A safety assessment method for steel frame structures based on boundary adaptive cloud network model

Abstract

The present invention discloses a steel frame structure safety assessment method based on a boundary adaptive cloud network model, comprising the following steps: 1) constructing a steel frame structure safety assessment system model; 2) determining interval thresholds for various damage behavior indicators at different structural safety levels, and calculating characteristic parameters of the antecedent-"3En" rule cloud and the antecedent-variable fuzzy cloud; 3) fusing information on the membership and membership degrees to construct the antecedent cloud of the boundary adaptive cloud network model; 4) calculating characteristic parameters of the posterior cloud of the boundary adaptive cloud network model and establishing a boundary adaptive cloud network model based on weighted Dependent-Scientific fusion; and 5) obtaining measured values ​​of structural monitoring indicators under the current load step and inputting them into the boundary adaptive cloud network model based on weighted Dependent-Scientific fusion to obtain a quantitative safety assessment value for the steel frame structure under the current load step. This invention addresses the issues of accurately quantifying the safety level of steel frame structures using multiple behavior indicators and the uncertainty caused by measurement noise.

Description

Technical Field

[0001] The present invention relates to the field of steel frame structure safety evaluation, and in particular to a steel frame structure safety evaluation method based on a boundary adaptive cloud network model. Background Art

[0002] Steel frame structures boast high strength, light weight, excellent seismic resistance, recyclability, suitability for modular construction, and ease of industrial production. In recent years, they have been widely used and rapidly developed in the construction industry. However, steel frame structures can suffer from fatigue and other damage under reciprocating loads. Accumulating structural damage can lead to a gradual decrease in bearing capacity, seriously impacting the overall safety of the structure. Furthermore, most steel frame structures are supported by supporting steel columns, and loosening of the supporting column restraints can also reduce structural stability, leading to instability and failure, reducing safety, and even potentially causing serious accidents such as collapse of the entire steel frame structure. Since steel frame structures can age over their service life, reducing their bearing capacity and support stability, real-time operation and maintenance monitoring and safety status assessment of building structures are particularly important.

[0003] Structural safety assessment methods include the Analytic Hierarchy Process (AHP), grey relational analysis, fuzzy comprehensive evaluation, reliability analysis, and artificial neural networks. The AHP is a multi-criteria comprehensive evaluation method that quantitatively analyzes qualitative issues and can reduce the influence of subjective factors to a certain extent. By logically decomposing the object to be evaluated into multiple levels, such as the target layer, criterion layer, and scheme layer, qualitative factors are quantified, and ultimately decisions are made, making the evaluation results more scientific. The grey relational analysis method analyzes the correlation between a small amount of known data and uses the correlation coefficient matrix as the basis for judgment to determine the degree of similarity between data sequences of several evaluation objects. The fuzzy comprehensive evaluation method is a specific application method of fuzzy mathematics. Utilizing the membership principle of fuzzy mathematics, it comprehensively considers the influencing factors of the evaluation object and obtains a fuzzy result set through fuzzy symbol operations, thereby expressing uncertain information using a quantitative evaluation method. Reliability methods refer to the probability that a structure will complete its pre-defined functions and requirements within specified time and conditions. The first-order second-moment method was the earliest and most widely used reliability calculation method in research. As research on structural reliability analysis deepened, subsequent reliability calculation methods, such as the second-order second-moment method, the higher-order moment method, and the response surface method, were proposed. Currently, reliability analysis methods are also widely used in structural safety assessment. Artificial neural networks are advanced intelligent algorithms that, through data training, can possess excellent memory and self-learning capabilities. They also excel at processing massive amounts of data, compensating for the computational limitations of traditional methods and reducing the influence of human subjectivity. They are therefore suitable for structural safety status assessment. However, because neural networks require large amounts of data for training, they can easily fail to generalize without sufficient training samples. Furthermore, because neural networks activate their behavior through neuron weights, the model lacks transparency, making it difficult to determine whether the network's assessments are based on erroneous factors.

[0004] In practical engineering applications, factors such as measurement noise, initial defects, and uncertainty in the standard values ​​of evaluation indicators often exist, making structural safety assessments challenging. Cloud models, leveraging their three digital features, offer the advantage of reducing uncertainty and hold great promise for application in building structural safety status assessments. However, despite their advantages in addressing uncertainty, their assessments of structural safety status typically rely on the principle of maximum membership to determine the current safety level of the research object, making it difficult to assess the actual safety of the structure. Summary of the Invention

[0005] The purpose of the present invention is to provide a steel frame structure safety assessment method based on a boundary adaptive cloud network model, comprising the following steps:

[0006] 1) Select the safety evaluation index of steel frame structure and construct the damage index system U={U1,U2,...,U i}, and determine the fusion weight of each damage index;

[0007] 2) Construct a steel frame structure safety evaluation system model M;

[0008] 3) Determine the interval thresholds of each damage behavior index at different structural safety levels, and calculate the characteristic parameters of the antecedent-"3En" rule cloud and the characteristic parameters of the antecedent-variable fuzzy cloud; the characteristic parameters of the antecedent-"3En" rule cloud include the expected Ex a,3En , Entropy a,3En , super entropy He a,3En , antecedent-"3En" rule cloud membership μ a,3En ; The characteristic parameters of the antecedent-variable fuzzy cloud include the variable fuzzy cloud expectation Ex a,VFC 、Entropy a,VFC 、Super entropy He a,VFC , membership degree μ of the antecedent-variable fuzzy cloud a,VFC ;

[0009] 4) Using weighted DS information fusion method to calculate the membership μ a,3En and membership μ a,VFC Perform information fusion to obtain the boundary adaptive antecedent cloud certainty μ a,BACN , and then construct the boundary adaptive cloud network model frontier cloud;

[0010] 5) Calculate the post-cloud characteristic parameters of the boundary adaptive cloud network model, thereby establishing a qualitative rule base including multiple "IF-THEN" rules, and establishing a boundary adaptive cloud network model based on weighted DS fusion;

[0011] 6) Obtain the measured values ​​of the structural monitoring indicators under the current load step (x1, x2, ···, x i ) and input it into the boundary adaptive cloud network model based on weighted DS fusion to obtain the quantitative value of the safety evaluation of the steel frame structure under the current load step.

