Method and system for detecting wind resistance of steel structure

Abstract

The invention relates to the technical field of steel structure wind resistance detection, in particular to a steel structure wind resistance detection method and system. The method comprises the following steps: constructing a steel structure geometric model, calculating a geometric instability angle between steel structure surfaces through a stereographic projection method, and identifying instability areas under different wind load conditions; arranging a sensor in the instability area, and collecting and normalizing dynamic response data to construct combined time sequence data of the instability area; setting a reference entropy value and an instability entropy threshold value, calculating a sample entropy of the instability region combined time sequence data, and determining an instability region; and calculating an instability risk index by using a combined risk assessment formula, and assessing the risk of the instability region based on the instability risk index. According to the method and system for detecting the wind resistance of the steel structure, the stereographic projection method and the entropy theory are combined, the instability risk area is recognized under different wind load conditions, the comprehensive instability risk index is defined and calculated, and multi-dimensional quantification and early warning of the instability risk of the structure are achieved.

Description

Technical Field

[0001] The present invention relates to the technical field of steel structure wind resistance performance detection technology, in particular to a steel structure wind resistance performance detection method and system. Background Art

[0002] The wind resistance performance testing method and system of steel structures is designed to improve the accuracy of stability assessment of steel structures under complex wind load conditions. By accurately identifying potential unstable areas and real-time dynamic response monitoring, the instability risk of the structure under extreme wind loads can be controlled, thus achieving efficient testing of the wind resistance performance of steel structures.

[0003] Existing methods and systems for detecting the wind resistance of steel structures are usually unable to fully capture the mutual influence between the geometric instability and dynamic response of the structure. In addition, due to the subtle and imperceptible changes in the spatial geometric relationship of the steel structure and the many changes in the internal response data of the steel structure, the instability risk assessment will be insufficiently accurate, false alarms and missed alarms will be serious, and key instability areas cannot be identified in time. Therefore, a method and system for detecting the wind resistance of steel structures are provided. Summary of the invention

[0004] The purpose of the present invention is to provide a method and system for detecting the wind resistance of steel structures, so as to solve the problems raised in the above background technology that the changes in the spatial geometric relationships of the steel structures are subtle and difficult to detect, and the internal response data of the steel structures change in many ways, which leads to insufficient accuracy in instability risk assessment, serious false alarms and omissions, and inability to timely identify key instability areas.

[0005] To achieve the above object, the present invention provides a method for testing the wind resistance of a steel structure, comprising: S1. Construct a geometric model of the steel structure, calculate the geometric instability angle between the steel structure surfaces by stereographic projection method, and identify the instability area under different wind load conditions; S2. Arrange sensors in the unstable area to collect and normalize the dynamic response data under wind load to construct the combined time series data of the unstable area; S3, setting a baseline entropy value and an instability entropy value threshold, calculating the sample entropy of the combined time series data of the instability region to determine the instability region; S4. Use the combined risk assessment formula to calculate the instability risk index of the unstable area by integrating the geometric instability angle and sample entropy.

[0006] The stereographic projection method is used to analyze the geometric relationship between steel structure surfaces and determine the unstable area; In S1, a geometric model of the steel structure is constructed, and the geometric instability angles between the steel structure surfaces are calculated by the stereographic projection method to identify the instability areas under different wind load conditions. The specific method steps are as follows: S1.1. Define each plane in the steel structure as , the node position in the steel structure is , define the distance between adjacent planes as ; ; in, For face The normal vector of For face exist The weight of the axis; For face exist The weight of the axis; For face exist The weight of the axis; is the number of the node in the steel structure; S1.2. Based on the geometric model of the steel structure, use the stereographic projection method to calculate the geometric instability angle between the steel structure surfaces, set the critical instability angle, and compare the geometric instability angle with the critical instability angle; S1.3. The area where the geometric instability angle is less than the critical instability angle is considered to be an unstable area.

