A method for long-term wind-induced fatigue and earthquake multi-disaster risk assessment of a latticed shell structure

Abstract

The present application relates to the technical field of risk assessment of multi-disaster of reticulated shell structure, and specifically provides a method for long-term wind-induced fatigue and multi-disaster risk assessment of reticulated shell structure. The main steps include: generating long-term wind load in accordance with local historical climate rules of the structure; evaluating fatigue damage and performance degradation degree of the reticulated shell structure under the action of long-term wind load; and calculating multi-disaster risk of the reticulated shell structure under the action of long-term wind-induced fatigue and earthquake. The present application solves the limitation that long-term wind-induced fatigue and randomness of wind direction are not considered in the current multi-disaster analysis technology. In addition, the uncertainty of structural material, the uncertainty of ground motion and the fitting model error are considered by statistical method, and an exponential equation is used to establish a multi-disaster probability demand model, so that the multi-disaster risk assessment result of the reticulated shell structure is more accurate.

Description

Technical Field

[0001] This invention relates to the field of multi-hazard risk assessment technology for reticulated shell structures, and specifically provides a method for assessing the long-term wind-induced fatigue and multi-hazard seismic risks of reticulated shell structures. Background Technology

[0002] Grid shell structures are widely used in important buildings such as opera houses, exhibition centers, and stadiums. Because these buildings require open spaces and are typically used on roofs and superstructures, wind-induced fatigue is unavoidable during their long service life. The degradation of material properties caused by long-term wind-induced fatigue can significantly impact the seismic performance of grid shell structures. Damage to such important public facilities would have a devastating impact on socio-economic development and public safety. However, current research and standards do not consider the long-term wind-induced fatigue effects on these structures.

[0003] Fatigue failure of steel structures is a hot topic in science and engineering. Reports indicate that 80%-90% of steel structure failures are caused by fatigue and fracture. For reticulated shell structures, wind-induced fatigue failure primarily leads to material property degradation. In long-term wind environments, structural fatigue damage develops, accumulates, and expands over time. Under seismic loading, long-term wind-induced fatigue failure can cause rapid collapse of the structure before its design service life, damage under seismic loading below its design intensity, or increase the risk of structural failure during earthquakes. However, research on long-term wind-induced fatigue in reticulated shell structures is still very limited, and analytical methods remain unclear.

[0004] In recent years, although multi-hazard problems have received attention in other structures, the methods proposed in these studies may not be directly applicable to reticulated shell structures due to certain limitations. Therefore, there is an urgent need for a multi-hazard analysis method to assess the multi-hazard risks of reticulated shell structures under long-term wind fatigue and seismic loading.

[0005] ①The first limitation is that previous multi-hazard structural assessment methods typically only considered the combined effects of a single strong wind event with other hazards, neglecting the fatigue effects caused by long-term wind. Under seismic loading, the additional risk of damage from the performance degradation of reticulated shell structures due to long-term wind-induced fatigue cannot be ignored.

[0006] ② Another limitation is that previous assessment techniques ignored the randomness of wind direction. In previous methods of structural wind-induced fatigue analysis, a fixed wind direction loading scheme would cause fatigue failure to continuously affect components in localized areas of the structure, gradually leading to significantly more severe fatigue failure in those areas compared to other parts. This neglect of the randomness of wind direction would result in an underestimation of the performance of reticulated shell structures, leading to inaccurate wind-induced fatigue assessments.

[0007] In view of the above background and limitations, it is necessary to design a risk assessment method that takes into account long-term wind-induced fatigue and multiple seismic hazards, so as to improve the current analytical methods in the field of multi-hazard assessment. Summary of the Invention

[0008] To address the aforementioned technical problems, this invention provides a method for assessing the long-term wind-induced fatigue and multiple seismic hazards of reticulated shell structures.

[0009] The technical solution of this invention is as follows:

[0010] A method for assessing long-term wind-induced fatigue and seismic multi-hazard risk of reticulated shell structures, comprising the following steps:

[0011] Step S1: Collect wind speed and meteorological data at the site of the structure.