[0012] Furthermore, the entropy En of the “3En” rule cloud model a,3En =(C R -C L ) / 6, where C L 、C R are the left and right boundary values ​​of the threshold interval; for a specific value x i The cloud membership μ a,3En (x i )=exp[-(x i -Ex a,3En ) 2 / (2En i' 2 )], where En i ′ is En a,3En is the expected value and He a,3En A normal random number generated for the variance.

[0013] Furthermore, the structural safety evaluation indicators include strain, deflection, angle, stress, and layer displacement.

[0014] Furthermore, the structural safety assessment system model M is as follows:

[0015]

[0016] Where U i represents the i-th damage behavior index in the structural safety evaluation index system; L j represents the jth safety level divided by the structural safety assessment model M;

[0017] C j =[C 1j C 2j …C ij ] T , where C ij =(a ij ,b ij ) represents the jth structural safety level threshold interval in the i-th damage behavior index. ij ,b ij It is the lower and upper limit of the safety level.

[0018] Furthermore, the variable fuzzy cloud expectation Ex a,VFC As shown below:

[0019]

[0020] Where x t,norm (i, j) represents the standardized training data of the i-th monitoring indicator at the j-th safety level, t represents the sequence number of the training data, μ t ′(i,j) is the standardized training data x of the i-th monitoring indicator at the j-th security level t,norm For the relative membership of the jth security level threshold interval, n t is the number of standardized training data for the i-th monitoring indicator at the j-th safety level;

[0021] Entropy a,VFC As shown below:

[0022]

[0023] Where η R(i, j) is the right limit value of the threshold interval of the i-th monitoring indicator obtained by appropriately expanding the softening factor at the j-th safety level, η L (i, j) is the left limit value of the indicator threshold interval obtained by appropriately expanding the i-th monitoring indicator at the j-th safety level through the softening factor.

[0024] Superentropy He a,VFC As shown below:

[0025] He a,VFC =0.1En a,VFC (i,j) (4)

[0026] Antecedent - membership degree μ of variable fuzzy cloud a,VFC As shown below:

[0027]

[0028] Where x norm (i,j) is the standardized measured value of the i-th monitoring indicator at the j-th safety level. a ' ,VFC (i,j) is En a,VFC (i,j) is the expectation, He a,VFC is a normal random distribution with a standard deviation.

[0029] Furthermore, the boundary adaptive antecedent cloud certainty μ a,BACN As shown below:

[0030]

[0031] Where, m1(A i ) comes from the evidence source of the antecedent-“3En” rule cloud, which is the i-th piece of evidence data in the evidence source, m2(B j ) comes from the evidence source in the antecedent-variable fuzzy cloud, which is the jth piece of evidence data in the evidence source;

[0032] Among them, the basic probability assignment m1(A i )’s kth monitoring indicator item m 1k (A i ) is as follows:

[0033]

[0034] Where μ ki,3En Assign m1(A i )’s cloud membership corresponding to the k-th monitoring indicator item; n j Indicates the number of security levels;

[0035] Basic probability assignment m2(B j)’s kth monitoring indicator item m 2k (B j ) is as follows:

[0036]

[0037] Where n j Indicates the number of security levels; μ kj,VFC Assign m2(B j ) corresponds to the cloud membership of the kth monitoring indicator item.

[0038] Furthermore, the boundary adaptive cloud network model post-processing cloud characteristic parameters include the boundary adaptive cloud network model post-processing cloud expectation Ex b,BACN , boundary adaptive cloud network model post-processing cloud entropy En b,BACN 、Super entropy He b,BACN .

[0039] Furthermore, the boundary adaptive cloud network model post-cloud expectation Ex b,BACN As shown below:

[0040]

[0041] Where, Ex b,BACN (j) is the expected cloud of the subsequent component of the j-th safety level score of the structure, s R (j) is the right boundary of the jth safety level scoring interval of the structure, s L (j) is the left boundary of the j-th safety level scoring interval of the structure;

[0042] Boundary Adaptive Cloud Network Model Post-processing Cloud Entropy En b,BACN is as follows:

[0043]

[0044] In the formula, En b,BACN (j) is the subsequent cloud entropy of the j-th safety level score of the structure;

[0045] Superentropy He b,BACN As shown below:

[0046] He b,BACN =0.1En b,BACN (11)

[0047] Furthermore, in the boundary adaptive cloud network model based on weighted DS fusion, the output of the post-processing cloud is the safety evaluation quantitative value x b,BACN (i,j), that is:

[0048]

[0049] Where Nb ' ,BACN (i,j) is En b,BACN For the expectation, He b,BACN is a normal random distribution with standard deviation; Ex b,BACN (j) is the expected cloud of the consequent of the j-th safety level score of the structure; μ a,BACN

[0050] is the membership degree of the subsequent cloud; x norm Ex is the standardized measured value. a,3En is the characteristic parameter of the antecedent-“3En” rule cloud.

