A method and device for selecting a pulse-type seismic motion intensity characterization parameter

Abstract

The present application relates to the selection method and device of the pulse type ground motion intensity characterization parameter, first, the ground motion parameters in N ground motion records are subjected to principal component analysis, and the first m principal components are selected as the alternative parameters; then, the maximum interlayer displacement angle corresponding to N ground motion records is obtained by using the elastic-plastic time history analysis method; the maximum interlayer displacement angle corresponding to N ground motion records and m alternative parameters are subjected to linear regression analysis, the relationship between the alternative parameters and the median of the maximum interlayer displacement angle is obtained, and the linear regression determination coefficient of the alternative parameters is calculated; the alternative parameter corresponding to the linear regression determination coefficient closest to 1 is selected as the optimal pulse type ground motion intensity characterization parameter. Compared with a single intensity parameter, the present application converts a single ground motion intensity characterization parameter into a composite ground motion intensity characterization parameter by using the principal component analysis method, which has better statistics and better correlation with the structural response.

Description

Technical Field

[0001] The present invention relates to the technical field of structural earthquake resistance, and particularly relates to a method and device for selecting parameters for characterizing the intensity of impulsive ground motion. Background Art

[0002] As an important part of the probabilistic decision-making framework of "performance-based earthquake engineering (PBEE)" proposed by the Pacific Earthquake Engineering Research Center (PEER), probabilistic seismic demand analysis (PSDA) has received extensive attention in the field of earthquake resistance research in recent years. PSDA evaluates the seismic performance of structures by establishing the relationship between the intensity measure (IM) of ground motion and the seismic demand measure of structures. Using the spectral acceleration at the first period as the intensity index can solve the problem of quantifying the ground motion intensity in most PSDA analyses. However, for impulsive ground motion with strong randomness and complexity, the evaluation method of using a single index to describe the ground motion intensity will bring large errors.

[0003] The methods described in this section are not necessarily methods that have been previously envisioned or adopted. Unless otherwise specified, any method described in this section should not be considered prior art solely because it is included in this section. Similarly, unless otherwise specified, the problems mentioned in this section should not be considered to have been recognized in any prior art. Summary of the Invention

[0004] In view of the technical problems existing in the prior art, the present invention provides a method and device for selecting parameters for characterizing the intensity of impulsive ground motion, which can be used for selecting parameters for characterizing the intensity of impulsive ground motion in seismic performance analysis.

[0005] The technical solutions of the present invention for solving the above technical problems are as follows:

[0006] In a first aspect, the present invention provides a method for selecting parameters for characterizing the intensity of impulsive ground motion, including:

[0007] Performing principal component analysis on the ground motion parameters in N ground motion records, and screening the first m principal components as alternative parameters, where m < n, n is the number of ground motion parameters, and N is a positive integer;

[0008] Using the elastoplastic time history analysis method, the maximum inter-story drift angles corresponding to the N ground motion records were obtained;

[0009] Linear regression analysis was performed on the maximum inter-story drift angles corresponding to the N ground motion records and the m candidate parameters to obtain the relationship between the candidate parameters and the median of the maximum inter-story drift angles: m θ =a k +b k Y k , where m θ a is the median of the maximum inter-story drift angle. k b k For alternative parameter Y k The corresponding regression coefficient when used as a parameter characterizing strength;

[0010] The linear regression coefficient of determination for the k-th candidate parameter is calculated using the following formula. As an evaluation index for the applicability of parameters characterizing seismic motion intensity:

[0011]

[0012] In the formula, θ p The most recent seismic motion record p obtained through elastoplastic time history analysis

[0013] Large inter-story drift angle; The median of the maximum inter-story drift angle is the p-th ground motion record and the k-th candidate parameter, calculated based on the relationship between the candidate parameters and the median of the maximum inter-story drift angle.

[0014] The candidate parameter corresponding to the linear regression coefficient of determination closest to 1 is selected as the optimal pulse-type ground motion intensity characterization parameter.

