A method for predicting low-cycle fatigue failure sequence of a plate-type connection steel support frame

Abstract

The application provides a low-cycle fatigue damage sequence prediction method for a plate connection steel support frame, comprising the following steps: considering different combination schemes of design parameters; obtaining the low-cycle fatigue life of two components of a joint plate and an I-shaped section support in a center support steel frame based on a ductility damage method and a critical surface method; calculating a low-cycle fatigue damage matching control index of the frame; establishing a low-cycle fatigue damage sequence prediction function of the plate connection I-shaped section center support steel frame based on a Bayesian updating theory; and establishing a low-cycle fatigue damage sequence prediction model of the center support steel frame under different reliabilities based on probability statistics. The application can directly utilize the plate connection support frame design parameters to judge and estimate the low-cycle fatigue damage sequence of the frame, avoids time-consuming numerical simulation, helps engineers to evaluate and control the seismic design safety level of the center support steel frame in the design stage, and provides a theoretical basis for the support frame design parameter value.

Description

A method for predicting the low-cycle fatigue failure sequence of a plate-connected steel support frame. Technical Field

[0001] This invention belongs to the field of low-cycle fatigue life prediction technology, and particularly relates to a prediction and control of the low-cycle fatigue failure sequence of a plate-connected steel support frame. Background Technology

[0002] The plate-connected center-supported steel frame structure is simple in construction, has clear force transmission, and a high degree of prefabrication, giving it certain advantages in prefabricated steel structure buildings that are being vigorously promoted in my country. At the same time, this structure possesses good horizontal stiffness and load-bearing capacity, making it promising for application in civil buildings.

[0003] Recent earthquake damage surveys have shown that in plate-connected center-braced steel frames, low-cycle fatigue damage and fracture not only occur at the supports but also frequently at the plate joints. Premature damage and fracture of the supports can severely degrade the stiffness of the floors they occupy, easily creating weak stories and leading to structural collapse. Conversely, if the joints fail or fracture before the supports, the force transmission path is disrupted, deteriorating the overall load-bearing capacity and ductility of the structure. Therefore, predicting and controlling the order of low-cycle fatigue damage and lifespan matching of the components of the braced frame during structural design is of paramount importance.

[0004] Existing research primarily focuses on the impact of single-factor design variables (such as geometric parameters, detailed construction, and bracing arrangement) on the seismic performance of braced frames. Currently, it is impossible to consider and predict the low-cycle fatigue failure sequence of individual components within the braced frame, and there is a lack of collaborative design strategies that can accommodate the adjustment of multiple design parameters. This, to some extent, limits the promotion and application of this structure. For plate-connected steel braced frames, both excessively strong (large and thick) and excessively weak (small and thin) gusset plates (relative to bracing) designs negatively impact the overall seismic performance of the braced frame. The gusset plates must participate in the plastic development of the structure while ensuring that low-cycle fatigue failure occurs after the bracing, in order to fully utilize the overall energy dissipation capacity of the structure. From a collaborative design perspective, if the low-cycle fatigue damage of different components can be predicted and controlled during seismic design, avoiding significant differences in low-cycle fatigue life among components that lead to uncoordinated seismic performance, then collaborative work among components can be achieved, thereby optimizing the overall seismic performance of the structure. Summary of the Invention

[0005] To address the aforementioned technical problems, this invention provides a method for predicting the low-cycle fatigue failure sequence of a plate-connected I-shaped cross-section centrally supported steel frame. This method avoids complex and inefficient numerical calculations and allows for the adjustment of various design parameters (including geometric dimensions and connection coefficients) of the support frame during the structural design phase, thereby improving its seismic performance.