[0012] Step S2: Establish a joint probability distribution model for wind speed and wind direction.

[0013] Step S3: Generate long-term wind loads.

[0014] Step S4: Establish an uncertainty model of the reticulated shell structure based on vector finite element method.

[0015] Step S5: Calculate the average annual fatigue damage of the reticulated shell structure components and assess the maximum service life of the structure.

[0016] Step S6: Evaluate the impact of long-term wind-induced fatigue on the seismic performance of the reticulated shell structure.

[0017] Step S7: Establish multi-hazard model uncertainty sample pairs.

[0018] Step S8: Divide the performance status levels of the reticulated shell structure; establish a multi-hazard probability demand model based on the exponential equation; establish a multi-hazard vulnerability model for each performance level of the reticulated shell structure; determine the target performance curve of the reticulated shell structure.

[0019] Furthermore, the data in step S1 needs to be obtained from a satellite database or a reanalysis database because satellite data has higher geographical resolution and can obtain wind speeds near the structure, overcoming the limitations of land stations being geographically far from the structure; the temporal resolution of meteorological data is at least 1 hour or more.

[0020] Furthermore, the correlation between wind speed and wind direction in step S2 cannot be ignored. Establishing a joint distribution of the two is an effective means of considering their correlation. The joint probability distribution model of wind speed and wind direction C(x,y) is realized through a single-parameter Frank Archimedes Copula equation (6).

[0021]

[0022] In the formula, x and y represent the probability density functions of average wind speed and wind direction, respectively; α is the model parameter.

[0023] Furthermore, the specific content of the generation method in step S3 is as follows: based on the service time T years of the structure, extract the dimension from the joint probability distribution model. average wind speed and its wind direction θ i Data; use Davenport spectrum and autoregression method to generate fluctuating wind corresponding to average wind speed; generate long-term wind speed sequence according to formula (7)(8); use Davenport spectrum formula (9)(10) and autoregression method to generate fluctuating wind speed corresponding to average wind speed; use wind speed and wind pressure conversion formula (11)(11) and load formula (12) to generate long-term wind load.

[0024]

[0025]

[0026] In the formula, V LTWS and u j θ represents the total wind speed and fluctuating wind speed at time t, respectively. LTWS This corresponds to the wind direction.

[0027]

[0028]

[0029] In the formula, S v (f n f represents the power spectrum of wind speed fluctuations. n Here, k represents the frequency, and k is the surface roughness coefficient. The wind speed is at a height of 10m above the ground.

[0030]

[0031]

[0032] F q =μ s w q A q (7)

[0033] In the formula, η represents the surface roughness; w q V q A q , and F q Let q represent the wind pressure, total wind speed, calculated area, and wind load at point q, respectively; ρ is the air density, assumed to be 1.2 (kg / m³). 3 );μ s This represents the shape coefficient of the reticulated shell structure.

[0034] Furthermore, the structural model in step S4 is established using the vector finite element method, which can significantly improve the efficiency of structural response solution and greatly shorten the solution time. Considering the nonlinear characteristics of the reticulated shell structure, a simplified buckling softening model is adopted for the constitutive relation of the members. The reticulated shell structure considers the uncertainties of three materials: steel yield strength, elastic modulus and damping ratio, and uses the Latin hypercube sampling method to generate the uncertainty set of the structure.

[0035] Further, the structural fatigue life calculation method in step S5 is as follows: using the Miner method and the SN curve of the connecting members, calculate the annual average fatigue damage value of all members of the worst-performing model in the structural uncertainty group; when a member D(T) is 1, the member fails due to fracture, and it is conservatively assumed that T is the healthy life of the structure at this time; find the maximum value D(1) of the annual average fatigue damage of all members, and then use formula (14) to calculate the maximum service time T of the structure. s .