[0015] Furthermore, the principal component analysis of the seismic parameters in the N seismic records, and the selection of the top m principal components as candidate parameters, includes:

[0016] Extract n initial ground motion parameters from the ground motion record and construct an n-dimensional random vector X = (X1, X2, ..., X...). n Then the covariance matrix R corresponding to the n-dimensional random vector X is expressed as:

[0017]

[0018] In the formula, c ij =E[(X i -E[X i ])(X j -E[X j ])];

[0019] Note: The eigenvalues of the covariance matrix R corresponding to the n-dimensional random vector X are λ1, λ2,..., λ in descending order. n , and the corresponding orthonormal eigenvectors are α1, α2,..., α n . Then the k-th principal component Y of the n-dimensional random vector X k is expressed as a linear combination of the initial ground motion parameters: Y k = α k1 X1 + α k2 X2 +... + α kn X n , and α kn is the n-th component of the eigenvector α k .

[0020] Calculate the variance λ of the principal component and the contribution rate β of the first m principal components with the largest variance to the overall variance according to equations (3) and (4). k m :

[0021]

[0022] Select the first m principal components corresponding to β m ≥ the preset threshold as alternative parameters.

[0023] Further, the preset threshold is 95%.

[0024] Further, when considering bidirectional seismic action, before performing principal component analysis on the ground motion parameters in N ground motion records, the method further includes merging the initial ground motion parameters in two directions according to the following formula: In the formula, X ix , X iy are the components of the initial ground motion parameter X i in the x and y directions respectively.

[0025] Further, the method further includes: judging the structural characteristics of the target building according to the site characteristic period and the structural natural vibration period, and screening the ground motion parameters according to the characteristics of the target building.

[0026] In a second aspect, the present invention provides a device for selecting pulse-type ground motion intensity characterization parameters, including:

[0027] A principal component analysis module that performs principal component analysis on the ground motion parameters in N ground motion records, and screens the first m principal components as alternative parameters, where m < n, n is the number of ground motion parameters, and N is a positive integer;

[0028] A maximum inter-story drift angle analysis module that uses the elastic-plastic time history analysis method to obtain the maximum inter-story drift angle corresponding to the N ground motion records;

[0029] The regression analysis module performs linear regression analysis on the maximum inter-story drift angles corresponding to the N ground motion records and the m candidate parameters to obtain the relationship between the candidate parameters and the median of the maximum inter-story drift angle: m θ =a k +b k Y k , where m θ a is the median of the maximum inter-story drift angle. k b k For alternative parameter Y k The corresponding regression coefficient when used as a parameter characterizing strength;

[0030] The coefficient of determination module calculates the coefficient of determination for the linear regression of the k-th candidate parameter according to the following formula. As an evaluation index for the applicability of parameters characterizing seismic motion intensity:

[0031]

[0032] In the formula, θ p The maximum inter-story drift angle is obtained from the elastoplastic time history analysis of the p-th ground motion record; The median of the maximum inter-story drift angle is the p-th ground motion record and the k-th candidate parameter, calculated based on the relationship between the candidate parameters and the median of the maximum inter-story drift angle.

[0033] The parameter selection module selects the candidate parameter corresponding to the linear regression coefficient of determination closest to 1 as the optimal pulse-type ground motion intensity characterization parameter.

[0034] Furthermore, the device also includes a parameter merging module, which is used to: when considering bidirectional seismic action, before performing principal component analysis on the seismic motion parameters in N seismic motion records, merge the initial seismic motion parameters in the two directions according to the following formula: In the formula X ix X iy These are the initial ground motion parameters X. i The components in the x and y directions.

[0035] Furthermore, the parameter screening module of this device is used to determine the structural characteristics of the target building based on the site characteristic period and the natural vibration period of the structure, and to screen the seismic motion parameters based on the characteristics of the target building.

[0036] Thirdly, the present invention provides an electronic device, comprising:

[0037] Memory, used to store computer software programs;

[0038] A processor is used to read and execute the computer software program, thereby implementing the pulse-type ground motion intensity characterization parameter selection method described in the first aspect of the present invention.

[0039] Fourthly, the present invention provides a non-transitory computer-readable storage medium storing a computer software program, which, when executed by a processor, implements a method for selecting pulse-type ground motion intensity characterization parameters as described in the first aspect of the present invention.

