Dynamic assessment method for failure probability of high-rise building group component under consideration of typhoon spatial-temporal variation characteristics

Abstract

The invention discloses a high-rise building group component failure probability dynamic evaluation method considering typhoon spatial-temporal variation characteristics, and the method comprises the steps: extracting structural wind load characteristics, obtaining a surface wind pressure time history through a wind tunnel experiment, and converting the surface wind pressure time history into a node load time history; structural damage is rapidly recognized, and based on a refined finite element model and a lumped damage mechanical model, two state variables of a damage driving rotation angle DDR and a damage variable DAM are defined to rapidly recognize a damage area; performing component failure assessment, and quantifying the failure probability by judging whether the damage exceeds a threshold value or not; the failure probability is quantified under the typhoon spatial-temporal change, typhoon early warning information is accessed in real time, node loads are dynamically updated, the component failure probability is calculated, and minute-level response is achieved. According to the method, failure probability dynamic evaluation of tens of thousands of building group components can be efficiently and accurately realized, and decision support is provided for urban disaster prevention.

Description

Technical Field

[0001] This invention belongs to the field of wind-resistant design technology for building structures, specifically a dynamic evaluation method for the failure probability of components in high-rise building complexes under the spatiotemporal variation characteristics of typhoons. Background Technology

[0002] With the intensification of global climate change, typhoons along my country's coast are showing an evolving trend of increasing intensity, frequency, and scope of impact. Meteorological monitoring data shows that more than fifty typhoons have made landfall in my country in the past decade, with the proportion of super typhoons increasing by about 1.5 times compared to the 1990s. Typhoon wind fields exhibit significant three-dimensionality, non-stationarity, and non-Gaussian characteristics, with instantaneous wind speed and direction fluctuating dramatically on a scale of seconds to minutes. Simultaneously, as the eyewall and rainband structure of the typhoon move, the wind direction undergoes continuous rotational changes. The canyon effect of urban underlying surfaces and aerodynamic disturbances from building clusters further amplify local wind pressure peaks, resulting in strong spatial variability and temporal abrupt changes in wind loads. However, current building wind-resistant design codes still use 10-minute average wind speed and a single prevailing wind direction as inputs, failing to reflect the spatiotemporal variations and unsteady aerodynamic coupling effects during typhoon processes. Extensive field measurements show that even super high-rise buildings designed according to these codes may still experience unexpected vibrations, component damage, and even the detachment of auxiliary components during extreme strong winds, posing safety hazards.

[0003] At the structural level, high-rise buildings under typhoon loads simultaneously experience coupled effects from alongwind, crosswind, and torsion, resulting in multiaxial stress, nonlinear response, and low-cycle fatigue degradation in key components. For thin-walled steel beams and steel-concrete composite columns, extreme wind loads can easily induce local buckling, leading to degradation in stiffness and bearing capacity. However, existing assessment techniques typically use macroscopic response indicators such as inter-story drift angle, base shear force, or apex acceleration as structural engineering requirement parameters (EDPs), which are insufficient to capture microscopic damage behaviors such as end plastic hinge evolution and local buckling of components. If solid element modeling is used for individual structures, nonlinear dynamic analysis often takes several days or even weeks, which cannot meet the batch assessment needs of tens of thousands of components in urban building complexes, resulting in a significant contradiction between computational accuracy and timeliness.

[0004] Furthermore, existing wind load-structural response analysis processes generally employ offline batch processing: wind tunnel pressure data is stored in the form of discrete wind direction angles and fixed wind speeds, making it impossible to adjust aerodynamic inputs based on real-time typhoon path, intensity changes, and wind circle radius; structural model updates rely on manual operation and lack connectivity mechanisms with meteorological early warning systems and IoT monitoring platforms, making it difficult to form a dynamic closed loop of "monitoring-early warning-assessment-decision-making." Typhoon early warning information is typically updated every 3-6 hours, while decisions such as urban traffic control and emergency evacuation require minute-level responses. Therefore, it is necessary to propose an assessment method that simultaneously considers real-time typhoon early warning, refined structural response analysis, and efficient computational capabilities to achieve a rapid closed loop from typhoon monitoring to structural risk assessment, providing timely evidence for emergency evacuation and urban resilience decisions. Summary of the Invention

[0005] In view of this, the purpose of this invention is to provide a dynamic assessment method for the failure probability of components of high-rise building complexes under the spatiotemporal variation characteristics of typhoons. This method can quantify the failure probability of structures / components of urban high-rise building complexes within a minute-level response cycle, so as to support urban emergency response and resilience management.