[0007] As a further improvement of the technical solution, in S1.2, based on the geometric model of the steel structure, the geometric instability angle between the steel structure surfaces is calculated using the stereographic projection method, the critical instability angle is set, and the geometric instability angle is compared with the critical instability angle. The specific method steps are as follows: S1.2.1. Define wind load direction vector for: ; in, For wind power Directional component; For wind power Directional component; For wind power Directional component; S1.2.2. Calculation of surface using stereographic projection Normal vector With face Normal vector The angle between : ; in, For face Normal vector Model; For face Normal vector Model; S1.2.3. Setting the critical instability angle , compare the geometric instability angle with the critical instability angle: like , then face With face There is a risk of instability; like , then face With face Relatively stable between.

[0008] As a further improvement of the technical solution, in S2, sensors are arranged in the unstable area to collect and normalize the dynamic response data under the action of wind load to construct the time series data of the unstable area. The specific method steps are as follows: S2.1. Set the number of unstable regions to be , The unstable regions are: , ; S2.2. In the unstable area Internal sensor , used to measure the dynamic response data under wind load, including stress, acceleration and displacement; in, is a stress sensor; is an acceleration sensor; is a displacement sensor; S2.3, set the sampling interval to The total sampling time is , total number of sampling times , using sensors The dynamic response data is collected and normalized.

[0009] As a further improvement of the technical solution, in S2.3, the sampling interval is set to The total sampling time is , total number of sampling times , using sensors The dynamic response data is collected and normalized. The specific steps are as follows: S2.3.1 Stress sensor At the moment The stress data is , then the total sampling time The collected stress time series is ; Accelerometer At the moment The acceleration data is , then the total sampling time The collected acceleration time series is ; Displacement Sensors At the moment The displacement data is , then the total sampling time The collected displacement time series is ; in, is the sampling time; S2.3.2, use the minimum-maximum normalization algorithm to normalize the stress data, acceleration data and displacement data to interval, and obtain normalized stress data , normalized acceleration data and normalized displacement data ; S2.3.2. Normalized unstable region The dynamic response data of the combination is the unstable area Combined time series data : ; in, The unstable region Combined time series data.

[0010] As a further improvement of the technical solution, in S3, a reference entropy value and an instability entropy value threshold are set, and the sample entropy of the combined time series data of the instability region is calculated to determine the instability region. The specific method steps are as follows: S3.1. Setting the baseline entropy value and the instability entropy threshold ; S3.2. Calculation of unstable area Combined time series data The sample entropy of ; S3.3. Sample entropy With the baseline entropy value and the instability entropy threshold , using risk assessment criteria to assess the risk of unstable areas.

[0011] As a further improvement of this technical solution, the S3.2, calculation of the unstable region Combined time series data The sample entropy of , the specific method is as follows: S3.2.1. Selecting the embedding dimension and similarity tolerance ; S3.2.2. According to the embedding dimension , from the combined time series data The length extracted is The template vector is: The template vector for the stress data series is: ; The template vector of the acceleration data sequence is: ; The template vector of the displacement data sequence is: ; in, It's time. ; For the moment Template vector of the stress data sequence; For the moment Template vector of the acceleration data sequence; For the moment The template vector of the displacement data sequence; S3.2.3. Calculate all lengths The template vector satisfies the similarity tolerance Similar template comparison : From combined time series data The length extracted is The template vector of For each pair of length Template vector and ,calculate and distance : ; like , then the template vector and resemblance; Calculate the number of all similar template vector pairs as ; Calculate the similarity tolerance Similar template comparison : ; The reconstructed length is The template vector satisfies the similarity tolerance Similar template comparison This calculation method is the same as that of same; S3.2.3. Comparison based on similar templates and , calculate the sample entropy : ; in, is the sample entropy.

[0012] As a further improvement of this technical solution, in S3.3, the sample entropy With the baseline entropy value and the instability entropy threshold , the risk of unstable area is assessed using risk assessment criteria, the specific method is as follows: The risk assessment criteria are: Normal state: If , then the unstable region The response complexity is low and the structure is in a safe and stable state; Warning status: If , then the structural area is in a warning state, the response complexity is high, and the monitoring frequency is increased; Instability: If , then the structural area may enter an unstable state and an early warning should be triggered immediately; Based on the above risk assessment criteria, a risk assessment report is generated and the unstable areas are marked.