[0040] The beneficial effects of this invention are: compared to a single intensity parameter, this invention utilizes principal component analysis to reduce the dimensionality of seismic motion parameters, transforming a single seismic motion intensity characterization parameter into a composite seismic intensity characterization parameter. The composite seismic motion intensity characterization parameter possesses superior statistical properties, exhibits better correlation with structural response, and is more suitable as a characterization parameter for pulse-type seismic motions. Attached Figure Description

[0041] Figure 1 This is a schematic flowchart of a method for selecting pulse-type ground motion intensity characterization parameters according to an embodiment of the present invention;

[0042] Figure 2 This is a finite element model of a building frame structure provided in an embodiment of the present invention;

[0043] Figure 3 This refers to the principal component variance contribution rate obtained by principal component analysis in this embodiment of the invention.

[0044] Figures 4a-4c These are the intensity characterization parameters Y1, Y2, and S in the embodiments of the present invention. a (T1) Regression results;

[0045] Figure 5 This is a schematic diagram of a device for selecting parameters for pulse-type ground motion intensity characterization provided in an embodiment of the present invention;

[0046] Figure 6 A structural block diagram of an exemplary electronic device that can be used to implement embodiments of the present invention is shown. Detailed Implementation

[0047] The technical solutions of the embodiments of this application will be clearly and completely described below with reference to the accompanying drawings. Obviously, the described embodiments are only some embodiments of this application, and not all embodiments. Based on the embodiments of this application, all other embodiments obtained by those skilled in the art without creative effort are within the scope of protection of this application.

[0048] In the description of the present application, the term "for example" is used to mean "serving as an example, illustration, or explanation". Any embodiment described in the present application as "for example" is not necessarily to be construed as more preferred or advantageous than other embodiments. The following description is given to enable any person skilled in the art to implement and use the present invention. In the following description, details are set forth for purposes of explanation. It should be understood that those of ordinary skill in the art can recognize that the present invention can be implemented without the use of these specific details. In other instances, well-known structures and processes are not elaborated in detail so as not to obscure the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but rather to be in line with the broadest scope consistent with the principles and features disclosed in the present application.

[0049] As Figure 1 shown, the present invention provides a method for selecting a characterization parameter of pulsed ground motion intensity, comprising the following steps:

[0050] S100, performing principal component analysis on the ground motion parameters in N ground motion records, and screening the first m principal components as alternative parameters, where m < n, n is the number of ground motion parameters, and N is a positive integer.

[0051] Principal component analysis is a common data dimensionality reduction method, which can transform high-dimensional correlated variables into low-dimensional linearly uncorrelated variables through orthogonal transformation, so as to achieve data dimensionality reduction. The new variables obtained after transformation are called principal components.

[0052] In this embodiment, n initial ground motion parameters in the ground motion records are extracted, and an n-dimensional random vector X = (X1, X2,..., X n ) is constructed; then the covariance matrix R corresponding to the n-dimensional random vector X is expressed as:

[0053]

[0054] In the formula, c ij = E[(X i - E[X i )(X j - E[X j )].

[0055] The problem of principal component analysis of ground motion parameters is transformed into the problem of solving the eigenvalues and eigenvectors of the corresponding covariance matrix R. Denote: the eigenvalues of the covariance matrix R corresponding to the n-dimensional random vector X are λ1, λ2,..., λ n , and the corresponding orthonormal eigenvectors are α1, α2,..., α n , then the kth principal component Y k of the n-dimensional random vector X is expressed as a linear combination of the initial ground motion parameters:

[0056] Y k =α k1 X1+α k2 X2 + ... + α kn X n (2);

[0057] In the formula, α kn For the eigenvector α k The nth component.

[0058] The variance λ of the principal components is calculated according to equations (3) and (4). k And the contribution rate β of the top m principal components with the largest variance to the overall variance. m :

[0059]

[0060] Choose β m The first m principal components corresponding to the condition ≥ the preset threshold are used as candidate parameters.

[0061] To preserve core data information, this embodiment of the invention selects β. m The top m principal components corresponding to ≥95% are used as candidate parameters.