[0051] The technical effect of the present invention is undoubted. The present invention combines variable fuzzy cloud and "3En"

[0052] The degree of certainty obtained from the rule-based normal cloud is subjected to DS information fusion, which then constructs a boundary-adaptive antecedent cloud in the normal cloud reasoning system. This is then mapped to the "3En" rule-based consequent cloud based on the safety level score, thereby establishing a boundary-adaptive cloud network model based on DS fusion. The corresponding multidimensional performance indicator measured data is input into the input layer, activating the boundary-adaptive cloud network model. After reasoning and fusion, the overall fused safety score output value for the structure in its current health state is finally obtained.

[0053] The present invention utilizes a boundary adaptive cloud network model based on multiple performance indicators to perform health identification of steel frame structures, solving the problem of inaccurate health status safety assessment of steel frame structures when considering multiple performance indicators.

[0054] Under the interference of uncertain factors such as measurement noise, the boundary adaptive cloud network model of the present invention can more accurately determine the safety level of the steel frame structure and has a higher accuracy in identifying the health and safety status. BRIEF DESCRIPTION OF THE DRAWINGS

[0055] Figure 1 is a flow chart of the method;

[0056] Figure 2 (a)-(c) show the size information, finite element model and measurement point locations of the four-story steel frame;

[0057] Figure 3 Extract the overall features of a four-story steel frame structure; Figure 3 (a) is the hysteresis curve; Figure 3 (b) is the skeleton curve; Figure 3 (c) is the secant stiffness degradation curve;

[0058] Figure 4 Safety level classification for four-story steel frame structures.

[0059] Figure 5 This is the overall security evaluation result of the structure based on the boundary adaptive cloud network model. DETAILED DESCRIPTION

[0060] The present invention will be further described below with reference to the following examples, but it should not be understood that the scope of the present invention is limited to the following examples. Without departing from the above technical ideas of the present invention, various substitutions and modifications can be made according to common technical knowledge and customary means in the art, and all should be included in the scope of protection of the present invention.

[0061] Example 1:

[0062] See also Figures 1 to 5 ,A steel frame structure safety evaluation method based on a boundary adaptive cloud network model, comprising the following steps:

[0063] 1) Select the safety evaluation index of steel frame structure and construct the damage index system U={U1,U2,...,U i}, and determine the fusion weight of each damage index;

[0064] 2) Construct a steel frame structure safety evaluation system model M;

[0065] 3) Determine the interval thresholds of each damage behavior index at different structural safety levels, and calculate the characteristic parameters of the antecedent-"3En" rule cloud and the characteristic parameters of the antecedent-variable fuzzy cloud; the characteristic parameters of the antecedent-"3En" rule cloud include the expected Ex a,3En , Entropy a,3En , super entropy He a,3En , antecedent-"3En" rule cloud membership μ a,3En ; The characteristic parameters of the antecedent-variable fuzzy cloud include the variable fuzzy cloud expectation Ex a,VFC 、Entropy a,VFC 、Super entropy He a,VFC , membership degree μ of the antecedent-variable fuzzy cloud a,VFC ;

[0066] 4) Using weighted DS information fusion method to calculate the membership μ a,3En and membership μ a,VFC Perform information fusion to obtain the boundary adaptive antecedent cloud certainty μ a,BACN , and then construct the boundary adaptive cloud network model frontier cloud;

[0067] 5) Calculate the post-cloud characteristic parameters of the boundary adaptive cloud network model, thereby establishing a qualitative rule base including multiple "IF-THEN" rules, and establishing a boundary adaptive cloud network model based on weighted DS fusion;

[0068] 6) Obtain the measured values ​​of the structural monitoring indicators under the current load step (x1, x2, ···, xi ) and input it into the boundary adaptive cloud network model based on weighted DS fusion to obtain the quantitative value of the safety evaluation of the steel frame structure under the current load step.

[0069] The structural safety evaluation indicators include strain, deflection, rotation angle, stress, and layer displacement.

[0070] The structural safety assessment system model M is as follows:

[0071]

[0072] Where U i represents the i-th damage behavior index in the structural safety evaluation index system; L j represents the jth safety level divided by the structural safety assessment model M;

[0073] C j =[C 1j C 2j … C ij ] T , where C ij =(a ij ,b ij ) represents the jth structural safety level threshold interval in the i-th damage behavior index. ij ,b ij It is the lower and upper limit of the safety level.

[0074] Variable Fuzzy Cloud Expectation Ex a,VFC As shown below:

[0075]

[0076] Where x t,norm (i, j) represents the standardized training data of the i-th monitoring indicator at the j-th safety level, t represents the sequence number of the training data, μ t ′(i,j) is the standardized training data x of the i-th monitoring indicator at the j-th security level t,norm For the relative membership of the jth security level threshold interval, n t is the number of standardized training data for the i-th monitoring indicator at the j-th safety level;

[0077] Entropy a,VFC As shown below:

[0078]

[0079] Where η R (i, j) is the right limit value of the threshold interval of the i-th monitoring indicator obtained by appropriately expanding the softening factor at the j-th safety level, η L(i, j) is the left limit value of the indicator threshold interval obtained by appropriately expanding the i-th monitoring indicator at the j-th safety level through the softening factor.

[0080] Superentropy He a,VFC As shown below:

[0081] He a,VFC =0.1En a,VFC (i,j) (4)

[0082] Antecedent - membership degree μ of variable fuzzy cloud a,VFC As shown below:

[0083]

[0084] Where x norm (i,j) is the standardized measured value of the i-th monitoring indicator at the j-th safety level. a ' ,VFC (i,j) is En a,VFC (i,j) is the expectation, He a,VFC is a normal random distribution with a standard deviation.

