Seismic oscillation record adjusting and selecting method and device in elastic time history analysis

Abstract

The invention relates to a method and device for adjusting and selecting seismic oscillation records in elastic time history analysis, and the method comprises the steps: carrying out the amplitude modulation of initial seismic oscillation records used in the elastic time history analysis through employing an effective ground peak acceleration EPA, and carrying out the screening of the initial seismic oscillation records according to a preset first adjustment and selection parameter, obtaining a first seismic oscillation record; adopting causal inference to screen out seismic oscillation parameters directly causing shear force of the structure base as second adjustment and selection parameters; and screening the first seismic oscillation record according to the second adjustment and selection parameter to obtain a final seismic oscillation record for time history analysis. By means of the method, discreteness of the shear force of the final structure base can be well controlled, and therefore the vibration mode decomposition reaction spectrum method result is corrected through the elastic time history analysis result.

Description

Technical Field

[0001] The present invention relates to the technical field of structural earthquake resistance, and in particular to a method and device for selecting earthquake motion records in elastic time history analysis based on causal inference. Background Art

[0002] According to relevant regulations, for particularly irregular buildings, Class A buildings and buildings that exceed the height limit, the time history analysis method needs to be used for supplementary calculations under frequent earthquakes. In the prior art, the effective ground peak acceleration EPA is used to adjust the seismic records. Although it can control the seismic records after adjustment to be roughly at the same intensity level as the design response spectrum, even if some seismic records are required to be recorded manually, the discreteness of the final structural base shear force cannot be well controlled, resulting in the results calculated by the elastic time history analysis often far exceeding the results calculated by the vibration mode decomposition response spectrum method. The results of the elastic time history analysis cannot be used to correct the results of the response spectrum method. Therefore, a seismic record adjustment method that can control the discreteness of the base shear force is required in the supplementary calculation of the elastic time history analysis.

[0003] The methods described in this section are not necessarily methods that have been previously conceived or employed. Unless otherwise indicated, it should not be assumed that any method described in this section is considered to be prior art simply because it is included in this section. Similarly, unless otherwise indicated, the issues mentioned in this section should not be considered to have been recognized in any prior art. Summary of the invention

[0004] The present invention aims at the technical problems existing in the prior art and provides a method for selecting earthquake records in elastic time history analysis based on causal inference, which can be used for selecting earthquake records in elastic time history verification.

[0005] The technical solution of the present invention to solve the above technical problems is as follows:

[0006] In a first aspect, the present invention provides a method for selecting earthquake records in elastic time history analysis, comprising:

[0007] The effective ground peak acceleration EPA is used to amplitude modulate the initial seismic motion record used in the elastic time history analysis, and the initial seismic motion record is screened according to a preset first selection parameter to obtain a first seismic motion record;

[0008] Causal inference is used to select the ground motion parameters that directly cause the shear force of the structural base as the second adjustment parameter;

[0009] The first seismic record is screened according to the second selection parameter to obtain a final seismic record for time history analysis.

[0010] Furthermore, the screening of the initial seismic motion record according to the preset first selection parameter to obtain the first seismic motion record includes: screening the initial seismic motion record after amplitude modulation, the difference between the average response spectrum of the initial seismic motion record and the design response spectrum at the main period point of the structure is not more than 20% as the first seismic motion record.

[0011] Furthermore, the use of causal inference to select the ground motion parameters that directly cause the shear force of the structural base as the second adjustment parameter includes:

[0012] extracting earthquake motion parameters contained in the first earthquake motion record as candidate parameters for causal inference;

[0013] Using a potential result model to make causal inferences between the alternative parameters and the structural base shear;

[0014] The dual machine learning method is used to quantitatively analyze the causal relationship between the candidate parameters and the base shear in the framework of the potential result model, and the causal effect index of each candidate parameter on the base shear is obtained;

[0015] The alternative parameter with the largest causal effect indicator is selected as the second adjustment parameter.

