Method for constructing earthquake vulnerability curve
By constructing a fragility curve and adjusting it using nonlinear interpolation and optimization algorithms, a target fragility curve is generated, which solves the problems of inaccurate assessment of complex structures and data dependence in existing technologies, and achieves more accurate building loss assessment.
Patent Information
- Application Number
- CN202511178965.4
- Authority / Receiving Office
- CN · China
- Patent Type
- Applications(China)
- Current Assignee / Owner
- Filing Date
- 2025-08-21
- Publication Date
- 2025-12-23
AI Technical Summary
In existing technologies, engineering analytical methods are difficult to accurately simulate the nonlinear behavior of complex structures and lack universality, while earthquake damage data methods rely on the quality and quantity of historical data, which leads to bias and overestimation risks when assessing building damage.
By acquiring ground motion intensity and actual loss data, a fragility curve is constructed. Nonlinear interpolation parameters are introduced, and an optimization algorithm with boundary constraints is used to iteratively adjust the initial fragility curve to generate the target fragility curve. Combining the advantages of engineering analytical methods and earthquake damage data methods, the model is dynamically verified.
It enables accurate damage assessment of complex structures, eliminates the universality defects of engineering analytical methods and the data sparsity bias of earthquake damage data methods, and generates more accurate vulnerability curves.
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Figure CN121190607A_ABST
Abstract
Description
TECHNICAL FIELD
[0001] The present disclosure relates to the technical field of vulnerability analysis of engineering modules in a seismic catastrophe model, and particularly relates to a method for constructing a seismic vulnerability curve. BACKGROUND
[0002] In the field of earthquake engineering, building loss assessment mainly relies on two independent technical routes: engineering analysis method and damage data method. Among them, the engineering analysis method is based on the principle of structural dynamics, simulates the response of building structure through IDA (Incremental Dynamic Analysis), and generates a vulnerability curve describing the damage probability; while the damage data method directly fits the vulnerability curve describing the MDR (Mean Damage Ratio) based on historical disaster statistics.
[0003] Although building loss can be assessed by engineering analysis method and damage data method, these two methods have the following shortcomings: the engineering analysis method is difficult to accurately simulate the nonlinear behavior of complex structures such as masonry structures because it needs to simplify the rich building structures in real life into specific building structures, and the vulnerability curve of the engineering analysis method is not suitable for specific structure types and is affected by the subjectivity of damage indicators and limit state selection, resulting in differences between the results and actual disaster losses; while the damage data method completely depends on the quality and quantity of historical damage data, and when the sample size is insufficient or the data quality is poor, the vulnerability curve established will deviate significantly from the actual loss, often leading to overestimation of loss. SUMMARY
[0004] The embodiments of the present disclosure provide a method for constructing a seismic vulnerability curve to solve the problems of model simplification being difficult to reflect the actual behavior of complex structures such as masonry and lack of universality of the assessment results caused by using the engineering analysis method to assess building loss, and the problems of excessive dependence on the quality and quantity of historical data, and easy to cause sample deviation and overestimation of loss caused by using the damage data method to assess building loss.
[0005] In a first aspect, the embodiments of the present disclosure provide a method for constructing a seismic vulnerability curve, the method comprising: obtaining seismic intensity data and actual seismic loss data of a target region; determining a target building corresponding vulnerability curve according to the seismic intensity data of the target building in the target region; the vulnerability curve is used to represent the exceedance probability of the target building in different damage states; determine, according to the fragility curve and preset nonlinear interpolation parameters, an initial seismic vulnerability curve corresponding to the target building; the preset nonlinear interpolation parameters include a steepness control parameter, a reference offset parameter, and a demarcation point parameter; the steepness control parameter is used to determine a function slope of the initial seismic vulnerability curve in a demarcation point neighborhood; the reference offset parameter is used to determine reference values of a numerator and a denominator of a function corresponding to the initial seismic vulnerability curve; and the demarcation point parameter is used to determine a demarcation point of an output value of the function corresponding to the initial seismic vulnerability curve. take the actual seismic loss data as an optimization target, and iteratively adjust parameters of the initial seismic vulnerability curve by using a preset optimization algorithm with boundary constraints to obtain a target seismic vulnerability curve corresponding to the target building; the parameters of the initial seismic vulnerability curve include regression parameters of the initial seismic vulnerability curve and the preset nonlinear interpolation parameters.
[0006] In a second aspect, the embodiments of the present disclosure provide a device for constructing a seismic vulnerability curve, and the device includes: an acquisition module configured to acquire seismic intensity data and actual seismic loss data of a target region; a first determination module configured to determine, according to the seismic intensity data of a target building in the target region, a fragility curve corresponding to the target building; the fragility curve is used to represent an exceedance probability of the target building in different damage states; a second determination module configured to determine, according to the fragility curve and preset nonlinear interpolation parameters, an initial seismic vulnerability curve corresponding to the target building; the preset nonlinear interpolation parameters include a steepness control parameter, a reference offset parameter, and a demarcation point parameter; the steepness control parameter is used to determine a function slope of the initial seismic vulnerability curve in a demarcation point neighborhood; the reference offset parameter is used to determine reference values of a numerator and a denominator of a function corresponding to the initial seismic vulnerability curve; and the demarcation point parameter is used to determine a demarcation point of an output value of the function corresponding to the initial seismic vulnerability curve; an optimization module configured to take the actual seismic loss data as an optimization target, and iteratively adjust parameters of the initial seismic vulnerability curve by using a preset optimization algorithm with boundary constraints to obtain a target seismic vulnerability curve corresponding to the target building; the parameters of the initial seismic vulnerability curve include regression parameters of the initial seismic vulnerability curve and the preset nonlinear interpolation parameters.