[0085] Boundary adaptive antecedent cloud certainty μ a,BACN As shown below:

[0086]

[0087] Where, m1(A i ) comes from the evidence source of the antecedent-“3En” rule cloud, which is the i-th piece of evidence data in the evidence source, m2(B j ) comes from the evidence source in the antecedent-variable fuzzy cloud, which is the jth piece of evidence data in the evidence source;

[0088] Among them, the basic probability assignment m1(A i )’s kth monitoring indicator item m 1k (A i ) is as follows:

[0089]

[0090] Where μ ki,3En Assign m1(A i )’s cloud membership corresponding to the k-th monitoring indicator item; n j Indicates the number of security levels;

[0091] Basic probability assignment m2(B j )’s kth monitoring indicator item m 2k (B j ) is as follows:

[0092]

[0093] Where n j Indicates the number of security levels; μ kj,VFC Assign m2(B j ) corresponds to the cloud membership of the kth monitoring indicator item.

[0094] The characteristic parameters of the post-processing cloud of the boundary adaptive cloud network model include the post-processing cloud expectation Ex b,BACN , boundary adaptive cloud network model post-processing cloud entropy En b,BACN 、Super entropy He b,BACN .

[0095] Boundary Adaptive Cloud Network Model Post-processing Cloud Expectation Ex b,BACN As shown below:

[0096]

[0097] Where, Ex b,BACN (j) is the expected cloud of the subsequent component of the j-th safety level score of the structure, s R (j) is the right boundary of the jth safety level scoring interval of the structure, s L (j) is the left boundary of the j-th safety level scoring interval of the structure;

[0098] Boundary Adaptive Cloud Network Model Post-processing Cloud Entropy En b,BACN is as follows:

[0099]

[0100] In the formula, En b,BACN (j) is the subsequent cloud entropy of the j-th safety level score of the structure;

[0101] Superentropy He b,BACN As shown below:

[0102] He b,BACN =0.1En b,BACN (11)

[0103] In the boundary adaptive cloud network model based on weighted DS fusion, the output of the post-processing cloud is the safety evaluation quantitative value x b,BACN (i,j), that is:

[0104]

[0105] Where N b ' ,BACN (i,j) is En b,BACN For the expectation, He b,BACNis a normal random distribution with standard deviation; Ex b,BACN (j) is the expected cloud of the consequent of the j-th safety level score of the structure; μ a,BACN

[0106] is the membership degree of the subsequent cloud; x norm Ex is the standardized measured value. a,3En is the characteristic parameter of the antecedent-“3En” rule cloud.

[0107] Example 2:

[0108] A steel frame structure safety assessment method based on a boundary adaptive cloud network model includes the following steps:

[0109] 1) Select the safety evaluation index of steel frame structure and construct the damage index system U={U1,U2,...,U i}, and determine the fusion weight of each damage index;

[0110] 2) Construct a steel frame structure safety evaluation system model M;

[0111] 3) Determine the interval thresholds of each damage behavior index at different structural safety levels, and calculate the characteristic parameters of the antecedent-"3En" rule cloud and the characteristic parameters of the antecedent-variable fuzzy cloud; the characteristic parameters of the antecedent-"3En" rule cloud include the expected Ex a,3En , Entropy a,3En , super entropy He a,3En , antecedent-"3En" rule cloud membership μ a,3En ; The characteristic parameters of the antecedent-variable fuzzy cloud include the variable fuzzy cloud expectation Ex a,VFC 、Entropy a,VFC 、Super entropy He a,VFC , membership degree μ of the antecedent-variable fuzzy cloud a,VFC ;

[0112] 4) Using weighted DS information fusion method to calculate the membership μ a,3En and membership μ a,VFC Perform information fusion to obtain the boundary adaptive antecedent cloud certainty μ a,BACN , and then construct the boundary adaptive cloud network model frontier cloud;

[0113] 5) Calculate the post-cloud characteristic parameters of the boundary adaptive cloud network model, thereby establishing a qualitative rule base including multiple "IF-THEN" rules, and establishing a boundary adaptive cloud network model based on weighted DS fusion;

[0114] 6) Obtain the measured values ​​of the structural monitoring indicators under the current load step (x1, x2, ···, x i) and input it into the boundary adaptive cloud network model based on weighted DS fusion to obtain the quantitative value of the safety evaluation of the steel frame structure under the current load step.

[0115] Example 3:

[0116] A steel frame structure safety evaluation method based on a boundary adaptive cloud network model, the technical content of which is the same as that of Example 2. Furthermore, the structural safety evaluation indicators include strain, deflection, rotation angle, stress, and layer displacement.

[0117] Example 4:

[0118] A steel frame structure safety evaluation method based on a boundary adaptive cloud network model, the technical content of which is the same as any one of Examples 2-3. Furthermore, a structural safety evaluation system model M is shown below:

[0119]

[0120] Where U i represents the i-th damage behavior index in the structural safety evaluation index system; L j represents the jth safety level divided by the structural safety assessment model M; C j =[C 1j C 2j … C ij ] T , where C ij =(a ij ,b ij ) represents the jth structural safety level threshold interval in the i-th damage behavior index.

[0121] Example 5:

[0122] A steel frame structure safety evaluation method based on a boundary adaptive cloud network model, the technical content is the same as any one of embodiments 2-4, further, the variable fuzzy cloud expectation Ex a,VFC As shown below:

[0123]

[0124] Where x t,norm (i, j) represents the standardized training data of the i-th monitoring indicator at the j-th safety level, t represents the sequence number of the training data, μ′ t (i,j) is the standardized training data x of the i-th monitoring indicator at the j-th safety level t,norm For the relative membership of the jth security level threshold interval, n t is the number of standardized training data for the i-th monitoring indicator at the j-th safety level;

[0125] Entropy a,VFCAs shown below:

[0126]

[0127] Where η R (i, j) is the right limit value of the threshold interval of the i-th monitoring indicator obtained by appropriately expanding the softening factor at the j-th safety level, η L (i, j) is the left limit value of the indicator threshold interval obtained by appropriately expanding the i-th monitoring indicator at the j-th safety level through the softening factor.