[0016] Furthermore, the use of the potential result model to perform causal inference between the candidate parameters and the structural base shear force includes:

[0017] Assuming that the alternative parameters include two types: cause variable T and confounding variable X, the causal relationship between the structural base shear and the alternative parameters is expressed as:

[0018] Y = θT + g(X) +;

[0019] T = f(X) +;

[0020] where Y is the base shear generated after the intervention of the alternative parameters; g(X) and f(X) are unknown functions of X; ∈ and η are both unknown random errors with a mean of 0; θ is the causal effect of the alternative parameters on the base shear, θ = Y[di(T = 1)] - [do(T = 0)], T = 1 and T = 0 represent intervention / no intervention, respectively; Y[do(T = 1)] represents the potential base shear result generated by the ground motion record under the intervention of the causal variable T; Y[do(T = 0)] represents the potential base shear result generated by the ground motion record without intervention.

[0021] Furthermore, the dual machine learning method is used to quantitatively analyze the causal relationship between the candidate parameters and the base shear in the framework of the potential result model, and the causal effect index of each candidate parameter on the base shear is obtained, including:

[0022] The XGBoost algorithm is used to fit the base shear force Y and the cause variable T generated after the intervention of the alternative parameters, and the Bayesian optimization algorithm is used to search for the optimal hyperparameters of the XGBoost algorithm to obtain the estimated values ​​of the base shear force Y and the cause variable T. and

[0023]

[0024]

[0025] Compute the fitted residuals:

[0026]

[0027]

[0028] Perform a linear regression with an intercept of 0 on the fitted residuals to calculate the estimated value of the causal effect θ of the alternative parameters on the base shear

[0029] ε Y =ε T +;

[0030]

[0031] Where n is the total amount of data in the specified training data set.

[0032] Furthermore, the dual machine learning method is used to quantitatively analyze the causal relationship between the candidate parameters and the base shear under the framework of the potential result model to obtain the causal effect index of each candidate parameter on the base shear, and further includes:

[0033] The first earthquake record D is randomly divided into b sub-datasets, denoted as D i ,=1,2,…,, and D in D i The complement of -i ;

[0034] For each D -i , respectively use the first half to train the XGBoost model l(X) and the second half to train the XGBoost model m(X), and use D i Perform Bayesian optimization to find the best hyperparameter combination for the model;

[0035] Use the trained XGBoost model to obtain the sub-dataset D i The corresponding estimated values ​​and residuals of base shear Y and causal variable T;

[0036] Using subdataset D iThe corresponding estimated values ​​and residuals of the base shear force Y and the cause variable T are calculated for the sub-dataset D i Corresponding causal effect Estimated value of but That is the final causal effect indicator corresponding to the alternative parameter.

[0037] Furthermore, the screening of the first earthquake motion record according to the second selection parameter to obtain the final earthquake motion record for time history analysis includes:

[0038] The median of the second selected parameter in the first seismic record is obtained by statistics, and the seismic record whose difference with the median is not greater than 25% is selected as the final seismic record for time history analysis.

[0039] In a second aspect, the present invention provides a device for selecting earthquake records in elastic time history analysis, comprising:

[0040] The first selection module uses the effective ground peak acceleration EPA to modulate the initial seismic motion record used in the elastic time history analysis, and selects the initial seismic motion record according to the preset first selection parameter to obtain the first seismic motion record;

[0041] The second selection module uses causal inference to select the seismic motion parameters that directly cause the shear force of the structural base as the second selection parameters; the first seismic motion record is selected according to the second selection parameters to obtain the final seismic motion record for time history analysis.

[0042] In a third aspect, the present invention provides an electronic device, comprising:

[0043] at least one processor; and

[0044] a memory communicatively coupled to the at least one processor;

[0045] 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 method for selecting seismic records in elastic time history analysis described in the first aspect of the present invention.

[0046] In a fourth aspect, the present invention provides a non-transitory computer-readable storage medium, characterized in that computer instructions are stored in the storage medium, and when the computer instructions are executed by a computer, a method for selecting seismic records in elastic time history analysis as described in the first aspect of the present invention is implemented.