[0013] As a further improvement of the technical solution, in S4, a combined risk assessment formula is used to calculate the instability risk index of the geometric instability angle of the unstable area and the sample entropy fusion, and the specific method is as follows: The combined risk assessment formula is defined as: ; in, is the instability risk index; The instability risk index is calculated Security Risk Index Compare: When the risk is low; , the risk is higher.

[0014] On the other hand, the present invention provides a steel structure wind resistance performance detection system, including a memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the processor executes the computer program to implement any one of the steel structure wind resistance performance detection methods described above.

[0015] Compared with the prior art, the present invention has the following beneficial effects: 1. In the steel structure wind resistance performance detection method and system, based on the calculation of the geometric instability angle between the steel structure surfaces by the stereographic projection method and combined with the quantitative analysis of the complexity of regional dynamic response by the entropy theory, the areas with higher instability risk can be accurately identified under different wind load conditions, and the instability of the areas with higher instability risk can be evaluated.

[0016] 2. In the steel structure wind resistance performance detection method and system, the geometric instability angle is combined with the regional dynamic response sample entropy to calculate the comprehensive instability risk index, thereby realizing multi-dimensional quantification and early warning of the structural instability risk. BRIEF DESCRIPTION OF THE DRAWINGS

[0017] Figure 1 The figure is a flow chart of the overall method of the present invention. DETAILED DESCRIPTION

[0018] The following will be combined with the drawings in the embodiments of the present invention to clearly and completely describe the technical solutions in the embodiments of the present invention. Obviously, the described embodiments are only part of the embodiments of the present invention, not all of the embodiments. Based on the embodiments of the present invention, all other embodiments obtained by ordinary technicians in this field without creative work are within the scope of protection of the present invention.

[0019] Example 1: Please refer to Figure 1 As shown, this embodiment provides a method for detecting wind resistance of steel structures, comprising the following steps: S1. Construct a geometric model of the steel structure, calculate the geometric instability angle between the steel structure surfaces by stereographic projection method, and identify the instability area under different wind load conditions; The stereographic projection method is used to analyze the geometric relationship between steel structure surfaces and determine the unstable area; In this embodiment S1, a geometric model of the steel structure is constructed, and the geometric instability angles between the steel structure surfaces are calculated by the stereographic projection method to identify the instability areas under different wind load conditions. The specific method steps are as follows: S1.1. Define each plane in the steel structure as , the node position in the steel structure is , define the distance between adjacent planes as ; ; in, For face The normal vector of For face exist The weight of the axis; For face exist The weight of the axis; For face exist The weight of the axis; is the number of the node in the steel structure; S1.2. Based on the geometric model of the steel structure, use the stereographic projection method to calculate the geometric instability angle between the steel structure surfaces, set the critical instability angle, and compare the geometric instability angle with the critical instability angle; S1.3. The area where the geometric instability angle is less than the critical instability angle is considered to be an unstable area.

[0020] In this embodiment S1.2, based on the geometric model of the steel structure, the geometric instability angle between the steel structure surfaces is calculated using the stereographic projection method, the critical instability angle is set, and the geometric instability angle is compared with the critical instability angle. The specific method steps are as follows: S1.2.1. Define wind load direction vector for: ; in, For wind power Directional component; For wind power Directional component; For wind power Directional component; S1.2.2. Calculation of surface using stereographic projection Normal vector With face Normal vector The angle between : ; in, For face Normal vector Model; For face Normal vector Model; S1.2.3. Setting the critical instability angle , compare the geometric instability angle with the critical instability angle: like , then face With face There is a risk of instability; like , then face With face Relatively stable between.

[0021] S2. Arrange sensors in the unstable area to collect and normalize the dynamic response data under wind load to construct the combined time series data of the unstable area; In this embodiment S2, sensors are arranged in the unstable area to collect and normalize the dynamic response data under the action of wind load to construct the time series data of the unstable area. The specific method steps are as follows: S2.1. Set the number of unstable regions to be , The unstable regions are: , ; S2.2. In the unstable area Internal sensor , used to measure the dynamic response data under wind load, including stress, acceleration and displacement; in, is a stress sensor; is an acceleration sensor; is a displacement sensor; S2.3, set the sampling interval to The total sampling time is , total number of sampling times , using sensors The dynamic response data is collected and normalized.