[0006] To achieve the above objectives, the present invention adopts the following technical solution:

[0007] A method for predicting the low-cycle fatigue failure sequence of a plate-connected steel support frame includes the following steps:

[0008] Step 1: Considering design parameters including component geometric parameters, connection structure, connection coefficient, and design inter-story drift angle, obtain different combinations of design parameters for the plate-connected I-section centrally supported steel frame, establish an ABAQUS finite element model and perform numerical analysis;

[0009] Step 2: Based on the ductile damage model and the critical surface method (LZH model), obtain the low-cycle fatigue life of the two components, the gusset plate and the I-section support, in the centrally supported steel frame;

[0010] Step 3: Measure the low-cycle fatigue life N of the gusset plate. C With support for low-cycle fatigue life N B The ratio of β to N is used as the low-cycle fatigue damage matching control index for the central support frame, i.e., β = N C / N B And calculate the result;

[0011] Step 4: Based on Bayesian update theory, establish a low-cycle fatigue failure sequence prediction function for a plate-connected I-shaped cross-section centrally supported steel frame.

[0012] Step 5: Based on probability statistics, establish a prediction model for the low-cycle fatigue failure sequence of the plate-connected center-supported steel frame under different reliability levels.

[0013] Preferably, the design parameters in step 1 specifically include: the support slenderness ratio λ. B Support flange width-to-thickness ratio b / t, support web width-to-thickness ratio h0 / t w Node plate slenderness ratio λ C Connection coefficient k, beam-to-column height ratio ξ, and inter-story drift angle δ.

[0014] Preferably, obtaining the low-cycle fatigue life of the node plate and support in step 2 specifically includes the following process:

[0015] Step 1: Add a ductile damage model to the constitutive model of the supporting frame material.

[0016]

[0017] In the formula, η is the critical value of equivalent plastic strain; η is the stress triaxiality, which represents the stress state of the element and is the ratio of the element's average stress to the Mises equivalent stress; η0 is a material constant, which is usually taken as 1 / 3 for metallic materials.

[0018] C2 represents the critical value of the equivalent plastic strain under uniaxial tension, which can be obtained by the following formula:

[0019] C2=-ln(1-A R )

[0020] In the formula, A R The reduction of area of ​​a material can be obtained from a monotonic tensile test.

[0021] C1 represents the critical value of the equivalent plastic strain under pure shear conditions, which can be obtained from the following formula:

[0022]

[0023] σ=Aε n A and n represent the strength coefficient and hardening index of the material, respectively, which can be calculated from the relationship between the true stress σ and the true strain ε in the monotonic tensile test of steel before necking. When the numerically simulated strain at the critical support position in the frame reaches this critical value, damage is considered. The cycle in which damage accumulates to 1 is its low-cycle fatigue life N. B ;

[0024] Step 2: Based on the LZH model using the critical surface method, establish the low-cycle fatigue life N of the node plate. C Prediction formula

[0025]

[0026] In the formula, Δγ max It is the maximum shear strain amplitude of the key element; σ n,max It is the maximum normal stress at the critical surface of the element (i.e., the plane containing the maximum shear strain amplitude); Δε n It is the normal strain amplitude of the element critical surface; σ′ f ε′ is the fatigue strength coefficient, a is the fatigue strength exponent, and ε′ is the fatigue strength coefficient. f ρ is the fatigue ductility coefficient, b is the fatigue ductility exponent, E is the elastic modulus of steel, and f is the fatigue ductility coefficient. y ω represents the yield strength of the steel, and all six items are material coefficients; ω is the life reduction factor considering the interaction of fatigue damage between the support and the gusset plate.

[0027] Preferably, the low-cycle fatigue damage matching control index β = N for the support frame in step 3 is... C / N BWhen β>1, it means that the low-cycle fatigue life of the plate node is greater than that of the support, which means the design is safe but conservative; when β=1, it means that the low-cycle fatigue life of the plate node is equal to that of the support, which can give full play to the synergistic deformation effect of the two and make the overall energy dissipation capacity of the frame optimal; when β<1, it means that the low-cycle fatigue life of the plate node is less than that of the support, and the design parameters need to be adjusted.