[0007] In a third aspect, the embodiments of the present disclosure provide a device for constructing a seismic vulnerability curve, and the device includes a processor and a memory configured to store computer executable instructions, which, when executed, cause the processor to implement steps of the method in the first aspect.
[0008] In a fourth aspect, the embodiments of the present disclosure provide a computer readable storage medium for storing computer executable instructions, which, when executed by a processor, implement the steps of the method of the first aspect.
[0009] In a fifth aspect, the embodiments of the present disclosure provide a computer program product comprising a computer program, which, when executed by a processor, implements the steps of the method of the first aspect.
[0010] The above at least one technical solution provided by the embodiments of the present disclosure can achieve the following technical effects: In the embodiments of the present disclosure, the seismic intensity data and the actual seismic loss data of the target region can be obtained first, and the seismic intensity data of the target building in the target region is used to determine the brittle vulnerability curve corresponding to the target building. Then, the initial fragility curve corresponding to the target building can be determined according to the brittle vulnerability curve and the preset nonlinear interpolation parameters, wherein the preset nonlinear interpolation parameters can include a steepness control parameter, a reference offset parameter and a demarcation point parameter. Specifically, the steepness control parameter can be used to determine the function slope of the initial fragility curve in the neighborhood of the demarcation point; the reference offset parameter can be used to determine the reference value of the numerator and the denominator of the function corresponding to the initial fragility curve; and the demarcation point parameter can be used to determine the demarcation point of the output value of the function corresponding to the initial fragility curve. After obtaining the initial fragility curve, the actual seismic loss data obtained can be used as the optimization target, and the parameters of the initial fragility curve, including the regression parameters and the nonlinear interpolation parameters of the initial fragility curve, can be iteratively adjusted by using the preset optimization algorithm with boundary constraints to obtain the target fragility curve corresponding to the target building.
[0011] From the above content, it can be known that in the embodiments of the present disclosure, the brittle vulnerability curve can be generated first, the nonlinear interpolation parameters including the steepness control parameter, the reference offset parameter and the demarcation point parameter can be introduced, and the damage state mapping relationship can be dynamically reconstructed. The physical basis of the engineering analysis method can be inherited, and the marginal diminishing effect of the high loss rate area can be accurately captured. Then, the parameters of the initial fragility curve can be iteratively adjusted by using the preset optimization algorithm with boundary constraints, so that the theoretical model matches the actual disaster distribution, the universality defects of the engineering analysis method depending on a single model and the data sparsity bias of the seismic damage data method are completely eliminated, the dynamic mutual verification of the engineering analysis method and the seismic damage data method is realized, and the problems of related technologies are effectively solved. BRIEF DESCRIPTION OF DRAWINGS
[0012] In order to make one or more embodiments of the present disclosure or the technical solutions in the prior art clearer, the drawings needed in the embodiment or prior art description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present disclosure, and all other drawings obtained by those of ordinary skill in the art without creative labor based on these drawings are within the protection scope of the present disclosure. Figure 1 A flowchart of a method for constructing a seismic vulnerability curve according to an embodiment of the present disclosure is shown in FIG. 1. Figure 2 A block diagram of a seismic vulnerability curve construction device 200 according to an embodiment of the present disclosure is shown in FIG. 2. Figure 3 A hardware structure diagram of a seismic vulnerability curve construction device according to an embodiment of the present disclosure is shown in FIG. 3. DETAILED DESCRIPTION
[0013] In order to make one or more embodiments of the present disclosure or the technical solutions in the prior art clearer, the drawings needed in the embodiment or prior art description will be briefly introduced below. Obviously, the drawings in the following description are only some embodiments of the present disclosure, and all other drawings obtained by those of ordinary skill in the art without creative labor based on these drawings are within the protection scope of the present disclosure.
[0014] The technical solutions provided by the embodiments of the present disclosure are described in detail below with reference to the drawings.
[0015] Please refer to Figure 1 , Figure 1 A flowchart of a method for constructing a seismic vulnerability curve according to an embodiment of the present disclosure is shown in FIG. 1. Figure 1 The method includes the following steps: Step 102: Obtain seismic intensity data and actual seismic loss data of a target region.
[0016] Step 104: Determine a seismic vulnerability curve corresponding to a target building according to the seismic intensity data of the target building in the target region. The seismic vulnerability curve is used to represent the exceeding probability of the target building in different damage states.
[0017] Step 106: determining the initial fragility curve corresponding to the target building according to the fragility curve and preset nonlinear interpolation parameters; the preset nonlinear interpolation parameters include a steepness control parameter, a reference offset parameter and a demarcation point parameter; the steepness control parameter is used to determine the function slope of the initial fragility curve in the neighborhood of the demarcation point; the reference offset parameter is used to determine the reference value of the numerator and denominator of the function corresponding to the initial fragility curve; and the demarcation point parameter is used to determine the demarcation point of the output value of the function corresponding to the initial fragility curve.
[0018] Step 108: taking the obtained actual seismic loss data as an optimization target, and iteratively adjusting the parameters of the initial fragility curve by using a preset optimization algorithm with boundary constraints to obtain the target fragility curve corresponding to the target building; the parameters of the initial fragility curve include the regression parameters of the initial fragility curve and the preset nonlinear interpolation parameters.
[0019] In an embodiment of the present application, the seismic intensity data and the actual seismic loss data of the target region can be obtained. The seismic intensity data can include SA (Spectral Acceleration) data, and the actual seismic loss data can include MDR, i.e., average loss rate data.
