Pulse type seismic oscillation intensity characterization parameter selection method and device

Abstract

The invention relates to a pulse type seismic oscillation intensity characterization parameter selection method and device, and the method comprises the steps: carrying out the principal component analysis of seismic oscillation parameters in N seismic oscillation records, and screening the first m principal components as alternative parameters; then an elastic-plastic time history analysis method is utilized to obtain the maximum interlayer displacement angle corresponding to the N seismic oscillation records; performing linear regression analysis on the maximum interlayer displacement angle corresponding to the N seismic oscillation records and the m alternative parameters to obtain a relationship between the alternative parameters and the median of the maximum interlayer displacement angle, and calculating a linear regression determination coefficient of the alternative parameters; and selecting an alternative parameter corresponding to the linear regression determination coefficient closest to 1 as an optimal pulse type seismic oscillation intensity characterization parameter. Compared with a single intensity parameter, the single seismic oscillation intensity characterization parameter is converted into the composite seismic intensity characterization parameter by using the principal component analysis method, so that the statistical property is better, and the correlation with the structural response is better.

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 intensity of ground motion 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 intensity of ground motion will bring large errors.

[0003] The methods described in this section are not necessarily methods that have been previously conceived or adopted. Unless otherwise specified, no method described in this section should 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 elastic-plastic time history analysis method, the maximum inter-story displacement angle corresponding to the N earthquake motion records is obtained;

[0009] A linear regression analysis is performed on the maximum inter-story displacement angle corresponding to the N earthquake records and the m candidate parameters to obtain the relationship between the candidate parameters and the median of the maximum inter-story displacement angle: θ =a k +b k Y k , where m θ is the median of the maximum inter-story displacement angle, a k , b k is the alternative parameter Y k The corresponding regression coefficient when used as a strength characterization parameter;

[0010] The linear regression coefficient of determination of the kth candidate parameter is calculated according to the following formula: As an evaluation index of the applicability of ground motion intensity characterization parameters:

[0011]

[0012] In the formula, θ p is the maximum value obtained by elastic-plastic time history analysis of the pth ground motion record.

[0013] Large inter-story displacement angle; is the median of the maximum inter-story displacement angle corresponding to the pth earthquake motion record and the kth alternative parameter calculated based on the relationship between the alternative parameters and the median of the maximum inter-story displacement angle;

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

[0015] Furthermore, the performing of principal component analysis on the seismic parameters in the N seismic records and selecting the first 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 , where α kn is the n-th component of the eigenvector α k ;

[0020] Calculate the variance λ of the principal components 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: where 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 target building characteristics.

[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-layer displacement angle corresponding to the N earthquake records and the m candidate parameters to obtain the relationship between the candidate parameters and the median of the maximum inter-layer displacement angle: m θ =a k +b k Y k , where m θ is the median of the maximum inter-story displacement angle, a k 、b k is the alternative parameter Y k The corresponding regression coefficient when used as a strength characterization parameter;

[0030] The coefficient of determination calculation module calculates the linear regression coefficient of determination of the kth candidate parameter according to the following formula As an evaluation index of the applicability of ground motion intensity characterization parameters:

[0031]

[0032] In the formula, θ p is the maximum inter-story displacement angle obtained through the elastoplastic time history analysis of the pth earthquake motion record; is the median of the maximum inter-story displacement angle corresponding to the pth ground motion record and the kth alternative parameter calculated based on the relationship between the alternative parameters and the median of the maximum inter-story displacement angle;

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

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

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

[0036] In a third aspect, the present invention provides an electronic device, comprising:

[0037] Memory for storing computer software programs;

[0038] The processor is used to read and execute the computer software program, thereby realizing the method for selecting parameters for characterizing the intensity of pulse-type ground motion described in the first aspect of the present invention.

[0039] In a fourth aspect, the present invention provides a non-transitory computer-readable storage medium, wherein the storage medium stores a computer software program, and when the computer software program is executed by a processor, the method for selecting parameters for characterizing pulse-type seismic intensity as described in the first aspect of the present invention is implemented.