[0006] To achieve the above objectives, the present invention provides the following technical solution: A dynamic assessment method for the failure probability of components in high-rise building complexes considering the spatiotemporal variation characteristics of typhoons includes the following steps: Step 1: Structural wind load feature extraction: Obtain spatial information of the building complex area and geometric features and material parameters of the target building. Based on the spatial information and geometric features, obtain the surface wind pressure time history at different wind speeds under discrete wind direction angles through wind tunnel pressure measurement experiments, and use interpolation methods to convert the surface wind pressure time history into nodal load time history. Step 2: Rapid identification of structural damage: Based on the geometric information and material properties of the target building, a refined finite element model is established. The lumped damage mechanics model LDM is used to concentrate the damage and plasticity of the component in the inelastic hinge region at the end of the element. Two state variables, damage-driving angle DDR and damage variable DAM, are defined. By analyzing the thresholds of damage-driving angle DDR and damage variable DAM, the structural damage area is quickly identified. Step 3: Group component failure assessment: For the structural damage areas identified in Step 2, call the corresponding refined finite element model, and determine whether the component damage exceeds the preset threshold based on the nodal load time history, thereby quantifying the failure probability of the group or component. Step 4: Quantification of component failure probability under typhoon spatiotemporal changes: Real-time access to typhoon meteorological early warning information, updating the inflow wind speed and direction at the location of the building complex based on the early warning information, calling the finite element node load time history corresponding to the wind speed and direction in Step 1, and applying it to the lumped damage mechanics model LDM in Step 2, dynamically updating the evolution process of damage-driven rotation angle DDR and damage variable DAM, and calculating the failure probability of tens of thousands of components in the building complex in batches based on the statistical method in Step 3.

[0007] Furthermore, in step one, the spatial information of the building complex area includes topographic features, surrounding building layout and urban ventilation corridor wind environment characteristics, and the geometric features and material parameters of the target building include structural component cross-sectional dimensions, floor height, component arrangement and material strength.

[0008] Furthermore, in step one, the surface wind pressure time history is converted into the nodal load time history using the cubic spline interpolation method, including the following steps: Based on the surface pressure coefficient obtained from wind tunnel pressure measurement experiments, the building surface is divided into a virtual pressure measurement grid; Cubic spline interpolation is performed on discrete wind direction angles, and continuous wind pressure time histories under any wind direction are obtained by extrapolation of data. The wind pressure is mapped to the finite element nodes according to the surface region, and the nodal load time history is generated by the region area integration. The load was scaled up using time and velocity scales to ensure that the nodal loads were consistent with the actual typhoon inflow wind speed.

[0009] Furthermore, in step two, the establishment of the refined finite element model takes into account the P-Delta effect and shear deformation.

[0010] Furthermore, in step two, the simplified method for concentrated damage at the component ends of the lumped damage mechanics model (LDM) is as follows: The damage areas of structural components are concentrated at both ends of the component unit, while the middle section of the component remains elastic; Equivalent nonlinear spring elements are used at the ends of the components to simulate buckling and damage evolution; Define the damage-driving angle DDR as the damage initiation variable, and its expression is: in: This is the cumulative value of the positive plastic rotation angle at the end of the component; The critical angle for local buckling; ; when When the time indicates that the component has entered the plastic development stage but local buckling has not been triggered; when This indicates that the end of the component has reached the point of local buckling initiation; The damage variable DAM is defined to describe the stiffness degradation process of a member after local buckling, and its expression is as follows: in: This represents the initial stiffness of the member before buckling. This is the equivalent stiffness after buckling; This represents the positive damage variable in local buckling, corresponding to the degree of local buckling caused by the positive bending moment, and its value ranges from [0,1]. This represents the negative damage variable in local buckling, corresponding to the degree of local buckling caused by negative bending moment, and its value ranges from [0,1]. when When the time indicates that the component is in the elastic stage and there is no damage; when When the time indicates that the component has entered the elastoplastic stage, damage accumulates and stiffness degrades; when This indicates that the component has reached the yielding stage, its load-bearing capacity is completely lost, and the component fails.

