.89 1.12 1.26 1.36 1.41

[0099] The calculation method of the consistency index \(CI\) is as follows. The formula is:

[0100]

[0101] The calculation method of the consistency ratio is as follows. The formula is:

[0102]

[0103] If the actually calculated \(CR\) value in the tunnel project is less than 0.1, it is determined that the matter-element judgment matrix meets the consistency requirement, and the weights determined by this matrix meet the requirements;

[0104] Step 5: Calculate the correlation degree of the evaluation index to the disease level:

[0105] The formula for calculating the correlation degree of evaluation index \(i\) with respect to tunnel disease level \(j\) is as follows:

[0106]

[0107] In the formula, k ji is the correlation degree of evaluation index i with respect to evaluation level j in the model; μ ji is the value range of the classical domain, and μ pi is the value range of the section domain; ρ(μ i , μ ji ) is the distance from the point μ i to the interval (μ jimin , μ jimax ), and ρ(μ i , μ pi ) is the distance from the point μ i to the interval (μ pimin , μ pimax );

[0108] Step 6, determination of the comprehensive correlation degree: After determining the proportion of each matter-element index in the evaluation model, combining with the correlation degree of the evaluation index with respect to the severity level of the engineering structure disease obtained from the formula in Step 5, the comprehensive correlation degree of each disease index in the evaluation system with respect to the evaluation level of the tunnel to be evaluated is obtained:

[0109]

[0110] In the formula, a i is the proportion of each evaluation index in the matter-element model, and k ji is the correlation degree of evaluation index i with respect to tunnel disease level j;

[0111] Step 7, determination of the tunnel structure disease level: After all the parameters in Step 6 are determined, the disease level of the target tunnel structure can be determined:

[0112] k = max{k j |j = 1, 2... n}.

[0113] The following uses a specific example of the Tiebeishan Tunnel to verify a tunnel disease evaluation method based on the external environment and fire action of the present invention. The specific steps are as follows:

[0114] 1. By collecting the disease data of operating highway tunnels, establish a tunnel disease database, and select lining cracking, leakage, material strength deterioration, lining delamination and peeling, lining effective thickness, freeze-thaw, bias pressure, and fire as evaluation indexes.

[0115] Table 12 Tiebeishan Tunnel disease data

[0116]

[0117] 2. Determine the classical domain and section domain of the tunnel structure disease.

[0118] The structural disease levels are divided into 4 categories, namely: N1 is in good condition, N2 is slightly damaged, N3 is moderately damaged, and N4 is severely damaged. According to the established evaluation indicators and quantitative grading criteria above, the classical domains of the matter elements corresponding to each level of the tunnel lining structure are represented as follows:

[0119]

[0120] After determining the classical domains of the main matter elements in the tunnel disease evaluation model, the matter-element's joint domain can be determined as follows:

[0121]

[0122] 3. Determine the matter element to be evaluated in the tunnel structure disease model.

[0123] Based on the regular tunnel inspection results and combined with the technical condition descriptions of various evaluation indicators, scores are given to the condition values of each indicator. There are 5 dripping water diseases at the left and right arch waists of the lining, and it has had a certain impact on vehicle traffic, so the evaluation value of the lining leakage index is taken as 2. The water sources of the tunnel leakage are mostly surface water. In cold weather, the leakage water is likely to freeze at positions such as the lining surface and drainage system, forming ice hanging, ice columns, and ice cones, etc., so the evaluation value of the freeze-thaw action index is 2.

[0124] Cracks occur at all construction joints in the whole tunnel: There are 11 circumferential cracks in the tunnel lining, the maximum crack width is 0.75 mm, and the length range is 1 m to 20 m; there are 30 longitudinal cracks, the maximum crack width is 2 mm, and the length range is 5 m to 15 m; there are 2 diagonal cracks, the maximum crack width is 1 mm, and the lengths are both 10 m, so the evaluation value of the lining cracking index is taken as 4. Considering that there is a phenomenon of poor bonding between the primary lining and the secondary lining in a large area in the tunnel, the evaluation value of the lining effective thickness index is taken as 3.

[0125] There are 5 peeling-off layers on the lining, all located at the construction joints of the side walls, with an area of 0.22 m2, so the evaluation value of the lining peeling-off is taken as 3. The test results show that the measured strength of the tunnel lining concrete is 21.1 MPa, while the designed strength of the concrete is 18 MPa, and the concrete strength meets the design requirements, so the evaluation value of the material strength deterioration is set as 1.

[0126] There are many circular, longitudinal, and diagonal cracks on the surface of the tunnel lining. The widths of some cracks have seriously exceeded the specification limit requirements, and the depths of some cracks exceed 14 cm, so the evaluation value of the eccentric loading effect is taken as 3.