[0022] In this embodiment S2.3, the sampling interval is set to The total sampling time is , total number of sampling times , using sensors The dynamic response data is collected and normalized. The specific steps are as follows: S2.3.1 Stress sensor At the moment The stress data is , then the total sampling time The collected stress time series is ; Accelerometer At the moment The acceleration data is , then the total sampling time The collected acceleration time series is ; Displacement Sensors At the moment The displacement data is , then the total sampling time The collected displacement time series is ; in, is the sampling time; S2.3.2, use the minimum-maximum normalization algorithm to normalize the stress data, acceleration data and displacement data to interval, and obtain normalized stress data , normalized acceleration data and normalized displacement data ; In this embodiment, the minimum-maximum normalization algorithm is used to normalize the stress data, acceleration data and displacement data to interval, and obtain normalized stress data , normalized acceleration data and normalized displacement data , the normalization formula is as follows: ; ; ; in, and are the minimum and maximum values ​​of stress data respectively; and They are the minimum and maximum values ​​of the acceleration data respectively; and are the minimum and maximum values ​​of the displacement data respectively; S2.3.2. Normalized unstable region The dynamic response data of the combination is the unstable area Combined time series data : ; in, The unstable region Combined time series data.

[0023] S3, setting a baseline entropy value and an instability entropy value threshold, calculating the sample entropy of the combined time series data of the instability region to determine the instability region; In this embodiment S3, a reference entropy value and an instability entropy value threshold are set, and the sample entropy of the combined time series data of the instability region is calculated to determine the instability region. The specific method steps are as follows: S3.1. Setting the baseline entropy value and the instability entropy threshold ; In this embodiment, the baseline entropy value represents the structural area under normal and low wind loads. The typical sample entropy value of the wind turbine is the benchmark entropy value, which can be calculated by the sample entropy measured by experimental data and historical data under low wind load conditions as a reference value before instability. The instability entropy threshold indicates the critical value of sample entropy at which the structure may enter an unstable state; when the sample entropy exceeds the threshold, the region The dynamic response of the structure is considered to be in a high-risk instability state. The threshold of the instability entropy value is usually set through experimental testing or simulation, reflecting the upper limit of the complexity of the structure's response under extreme wind loads. S3.2. Calculation of unstable area Combined time series data The sample entropy of ; S3.3. Sample entropy With the baseline entropy value and the instability entropy threshold , using risk assessment criteria to assess the risk of unstable areas.

[0024] In this embodiment S3.2, calculate the unstable region Combined time series data The sample entropy of , the specific method is as follows: S3.2.1. Selecting the embedding dimension and similarity tolerance ; In this embodiment, the embedding dimension The dimension along which to segment the time series; Similarity tolerance Set it as a certain proportion of the standard deviation of the time series, and choose 0.25 times the standard deviation; S3.2.2. According to the embedding dimension , from the combined time series data The length extracted is The template vector is: The template vector for the stress data series is: ; The template vector of the acceleration data sequence is: ; The template vector of the displacement data sequence is: ; in, It's time. ; For the moment Template vector of the stress data sequence; For the moment Template vector of the acceleration data sequence; For the moment The template vector of the displacement data sequence; S3.2.3. Calculate all lengths The template vector satisfies the similarity tolerance Similar template comparison : From combined time series data The length extracted is The template vector of For each pair of length Template vector and ,calculate and distance : ; like , then the template vector and resemblance; Calculate the number of all similar template vector pairs as ; Calculate the similarity tolerance Similar template comparison : ; The reconstructed length is The template vector satisfies the similarity tolerance Similar template comparison This calculation method is the same as that of same; S3.2.3. Comparison based on similar templates and , calculate the sample entropy : ; in, is the sample entropy.

[0025] In this embodiment, the sample entropy Indicates the unstable area The dynamic response complexity, sample entropy The larger it is, the higher the complexity and instability of the structural area.