[0028] Preferably, the low-cycle fatigue failure sequence prediction function of the frame is,

[0029]

[0030] Based on Bayesian update theory

[0031] f(α)=γL(α)p(α)

[0032] In the formula, α=(a,σ) are the unknown model coefficients, f(α) is the posterior distribution function of α, γ is the regularization factor, L(α) is the likelihood function, and p(α) is the prior distribution function of α. Using Markov Chain Monte Carlo simulation, combined with the β data from step 3, the key factor influencing the low-cycle fatigue damage matching relationship of the frame is obtained, namely the support slenderness ratio λ. B Support flange width-to-thickness ratio b / t, gusset plate slenderness ratio λ C The connection coefficient k and the beam-column height ratio ξ are calculated, and the fitting coefficients α = (a0, a1, a2, a3, a4, a5) in the formula are solved simultaneously.

[0033] Preferably, the frame low-cycle fatigue failure sequence prediction function in step 4 does not need to consider external loads, which facilitates its use in the seismic design of the supporting frame.

[0034] Preferably, the frame low-cycle fatigue failure sequence prediction function in step 4 can satisfy the joint adjustment of multiple design parameters, has high computational efficiency and strong applicability.

[0035] Preferably, step 5 involves taking the logarithm of both sides of the disorder prediction function from step 4.

[0036]

[0037]

[0038]

[0039] In the formula, μ p s is the standard normality skewness, s is the sample standard deviation, and μ is the standard normality skewness ps is the model error of this linear model, and p is the reliability of each model. i corresponding μ pi The number of supporting frame samples is derived from the standard normal skewness scale. and These represent the average values ​​of lna0 and β, respectively; different levels of reliability correspond to different estimates in the prediction formula.

[0040] Compared with the prior art, the present invention has the following beneficial effects:

[0041] 1. The low-cycle fatigue failure sequence prediction method provided by this invention can obtain the low-cycle fatigue failure sequence and component low-cycle fatigue life matching relationship of plate-connected centrally supported steel frames during the structural design stage. It can simultaneously consider and adjust multiple design parameters, so that the seismic design of the support frame not only meets the strength requirements, but also meets the requirement that the node fails after the component, realizing the design principle of "strong node and weak component", and further achieving the "knowable and controllable" level of structural design safety.

[0042] 2. This invention can directly use the design parameters of the plate-connected support frame to determine and predict its low-cycle fatigue damage sequence, avoiding tedious and time-consuming numerical simulation. It can help engineers assess and control the seismic design safety level of the central support steel frame during the design phase, and at the same time provide a theoretical basis for the selection of support frame design parameters.

[0043] 3. The low-cycle fatigue failure sequence prediction function for plate-connected I-section supported steel frames provided by this invention does not require consideration of external loads and can satisfy the joint adjustment of multiple design parameters. It has high calculation efficiency and strong applicability, and is suitable for seismic design of supported frames. Attached Figure Description

[0044] Figure 1 is a flowchart of a method for predicting the low-cycle fatigue failure sequence of a plate-connected steel support frame according to the present invention.

[0045] Figure 2 is a numerical simulation result of the low-cycle fatigue failure sequence of the centrally supported steel frame with plate connection I-shaped section according to the present invention.

[0046] Figure 3 is a comparison of the predicted results and numerical simulation results of the low-cycle fatigue failure sequence of the plate-type connected support frame of Q355B steel using the method of the present invention.

[0047] Figure 4 is a schematic diagram of the low-cycle fatigue failure sequence prediction model of the support frame under the conditions of connection coefficient k = 1.2 and beam-column height ratio ξ = 1. Detailed Implementation

[0048] To enable those skilled in the art to better understand the technical solutions of the present invention, the technical solutions of the present invention will be clearly and completely described below with reference to the accompanying drawings of the embodiments of the present invention. Obviously, the described embodiments are only some embodiments of the present invention, and not all embodiments of the present invention.