[0128] Superentropy He a,VFC As shown below:

[0129] He a,VFC =0.1En a,VFC (i,j) (4)

[0130] Antecedent - membership degree μ of variable fuzzy cloud a,VFC As shown below:

[0131]

[0132] Where x norm (i,j) is the standardized measured value of the i-th monitoring indicator at the j-th safety level.

[0133] Example 6:

[0134] A steel frame structure safety evaluation method based on a boundary adaptive cloud network model, the technical content is the same as any one of embodiments 2-5, further, the boundary adaptive antecedent cloud certainty μ a,BACN As shown below:

[0135]

[0136] Where, m1(A i ) represents the evidence source from the antecedent-“3En” rule cloud, which is the i-th piece of evidence data in the evidence source, m2(B j ) represents the evidence source from the antecedent-variable fuzzy cloud, which is the jth piece of evidence data in the evidence source;

[0137] Among them, the basic probability assignment m1(A i ) is the kth monitoring indicator item

[0138]

[0139] Basic probability assignment m2(B j ) is the kth monitoring indicator item

[0140]

[0141] Where n j Indicates the number of security levels.

[0142] Example 7:

[0143] A steel frame structure safety evaluation method based on a boundary adaptive cloud network model, the technical content is the same as any one of embodiments 2-6, further, the boundary adaptive cloud network model post-cloud feature parameters include the boundary adaptive cloud network model post-cloud expected Ex b,BACN , boundary adaptive cloud network model post-processing cloud entropy En b,BACN 、Super entropy He b,BACN .

[0144] Example 8:

[0145] A steel frame structure safety evaluation method based on a boundary adaptive cloud network model, the technical content is the same as any one of embodiments 2-7, further, the boundary adaptive cloud network model post-cloud expectation Ex b,BACN As shown below:

[0146]

[0147] Where, Ex b,BACN (j) is the expected cloud of the subsequent component of the j-th safety level score of the structure, s R (j) is the right boundary of the jth safety level scoring interval of the structure, s L (j) is the left boundary of the j-th safety level scoring interval of the structure;

[0148] Boundary Adaptive Cloud Network Model Post-processing Cloud Entropy En b,BACN is as follows:

[0149]

[0150] In the formula, En b,BACN (j) is the subsequent cloud entropy of the j-th safety level score of the structure;

[0151] Superentropy He b,BACN As shown below:

[0152] He b,BACN =0.1En b,BACN (11)

[0153] Example 9:

[0154] A steel frame structure safety evaluation method based on a boundary adaptive cloud network model, the technical content is the same as any one of embodiments 2-8, further, in the boundary adaptive cloud network model based on weighted DS fusion, the output of the post-cloud is the safety evaluation quantitative value x b,BACN (i,j), that is:

[0155]

[0156] Where N b ' ,BACN (i,j) is En b,BACN For the expectation, He b,BACN is a normal random distribution with standard deviation; Ex b,BACN (j) is the expected cloud of the consequent of the j-th safety level score of the structure; μ a,BACN

[0157] is the membership degree of the subsequent cloud; x norm is the standardized measured value.

[0158] Example 10:

[0159] A steel frame structure safety evaluation method based on a boundary adaptive cloud network model, the technical content is the same as any one of embodiments 2-9, further, the entropy En of the "3En" rule cloud model a,3En =(C R -C L ) / 6, where C L 、C R is the left and right boundary value of the threshold interval, and the super entropy He a,3En =En / 10; for a specific value x i Cloud membership

[0160] μ a,3En (x i )=exp[-(x i -Ex a,3En ) 2 / (2En i ' 2 )], where En i ′ is En a,3En is the expected value and He a,3En A normal random number generated for the variance.

[0161] Example 11:

[0162] A safety assessment method for steel frame structures based on a boundary adaptive cloud network model is proposed. The steps are as follows:

[0163] (1) Select structural safety evaluation indicators and construct a damage index system U = {U1, U2, ..., U i}, and determine the fusion weight of each damage index, U represents the damage behavior parameter of structural safety assessment, U1, U2, ..., U i Characterize specific evaluation indicators in safety evaluation, such as strain, deflection, angle, stress, layer displacement, etc.

[0164] (2) Divide the structural safety levels and regard the thresholds of each level of the structural safety index as a double-constraint space. Fully consider the uncertainty of the boundary value of the constraint space and appropriately expand it to form a normal cloud. For the structural safety evaluation model M, its safety state is divided into j levels, and i structural damage multi-state indicators are selected. The relevant structural safety evaluation system model M can be expressed as:

[0165]

[0166] Where U i represents the i-th damage behavior index in the structural safety evaluation index system; L j represents the jth safety level divided by the structural safety assessment model M; C j =[C 1j C 2j …C ij ] T , where C ij =(a ij ,b ij ) represents the jth structural safety level threshold interval in the i-th damage behavior index.

[0167] (3) Determine the interval thresholds of each damage performance indicator at different structural safety levels, and calculate the characteristic parameters of the antecedent-"3En" rule cloud and the antecedent-variable fuzzy cloud: Ex a,3En ,En a,3En , He a,3En , μ a,3En and Ex a,VFC ,En a,VFC , He a,VFC , μ a,VFC .