[0026] In this embodiment S3.3, the sample entropy With the baseline entropy value and the instability entropy threshold , the risk of unstable area is assessed using risk assessment criteria, the specific method is as follows: The risk assessment criteria are: Normal state: If , then the unstable region The response complexity is low and the structure is in a safe and stable state; Warning status: If , then the structural area is in a warning state, the response complexity is high, and the monitoring frequency is increased; Instability: If , then the structural area may enter an unstable state and an early warning should be triggered immediately; Based on the above risk assessment criteria, a risk assessment report is generated and the unstable areas are marked.

[0027] S4, using the combined risk assessment formula to calculate the instability risk index of the instability area by integrating the geometric instability angle and sample entropy; In this embodiment S4, a combined risk assessment formula is used to calculate the instability risk index of the geometric instability angle of the instability region and the sample entropy fusion, and the specific method is as follows: The combined risk assessment formula is defined as: ; in, is the instability risk index; The instability risk index is calculated Security Risk Index Compare: When the risk is low; , the risk is higher.

[0028] Embodiment 2: This embodiment provides a steel structure wind resistance performance detection system, including a memory, a processor, and a computer program stored in the memory and executable on the processor, and the processor executes the computer program to implement any one of the steel structure wind resistance performance detection methods described above.

[0029] The above shows and describes the basic principles, main features and advantages of the present invention. Those skilled in the art should understand that the present invention is not limited by the above embodiments, and the above embodiments and descriptions are only preferred examples of the present invention, and are not intended to limit the present invention. Without departing from the spirit and scope of the present invention, the present invention may have various changes and improvements, and these changes and improvements all fall within the scope of the present invention to be protected.

Claims

1. Testing methods for wind resistance of steel structures, It is characterized in that The following steps are involved: S1. Construct a geometric model of the steel structure, calculate the geometric instability angle between the steel structure surfaces by stereographic projection method, and identify the instability area under different wind load conditions; S2. Arrange sensors in the unstable area to collect and normalize the dynamic response data under wind load to construct the combined time series data of the unstable area; S3, setting a baseline entropy value and an instability entropy value threshold, calculating the sample entropy of the combined time series data of the instability region to determine the instability region; S4. Use the combined risk assessment formula to calculate the instability risk index of the unstable area by integrating the geometric instability angle and sample entropy, and evaluate the risk level of the unstable area based on the instability risk index.

2. The method for detecting wind resistance of steel structures according to claim 1, Features: The stereographic projection method is used to analyze the geometric relationship between steel structure surfaces and determine the unstable area; In S1, a geometric model of the steel structure is constructed, and the geometric instability angles between the steel structure surfaces are calculated by the stereographic projection method to identify the instability areas under different wind load conditions. The specific method steps are as follows: S1.

1. Define each plane in the steel structure as , the node position in the steel structure is , define the distance between adjacent planes as ; ; in, For face The normal vector of For face exist The weight of the axis; For face exist The weight of the axis; For face exist The weight of the axis; is the number of the node in the steel structure; S1.

2. Based on the geometric model of the steel structure, use the stereographic projection method to calculate the geometric instability angle between the steel structure surfaces, set the critical instability angle, and compare the geometric instability angle with the critical instability angle; S1.

3. The area where the geometric instability angle is less than the critical instability angle is considered to be an unstable area.

3. The method for detecting wind resistance of steel structures according to claim 2, Features: In S1.2, based on the geometric model of the steel structure, the geometric instability angle between the steel structure surfaces is calculated using the stereographic projection method, the critical instability angle is set, and the geometric instability angle is compared with the critical instability angle. The specific method steps are as follows: S1.2.

1. Define wind load direction vector for: ； in, For wind power Directional component; For wind power Directional component; For wind power Directional component; S1.2.

2. Calculation of surface using stereographic projection Normal vector With face Normal vector The angle between : ； in, For face Normal vector Model; For face Normal vector Model; S1.2.

3. Setting the critical instability angle , compare the geometric instability angle with the critical instability angle: like , then face With face There is a risk of instability; like , then face With face Relatively stable between.

4. The method for detecting wind resistance of steel structures according to claim 1, Features: In S2, sensors are arranged in the unstable area to collect and normalize dynamic response data under wind load to construct time series data of the unstable area. The specific method steps are as follows: S2.

