Method, apparatus, and storage medium for determining characteristic parameters of a building

Abstract

The present application discloses a method, an apparatus, and a storage medium for determining characteristic parameters of a building, including: establishing an initial finite element model for indicating a mapping relationship between the characteristic parameters and response parameters of the building, where the characteristic parameters include the elastic modulus, density, and stiffness of the building, and the response parameters include the response frequencies of the building; establishing a first characteristic parameter sample set of the characteristic parameters, and using the initial finite element model, determining a first response parameter sample set of the response parameters according to the first characteristic parameter sample set; establishing a first response surface model for indicating the mapping relationship between the characteristic parameters and response parameters, and correcting the first response surface model according to the correlation between the characteristic parameters and response parameters to obtain a second response surface model; determining the parameters of the second response surface model according to the first characteristic parameter sample set and the first response parameter sample set; measuring the response parameters of the target building; and using the second response surface model to determine the characteristic parameters of the building according to the measured response parameters.

Description

Technical Field

[0001] The present application relates to the field of building construction, and particularly to a method, an apparatus, and a storage medium for determining characteristic parameters of a building. Background Art

[0002] With the rapid development of the national economy, the number of newly built buildings in China is increasing continuously, and the demand for safety assessment of existing old bridges is also growing. Since buildings may be affected by adverse factors such as environmental erosion, long-term effects of various loads, and material aging during long-term use, various damages may occur in local key components of the building structure, affecting the safety and durability of the building structure. Due to changes in the elastic modulus, density, structural dimensions, etc. of the materials of existing buildings, there is often a large gap between the calculation results of the initial finite element model and the measured data. Therefore, establishing an accurate finite element model that can reflect the true stress state of the building structure has become a research hotspot at home and abroad.

[0003] The response surface method refers to fitting a response surface model between the structural response and the parameters to be corrected based on the experimental design method. Using the response surface model to replace the finite element model can achieve the optimization and correction of characteristic parameters. The finite element model correction method based on the response surface can accurately evaluate the stress state and predict the performance of existing buildings, and has been widely used in the field of building engineering in recent years.

[0004] However, the finite element model correction method based on the response surface often has certain problems. For example, the existing finite element model correction method based on the response surface method cannot well simulate the internal relationship between the characteristic parameters to be corrected and the response characteristic values, so it cannot improve the fitting accuracy of the response surface model, and thus cannot conduct safety assessment and risk prediction on the current state of existing buildings.

[0005] Aiming at the technical problem that the existing response surface model of a building in the above-mentioned prior art cannot well indicate the internal relationship between the characteristic parameters and the response parameters, so more accurate characteristic parameters cannot be obtained, and thus accurate safety assessment and risk prediction of the building cannot be carried out, no effective solution has been proposed yet. Summary of the Invention

[0006] Embodiments of the present disclosure provide a method, an apparatus, and a storage medium for determining characteristic parameters of a building, so as to at least solve the technical problem that the existing response surface model of a building in the prior art cannot well indicate the correlation between the characteristic parameters and the response parameters, and thus more accurate characteristic parameters cannot be obtained.

[0007] According to one aspect of the embodiments of the present disclosure, a method for determining characteristic parameters of a building is provided, including: establishing an initial finite element model for indicating the mapping relationship between the characteristic parameters and the response parameters of the building, where the characteristic parameters include the elastic modulus, density, and stiffness of the building, and the response parameters include the response frequency of the building; establishing a first characteristic parameter sample set of the characteristic parameters, and using the initial finite element model, determining a first response parameter sample set of the response parameters according to the first characteristic parameter sample set; establishing a first response surface model for indicating the mapping relationship between the characteristic parameters and the response parameters, and correcting the first response surface model according to the correlation between the characteristic parameters and the response parameters to obtain a second response surface model; determining the parameters of the second response surface model according to the first characteristic parameter sample set and the first response parameter sample set; measuring the response parameters of the target building; and using the second response surface model to determine the characteristic parameters of the building according to the measured response parameters.

[0008] According to another aspect of the embodiments of the present disclosure, a storage medium is further provided. The storage medium includes a stored program, wherein the method described in any one of the above is executed by a processor when the program runs.