[0168] The characteristic parameters of the “3En” regular cloud are the traditional characteristic values, while the variable fuzzy cloud expects Ex a,VFC The regulations are as follows:

[0169]

[0170] Where x t,norm (i, j) represents the standardized training data of the i-th monitoring indicator at the j-th safety level, t represents the sequence number of the training data, μ t ′(i,j) is the standardized training data x of the i-th monitoring indicator at the j-th security level t,norm For the relative membership of the jth security level threshold interval, n t is the number of standardized training data for the i-th monitoring indicator at the j-th security level.

[0171] Entropy a,VFC The calculation is as follows:

[0172]

[0173] Where η R (i, j) is the right limit value of the threshold interval of the i-th monitoring indicator obtained by appropriately expanding the softening factor at the j-th safety level, η L (i, j) is the left limit value of the indicator threshold interval obtained by appropriately expanding the i-th monitoring indicator at the j-th safety level through the softening factor.

[0174] Superentropy He a,VFC Generally, it is entropy En a,VFC 0.1 times of .

[0175] Generate En a,VFC For the expectation, He a,VFC Normal random number N with standard deviation a ' ,VFC (i, j), and then calculate the membership of the antecedent-variable fuzzy cloud μ a,VFC :

[0176]

[0177] Where x norm (i,j) is the standardized measured value of the i-th monitoring indicator at the j-th safety level.

[0178] (4) Using the weighted DS information fusion method to a,3En and μ a,VFC Perform information fusion to obtain the boundary adaptive antecedent cloud certainty μ a,BACN ,construct the boundary adaptive cloud network model frontier cloud.

[0179] Need to calculate the membership degree μ of the antecedent-“3En” rule cloud a,3En and antecedent-variable fuzzy cloud membership μ a,VFC Perform normalization to determine the basic probability assignment

[0180]

[0181] Where m i (A j ) represents the membership degree μ of the i-th monitoring indicator in the j-th security level antecedent cloud ij The basic probability assignment of

[0182] Perform DS information fusion to obtain a more accurate membership evidence source.

[0183]

[0184] Where m(μ a,BACN) is the basic probability assignment obtained after the fusion of evidence sources, that is, the antecedent cloud membership of the boundary adaptive cloud network model μ a,BACN .

[0185] (5) Calculate the post-cloud characteristic parameters of the boundary adaptive cloud network model: Ex b,BACN ,En b,BACN and He b,BACN , thereby establishing a qualitative rule base, using the boundary adaptive antecedent cloud and the 3En rule post-cloud to establish a qualitative rule base consisting of several "IF-THEN" rules containing qualitative concepts. On this basis, a boundary adaptive cloud network model based on weighted DS fusion is established.

[0186] Boundary Adaptive Cloud Network Model Post-processing Cloud Expectation Ex b,BACN The calculation is as follows:

[0187]

[0188] Where, Ex b,BACN (j) is the expected cloud of the subsequent component of the j-th safety level score of the structure, s R (j) is the right boundary of the jth safety level scoring interval of the structure, s L (j) is the left boundary of the j-th safety level scoring interval of the structure.

[0189] Boundary Adaptive Cloud Network Model Post-processing Cloud Entropy En b,BACN for:

[0190]

[0191] In the formula, En b,BACN (j) is the subsequent cloud entropy of the j-th safety level score of the structure.

[0192] Superentropy He b,BACN Generally take entropy En b,BACN One tenth of.

[0193] Calculate the quantitative value x of the boundary adaptive cloud network model after-component cloud b,BACN When En is generated b,BACN For the expectation, He b,BACN N is a normal random distribution with standard deviation b ' ,BACN (i, j), the safety evaluation quantitative value x output by the boundary adaptive cloud network model is calculated by the following formula b,BACN (i,j):

[0194]

[0195] (6) Input the measured values ​​of the structural monitoring indicators under a certain load step (x1, x2, ···, xi ), activate the boundary adaptive cloud network model, and after reasoning and fusion, finally obtain the overall fusion score of the structure under the current load step.

[0196] Example 12:

[0197] A validation of a steel frame structure safety assessment method based on a boundary-adaptive cloud network model is presented below:

[0198] The verification was conducted by low-cycle reciprocating loading of a single-span four-story steel frame structure with a span of 6m, a first floor height of 4.2m, and a second to fourth floor height of 3.9m. The beams and columns are made of Q235 steel, and the beam cross-section size is

[0199] H300mm×300mm×10mm×15mm, column section size is

[0200] H400mm×300mm×10mm×16mm. In order to improve the calculation efficiency, the finite element beams and columns are all two-dimensional beam elements. The grid size of the beam and column elements of the finite element model of the four-story steel frame structure is divided into 300mm. Figure 2 As shown. To simulate the vertical loads to which the steel frame structure is subjected during actual use, a constant vertical concentrated force of 400 kN was applied to the nodes of the finite element model. The vertical concentrated force applied to each node on each floor was calculated to be 0.15 for the fourth-floor frame columns, 0.3 for the third-floor frame columns, 0.45 for the second-floor frame columns, and 0.6 for the first-floor frame columns. To simulate the connection between the steel frame structure and the ground, the bottom of the four-story steel frame finite element model was set as a fixed constraint.

[0201] The division of the four-story steel frame structure and the location of monitoring index measurement points are as follows: Figure 2 As shown in (c), each substructure includes: beam end strain, column upper end strain, column lower end strain, node rotation angle and layer displacement monitoring indicators.