[0062] S200, using the elastoplastic time history analysis method, the maximum inter-story drift angle corresponding to the N ground motion records is obtained.

[0063] S300, Perform linear regression analysis on the maximum inter-story drift angles corresponding to the N ground motion records and the m candidate parameters to obtain the relationship between the candidate parameters and the median of the maximum inter-story drift angle:

[0064] m θ =a k +b k Y k (5);

[0065] Where, m θ a is the median of the maximum inter-story drift angle. k b k For alternative parameter Y k The corresponding regression coefficient when used as a parameter to characterize intensity.

[0066] Specifically, in this embodiment, the least squares method is used for linear regression analysis to perform linear fitting. It should be understood that those skilled in the art can also use other mathematical methods to perform linear fitting in linear regression analysis according to actual needs. After linear fitting, the candidate parameter Y... k The corresponding inter-story drift angle is the median of the maximum inter-story drift angle.

[0067] S400, calculate the linear regression coefficient of determination of the kth candidate parameter according to equation (6). As an evaluation index for the applicability of parameters characterizing seismic motion intensity:

[0068]

[0069] In the formula, θ p The maximum inter-story drift angle is obtained from the elastoplastic time history analysis of the p-th ground motion record; The median of the maximum inter-story drift angle corresponding to the p-th ground motion record and the k-th candidate parameter is calculated based on formula (5);

[0070] S500 selects the candidate parameter corresponding to the linear regression coefficient of determination closest to 1 as the optimal pulse-type ground motion intensity characterization parameter.

[0071] Linear regression coefficient of determination The closer the value is to 1, the better the correlation between the principal component and the structural response, and the more suitable it is as a parameter for characterizing seismic intensity.

[0072] As a preferred embodiment, when considering bidirectional seismic action, before performing principal component analysis on the seismic motion parameters in the N seismic motion records, the method further includes merging the initial seismic motion parameters in both directions according to the following formula: In the formula X ix X iy These are the initial ground motion parameters X. i The components in the x and y directions.

[0073] Preferably, the method further includes: determining the structural characteristics of the target building based on the site characteristic period and the natural vibration period of the structure, and selecting seismic motion parameters based on the characteristics of the target building.

[0074] The above method will be further explained below with specific examples.

[0075] This embodiment uses a multi-story frame structure in a museum as an engineering case study. This multi-story frame structure was severely damaged in a magnitude 6.8 earthquake in its area; this earthquake was a pulse-type near-fault earthquake. The multi-story frame structure is a two-story frame structure, with the first and second stories measuring 4.8m and 4.2m in height, respectively. According to the seismic zoning map, the location of frame W2 belongs to a seismic intensity zone of 9 degrees (0.4g), a Class II site, with a ground motion group of Group 3 and a characteristic period of 0.45s.

[0076] Elastic time-history analysis of frame W2 was performed using the general-purpose analysis software ANSYS. Based on the field rebound test and measurement results, the concrete strength of the frame columns, frame beams, and floor slabs was C30, and the steel reinforcement was HRB400, with all material strength values ​​being standard values. The concrete and steel reinforcement were modeled using multi-segmented and bi-segmented ideal elastoplastic constitutive relations, respectively, both employing isotropic hardening models and neglecting the Bauschinger effect. The frame beams and the "imitation corbel" were modeled using the 3D linear finite strain beam element BEAM188; the floor slab was modeled using the 3D finite strain shell element SHELL181. To accurately account for the influence of stirrups on the plastic development of the frame columns, the bottom 1.5m range of the first-floor frame columns was simulated using the 3D solid element reinforced SOLID65, while the remaining frame columns were simulated using the 3D quadratic finite strain beam element BEAM189. The longitudinal reinforcement of the components was dispersed throughout the entire cross-section. The solid element portion at the bottom of the columns was meshed using hexahedral mapping and refined; the finite element mesh generation is shown in [link to finite element mesh generation documentation]. Figure 2 .

