CLA-based earthquake vulnerability model construction method, apparatus and device, and medium

Abstract

The embodiment of the invention provides a CLA-based earthquake vulnerability model construction method and device, equipment and a medium, and the method comprises the steps: analyzing an earthquake intensity parameter in historical engineering dynamic response parameters and a corresponding engineering demand parameter, and determining a first relation between the two parameters; based on the historical engineering dynamic response parameters, probability anti-seismic demand analysis is carried out, and a second relation between the change values of the engineering demand parameters and the change values of the corresponding seismic intensity parameters is determined; and based on the first relationship and the second relationship, determining an earthquake vulnerability function corresponding to the engineering demand parameter as an earthquake vulnerability model. According to the invention, a scientific and rigorous gravity dam earthquake vulnerability model can be constructed.

Description

Technical Field

[0001] The present disclosure relates to the technical field of earthquake monitoring, and in particular to a method, device, equipment and medium for constructing an earthquake vulnerability model based on CLA. Background Art

[0002] The output variable of the demand analysis is the engineering demand parameter (EDP), and this step is completed through structural dynamic response analysis. A commonly used dynamic response analysis method, namely Cloud Analysis (CLA), is selected. CLA mainly uses a series of unscaled or measured earthquake records for dynamic response analysis. It is usually used in conjunction with probabilistic seismic demand analysis. When selecting earthquake records in CLA, the critical values ​​of M and R are first selected. Based on this, the MR plane is divided into four intervals. Then, as many earthquake records as possible are selected in each interval. These earthquake records are required to be distributed as evenly as possible in their respective MR intervals. They are considered to represent a certain earthquake scenario defined by the MR interval in which they are located.

[0003] In the usual dynamic response analysis of gravity dams, predictions are usually made based on data and staff experience, which are subjectively set by staff and cannot guarantee the scientificity and rigor of the analysis process. Summary of the invention

[0004] The present invention provides a method, device, equipment and medium for constructing an earthquake vulnerability model based on CLA, aiming to construct a scientific and rigorous gravity dam earthquake vulnerability model.

[0005] The technical solution of the present disclosure is as follows:

[0006] In a first aspect, an embodiment of the present disclosure provides a method for constructing an earthquake vulnerability model based on CLA, comprising:

[0007] Analyze the earthquake intensity parameters and corresponding engineering demand parameters in the historical engineering dynamic response parameters to determine the first relationship between the two;

[0008] Performing probabilistic seismic demand analysis based on historical engineering dynamic response parameters to determine a second relationship between a change in the engineering demand parameter and a change in the corresponding earthquake intensity parameter;

[0009] Based on the first relationship and the second relationship, a seismic vulnerability function corresponding to the engineering demand parameter is determined as a seismic vulnerability model.

[0010] In a possible implementation manner, in the method provided in the embodiment of the present invention, the earthquake intensity parameter in the historical engineering dynamic response parameter and the corresponding engineering demand parameter are analyzed to determine the first relationship between the two, including:

[0011] When the earthquake intensity parameter is given, the corresponding engineering demand parameter pays log-normal distribution, and the first relationship formula is expressed as:

[0012] EDP|IM~LN(μ EDP|IM , β EDP|IM )

[0013] Among them, IM is the earthquake intensity parameter; EDP is the engineering demand parameter; μ EDP|IM is the conditional logarithmic mean of EDP under a given IM; β EDP|IM is the conditional logarithmic standard deviation of EDP under a given IM.

[0014] In a possible implementation, in the method provided by an embodiment of the present invention, in the step of determining the second relationship between the change value of the engineering demand parameter and the change value of the corresponding earthquake intensity parameter, when the change value of the engineering demand parameter is the conditional logarithmic mean, given the earthquake intensity parameter IM=im, the conditional logarithmic mean is linearly related to the logarithmic value ln(im) of the earthquake intensity parameter IM, and the linear second relationship formula is expressed as:

[0015] μ EDP|IM＝im =BBln(im)+ln(A)

[0016] The change value of the engineering demand parameter is the conditional median value When , it has a power function relationship with the earthquake intensity parameter IM. At this time, the second relationship formula of the power function is expressed as:

[0017]

[0018] Among them, A and B are coefficients.