[0047] The beneficial effect of the present invention is that, based on the initial adjustment of the seismic records using the effective ground peak acceleration EPA, the present invention uses a causal inference method to adjust the results of the initial adjustment again, which can well control the discreteness of the final structural base shear force, thereby realizing the correction of the vibration mode decomposition response spectrum method results using the elastic time history analysis results. BRIEF DESCRIPTION OF THE DRAWINGS

[0048] Figure 1 A schematic flow chart of a method for selecting earthquake records in elastic time history analysis provided by an embodiment of the present invention;

[0049] Figure 2 A finite element model of a certain building frame structure provided by an embodiment of the present invention;

[0050] Figure 3 is the causal inference analysis result of the earthquake motion parameters in the embodiment of the present invention;

[0051] Figure 4a to Figure 4d They are respectively the frequency distribution histograms of the main cause variables in the embodiments of the present invention;

[0052] Figure 5 A schematic diagram of a cross-validation process in an embodiment of the present invention;

[0053] 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

[0054] 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.

[0055] In the description of this application, the terms "first" and "second" are used for descriptive purposes only and should not be understood as indicating or implying relative importance or implicitly indicating the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include one or more of the features. In the description of this application, "plurality" means two or more, unless otherwise clearly and specifically defined.

[0056] In the description of the present application, the term "for example" is used to mean "used as an example, illustration or description". Any embodiment described as "for example" in the present application is not necessarily to be construed as being more preferred or advantageous than other embodiments. The following description is given to enable any technician in the field to implement and use the present invention. In the following description, details are listed for the purpose of explanation. It should be understood that a person of ordinary skill in the art can recognize that the present invention can be implemented without using these specific details. In other examples, well-known structures and processes will not be elaborated in detail to avoid unnecessary details to obscure the description of the present invention. Therefore, the present invention is not intended to be limited to the embodiments shown, but is consistent with the widest scope consistent with the principles and features disclosed in the present application.

[0057] like Figure 1 As shown, an embodiment of the present invention provides a method for selecting earthquake records in elastic time history analysis, comprising:

[0058] S100, using effective ground peak acceleration EPA to amplitude modulate the initial seismic record used in elastic time history analysis, and screening the initial seismic record according to a preset first selection parameter to obtain a first seismic record.

[0059] Specifically, this embodiment uses the effective ground peak acceleration EPA to modulate the initial seismic motion record used in the elastic time history analysis, and selects the initial seismic motion record whose average response spectrum after amplitude modulation is not more than 20% different from the design response spectrum at the main period point of the structure as the first seismic motion record. (The method of selecting the first seismic motion record is referred to as the standard method hereinafter).

[0060] S200 uses causal inference to select the ground motion parameters that directly cause the shear force of the structural base as the second adjustment parameter.

[0061] Specifically include:

[0062] S201, extracting earthquake motion parameters included in the first earthquake motion record as candidate parameters for causal inference.

[0063] The embodiment of the present invention selects commonly used pulse, acceleration and velocity related parameters, a total of 17 seismic motion parameters as candidate parameters. 300 seismic motion records with a fault distance of less than 60 km were selected from the Pacific Earthquake Engineering Research (PEER) ground motion database, and the corresponding seismic motion parameters were counted, including 90 pulse seismic motion records. The 17 candidate seismic motion parameters and statistical results are shown in Table 1.

[0064] Table 1 Multi-parameters of ground motion and the statistics results

[0065]

[0066]

[0067] Note: The major axis of the structure is in the X direction, and the minor axis is in the Y direction.

[0068] S202: Using a potential result model to perform causal inference between the candidate parameters and the structural base shear.

[0069] The Rubin Causal Model (RCM) is used to perform causal inference between earthquake parameters and structural base shear. RCM assumes that all variables except intervention and potential results are confounding variables, and completes causal inference by predicting the changes in potential results before and after intervention. This paper introduces the do operator to represent the intervention of input earthquake parameters. For any earthquake record, the causal effect index of the alternative parameters on the base shear can be written as,

[0070] θ=Y[do(T=1)]- [do(T=0)] (3)

[0071] Where T is a certain intervention on the seismic parameters, T = 1 and T = 0 represent intervention / no intervention, respectively; Y[do(T = 1)] represents the potential base shear result generated by the seismic record under the intervention of the cause variable T; Y[do(T = 0)] represents the potential base shear result generated by the seismic record without intervention.