[0077] Based on existing studies of pulse-type ground motions, this example selects 90 pulse-type ground motion records from the Pacific Earthquake Engineering Research (PEER) ground motion database for principal component analysis. Given that frame W2 is a short-period structure, the displacement-related parameters have a relatively small impact on its structural response. Therefore, this example uses 17 ground motion parameters—pulse-related, acceleration-related, and velocity-related parameters—for principal component analysis. The names and symbols of the 17 ground motion parameters are shown in Table 1.

[0078] Table 1

[0079]

[0080]

[0081] Using 90 pulse-type ground motion parameters as initial ground motion parameters, principal component analysis was performed on the pulse-type ground motion parameters. The variance contribution rates of each principal component are shown in the figure. Figure 3 .like Figure 3 As shown, for multivariate parameters of pulse-type ground motion, the variance contribution rates of the principal components are relatively concentrated. The variance contribution rates of the first two principal components are 84.98% and 12.98%, respectively, with a cumulative contribution rate of 97.96%. The first two principal components can well explain the variance of multivariate parameters of ground motion.

[0082] This invention selects seismic motion multivariate parameters with a correlation coefficient greater than 0.3 and eigenvector components α with large absolute values. kn This represents the first two principal components, Y1 and Y2. Y1 and Y2 can be expressed using multivariate ground motion parameters as follows:

[0083] Y1 = 0.80P v + 0.49IF + 0.26PPV + 0.17A p + 0.13S v + 0.10I v + 0.005I A (7)

[0084] Y2 = -0.88S v + 0.36P v - 0.22PPV - 0.20IF - 0.09A p + 0.007T p (8)

[0085] After removing the outliers in the data, the principal components Y1, Y2 and the first-period spectral acceleration S a (T1) are regressed. The analysis results are shown in Figures 4a-4c . As Figures 4a-4c shown, the principal component Y1 is positively correlated with the maximum inter-story drift angle, and the determination coefficient of the linear regression reaches 0.54. The principal component Y2 is negatively correlated with the structural response, and the determination coefficient of the regression result is 0.48, and the correlation between the two principal components and the maximum inter-story drift angle is much greater than that of the first-period spectral acceleration S a (T1). According to the judgment of the site characteristic period and the structural natural vibration period, the frame W2 belongs to a short-period structure. According to the design response spectrum in the code, the seismic response of the frame W2 is controlled by acceleration. However, for the pulse-type ground motion records, the first-period spectral acceleration S a (T1) cannot effectively reflect the seismic response of short-period structures and is no longer suitable as an intensity characterization parameter. According to the analysis results of the principal component variance contribution rate, for the pulse-type ground motions selected in the present invention, the principal component Y1 is more suitable as an intensity characterization parameter for PSDA analysis.

[0086] As Figure 5 shown, the embodiment of the present invention provides a device for selecting an intensity characterization parameter of pulse-type ground motion, including:

[0087] A principal component analysis module that performs principal component analysis on the ground motion parameters in N ground motion records, and screens the first m principal components as alternative parameters, where m < n, n is the number of ground motion parameters, and N is a positive integer;

[0088] A maximum inter-story drift angle analysis module that uses the elastic-plastic time history analysis method to obtain the maximum inter-story drift angles corresponding to the N ground motion records;

[0089] The regression analysis module performs linear regression analysis on the maximum inter-story drift angles corresponding to the N ground motion records and the m candidate parameters to obtain the relationship between the candidate parameters and the median of the maximum inter-story drift angle: m θ =a k +b k Y k , where m θ a is the median of the maximum inter-story drift angle. k b k For alternative parameter Y k The corresponding regression coefficient when used as a parameter characterizing strength;

[0090] The coefficient of determination module calculates the coefficient of determination for the linear regression of the k-th candidate parameter according to the following formula. As an evaluation index for the applicability of parameters characterizing seismic motion intensity:

[0091]

[0092] In the formula, θ p The maximum inter-story drift angle is obtained from the elastoplastic time history analysis of the p-th ground motion record; The median of the maximum inter-story drift angle is the p-th ground motion record and the k-th candidate parameter, calculated based on the relationship between the candidate parameters and the median of the maximum inter-story drift angle.

[0093] The parameter selection module selects the candidate parameter corresponding to the linear regression coefficient of determination closest to 1 as the optimal pulse-type ground motion intensity characterization parameter.