[0036]

[0037] Furthermore, the evaluation method in step S6 is as follows: generate long-term wind loads with different service times, perform fatigue analysis on the reticulated shell structure respectively, and use the steel degradation model to obtain the structure after performance degradation; the maximum nodal displacement (λ) can reflect the process of the reticulated shell structure from local instability to overall instability, so it is used as the DM of the reticulated shell structure; perform incremental dynamic analysis on structures with different damage levels, plot the seismic incremental dynamic curve, and calculate the changes of the DM of structures with different damage levels under different seismic intensities.

[0038] Further, the sample pair establishment method in step S7 is as follows: PGA is selected as the disaster intensity parameter IM1 to characterize earthquake disasters; ground motion records are collected; ground motion PGA is used as the amplitude modulation term; each record is amplitude-modulated according to a certain PGA range to form a ground motion database composed of j types of ground motions; j types of structures and ground motion PGA are extracted from the uncertain group of structures to form j earthquake-structure sample pairs; the maximum service time T of the structure calculated in step S6 is used as the basis for the sample pair establishment. s To maximize the value, random service time T is generated according to a uniform distribution. j Service time T is selected as the disaster intensity IM2, and T is... j By randomly matching the earthquake-structure sample pairs, j multi-hazard-model uncertainty sample pairs are generated.

[0039] Furthermore, the specific content of step S8 is as follows: using the maximum nodal displacement ratio (λ) as an indicator, the performance state level S of the structure is... CThe damage is categorized into three levels: minor damage, severe damage, and collapse. All multi-hazard model uncertainty sample pairs in step S7 are solved, and structural response data d is collected. j The exponential regression model has higher fitting accuracy than the linear regression model and avoids the non-monotonicity of quadratic or higher-order models. Using Equation (15) to fit the multi-hazard probability demand model based on the exponential equation, the relationship between the demand λ of the reticulated shell structure and the disaster intensity PGA-T and DM can be described more accurately. Using Equations (16) and (17), a multi-hazard vulnerability model of the reticulated shell structure at each performance state level is established. The curve at the collapse failure exceedance probability of 10% is taken as the performance target curve of the structure.

[0040] ln(S D ) = r a ·exp(r b ·ln(IM1))+r c ln(IM2)+r d +εσ (9)

[0041]

[0042]

[0043] In the formula, r a ,r b ,r c and r d Let εσ be the fitting parameters, and let S be a zero-mean normal distribution with a constant standard deviation σ. D and Let S represent the median and logarithmic standard deviation of DM, respectively; C and β C These represent the structural performance state level S respectively. C The median and logarithmic standard deviation of the corresponding indicators; φ(·) is the standard normal cumulative distribution function.

[0044] The beneficial effects of this patent are as follows:

[0045] This invention provides a long-term wind load simulation method that avoids the problem of distant meteorological stations or even the absence of stations near the structure site, and improves the time resolution of wind sequences to 10 minutes. Previous methods of structural fatigue analysis using continuous wind loads with a fixed wind direction overestimated structural fatigue damage. The fatigue analysis method in this invention considers the randomness of wind direction, making fatigue damage prediction more accurate. This invention also considers the uncertainties of structural materials, seismic events, and fitting model errors, utilizing an exponential multi-hazard probability demand model to describe the relationship between the intensity of various hazards and structural requirements, thus making the multi-hazard vulnerability probability assessment of structures more accurate. Attached Figure Description

[0046] Figure 1 A flowchart of an embodiment of the present invention.

[0047] Figure 2 Schematic diagram of the performance target curve of the reticulated shell structure. Detailed Implementation

[0048] The technical solutions of the embodiments of the present invention will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only a part of the embodiments of the present invention, and not all of them. All other embodiments obtained by those skilled in the art based on the embodiments of the present invention without creative effort are within the protection scope of the present invention. Figure 1 As shown in the figure, this embodiment provides a method for assessing the long-term wind-induced fatigue and seismic multi-hazard risks of reticulated shell structures, including the following steps:

[0049] Step S1: Collect wind speed and meteorological data at the site of the structure.

[0050] Step S2: Establish a joint probability distribution model for wind speed and wind direction.

[0051] Step S3: Generate long-term wind loads.