[0072] The intervention T of the local vibration parameter is a pseudo-causal variable of the potential base shear result Y, then,

[0073] P[Y|T=t]≠P[Y|do(T=t)] (4)

[0074] Where P[Y|T=t] is the conditional probability of base shear under intervention T=t, where base shear is the measured value; P[Y|do(T=t)] is the conditional probability of base shear under intervention T=t, where base shear is the potential outcome.

[0075] The dual machine learning method is used to quantitatively analyze the causal relationship between the alternative parameters and the base shear in the framework of the potential result model, and the causal effect indicators of each alternative parameter on the base shear are obtained.

[0076] Traditional machine learning methods can model high-dimensional data and are suitable for solving causal inference problems of seismic motion parameters containing high-dimensional confounding variables. However, traditional machine learning methods only focus on the prediction effect and ignore possible overfitting, resulting in biased estimation results. According to existing research, the double machine learning method (DML) can effectively avoid overfitting and eliminate bias by performing a secondary fit on the residuals generated by the first fitting result, and is more suitable for causal inference. The embodiment of the present invention uses a double machine learning method to quantitatively analyze the causal relationship between alternative parameters and base shear under the RCM framework. When a sufficient number of samples are selected, assuming that all alternative parameters are divided into two categories: causal variables T and confounding variables X, the causal relationship between the structural base shear and the seismic motion parameters can be expressed as follows:

[0077] Y=θT+g(X)+∈ (5)

[0078] T=f(X)+η (6)

[0079] Where Y is the base shear generated after the intervention of the alternative parameters; θ is the causal effect of the alternative parameters on the base shear, g(X) and f(X) are unknown functions of X; ∈ and η are both unknown random errors with a mean of 0.

[0080] For the first step of fitting in the dual machine learning method, the embodiment of the present invention introduces a regularization term into the XGBoost algorithm (Extreme gradient boosting algorithm), and fits Y and T separately to reduce the possibility of overfitting; at the same time, given that the prediction accuracy of the XGBoost algorithm depends on the selection of hyperparameters, the embodiment of the present invention uses Bayesian optimization technique to search for the optimal hyperparameters of the XGBoost algorithm. The principle of regression analysis is to fit the best projection of the dependent variable on the feature space formed by the independent variable. The residual is perpendicular to the sample space formed by the independent variable. Therefore, fitting the residual can eliminate the influence of the correlation of the independent variable to the greatest extent. The model obtained by regression analysis is recorded as

[0081]

[0082]

[0083] For the second step of fitting in the dual machine learning method, this paper performs linear regression on the residuals of the XGBoost algorithm fitting results to eliminate the estimation bias caused by the correlation between the cause variable T and the confounding variable X. The residuals corresponding to Y and T can be written as,

[0084]

[0085]

[0086] By performing linear regression with an intercept of 0 on the residuals of Y and T, we can obtain:

[0087] ε Y =θε T +∈ (11)

[0088]

[0089] Where n is the total amount of data in the specified training data set.

[0090] S203, selecting the candidate parameter with the largest causal effect index as the second adjustment parameter.

[0091] S300: Screen the first earthquake motion record according to the second selection parameter to obtain a final earthquake motion record for time history analysis.

[0092] Specifically, the median of the second selected parameter in the first seismic record is obtained by statistics, and the seismic record whose difference from the median is no more than 25% is selected as the final seismic record for time history analysis.

[0093] In addition, dual machine learning methods usually require the data set to be divided multiple times by cross validation. The average value of to take into account the randomness of the data. Therefore, in a preferred embodiment, Figure 5 As shown, step S202 also includes randomly dividing the first earthquake record D into b sub-data sets, denoted as D i ,=1,2,…,, and D in D i The complement of -i ; For each D -i , respectively use the first half to train the XGBoost model l(X) and the second half to train the XGBoost model m(X), and use D i Perform Bayesian optimization to find the best hyperparameter combination for the model; use the trained XGBoost model to obtain the sub-dataset D i The corresponding estimated values ​​of base shear Y and causal variable T (Equations (7) and (8)) and residuals (Equations (9) and (10)); using the sub-dataset D i The corresponding estimated values ​​and residuals of the base shear force Y and the cause variable T are calculated for the sub-dataset D i Corresponding causal effect Estimated value of but That is the final causal effect indicator corresponding to the alternative parameter.