1. Set the number of unstable regions to be , The unstable regions are: , ; S2.

2. In the unstable area Internal sensor , used to measure the dynamic response data under wind load, including stress, acceleration and displacement; in, is a stress sensor; is an acceleration sensor; is a displacement sensor; S2.3, set the sampling interval to The total sampling time is , total number of sampling times , using sensors The dynamic response data is collected and normalized.

5. The method for detecting wind resistance of steel structure according to claim 4, Features: In S2.3, the sampling interval is set to The total sampling time is , total number of sampling times , using sensors The dynamic response data is collected and normalized. The specific steps are as follows: S2.3.1 Stress sensor At the moment The stress data is , then the total sampling time The collected stress time series is ; Accelerometer At the moment The acceleration data is , then the total sampling time The collected acceleration time series is ; Displacement Sensors At the moment The displacement data is , then the total sampling time The collected displacement time series is ; in, is the sampling time; S2.3.2, use the minimum-maximum normalization algorithm to normalize the stress data, acceleration data and displacement data to interval, and obtain normalized stress data , normalized acceleration data and normalized displacement data ; S2.3.

2. Normalized unstable region The dynamic response data of the combination is the unstable area Combined time series data : ； in, The unstable region Combined time series data.

6. The method for detecting wind resistance of steel structures according to claim 1, Features: In S3, a reference entropy value and an instability entropy value threshold are set, and the sample entropy of the combined time series data of the instability region is calculated to determine the instability region. The specific method steps are as follows: S3.

1. Setting the baseline entropy value and the instability entropy threshold ; S3.

2. Calculation of unstable area Combined time series data The sample entropy of ; S3.

3. Sample entropy With the baseline entropy value and the instability entropy threshold , using risk assessment criteria to assess the risk of unstable areas.

7. The method for detecting wind resistance of steel structure according to claim 6, Features: S3.2, calculation of unstable area Combined time series data The sample entropy of , the specific method is as follows: S3.2.

1. Selecting the embedding dimension and similarity tolerance ; S3.2.

2. According to the embedding dimension , from the combined time series data The length extracted is The template vector is: The template vector for the stress data series is: ； The template vector of the acceleration data sequence is: ； The template vector of the displacement data sequence is: ； in, It's time. ; For the moment Template vector of the stress data sequence; For the moment Template vector of the acceleration data sequence; For the moment The template vector of the displacement data sequence; S3.2.

3. Calculate all lengths The template vector satisfies the similarity tolerance Similar template comparison : From combined time series data The length extracted is The template vector of For each pair of length Template vector and ,calculate and distance : ； like , then the template vector and resemblance; Calculate the number of all similar template vector pairs as ; Calculate the similarity tolerance Similar template comparison : ； The reconstructed length is The template vector satisfies the similarity tolerance Similar template comparison This calculation method is the same as that of same; S3.2.

3. Comparison based on similar templates and , calculate the sample entropy : ； in, is the sample entropy.

8. The method for detecting wind resistance of steel structures according to claim 7, Features: In S3.3, the sample entropy With the baseline entropy value and the instability entropy threshold , using the risk assessment criteria to assess the risk of unstable areas, the specific method is as follows: The risk assessment criteria are: Normal state: If , then the unstable region The response complexity is low and the structure is in a safe and stable state; Warning status: If , then the structural area is in a warning state, the response complexity is high, and the monitoring frequency is increased; Instability: If , then the structural area may enter an unstable state and an early warning should be triggered immediately; Based on the above risk assessment criteria, a risk assessment report is generated and the unstable areas are marked.

9. The method for detecting wind resistance of steel structures according to claim 1, Features: In S4, a combined risk assessment formula is used to calculate the instability risk index of the geometric instability angle of the instability region and the sample entropy fusion, and the specific method is as follows: The combined risk assessment formula is defined as: ； in, is the instability risk index; The instability risk index is calculated Security Risk Index Compare: When the risk is low; , the risk is higher.

10. A steel structure wind resistance performance detection system, comprising a memory, a processor, and a computer program stored in the memory and executable on the processor, Features: The processor executes a computer program to implement the method for detecting wind resistance of a steel structure as described in any one of claims 1 to 9.

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