[0009] According to another aspect of the embodiments of the present disclosure, a device for determining characteristic parameters of a building is further provided, including: a model establishment module for establishing an initial finite element model for indicating the mapping relationship between the characteristic parameters and the response parameters of the building, where the characteristic parameters include the elastic modulus, density, and stiffness of the building, and the response parameters include the response frequency of the building; a first determination module for establishing a first characteristic parameter sample set of the characteristic parameters and using the initial finite element model to determine a first response parameter sample set of the response parameters according to the first characteristic parameter sample set; a model correction module for establishing a first response surface model for indicating the mapping relationship between the characteristic parameters and the response parameters and correcting the first response surface model according to the correlation between the characteristic parameters and the response parameters to obtain a second response surface model; a coefficient to be determined determination module for determining the parameters of the second response surface model according to the first characteristic parameter sample set and the first response parameter sample set; a response parameter measurement module for measuring the response parameters of the target building; and a parameter solution module for determining the characteristic parameters of the building according to the measured response parameters by using the second response surface model.

[0010] According to another aspect of the embodiments of the present disclosure, there is also provided a finite element model updating device based on the response surface method, including: a processor; and a memory connected to the processor for providing instructions for the processor to perform the following processing steps: establishing an initial finite element model for indicating the mapping relationship between the characteristic parameters and response parameters of a building, where the characteristic parameters include the elastic modulus, density, and stiffness of the building, and the response parameters include the response frequencies of the building; establishing a first characteristic parameter sample set of the characteristic parameters, and using the initial finite element model to determine a first response parameter sample set of the response parameters according to the first characteristic parameter sample set; establishing a first response surface model for indicating the mapping relationship between the characteristic parameters and response parameters, and correcting the first response surface model according to the correlation between the characteristic parameters and response parameters to obtain a second response surface model; determining the parameters of the second response surface model according to the first characteristic parameter sample set and the first response parameter sample set; measuring the response parameters of the target building; and using the second response surface model to determine the characteristic parameters of the building according to the measured response parameters.

[0011] In the embodiments of the present disclosure, first, an initial finite element model capable of characterizing the relationship between the characteristic parameters and response parameters of a building is established, and then the characteristic parameters that need to be corrected and the response parameters corresponding to the characteristic parameters are determined. Then, a first characteristic parameter sample set is established, and the first characteristic parameter sample set is substituted into the initial finite element model to obtain a first response parameter sample set. Then, a first response surface model capable of characterizing the mapping relationship between the characteristic parameters and response parameters is established, and the correlation between the characteristic parameters and response parameters is analyzed. According to the correlation between the characteristic parameters and response parameters, the first response surface model is corrected to obtain a second response surface model. Then, the parameters of the second response surface model are determined according to the first characteristic parameter sample set and the first response parameter sample set. The response parameters of the building are measured, and the measured response parameters are substituted into the second response surface model to obtain the characteristic parameters of the building. Since the corrected second response surface model can well characterize the correlation between the characteristic parameters and response parameters, the second response surface model can improve the calculation accuracy of the characteristic parameters of the building. Thus, through the above operations, the technical effect of being able to correct the first response surface model by analyzing the correlation between the characteristic parameters and response parameters to obtain a second response surface model and using the second response surface model to obtain more accurate characteristic parameters is achieved. Furthermore, the technical problem in the prior art that the existing response surface model of a building cannot well indicate the internal relationship between the characteristic parameters and response parameters, so more accurate characteristic parameters cannot be obtained, and thus the building cannot be accurately safety evaluated and risk predicted is solved. Description of the Drawings

[0012] The accompanying drawings described herein are used to provide a further understanding of the present disclosure and form a part of this application. The schematic embodiments of the present disclosure and their descriptions are used to explain the present disclosure and do not constitute an improper limitation of the present disclosure. In the drawings:

[0013] Figure 1 is a hardware structure block diagram of a computing device for implementing the method according to Embodiment 1 of the present disclosure;

[0014] Figure 2 is a schematic diagram of a system for determining characteristic parameters of a building according to the first aspect of Embodiment 1 of the present disclosure;

[0015] Figure 3 is a schematic flowchart of a method for determining characteristic parameters of a building according to the first aspect of Embodiment 1 of the present disclosure;

[0016] Figure 4 is a schematic diagram of a building according to the first aspect of Embodiment 1 of the present disclosure;

[0017] Figure 5A is a schematic diagram of the significance of the influence of elastic modulus on the vertical first-order frequency according to the first aspect of Embodiment 1 of the present disclosure;

[0018] Figure 5B is a schematic diagram of the significance of the influence of density on the vertical first-order frequency according to the first aspect of Embodiment 1 of the present disclosure;

[0019] Figure 5C is a schematic diagram of the significance of the influence of stiffness on the vertical first-order frequency according to the first aspect of Embodiment 1 of the present disclosure;

[0020] Figure 6A is a schematic diagram of a correlation curve between elastic modulus and vertical first-order frequency according to the first aspect of Embodiment 1 of the present disclosure;