[0094] The following is a further explanation of the method for selecting earthquake records in elastic time history analysis described in an embodiment of the present invention with reference to a specific example.

[0095] The multi-story frame structure (frame W2) of a museum was selected as an example. The frame was severely damaged in a 6.8-magnitude earthquake. The multi-story building is a two-story frame structure. The heights of the first and second floors are 4.8m and 4.2m respectively, and the typical column grid span is 7.8m. According to the earthquake zoning map, the location of the museum belongs to the 9-degree zone (0.4g), Class II site, the seismic motion group is the third group, and the characteristic period is 0.45s. This example uses the general analysis software ANSYS to perform elastic time-history analysis on frame W2. The finite element model of frame W2 is shown in Figure 2 The first four natural vibration periods of frame W2 are 0.43s, 0.42s, 0.36s, and 0.16s, respectively. According to the results of finite element analysis and the provisions of relevant specifications, frame W2 has torsional irregularity, concave-convex irregularity, and discontinuity of vertical lateral force resisting components, a total of three irregular items, which is a particularly irregular structure and requires elastic time-history analysis for supplementary calculation.

[0096] In this example, causal inference is used to perform causal inference on the seismic parameters in Table 1 based on 300 sets of base shear data of frame W2. The algebraic mean of the causal effect index of the seismic parameters in the two horizontal directions is taken as the causal effect index θ of the seismic parameters, and θ is normalized. The final analysis results are shown in Figure 3 .like Figure 3 As shown in Figure 1, the main cause of the structural base shear force is the acceleration-related parameters. Park-Ang index and Housner strength are the main causes of the structural base shear force. As mentioned above, the basic natural vibration period of frame W2 is smaller than the site characteristic period, and the seismic response is in the acceleration control section. The causal inference analysis results are consistent with engineering experience. The frequency distribution histogram of the seismic parameters with higher causal effect indicators is shown in Figure 1. Figure 4a to Figure 4d .

[0097] According to this method, the seismic records are adjusted: (1) The seismic records are adjusted according to the standard method and the amplitude modulation coefficient S is controlled. F ≤3;

[0098]

[0099] In the formula, EPA max It is the maximum value of the effective ground peak acceleration, which is determined according to the relevant regulations; EPA iis the effective peak ground acceleration of the ith earthquake record;

[0100] (2) Based on the seismic motion records selected by the standard method, the data with discrete control parameter values ​​between the 25% and 75% quantiles are used as the final seismic motion record samples.

[0101] This method reduces the discreteness of the base shear force by controlling the fluctuation range of the screening parameters, so that the results of the elastic time history analysis verification can be better used to correct the design results of the vibration mode decomposition response spectrum method. When the discreteness of the base shear force is effectively controlled, the number of seismic motion records required for the elastic time history analysis should also be adjusted accordingly. The sample size of the seismic motion records used in the time history analysis can be determined according to the following formula,

[0102]

[0103] Wherein, m is the number of seismic motion records to be used in elastic time history analysis; Z is related to the confidence level. When the confidence level is 95%, Z=1.96; σ is the standard deviation of the overall sample; E is the allowable error. According to the relevant specifications, when three seismic motion records are used for time history analysis, E=35%, and when seven seismic motion records are used for time history analysis, E=20%; μ is the mean of the overall sample.

[0104] According to the standard method and the method provided in the embodiment of the present invention, 120 and 60 earthquake motion records meeting the requirements are obtained respectively.