[0040] The beneficial effect of the present invention is that compared with a single intensity parameter, the present invention uses the principal component analysis method to reduce the dimension of the seismic motion parameter, and converts the single seismic motion intensity characterization parameter into a composite seismic intensity characterization parameter. The composite seismic motion intensity characterization parameter has better statistics, better correlation with structural response, and is more suitable as a pulse seismic motion intensity characterization parameter. BRIEF DESCRIPTION OF THE DRAWINGS

[0041] Figure 1 A schematic flow chart of a method for selecting parameters for characterizing pulse earthquake intensity provided by an embodiment of the present invention;

[0042] Figure 2 A finite element model of a certain building frame structure provided by an embodiment of the present invention;

[0043] Figure 3 is the principal component variance contribution rate obtained by principal component analysis in the embodiment of the present invention;

[0044] Figure 4a to Figure 4c are the strength characterization parameters Y1, Y2, and S in the embodiment of the present invention respectively. a (T1) Regression results;

[0045] Figure 5 A schematic diagram of the structure of a device for selecting parameters for characterizing pulse-type earthquake intensity provided by an embodiment of the present invention;

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

[0047] The following will be combined with the drawings in the embodiments of the present application to clearly and completely describe the technical solutions in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all of the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative work 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 as "for example" in the present application is not necessarily construed as being more preferred or advantageous than other embodiments. The following description is provided 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 using these specific details. In other instances, well-known structures and processes are not elaborated in detail to avoid obscuring the description of the present invention with unnecessary details. Therefore, the present invention is not intended to be limited to the embodiments shown, but is to be accorded the widest 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 impulse-type ground motion intensity, including the following steps:

[0050] S100, perform principal component analysis on the ground motion parameters in N ground motion records, and screen 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 that can transform high-dimensional correlated variables into low-dimensional linearly uncorrelated variables through orthogonal transformation, thereby achieving 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 k-th 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 is the feature vector α k The nth component of .

[0058] According to formula (3) and formula (4), the variance λ of the principal component is calculated k And the contribution rate of the first m principal components with the largest variance to the overall variance β m :

[0059]

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

[0061] In order to retain the core information of the data, the embodiment of the present invention selects β m The first m principal components corresponding to ≥95% are taken as candidate parameters.

[0062] S200, using an elastic-plastic time history analysis method, obtaining the maximum inter-story displacement angle corresponding to the N earthquake motion records.

[0063] S300, performing linear regression analysis on the maximum inter-story displacement angle corresponding to the N earthquake records and the m candidate parameters, to obtain a relationship between the candidate parameters and the median of the maximum inter-story displacement angle:

[0064] m θ =a k +b k Y k (5);

[0065] Among them, m θ is the median of the maximum inter-story displacement angle, a k , b k is the alternative parameter Y k The corresponding regression coefficient when used as an intensity characterization parameter.

[0066] Specifically, the linear regression analysis in this embodiment uses the least square method for linear fitting. It should be known 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 displacement angle is the median of the maximum inter-story displacement angle.

[0067] S400, calculating the linear regression determination coefficient of the kth candidate parameter according to formula (6) As an evaluation index of the applicability of ground motion intensity characterization parameters:

[0068]

[0069] In the formula, θ p is the maximum inter-story displacement angle obtained through the elastoplastic time history analysis of the pth earthquake motion record; is the median of the maximum inter-story displacement angle corresponding to the pth ground motion record and the kth candidate parameter calculated based on formula (5);

[0070] S500: Selecting a candidate parameter corresponding to a linear regression determination coefficient closest to 1 as an optimal pulse-type ground motion intensity characterization parameter.

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

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

[0073] Preferably, the method further comprises: judging the structural characteristics of the target building according to the site characteristic period and the structural natural vibration period, and screening the seismic motion parameters according to the characteristics of the target building.

[0074] The above method is further explained below with reference to specific examples.

[0075] This embodiment uses a multi-story frame structure in a museum as an engineering case for analysis. The multi-story frame structure was severely damaged in a 6.8-magnitude earthquake in the area where it was located. The earthquake was a pulse-type near-fault earthquake. The multi-story frame structure is a two-story frame structure, and the heights of the first and second floors are 4.8m and 4.2m respectively. According to the earthquake zoning map, the location of frame W2 belongs to the 9-degree zone (0.4g), Class II site, the seismic grouping is the third group, and the characteristic period is 0.45s.

