Method and device for conditioning seismic records in an elastic time history analysis

Abstract

The application relates to a method and device for selecting seismic records in elastic time-history analysis, which comprises the following steps: using effective peak ground acceleration (EPA) to adjust the amplitude of initial seismic records used in elastic time-history analysis, and screening the initial seismic records according to preset first selection parameters to obtain first seismic records; using causal inference to screen seismic parameters directly causing structure base shear, as second selection parameters; and screening the first seismic records according to the second selection parameters to obtain final seismic records used in time-history analysis. Through the method, the dispersion of final structure base shear can be well controlled, so that the results of mode decomposition response spectrum method can be corrected by using the results of elastic time-history analysis.

Description

TECHNICAL FIELD

[0001] The present application relates to the field of structural seismic technology, and particularly relates to a method and device for selecting seismic records in elastic time history analysis based on causal inference. BACKGROUND

[0002] According to relevant regulations, time history analysis method needs to be used for supplementary calculation of buildings with special irregularities, Class A buildings and buildings with height exceeding the limit under frequent earthquakes. In the prior art, effective ground peak acceleration (EPA) is used to select seismic records, which can control the seismic records after selection to be at the same intensity level as the design response spectrum, but even if part of the seismic records are required to be artificially selected, the dispersion of the final structure base shear force cannot be well controlled, resulting in that the result of elastic time history analysis is often much higher than that of modal decomposition response spectrum method, and the result of elastic time history analysis cannot be used to modify the result of response spectrum method. Therefore, a seismic record selection method capable of controlling the dispersion of base shear force needs to be used in the elastic time history analysis supplementary calculation.

[0003] The methods described in this section can not have been previously conceived or made. Unless otherwise indicated herein, the methods described in this section are not to be assumed to have been in the prior art merely because they are described in this section. Similarly, issues mentioned in this section should not be assumed to have been admitted to be prior art in any jurisdiction merely because of their mention in this section. SUMMARY

[0004] The present application provides a method for selecting seismic records in elastic time history analysis based on causal inference, which can be used for the selection of seismic records in elastic time history analysis.

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

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

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

[0008] The seismic parameters directly causing the structure base shear force are screened by using causal inference as a second selection parameter;

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

[0010] Further, the filtering of the initial seismic records according to the preset first adjustment parameter to obtain the first seismic record comprises: filtering the initial seismic records whose difference between the average response spectrum of the amplitude-modulated initial seismic record and the design response spectrum at the main period point of the structure is not greater than 20% as the first seismic record.

[0011] Further, the filtering of the initial seismic records according to the preset first adjustment parameter to obtain the first seismic record comprises: filtering the initial seismic records whose difference between the average response spectrum of the amplitude-modulated initial seismic record and the design response spectrum at the main period point of the structure is not greater than 20% as the first seismic record.

[0012] Further, the filtering of the initial seismic records according to the preset first adjustment parameter to obtain the first seismic record comprises: filtering the initial seismic records whose difference between the average response spectrum of the amplitude-modulated initial seismic record and the design response spectrum at the main period point of the structure is not greater than 20% as the first seismic record.

[0013] Further, the filtering of the initial seismic records according to the preset first adjustment parameter to obtain the first seismic record comprises: filtering the initial seismic records whose difference between the average response spectrum of the amplitude-modulated initial seismic record and the design response spectrum at the main period point of the structure is not greater than 20% as the first seismic record.

[0014] Further, the filtering of the initial seismic records according to the preset first adjustment parameter to obtain the first seismic record comprises: filtering the initial seismic records whose difference between the average response spectrum of the amplitude-modulated initial seismic record and the design response spectrum at the main period point of the structure is not greater than 20% as the first seismic record.

[0015] Further, the filtering of the initial seismic records according to the preset first adjustment parameter to obtain the first seismic record comprises: filtering the initial seismic records whose difference between the average response spectrum of the amplitude-modulated initial seismic record and the design response spectrum at the main period point of the structure is not greater than 20% as the first seismic record.