[0011] Furthermore, in step three, the method for quantifying the failure probability of a group / component includes: Set local buckling initiation threshold With complete failure threshold ; Under the action of nodal load time history, record the number of times the component damage-driven rotation angle DDR or damage variable DAM exceeds the corresponding threshold; The proportion of components exceeding the threshold under different combinations of wind speed and wind direction angle was statistically analyzed, and a component failure probability-wind speed vulnerability analysis curve was established, the expression of which is: in: Wind speed The failure probability of a component under certain conditions.

[0012] Furthermore, in step four, typhoon weather warning information is obtained in real time through the meteorological bureau's release platform and connected with the Internet of Things monitoring platform to form a dynamic closed loop of "monitoring-early warning-assessment-decision".

[0013] Furthermore, typhoon weather warning information includes the typhoon's path, wind speed, direction of movement, and radius of its wind circle.

[0014] Furthermore, in step four, the quantification of the failure probability of the group / component based on the spatiotemporal variation characteristics of the typhoon includes: Based on real-time typhoon weather warning information, the inflow wind speed and direction at the location of the building complex are updated in real time. Based on real-time wind speed and direction, the corresponding nodal load time history is automatically retrieved from the nodal load time history database established in step one and applied to the lumped damage mechanics model LDM. The evolution process of damage-driven rotation angle (DDR) and damage variable (DAM) is dynamically updated, and the latest component failure probability is output based on the probabilistic statistical method in step three, realizing minute-level component failure probability updates and visualization output.

[0015] The beneficial effects of this invention are as follows: This invention presents a dynamic assessment method for the failure probability of components in high-rise building complexes under the spatiotemporal variation characteristics of typhoons. By integrating the spatiotemporal variation characteristics of typhoons with refined structural modeling, it achieves minute-level dynamic assessment of the failure probability of components in high-rise building complexes, and has the following significant technical effects: (1) Breaking through the limitations of traditional wind load modeling: By combining wind tunnel experiments with cubic spline interpolation, discrete wind direction angle and wind pressure data are transformed into continuous nodal load time histories, which accurately reflects the unsteady characteristics of typhoons and solves the shortcomings of the fixed wind direction assumption in the specification. (2) Achieve accurate cross-scale capture of damage identification: By using the lumped damage mechanics model LDM, the damage at the end of the component is condensed into two state variables, DDR and DAM, which can accurately identify micro-damage behaviors such as local buckling and stiffness degradation while ensuring computational efficiency. (3) Construct an efficient dynamic evaluation system: Based on real-time typhoon warning data, the system automatically calls the pre-stored load time history and combines the lumped damage mechanics model LDM to realize the dynamic tracking of damage evolution, supporting batch failure probability statistics of tens of thousands of components, and improving the calculation efficiency by several orders of magnitude. (4) Form a closed-loop decision support mechanism: connect the entire chain of "meteorological monitoring - load mapping - damage identification - probability assessment" to achieve minute-level response from typhoon warning to structural safety assessment, and provide real-time technical support for urban emergency evacuation and resilience management.

[0016] In summary, the method of this invention effectively solves the industry problem of balancing accuracy and efficiency in traditional assessment methods, and provides an innovative solution for typhoon disaster prevention in coastal cities. Attached Figure Description

[0017] To make the objectives, technical solutions, and beneficial effects of this invention clearer, the following figures are provided for illustration: Figure 1 This is a flowchart of an embodiment of the dynamic evaluation method for the failure probability of high-rise building components under the spatiotemporal variation characteristics of typhoons, according to the present invention. Figure 2 A schematic diagram of the wind tunnel test setup and pressure measurement points; Figure 3 Flowchart of nodal load transformation for cubic spline interpolation method; Figure 4 A simplified model of inelastic hinge damage at the end of the LDM model; Figure 5DAM contour plot of the build number and damage variable in the LDM model; Figure 6 This is a DDR contour plot of the damage-driven rotation angle in the LDM model. Detailed Implementation

[0018] The present invention will be further described below with reference to the accompanying drawings and specific embodiments, so that those skilled in the art can better understand and implement the present invention. However, the embodiments described are not intended to limit the present invention.