[0105] The seismic records selected by the standard method are imported into the finite element model of frame W2 for time history analysis, and the elastic time history analysis results and the number of seismic records required are statistically analyzed with an allowable error of 35% and 20%, respectively. The statistical results are shown in Table 2. As shown in Table 2, when the discreteness control parameter in this method uses the main cause variable, the discreteness of the base shear obtained by the elastic time history analysis can be effectively controlled; on the contrary, when the seismic parameters with smaller causal effect indicators or non-causal variables are selected, the discreteness of the base shear cannot be effectively controlled. When the discreteness control parameters are Housner intensity Pa and Park-Ang index IC, the standard deviation of the base shear obtained by the time history analysis is reduced by 26% and 25% respectively compared with the standard method. It is worth noting that the discreteness of the seismic parameters themselves will affect the discreteness of the final base shear results. Since the Park-Ang index and Housner intensity data obtained by selecting seismic records have large discreteness, the standard deviation of the base shear statistically obtained by the seismic records selected according to the two indicators may be overestimated. After secondary screening of seismic records according to discrete control parameters, artificial seismic records can be omitted in elastic time history analysis. On the contrary, when this method is not used or the discrete control parameters do not use the main cause variables, the discreteness of most base shear samples is large, and more seismic records or a certain amount of artificial records are needed for elastic time history analysis to reduce the discreteness of the results. According to the causal inference results and the elastic time history analysis results of the selected seismic records, for the elastic time history verification of frame W2 in the museum, this example recommends using effective peak acceleration EPA and Park-Ang index IC as the selection parameters for seismic records.

[0106] Table 2 The statistics of elastic time-history analysis results

[0107]

[0108] Note: The discrete control parameter data in this method adopts the main direction data of the structure.

[0109] The embodiment of the present invention further provides a device for selecting earthquake records in elastic time history analysis, comprising:

[0110] The first selection module uses the effective ground peak acceleration EPA to modulate the initial seismic motion record used in the elastic time history analysis, and selects the initial seismic motion record according to the preset first selection parameter to obtain the first seismic motion record;

[0111] The second selection module uses causal inference to select the seismic motion parameters that directly cause the shear force of the structural base as the second selection parameters; the first seismic motion record is selected according to the second selection parameters to obtain the final seismic motion record for time history analysis.

[0112] According to one aspect of the present disclosure, an electronic device is also disclosed, 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, and the instructions are executed by the at least one processor so that the at least one processor can execute the above method.

[0113] According to one aspect of the present disclosure, a non-transitory computer-readable storage medium is also disclosed, wherein the storage medium stores computer instructions, and the computer instructions implement the above method when executed by a computer.

[0114] According to one aspect of the present disclosure, a computer program product is also disclosed, including a computer program, wherein:

[0115] The computer program implements the above method when executed by a processor.

[0116] refer to Figure 6 , a block diagram of an electronic device 600 that can be used as a server or client of the present disclosure will now be described, which is an example of a hardware device that can be applied to various aspects of the present disclosure. 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 disclosure described and / or required herein.

[0117] 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.

[0118] 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.

[0119] 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).

[0120] 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.

[0121] The program code for implementing the method of the present disclosure may be written in any combination of one or more programming languages. These program codes may 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 may be executed entirely on the machine, partially on the machine, partially on the machine and partially on a remote machine as a stand-alone software package, or entirely on a remote machine or server.

[0122] In the context of the present disclosure, a machine-readable medium may be a tangible medium that may contain or store a program for use by or in conjunction 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, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, device, 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.

[0123] 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).

[0124] 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.

[0125] 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.

[0126] 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 disclosure can be performed in parallel, sequentially or in different orders, as long as the desired results of the technical solutions disclosed in this disclosure can be achieved, and this document does not limit this.

[0127] Although the embodiments or examples of the present disclosure 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 only 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 disclosure. 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 disclosure.

Claims

1. A method for selecting earthquake records in elastic time history analysis, characterized in that: include: The effective ground peak acceleration EPA is used to amplitude modulate the initial seismic motion record used in the elastic time history analysis, and the initial seismic motion record is screened according to a preset first selection parameter to obtain a first seismic motion record; Causal inference is used to select the ground motion parameters that directly cause the shear force of the structural base as the second adjustment parameter; The first seismic record is screened according to the second selection parameter to obtain a final seismic record for time history analysis.

2. The selection method according to claim 1, characterized in that: The method of screening the initial seismic motion record according to the preset first selection parameter to obtain the first seismic motion record includes: screening the initial seismic motion record after amplitude modulation, the difference between the average response spectrum of the initial seismic motion record after amplitude modulation and the design response spectrum at the main period point of the structure is not more than 20% as the first seismic motion record.