[0049] As shown in Figure 1, a method for predicting the low-cycle fatigue failure sequence of a plate-connected steel support frame includes:

[0050] Step 1: Based on the selected component geometric parameters, connection structure, connection coefficient, design inter-story drift angle, and other design parameters, obtain different parameter combinations for the seismic design of the plate-connected I-section centrally supported steel frame, establish an ABAQUS finite element model, and perform numerical analysis.

[0051] Step 2: Based on the ductile damage model in the ductile damage method and the LZH model in the critical surface method, obtain the low-cycle fatigue life of the two components, the gusset plate and the I-section support, in the centrally supported steel frame.

[0052] Step 3: Measure the low-cycle fatigue life (N) of the gusset plate. C ) and support low-cycle fatigue life (N) B The ratio of β to N C / N B The low-cycle fatigue damage matching control index of the central support frame is used, and its results are calculated.

[0053] Step 4: Based on Bayesian update theory, establish a low-cycle fatigue failure sequence prediction function for a plate-connected I-shaped cross-section centrally supported steel frame.

[0054] Step 5: Based on probability statistics, establish a prediction model for the low-cycle fatigue failure sequence of the plate-connected center-supported steel frame under different reliability levels.

[0055] The low-cycle fatigue failure sequence prediction method provided in this embodiment can obtain the low-cycle fatigue failure sequence and component low-cycle fatigue life matching relationship of the plate-connected centrally supported steel frame during the structural design stage. It can simultaneously consider and adjust multiple design parameters, so that the seismic design of the support frame not only meets the strength requirements, but also meets the requirement that the node fails after the component, realizing the design principle of "strong node and weak component", and further achieving the "knowable and controllable" level of structural design safety.

[0056] In a plate-connected I-section centrally supported steel frame, the bracing is connected to the beams and columns via gusset plates; the bracing is connected to the gusset plates via connectors, including welding and bolting; the gusset plates are connected to the beams and columns via end plates.

[0057] In this embodiment, step 1 involves a total of 192 finite element models of the support frame. The design parameter variables basically include all the influencing factors that need to be considered during structural design, specifically: the slenderness ratio λ of the support. B The support flange width-to-thickness ratio (b / t) is divided into six levels: 50, 60, 70, 80, 90, and 100. The web width-to-thickness ratio (h0 / t) is also divided into six levels: 6, 7, 8, 9, 10, and 11. w It is divided into six levels, namely 20, 21, 22, 23, 25, and 27; the net distance ratio is the distance L from the end of the support to the diagonal line connecting the node plate. off With the thickness t of the node plate p The ratio is divided into three levels: 2, -2, and -6. A negative number indicates that the support end extends into the diagonal side of the node plate, meaning the support is closer to the beam and column; the connection coefficient k = W w t p / A b , where A b To support the cross-sectional area, W w To support the 30° end, the effective Whitmore width with a diffusion angle is divided into seven levels: 1, 1.1, 1.2, 1.3, 1.4, 1.5, and 1.6. The beam-to-column height ratio ξ is divided into three levels: 0.7, 0.84, and 1, corresponding to column-to-beam angles of 35°, 40°, and 45°. The inter-story drift angle δ is also divided into three levels: 1 / 100, 1 / 70, and 1 / 50. To facilitate the establishment of a prediction model, the geometric information of the gusset plate is converted into a slenderness ratio λ. C express.

[0058] The low-cycle fatigue life of the support in step 2 is obtained using the ductile damage method, by incorporating a ductile damage model into the constitutive model of the support frame material.

[0059]

[0060] In the formula, η is the critical value of equivalent plastic strain; η is the stress triaxiality, which represents the stress state of the element and is the ratio of the element's average stress to the Mises equivalent stress; η0 is a material constant, which is usually taken as 1 / 3 for metallic materials.

[0061] C2 represents the critical value of the equivalent plastic strain under uniaxial tension, which can be obtained by the following formula:

[0062] C2=-ln(1-A R )

[0063] In the formula, A R The reduction of area of ​​a material can be obtained from a monotonic tensile test.

