Method and system for determining structure response danger curve based on simulated earthquake

Abstract

The invention relates to the technical field of earthquake risk analysis, in particular to a structural response danger curve determination method based on simulated earthquake, which comprises the following steps of: 1, dividing an earthquake potential source based on an earthquake activity model of a target site, and generating a random earthquake event directory in target observation time; 2, carrying out random seismic oscillation simulation on the seismic events in the random seismic directory, and constructing a simulated seismic oscillation database; 3, comparing the simulated seismic oscillation data with the seismic oscillation intensity index of the seismic oscillation experience prediction equation, and screening out a qualified time history subset meeting the physical consistency requirement; 4, inputting the qualified time history subset into a finite element model of a target structure to obtain a sample set of structural engineering demand parameters; 5, generating a structure response probability risk curve based on the engineering demand parameter sample set; and key scenes such as near-field large earthquakes can be included, the regional adaptability and authenticity are better, and the seismic oscillation input is more comprehensive.

Description

TECHNICAL FIELD

[0001] The present application relates to the technical field of seismic risk analysis, in particular to a structure response risk curve determination method and system based on simulated earthquakes. BACKGROUND

[0002] With the deepening of urbanization process in China, engineering structure forms tend to diversification, and the demand for "precise assessment of future seismic risk" in building seismic design is increasingly urgent. Traditional structure response probability risk analysis method mainly relies on the selection of ground motion records from strong motion record database according to empirical attenuation relationship to replace the actual ground motion of target site for analysis. If the selected records fail to cover the potential source diversity and the randomness of ground motion in the target area, it is easy to cause systematic deviation in the evaluation results, and it is also difficult to provide a unified evaluation standard for the results of different analysis methods.

[0003] At present, the method for calculating seismic risk analysis is to output the results of seismic risk analysis under its own improved method, which cannot provide calibration curves for other methods. For example, the "seismic risk analysis method, device, equipment and storage medium" with the publication number CN114693066A simplifies the annual occurrence rate calculation of vector type ground motion parameters through "equivalent earthquake" (magnitude-distance combination), but the equivalent earthquake cannot reflect the randomness of ground motion time history.

[0004] The existing technology focuses on the output of its own results in the process of seismic risk analysis, and the data generation capability is insufficient, which depends on external database or simplified model, has poor regional adaptability, and the final output result does not have the function of calibration. There is an urgent need for a structure response probability risk demand determination method that can cover the potential source diversity and does not need to rely on external database. SUMMARY

[0005] The purpose of the present application is to provide a structure response risk curve determination method and system based on simulated earthquakes, which aims to solve the problems in the prior art.

[0006] The present application provides a structure response risk curve determination method based on simulated earthquakes, which comprises the following steps:

[0007] Step 1: dividing the seismic potential source based on the seismic activity model of the target site, and generating a random earthquake event catalog within the target observation time;

[0008] Step 2: performing random ground motion simulation on the earthquake events in the random earthquake catalog to construct a simulated ground motion database;

[0009] Step 3: comparing the simulated ground motion data with the ground motion intensity index of the empirical prediction equation, and screening out a qualified time history subset meeting the physical consistency requirement;

[0010] Step 4: input the qualified time-history subset into the finite element model of the target structure, perform dynamic time-history analysis, and obtain a sample set of engineering demand parameters of the structure;

[0011] Step 5: based on the sample set of engineering demand parameters obtained above, calculate the annual average exceedance probability of the engineering demand parameters, generate a structure response probability hazard curve, and the calculation formula of the annual average exceedance probability is as follows:

[0012]

[0013] In the formula: is the annual exceedance probability of the engineering demand parameter, is the number of seismic events, is the probability of the seismic event, is the probability of the seismic event, is the annual occurrence rate of the random simulated seismic event.

[0014] Preferably, the specific steps of step one are: based on the seismic activity model of the target region, determining the potential source type, dividing the potential source boundary combined with the spatial data of the target region, marking the geographic coordinate information of each potential source, determining the relative position of the target site and each potential source, and obtaining the activity parameters and magnitude frequency parameters of each potential source; and then generating a random seismic catalog within the observation time through Monte Carlo random sampling.

[0015] Preferably, the potential source type is a fault potential source or a surface source; the activity parameters include the minimum magnitude, the maximum magnitude, and the magnitude frequency decay relationship; and the generated random seismic catalog contains the magnitude, the source distance, the longitude and latitude, the fault size, and the frequency of occurrence.