[0019] In a possible implementation manner, in the method provided in the embodiment of the present invention, determining the seismic vulnerability function corresponding to the engineering demand parameter based on the first relationship and the second relationship includes:

[0020] Based on the vulnerability cloud analysis method CLA, the seismic intensity parameters and the corresponding engineering demand parameters in the historical engineering dynamic response parameters are taken logarithmically and linearly fitted to obtain the coefficients A and B, and then the corresponding EDP logarithmic mean μ when IM=im is determined. EDP|IM ;

[0021] In the first relationship, the conditional logarithmic standard deviation does not change with the change of IM, that is, it remains constant globally. The conditional logarithmic standard deviation is calculated by the following formula:

[0022]

[0023] Where n is the number of earthquake records in CLA;

[0024] The limit state of the gravity dam structure is defined as EDP exceeding the preset threshold, and the corresponding initial fragility function is:

[0025]

[0026] In a possible implementation, in the method provided by the embodiment of the present invention, an intermediate vulnerability function is obtained based on the initial vulnerability function:

[0027]

[0028] make:

[0029]

[0030] The IM value corresponding to the demand level edp in the curve obtained by linear fitting is that when the conditional median value of EDP is equal to the given demand level edp, it corresponds to The earthquake intensity parameter IM.

[0031] In a possible implementation, in the method provided in the embodiment of the present invention, the earthquake vulnerability model formula is expressed as:

[0032]

[0033] Among them, based on the earthquake vulnerability model, the obtained EDP and the corresponding IM (seismic intensity parameter, IntensityMeasure) constitute the cloud response on the EDP-IM plane.

[0034] In a second aspect, the embodiment of the present disclosure further provides a device for constructing an earthquake vulnerability model based on CLA, comprising:

[0035] A first relationship determination module is used to analyze the earthquake intensity parameter in the historical engineering dynamic response parameter and the corresponding engineering demand parameter to determine the first relationship between the two;

[0036] A second relationship determination module is used to perform a probabilistic seismic demand analysis based on historical engineering dynamic response parameters to determine a second relationship between a change value of an engineering demand parameter and a change value of a corresponding earthquake intensity parameter;

[0037] The model building module is used to determine the earthquake vulnerability function corresponding to the engineering demand parameter based on the first relationship and the second relationship as the earthquake vulnerability model.

[0038] In a third aspect, an embodiment of the present disclosure further provides an electronic device, including:

[0039] processor;

[0040] a memory for storing processor-executable instructions;

[0041] The processor is configured to execute instructions to implement the method of the first aspect.

[0042] In a fourth aspect, an embodiment of the present disclosure further provides a computer-readable storage medium having a computer program stored thereon, wherein the computer program implements the method of the first aspect when executed by a processor.

[0043] In a fifth aspect, an embodiment of the present disclosure further provides a computer program product, comprising a computer program / instructions, wherein the computer program / instructions implement the method of the first aspect when executed by a processor.

[0044] The technical solution provided by the embodiments of the present disclosure brings at least the following beneficial effects:

[0045] In the embodiment of the present disclosure, the earthquake intensity parameter and the corresponding engineering demand parameter in the historical engineering dynamic response parameter are analyzed to determine the first relationship between the two; the probabilistic seismic demand analysis is performed based on the historical engineering dynamic response parameter to determine the second relationship between the change value of the engineering demand parameter and the change value of the corresponding earthquake intensity parameter; based on the first relationship and the second relationship, the earthquake vulnerability function corresponding to the engineering demand parameter is determined as the earthquake vulnerability model. Through the present invention, a scientific and rigorous gravity dam earthquake vulnerability model can be constructed.

[0046] It is to be understood that the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the present disclosure. BRIEF DESCRIPTION OF THE DRAWINGS

[0047] The drawings herein are incorporated into and constitute a part of the specification, illustrate embodiments consistent with the present disclosure, and together with the description are used to explain the principles of the present disclosure, and do not constitute improper limitations on the present disclosure.