[0076] The general analysis software ANSYS was used to conduct elastic time-history analysis on frame W2: According to the on-site rebound test and measurement results, the concrete strength of the frame columns, frame beams and floor slabs is C30, and the steel bars are HRB400, and the material strength values ​​are all standard values. Concrete and steel bars adopt multi-fold line and double-fold line ideal elastic-plastic constitutive relations respectively, and both use isotropic strengthening models without considering the Bausinger effect. The frame beams and "imitation brackets" are modeled using 3D linear finite strain beam elements BEAM188; the floor slabs are modeled using 3D finite strain shell elements SHELL181; in order to accurately take into account the influence of stirrups on the plastic development of frame columns, 3D solid unit reinforced SOLID65 is used within 1.5m of the bottom of the first-floor frame columns, and the remaining frame columns are simulated using 3D secondary finite strain beam elements BEAM189; the longitudinal reinforcement of the components is dispersed throughout the entire section. The solid unit part at the lower end of the column is divided by hexahedral mapping, and the mesh is encrypted. The finite element mesh division is shown in Figure 2 .

[0077] According to the existing pulse-type ground motion research, this example selects 90 pulse-type ground motion records from the Pacific Earthquake Engineering Research (PEER) ground motion database for principal component analysis. Since frame W2 is a short-period structure, the displacement-related parameters have little effect on its structural response. In this example, pulse-related, acceleration-related and velocity-related parameters are selected, a total of 17 ground motion parameters are used 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] The multivariate parameters of 90 pulse-type ground motions were used as the initial ground motion parameters, and the principal components of the pulse-type ground motion parameters were analyzed. The variance contribution rate of each principal component is shown in Figure 3 .like Figure 3 As shown in the figure, for pulse-type seismic multivariate parameters, the variance contribution rate of the principal components is relatively concentrated. The variance contribution rates of the first two principal components are 84.98% and 12.98% respectively, and the cumulative contribution rate reaches 97.96%. The first two principal components can well explain the variance of the seismic multivariate parameters.

[0082] The present invention selects the multivariate parameters of earthquake motion with a correlation coefficient greater than 0.3 and the eigenvector component α with a larger absolute value. kn represents the first two principal components Y1 and Y2. Y1 and Y2 can be expressed by multivariate ground motion parameters as:

[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 subjected to regression analysis, and the analysis results are shown in Figure 4a to Figure 4c . As Figure 4a to Figure 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. The correlations between the two principal components and the maximum inter - story drift angle are much greater than that of the first - period spectral acceleration S a (T1). Judging from the site characteristic period and the natural vibration period of the structure, 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 the short - period structure 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 the 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-layer displacement angle corresponding to the N earthquake records and the m candidate parameters to obtain the relationship between the candidate parameters and the median of the maximum inter-layer displacement angle: m θ =a k +b k Y k , where m θ is the median of the maximum inter-story displacement angle, a k , b k is the alternative parameter Y k The corresponding regression coefficient when used as a strength characterization parameter;

[0090] The coefficient of determination calculation module calculates the linear regression coefficient of determination of the kth candidate parameter according to the following formula As an evaluation index of the applicability of ground motion intensity characterization parameters:

[0091]

[0092] In the formula, θ p is the maximum inter-story displacement angle obtained through the elastoplastic time history analysis of the pth earthquake motion record; is the median of the maximum inter-story displacement angle corresponding to the pth ground motion record and the kth alternative parameter calculated based on the relationship between the alternative parameters and the median of the maximum inter-story displacement angle;

[0093] The parameter selection module selects the candidate parameter corresponding to the linear regression determination coefficient 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, comprising: 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, and the instructions are executed by the at least one processor so that the at least one processor can execute the above method.

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

[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 a processor.

[0098] refer to Figure 6, the structural block diagram of the electronic device 600 that can be used as the server or client of the present embodiment will now be described, which is an example of a hardware device that can be applied to various aspects of the present embodiment. The electronic device is intended to represent various forms of digital electronic computer devices, such as laptop computers, desktop computers, workbenches, 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 processing, cellular phones, smart phones, 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 present embodiment described and / or required herein.