[0016] Preferably, the specific operation of step two is: using a wavelet transform-based random simulation method to simulate the seismic events in the random seismic catalog, generating artificial ground motions with time-frequency non-stationary characteristics, and constructing a simulated ground motion database.

[0017] Preferably, the wavelet transform-based random simulation method has the following specific operation stages:

[0018] S1, wavelet packet decomposition and time-frequency feature extraction: using wavelet packet transform to decompose the time-history data of the target site into wavelet packet subsets in time and frequency, and the calculation formula is as follows:

[0019]

[0020] In the formula: is the i-th group of wavelet packets of the j-th level of decomposition in frequency, k is the time translation parameter, is the wavelet packet function, Time sequence; time-frequency characteristic parameters are extracted from the wavelet packet coefficients obtained by decomposition;

[0021] S2, regression model analysis: based on the time-frequency characteristic parameters extracted in the first stage, a regression model of time-frequency characteristic parameters and seismological variables is used, and the formula is as follows:

[0022] In the formula: represents the natural logarithm of the time-frequency characteristic parameters extracted by the wavelet packet, is the matrix magnitude, is the source distance, is the fault distance, is the average shear wave velocity of the site 30m deep; is the intra-event residual, is the inter-event residual, which is used to quantify the uncertainty of the parameters; h is a near-field correction parameter, which is used to avoid the occurrence of maximum value in the near field so as to improve the accuracy of the source distance calculation; 、 is the linear term coefficient;

[0023] S3, time history reconstruction: based on the parameter constraint of the regression model, the wavelet packet inverse transform is used to reconstruct the artificial ground motion time history, and the calculation formula is as follows:

[0024]

[0025] In the formula: is the wavelet packet coefficient satisfying the constraint of the regression model; for each earthquake event in the random earthquake catalog, the corresponding ground motion time history is simulated to calculate the corresponding ground motion intensity index IM, and the simulated ground motion database is obtained by summarizing.

[0026] Preferably, the value of the simulated ground motion data in step three is compared with the corresponding ground motion intensity index obtained from the empirical attenuation relationship, and the value falls within ±1 times the standard deviation of the empirical attenuation relationship, verifying the matching degree of the simulated data and the empirical attenuation relationship value; at the same time, the simulated data value falling within ±3 times the standard deviation of the empirical attenuation relationship is screened, and a qualified ground motion time history subset is obtained.

[0027] A system based on the above structure response hazard curve determination method, the system comprises the following modules:

[0028] Potential source and random catalog generation module: used for dividing potential sources based on a seismic activity model, determining activity parameters, and generating a random earthquake catalog by Monte Carlo random sampling;

[0029] Seismic record random simulation and verification module: for obtaining the target site seismic record, obtaining the seismic time history data of each seismic event based on the random simulation method, constructing the simulated seismic database, and screening the qualified time history subset by comparing with the empirical attenuation relationship value;

[0030] Structure response calculation module: for inputting the structure finite element model of the qualified time history subset, and calculating to obtain the engineering demand parameter sample set;

[0031] Structure response hazard calculation module: calculating the annual average exceeding probability of the engineering demand parameter, and generating the structure response probability hazard curve with calibration function.

[0032] The beneficial effects of the present application are: the random simulation method is used to construct the simulated seismic database, which can cover the diversity of potential sources in the target area, reflect the real potential source distribution of the target area, overcome the defects of insufficient representativeness of the selected wave from the external database compared with the method of selecting similar historical records from the strong earthquake record database, especially can contain the key scene of near-field large earthquake, and the regional adaptability and authenticity are better, and the seismic input is more comprehensive;

[0033] The present application adopts random simulation seismic waves with time-frequency non-stationary characteristics, and combines strict empirical attenuation relationship verification and probability hazard analysis framework, so that the controllability and accuracy of the whole process from seismic input to structure response are ensured, and the reliability of the probability seismic demand evaluation is significantly improved.

[0034] The structure response probability hazard curve finally output by the present application considers all possible seismic events of the site in the observed events, and contains information other than IM through the simulated seismic time history; therefore, it can be used to calibrate and evaluate the effectiveness and accuracy of other seismic hazard analysis or wave selection method. BRIEF DESCRIPTION OF DRAWINGS

[0035] Figure 1 It is the flowchart of the method of the present application.

[0036] Figure 2 Fig. (a) is the potential source magnitude frequency relationship diagram in the embodiment; (b) is a potential source and site distribution schematic diagram.