[0048] Figure 1 A schematic diagram of a flow chart of a method for constructing an earthquake vulnerability model based on CLA provided in an embodiment of the present disclosure;

[0049] Figure 2 A schematic diagram of the distribution of the Y component Sa(T1) of the seismic record selected for a method for constructing a seismic vulnerability model based on CLA provided in an embodiment of the present disclosure;

[0050] Figure 3 The first-order natural frequency drop ratio d of the dam body after an earthquake in a method for constructing an earthquake vulnerability model based on CLA provided in an embodiment of the present disclosure is F and dam body damage volume ratio R dt Schematic diagram of the corresponding relationship;

[0051] Figure 4The first-order natural frequency drop ratio d of the dam body after an earthquake in a method for constructing an earthquake vulnerability model based on CLA provided in an embodiment of the present disclosure is F and dam body damage volume ratio R dt＞0.8 Schematic diagram of the corresponding relationship;

[0052] Figure 5 A schematic diagram of the structure of a CLA-based earthquake vulnerability model building device provided in an embodiment of the present disclosure;

[0053] Figure 6 A schematic diagram of the structure of an electronic device provided in an embodiment of the present disclosure. DETAILED DESCRIPTION

[0054] In order to enable ordinary persons in the art to better understand the technical solutions of the present disclosure, the technical solutions in the embodiments of the present disclosure will be clearly and completely described below in conjunction with the accompanying drawings.

[0055] It should be noted that the terms "first", "second", etc. in the specification and claims of the present disclosure and the above-mentioned drawings are used to distinguish similar objects, and are not necessarily used to describe a specific order or sequence. It should be understood that the data used in this way can be interchanged where appropriate, so that the embodiments of the present disclosure described herein can be implemented in an order other than those illustrated or described herein. The embodiments described in the following exemplary embodiments do not represent all embodiments consistent with the present disclosure. Instead, they are merely examples of devices and methods consistent with some aspects of the present disclosure as detailed in the appended claims.

[0056] Figure 1 A flowchart of a method for constructing an earthquake vulnerability model based on CLA is provided in the first embodiment of the present disclosure, as shown in FIG. Figure 1 As shown, the CLA-based earthquake vulnerability model construction method may include the following steps:

[0057] S101, analyzing the earthquake intensity parameters in the historical engineering dynamic response parameters and the corresponding engineering demand parameters to determine a first relationship between the two.

[0058] In earthquake vulnerability analysis, researchers estimate the parameter values ​​(mean μ and logarithmic standard deviation β) of the earthquake vulnerability function based on calculation results (or observational data). These parameter values, like the observational data, depend on the dynamic response analysis method used.

[0059] In the present invention, CLA is used to explore the correlation between EDP and IM. Generally, CLA is not used for seismic vulnerability analysis of structural damage. It is usually assumed that EDP obeys lognormal distribution when IM is given, and the first relationship formula obtained is expressed as:

[0060] EDP|IM~LN(μ EDP|IM , β EDP|IM )

[0061] Among them, IM is the earthquake intensity parameter; EDP is the engineering demand parameter; μ EDP|IM is the conditional logarithmic mean of EDP under a given IM; β EDP|IM is the conditional logarithmic standard deviation of EDP under a given IM.

[0062] S102, performing a probabilistic seismic demand analysis based on historical engineering dynamic response parameters, and determining a second relationship between a change value of the engineering demand parameter and a change value of a corresponding earthquake intensity parameter.

[0063] Through a large number of probabilistic seismic demand analyses, Cornell found that when IM = im, the conditional logarithmic mean μ of EDP is EDP|IM It is linearly related to the logarithm value of IM, ln(im), and the linear second relationship formula is expressed as:

[0064] μ EDP|IM＝im =Bln(im)+ln(A)

[0065] The change value of the engineering demand parameter is the conditional median value When , it has a power function relationship with the earthquake intensity parameter IM. At this time, the second relationship formula of the power function is expressed as:

[0066]

[0067] Among them, A and B are coefficients.

[0068] S103: Based on the first relationship and the second relationship, determine a seismic vulnerability function corresponding to the engineering demand parameter as a seismic vulnerability model.

[0069] Based on the vulnerability cloud analysis method CLA, the logarithms of IM and the corresponding EDP in CLA are taken and linear fitting is performed, such as Figure 2 As shown, by obtaining the coefficients A and B, we can find the logarithmic mean μ of EDP corresponding to IM = im EDP|IM .

[0070] In the first relationship, the conditional logarithmic standard deviation does not change with the change of IM, that is, it remains constant globally. The conditional logarithmic standard deviation is calculated by the following formula:

[0071]

[0072] Where n is the number of earthquake records in CLA;

[0073] The limit state of the gravity dam structure is defined as EDP exceeding the preset threshold, and the corresponding initial fragility function is:

[0074]

[0075] Based on the initial vulnerability function, the intermediate vulnerability function is obtained:

[0076]

[0077] make:

[0078]

[0079] The IM value corresponding to the demand level edp in the curve obtained by linear fitting is that when the conditional median value of EDP is equal to the given demand level edp, it corresponds to The earthquake intensity parameter IM is the limit state.