[0064] C1 represents the critical value of the equivalent plastic strain under pure shear conditions, which can be obtained from the following formula:

[0065]

[0066] σ=Aε n A and n represent the strength coefficient and hardening index of the material, respectively, which can be deduced from the relationship between the true stress σ and the true strain ε of the monotonic tensile test before necking of the steel. The results of the monotonic tensile test on Q355B steel, including the acquisition and processing of various parameters, are shown in Table 1.

[0067] Table 1. Parameter values ​​of the ductile damage model for Q355B steel.

[0068]

[0069] When the numerically simulated strain at the critical support location in the frame reaches this critical value, damage is started to be accounted for. The cycle in which the accumulated damage reaches 1 is its low-cycle fatigue life N. B ;

[0070] The low-cycle fatigue life of the gusset plate in step 2 is obtained using the critical surface method. Based on the LZH model, the low-cycle fatigue life N of the gusset plate is established. C Prediction formula

[0071]

[0072] In the formula, Δγ max It is the maximum shear strain amplitude of the key element; σ n,max It is the maximum normal stress at the critical surface of the element (i.e., the plane containing the maximum shear strain amplitude); Δε n It is the normal strain amplitude of the element critical surface; σ′ f ε′ is the fatigue strength coefficient, a is the fatigue strength exponent, and ε′ is the fatigue strength coefficient. f ρ is the fatigue ductility coefficient, b is the fatigue ductility exponent, E is the elastic modulus of steel, and f is the fatigue ductility coefficient. y The six parameters are material coefficients, representing the yield strength of the steel. ω is the life reduction factor considering the interaction of fatigue damage between the support and the gusset plate, taken as 2. Low-cycle fatigue tests were conducted on Q355B steel, and the collected and processed parameters are shown in Table 2.

[0073] Table 2. Parameter values ​​of LZH model for Q355B steel.

[0074]

[0075] Step 3: Low-cycle fatigue damage matching control index β = N for the support frame C / NB The β data for the 192 plate-type connection support frames are shown in Figure 2. β>1 indicates that the low-cycle fatigue life of the plate nodes is greater than that of the supports, which means the design is safe but conservative; β=1 indicates that the low-cycle fatigue life of the plate nodes is equal to that of the supports, which can give full play to the synergistic deformation effect of the two and make the overall energy dissipation capacity of the frame optimal; β<1 indicates that the low-cycle fatigue life of the plate nodes is less than that of the supports, and the design parameters need to be adjusted.

[0076] This embodiment, through data analysis, reveals that the low-cycle fatigue failure sequence of the frame is related to only five factors, namely, the slenderness ratio of the support λ. B Support flange width-to-thickness ratio b / t, gusset plate slenderness ratio λ C The connection coefficient k and the beam-to-column height ratio ξ are considered. Furthermore, β exhibits a good logarithmic linear relationship with each factor. Based on this, the prediction function for the low-cycle fatigue failure sequence of the frame in step 4 is derived.

[0077]

[0078] Based on Bayesian update theory, Markov Chain Monte Carlo simulation is used, combined with the β data from step 3, to solve for the fitting coefficients in the equation:

[0079] α = (a0, a1, a2, a3, a4, a5)

[0080] f(α)=γL(α)p(α)

[0081] In the formula, α=(a,σ) are the unknown model coefficients, f(α) is the posterior distribution function of α, γ is the regularization factor, L(α) is the likelihood function, and p(α) is the prior distribution function of α; the coefficient values ​​of the low-cycle fatigue failure sequence prediction function are shown in Table 3:

[0082] Table 3. Coefficients of the Low-Cycle Fatigue Failure Sequence Prediction Function for Plate-Connected Support Frames

[0083]

[0084] Once the low-cycle fatigue failure sequence prediction model is established, the low-cycle fatigue failure sequence of the corresponding plate-type connection support frame can be predicted by inputting the given design parameters into the model. The design parameters can be adjusted according to the β value to meet the design requirements.