[0020] In the embodiment of the present application, it is considered that the disaster claim matrix data is an important basis for constructing the fragility curve. Therefore, in order to accurately depict the relationship between the disaster intensity and the loss, it is considered in the embodiment of the present application that the PGA (Peak Ground Acceleration) parameter is a ground motion parameter independent of the structure, and the dynamic characteristics of the structure itself are basically not considered, regardless of the natural period of the structure, only the peak acceleration of the ground motion is reflected. The SA parameter is closely related to the natural period of the structure, can reflect the action strength of the earthquake on structures of different periods, and is more in line with the actual situation, so the SA parameter in the earthquake event is extracted as the input disaster intensity data, i.e., as the X-axis of the subsequent fragility curve; and the MDR represents the average proportion of different types of buildings suffering losses under a specific disaster intensity, and directly reflects the damage degree caused by the disaster, as the corresponding loss ratio data, i.e., as the Y-axis of the subsequent fragility curve.
[0021] In an embodiment of the present application, after the seismic intensity data of the target region is obtained, the fragility curve corresponding to the target building can be determined according to the seismic intensity data of the target building in the target region. The fragility curve can be used to represent the exceedance probability of the target building in different damage states.
[0022] In one example, when determining the brittleness curve of a target building based on the seismic intensity data of the target building in the target area, the different damage states of the target building can be determined first based on the degree of damage to the target building.
[0023] In this example, referring to Table 1 of GB / T18208.4—2011 "Earthquake Field Work Part 4: Assessment of Direct Disaster Losses," the damage levels are divided into five categories: basically intact, slightly damaged, moderately damaged, severely damaged, and completely destroyed, with ranges of 0%-5%, 6%-15%, 16%-45%, 46%-100%, and 81%-100%, respectively. This example continuously divides the degree of structural damage under seismic loading, establishing a continuous damage state mapping relationship. Specifically, the damage level is divided into the following five intervals: 0%-5%, 6%-15%, 16%-45%, 46%-100%, and 81%-100%. These five intervals correspond to the five different loss states: basically intact, slightly damaged, moderately damaged, severely damaged, and completely destroyed. Each interval represents a different degree of damage that the structure may suffer during an earthquake, from minor damage to severe destruction, and then to a state close to collapse. By dividing the structure into intervals, the loss situation under different earthquake intensities can be assessed more accurately.
[0024] After determining the different damage states of the target building, the corresponding fragility curve of the target building can be constructed using incremental dynamic analysis based on the ground motion intensity data and the different damage states of the target building.
[0025] In one example, when constructing the fragility curve corresponding to the target building, the Incremental Dynamic Analysis (IDA) method can be used. Therefore, when constructing the fragility curve corresponding to the target building, the exceedance probability under the following states can be constructed using the IDA analysis method: basically intact, slightly damaged, moderately damaged, severely damaged, and collapsed.
[0026] in, This represents the probability that the structure's response under seismic loading will exceed a certain failure state; IDA analysis can yield the maximum inter-story drift angle of the structure under different seismic intensities (such as PGA and SA). The regression parameters α and β; the structural performance parameters corresponding to each failure state point. It can be obtained through static elastoplastic analysis (pushover); of and The statistical value can also be obtained according to a regulation in the HAZUS99 brittle vulnerability curve parameter, when the brittle vulnerability curve takes the PGA as an independent variable 0.4 is taken, is a normal cumulative distribution function, and the value is determined by referring to a standard normal distribution table.
[0027] In an embodiment of the present application, after the brittle vulnerability curve is determined, the initial vulnerability curve corresponding to the target building can be determined according to the brittle vulnerability curve and preset nonlinear interpolation parameters. The preset nonlinear interpolation parameters can include a steepness control parameter, a reference offset parameter and a demarcation point parameter.
[0028] Specifically, the steepness control parameter can be used to determine the function slope of the initial vulnerability curve in the neighborhood of the demarcation point; the reference offset parameter can be used to determine the reference value of the numerator and denominator of the function corresponding to the initial vulnerability curve; and the demarcation point parameter can be used to determine the demarcation point of the output value of the function corresponding to the initial vulnerability curve.
[0029] In an embodiment of the present application, when the initial vulnerability curve corresponding to the target building is determined according to the brittle vulnerability curve and the preset nonlinear interpolation parameters, the exceeding probability of the target building under different damage states can be obtained from the brittle vulnerability curve. For example, according to the above formula, the exceeding probability of each damage state can be obtained through IDA analysis .
[0030] Then, the discrete loss probability data corresponding to each damage state can be determined according to the obtained exceeding probability of the target building under different damage states. For example, the damage degree can be divided into five continuous intervals: 0%-5% (essentially intact), 6%-15% (slightly damaged), 16%-45% (moderately damaged), 46%-100% (severely damaged) and 81%-100% (collapsed state).
[0031] The continuous loss probability data is obtained by inputting the discrete loss probability data into the preset nonlinear interpolation function; wherein the nonlinear interpolation function is composed of the preset nonlinear interpolation parameters, i.e. the steepness control parameter, the reference offset parameter and the demarcation point parameter.
[0032] In an example, three parameters a, b and c can be introduced during nonlinear interpolation, wherein a is the steepness control parameter, used to control the steepness of the initial vulnerability curve near c; b is the reference offset parameter, used to adjust the reference value of the numerator and denominator of the function corresponding to the initial vulnerability curve, and affects the overall horizontal position of the function; and c is the demarcation point parameter, which is a calculation range and can define the demarcation point of the function behavior, and when x>c, the function output is limited to 1, wherein x is the input value. The interpolation ratio calculation formula is: By adjusting the values of the parameters a, b and c, the nonlinear interpolation of different engineering scenarios can be adapted, and the optimization goal is to minimize the error between the model output and the actual observation value.