[0094] According to one aspect of this embodiment, an electronic device is also provided, including: at least one processor; and a memory communicatively connected to the at least one processor; wherein the memory stores instructions executable by the at least one processor, the instructions being executed by the at least one processor to enable the at least one processor to perform the above-described method.

[0095] According to one aspect of this embodiment, a non-transitory computer-readable storage medium is also provided, wherein computer instructions are stored therein, which, when executed by a computer, implement the above-described method.

[0096] According to one aspect of this embodiment, a computer program product is also provided, including a computer program, wherein,

[0097] The computer program implements the above method when executed by the processor.

[0098] refer to Figure 6The following is a structural block diagram of an electronic device 600 that can serve as a server or client in this embodiment, which is an example of a hardware device that can be applied to various aspects of this embodiment. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workstations, personal digital assistants, servers, blade servers, mainframe computers, and other suitable computers. The electronic device can also represent various forms of mobile devices, such as personal digital processors, cellular phones, smartphones, wearable devices, and other similar computing devices. The components shown herein, their connections and relationships, and their functions are merely examples and are not intended to limit the implementation of the embodiments described and / or claimed herein.

[0099] like Figure 6 As shown, the electronic device 600 includes a computing unit 601, which can perform various appropriate actions and processes based on a computer program stored in a read-only memory (ROM) 602 or a computer program loaded from a storage unit 608 into a random access memory (RAM) 603. The RAM 603 may also store various programs and data required for the operation of the electronic device 600. The computing unit 601, ROM 602, and RAM 603 are interconnected via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0100] Multiple components in electronic device 600 are connected to I / O interface 605, including: input unit 606, output unit 607, storage unit 608, and communication unit 609. Input unit 606 can be any type of device capable of inputting information to electronic device 600. Input unit 606 can receive input digital or character information and generate key signal inputs related to user settings and / or function control of electronic device, and can include, but is not limited to, a mouse, keyboard, touchscreen, trackpad, trackball, joystick, microphone, and / or remote control. Output unit 607 can be any type of device capable of presenting information, and can include, but is not limited to, a monitor, speaker, video / audio output terminal, vibrator, and / or printer. Storage unit 608 can include, but is not limited to, disk and optical disk. Communication unit 609 allows electronic device 600 to exchange information / data with other devices through computer networks such as the Internet and / or various telecommunications networks, and can include, but is not limited to, modems, network cards, infrared communication devices, wireless communication transceivers, and / or chipsets, such as Bluetooth™ devices, 802.11 devices, WiFi devices, WiMax devices, cellular communication devices, and / or the like.

[0101] The computing unit 601 can be a variety of general-purpose and / or special-purpose processing components with processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any suitable processor, controller, microcontroller, etc. The computing unit 601 performs the various methods and processes described above, such as image processing methods. For example, in some embodiments, the image processing method may be implemented as a computer software program tangibly contained in a machine-readable medium, such as storage unit 608. In some embodiments, part or all of the computer program may be loaded and / or installed on the electronic device 600 via ROM 602 and / or communication unit 609. When the computer program is loaded into RAM 603 and executed by the computing unit 601, one or more steps of the image processing method described above may be performed. Alternatively, in other embodiments, the computing unit 601 may be configured to perform image processing methods by any other suitable means (e.g., by means of firmware).

[0102] Various embodiments of the systems and techniques described above herein can be implemented in digital electronic circuit systems, integrated circuit systems, field-programmable gate arrays (FPGAs), application-specific integrated circuits (ASICs), application-specific standard products (ASSPs), systems-on-a-chip (SoCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various embodiments may include implementations in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which may be a dedicated or general-purpose programmable processor, capable of receiving data and instructions from a storage system, at least one input device, and at least one output device, and transmitting data and instructions to the storage system, the at least one input device, and the at least one output device.

[0103] The program code used to implement the methods of this embodiment may be written in any combination of one or more programming languages. This program code may be provided to a processor or controller of a general-purpose computer, special-purpose computer, or other programmable data processing device, such that when executed by the processor or controller, the program code causes the functions / operations specified in the flowcharts and / or block diagrams to be implemented. The program code may be executed entirely on the machine, partially on the machine, as a standalone software package partially on the machine and partially on a remote machine, or entirely on a remote machine or server.