[0085] Figure 3 shows a comparison between the predicted low-cycle fatigue failure sequence of a plate-connected support frame for Q355B steel using the method of this invention and the results of numerical simulation. Most of the predicted results fall within the 1.5-fold dispersion band of the numerical analysis results, indicating that the prediction model has good accuracy.

[0086] The low-cycle fatigue failure sequence prediction function for the plate-connected I-section supported steel frame in this embodiment does not need to consider external loads, and can satisfy the joint adjustment of multiple design parameters. It has high calculation efficiency and strong applicability, and is suitable for seismic design of supported frames.

[0087] Using the method of this invention, the low-cycle fatigue failure sequence of Class I, II, III, and IV frames in the current specifications is predicted. Based on the connection coefficient k = 1.2 and the beam-column height ratio ξ = 1, the prediction results of the low-cycle fatigue failure sequence β of the braced frame are shown in Figure 4. The vast majority of β values ​​are much greater than 1, indicating the conservatism of the existing frame design requirements. The method for predicting the low-cycle fatigue failure sequence of plate-connected braced frames provided in this example provides a theoretical basis for the low-cycle fatigue damage assessment of braced frames and can be used for the seismic design safety evaluation of plate-connected braced frames.

[0088] Step 5: Taking the logarithm of both sides of the prediction function in Step 4 yields the low-cycle fatigue failure sequence prediction model for plate-connected support frames under different reliability levels.

[0089]

[0090]

[0091]

[0092] In the formula, μ p s is the standard normality skewness, s is the sample standard deviation, and μ is the standard normality skewness p s is the model error of this linear model, and p is the reliability of each model. i corresponding μ pi The number of supporting frame samples is derived from the standard normal skewness scale. and These represent the average values ​​of lna0 and β, respectively; in this embodiment, different reliability levels correspond to different estimated values ​​in the prediction formula. Specific values ​​are shown in Table 4.

[0093] Table 4. Coefficient values ​​of a0 for prediction models under different reliability levels.

[0094]

[0095] The above description is merely a preferred embodiment of the present invention and is not intended to limit the present invention. It should be noted that for those skilled in the art, several improvements and modifications can be made without departing from the technical principles of the present invention, and these improvements and modifications should also be considered within the scope of protection of the present invention.

Claims

1. A method for predicting the low-cycle fatigue failure sequence of a plate-connected steel support frame, characterized in that, The process includes the following steps: Step 1: Considering the component geometric parameters, connection details, connection coefficients, and design parameters such as the inter-story drift angle, obtain different combinations of design parameters for the plate-connected I-section centrally supported steel frame, establish an ABAQUS finite element model, and perform numerical analysis; Step 2: Based on the ductile damage method and the critical surface method, obtain the low-cycle fatigue life of the gusset plate and the I-section support components in the centrally supported steel frame; Step 3: Calculate the low-cycle fatigue life N of the gusset plate. C With support for low-cycle fatigue life N B The ratio of β to N is used as the low-cycle fatigue damage matching control index for the central support frame, i.e., β = N C / N B Step 4: Based on Bayesian update theory, establish a low-cycle fatigue failure sequence prediction function for the plate-connected I-shaped cross-section centrally supported steel frame; the low-cycle fatigue failure sequence prediction function for the frame is, Based on Bayesian update theory In the formula, For unknown model coefficients, It is the posterior distribution function of α. It is a regularization factor. It is the likelihood function. It is the prior distribution function of α; using Markov Chain Monte Carlo simulation and combining it with the β data from step 3, the key factor affecting the low-cycle fatigue damage matching relationship of the frame is obtained, namely the support slenderness ratio λ. B Support flange width-to-thickness ratio b / t, gusset plate slenderness ratio λ C The connection coefficient k and the beam-column height ratio ξ are calculated simultaneously, along with the fitting coefficients in the formula. Step 5: Based on probability statistics, establish a low-cycle fatigue failure sequence prediction model for a plate-connected centrally supported steel frame under different reliability levels; Step 5 involves taking the logarithm of both sides of the failure sequence prediction function from Step 4. In the formula, μ p s is the standard normality skewness, s is the sample standard deviation, and μ is the standard normality skewness. p s is the model error of this linear model, and p is the reliability of each model. i corresponding μ pi The number of supporting frame samples is derived from the standard normal skewness scale. and They represent The average value of β; different reliability levels correspond to different estimates in the prediction formula. 。 2. The method for predicting the low-cycle fatigue failure sequence of a plate-connected steel support frame according to claim 1, characterized in that, The design parameters in step 1 specifically include: the slenderness ratio λ of the support. B Support flange width-to-thickness ratio b / t, support web width-to-thickness ratio h0 / t w Node plate slenderness ratio λ C Connection coefficient k, beam-to-column height ratio ξ, and inter-story drift angle δ.