[0033] Then, the continuous loss probability data can be weighted with the standard loss value to obtain average loss rate data. First, the national standard GB / T18208.4-2011 shown in Table 1 can be obtained: Table 1
[0034] Then, the weight value required for weighted calculation can be determined:
[0035] Wherein, MDR is the average loss rate under a given intensity (the intensity can be an engineering quantification index of ground motion intensity parameter (such as PGA or SA)); is the damage grade (D =1, 2, 3, 4, 5 respectively represent basic intact, slight damage, moderate damage, severe damage, collapse); is the probability of a given damage grade under a certain intensity; is the loss rate under a given damage grade, which is generally taken as 3%, 11%, 31%, 73% and 91% according to the above national standard; and are nonlinear transformation functions, which are power functions with natural logarithm as base, for example, .
[0036] Finally, the corresponding relationship between the ground motion intensity data and the average loss rate data can be established to obtain the initial vulnerability curve.
[0037] In an embodiment of the present application, after obtaining the initial vulnerability curve, the actual seismic loss data obtained can be taken as the optimization goal, and a preset optimization algorithm with boundary constraints is used to iteratively adjust the parameters of the initial vulnerability curve to obtain the target vulnerability curve corresponding to the target building. The parameters of the initial vulnerability curve can include the regression parameters of the initial vulnerability curve and the preset nonlinear interpolation parameters.
[0038] In an embodiment of the present application, before the parameters of the initial vulnerability curve are iteratively adjusted by using the preset optimization algorithm with boundary constraints, the boundary constraints for the preset optimization algorithm can also be set. Specifically, the feasible region boundary of the inter-story drift angle regression parameters of the target building can be obtained according to the incremental dynamic analysis method, and the standard deviation value range of the limit state displacement angle of the target building can be obtained by static elastic-plastic analysis, to set the boundary constraints for the preset optimization algorithm.
[0039] In one example, the preset optimization algorithm can be an L-BFGS-B algorithm. When setting the boundary constraint for the preset optimization algorithm, the regression parameters of the maximum inter-story drift angle of the target building under different seismic intensity can be obtained by IDA analysis . And the boundary constraint can be set according to the feasible region boundary of the regression parameters . And the standard deviation of the structure performance parameters corresponding to each damage state point can be obtained by the manager elastic-plastic analysis, which can be valued according to the HAZUS99 regulation. Therefore, the boundary constraint can be set according to the value range of the standard deviation.
[0040] In the embodiment of the present application, before the preset optimization algorithm with boundary constraint is used to iteratively adjust the parameters of the initial fragility curve with the obtained actual seismic loss data as the optimization target, the weighting rule can be determined according to the distribution density of the seismic intensity data and the confidence of the preset optimization algorithm in the iteration process. Then, the weighted root mean square error can be obtained by weighting the root mean square error according to the determined weighting rule, wherein the root mean square error is the difference between the initial fragility curve and the actual seismic loss data. And the weighted root mean square error can be set as the evaluation index, and the low seismic intensity data in the seismic intensity data is assigned a high weight.
[0041] Considering the distribution characteristics of the actual disaster data, the frequency of small earthquakes is naturally more, and the amount of data with low SA is relatively sufficient. More data can reduce the influence of accidental factors, make the law reflected by the data closer to the real situation, and have higher confidence in the statistical level. Based on this, the embodiment of the present application further defines a weight array weights, and different weights are assigned to different data points according to the importance and reliability of data distribution, so as to improve the fitting ability of the model to specific regional data, so that the model pays more attention to the data with high confidence in the fitting process, and thus can better reflect the real relationship between actual disaster intensity and related parameters, and make the model output more in line with the actual situation.
[0042] In order to accurately estimate the parameters in the constructed objective function, the embodiment of the present application uses weighted RMSE (Root Mean Square Error) as the evaluation index. RMSE can comprehensively reflect the prediction accuracy of the model by calculating the square root of the mean value of the sum of squares of the prediction error and the actual value.
[0043] The preset optimization algorithm with boundary constraint can be used to solve parameters in the embodiment of the present application. In actual disaster model parameter estimation, the parameters often have a constraint boundary. The algorithm can more accurately search the parameter space under the condition of such a constraint to find the parameter value that minimizes the evaluation function. The preset optimization algorithm with boundary constraint can be used to iteratively search for the optimal solution of the target parameter in the embodiment of the present application, with the core target of minimizing the weighted root mean square error (RMSE). Specifically, the algorithm optimizes efficiently within the parameter constraint boundary, minimizes the RMSE of the fitting result, minimizes the average error between the model prediction value and the actual disaster claim data, accurately depicts the real relationship between disaster intensity and loss ratio, and obtains a more accurate vulnerability curve.
[0044] The embodiment of the present application can realize the construction of a nonlinear vulnerability curve. The nonlinear curve relies on a flexible function form, breaks through the linear hypothesis limit, can deeply capture the complex nonlinear coupling relationship between the earthquake intensity index (such as SA and PGA) and the loss ratio, and can output a fitting effect that is more in line with actual disaster data. However, the calculation complexity of the nonlinear curve is significantly higher than that of the linear curve due to the introduction of more to-be-estimated parameters and complex function forms. In actual application, if the disaster data distribution law is clear and the computing resources are limited, the linear curve can realize efficient and basic-precision fitting; if the nonlinear characteristics of the disaster loss data need to be deeply mined, the nonlinear curve is preferred, so as to balance the model accuracy, the calculation cost, and the adaptability to the actual disaster scene.