[0080] The seismic vulnerability function is:

[0081]

[0082] Obviously, the CLA method can conveniently calculate the seismic vulnerability function, thanks to several important assumptions in the CLA method, namely, when the IM is given, the EDP is log-normally distributed, the EDP and IM are linearly related on a logarithmic scale, and the logarithmic standard deviation remains constant.

[0083] Specifically, the present invention selected 80 earthquake records as historical engineering dynamic response parameters based on actual measurement and manual fitting, carried out seismic vulnerability analysis based on the CLA method taking an arch dam as an example, studied the correlation between the optimal IM and EDP of the arch dam, defined a limit state according to the research results, and gave the corresponding seismic vulnerability curve.

[0084] The nonlinear behavior of the dam concrete was considered in the calculation, and the plastic damage constitutive model of concrete proposed by Lee and Fenves was used to simulate the damage and cracking of the dam concrete. The foundation damping ratio should be ignored when simulating earthquake input using the incident wave method, and only the dam body should consider a damping ratio of 5%, and it is recommended to take the foundation truncation coefficient r = 2.0 (the foundation extension range is twice the dam height). Therefore, in order to improve the finite element model of the foundation and make the results of the seismic vulnerability analysis more reliable, the foundation model includes rock layers and faults. The fluid-solid coupling method is used to simulate the interaction between the dam body and the reservoir water, and 40,584 fluid units are established to simulate the reservoir water.

[0085] The radiation damping effect is taken into account in the foundation, and the nonlinearity of the foundation material around the dam is also taken into account. Its nonlinear constitutive relationship is simulated by the Drucker-Prager elastic-plastic model (DP model). Table 1 gives the parameters of each material in the finite element model.

[0086]

[0087] Table 1 Material parameter statistics

[0088] In the CLA method, seismic records are selected according to certain steps and conditions to fully consider the uncertainty of earthquakes. First, the seismic parameter regions are divided. The magnitude M and the epicentral distance R are used as seismic parameters to construct the MR plane describing the seismic records. The critical values ​​of M and R are selected to divide the MR plane into four regions. Secondly, the corresponding seismic records are selected in each divided region. The selected seismic records are evenly distributed in the four regions of the MR plane, and the number of seismic records in each region is roughly the same. In addition, the following requirements should be met when selecting seismic records: first, the shear wave velocity Vs30 meets the site conditions of the structure; second, the distribution range of seismic intensity parameters (such as Sa(T1)) is wide enough to reduce the slope error of IM-EDP linear regression under the logarithmic scale; third, avoid selecting too many seismic records from the same seismic event (for example, no more than 10% of the total). Following the above principles, this chapter requires that the magnitude be between 5-8, with 7 as the boundary, and the epicentral distance considers 5-15Km and above 15Km, thereby dividing the MR plane into four regions. At the same time, considering the site conditions as hard site soil or hard rock, Vs30>500m / s is required. 20 earthquake records were selected in each interval, and a total of 80 earthquake records were selected. It should be pointed out that, since the earthquake records are required to meet the conditions of M, R and Vs30 at the same time, only 36 actual earthquakes were selected, and the number of 4 regions differed greatly, which is not suitable for the CLA method. Therefore, the 80 earthquake records in this embodiment actually also include 44 artificial synthetic earthquake records. In order to make the artificial earthquake records also meet the conditions of M, R and Vs30, a regional seismic random simulation method based on wavelet packets and cokriging analysis is adopted. This method can simulate the accumulation process of seismic motion, target acceleration response spectrum ARS and Arias intensity IA by giving specific earthquake scene parameters (M, R, Vs30). Due to the orthogonal characteristics of the wavelet packet spectrum, the wavelet packet coefficients can be adjusted in the time domain and frequency domain respectively. After repeated modifications, the generated seismic motion not only matches the target ARS and IA accumulation process, but also maintains non-stationarity.