[0127] Under the action of high temperature in a fire or the like, the lining cracks of the tunnel further develop, causing an obvious instability trend in the side walls and the lining layer, but it has not reached the degree where obvious instability signs appear in the side walls and the vault. Moreover, the tunnel lining is made of non-reinforced concrete. Referring to the fire resistance performance of ordinary plain concrete, for concrete of general strength grades without fire protection measures, the fire resistance limit may be about 1 to 2 hours. Therefore, the evaluation value of the fire action index is 2.

[0128] Based on the above analysis, the matter-elements to be evaluated are initially obtained as follows:

[0129]

[0130] Among them, c1, c2, c3, c4, c5, c6, c7, and c8 represent lining cracking, leakage, material strength deterioration, lining delamination and peeling, effective lining thickness, freeze-thaw, eccentric pressure, and fire.

[0131] 4. Determination of the weights of evaluation indicators and consistency test.

[0132] Form a judgment matrix with the 8 evaluation indicators in the above evaluation model, as shown in Table 13.

[0133] Table 13 Judgment matrix of the tunnel structure disease evaluation model

[0134]

[0135]

[0136] After obtaining the above judgment matrix, the weight set of the evaluation indicators and the maximum eigenvalue of the judgment matrix can be calculated using the square root method. First, calculate the weight values of the evaluation indicators in the judgment matrix, and the process is as follows:

[0137]

[0138] Among them, A i is the value obtained by taking the square root of the product of the numerical values of the row vector corresponding to the i-th evaluation indicator in the judgment matrix; a ij is the element in the i-th row and j-th column of the judgment matrix.

[0139] Normalize the result of taking the square root of the product of the above-obtained judgment matrix to get:

[0140] A = (0.373 0.176 0.062 0.113 0.062 0.113 0.062 0.038) T

[0141] That is, the proportion of each evaluation indicator in the lining structure disease evaluation model. Subsequently, the maximum eigenvalue λ of the judgment matrix can be calculated, and the process is as follows:

[0142]

[0143] Obtain the maximum eigenvalue:

[0144]

[0145] The maximum eigenvalue is 8.123, and the consistency test can be carried out:

[0146]

[0147] The consistency index can be obtained as:

[0148]

[0149] The results show that the judgment matrix meets the consistency requirements, and the calculation weight set of the above matter elements can be determined as the proportion of each evaluation index.

[0150] 5. Calculation of the correlation degree of evaluation indexes to the disease level.

[0151] The correlation degree of evaluation index i with respect to tunnel disease level j is shown in the following formula:

[0152]

[0153] In the formula, k ji is the correlation degree of evaluation index i with respect to evaluation level j in the model; μ ji is the value range of the classical domain, and μ pi is the value range of the section domain; ρ(μ i , μ ji ) is the distance from point μ i to the interval (μ jimin , μ jimax ), and ρ(μ i , μ pi ) is the distance from point μ i to the interval (μ pimin , μ pimax ).

[0154] According to the formula, calculate the correlation degrees of the first and second evaluation indexes with evaluation levels 1 and 2. The specific process is as follows:

[0155]

[0156] According to the above formula, the calculation results of the correlation degree matrix are shown in Table 14:

[0157] Table 14 Correlation degrees of evaluation indexes with respect to evaluation levels

[0158]

[0159] 6. Calculate the comprehensive correlation degree.

[0160] After calculating the correlation degrees of each evaluation index with respect to the 4 levels, the comprehensive correlation degree of the disease level of the evaluation model can be calculated as follows: It can be calculated that:

[0161]

[0162] In summary, the comprehensive correlation degrees of the 4 levels are k1 = -0.819, k2 = -0.212, k3 = -0.454, and k4 = -0.192 respectively.

[0163] 7. Determination of the disease level of the tunnel structure.

[0164] According to the grade evaluation rules specified above, the tunnel disease level is determined as follows:

[0165] k = max{k j | j = 1, 2…n} = -0.192 (j = 4)

[0166] It can be known that the disease evaluation level of the Tiebeishan Tunnel structure is level 4, that is, the tunnel is severely damaged, which is consistent with the actual structural state of the tunnel.

[0167] From the above description, it can be seen that when evaluating the tunnel disease, the present invention can determine the tunnel disease level according to the tunnel disease evaluation index, reducing the risk of subjective evaluation.