[0016] Further, the filtering of the initial seismic records according to the preset first adjustment parameter to obtain the first seismic record comprises: filtering the initial seismic records whose difference between the average response spectrum of the amplitude-modulated initial seismic record and the design response spectrum at the main period point of the structure is not greater than 20% as the first seismic record.

[0017] Further, the filtering of the initial seismic records according to the preset first adjustment parameter to obtain the first seismic record comprises: filtering the initial seismic records whose difference between the average response spectrum of the amplitude-modulated initial seismic record and the design response spectrum at the main period point of the structure is not greater than 20% as the first seismic record.

[0018] Further, the filtering of the initial seismic records according to the preset first adjustment parameter to obtain the first seismic record comprises: filtering the initial seismic records whose difference between the average response spectrum of the amplitude-modulated initial seismic record and the design response spectrum at the main period point of the structure is not greater than 20% as the first seismic record.

[0019] Further, the filtering of the initial seismic records according to the preset first adjustment parameter to obtain the first seismic record comprises: filtering the initial seismic records whose difference between the average response spectrum of the amplitude-modulated initial seismic record and the design response spectrum at the main period point of the structure is not greater than 20% as the first seismic record.

[0020] Further, the filtering of the initial seismic records according to the preset first adjustment parameter to obtain the first seismic record comprises: filtering the initial seismic records whose difference between the average response spectrum of the amplitude-modulated initial seismic record and the design response spectrum at the main period point of the structure is not greater than 20% as the first seismic record.

[0021] Further, the filtering of the initial seismic records according to the preset first adjustment parameter to obtain the first seismic record comprises: filtering the initial seismic records whose difference between the average response spectrum of the amplitude-modulated initial seismic record and the design response spectrum at the main period point of the structure is not greater than 20% as the first seismic record.

[0022] The XGBoost algorithm is used to fit the base shear Y and the cause variable T generated after the intervention of the candidate 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 Y and the cause variable T and

[0023]

[0024]

[0025] The fitting residual is calculated as follows:

[0026]

[0027]

[0028] The fitting residual is calculated as follows:

[0029] ε Y = ε T +.

[0030]

[0031] In the formula, n is the total amount of data in the specified training data set.

[0032] Further, the method for quantitatively analyzing the causal relationship between the candidate parameters and the base shear under the potential result model framework using the double machine learning method comprises the following steps:

[0033] The first seismic motion record D is randomly divided into b sub-data sets, denoted as D i , i = 1, 2, …, b, and the complement of D i in D is denoted as D -i .

[0034] For each D -i , the first half is used to train the XGBoost model l(X) and the second half is used to train the XGBoost model m(X), and the Bayesian optimization is used to find the best combination of model hyperparameters using D i .

[0035] The trained XGBoost model is used to calculate the estimated values of the base shear Y and the cause variable T corresponding to the sub-data set D i and the residual.

[0036] The sub-data set D icorresponding base shear Y and cause variable T and residual error, calculate sub-data set D i corresponding causal effect estimate value then is the final causal effect index corresponding to the alternative parameter.

[0037] Further, the filtering of the first ground motion record according to the second selection parameter to obtain the final ground motion record for time history analysis comprises:

[0038] The median of the second selection parameter in the first ground motion record is statistically obtained, and the ground motion record with a difference of not more than 25% from the median is selected as the final ground motion record for time history analysis.

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

[0040] A first selection module is configured to adjust the amplitude of the initial ground motion record used in elastic time history analysis by using effective peak ground acceleration (EPA), and filter the initial ground motion record according to a preset first selection parameter to obtain a first ground motion record.

[0041] A second selection module is configured to filter out the ground motion parameter directly causing the base shear of the structure by using causal inference as a second selection parameter, and filter the first ground motion record according to the second selection parameter to obtain a final ground motion record for time history analysis.

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

[0043] at least one processor; and

[0044] a memory in communication with the at least one processor;

[0045] wherein the memory stores instructions executable by the at least one processor, and the instructions are executed by the at least one processor to enable the at least one processor to perform the method for selecting ground motion records in elastic time history analysis according to the first aspect of the present application.