[0104] In the context of this embodiment, a machine-readable medium can be a tangible medium that may contain or store a program for use by or in conjunction with an instruction execution system, apparatus, or device. A machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. Machine-readable media can include, but are not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, apparatus, or devices, or any suitable combination of the foregoing. More specific examples of machine-readable storage media include electrical connections based on one or more wires, portable computer disks, hard disks, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or flash memory), optical fibers, portable compact disk read-only memory (CD-ROM), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0105] To provide interaction with a user, the systems and techniques described herein can be implemented on a computer having: a display device for displaying information to the user (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor); and a keyboard and pointing device (e.g., a mouse or trackball) through which the user provides input to the computer. Other types of devices can also be used to provide interaction with the user; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form (including sound input, voice input, or tactile input).

[0106] The systems and technologies described herein can be implemented in computing systems that include backend components (e.g., as a data server), or computing systems that include middleware components (e.g., an application server), or computing systems that include frontend components (e.g., a user computer with a graphical user interface or web browser through which a user can interact with embodiments of the systems and technologies described herein), or any combination of such backend, middleware, or frontend components. The components of the system can be interconnected via digital data communication of any form or medium (e.g., a communication network). Examples of communication networks include local area networks (LANs), wide area networks (WANs), and the Internet.

[0107] Computer systems can include clients and servers. Clients and servers are generally located far apart and typically interact via communication networks. Client-server relationships are created by computer programs running on the respective computers and having a client-server relationship with each other. Servers can be cloud servers, servers in distributed systems, or servers incorporating blockchain technology.

[0108] It should be understood that the various forms of processes shown above can be used to reorder, add, or delete steps. For example, the steps described in this embodiment can be executed in parallel, sequentially, or in a different order, as long as the desired result of the technical solution disclosed in this embodiment can be achieved, and this document does not impose any restrictions.

[0109] Although embodiments or examples of this invention have been described with reference to the accompanying drawings, it should be understood that the methods, systems, and devices described above are merely exemplary embodiments or examples, and the scope of the invention is not limited by these embodiments or examples, but only by the granted claims and their equivalents. Various elements in the embodiments or examples may be omitted or replaced by their equivalents. Furthermore, the steps may be performed in a different order than that described in this embodiment. Further, various elements in the embodiments or examples may be combined in various ways. Importantly, as technology evolves, many elements described herein can be replaced by equivalents that appear after this embodiment.

Claims

1. A method for selecting parameters to characterize the intensity of pulse-type ground motion, characterized in that, Including: Performing principal component analysis on the ground motion parameters in N ground motion records, and screening the first m principal components as alternative parameters, where m < n, n is the number of ground motion parameters, and N is a positive integer; Using the elastoplastic time history analysis method to obtain the maximum inter-story drift angle corresponding to the N ground motion records; Linear regression analysis was performed on the maximum inter-story drift angles corresponding to the N ground motion records and the m candidate parameters to obtain the relationship between the candidate parameters and the median of the maximum inter-story drift angles: m θ =a k +b k Y k , where m θ a is the median of the maximum inter-story drift angle. k b k For alternative parameter Y k The corresponding regression coefficient when used as a parameter characterizing strength; The linear regression coefficient of determination for the k-th candidate parameter is calculated using the following formula. As an evaluation index for the applicability of parameters characterizing seismic motion intensity: In the formula, θ p The maximum inter-story drift angle is obtained from the elastoplastic time history analysis of the p-th ground motion record; The median of the maximum inter-story drift angle is the p-th ground motion record and the k-th candidate parameter, calculated based on the relationship between the candidate parameters and the median of the maximum inter-story drift angle. Selecting the alternative parameter corresponding to the linear regression determination coefficient closest to 1 as the optimal pulse-type ground motion intensity characterization parameter.