[0099] like Figure 6 As shown, the electronic device 600 includes a computing unit 601, which can perform various appropriate actions and processes according to 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. In the RAM 603, various programs and data required for the operation of the electronic device 600 can also be stored. The computing unit 601, the ROM 602, and the RAM 603 are connected to each other via a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0100] Multiple components in the electronic device 600 are connected to the I / O interface 605, including: an input unit 606, an output unit 607, a storage unit 608, and a communication unit 609. The input unit 606 can be any type of device that can input information to the electronic device 600. The input unit 606 can receive input digital or character information and generate key signal input related to user settings and / or function control of the electronic device, and can include but is not limited to a mouse, a keyboard, a touch screen, a track pad, a track ball, a joystick, a microphone, and / or a remote controller. The output unit 607 can be any type of device that can present information, and can include but is not limited to a display, a speaker, a video / audio output terminal, a vibrator, and / or a printer. The storage unit 608 can include but is not limited to a disk, an optical disk. The communication unit 609 allows the electronic device 600 to exchange information / data with other devices through a computer network such as the Internet and / or various telecommunication networks, and can include but is not limited to a modem, a network card, an infrared communication device, a wireless communication transceiver, and / or a chipset, such as a Bluetooth™ device, an 802.11 device, a WiFi device, a WiMax device, a cellular communication device, and / or the like.

[0101] The computing unit 601 may be a variety of general and / or special 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 dedicated artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, digital signal processors (DSPs), and any appropriate processors, controllers, microcontrollers, 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, which is tangibly contained in a machine-readable medium, such as a 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 the image processing method in any other appropriate manner (e.g., by means of firmware).

[0102] Various implementations 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 chips (SOCs), complex programmable logic devices (CPLDs), computer hardware, firmware, software, and / or combinations thereof. These various implementations can include: being implemented in one or more computer programs that can be executed and / or interpreted on a programmable system including at least one programmable processor, which can be a special purpose or general purpose programmable processor that can receive data and instructions from a storage system, at least one input device, and at least one output device, and transmit data and instructions to the storage system, the at least one input device, and the at least one output device.

[0103] The program code for implementing the method of the present embodiment can be written in any combination of one or more programming languages. These program codes can be provided to a processor or controller of a general-purpose computer, a special-purpose computer or other programmable data processing device, so that the program code, when executed by the processor or controller, enables the functions / operations specified in the flow chart and / or block diagram to be implemented. The program code can be executed entirely on the machine, partially on the machine, partially on the machine as an independent software package and partially on a remote machine, or entirely on a remote machine or server.

[0104] In the context of the present embodiment, a machine-readable medium may be a tangible medium that may contain or store a program for use by an instruction execution system, device or equipment or used in combination with an instruction execution system, device or equipment. A machine-readable medium may be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium may include, but is not limited to, electronic, magnetic, optical, electromagnetic, infrared, or semiconductor systems, devices or equipment, or any suitable combination of the foregoing. A more specific example of a machine-readable storage medium may include an electrical connection based on one or more lines, a portable computer disk, a hard disk, a random access memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disk read-only memory (CD ROM), an optical storage device, a magnetic storage device, 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 (e.g., a CRT (cathode ray tube) or LCD (liquid crystal display) monitor) for displaying information to the user; and a keyboard and pointing device (e.g., a mouse or trackball) through which the user can provide input to the computer. Other types of devices can also be used to provide interaction with the user; for example, the 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 acoustic input, voice input, or tactile input).

[0106] The systems and techniques described herein may be implemented in a computing system that includes back-end components (e.g., as a data server), or a computing system that includes middleware components (e.g., an application server), or a computing system that includes front-end components (e.g., a user computer with a graphical user interface or a web browser through which a user can interact with implementations of the systems and techniques described herein), or a computing system that includes any combination of such back-end components, middleware components, or front-end components. The components of the system may be interconnected by any form or medium of digital data communication (e.g., a communication network). Examples of communication networks include: a local area network (LAN), a wide area network (WAN), and the Internet.