[0089] In this section, when simulating artificial earthquakes, the earthquake scenario parameters (M, R, Vs30) are randomly generated within the specified range, and there is no given target ARS and IA accumulation process, so the generated earthquake records can reflect the randomness of ground motion. In addition, since the dynamic response of the arch dam is weakened compared with the massless foundation model after considering the foundation radiation damping effect, in order to obtain the dynamic response results of the arch dam in a larger range and ensure the reliability of the seismic vulnerability analysis results, all earthquake records are uniformly magnified by 2 times. Figure 2 The intensity parameter distribution of the Y component (downstream) of the selected seismic record is given. Taking the spectral acceleration Sa(T1) at the first-order natural vibration period T1 of the arch dam as an example, it can be seen that the distribution range of Sa(T1) of the selected seismic record after overall amplitude modulation is relatively wide and obeys the log-normal distribution. It is expected that the nonlinear dynamic response results of the arch dam will also be distributed in a larger range.

[0090] For the optimal earthquake intensity parameters (such as and ), they and the maximum displacement Δ u and the dam body damage volume ratio R dt＞0 There is a good correlation between them. On the logarithmic scale, they are approximately linear and have a high goodness of fit. u and R dt＞0 are all expressed as functions of the optimal earthquake intensity parameters, then Δ u and R dt＞0 can also be expressed as functions of each other, in other words, Δ u and R dt＞0 There is a certain correlation.

[0091] Although the limit state cannot be determined by the relationship between damage and displacement, the correlation between the two indicates that the characteristics of the structure itself may be expressed in the form of correlation between EDPs. The damage to the dam body after the earthquake leads to an increase in the first-order vibration period (frequency decrease). The percentage of the first-order frequency decrease of the dam body after the earthquake is used as a new EDP, and an attempt is made to determine the change in the dam body's working performance by exploring its correlation with damage and displacement. The expression of this EDP is:

[0092]

[0093] where d F represents the percentage of the first-order natural frequency decrease of the dam body after the earthquake, f 1，d It represents the first-order natural frequency of the dam body after the earthquake, and f1 represents the initial first-order natural frequency of the dam body.

[0094] Figure 3 and Figure 4 The R dt＞0 and R dt＞0.8 With d F Corresponding to the situation, where R dt＞0.8 The damage factor d t >0.8 of the volume ratio of concrete (cracking volume ratio), and d t >0.8 is usually considered as macroscopic cracking of concrete in engineering. Figure 3 and Figure 4 As can be seen in FIt has a good linear relationship with the damage of the dam body and R dt＞0.8 The linear fit goodness of fit reached 0.9777, which is consistent with the R dt＞0 The goodness of fit is also as high as 0.9693. However, it should be noted that d F With R dt＞0 During the fitting process, dt＞0 When it is small (about 10% or less), there is no strong linear relationship, such as Figure 3 As shown in the black box, d F First follow R dt＞0 The growth rate is growing rapidly, and also around R dt＞0 = 2% and then the growth slows down. dt＞0 After approaching 10%, it starts to grow linearly again. This turning point corresponds to d F About 2.5%. The reason for this disturbance is still that when R dt＞0 When the damage is small, it is mainly at the interface of the dam foundation, with a complex distribution, while the dam body is basically intact. At this time, the frequency decrease is not large, and it is difficult to find the R dt＞0 The second inflection point is related to the damage of the dam surface. After that, the damage of the dam foundation interface is no longer dominant, and the damage of the dam body causes the frequency to continue to decrease. dt＞0 Replace with R dt＞0.8 After that, F With R dt＞0.8 There is no obvious inflection point in the linear relationship between them. This is because the complex minor damage in the dam foundation interface has been eliminated, leaving only the cracking volume ratio with practical significance. On the other hand, this also shows that the frequency drop and concrete cracking have a good correlation.

[0095] Based on the same inventive concept, the embodiment of the present disclosure also provides a CLA-based earthquake vulnerability model construction device. Figure 5 As shown, the CLA-based earthquake vulnerability model building device 400 includes:

[0096] A first relationship determination module 410 is used to analyze the earthquake intensity parameter in the historical engineering dynamic response parameter and the corresponding engineering demand parameter to determine a first relationship between the two;

[0097] A second relationship determination module 420, for performing a probabilistic seismic demand analysis based on historical engineering dynamic response parameters, and determining a second relationship between a change value of an engineering demand parameter and a change value of a corresponding earthquake intensity parameter;

[0098] The model building module 430 is used to determine the earthquake vulnerability function corresponding to the engineering demand parameter based on the first relationship and the second relationship as the earthquake vulnerability model.

[0099] The specific implementation method and technical effect of the device provided in the embodiment of the present disclosure are similar to those of the above-mentioned method embodiment, and will not be repeated here.