2. The method according to claim 1, characterized in that, The performing principal component analysis on the ground motion parameters in N ground motion records and screening the first m principal components as alternative parameters includes: Extract n initial ground motion parameters from the ground motion record and construct an n-dimensional random vector X = (X1, X2, ..., X...). n Then the covariance matrix R corresponding to the n-dimensional random vector X is expressed as: In the formula, c ij =E[(X i -E[X i ])(X j -E[X j ])]; Let the eigenvalues ​​of the covariance matrix R corresponding to the n-dimensional random vector X be λ1, λ2, ..., λ3 in descending order. n The corresponding orthogonal normalized eigenvectors are α1, α2, ..., α n Then the k-th principal component Y of the n-dimensional random vector X k The linear combination of initial ground motion parameters is expressed as: Y k =α k1 X1+α k2 X2 + ... + α kn X n α kn For the eigenvector α k The nth component; The variance λ of the principal components is calculated according to equations (3) and (4). k And the contribution rate β of the top m principal components with the largest variance to the overall variance. m : Choose β m The first m principal components corresponding to the condition ≥ the preset threshold are used as candidate parameters.

3. The method according to claim 2, characterized in that, The preset threshold is 95%.

4. The method according to claim 1, characterized in that, When considering bidirectional seismic action, before performing principal component analysis on the seismic motion parameters in N seismic motion records, the method further includes merging the initial seismic motion parameters in both directions according to the following formula: In the formula X ix X iy These are the initial ground motion parameters X. i The components in the x and y directions.

5. The method according to claim 1, characterized in that, It further includes: Judging the structural characteristics of the target building according to the site characteristic period and the structural natural vibration period, and screening the ground motion parameters according to the characteristics of the target building.

6. A device for selecting parameters for pulse-type ground motion intensity characterization, characterized in that, Including: A principal component analysis module that performs principal component analysis on the ground motion parameters in N ground motion records and screens the first m principal components as alternative parameters, where m < n, n is the number of ground motion parameters, and N is a positive integer; A maximum inter-story drift angle analysis module that uses the elastoplastic time history analysis method to obtain the maximum inter-story drift angle corresponding to the N ground motion records; The regression analysis module performs linear regression analysis on the maximum inter-story drift angles corresponding to the N ground motion records and the m candidate parameters to obtain the relationship between the candidate parameters and the median of the maximum inter-story drift angle: m θ =a k +b k Y k , where m θ a is the median of the maximum inter-story drift angle. k b k For alternative parameter Y k The corresponding regression coefficient when used as a parameter characterizing strength; The coefficient of determination module calculates the coefficient of determination for the linear regression of the k-th candidate parameter according to the following formula. As an evaluation index for the applicability of parameters characterizing seismic motion intensity: In the formula, θ p The maximum inter-story drift angle is obtained from the elastoplastic time history analysis of the p-th ground motion record; The median of the maximum inter-story drift angle is the p-th ground motion record and the k-th candidate parameter, calculated based on the relationship between the candidate parameters and the median of the maximum inter-story drift angle. A parameter selection module that selects the alternative parameter corresponding to the linear regression determination coefficient closest to 1 as the optimal pulse-type ground motion intensity characterization parameter.

7. The apparatus according to claim 6, characterized in that, It also includes a parameter merging module, which is used to: when considering bidirectional seismic action, before performing principal component analysis on the seismic motion parameters in N seismic motion records, merge the initial seismic motion parameters in the two directions according to the following formula: In the formula X ix X iy These are the initial ground motion parameters X. i The components in the x and y directions.

8. The apparatus according to claim 6, characterized in that, It further includes a parameter screening module for judging the structural characteristics of the target building according to the site characteristic period and the structural natural vibration period, and screening the ground motion parameters according to the characteristics of the target building.

9. An electronic device, characterized in that, Including: A memory for storing computer software programs; A processor for reading and executing the computer software program, and further implementing a method for selecting a pulse-type ground motion intensity characterization parameter according to any one of claims 1-5.

10. A non-transitory computer-readable storage medium, characterized in that, The computer software program is stored in the storage medium, and when the computer software program is executed by the processor, it implements a method for selecting a pulse-type ground motion intensity characterization parameter according to any one of claims 1-5.

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