[0107] A computer system may include a client and a server. The client and the server are generally remote from each other and usually interact through a communication network. The relationship of client and server is generated by computer programs running on respective computers and having a client-server relationship with each other. The server may be a cloud server, a server of a distributed system, or a server combined with a blockchain.

[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 recorded in this embodiment can be executed in parallel, sequentially or in different orders, as long as the desired results of the technical solution disclosed in this embodiment can be achieved, and this document does not limit this.

[0109] Although the embodiments or examples of the present embodiment have been described with reference to the accompanying drawings, it should be understood that the above-mentioned methods, systems and devices are merely exemplary embodiments or examples, and the scope of the present invention is not limited by these embodiments or examples, but is only limited by the claims after authorization and their equivalent scope. Various elements in the embodiments or examples can be omitted or replaced by their equivalent elements. In addition, each step can be performed in an order different from that described in the present embodiment. Further, the various elements in the embodiments or examples can be combined in various ways. It is important that with the evolution of technology, many elements described herein can be replaced by equivalent elements that appear after the present embodiment.

Claims

1. A method for selecting parameters for characterizing pulse earthquake intensity, 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 elastic-plastic time history analysis method to obtain the maximum inter-story drift angle corresponding to the N ground motion records; A linear regression analysis is performed on the maximum inter-story displacement angle corresponding to the N earthquake records and the m candidate parameters to obtain the relationship between the candidate parameters and the median of the maximum inter-story displacement angle: θ =a k +b k Y k , where m θ is the median of the maximum inter-story displacement angle, a k , b k is the alternative parameter Y k The corresponding regression coefficient when used as a strength characterization parameter; The linear regression coefficient of determination of the kth candidate parameter is calculated according to the following formula: As an evaluation index of the applicability of ground motion intensity characterization parameters: In the formula, θ p is the maximum inter-story displacement angle obtained through the elastoplastic time history analysis of the pth earthquake motion record; is the median of the maximum inter-story displacement angle corresponding to the pth ground motion record and the kth alternative parameter calculated based on the relationship between the alternative parameters and the median of the maximum inter-story displacement 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 ])]; Note: The eigenvalues ​​of the covariance matrix R corresponding to the n-dimensional random vector X are λ1,λ2,...,λ n , the corresponding orthogonal normalized eigenvectors are α1,α2,...,α n , then the kth 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 is the feature vector α k The nth component of ; According to formula (3) and formula (4), the variance λ of the principal component is calculated k And the contribution rate of the first m principal components with the largest variance to the overall variance β m : Select β m The first m principal components corresponding to 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 earthquake action, before performing principal component analysis on the seismic parameters in the N seismic records, the method further includes combining the initial seismic parameters in the two directions according to the following formula: Where X ix , X iy are the initial ground motion parameters X i Components in the x and y directions.

5. The method according to claim 1, characterized in that It also 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 characterizing pulse earthquake intensity, 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 elastic-plastic 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-layer displacement angle corresponding to the N earthquake records and the m candidate parameters to obtain the relationship between the candidate parameters and the median of the maximum inter-layer displacement angle: m θ =a k +b k Y k , where m θ is the median of the maximum inter-story displacement angle, a k , b k is the alternative parameter Y k The corresponding regression coefficient when used as a strength characterization parameter; The coefficient of determination calculation module calculates the linear regression coefficient of determination of the kth candidate parameter according to the following formula As an evaluation index of the applicability of ground motion intensity characterization parameters: In the formula, θ p is the maximum inter-story displacement angle obtained through the elastoplastic time history analysis of the pth earthquake motion record; is the median of the maximum inter-story displacement angle corresponding to the pth ground motion record and the kth alternative parameter calculated based on the relationship between the alternative parameters and the median of the maximum inter-story displacement 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 device according to claim 6, characterized in that It also includes a parameter merging module, which is used to: when considering bidirectional earthquake action, before performing principal component analysis on the seismic parameters in N seismic records, merge the initial seismic parameters in two directions according to the following formula: Where X ix , X iy are the initial ground motion parameters X i Components in the x and y directions.

8. The device according to claim 6, characterized in that It also 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.

Images