[0100] In addition, combined Figure 1-Figure 5 The battery capacity prediction method and device described in the embodiments of the present application can be implemented by an electronic device. Figure 6 A schematic diagram of the hardware structure of an electronic device provided in an embodiment of the present application is shown.

[0101] like Figure 6 As shown, the electronic device 1000 may include a processing device (e.g., a central processing unit, a graphics processor, etc.) 1001, which can perform various appropriate actions and processes according to a program stored in a read-only memory (ROM) 1002 or a program loaded from a storage device 1008 to a random access memory (RAM) 1003 to implement the battery capacity prediction method of the embodiment described in the present disclosure. In the RAM 1003, various programs and data required for the operation of the electronic device 1000 are also stored. The processing device 1001, the ROM 1002, and the RAM 1003 are connected to each other via a bus 1004. An input / output (I / O) interface 1005 is also connected to the bus 1004.

[0102] Typically, the following devices may be connected to the I / O interface 1005: an input device 1006 including, for example, a touch screen, a touch pad, a keyboard, a mouse, a camera, a microphone, an accelerometer, a gyroscope, etc.; an output device 1007 including, for example, a liquid crystal display (LCD), a speaker, a vibrator, etc.; a storage device 1008 including, for example, a magnetic tape, a hard disk, etc.; and a communication device 1009. The communication device 1009 may allow the electronic device 1000 to communicate with other devices wirelessly or by wire to exchange data. Although Figure 6 The electronic device 1000 is shown with various devices, but it should be understood that it is not required to implement or possess all the devices shown. More or fewer devices may be implemented or possessed instead.

[0103] In particular, according to an embodiment of the present disclosure, the process described above with reference to the flowchart can be implemented as a computer software program. For example, an embodiment of the present disclosure includes a computer program product, which includes a computer program carried on a non-transitory computer-readable medium, and the computer program contains a program code for executing the method shown in the flowchart, thereby implementing the voice control method as described above. In such an embodiment, the computer program can be downloaded and installed from the network through the communication device 1009, or installed from the storage device 1008, or installed from the ROM 1002. When the computer program is executed by the processing device 1001, the above-mentioned functions defined in the method of the embodiment of the present disclosure are executed.

[0104] It should be noted that the computer-readable medium disclosed above may be a computer-readable signal medium or a computer-readable storage medium or any combination of the above two. The computer-readable storage medium may be, for example, but not limited to, an electrical, magnetic, optical, electromagnetic, infrared, or semiconductor system, device or device, or any combination of the above. More specific examples of computer-readable storage media may include, but are not limited to: an electrical connection with one or more wires, 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 above. In the present disclosure, a computer-readable storage medium may be any tangible medium containing or storing a program that may be used by or in combination with an instruction execution system, device or device. In the present disclosure, a computer-readable signal medium may include a data signal propagated in a baseband or as part of a carrier wave, in which a computer-readable program code is carried. This propagated data signal may take a variety of forms, including but not limited to an electromagnetic signal, an optical signal, or any suitable combination of the above. The computer readable signal medium may also be any computer readable medium other than a computer readable storage medium, which may send, propagate or transmit a program for use by or in conjunction with an instruction execution system, apparatus or device. The program code contained on the computer readable medium may be transmitted using any suitable medium, including but not limited to: wires, optical cables, RF (radio frequency), etc., or any suitable combination of the above.

[0105] In some embodiments, the client and the server may communicate using any currently known or future developed network protocol such as HTTP (HyperText Transfer Protocol), and may be interconnected with 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"), an internet (e.g., the Internet), and a peer-to-peer network (e.g., an ad hoc peer-to-peer network), as well as any currently known or future developed network.

[0106] The computer-readable medium may be included in the electronic device, or may exist independently without being incorporated into the electronic device.

[0107] The computer-readable medium carries one or more programs. When the one or more programs are executed by the electronic device, the electronic device:

[0108] Analyze the earthquake intensity parameters and corresponding engineering demand parameters in the historical engineering dynamic response parameters to determine the first relationship between the two;

[0109] Performing a probabilistic seismic demand analysis based on the historical engineering dynamic response parameters to determine a second relationship between a change value of the engineering demand parameter and a change value of a corresponding earthquake intensity parameter;

[0110] Based on the first relationship and the second relationship, a seismic vulnerability function corresponding to the engineering demand parameter is determined as the seismic vulnerability model.

[0111] Optionally, when the above one or more programs are executed by the electronic device, the electronic device may also execute other steps described in the above embodiments.