Claims

1. A tunnel disease evaluation method based on external environment and fire action, characterized in that It includes the following steps: Step 1: Establish a tunnel disease database by collecting tunnel disease data; Step 2: Determine the classical domain and section domain of tunnel structure diseases: Combining the matter-element theory, determine the classical domain of the matter-elements in the disease evaluation system according to the types of tunnel disease evaluation indicators in the tunnel disease database; Determine the section domain of the tunnel disease evaluation grade model according to the evaluation grade and the maximum and minimum values of the index classical domain; Step 3: Determine the matter-elements to be evaluated in the disease evaluation model: Determine the matter-elements to be evaluated according to the disease evaluation indicators in the tunnel disease evaluation grade model. Then, through the analytic hierarchy process combined with the Saaty scale method, make pairwise comparisons of the evaluation indicators of the tunnel disease evaluation grade model, and finally form a judgment matrix. Then list the scales between the indicators in the judgment matrix; Step 4: Determination and consistency test of the weights of evaluation indicators: After obtaining the judgment matrix in Step 3, use mathematical methods to calculate values such as the mean or square root of each row of judgment indicators. Subsequently, perform normalization processing on the corresponding values of each indicator to initially obtain the weights of each indicator corresponding to the target problem, so as to determine the weights of each matter-element disease indicator in the tunnel disease evaluation grade; Then calculate the consistency ratio according to the maximum eigenvalue and the order of the matrix, and thus determine whether the weights of the matrix meet the requirements; Step 5: Calculate the correlation degree of the evaluation indicators to the disease grade: Calculate the correlation degree of the tunnel disease grade according to the evaluation indicators; Step 6: Determination of the comprehensive correlation degree: After determining the proportion of each matter-element index in the evaluation model, combine the correlation degree of the evaluation indicators obtained from the formula in Step 5 regarding the severity grade of engineering structure diseases, and then obtain the comprehensive correlation degree of each disease indicator in the evaluation system regarding the evaluation grade of the tunnel to be evaluated: where a i is the proportion of each evaluation index in the matter-element model, and k ji is the correlation degree of evaluation index i with respect to tunnel disease level j; Step 7: Determination of the tunnel structure disease grade: After all the parameters in Step 6 are determined, the disease grade of the target tunnel structure can be determined: k = max{k j | j = 1, 2... n}.

2. The tunnel disease evaluation method based on external environment and fire action according to claim 1, wherein The classical domain of the matter-elements in Step 1 is expressed as: In the formula, R ab represents the classical domain of the matter element of the tunnel disease evaluation system; N i represents the i-th system evaluation level, C im represents the m-th evaluation index in the i-th evaluation level of the evaluation system; and (μi mmin , μi mmax ) represents the classical domain range of the i-th evaluation level with respect to the value of the evaluation index C m , where the value range of i corresponds to the value range of the system evaluation level.

3. The tunnel disease evaluation method based on external environment and fire action according to claim 1, characterized in that The section domain of the matter-elements of the tunnel disease evaluation grade model in Step 1 is expressed as: In the formula, R P represents the range of the matter element of the tunnel disease evaluation system; P represents the value range of various evaluation indicators corresponding to all evaluation levels; (μ mmin , μ mmax ) represents the value range of the classical domain of the index m in all evaluation levels, that is, the range of the matter element corresponding to the index m.

4. The tunnel disease evaluation method based on the external environment and the action of fire according to claim 1, wherein The construction process of the matter-elements to be evaluated model is to obtain the formula of the matter-elements to be evaluated model through the construction methods of the section domain and the classical domain: where μ m is the measured value of the tunnel to be evaluated with respect to the evaluation index C m .

5. The tunnel disease evaluation method based on external environment and fire action according to claim 1, characterized in that The formula for multiplying the row vectors corresponding to each evaluation indicator in Step 3 and taking the square root is: where A i is the value obtained by taking the square root of the product of the numerical values of the row vector corresponding to the i-th evaluation index in the judgment matrix; a ij is the element in the i-th row and j-th column of the judgment matrix; Subsequently, perform normalization processing on the square root values corresponding to each indicator, and the proportion vector of each indicator in the target model can be obtained. The formula is: The calculation formula for the maximum eigenvalue of the matrix is: Wherein, R is a judgment matrix; A is the vector obtained after normalization, i.e., (α1α2…α i …α i …α n ); n is the number of evaluation indicators, which is the order of the judgment matrix; In the inspection process, the consistency index is CI, and the random consistency index is RI. Its value is determined by the order of the matter-element judgment matrix. The calculation method of the consistency index CI is as follows. The formula is: The calculation method formula for the consistency ratio is: If the actually calculated CR value in the tunnel project is less than 0.1, it is determined that the matter-element judgment matrix meets the consistency requirements, and the weights determined by this matrix meet the requirements.

6. The tunnel disease evaluation method based on external environment and fire effect according to claim 1, wherein The calculation formula for the correlation degree of evaluation indicator i regarding tunnel disease grade j in Step 5 is as follows: where k ji is the correlation degree of evaluation index i with respect to evaluation level j in the model; μ ji is the value range of the classical domain, and μ pi is the value range of the section domain; ρ(μ i , μ ji ) is the distance from point μ i to the interval (μ jimin , μ jimax ), and ρ(μ i , μ pi ) is the distance from point μ i to the interval (μ pimin , μ pimax ).

7. The tunnel disease evaluation method based on external environment and fire action according to claim 1, characterized in that The types of tunnel disease evaluation indicators in Step 1 are lining cracking, water seepage, material strength deterioration, lining delamination and peeling, lining effective thickness, freeze-thaw, bias pressure, and fire.

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