3. The method for predicting the low-cycle fatigue failure sequence of a plate-connected steel support frame according to claim 1, characterized in that, The process of obtaining the low-cycle fatigue life of the node plate and support in step 2 includes the following steps: Step 1: Add a ductile damage model to the constitutive model of the support frame material. In the formula, This is the critical value of the equivalent plastic strain; η is the stress triaxiality, which represents the stress state of the element and is the ratio of the element's average stress to the Mises equivalent stress. η0 is a material constant, which is taken as 1 / 3 for metallic materials; C2 represents the critical value of equivalent plastic strain under uniaxial tension, obtained by the following formula: In the formula, A R C1 represents the reduction of area of ​​the material, obtained from a monotonic tensile test; C1 represents the critical value of the equivalent plastic strain under pure shear conditions, obtained by the following formula: A and n represent the strength coefficient and hardening index of the material, respectively, which are derived from the relationship between the true stress σ and the true strain ε in the monotonic tensile test of steel before necking. When the numerically simulated strain at the critical support position in the frame reaches this critical value, damage is considered. The cycle in which damage accumulates to 1 is its low-cycle fatigue life N. B Step 2: Based on the LZH model using the critical surface method, establish the low-cycle fatigue life N of the node plate. C Prediction formula In the formula, It is the maximum shear strain amplitude of the key element; σ n,max It is the maximum normal stress on the critical surface of the unit; It is the normal strain amplitude of the unit critical surface; Here, is the fatigue strength coefficient, and 'a' is the fatigue strength index. ρ is the fatigue ductility coefficient, b is the fatigue ductility index, E is the elastic modulus of steel, and f is the fatigue ductility coefficient. y ω represents the yield strength of the steel, and all six items are material coefficients; ω is the life reduction factor considering the interaction of fatigue damage between the support and the gusset plate.

4. The method for predicting the low-cycle fatigue failure sequence of a plate-connected steel support frame according to claim 1, characterized in that, The low-cycle fatigue damage matching control index β=N for the support frame in step 3 C / N B When β>1, it means that the low-cycle fatigue life of the plate node is greater than that of the support, which means the design is safe but conservative; when β=1, it means that the low-cycle fatigue life of the plate node is equal to that of the support, which can give full play to the synergistic deformation effect of the two and make the overall energy dissipation capacity of the frame optimal; when β<1, it means that the low-cycle fatigue life of the plate node is less than that of the support, and the design parameters need to be adjusted.

5. The method for predicting the low-cycle fatigue failure sequence of a plate-connected steel support frame according to claim 1, characterized in that, The frame low-cycle fatigue failure sequence prediction function in step 4 does not need to consider external loads, which makes it convenient for use in the seismic design of supporting frames.

6. The method for predicting the low-cycle fatigue failure sequence of a plate-connected steel support frame according to claim 1, characterized in that, The frame low-cycle fatigue failure sequence prediction function in step 4 satisfies the joint adjustment of multiple design parameters.

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