[0112] Computer program code for performing the operations of the present disclosure may be written in one or more programming languages ​​or a combination thereof, including, but not limited to, object-oriented programming languages, such as Java, Smalltalk, C++, and conventional procedural programming languages, such as "C" or similar programming languages. The program code may be executed entirely on the user's computer, partially on the user's computer, as a separate software package, partially on the user's computer and partially on a remote computer, or entirely on a remote computer or server. In cases involving a remote computer, the remote computer may be connected to the user's computer through any type of network, including a local area network (LAN) or a wide area network (WAN), or may be connected to an external computer (e.g., through the Internet using an Internet service provider).

[0113] The flow chart and block diagram in the accompanying drawings illustrate the possible architecture, function and operation of the system, method and computer program product according to various embodiments of the present disclosure. In this regard, each square box in the flow chart or block diagram can represent a module, a program segment or a part of a code, and the module, the program segment or a part of the code contains one or more executable instructions for realizing the specified logical function. It should also be noted that in some implementations as replacements, the functions marked in the square box can also occur in a sequence different from that marked in the accompanying drawings. For example, two square boxes represented in succession can actually be executed substantially in parallel, and they can sometimes be executed in the opposite order, depending on the functions involved. It should also be noted that each square box in the block diagram and / or flow chart, and the combination of the square boxes in the block diagram and / or flow chart can be implemented with a dedicated hardware-based system that performs a specified function or operation, or can be implemented with a combination of dedicated hardware and computer instructions.

[0114] The units involved in the embodiments described in the present disclosure may be implemented by software or hardware, wherein the name of a unit does not, in some cases, limit the unit itself.

[0115] The functions described above herein may be performed at least in part by one or more hardware logic components. For example, without limitation, exemplary types of hardware logic components that may be used include: field programmable gate arrays (FPGAs), application specific integrated circuits (ASICs), application specific standard products (ASSPs), systems on chips (SOCs), complex programmable logic devices (CPLDs), and the like.

[0116] 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.

[0117] Those skilled in the art will appreciate that the embodiments of the present application may be provided as methods, systems, or computer program products. Therefore, the present application may adopt the form of a complete hardware embodiment, a complete software embodiment, or an embodiment in combination with software and hardware. Moreover, the present application may adopt the form of a computer program product implemented in one or more computer-usable storage media (including but not limited to disk storage, CD-ROM, optical storage, etc.) that include computer-usable program code.

[0118] The present application is described with reference to the flowcharts and / or block diagrams of the methods, devices (systems), and computer program products according to the embodiments of the present application. It should be understood that each process and / or box in the flowchart and / or block diagram, as well as the combination of the processes and / or boxes in the flowchart and / or block diagram, can be implemented by computer program instructions. These computer program instructions can be provided to a processor of a general-purpose computer, a special-purpose computer, an embedded processor, or other programmable data processing device to generate a machine, so that the instructions executed by the processor of the computer or other programmable data processing device generate instructions for implementing the processes in the flowchart and / or block diagram. Figure 1 A process or multiple processes and / or boxes Figure 1 A device that provides the functions specified in a block or multiple blocks.

[0119] These computer program instructions may also be stored in a computer-readable memory capable of directing a computer or other programmable data processing device to operate in a specific manner, so that the instructions stored in the computer-readable memory produce an article of manufacture comprising an instruction device, which implements the process Figure 1 A process or multiple processes and / or boxes Figure 1 A function specified in one or more boxes.

[0120] These computer program instructions can also be loaded onto a computer or other programmable data processing device so that a series of operating steps are executed on the computer or other programmable device to produce a computer-implemented process, thereby providing instructions for implementing the process. Figure 1 A process or multiple processes and / or boxes Figure 1 The steps for the functions specified in one or more boxes.

[0121] Although the preferred embodiments of the present application have been described, those skilled in the art may make additional changes and modifications to these embodiments once they have learned the basic creative concept. Therefore, the appended claims are intended to be interpreted as including the preferred embodiments and all changes and modifications falling within the scope of the present application.

[0122] Obviously, those skilled in the art can make various changes and modifications to the present invention without departing from the spirit and scope of the present invention. Thus, if these modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include these modifications and variations.