[0045] In the embodiment of the present application, the seismic intensity data and the actual seismic loss data of a target region can be obtained first, and the vulnerability curve corresponding to a target building in the target region can be determined according to the seismic intensity data of the target building. Then, the initial vulnerability curve corresponding to the target building can be determined according to the vulnerability curve and preset nonlinear interpolation parameters. The preset nonlinear interpolation parameters can include a steepness control parameter, a reference offset parameter, and a demarcation point parameter. Specifically, the steepness control parameter can be used to determine the function slope of the initial vulnerability curve in the neighborhood of the demarcation point; the reference offset parameter can be used to determine the reference value of the numerator and the denominator of the function corresponding to the initial vulnerability curve; and the demarcation point parameter can be used to determine the demarcation point of the output value of the function corresponding to the initial vulnerability curve. After obtaining the initial vulnerability curve, the actual seismic loss data obtained can be used as an optimization target, and a preset optimization algorithm with boundary constraint can be used to iteratively adjust the parameters of the initial vulnerability curve, including the regression parameters and the nonlinear interpolation parameters of the initial vulnerability curve, to obtain the target vulnerability curve corresponding to the target building.
[0046] From the above, in the embodiment of the present application, the brittle vulnerability curve can be generated first, the nonlinear interpolation parameters including the steepness control parameter, the reference offset parameter and the demarcation point parameter are introduced, and the damage state mapping relationship is dynamically reconstructed, which can not only inherit the physical basis of the engineering analysis method, but also accurately capture the marginal diminishing effect of the high loss rate area, and then the actual seismic loss data is taken as the optimization target, the preset optimization algorithm with boundary constraint is used to iteratively adjust the parameters of the initial vulnerability curve, so that the theoretical model matches the actual disaster distribution, the universality defects of the engineering analysis method relying on a single model and the data sparsity deviation of the seismic damage data method are completely eliminated, the dynamic mutual verification of the engineering analysis method and the seismic damage data method is realized, and the problems of related technologies are effectively solved.
[0047] Corresponding to the construction method of the seismic vulnerability curve, the embodiment of the present application further provides a construction device of a seismic vulnerability curve, Figure 2 The module composition schematic diagram of the construction device 200 of the seismic vulnerability curve provided by the embodiment of the present application is shown in Figure 2 The construction device 200 of the seismic vulnerability curve includes: An acquisition module 201 is configured to acquire seismic intensity data and actual seismic loss data of a target region. A first determination module 202 is configured to determine a brittle vulnerability curve corresponding to a target building in the target region according to the seismic intensity data of the target building; the brittle vulnerability curve is used to represent the exceeding probability of the target building in different damage states. A second determination module 203 is configured to determine an initial vulnerability curve corresponding to the target building according to the brittle vulnerability curve and preset nonlinear interpolation parameters; the preset nonlinear interpolation parameters include a steepness control parameter, a reference offset parameter and a demarcation point parameter; the steepness control parameter is used to determine the function slope of the initial vulnerability curve in the neighborhood of the demarcation point; the reference offset parameter is used to determine the reference value of the numerator and the denominator of the function corresponding to the initial vulnerability curve; and the demarcation point parameter is used to determine the demarcation point of the output value of the function corresponding to the initial vulnerability curve. An optimization module 204 is configured to take the acquired actual seismic loss data as an optimization target, iteratively adjust the parameters of the initial vulnerability curve by using a preset optimization algorithm with boundary constraint, and obtain a target vulnerability curve corresponding to the target building; the parameters of the initial vulnerability curve include the regression parameters of the initial vulnerability curve and the preset nonlinear interpolation parameters.
[0048] Optionally, the first determination module 202 is configured to: Determine different damage states of the target building according to the damage degree of the target building. Based on the ground motion intensity data of the target building and the different damage states of the target building, the brittleness curve corresponding to the target building is constructed by incremental dynamic analysis.
[0049] Optionally, the second determining module 203 is used to: From the fragility curve, the exceedance probability of the target building under different damage states is obtained; Based on the obtained exceedance probability of the target building under different damage states, determine the discrete loss probability data corresponding to each damage state; The discrete loss probability data is input into a preset nonlinear interpolation function to obtain continuous loss probability data; wherein the nonlinear interpolation function is composed of the preset nonlinear interpolation parameters; The continuous loss probability data is weighted and calculated with the standard loss value to obtain the average loss rate data; Establish the correspondence between the ground motion intensity data and the average loss rate data to obtain the initial vulnerability curve.
[0050] Optionally, the ground motion intensity data includes spectral acceleration data; the actual earthquake loss data includes average loss rate data.
[0051] Optionally, the device further includes ( Figure 2 (not shown in the image) The first setting module 205 is used to set boundary constraints for the preset optimization algorithm before iteratively adjusting the parameters of the initial vulnerability curve using a preset optimization algorithm with boundary constraints, based on the actual earthquake loss data obtained as the optimization target, to obtain the target vulnerability curve corresponding to the target building. The first setting module 205 is used for: The feasible domain boundary of the inter-story drift angle regression parameters of the target building is obtained by incremental dynamic analysis, and the standard deviation range of the ultimate state drift angle of the target building is obtained by static elastoplastic analysis, so as to set boundary constraints for the preset optimization algorithm.
[0052] Optionally, the device further includes ( Figure 2 (not shown in the image) The second setting module 206 is used to determine the weighting rules based on the distribution density of the ground motion intensity data and the confidence level of the preset optimization algorithm during the iteration process, before iteratively adjusting the parameters of the initial vulnerability curve using the acquired actual earthquake loss data as the optimization target and a preset optimization algorithm with boundary constraints to obtain the target vulnerability curve corresponding to the target building. The weighting module 207 is configured to weight the root mean square error according to the determined weighting rule to obtain a weighted root mean square error; wherein the root mean square error is a difference between the initial vulnerability curve and the actual seismic loss data. The third setting module 208 sets the weighted root mean square error as an evaluation index, and assigns a high weight to low seismic intensity data in the seismic intensity data.