[0052] Step S4: Establish an uncertainty model of the reticulated shell structure based on vector finite element method.

[0053] Step S5: Calculate the average annual fatigue damage of the reticulated shell structure components and assess the maximum service life of the structure.

[0054] Step S6: Evaluate the impact of long-term wind-induced fatigue on the seismic performance of the reticulated shell structure.

[0055] Step S7: Establish multi-hazard model uncertainty sample pairs.

[0056] Step S8: Divide the performance status levels of the reticulated shell structure; establish a demand model based on exponential multi-hazard probability; establish a multi-hazard vulnerability model for each performance level of the reticulated shell structure; determine the target performance curve of the reticulated shell structure.

[0057] In this embodiment, the structure location in step S1 is assumed to be Dalian, a coastal city in China. Because the reanalysis data has higher geographical resolution, it can obtain wind speeds near the structure, overcoming the limitation of land stations being geographically distant from the structure. Therefore, 10-meter upper-level wind data for the suburbs of Dalian (longitude 121.04°-121.34°, latitude 38.72°-38.97°) were collected from the ERA5 (Reanalysis v5) reanalysis database of the European Centre for Medium-Range Weather Forecasts (ECMWF). The temporal resolution of the data is 1 hour.

[0058] In this embodiment, the probability density function of average wind speed in step S2 is constructed using a commonly used extreme value distribution, the Weibull distribution, formula (18); considering that wind direction is a multi-peak cyclic statistic, the mixed VonMises distribution (19) is used to construct its probability density function.

[0059]

[0060]

[0061] In the formula, a, b, ω j ,κ j and μ j The distribution parameters are unknown.

[0062] In this embodiment, in step S2, to consider the correlation between wind speed and wind direction, the joint probability distribution model C(x,y) of wind speed and wind direction is implemented through a single-parameter Frank Archimedean Copula equation (20).

[0063]

[0064] In the formula, x and y represent the probability density functions of average wind speed and wind direction, respectively, and α is the model parameter.

[0065] In this embodiment, the generation method in step S3 is as follows: Considering that the wind speed in nature changes very slowly within a 10-minute time interval, the wind can be regarded as the superposition of a constant average wind speed and a fluctuating wind speed. Therefore, based on the service life T years of the structure, the dimension is extracted from the joint probability distribution model. The average wind speed is composed of 10-minute intervals. and its wind direction θ i Data; generate long-term wind speed sequences according to formulas (21) and (22); generate pulsating winds corresponding to average wind speeds using Davenport spectrum formulas (23) and (24) and autoregression method; generate long-term wind loads using wind speed and wind pressure conversion formulas (25) and (26) and wind load formula (27).

[0066]

[0067]

[0068] In the formula, V LTWS and u j θ represents the total wind speed and fluctuating wind speed at time t, respectively. LTWS This corresponds to the wind direction.

[0069]

[0070]

[0071] In the formula, S v (f n f represents the power spectrum of wind speed fluctuations. n Here, k represents the frequency, and k is the surface roughness coefficient. The wind speed is at a height of 10m above the ground.

[0072]

[0073]

[0074] F q =μ s w q A q (twenty one)

[0075] In the formula, η represents the surface roughness, which is taken as 0.16 in this embodiment according to the load specification; w q V q A q , and F q Let q represent the wind pressure, total wind speed, calculated area, and wind load at point q, respectively; ρ is the air density, assumed to be 1.2 (kg / m³). 3 );μ s This represents the shape coefficient of the reticulated shell structure.

[0076] In this embodiment, the structural model in step S4 adopts a typical Kiewitt-8 type single-layer spherical reticulated shell; the welded hollow sphere-steel pipe connection node is the most widely used connection method in reticulated shell structures, so this node is used as the structural node; the finite element model of the reticulated shell structure is established using a vector finite element method program based on Matlab software, which can significantly improve the efficiency of structural response solution and greatly shorten the solution time; in the vector finite element model, the gravity load near each node is converted into concentrated mass applied to the reticulated shell node. In addition, the nonlinear effects of the structure are considered when modeling and analyzing the dynamic response of the reticulated shell structure; since the failure of spatial structural members is mostly buckling failure, the stress-strain relationship of the steel pipe adopts a simplified buckling softening model and is converted into the mechanical relationship between nodes; the reticulated shell structure model considers the uncertainties of three materials: steel yield strength, elastic modulus and damping ratio, and the distribution models of each parameter are shown in Table 1. The uncertain groups of the structure are generated using the Latin hypercube sampling method.