[0019] like Figure 1 As shown in the figure, this embodiment considers the dynamic evaluation method for the failure probability of components in high-rise building complexes under the spatiotemporal variation characteristics of typhoons, and includes the following steps.

[0020] Step 1: Structural wind load feature extraction: Transform the typhoon wind field characteristics into nodal load time histories that can be used for structural analysis.

[0021] First, spatial information about the building complex area and the geometric features and material parameters of the target building are collected. The spatial information includes topographic features, the layout of surrounding buildings, and wind environment characteristics such as urban ventilation corridors; the geometric features and material parameters include structural component cross-sectional dimensions, floor height, component arrangement, and material strength. Specifically, spatial information such as building coordinates, height, and roof shape can be obtained through platforms such as OpenStreetMap and Zenodo, and component cross-sectional dimensions, materials, and connection details can be extracted using Building Information Modeling (BIM).

[0022] Then, based on the above information, a wind tunnel pressure measurement experiment was conducted. For example... Figure 2 As shown, a 1:400 rigid building complex model was arranged in a recirculating wind tunnel, with 308 pressure measurement points on the model surface. A 64-channel electronic scanning valve was used to record the surface wind pressure time history at discrete wind direction angles of 0°, 15°, 30°, 45°, 60°, 75°, and 90°, with wind speeds of 15 m / s, 30 m / s, and 40 m / s, respectively. The ground roughness type was classified as D.

[0023] Finally, the discrete surface wind pressure time history is transformed into a continuous finite element nodal load time history using cubic spline interpolation, such as... Figure 3 As shown, the steps include the following.

[0024] (1) Based on the surface pressure coefficient measured by the wind tunnel, the building surface is divided into a virtual pressure measurement grid.

[0025] (2) Use MATLAB to perform cubic spline interpolation on the discrete wind direction angles above, and obtain the continuous wind pressure time history under any wind direction by extrapolation of the data.

[0026] (3) Map the wind pressure to the finite element nodes according to the surface region, and generate the nodal load time history by integrating the region area. The integration formula is as follows: in: For measuring points Wind pressure time history; For nodes For measuring points The area affected The angle between wind directions.

[0027] (4) Utilizing time scales and velocity scale The generated nodal load time histories are prototype-scaled to ensure that the nodal loads are consistent with the actual typhoon inflow wind speed, thereby establishing a "nodal load time history database" covering different wind speeds and directions. Among them: and These are the characteristic lengths of the prototype structure and the wind tunnel test model, respectively. and These are the actual inflow wind speed of the prototype typhoon and the model inflow wind speed in the wind tunnel test, respectively. This refers to the time step of the nodal load time history in the wind tunnel test. The time step is the time history of the load at the nodes of the prototype structure.

[0028] Step 2: Rapid identification of structural damage: Establish a lumped damage mechanics model (LDM) that can quickly identify local damage in components.

[0029] First, based on the geometric information and material properties of the target building, a refined finite element model is established using Abaqus or SAP2000 software, considering the P-Delta effect and shear deformation. Simultaneously, time histories of nodal loads under different wind speeds and discrete wind direction angles are applied to verify the structure and determine its overall stability. Based on the refined finite element model, a frame is selected for node and component labeling.

[0030] Then, the lumped damage mechanics model LDM is introduced to simplify the damage. For example... Figure 4 As shown, the damage zones of structural members such as beams and columns are concentrated at both ends of the member element, while the middle section of the member remains elastic. Equivalent nonlinear spring elements (plastic hinges) are used at the ends of the members to simulate buckling and damage evolution.