[0053] In the embodiment of the present application, the seismic intensity data and the actual seismic loss data of the target region can be obtained first, and the seismic vulnerability curve corresponding to the target building in the target region can be determined according to the seismic intensity data of the target building. Then, the initial vulnerability curve corresponding to the target building can be determined according to the seismic vulnerability curve and the preset nonlinear interpolation parameters. The preset nonlinear interpolation parameters can include a steepness control parameter, a reference offset parameter and a demarcation point parameter. Specifically, the steepness control parameter can be used to determine the function slope of the initial vulnerability curve in the neighborhood of the demarcation point. The reference offset parameter can be used to determine the reference value of the numerator and the denominator of the function corresponding to the initial vulnerability curve. The demarcation point parameter can be used to determine the demarcation point of the output value of the function corresponding to the initial vulnerability curve. After obtaining the initial vulnerability curve, the parameters of the initial vulnerability curve, including the regression parameters and the nonlinear interpolation parameters, can be iteratively adjusted by using the preset optimization algorithm with boundary constraints, taking the actual seismic loss data as the optimization target, to obtain the target vulnerability curve corresponding to the target building.
[0054] From the above, it can be seen that in the embodiment of the present application, the seismic vulnerability curve can be generated first, the nonlinear interpolation parameters including the steepness control parameter, the reference offset parameter and the demarcation point parameter are introduced, and the damage state mapping relationship is dynamically reconstructed. The embodiment of the present application can not only inherit the physical basis of the engineering analysis method, but also accurately capture the marginal diminishing effect of the high loss rate area. Then, the parameters of the initial vulnerability curve are iteratively adjusted by using the preset optimization algorithm with boundary constraints, taking the actual seismic loss data as the optimization target, so that the theoretical model matches the actual disaster distribution. The embodiment of the present application completely eliminates the universality defects of the engineering analysis method relying on a single model and the data sparsity bias of the seismic damage data method, realizes the dynamic mutual verification of the engineering analysis method and the seismic damage data method, and effectively solves the problems of related technologies.
[0055] Corresponding to the construction method of the seismic vulnerability curve, the embodiment of the present application further provides a construction device of a seismic vulnerability curve, Figure 3 The hardware structure schematic diagram of the construction device of the seismic vulnerability curve provided by one embodiment of the present application is shown.
[0056] The construction device of the seismic vulnerability curve can be a terminal device or a server provided by the above-mentioned embodiments for constructing the seismic vulnerability curve.
[0057] The construction device of the seismic fragility curve can have a large difference in configuration or performance, and can include one or more processors 301 and memories 302, and the memories 302 can store one or more stored applications or data. The memories 302 can be temporary storage or persistent storage. The applications stored in the memories 302 can include one or more modules (not shown in the figure), and each module can include a series of computer executable instructions in the construction device of the seismic fragility curve. Further, the processor 301 can be configured to communicate with the memory 302 and execute a series of computer executable instructions in the memory 302 on the construction device of the seismic fragility curve. The construction device of the seismic fragility curve can also include one or more power supplies 303, one or more wired or wireless network interfaces 304, one or more input / output interfaces 305, and one or more keyboards 306.
[0058] In particular, in the embodiment, the construction device of the seismic fragility curve includes a memory and one or more programs, wherein one or more programs are stored in the memory, and the one or more programs can include one or more modules, and each module can include a series of computer executable instructions in the construction device of the seismic fragility curve, and configured to be executed by one or more processors according to the above embodiment.
[0059] In the embodiment of the present application, the seismic intensity data and the actual seismic loss data of the target region can be obtained first, and then the seismic fragility curve corresponding to the target building in the target region can be determined according to the seismic intensity data of the target building. Then, the initial seismic fragility curve corresponding to the target building can be determined according to the seismic fragility curve and the preset nonlinear interpolation parameters. The preset nonlinear interpolation parameters can include a steepness control parameter, a reference offset parameter, and a demarcation point parameter. Specifically, the steepness control parameter can be used to determine the function slope of the initial seismic fragility curve in the neighborhood of the demarcation point. The reference offset parameter can be used to determine the reference value of the numerator and the denominator of the function corresponding to the initial seismic fragility curve. The demarcation point parameter can be used to determine the demarcation point of the output value of the function corresponding to the initial seismic fragility curve. After obtaining the initial seismic fragility curve, the actual seismic loss data obtained can be used as the optimization target, and the preset optimization algorithm with boundary constraints can be used to iteratively adjust the parameters of the initial seismic fragility curve, including the regression parameters and the nonlinear interpolation parameters of the initial seismic fragility curve, to obtain the target seismic fragility curve corresponding to the target building.
[0060] From the above, in the embodiment of the present application, the brittle curve can be generated first, the non-linear interpolation parameters including the steepness control parameter, the reference offset parameter and the demarcation point parameter are introduced, and the damage state mapping relationship is dynamically reconstructed, which can not only inherit the physical basis of the engineering analytical method, but also accurately capture the marginal diminishing effect of the high loss rate area, and then the actual seismic loss data is taken as the optimization target, the preset optimization algorithm with boundary constraint is used to iteratively adjust the parameters of the initial vulnerability curve, so that the theoretical model matches the actual disaster distribution, the universality defects of the engineering analytical method depending on a single model and the data sparsity deviation of the seismic damage data method are completely eliminated, the dynamic mutual verification of the engineering analytical method and the seismic damage data method is realized, and the problems of related technologies are effectively solved.
[0061] Another embodiment of the present disclosure also provides a computer readable storage medium for storing computer executable instructions, which realize the above process when executed by a processor.
[0062] The storage medium in the embodiment of the present disclosure can realize each process of the above-mentioned seismic vulnerability curve construction method embodiment and achieve the same effect and function, which is not repeated here.