[0077] Table 1. Probability distribution model of structural uncertainty parameters

[0078]

[0079] In this embodiment, the structural fatigue life calculation method in step S5 is as follows: using the Miner method and the SN curve of the connecting members, the average annual fatigue damage value of all members in the worst-performing model in the structural uncertainty group is calculated; when a member D(T) is 1, the member fails due to fracture, and it is conservatively assumed that T is the healthy life of the structure at this time; find the maximum average annual fatigue damage value D(1) among all members, and then use formula (28) to calculate the maximum service time T of the structure. s .

[0080]

[0081] In this embodiment, the evaluation method in step S6 is as follows: Long-term wind loads with different service times are generated to perform fatigue analysis on the reticulated shell structure. The steel degradation model formulas (29) and (30) are used to obtain the structure after performance degradation. The Northridge Canyon Country-W Lost Canyon earthquake event is selected, and seismic incremental dynamic analysis is performed on structures with different damage levels. The ground motion PGA is used as the amplitude modulation term, and the ground motion is amplitude-modulated according to the range of PGA from 0.1g to 1g. The maximum node displacement (λ) can reflect the process of the reticulated shell structure from local instability to overall instability, so it is used as the DM of the reticulated shell structure. After performing seismic incremental dynamic analysis on the reticulated shell structure, the displacement time histories of all nodes of the reticulated shell structure are collected, and the maximum node displacement is selected. Seismic incremental dynamic curves with PGA and λ as varying parameters are plotted. The response changes of structures with different damage levels under different earthquake intensities are calculated, and the influence of long-term wind-induced fatigue of different durations on the seismic performance of the structure can be evaluated.

[0082] f y (T)=f y [1-ζ σ D(T)] (23)

[0083] E S (T)=E S [1-ζ E D(T)] (24)

[0084] In the formula f y (T) and E S (T) represents the yield strength and elastic modulus of steel as varying over time, respectively, and ζ σ and ζ E These are the response coefficients, determined according to relevant literature.

[0085] In this embodiment, the method for establishing multi-hazard sample pairs in step S7 is as follows: In this scheme, PGA is selected as the hazard intensity parameter IM1 to characterize earthquake hazards; considering that the randomness of ground motion records will affect the earthquake risk assessment results, a ground motion database with a certain PGA range and a certain number of ground motion records should be established first; since large-span spatial structures are usually used for important buildings, it is necessary to conduct detailed on-site geological exploration to avoid the site being located on fractured rock layers, therefore, near-field ground motion records are not used; it is recommended to select at least 20 ground motion records, each ground motion record should have three components, and the ground motion PGA is used as the amplitude modulation term, and each record is amplitude-modulated in the range of 0.1g to 1g to form a ground motion database composed of j types of ground motions; j types of structures and ground motion PGA are extracted from the uncertain group of structures to form j earthquake-structure sample pairs; obviously, the structural damage caused by long-term wind-induced fatigue of the structure is only related to the structural service time T, therefore, the service time T is selected as the hazard intensity IM2; the maximum service time T of the structure calculated in step S6 is used as the basis for the analysis. s To maximize the value, random service time T is generated according to a uniform distribution. j ; T j Randomly match the earthquake-structure sample pairs and generate j multi-hazard-model uncertainty sample pairs using Monte Carlo random sampling.