[0046] In a fourth aspect, the present application provides a non-transitory computer readable storage medium, wherein the storage medium stores computer instructions, and the computer instructions are executed by a computer to implement the method for selecting ground motion records in elastic time history analysis according to the first aspect of the present application.

[0047] The beneficial effect of the present application is that: on the basis of initial selection of ground motion records by using effective ground peak acceleration (EPA), the results of initial selection are selected again by using the method of causal inference, which can well control the discreteness of the final structure base shear force, so as to realize the correction of the mode decomposition response spectrum method result by using the elastic time history analysis result. BRIEF DESCRIPTION OF DRAWINGS

[0048] Figure 1 The flowchart of the selection method of ground motion records in the elastic time history analysis provided by the embodiment of the present application is shown in the figure.

[0049] Figure 2 The finite element model of a certain building frame structure provided by the embodiment of the present application is shown in the figure.

[0050] Figure 3 The causal inference analysis result of the ground motion parameters in the embodiment of the present application is shown in the figure.

[0051] Figures 4a to 4d The frequency distribution histogram of the main reason variable in the embodiment of the present application is shown in the figure.

[0052] Figure 5 The cross-validation flowchart in the embodiment of the present application is shown in the figure.

[0053] Figure 6 The structural block diagram of an exemplary electronic device capable of implementing the embodiment of the present application is shown in the figure. DETAILED DESCRIPTION

[0054] The technical solutions in the embodiments of the present application will be described clearly and completely below with reference to the drawings in the embodiments of the present application. Obviously, the described embodiments are only part of the embodiments of the present application, not all the embodiments. Based on the embodiments in the present application, all other embodiments obtained by those skilled in the art without creative labor are within the scope of protection of the present application.

[0055] In the description of the present application, the terms "first", "second" are only used for the purpose of description, and cannot be understood as indicating or implying relative importance or implicitly indicating the number of the indicated technical features. Therefore, the features defined with "first", "second" can explicitly or implicitly include one or more of the features. In the description of the present application, the meaning of "multiple" is two or more, unless otherwise specifically limited.

[0056] In the description of the present application, the term "for example" is used to mean "serving as an example, instance, or illustration." Any embodiment described as "for example" in this application is not necessarily to be construed as preferred or advantageous over other embodiments. The following description is presented to enable any person skilled in the art to make and use the application. In the following description, for the purposes of explanation, numerous specific details are set forth in order to provide a thorough understanding of the present application. It will be apparent, however, to one skilled in the art that the present application can be practiced without using these specific details. In other instances, well-known structures and processes are not elaborated upon in order to avoid unnecessary detail, which can obscure the description of the present application. Thus, the present application is not intended to be limited by the embodiments shown, but is to be accorded the widest scope consistent with the principles and features disclosed herein.

[0057] As Figure 1 shown, the embodiment of the present application provides a method for selecting ground motion records in elastic time history analysis, comprising:

[0058] S100, using effective peak ground acceleration (EPA) to adjust the amplitude of the initial ground motion record used in elastic time history analysis, and selecting the initial ground motion record according to the first selection parameter to obtain the first ground motion record.

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

[0060] S200, using causal inference to select the ground motion parameter directly causing the base shear of the structure as the second selection parameter.

[0061] Specifically, it comprises:

[0062] S201, extracting the ground motion parameters contained in the first ground motion record as the alternative parameters for causal inference.

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

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

[0065]

[0066]

[0067] Note: The long axis direction of the structure is the X direction, and the short axis direction is the Y direction.

[0068] S202, using a potential outcome model to perform causal inference between the alternative parameters and the structural base shear.

[0069] The potential outcome model (Rubin Causal Model, RCM) is used to perform causal inference between the ground motion parameters and the structural base shear. The RCM assumes that all variables except the intervention and the potential outcome are confounding variables, and performs causal inference by predicting the change in the potential outcome before and after the intervention. In this paper, the do operator is introduced to represent the intervention on the input ground motion parameters. For any ground motion 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] In the formula, T is an intervention on the ground motion parameters, T = 1 and T = 0 respectively represent intervention / no intervention; Y [do (T = 1)] represents the potential base shear result of the ground motion record under the intervention of the cause variable T; Y [do (T = 0)] represents the potential base shear result of the ground motion record without intervention.