[0063] Another embodiment of the present disclosure also provides a computer program product, which includes a computer program, and the computer program realizes the above process when executed by a processor.
[0064] The computer program product in the embodiment of the present disclosure can realize each process of the above-mentioned seismic vulnerability curve construction method embodiment and achieve the same effect and function, which is not repeated here.
[0065] In various embodiments of the present disclosure, the computer readable storage medium includes a read-only memory (ROM), a random access memory (RAM), a magnetic disk or an optical disk, etc.
[0066] In the 1990s, it was possible to distinguish whether an improvement in a technology was a hardware improvement (e.g., an improvement in the circuit structure of a diode, transistor, switch, etc.) or a software improvement (an improvement in a method flow). However, as technology has advanced, many improvements in method flows today can be considered as direct improvements in hardware circuit structures. Designers almost always obtain the corresponding hardware circuit structure by programming the improved method flow into a hardware circuit. Therefore, it cannot be said that an improvement in a method flow cannot be implemented using a hardware entity module. For example, a programmable logic device (PLD) (e.g., a field programmable gate array (FPGA)) is an integrated circuit whose logic function is determined by the user programming the device. A digital system is "integrated" on a PLD by the designer programming it himself, without having to ask a chip manufacturer to design and manufacture a special integrated circuit chip. Moreover, instead of manually manufacturing an integrated circuit chip, this programming is now mostly implemented using "logic compiler" software, which is similar to the software compiler used when developing a program, and the original code before compilation must also be written in a specific programming language, which is called a hardware description language (HDL), and there are many types of HDL, such as ABEL (Advanced Boolean Expression Language), AHDL (Altera Hardware Description Language), Confluence, CUPL (Cornell University Programming Language), HDCal, JHDL (Java Hardware Description Language), Lava, Lola, MyHDL, PALASM, RHDL (Ruby Hardware Description Language), etc., and the most commonly used are VHDL (Very-High-Speed Integrated Circuit Hardware Description Language) and Verilog. Those skilled in the art should also be aware that it is only necessary to logically program the method flow using the above-mentioned hardware description languages and program it into an integrated circuit to easily obtain a hardware circuit that implements the logical method flow.
[0067] The controller can be implemented in any suitable way, for example, the controller can take the form of a microprocessor or processor and a computer readable medium storing computer readable program code, such as software or firmware, executable by the microprocessor, logic gates, switches, an application specific integrated circuit (ASIC), a programmable logic controller and an embedded microcontroller, examples of which include but are not limited to the following microcontrollers: ARC 625D, Atmel AT91SAM, Microchip PIC18F26K20 and Silicone Labs C8051F320, the memory controller can also be implemented as part of the control logic of the memory. Those skilled in the art will also know that, in addition to implementing the controller in pure computer readable program code, it is also possible to implement the controller in the form of logic gates, switches, application specific integrated circuits, programmable logic controllers and embedded microcontrollers, etc. to achieve the same functionality by logically programming the method steps. Such a controller can therefore be considered as a hardware component, and the means included therein for implementing the various functions can also be considered as structures within the hardware component. Alternatively, the means for implementing the various functions can even be considered as both a software module implementing the method and a structure within the hardware component.
[0068] The systems, apparatuses, modules or units illustrated by the above embodiments can be implemented by computer chips or entities, or by products with certain functions. A typical implementation device is a computer. Specifically, the computer can be a personal computer, a laptop computer, a cellular phone, a camera phone, a smart phone, a personal digital assistant, a media player, a navigation device, an email device, a game console, a tablet computer, a wearable device, or a combination of any of these devices.
[0069] For the sake of brevity, the above apparatuses are described in functional form in various units. Of course, the functions of each unit can be implemented in the same or multiple software and / or hardware.
[0070] Those skilled in the art will appreciate that one or more embodiments of the disclosure can provide a method, a system or a computer program product. Accordingly, one or more embodiments of the disclosure can take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, one or more embodiments of the disclosure can take the form of a computer program product on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROMs, optical storage devices, etc.) embodying computer readable program code.
[0071] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.
[0072] These computer program instructions can also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instructions which implement the function specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.
[0073] The computer program instructions can also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer-implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart block or blocks. Figure 1 one or more flow or blocks Figure 1 means for functionally implementing the steps listed in the flowchart block or blocks.
[0074] It should also be noted that the term "comprising" or "including" or any other variation thereof is intended to cover the non-exclusive inclusion such that processes, methods, articles, or apparatuses that comprise a list of elements are not limited to those elements but can include other elements not expressly listed or inherent to such processes, methods, articles, or apparatuses. Without limitation, an element preceded by "comprises a" or "comprises" does not, without more constraints, foreclose the existence of additional identical elements in the processes, methods, articles, or apparatuses that comprise the identified element.
[0075] One or more embodiments of the disclosure can be described in the general context of computer-executable instructions, such as program modules, being executed by a computer. Generally, program modules include routines, programs, objects, components, data structures, and the like, that perform particular tasks or implement particular abstract data types. One or more embodiments of the disclosure can also be practiced in distributed computing environments where tasks are performed by remote processing devices that are linked through a communications network. In a distributed computing environment, program modules can be located in local and remote computer storage media including memory storage devices.
[0076] Various embodiments in the present disclosure are described in progressive manner, and the same or similar parts between various embodiments can be referred to each other. Each embodiment focuses on the difference from other embodiments. Especially, the system embodiments are described simply because they are basically similar to the method embodiments, and the relevant parts can be referred to the description of the method embodiments.
[0077] The above only describes the embodiments of the present disclosure and is not intended to limit the present disclosure. The present disclosure can have various modifications and changes for those skilled in the art. Any modification, equivalent replacement, improvement, etc. within the spirit and principle of the present disclosure shall be included in the scope of claims of the present disclosure.