[0086] In this embodiment, step S8 specifically involves: classifying the performance state levels of the reticulated shell structure, as shown in Table 2, using λ as an index to classify the performance state levels S of the structure. C The damage is categorized into three levels: minor damage, severe damage, and collapse. All multi-hazard model uncertainty sample pairs in step S7 are solved, with the ground motion input employing the acceleration method, and the maximum nodal displacement data collected as the structural response data d. j Under seismic loading, reticulated shell structures are structures with strong nonlinear structural response. Exponential regression models have higher fitting accuracy than linear regression models and avoid the non-monotonicity of quadratic or higher-order models. Therefore, using equation (31) to fit a multi-hazard probability demand model based on the exponential equation can more accurately describe the relationship between the reticulated shell structure's demand λ and the hazard intensity PGA-T and DM. Formulas (32) and (33) are used to establish multi-hazard vulnerability models for each performance state level. Finally, the performance target curve of the reticulated shell structure is determined. In this invention, the curve at which the collapse failure exceedance probability is 10% is used as the performance target curve of the reticulated shell structure, such as... Figure 2 As shown.

[0087] ln(S D ) = r a ·exp(r b ·ln(IM1))+r c ln(IM2)+rd +εσ (25)

[0088]

[0089]

[0090] In the formula, r a ,r b ,r c and r d Let εσ be the fitting parameters, and let S be a zero-mean normal distribution with a constant standard deviation σ. D and Let S represent the median and logarithmic standard deviation of the structural demand DM, respectively; C and β C These represent the structural performance state level S respectively. C The median and logarithmic standard deviation of the corresponding indicators; φ(·) is the standard normal cumulative distribution function.

[0091] Table 2: Performance Status Level of Reticulated Shell Structures

[0092]

[0093] Matters not covered in this invention are common knowledge. The above embodiments are only for illustrating the technical concept and features of this invention, and are intended to enable those skilled in the art to understand the content of this invention and implement it accordingly. They should not be construed as limiting the scope of protection of this invention. All equivalent changes or modifications made in accordance with the spirit and essence of this invention should be covered within the scope of protection of this invention.

Claims

1. A method for assessing long-term wind-induced fatigue and seismic multi-hazard risks of reticulated shell structures, characterized in that, Includes the following steps: Step S1: Collect historical wind speed and meteorological data at the site of the structure selection; Step S2: Establish a joint probability distribution model for wind speed and wind direction; Step S3: Generate long-term wind loads; The generation method in step S3 is as follows: Based on the service life of the structure T In 2010, the dimension was extracted from the joint probability distribution model. average wind speed and its wind direction Data; using the Davenport spectrum and autoregression method to generate fluctuating wind speeds corresponding to the average wind speed; generating long-term wind speed sequences according to formulas (1) and (2); calculating long-term wind loads; (1) (2) In the formula, and u i Represent t Total wind speed and pulsating wind speed at any given time To correspond to the wind direction; Step S4: Establish an uncertainty model for the reticulated shell structure based on vector finite element method; Step S5: Calculate the average annual fatigue damage of the reticulated shell structure components and assess the maximum service life of the structure; Step S6: Evaluate the impact of long-term wind-induced fatigue on the seismic performance of the reticulated shell structure; Step S7: Establish multi-hazard model uncertainty sample pairs; The method for establishing multi-hazard uncertainty model sample pairs in step S7 is as follows: collect ground motion records, use the ground motion PGA as the amplitude modulation term, and perform amplitude modulation processing on each record according to a certain PGA range to form a sample pair. j A seismic motion database composed of various types of ground motion; Extract from the uncertainty set of the structure j This structure, randomly matched with the seismic ground motion PGA, forms... j One earthquake-structure sample pair; The maximum service time of the structure calculated in step S5 T s To maximize the value, random service times are generated according to a uniform distribution. T j Using Monte Carlo random sampling T j Randomly match earthquake-structure sample pairs to generate multi-hazard-model uncertainty sample pairs; Step S8: Divide the performance state levels of the reticulated shell structure; establish a multi-hazard probability demand model based on the exponential equation; establish a multi-hazard vulnerability model for each performance level of the reticulated shell structure; determine the target performance curve of the reticulated shell structure; The specific content of step S8 is as follows: using the maximum nodal displacement ratio (λ) as an indicator, the performance state level S of the structure is determined. C The damage is categorized into three levels: minor damage, severe damage, and collapse. All multi-hazard model uncertainty sample pairs in step S7 are solved to collect structural response data. d j ; Use equation (3) to fit a multi-hazard probability demand model based on the exponential equation; use formulas (4) and (5) to establish a multi-hazard vulnerability model for each performance state level of the reticulated shell structure; use the curve at the point where the collapse failure exceedance probability is 10% as the performance target curve of the structure; (3) (4) (5) In the formula, DM For the requirements of reticulated shell structures, IM 1 and IM 2 represents the disaster intensity corresponding to the two disasters; r a , r b ，r c and r d For fitting parameters, To have a constant standard deviation σ The zero-mean normal distribution; and To represent respectively DM The median and logarithmic standard deviation; and These represent the structural performance status levels, respectively. S C The median and logarithmic standard deviation of the corresponding indicators; It is the standard normal cumulative distribution function.