[0072] When the intervention T of the ground motion parameters is a spurious cause variable of the potential base shear result Y, then,

[0073] P[Y|T=t]≠P[Y|do(T=t)] (4)

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

[0075] The double machine learning method is used to quantitatively analyze the causal relationship between the alternative parameters and the base shear under the potential outcome model framework, and the causal effect index of each alternative parameter on the base shear is obtained.

[0076] Traditional machine learning methods can model high-dimensional data and are suitable for solving ground motion parameter causal inference problems containing high-dimensional confounding variables, but traditional machine learning methods only focus on prediction effects 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 fitting the residuals generated by the first fitting result twice, and is more suitable for causal inference. The embodiments of the present application use the double machine learning method to quantitatively analyze the causal relationship between the candidate parameters and the base shear in the RCM framework. Under the condition of selecting sufficient samples, assuming that all candidate parameters are divided into two categories of cause variables T and confounding variables X, the causal relationship between the base shear of the structure and the ground motion parameters can be expressed as,

[0077] Y = θT + g(X) + ∈ (5)

[0078] T = f(X) + η (6)

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

[0080] For the first step fitting in the double machine learning method, the embodiments of the present application introduce a regularization term into the XGBoost algorithm (Extreme gradient boosting algorithm) to fit Y and T respectively, and reduce the possibility of overfitting; at the same time, since the prediction accuracy of the XGBoost algorithm depends on the selection of hyperparameters, the embodiments of the present application use 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 in the characteristic space formed by the independent variable, and the residual is perpendicular to the sample space formed by the independent variable, so 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 denoted as

[0081]

[0082]

[0083] For the second step fitting in the double machine learning method, the residuals of the XGBoost algorithm fitting result are linearly regressed (Linear regression) in this paper, so as 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 a linear regression with an intercept of 0 on the residuals of Y and T, θ can be calculated. Combining equations (5), (7), (9), and (10), we can obtain...

[0087] ε Y =θε T +∈ (11)

[0088]

[0089] In the formula, n is the total amount of data in the specified training dataset.

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

[0091] S300, the first ground motion record is filtered according to the second selection parameters to obtain the final ground motion record for time history analysis.

[0092] Specifically, the median of the second tuning parameter in the first ground motion record is obtained statistically, and ground motion records that differ from the median by no more than 25% are selected as the final ground motion records for time history analysis.

[0093] Furthermore, dual machine learning methods typically require multiple partitions of the dataset for cross-validation to obtain the desired results. The average value is used to account for the randomness of the data. Therefore, in a preferred embodiment, such as Figure 5 As shown, step S202 further includes randomly dividing the first ground motion record D into b subsets, denoted as D0. i =1,2,…, and D in D i The complement of the set is denoted as D. -i For each D -i The XGBoost model l(X) is trained using the first half and the XGBoost model m(X) is trained using the second half, and D is used. i Bayesian optimization is performed to find the optimal combination of hyperparameters for the model; the trained XGBoost model is used to obtain the subset D. i The corresponding base shear force Y and the estimated values ​​of the causal variable T (Equations (7) and (8)) and the residuals (Equations (9) and (10)); using the subset D i The estimated values ​​and residuals of the corresponding base shear force Y and causal variable T are used to calculate the subset D. i Corresponding causal effect the estimated value of the acceleration then is the final causal effect index corresponding to the alternative parameter.

[0094] The method for selecting and adjusting seismic records in elastic time-history analysis is further described below with reference to a specific example.

[0095] A multi-story frame structure (frame W2) of a museum is selected as an example, which was severely damaged in a 6.8 magnitude earthquake. The multi-story building is a two-story frame structure, and the heights of the first and second stories are 4.8 m and 4.2 m, respectively. The typical column grid span is 7.8 m. According to the seismic zoning map, the location of the museum belongs to a 9-degree region (0.4g), a class II site, and the seismic motion is grouped into the third group, with a characteristic period of 0.45 s. In this example, the general analysis software ANSYS is used 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 0.43 s, 0.42 s, 0.36 s, and 0.16 s, respectively. According to the results of finite element analysis and the provisions of relevant specifications, frame W2 has three irregularities, including torsional irregularity, concave-convex irregularity, and discontinuity of vertical lateral force resisting members, and is a particularly irregular structure, which requires supplemental calculation by elastic time-history analysis.