Claims
1. A method for constructing a seismic vulnerability curve, characterized in that, The method includes: Acquire seismic intensity data and actual earthquake loss data for the target area; Based on the seismic intensity data of the target building in the target area, a fragility curve corresponding to the target building is determined; the fragility curve is used to represent the exceedance probability of the target building under different damage states. Based on the fragility curve and preset nonlinear interpolation parameters, an initial fragility curve corresponding to the target building is determined. The preset nonlinear interpolation parameters include a steepness control parameter, a reference offset parameter, and a boundary point parameter. The steepness control parameter is used to determine the slope of the function in the neighborhood of the boundary point of the initial fragility curve. The reference offset parameter is used to determine the reference values of the numerator and denominator of the function corresponding to the initial fragility curve. The boundary point parameter is used to determine the boundary point of the output value of the function corresponding to the initial fragility curve. Using the actual earthquake loss data obtained as the optimization target, a preset optimization algorithm with boundary constraints is used to iteratively adjust the parameters of the initial vulnerability curve to obtain the target vulnerability curve corresponding to the target building; the parameters of the initial vulnerability curve include the regression parameters of the initial vulnerability curve and the preset nonlinear interpolation parameters.
2. The method according to claim 1, characterized in that, The step of determining the brittleness curve corresponding to the target building based on the seismic intensity data of the target building in the target area includes: Based on the degree of damage to the target building, determine the different damage states of the target building; Based on the ground motion intensity data of the target building and the different damage states of the target building, the brittleness curve corresponding to the target building is constructed by incremental dynamic analysis.
3. The method according to claim 2, characterized in that, The step of determining the initial fragility curve corresponding to the target building based on the fragility curve and preset nonlinear interpolation parameters includes: From the fragility curve, the exceedance probability of the target building under different damage states is obtained; Based on the obtained exceedance probability of the target building under different damage states, determine the discrete loss probability data corresponding to each damage state; The discrete loss probability data is input into a preset nonlinear interpolation function to obtain continuous loss probability data; wherein the nonlinear interpolation function is composed of the preset nonlinear interpolation parameters; The continuous loss probability data is weighted and calculated with the standard loss value to obtain the average loss rate data; Establish the correspondence between the ground motion intensity data and the average loss rate data to obtain the initial vulnerability curve.
4. The method according to claim 1, characterized in that, The ground motion intensity data includes spectral acceleration data; the actual earthquake loss data includes average loss rate data.
5. The method according to claim 1, characterized in that, Before using the acquired actual earthquake loss data as the optimization target and employing a preset optimization algorithm with boundary constraints to iteratively adjust the parameters of the initial vulnerability curve to obtain the target vulnerability curve corresponding to the target building, the method further includes: Set boundary constraints for the preset optimization algorithm; Setting boundary constraints for the preset optimization algorithm includes: The feasible domain boundary of the inter-story drift angle regression parameters of the target building is obtained by incremental dynamic analysis, and the standard deviation range of the ultimate state drift angle of the target building is obtained by static elastoplastic analysis, so as to set boundary constraints for the preset optimization algorithm.
6. The method according to claim 1, characterized in that, Before using the acquired actual earthquake loss data as the optimization target and employing a preset optimization algorithm with boundary constraints to iteratively adjust the parameters of the initial vulnerability curve to obtain the target vulnerability curve corresponding to the target building, the method further includes: The weighting rules are determined based on the dense distribution of the ground motion intensity data and the confidence level of the preset optimization algorithm during the iteration process. The root mean square error is weighted according to the determined weighting rule to obtain the weighted root mean square error; wherein, the root mean square error is the difference between the initial vulnerability curve and the actual earthquake loss data; The weighted root mean square error is set as the evaluation metric, and high weights are assigned to the low ground motion intensity data in the ground motion intensity data.
7. A device for constructing seismic vulnerability curves, characterized in that, The device includes: The acquisition module is used to acquire ground motion intensity data and actual earthquake loss data for the target area. The first determining module is used to determine the fragility curve corresponding to the target building based on the seismic intensity data of the target building in the target area; the fragility curve is used to represent the exceedance probability of the target building under different damage states; The second determining module is used to determine the initial vulnerability curve corresponding to the target building based on the fragility curve and preset nonlinear interpolation parameters. The preset nonlinear interpolation parameters include a steepness control parameter, a reference offset parameter, and a boundary point parameter. The steepness control parameter is used to determine the slope of the function in the neighborhood of the boundary point of the initial vulnerability curve. The reference offset parameter is used to determine the reference values of the numerator and denominator of the function corresponding to the initial vulnerability curve. The boundary point parameter is used to determine the boundary point of the output value of the function corresponding to the initial vulnerability curve. The optimization module is used to take the acquired actual earthquake loss data as the optimization target, and iteratively adjust the parameters of the initial vulnerability curve using a preset optimization algorithm with boundary constraints to obtain the target vulnerability curve corresponding to the target building; the parameters of the initial vulnerability curve include the regression parameters of the initial vulnerability curve and the preset nonlinear interpolation parameters.
8. A device for constructing seismic vulnerability curves, characterized in that, include: A memory, a processor, and a computer program stored in the memory and executable on the processor, wherein the computer program, when executed by the processor, implements the steps of the method as described in any one of claims 1 to 6.
9. A computer-readable storage medium, characterized in that, The computer-readable storage medium is used to store computer-executable instructions that, when executed by a processor, implement the steps of the method described in any one of claims 1 to 6.
10. A computer program product, characterized in that, The computer program product includes a computer program that, when executed by a processor, implements the steps of the method described in any one of claims 1 to 6.