2. The method for assessing long-term wind-induced fatigue and seismic multi-hazard risks of a reticulated shell structure according to claim 1, characterized in that: The evaluation method in step S6 is as follows: generating long-term wind loads with different service times to perform fatigue analysis on the reticulated shell structure, using a steel degradation model to obtain the structure after performance degradation; performing incremental dynamic analysis on structures with different damage levels, plotting earthquake incremental dynamic curves, and calculating the response changes of structures with different damage levels under different earthquake intensities.

3. The method for assessing long-term wind-induced fatigue and seismic multi-hazard risk of a reticulated shell structure according to claim 1, characterized in that: The evaluation method in step S6 is as follows: generating long-term wind loads with different service times to perform fatigue analysis on the reticulated shell structure, using a steel degradation model to obtain the structure after performance degradation; performing incremental dynamic analysis on structures with different damage levels, plotting earthquake incremental dynamic curves, and calculating the response changes of structures with different damage levels under different earthquake intensities.

4. The method for assessing long-term wind-induced fatigue and seismic multi-hazard risk of a reticulated shell structure according to claim 1 or 3, characterized in that: The structural model in step S4 is established using the vector finite element method, and the constitutive relation of the members adopts a simplified buckling softening model. The uncertainty of the reticulated shell structure is considered for three materials: steel yield strength, elastic modulus and damping ratio. The uncertainty set of the structure is generated using the Latin hypercube sampling method.

5. The method for assessing long-term wind-induced fatigue and seismic multi-hazard risk of a reticulated shell structure according to claim 1, characterized in that: The structural model in step S4 is established using the vector finite element method, and the constitutive relation of the members adopts a simplified buckling softening model. The uncertainty of the reticulated shell structure is considered for three materials: steel yield strength, elastic modulus and damping ratio. The uncertainty set of the structure is generated using the Latin hypercube sampling method.

6. The method for assessing long-term wind-induced fatigue and seismic multi-hazard risks of a reticulated shell structure according to claim 2, characterized in that: The structural model in step S4 is established using the vector finite element method, and the constitutive relation of the members adopts a simplified buckling softening model. The uncertainty of the reticulated shell structure is considered for three materials: steel yield strength, elastic modulus and damping ratio. The uncertainty set of the structure is generated using the Latin hypercube sampling method.

7. A method for assessing long-term wind-induced fatigue and seismic multi-hazard risk of a reticulated shell structure according to claim 1, 3, 5, or 6, characterized in that: The method for calculating the structural fatigue life in step S5 is as follows: find the maximum value of the average annual fatigue damage of all components. D (1) Using the formula Calculate the maximum service time of the structure.

8. The method for assessing long-term wind-induced fatigue and seismic multi-hazard risk of a reticulated shell structure according to claim 1, characterized in that: The method for calculating the structural fatigue life in step S5 is as follows: find the maximum annual average fatigue damage of all components in the worst-performing model within the structural uncertainty group. D (1) Using the formula Calculate the maximum service time of the structure.

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