[0096] In this example, causal inference is used to perform causal inference on the seismic motion parameters in Table 1 based on the 300 sets of base shear data of frame W2. The algebraic average of the causal effect indexes of the horizontal two directions of the seismic motion parameters is taken as the causal effect index θ of the seismic motion parameter, and θ is normalized. The final analysis results are shown in Figure 3 As shown in Figure 3 , the main reason variable for causing the base shear of the structure is the acceleration-related parameter, and the Park-Ang index and Housner intensity are the main reason variables for causing the base shear of the structure. As described above, the basic natural vibration period of frame W2 is less than the characteristic period of the site, and the seismic response is in the acceleration control section, so the causal inference analysis results are consistent with engineering experience. The frequency distribution histogram of the seismic motion parameters with high causal effect indexes is shown in Figures 4a to 4d .

[0097] The seismic records are selected and adjusted according to the method as follows: (1) The seismic records are selected and adjusted according to the specification method, and the amplitude adjustment coefficient S F ≤3 is controlled;

[0098]

[0099] In the formula, EPA max is the maximum value of the effective peak ground acceleration, which is determined according to the relevant specifications; and EPA iEffective peak ground acceleration of the i th seismic record;

[0100] (2) Based on the seismic records selected by the specification method, the data with the value of the discrete control parameter between the 25% and 75% quantile is taken as the final seismic record sample.

[0101] The method can reduce the dispersion of the base shear by controlling the fluctuation range of the screening parameter, so that the result of the elastic time-history analysis can be better used for the correction of the design result of the mode-superposition response spectrum method. When the dispersion of the base shear is effectively controlled, the number of the seismic records used in the elastic time-history analysis should also be adjusted. The sample amount of the seismic records used in the time-history analysis can be determined according to the following formula,

[0102]

[0103] In the formula, m is the number of the seismic records used in the elastic time-history analysis; Z is related to the confidence level, when the confidence level is 95%, Z = 1.96; sigma is the standard deviation of the total sample; E is the allowable error, according to the relevant specification, when three seismic records are used for time-history analysis, E = 35%, when seven seismic records are used for time-history analysis, E = 20%; mu is the mean of the total sample.

[0104] According to the specification method and the method provided in the embodiment of the application, 120 and 60 seismic records meeting the requirements are obtained respectively.

[0105] The seismic motion records selected by the specification method are introduced into the finite element model of the frame W2 to perform time-history analysis, and the elastic time-history analysis results and the number of seismic motion records required are counted according to the allowable error of 35% and 20% respectively, and the statistical results are shown in Table 2. As shown in Table 2, when the dispersion control parameter in the method is the main reason variable, the dispersion of the base shear obtained by the elastic time-history analysis can be effectively controlled. On the contrary, when the seismic motion parameter of the smaller or non-causal variable is selected, the dispersion of the base shear cannot be effectively controlled. When the dispersion control parameter is selected as the Housner intensity Pa and the 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 specification method. It is worth noting that the dispersion of the seismic motion parameter itself will affect the dispersion of the final base shear result. Since the dispersion of the Park-Ang index and the Housner intensity data obtained by the selected seismic motion record is large, the standard deviation of the base shear counted by the selected seismic motion record according to the two indexes may be overestimated. After the secondary screening of the seismic motion record according to the dispersion control parameter, the artificial seismic motion record can not be used in the elastic time-history analysis. On the contrary, when the method is not used or the dispersion control parameter is not the main reason variable, the dispersion of most base shear samples is large, and more seismic motion records or a certain amount of artificial records are required to perform elastic time-history analysis to reduce the dispersion of the results. According to the causal inference results and the elastic time-history analysis results of the selected seismic motion record, for the elastic time-history checking of the frame W2 in the museum, the effective peak acceleration EPA and the Park-Ang index IC are recommended as the selection parameters of the seismic motion record.