[0037] Figure 3 It is the sampling schematic diagram of the magnitude of the random seismic catalog in the embodiment.

[0038] Figure 4 It is a typical seismic acceleration time history diagram obtained by random simulation in the embodiment.

[0039] Figure 5The following are comparison charts of simulated data and empirical attenuation relationship in the embodiments: (a) Peak acceleration PGA comparison chart after screening; (b) Natural period acceleration response spectrum SA (T=0.9s) comparison chart after screening; (c) Natural period acceleration response spectrum SA (T=1.2s) comparison chart after screening; (d) PGA comparison chart before screening; (e) SA (T=0.9s) comparison chart before screening; (f) SA (T=1.2s) comparison chart before screening.

[0040] Figure 6 (a) is a schematic diagram of the target structure in the embodiment; (b) is the structural response probability hazard curve of the maximum inter-story drift angle. Detailed Implementation

[0041] 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 some embodiments of the present invention, and not all embodiments. Based on the embodiments of the present invention, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of the present invention.

[0042] like Figure 1 The method for determining structural response hazard curves based on simulated earthquakes, as shown, specifically includes the following steps:

[0043] Step 1: Based on the seismic activity model of the target site, identify potential seismic sources and generate a catalog of random seismic events within the target observation period;

[0044] Step 2: Perform random ground motion simulations on earthquake events in the random earthquake catalog to construct a simulated ground motion database;

[0045] Step 3: Compare the simulated ground motion data with the ground motion intensity index of the empirical ground motion prediction equation, and select a qualified time history subset that meets the physical consistency requirements.

[0046] Step 4: Input the qualified seismic motion time history subsets one by one into the finite element model of the target structure, perform elastoplastic dynamic time history analysis, and obtain a sample set of structural engineering requirement parameters;

[0047] Step 5: Based on the obtained sample set of engineering demand parameters, calculate the annual average exceedance probability of the engineering demand parameters and generate the structural response probability hazard curve; plot the structural response probability hazard curve with the engineering demand parameters as the abscissa and the annual average exceedance probability as the ordinate. This curve is the structural response probability hazard curve; the formula for calculating the annual average exceedance probability is as follows:

[0048]

[0049] wherein: is the annual exceedance probability of the engineering demand parameter, is the number of seismic events, is the probability of a certain seismic event, is the probability of a certain seismic event, is the annual occurrence rate of the random simulated seismic event.

[0050] In one embodiment, the specific step of step one is: based on the seismic activity model of the target region, the potential source type (such as strike-slip fault, normal fault, etc.) is determined, the potential source boundary is divided in combination with the spatial data of the target region, the geographic coordinate information of each potential source is marked, the relative position of the target site and each potential source is determined, and the activity parameter and magnitude frequency parameter of each potential source are obtained; and then a random seismic catalog within the observation time is generated by Monte Carlo random sampling.

[0051] In one embodiment, the potential source type is a fault potential source or a surface source; the activity parameter includes the minimum magnitude, the maximum magnitude, and the magnitude frequency decay relationship; and the generated random seismic catalog contains the magnitude, the source distance, the longitude and latitude, the fault size, and the frequency of occurrence.

[0052] In one embodiment, the specific operation of step two is: a random simulation method based on wavelet transform is used to simulate the seismic events in the random seismic catalog, artificial ground motions with time-frequency non-stationary characteristics are generated, and a simulated ground motion database is constructed. The random ground motion simulation in accordance with the physical consistency is realized through the three stages of "wavelet packet decomposition and time-frequency feature extraction-regression model analysis-time history reconstruction".

[0053] The specific operation stages of the wavelet transform random simulation method are as follows:

[0054] S1, wavelet packet decomposition and time-frequency feature extraction: the wavelet packet transform is used to decompose the time history data of the target site into wavelet packet subsets in time and frequency, and the calculation formula is as follows:

[0055]

[0056] wherein: is the frequency of the jth level of decomposition, i is the wavelet packet group, k is the time translation parameter, and is the wavelet packet function, is the time sequence; the time-frequency feature parameters are extracted from the wavelet packet coefficients obtained by decomposition;

[0057] S2, regression model analysis: based on the time-frequency feature parameters extracted in the first stage, in order to ensure the physical rationality of the simulated ground motion, the regression model of the time-frequency feature parameters and the seismological variables is used, and the formula is as follows:

[0058] wherein: The natural logarithm represents the time-frequency feature parameters extracted by the wavelet packet. Moment magnitude, The distance from the epicenter, This is the fault distance. The average shear wave velocity at a depth of 30m in the field; For the residual within the event, is the inter-event residual, used to quantify the uncertainty of parameters; h is the near-field correction parameter, used to avoid maxima in the near field, thereby improving the accuracy of source distance calculation; , The coefficients of the linear terms;

[0059] S3. Time History Reconstruction: Based on the parameter constraints of the regression model, the artificial ground motion time history is reconstructed using wavelet packet inverse transform. The calculation formula is as follows:

[0060]

[0061] In the formula: To satisfy the wavelet packet coefficients of the regression model constraints, the corresponding ground motion time history is generated for each earthquake event in the random earthquake catalog, the corresponding ground motion intensity index IM is calculated, and the simulated ground motion database is obtained.

[0062] In one implementation, in step three, the median of the simulated ground motion data is compared with the ground motion intensity index corresponding to the empirical attenuation relationship equation. If the median falls within ±1 standard deviation of the empirical attenuation relationship value, the matching degree between the simulated data and the empirical attenuation relationship value is verified. Simultaneously, simulated data values ​​falling within ±3 standard deviations of the empirical attenuation relationship value are selected to obtain a qualified subset of ground motion time histories, ensuring that the coverage of the randomly simulated ground motion matches the potential seismic scenario of the target site. For example, the empirical attenuation relationship is selected as a reference for data comparison. The seismic parameters of the random simulated earthquake time rupi are input, including its magnitude, focal distance, fault distance, and average shear wave velocity at a depth of 30m. The mean and standard deviation of IM under the empirical attenuation relationship are calculated; the IM value of the corresponding earthquake event in the simulated ground motion database is calculated; ground motion time histories data whose median simulated IM falls within ±1 standard deviation of the median of the empirical attenuation relationship and whose IM value is within ±3 standard deviations of the median of the empirical attenuation relationship are retained.

[0063] A system for determining structural response hazard curves includes the following modules:

[0064] Potential source and random catalog generation module: used to classify potential sources and determine activity parameters based on seismic activity models, and generate a random earthquake catalog through Monte Carlo random sampling;

[0065] Seismic record random simulation and verification module: for obtaining the target site seismic record, based on random simulation method to obtain the seismic time history data of each earthquake event, to build simulation seismic database, through the comparison with the empirical attenuation relationship value screening qualified time course subset;

[0066] Structure response calculation module: for qualified time course subset input structure finite element model, calculation of engineering demand parameter sample set;

[0067] Structure response hazard calculation module: calculate the annual average exceedance probability of engineering demand parameter, generate the structure response probability hazard curve with calibration function.

[0068] The following is described in conjunction with specific embodiments:

[0069] Example 1,

[0070] Step S1: potential source division and random earthquake catalog generation. According to the target site, the potential source division and seismic parameter determination can be carried out, based on the magnitude frequency relationship, through random sampling to generate the earthquake catalog;

[0071] Step S2: random seismic simulation. Based on the random earthquake catalog, the random seismic simulation is carried out using the wavelet transform random simulation method. The simulation database containing seismic time history record and each seismic intensity index IM value is established, and the seismic time history is synthesized based on the time-frequency distribution of wavelet packet coefficient through inverse transformation, as follows:

[0072]

[0073] In the formula: is the time sequence, is the wavelet packet coefficient, is the wavelet packet function, which has time-frequency locality;

[0074] Step S3: comparison and verification of simulation data and empirical attenuation relationship value. According to the random earthquake catalog, the seismic parameters are input into the selected empirical attenuation relationship, and the selected BA2008 is used as the empirical attenuation relationship. The seismic intensity index IM value obtained from the simulation database, including the peak acceleration PGA and the median value of the acceleration intensity index SA, is compared with the corresponding IM value obtained from the attenuation relationship. The seismic time history data is retained when the simulation IM median value falls within the ±1 standard deviation range of the empirical attenuation relationship median value, and the IM value is within the ±3 standard deviation range of the empirical attenuation relationship median value.

[0075] Step S4: Structure response and probabilistic seismic hazard analysis. The retained time histories of ground motion are input into the finite element model of the structure, and the engineering demand parameter data are calculated. The sample probability and the annual average earthquake occurrence number are combined to calculate the annual average exceeding probability of each engineering demand parameter, and the probabilistic seismic hazard curve of the structure response is obtained.