[0031] Define two key state variables to quantify the damage: (1) Damage-driving angle DDR: As the damage initiation variable, its expression is: in: This is the cumulative value of the positive plastic rotation angle at the end of the component; ; The critical plastic rotation angle at which local buckling begins is a key threshold parameter characterizing the transition of a member from an elastoplastic state to a local buckling state under load. As an inherent parameter of material properties and section geometry, a nonlinear shell element model is established using ABAQUS software, and the force-displacement curve is output. The tangent stiffness of the curve is then used to... The change identifies the local buckling initiation point. When the tangential stiffness drops to 70% of the average stiffness in the elastoplastic stage (a commonly used engineering threshold), the corresponding point is the local buckling initiation point; the total displacement corresponding to this initiation point is recorded. And separate the plastic displacements within them. Thus obtain The relevant expressions are as follows: , , , in, For load increments; For displacement increment; The load corresponding to this location; This represents the initial stiffness of the member before buckling. The characteristic length of the component.

[0032] when When the time indicates that the component has entered the plastic development stage but local buckling has not been triggered; when The time indicates that the end of the component has reached the point of local buckling initiation.

[0033] (2) Damage Variable (DAM): Used to describe the stiffness degradation process of a member after local buckling, its expression is: in: This represents the initial stiffness of the member before buckling. This is the post-buckling equivalent stiffness, which changes in real time with the load history and damage accumulation. For example, when a positive bending moment is applied... When, the equivalent stiffness of the component in the positive direction It will decrease to the initial stiffness Approximately 50% (the specific calculation requires calibrating damage parameters → calculating damage variables → solving the flexibility matrix → inverting the stiffness matrix), corresponding to (Stiffness degradation of 50%) This represents the positive damage variable in local buckling, corresponding to the degree of local buckling caused by the positive bending moment, and its value ranges from [0,1]. This represents the negative damage variable in local buckling, corresponding to the degree of local buckling caused by negative bending moment, and its value range is also [0,1].

[0034] when When the time indicates that the component is in the elastic stage and there is no damage; when When the time indicates that the component has entered the elastoplastic stage, damage accumulates and stiffness degrades; when This indicates that the component has reached the yielding stage, its load-bearing capacity is completely lost, and the component fails.

[0035] The nodal load time histories obtained in step one are applied to the LDM model for dynamic time history analysis (e.g., using the Newmark-β method with an integration step size of 0.01 s). By monitoring the evolution of DDR and DAM, the damage regions of the structure can be quickly identified, and a model can be generated as follows: Figure 5 and Figure 6 The diagram shows the spatial distribution of component damage.

[0036] Specifically, the component parameters of the lumped damage mechanics model (LDM) are calculated as follows.

[0037] Read the defined material parameters from SAP2000: mass density elastic modulus Poisson's ratio Material yield strength hardening ratio Damping ratio .

[0038] ABAQUS is used to simulate the loading of a single member. The constitutive model of the member is calculated based on the cross-sectional area A, the moment of inertia I, the axial stiffness EA, the bending stiffness EI, and the yield moment My, and the force-displacement curve is output.

[0039] The displacement matrix and deformation matrix of the structure are linked based on the kinematic equations. The lumped damage mechanics model (LDM) is used to characterize the plastic deformation and damage set of the structure. The above parameters are incorporated to construct the damage set of the structure under different loads.

[0040] The plastic hinge embedding method of the lumped damage mechanics model LDM is as follows: According to Figure 4 The method shown uses damage-driven rotation. and damage variables To characterize the damage status of components; to calculate structural damage evolution parameters: Euler critical force. For node / component rotational stiffness Km, input the corresponding deformation matrix and displacement matrix.

[0041] Step 3: Group / Component Failure Assessment: Based on the damage identification results above, quantify the failure probability of the group / component.

[0042] Set a clear failure threshold: As a threshold for determining the initiation of local buckling, This serves as the threshold for determining complete failure of a component.

[0043] For different combinations of wind speed and wind direction angle in the database from step one, dynamic analysis was performed by applying nodal load time histories multiple times. In each analysis, it was recorded whether the DDR or DAM values ​​of each component exceeded the corresponding threshold. or ).

[0044] The proportion of instances where the component's DAM exceeded 1.0 under different combinations of wind speed and wind direction angle was statistically analyzed. Based on these statistical results, a component failure probability-wind speed vulnerability analysis curve was established, the expression of which is: in: That is, at wind speed The failure probability of the component under certain conditions.