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

[0107]

[0108] Note: The dispersion control parameter data in the method adopts the structural main direction data.

[0109] The embodiment of the application further provides a kind of selection device of seismic motion record in elastic time-history analysis, comprising:

[0110] The first selection module is selected by effective ground peak acceleration EPA to the initial seismic motion record used in elastic time-history analysis Amplitude modulation, and the initial seismic motion record is selected according to the first selection parameter, and the first seismic motion record is obtained;

[0111] The second adjustment module adopts a causal inference to screen out an earthquake ground motion parameter directly causing a structural base shear force as a second adjustment parameter; and screens the first earthquake ground motion record according to the second adjustment parameter to obtain a final earthquake ground motion record used for time history analysis.

[0112] According to an aspect of the present disclosure, an electronic device is also disclosed, comprising: at least one processor; and a memory communicatively connected with 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 to enable the at least one processor to perform the above method.

[0113] According to an 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 are executed by a computer to implement the above method.

[0114] According to an aspect of the present disclosure, a computer program product is also disclosed, comprising a computer program, wherein,

[0115] The computer program is executed by a processor to implement the above method.

[0116] Reference Figure 6 A block diagram of an electronic device 600 that can be a server or a client of the present disclosure will now be described, which is an example of a hardware device that can be applied to aspects of the present disclosure. The electronic device is intended to represent various forms of digital electronic computing devices such as laptops, desktops, workstations, personal digital assistants, servers, blade servers, mainframes, and other appropriate computing devices. The electronic device can also represent various forms of mobile devices such as personal digital processing, cellular telephones, smart phones, wearable devices, and other similar computing devices. The components, their connections, and their functions as shown in the figures, and their functions, are by way of example only, and are not meant to limit implementations of the present disclosure described and / or claimed in this document.

[0117] As Figure 6 shown, the electronic device 600 includes a computing unit 601 that can perform various appropriate actions and processes in accordance with 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. Various programs and data required for the operation of the electronic device 600 can also be stored in the RAM 603. The computing unit 601, the ROM 602, and the RAM 603 are connected to each other through a bus 604. An input / output (I / O) interface 605 is also connected to the bus 604.

[0118] A plurality of 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, and can receive inputted digital or character information, and generate key signal inputs related to user settings and / or function controls 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 magnetic 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 can be various general and / or special purpose processing components having processing and computing capabilities. Some examples of the computing unit 601 include, but are not limited to, a central processing unit (CPU), a graphics processing unit (GPU), various special-purpose artificial intelligence (AI) computing chips, various computing units running machine learning model algorithms, a digital signal processor (DSP), and any appropriate processor, controller, microcontroller, etc. The computing unit 601 performs various methods and processes described above, such as the image processing method. For example, in some embodiments, the image processing method can be implemented as a computer software program that is tangibly embodied in a machine-readable medium, such as the storage unit 608. In some embodiments, part or all of the computer program can be loaded and / or installed onto the electronic device 600 via the ROM 602 and / or the communication unit 609. When the computer program is loaded onto the RAM 603 and executed by the computing unit 601, one or more steps of the image processing method described above can be performed. Alternatively, in other embodiments, the computing unit 601 can be configured to perform the image processing method by any other appropriate means, such as by means of firmware.

[0120] The various embodiments of the systems and techniques described above can be implemented in digital electronic circuitry, integrated circuitry, a field programmable gate array (FPGA), an application specific integrated circuit (ASIC), a system on a chip (SOC), a complex programmable logic device (CPLD), computer hardware, firmware, software, and / or combinations thereof. These various embodiments can include implementation in one or more computer programs that are executable and / or interpretable on a programmable system including at least one programmable processor, which can be special or general purpose, coupled to receive data and instructions from, and to transmit data and instructions to, a storage system, at least one input device, and at least one output device.