Claims

1. A method for constructing an earthquake vulnerability model based on CLA, characterized in that: include: Analyze the earthquake intensity parameters and corresponding engineering demand parameters in the historical engineering dynamic response parameters to determine the first relationship between the two; Performing a probabilistic seismic demand analysis based on the historical engineering dynamic response parameters to determine a second relationship between a change value of the engineering demand parameter and a change value of a corresponding earthquake intensity parameter; Based on the first relationship and the second relationship, a seismic vulnerability function corresponding to the engineering demand parameter is determined as the seismic vulnerability model.

2. The method for constructing a seismic vulnerability model based on CLA according to claim 1, characterized in that: The earthquake intensity parameters in the historical engineering dynamic response parameters and the corresponding engineering demand parameters are analyzed to determine the first relationship between the two, including: When the earthquake intensity parameter is given, the corresponding engineering demand parameter pays log-normal distribution, and the first relationship formula is expressed as: EDP|IM~LN(μ EDP|IM ,β EDP|IM ) Among them, IM is the earthquake intensity parameter; EDP is the engineering demand parameter; μ EDP|IM is the conditional logarithmic mean of EDP under a given IM; β EDP|IM is the conditional logarithmic standard deviation of EDP under a given IM.

3. The method for constructing a seismic vulnerability model based on CLA according to claim 2, characterized in that: In the step of determining the second relationship between the change value of the engineering demand parameter and the change value of the corresponding earthquake intensity parameter, when the change value of the engineering demand parameter is the conditional logarithmic mean, given the earthquake intensity parameter IM=im, the conditional logarithmic mean and the logarithmic value ln(im) of the earthquake intensity parameter IM are in a linear relationship, and the linear second relationship formula is expressed as: m EDP|IM＝im =Bln(im)+ln(A) The change value of the engineering demand parameter is the conditional median value When , it has a power function relationship with the earthquake intensity parameter IM. At this time, the second relationship formula of the power function is expressed as: Among them, A and B are coefficients.

4. The method for constructing a seismic vulnerability model based on CLA according to claim 3, characterized in that: Determining the seismic vulnerability function corresponding to the engineering demand parameter based on the first relationship and the second relationship includes: Based on the vulnerability cloud analysis method CLA, the seismic intensity parameters and the corresponding engineering demand parameters in the historical engineering dynamic response parameters are taken logarithmically and linearly fitted to obtain the coefficients A and B, and then determine the EDP logarithmic mean μ corresponding to IM=im EDP|IM ; In the first relationship, it is assumed that the conditional logarithmic standard deviation does not change with the change of IM, that is, it remains constant globally, and the conditional logarithmic standard deviation is calculated by the following formula: Where n is the number of earthquake records in CLA; The limit state of the gravity dam structure is defined as EDP exceeding the preset threshold, and the corresponding initial fragility function is:

5. The method for constructing a seismic vulnerability model based on CLA according to claim 4, characterized in that: Based on the initial vulnerability function, the intermediate vulnerability function is obtained: make: The IM value corresponding to the demand level edp in the curve obtained by linear fitting is that when the conditional median value of EDP is equal to the given demand level edp, it corresponds to The earthquake intensity parameter IM.

6. The method for constructing a seismic vulnerability model based on CLA according to claim 5, characterized in that: The earthquake vulnerability model formula is expressed as:

7. The method for constructing a seismic vulnerability model based on CLA according to claim 1, characterized in that: Based on the seismic vulnerability model, the obtained EDP and the corresponding IM (seismic intensity parameter, Intensity Measure) constitute the cloud response on the EDP-IM plane.

8. A CLA-based earthquake vulnerability model construction device, characterized in that: include: A first relationship determination module is used to analyze the earthquake intensity parameter in the historical engineering dynamic response parameter and the corresponding engineering demand parameter to determine the first relationship between the two; A second relationship determination module, configured to perform a probabilistic seismic demand analysis based on the historical engineering dynamic response parameters, and determine a second relationship between a change value of the engineering demand parameter and a change value of a corresponding earthquake intensity parameter; The model building module is used to determine the earthquake vulnerability function corresponding to the engineering demand parameter based on the first relationship and the second relationship as the earthquake vulnerability model.

9. An electronic device, characterized in that: include: processor; a memory for storing instructions executable by the processor; The processor is configured to execute the instructions to implement the battery capacity prediction method according to any one of claims 1 to 7.

10. A computer-readable storage medium having a computer program stored thereon, characterized in that: When the computer program is executed by a processor, the lithium-ion battery life prediction method according to any one of claims 1 to 7 is implemented.

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