[0045] Step 4: Dynamic assessment under the spatiotemporal changes of typhoons: Achieve dynamic assessment of component failure probability in conjunction with real-time typhoon warnings.

[0046] The system accesses real-time typhoon warnings issued by the meteorological bureau via an interface, including typhoon path, wind speed, direction of movement, and radius of the wind circle. Based on this real-time information, it determines and updates the inflow wind speed and direction at the location of the building complex.

[0047] Based on the real-time updated wind speed and direction, the corresponding nodal load time history is automatically retrieved from the "Nodal Load Time History Database" pre-built in Step 1.

[0048] The retrieved node load time histories are automatically applied to the total damage mechanics model (LDM) of the building cluster that was constructed in step two, and a rapid dynamic response analysis is performed to dynamically update the evolution of the damage-driven rotation angle (DDR) and damage variable (DAM) of all components.

[0049] Finally, based on the statistical methods described in step three, and utilizing the latest analysis results, the latest failure probabilities of tens of thousands of components within the building complex are calculated in batches, achieving minute-level updates and visualization output. This method, through connection with an IoT monitoring platform, forms a dynamic closed loop of "monitoring-early warning-assessment-decision-making," providing real-time technical support for urban emergency evacuation and resilience management.

[0050] The above-described embodiments are merely preferred embodiments provided to fully illustrate the present invention, and the scope of protection of the present invention is not limited thereto. Equivalent substitutions or modifications made by those skilled in the art based on the present invention are all within the scope of protection of the present invention. The scope of protection of the present invention is defined by the claims.

Claims

1. A dynamic evaluation method for the failure probability of components in high-rise building complexes considering the spatiotemporal variation characteristics of typhoons, characterized in that: Includes the following steps: Step 1: Structural wind load feature extraction: Obtain spatial information of the building complex area and geometric features and material parameters of the target building. Based on the spatial information and geometric features, obtain the surface wind pressure time history at different wind speeds under discrete wind direction angles through wind tunnel pressure measurement experiments, and use interpolation methods to convert the surface wind pressure time history into nodal load time history. Step 2: Rapid identification of structural damage: Based on the geometric information and material properties of the target building, a refined finite element model is established. The lumped damage mechanics model LDM is used to concentrate the damage and plasticity of the component in the inelastic hinge region at the end of the element. Two state variables, damage-driving angle DDR and damage variable DAM, are defined. By analyzing the thresholds of damage-driving angle DDR and damage variable DAM, the structural damage area is quickly identified. Step 3: Group component failure assessment: For the structural damage areas identified in Step 2, call the corresponding refined finite element model, and determine whether the component damage exceeds the preset threshold based on the nodal load time history, thereby quantifying the failure probability of the group or component. Step 4: Quantification of component failure probability under typhoon spatiotemporal changes: Real-time access to typhoon meteorological early warning information, updating the inflow wind speed and direction at the location of the building complex based on the early warning information, calling the finite element node load time history corresponding to the wind speed and direction in Step 1, and applying it to the lumped damage mechanics model LDM in Step 2, dynamically updating the evolution process of damage-driven rotation angle DDR and damage variable DAM, and calculating the failure probability of tens of thousands of components in the building complex in batches based on the statistical method in Step 3.

2. The dynamic evaluation method for the failure probability of high-rise building components considering the spatiotemporal variation characteristics of typhoons, as described in claim 1, is characterized in that: In step one, the spatial information of the building complex area includes topographic features, surrounding building layout and urban ventilation corridor wind environment characteristics, and the geometric features and material parameters of the target building include structural component cross-sectional dimensions, floor height, component arrangement and material strength.

3. The dynamic evaluation method for the failure probability of high-rise building components considering the spatiotemporal variation characteristics of typhoons, as described in claim 1, is characterized in that: In step one, the surface wind pressure time history is converted into the nodal load time history using cubic spline interpolation, including the following steps: Based on the surface pressure coefficient obtained from wind tunnel pressure measurement experiments, the building surface is divided into a virtual pressure measurement grid; Cubic spline interpolation is performed on discrete wind direction angles, and continuous wind pressure time histories under any wind direction are obtained by extrapolation of data. The wind pressure is mapped to the finite element nodes according to the surface region, and the nodal load time history is generated by the region area integration. The load was scaled up using time and velocity scales to ensure that the nodal loads were consistent with the actual typhoon inflow wind speed.