[0121] Program code for carrying out methods of the present disclosure can be written in any combination of one or more programming languages. The program code can be provided to a processor or controller of a general purpose computer, special purpose computer, or other programmable data processing apparatus to produce a machine, such that the program code, when executed by the processor or controller, produces the functions / operations specified in the flowcharts and / or the block diagrams. The program code can be executed entirely on a machine, partially on a machine, partially on a 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 can be a tangible medium that contains or stores a program for use by or in connection with an instruction execution system, apparatus, or device. The machine-readable medium can be a machine-readable signal medium or a machine-readable storage medium. A machine-readable medium can include but is not limited to an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any suitable combination of the foregoing. More specific examples of the machine-readable storage medium will include one or more lines of electrical connections, portable computer disks, hard disk drives, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), optical fibers, portable compact disc read-only memories (CD-ROMs), optical storage devices, magnetic storage devices, or any suitable combination of the foregoing.

[0123] To provide for interaction with a user, the systems and techniques described here 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 a pointing device (e.g., a mouse or a trackball) by which the user can provide input to the computer. Other kinds of devices can be used to provide for interaction with a user as well; for example, feedback provided to the user can be any form of sensory feedback (e.g., visual feedback, auditory feedback, or tactile feedback); and input from the user can be received in any form, including acoustic, speech, or tactile input.

[0124] The systems and techniques described here can be implemented in a computing system that includes a back end component (e.g., as a data server), or that includes a middleware component (e.g., an application server), or that includes a front end component (e.g., a user computer having a graphical user interface or a Web browser through which a user can interact with an implementation of the systems and techniques described here), or any combination of such back end, middleware, or front end components. The components of the system can 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] The computer system can include clients and servers. A client and server are generally remote from each other and typically interact through a communication network. The relationship of client and server can arise by virtue of computer programs running on the respective computers and having a client-server relationship to each other. The server can 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 flow shown above can be re-ordered, added to, or deleted from without departing from the scope of the present disclosure. For example, the steps recited in the present disclosure can be performed in parallel, sequentially, or in a different order, as long as the desired results of the technology disclosed in the present disclosure are achieved, which is not limited herein.

[0127] While embodiments or examples of this disclosure have been described with reference to the figures, it will be understood that the methods, systems, and devices described above are merely exemplary embodiments or examples, and the scope of the application is not limited to these embodiments or examples. Various elements of the embodiments or examples can be omitted or substituted by equivalents thereof. Furthermore, the steps can be performed in a different order than described in the disclosure. Further, various elements of the embodiments or examples can be combined in various ways. It is important that as technology evolves, many of the elements described herein can be substituted by equivalents which serve the same function.

Claims

1. A method of conditioning a seismic ground motion record for use in elastic time history analysis, characterized by, The method comprises the following steps: amplitude-modulating initial ground motion records used in elastic time-history analysis by using effective peak ground acceleration (EPA), and screening the initial ground motion records according to preset first screening parameters to obtain first ground motion records; screening ground motion parameters directly causing base shear of a structure by using causal inference as second screening parameters; screening the first ground motion records according to the second screening parameters to obtain final ground motion records used in time-history analysis; the step of screening ground motion parameters directly causing base shear of a structure by using causal inference as second screening parameters comprises the following steps: extracting ground motion parameters contained in the first ground motion records as candidate parameters for causal inference; performing causal inference between the candidate parameters and base shear of the structure by using a potential outcome model; quantitatively analyzing causal relationships between the candidate parameters and the base shear under the framework of the potential outcome model by using a double-machine learning method to obtain an index of causal effect of each candidate parameter on the base shear; selecting a candidate parameter with the largest index of causal effect as the second screening parameter.

2. The brewing method according to claim 1, characterized in that, the step of screening the initial ground motion records according to preset first screening parameters to obtain first ground motion records comprises the following step: screening initial ground motion records whose difference between average response spectra and design response spectra at main period points of the structure is not greater than 20% as the first ground motion records.