[0076] Example 2: Application in a specific target site structure

[0077] Step S1: Potential source division and random earthquake catalog generation. The fault potential source type of the target site is strike-slip fault. The seismic activity parameters of the potential source are as follows: the value of the slope b of the used cut-off magnitude frequency relationship is 1, the value of the intercept a, i.e. the earthquake occurrence rate, is 5.301, the value of the minimum magnitude is 6, the value of the maximum magnitude is 8, the value of the characteristic lower limit magnitude is 7.4, the value of the characteristic magnitude is 6.4, and the magnitude frequency relationship is shown in Figure 2 (a); the potential source distance range is 15-200 km, the site category is type II, and the site condition V s30 =400 m / s, the total sample size is 5000, the observation time is 10,000 years, and then a random earthquake catalog is generated. The relative position of the potential source and the site is shown in Figure 2 (b), and the magnitude sampling results in the random earthquake catalog are shown in Figure 3 ;

[0078] Step S2: Random earthquake simulation. Based on wavelet transform, 5000 simulated ground motion records in the target area are generated under the seismic parameter range. The simulated data are stored as a simulated ground motion database. The selected 10 simulated time history graphs of magnitudes 6, 7 and 8 are shown in Figure 4 .

[0079] Step S3: Comparison and verification of simulated data and empirical attenuation relationship. The potential source parameters are input into the selected BA2008 attenuation relationship to obtain the predicted values of the attenuation relationship of each IM value. The peak ground acceleration PGA, the acceleration response spectrum value SA(0.9s) at a period of 0.9s, and the acceleration response spectrum value SA(1.2s) at a period of 1.2s obtained from the simulated data are compared with the corresponding ground motion intensity indicators IM obtained from the empirical attenuation relationship (BA2008) to verify whether the data are qualified. The comparison graphs of PGA, SA(0.9s) and SA(1.2s) before screening are shown in Figure 5 (a)(b)(c), and the comparison graphs of PGA, SA(0.9s) and SA(1.2s) before screening are shown in Figure 5 (e)(f)(g).

[0080] Step S4; structure response and probability risk analysis calculation. The retained seismic record is input into the target structure to obtain maximum inter-story drift angle data, and the annual average exceeding probability of engineering demand parameter is calculated according to the probability seismic risk analysis, and the structure response probability risk curve with calibration function is output.

[0081] Wherein the target structure is a multi-degree-of-freedom story shear structure, the constitutive relation is a three-line stiffness degradation model, the first stiffness reduction coefficient is 0.4, and the second stiffness reduction coefficient is 0.1. The schematic diagram of the multi-degree-of-freedom story shear structure is shown in Figure 6 (a). The structure response probability risk curve of the maximum inter-story drift angle is shown in Figure 6 (b).

[0082] The above embodiments are not a limitation of the present application, unless otherwise explicitly specified and limited, the terms "arrangement", "installation", "connection", "linkage" should be understood broadly, for example, it can be fixed connection, or detachable connection, or integrally connected; it can be mechanical connection, or electrical connection; it can be directly connected, or indirectly connected through intermediate medium, or internal communication of two elements. For those skilled in the art, the specific meaning of the above terms in the present application can be understood according to the specific circumstances. The present application is also not limited to the above examples, the changes, modifications, additions or replacements made by those skilled in the art within the scope of the technical solutions of the present application also belong to the protection scope of the present application. In addition, the technical features involved in the different embodiments of the present application described above can be combined with each other as long as there is no conflict.

[0083] It is obvious for those skilled in the art that the present application is not limited to the details of the above exemplary embodiments, and the present application can be realized in other specific forms without departing from the spirit or essential characteristics of the present application. Therefore, the embodiments should be regarded as exemplary and non-limiting, the scope of the present application is defined by the appended claims rather than the above description, and therefore all changes falling within the meaning and scope of the equivalent elements of the claims are intended to be included in the present application. Any reference signs in the claims should not be regarded as limiting the involved claims.