3. The selection method according to claim 1, characterized in that: The method of using causal inference to select the ground motion parameters that directly cause the shear force of the structural base as the second adjustment parameter includes: extracting earthquake motion parameters contained in the first earthquake motion record as candidate parameters for causal inference; Using a potential result model to make causal inferences between the alternative parameters and the structural base shear; The dual machine learning method is used to quantitatively analyze the causal relationship between the candidate parameters and the base shear in the framework of the potential result model, and the causal effect index of each candidate parameter on the base shear is obtained; The alternative parameter with the largest causal effect indicator is selected as the second adjustment parameter.

4. The selection method according to claim 3, characterized in that: The use of the potential result model to perform causal inference between the alternative parameters and the structural base shear force includes: Assuming that the alternative parameters include two types: cause variable T and confounding variable X, the causal relationship between the structural base shear and the alternative parameters is expressed as: Y = θT + g(X) + ∈; T = f(X) + η; where Y is the base shear generated after the intervention of the alternative parameters; g(X) and f(X) are unknown functions of X; ∈ and η are both unknown random errors with mean 0; θ is the causal effect of the alternative parameters on the base shear, θ = Y[do(T = 1)] - Y[do(T = 0)], T = 1 and T = 0 represent intervention / no intervention, respectively; Y[do(T = 1)] represents the potential base shear result generated by the seismic record under the intervention of the causal variable T; Y[do(T = 0)] represents the potential base shear result generated by the seismic record without intervention.

5. The selection method according to claim 4, characterized in that: The dual machine learning method is used to quantitatively analyze the causal relationship between the candidate parameters and the base shear in the framework of the potential result model, and obtain the causal effect index of each candidate parameter on the base shear, including: The XGBoost algorithm is used to fit the base shear force Y and the cause variable T generated after the intervention of the alternative parameters, and the Bayesian optimization algorithm is used to search for the optimal hyperparameters of the XGBoost algorithm to obtain the estimated values ​​of the base shear force Y and the cause variable T. and Compute the fitted residuals: Perform a linear regression with an intercept of 0 on the fitted residuals to calculate the estimated value of the causal effect θ of the alternative parameters on the base shear e Y =god T +∈; Where n is the total amount of data in the specified training data set.

6. The selection method according to claim 5, characterized in that: The method of using the dual machine learning method to quantitatively analyze the causal relationship between the candidate parameters and the base shear under the framework of the potential result model to obtain the causal effect index of each candidate parameter on the base shear also includes: The first earthquake record D is randomly divided into b sub-datasets, denoted as D i ,i=1,2,…,b, and D in D i The complement of -i ; For each D -i , respectively use the first half to train the XGBoost model l(X) and the second half to train the XGBoost model m(X), and use D i Perform Bayesian optimization to find the best hyperparameter combination for the model; Use the trained XGBoost model to obtain the sub-dataset D i The corresponding estimated values ​​and residuals of base shear Y and causal variable T; Using subdataset D i The corresponding estimated values ​​and residuals of the base shear force Y and the cause variable T are calculated for the sub-dataset D i Corresponding causal effect Estimated value of but That is the final causal effect indicator corresponding to the alternative parameter.

7. The selection method according to claim 1, characterized in that: The step of screening the first seismic record according to the second selection parameter to obtain a final seismic record for time history analysis includes: The median of the second selected parameter in the first seismic record is obtained by statistics, and the seismic record whose difference with the median is not greater than 25% is selected as the final seismic record for time history analysis.

8. A device for selecting earthquake records in elastic time history analysis, characterized in that: include: The first selection module uses the effective ground peak acceleration EPA to modulate the initial seismic motion record used in the elastic time history analysis, and selects the initial seismic motion record according to the preset first selection parameter to obtain the first seismic motion record; The second selection module uses causal inference to select the seismic motion parameters that directly cause the shear force of the structural base as the second selection parameters; the first seismic motion record is selected according to the second selection parameters to obtain the final seismic motion record for time history analysis.

9. An electronic device, characterized in that: include: at least one processor; as well as a memory communicatively coupled to the at least one processor; 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 method for selecting seismic records in elastic time history analysis as described in any one of claims 1-7.

10. A non-transitory computer-readable storage medium, characterized in that: The storage medium stores computer instructions, which, when executed by a computer, implement a method for selecting seismic records in elastic time history analysis as described in any one of claims 1 to 7.

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