[0202] The four-story steel frame structure was subjected to low-cycle reciprocating loading, and its hysteresis curve, skeleton curve and secant stiffness curve were extracted, as shown in the following figure. Figure 3 As shown. By analyzing the hysteresis curve, we can see that the hysteresis curve of the four-story steel frame structure is full and there is no obvious pinching phenomenon, which shows that the structure has good energy dissipation capacity. Figure 3It can be seen that before loading to a displacement of 60mm, the hysteresis curve of the four-story steel frame structure is nearly a straight line, with no significant change in stiffness. The skeleton curve is also nearly a straight line, indicating that the structural force is nearly proportional to the displacement, and the structure can be considered to be in the elastic stage. When the four-story steel frame structure is loaded with a displacement exceeding 60mm, its hysteresis curve is no longer a straight line, but instead includes a hysteresis loop with a certain area. The skeleton curve gradually bends, and the structural stiffness gradually decreases. As loading progresses, the structural stiffness decreases more and more rapidly. At the end of loading, the structural stiffness has dropped to approximately 60% of the initial stiffness. The skeleton curve shows that the structural bearing capacity reaches its maximum at a loading displacement of 250mm, and then begins to gradually decrease as loading progresses. At the last load step, the structural bearing capacity has dropped to approximately 60% of the maximum structural bearing capacity.

[0203] Before establishing the structural safety evaluation model, the structural safety level is divided according to the structural stiffness degradation curve, skeleton curve and structural damage characteristics. Figure 4 As shown in the figure, the four-story steel frame structure is divided into five safety levels: intact, slightly damaged, moderately damaged, severely damaged, and failed, corresponding to Level 1, Level 2, Level 3, Level 4, and Level 5, respectively. In the figure, the stiffness ratio represents the ratio of the structural secant stiffness of the current load step to the initial (first load step) structural secant stiffness, and the peak load ratio represents the ratio of the peak load of the current loading step in the positive segment of the skeleton curve to the maximum bearing capacity of the structure.

[0204] Using the measured data and the boundary adaptive cloud network model, the safety evaluation results of the steel frame structure under low-cycle reciprocating loading based on the boundary adaptive cloud network model are as follows: Figure 4 shown.

[0205] When the structure is actually at safety level 1, the overall safety score of the structure should be in the range of (80,100]. In the safety evaluation results based on the boundary adaptive cloud network model: the load step 6 score is less than 80, and the evaluation accuracy of the four-story steel frame structure at this safety level is 83%; when the structure is actually at safety level 2, the overall safety score of the structure should be in the range of (60,80]. In the safety evaluation results based on the boundary adaptive cloud network model: the load step 18 score is less than 60, and the evaluation accuracy of the four-story steel frame structure at this safety level is 92%; when the structure is actually at safety level 3, the overall safety score of the structure should be in the range of (40,60]. In the safety evaluation results based on the boundary adaptive cloud network model: The load step scores are all within the range of (40,60], and the evaluation accuracy of the four-story steel frame structure at this safety level is 100%; when the structure is actually at safety level 4, the overall safety score of the structure should be within the range of (20,40]. In the safety evaluation results based on the boundary adaptive cloud network model: the load step scores are all within the range of (20,40], and the evaluation accuracy of the four-story steel frame structure at this safety level is 100%; when the structure is actually at safety level 5, the overall safety score of the structure should be within the range of (0,20]. In the safety evaluation results based on the boundary adaptive cloud network model: the load step scores are all within the range of (0,20], and the evaluation accuracy of the four-story steel frame structure at this safety level is 100%.

[0206] The overall safety evaluation accuracy of the four-story steel frame structure under low-cycle reciprocating loading based on the boundary adaptive cloud network model is shown in Table 1. The table also lists the calculation results of the "50% certainty" rule normal cloud network model and the "3En" rule normal cloud network model.

[0207] Table 1 Overall safety evaluation accuracy of four-story steel frame structure

[0208]

[0209]

[0210] Table 1 shows that the boundary-adaptive cloud network model has strong structural safety assessment capabilities. Its assessment accuracy for each safety level and for the overall structure is higher than that of the "3En" rule-based normal cloud network model and the "50% certainty" rule-based normal cloud network model, effectively assessing the structural health status. Furthermore, the overall score of the four-story steel frame structure shows that, compared to the "50% certainty" rule-based normal cloud network model and the "3En" rule-based normal cloud network model, which have a higher error rate in assessing load steps at the safety level boundary, the boundary-adaptive cloud network model proposed in this paper can better assess the safety status of boundary load steps and has higher recognition reliability.

Claims

1. A steel frame structure safety assessment method based on a boundary adaptive cloud network model, characterized in that: The following steps are involved: 1) Select the safety evaluation index of steel frame structure and construct the damage index system U={U1,U2,...,U i }, and determine the fusion weight of each damage index; 2) Construct a steel frame structure safety evaluation system model M; 3) Determine the interval thresholds of each damage behavior index at different structural safety levels, and calculate the characteristic parameters of the antecedent-"3En" rule cloud and the characteristic parameters of the antecedent-variable fuzzy cloud; the characteristic parameters of the antecedent-"3En" rule cloud include the expected Ex a,3En , Entropy a,3En , super entropy He a,3En , the antecedent-"3En" rule cloud membership μ a,3En ; The characteristic parameters of the antecedent-variable fuzzy cloud include the variable fuzzy cloud expectation Ex a,VFC 、Entropy a,VFC 、Superentropy He a,VFC , membership degree μ of the antecedent-variable fuzzy cloud a,VFC ; 4) Using weighted DS information fusion method to calculate the membership μ a,3En and membership μ a,VFC Perform information fusion to obtain the boundary adaptive antecedent cloud certainty μ a,BACN , and then construct the boundary adaptive cloud network model frontier cloud; 5) Calculate the post-cloud characteristic parameters of the boundary adaptive cloud network model, thereby establishing a qualitative rule base including multiple "IF-THEN" rules, and establishing a boundary adaptive cloud network model based on weighted DS fusion; 6) Obtain the measured values ​​of the structural monitoring indicators under the current load step (x1, x2, ···, x i ) and input it into the boundary adaptive cloud network model based on weighted DS fusion to obtain the quantitative value of the safety evaluation of the steel frame structure under the current load step.