3. The brewing method according to claim 1, characterized in that, the step of performing causal inference between the candidate parameters and base shear of the structure by using a potential outcome model comprises the following steps: assuming that the candidate parameters include two types of cause variables T and confounding variables X, the causal relationship between the base shear of the structure and the candidate parameters is expressed as: ； ； where is the base shear resulting from the intervention on the alternative parameters; and are unknown functions of ; and are both random errors with mean 0 and unknown; is the causal effect of the alternative parameters on the base shear, , T = 1 and T = 0 indicate intervention / no intervention, respectively; represents the potential base shear outcome resulting from the intervention on the cause variable T; represents the potential base shear outcome resulting from the non-intervention on the cause variable T.

4. The brewing method according to claim 3, characterized in that, the step of quantitatively analyzing causal relationships between the candidate parameters and the base shear under the framework of the potential outcome model by using a double-machine learning method to obtain an index of causal effect of each candidate parameter on the base shear comprises the following steps: The base shear Y and the cause variable T generated after intervention on the candidate parameters are fitted by using an XGBoost algorithm, and a Bayesian optimization algorithm is used to search for optimal hyperparameters of the XGBoost algorithm, to obtain estimated values of the base shear Y and the cause variable T and : ； ； calculating fitting residuals: ； ； Performing a linear regression with zero intercept on the fitting residuals, to calculate the estimated causal effect of the alternative parameter on the basement shear :​ ； ； wherein n is the total amount of data in a specified training data set.

5. The brewing method according to claim 4, characterized in that, the step of quantitatively analyzing causal relationships between the candidate parameters and the base shear under the framework of the potential outcome model by using a double-machine learning method to obtain an index of causal effect of each candidate parameter on the base shear further comprises the following steps: The first ground motion record is divided into b subsets, denoted as The first ground motion record is divided into b subsets, denoted as The first ground motion record is divided into b subsets, denoted as The first ground motion record is divided into b subsets, denoted as The first ground motion record is divided into b subsets, denoted as The first ground motion record is divided into b subsets, denoted as For each , an XGBoost model is trained using the first half of the data and an XGBoost model is trained using the second half of the data , and Bayesian optimization is performed to find the best combination of hyperparameters for the models ; Using the trained XGBoost model to find the sub-dataset corresponding base shear Y and the estimated value of the cause variable T and residual error; Utilizing sub-datasets The estimated value of the corresponding base shear Y and the cause variable T and the residual, calculate the sub-dataset The estimated value of the corresponding causal effect Then That is the final causal effect index corresponding to the alternative parameter.​ 6. The brewing method according to claim 1, characterized in that, the step of screening the first ground motion records according to the second screening parameters to obtain final ground motion records used in time-history analysis comprises the following step: statistically obtaining a median of the second screening parameters in the first ground motion records, and selecting ground motion records with a difference of not greater than 25% from the median as the final ground motion records used in time-history analysis.

7. An apparatus for conditioning a seismic ground motion record in an elastic time history analysis, characterized by, The method comprises the following steps: a first screening module is configured to amplitude-modulate initial ground motion records used in elastic time-history analysis by using effective peak ground acceleration (EPA), and screen the initial ground motion records according to preset first screening parameters to obtain first ground motion records; a second screening module is configured to screen ground motion parameters directly causing base shear of a structure by using causal inference as second screening parameters, and screen the first ground motion records according to the second screening parameters to obtain final ground motion records used in time-history analysis. The ground motion parameter directly causing the structural base shear force is screened out by the causality inference, as the second selected parameter, and the method comprises the following steps: extracting ground motion parameters contained in the first ground motion record as candidate parameters for the causality inference; performing the causality inference between the candidate parameters and the structural base shear force by using a potential result model; quantitatively analyzing the causality between the candidate parameters and the base shear force under the framework of the potential result model by using a double-machine learning method, to obtain a causality effect index of each candidate parameter on the base shear force; selecting the candidate parameter with the largest causality effect index as the second selected parameter.

8. An electronic device, comprising: comprise: at least one processor; and a memory connected in communication with 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 to enable the at least one processor to perform the method for selecting a ground motion record in elastic time history analysis according to any one of claims 1-6.

9. A non-transitory computer-readable storage medium, comprising: The storage medium stores computer instructions, and the computer instructions are executed by a computer to implement the method for selecting a ground motion record in elastic time history analysis according to any one of claims 1-6.

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