Claims

1. A method for determining a structure response hazard curve based on simulated earthquakes, characterized by, Specifically comprising the following steps: Step 1: based on the seismic activity model of the target site, dividing the seismic potential source, generating a random earthquake event catalog within the target observation time; Step 2: random seismic simulation of the seismic events in the random earthquake catalog, constructing a simulated ground motion database; Step 3: comparing the simulated ground motion data with the ground motion intensity index of the empirical prediction equation, screening out the qualified time history subset that meets the physical consistency requirements; Step 4: inputting the qualified time history subset into the finite element model of the target structure, performing dynamic time history analysis to obtain a sample set of structural engineering demand parameters; Step 5: based on the above obtained engineering demand parameter sample set, calculating the annual average exceedance probability of the engineering demand parameter, generating the structural response probability hazard curve, and the formula for calculating the annual average exceedance probability is as follows: where: is the annual exceedance probability of the engineering demand parameter, is the number of seismic events, is the probability of exceeding the engineering demand parameter for a given seismic event, is the probability of exceeding the engineering demand parameter for a given seismic event, is the annual occurrence rate of the random simulated seismic event.

2. The method of claim 1, wherein, The specific steps of the step one are: based on the seismic activity model of the target region, determining the potential source type, dividing the potential source boundary combined with the spatial data of the target region, marking the geographic coordinate information of each potential source, determining the relative position of the target site and each potential source, and obtaining the activity parameters and magnitude frequency parameters of each potential source; then generating a random earthquake catalog within the observation time through Monte Carlo random sampling.

3. The method of claim 2, wherein, The potential source type is a fault potential source or a surface source; the activity parameters include the minimum magnitude, the maximum magnitude and the magnitude frequency decay relationship; the generated random earthquake catalog contains magnitude, source distance, longitude and latitude, fault size and frequency of occurrence.

4. The method of claim 1, wherein, The specific operation of the step two is: using a random simulation method based on wavelet transform to simulate the seismic events in the random earthquake catalog, generating artificial ground motion with time-frequency non-stationary characteristics, and constructing a simulated ground motion database.

5. The method of claim 4, wherein, The specific operation stages of the wavelet transform random simulation method are as follows: S1, wavelet packet decomposition and time-frequency feature extraction: using wavelet packet transform to decompose the target site time history data into wavelet packet subsets in time and frequency, and the calculation formula is as follows: wherein: is the i-th group wavelet packet of the j-th order decomposition in frequency, k is the time shift parameter, is the wavelet packet function, is a time sequence; extracting time-frequency characteristic parameters from the wavelet packet coefficients obtained from the decomposition; S2, regression model analysis: based on the time-frequency feature parameters extracted in the first stage, a regression model of time-frequency feature parameters and seismological variables is used, and the formula is as follows: In the formula, represents the natural logarithm of the time-frequency feature parameter extracted by the wavelet packet, is the magnitude, is the source distance, is the fault distance, is the average shear wave velocity at a depth of 30 m of the site; is the intra-event residual, is the inter-event residual, used to quantify the uncertainty of the parameters; h is a near-field correction parameter, used to avoid the occurrence of a maximum value in the near field, thereby improving the accuracy of the source distance calculation; , is the linear term coefficient; S3, time history reconstruction: based on the parameter constraint of the regression model, using wavelet packet inverse transform to reconstruct the artificial ground motion time history, and the calculation formula is as follows: wherein: The wavelet packet coefficients constrained by the regression model are used to simulate the time histories of ground motion for each earthquake event in the stochastic earthquake catalog, calculate the corresponding intensity measure IM, and aggregate the simulated ground motion database.

6. The method of claim 1, wherein, In the step three, the comparison between the median value of the simulated ground motion data and the corresponding ground motion intensity index obtained from the empirical attenuation relationship equation falls within the value of the empirical attenuation relationship ± 1 times the standard deviation, verifying the matching degree of the simulated data and the value of the empirical attenuation relationship; at the same time, the simulated data value falling within the value of the empirical attenuation relationship ± 3 times the standard deviation is screened out, and the qualified ground motion time history subset is obtained.

7. A system for determining a response-to-hazard curve based on the method of any one of claims 1-6, wherein, The system comprises the following modules: Potential source and random catalog generation module: used for dividing potential sources based on the seismic activity model, determining activity parameters, and generating a random earthquake catalog through Monte Carlo random sampling; Seismic record random simulation and verification module: used for obtaining target site seismic records, obtaining seismic time history data of each seismic event based on random simulation method, constructing a simulated ground motion database, and screening out qualified time history subsets through comparison with empirical attenuation relationship values; Structural response calculation module: used for inputting the qualified time history subset into the structure finite element model to calculate the engineering demand parameter sample set; The structural response risk calculation module calculates the annual average exceeding probability of the engineering demand parameter, and generates a structural response probability risk curve with a calibration function.

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