2. A steel frame structure safety assessment method based on a boundary adaptive cloud network model according to claim 1, characterized in that: The structural safety evaluation indicators include strain, deflection, rotation angle, stress, and layer displacement.

3. The steel frame structure safety assessment method based on the boundary adaptive cloud network model according to claim 1 is characterized by: Entropy En of the "3En" rule cloud model a,3En =(C R -C L ) / 6, where C L 、C R are the left and right boundary values ​​of the threshold interval; for a specific value x i The cloud membership μ a,3En (x i )=exp[-(x i -Ex a,3En ) 2 / (2En i ' 2 )], where En i ′ is En a,3En is the expected value and He a,3En A normal random number generated for the variance.

4. The steel frame structure safety assessment method based on the boundary adaptive cloud network model according to claim 1 is characterized in that: The structural safety assessment system model M is as follows: Where U i represents the i-th damage behavior index in the structural safety evaluation index system; L j represents the jth safety level divided by the structural safety assessment model M; C j =[C 1j C 2j …C ij ] T , where C ij =(a ij ,b ij ) represents the jth structural safety level threshold interval in the i-th damage behavior index; a ij ,b ij It is the lower and upper limit of the safety level.

5. The steel frame structure safety assessment method based on the boundary adaptive cloud network model according to claim 1 is characterized in that: Variable Fuzzy Cloud Expectation Ex a,VFC As shown below: Where x t,norm (i, j) represents the standardized training data of the i-th monitoring indicator at the j-th safety level, t represents the sequence number of the training data, μ t ′(i,j) is the standardized training data x of the i-th monitoring indicator at the j-th security level t,norm For the relative membership of the jth security level threshold interval, n t is the number of standardized training data for the i-th monitoring indicator at the j-th safety level; Entropy a,VFC As shown below: Where η R (i, j) is the right limit value of the threshold interval of the i-th monitoring indicator obtained by appropriately expanding the softening factor at the j-th safety level, η L (i, j) is the left limit value of the threshold interval of the i-th monitoring indicator obtained by appropriately expanding the softening factor at the j-th safety level; Superentropy He a,VFC As shown below: He a,VFC =0.1En a,VFC (i,j) (4) Antecedent - membership degree μ of variable fuzzy cloud a,VFC As shown below: μ a,VFC (i,j)=exp[-(x norm (i,j)-Ex a,VFC ) 2 / (2N a ′ ,VFC (i,j) 2 )] (5) Where x norm (i, j) is the standardized measured value of the i-th monitoring indicator at the j-th safety level; N a ' ,VFC (i,j) is En a,VFC (i,j) is the expectation, He a,VFC is a normal random distribution with a standard deviation of .

6. The steel frame structure safety assessment method based on the boundary adaptive cloud network model according to claim 1 is characterized in that: Boundary adaptive antecedent cloud certainty μ a,BACN As shown below: Where, m1(A i ) The evidence source from the antecedent-"3En" rule cloud is the i-th piece of evidence data in the evidence source; m2(B j ) comes from the evidence source in the antecedent-variable fuzzy cloud, which is the jth piece of evidence data in the evidence source; Among them, the basic probability assignment m1(A i )’s kth monitoring indicator item m 1k (A i ) is as follows: Where μ ki,3En Assign m1(A i )’s cloud membership corresponding to the k-th monitoring indicator item; n j Indicates the number of security levels; Basic probability assignment m2(B j )’s kth monitoring indicator item m 2k (B j ) is as follows: Where n j Indicates the number of security levels; μ kj,VFC Assign m2(B j ) corresponds to the cloud membership of the kth monitoring indicator item.

7. The steel frame structure safety assessment method based on the boundary adaptive cloud network model according to claim 1 is characterized in that: The characteristic parameters of the post-processing cloud of the boundary adaptive cloud network model include the post-processing cloud expectation Ex b,BACN , boundary adaptive cloud network model post-processing cloud entropy En b,BACN 、Superentropy He b,BACN .

8. A steel frame structure safety assessment method based on a boundary adaptive cloud network model according to claim 7, characterized in that: Boundary Adaptive Cloud Network Model Post-processing Cloud Expectation Ex b,BACN As shown below: Where, Ex b,BACN (j) is the expected cloud of the subsequent component of the j-th safety level score of the structure, s R (j) is the right boundary of the jth safety level scoring interval of the structure, s L (j) is the left boundary of the j-th safety level scoring interval of the structure; Boundary Adaptive Cloud Network Model Post-processing Cloud Entropy En b,BACN is as follows: In the formula, En b,BACN (j) is the subsequent cloud entropy of the j-th safety level score of the structure; Superentropy He b,BACN As shown below:

9. The steel frame structure safety assessment method based on the boundary adaptive cloud network model according to claim 1 is characterized in that: In the boundary adaptive cloud network model based on weighted DS fusion, the output of the post-processing cloud is the safety evaluation quantitative value x b,BACN (i,j), that is: Where N b ' ,BACN (i,j) is En b,BACN For the expectation, He b,BACN is a normal random distribution with standard deviation; Ex b,BACN (j) is the expected cloud of the consequent of the j-th safety level score of the structure; μ a,BACN is the membership degree of the subsequent cloud; x norm is the standardized measured value; Ex a,3En is the characteristic parameter of the antecedent-"3En" rule cloud.

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