4. The dynamic evaluation method for the failure probability of high-rise building components considering the spatiotemporal variation characteristics of typhoons, as described in claim 1, is characterized in that: In step two, the establishment of the refined finite element model takes into account the P-Delta effect and shear deformation.

5. The dynamic evaluation method for the failure probability of high-rise building components considering the spatiotemporal variation characteristics of typhoons, as described in claim 1, is characterized in that: In step two, the simplified method for concentrated damage at the component ends of the lumped damage mechanics model (LDM) is as follows: The damage areas of structural components are concentrated at both ends of the component unit, while the middle section of the component remains elastic; Equivalent nonlinear spring elements are used at the ends of the components to simulate buckling and damage evolution; Define the damage-driving angle DDR as the damage initiation variable, and its expression is: in: This is the cumulative value of the positive plastic rotation angle at the end of the component; This is the critical angle for local buckling; ; when When the time indicates that the component has entered the plastic development stage but local buckling has not been triggered; when This indicates that the end of the component has reached the point of local buckling initiation; The damage variable DAM is defined to describe the stiffness degradation process of a member after local buckling, and its expression is as follows: in: This represents the initial stiffness of the member before buckling. This is the equivalent stiffness after buckling; This represents the positive damage variable in local buckling, corresponding to the degree of local buckling caused by the positive bending moment, and its value ranges from [0,1]. This represents the negative damage variable in local buckling, corresponding to the degree of local buckling caused by negative bending moment, and its value ranges from [0,1].

6. When When the time indicates that the component is in the elastic stage and there is no damage; when When the time indicates that the component has entered the elastoplastic stage, damage accumulates and stiffness degrades; when This indicates that the component has reached the yielding stage, its load-bearing capacity is completely lost, and the component fails.

7. The dynamic evaluation method for the failure probability of high-rise building components considering the spatiotemporal variation characteristics of typhoons, as described in claim 1, is characterized in that: In step three, the quantification method for the failure probability of a group / component includes: Set local buckling initiation threshold With complete failure threshold ; Under the action of nodal load time history, record the number of times the component damage-driven rotation angle DDR or damage variable DAM exceeds the corresponding threshold; The proportion of components exceeding the threshold under different combinations of wind speed and wind direction angle was statistically analyzed, and a component failure probability-wind speed vulnerability analysis curve was established, the expression of which is: in: Wind speed The failure probability of a component under certain conditions.

8. The dynamic evaluation method for the failure probability of high-rise building components considering the spatiotemporal variation characteristics of typhoons, as described in claim 1, is characterized in that: In step four, typhoon weather warning information is obtained in real time through the meteorological bureau's release platform and connected with the Internet of Things monitoring platform to form a dynamic closed loop of "monitoring-early warning-assessment-decision".

9. The dynamic evaluation method for the failure probability of high-rise building components considering the spatiotemporal variation characteristics of typhoons, as described in claim 7, is characterized in that: Typhoon weather warning information includes the typhoon's path, wind speed, direction of movement, and radius of its wind circle.

10. The dynamic evaluation method for the failure probability of high-rise building components considering the spatiotemporal variation characteristics of typhoons, as described in claim 1, is characterized in that: In step four, the quantification of the failure probability of components / groups based on the spatiotemporal variation characteristics of typhoons includes: Based on real-time typhoon weather warning information, the inflow wind speed and direction at the location of the building complex are updated in real time. Based on real-time wind speed and direction, the corresponding nodal load time history is automatically retrieved from the nodal load time history database established in step one and applied to the lumped damage mechanics model LDM. The evolution process of damage-driven rotation angle (DDR) and damage variable (DAM) is dynamically updated, and the latest component failure probability is output based on the probabilistic statistical method in step three, realizing minute-level component failure probability updates and visualization output.

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