[0021] Figure 6B is a schematic diagram of a correlation curve between density and vertical first-order frequency according to the first aspect of Embodiment 1 of the present disclosure;

[0022] Figure 7 is a schematic flowchart of a method for verifying whether the accuracy of response parameters of a building meets the standard according to the first aspect of Embodiment 1 of the present disclosure;

[0023] Figure 8 is a schematic diagram of a device for determining characteristic parameters of a building according to the first aspect of Embodiment 2 of the present disclosure; and

[0024] Figure 9 is a schematic diagram of a device for determining characteristic parameters of a building according to the first aspect of Embodiment 3 of the present disclosure. Detailed implementation manners

[0025] In order to enable those skilled 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 in the embodiments of the present disclosure. Obviously, the described embodiments are only a part of the embodiments of the present disclosure, rather than all the embodiments. Based on the embodiments in the present disclosure, all other embodiments obtained by those of ordinary skill in the art without creative efforts shall fall within the scope of protection of the present disclosure.

[0026] 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 do not necessarily need to be used to describe a specific order or sequence. It should be understood that such data can be interchanged under appropriate circumstances so that the embodiments of the present disclosure described herein can be implemented in an order different from those illustrated or described herein. In addition, the terms "including" and "having" and any variations thereof are intended to cover non-exclusive inclusion. For example, a process, method, system, product or device including a series of steps or units does not necessarily have to be limited to those steps or units clearly listed, but may include other steps or units not clearly listed or inherent to these processes, methods, products or devices.

[0027] Embodiment 1

[0028] According to this embodiment, a method embodiment for determining characteristic parameters of a building is provided. It should be noted that the steps shown in the flowchart of the accompanying drawings can be executed in a computer system such as a set of computer-executable instructions, and although the logical order is shown in the flowchart, in some cases, the steps shown or described can be executed in an order different from that here.

[0029] The method embodiment provided in this embodiment can be executed on a mobile terminal, a computer terminal, a server or a similar computing device. Figure 1 A hardware structure block diagram of a computing device for implementing the determination of characteristic parameters of a building is shown. As Figure 1 shown, the computing device may include one or more processors (the processor may include, but is not limited to, a processing device such as a microprocessor MCU or a field programmable gate array FPGA), a memory for storing data, and a transmission device for communication functions. In addition, it may further include: a display, an input / output interface (I / O interface), a universal serial bus (USB) port (which may be included as one of the ports of the I / O interface), a network interface, a power supply, and / or a camera. Those of ordinary skill in the art can understand, Figure 1The structure shown is only schematic and does not limit the structure of the above-mentioned electronic device. For example, the computing device may also include more or fewer components than those shown in Figure 1 or have a different configuration from that shown in Figure 1 .

[0030] It should be noted that the above one or more processors and / or other data processing circuits can generally be referred to as "data processing circuits" herein. The data processing circuit can be embodied in software, hardware, firmware or any combination thereof, in whole or in part. In addition, the data processing circuit can be a single independent processing module, or be incorporated in whole or in part into any one of other elements in the computing device. As involved in the embodiments of the present disclosure, the data processing circuit is a kind of processor control (such as the selection of a variable resistor terminal path connected to an interface).

[0031] The memory can be used to store software programs and modules of application software, such as the program instructions / data storage device for determining the characteristic parameters of a building in the embodiments of the present disclosure. The processor executes various functional applications and data processing by running the software programs and modules stored in the memory, that is, realizes the determination of the characteristic parameters of the building in the above-mentioned application program. The memory can include high-speed random access memory, and can also include non-volatile memory, such as one or more magnetic storage devices, flash memory, or other non-volatile solid-state memories. In some instances, the memory can further include a memory remotely provided relative to the processor, and these remote memories can be connected to the computing device through a network. Examples of the above network include but are not limited to the Internet, intranet, local area network, mobile communication network and their combinations.

[0032] The transmission device is used to receive or send data via a network. Specific examples of the above network can include a wireless network provided by a communication provider of the computing device. In one instance, the transmission device includes a network adapter (Network Interface Controller, NIC), which can be connected to other network devices through a base station and thus communicate with the Internet. In one instance, the transmission device can be a radio frequency (RF) module, which is used to communicate with the Internet wirelessly.

[0033] The display can be, for example, a touch-screen liquid crystal display (LCD), which enables a user to interact with the user interface of the computing device.

[0034] It should be noted here that in some alternative embodiments, the above Figure 1The computing device shown may include hardware elements (including circuits), software elements (including computer code stored on a computer-readable medium), or a combination of both hardware and software elements. It should be noted that Figure 1 is only an example of a specific concrete instance and is intended to illustrate the types of components that may exist in the computing device described above.

[0035] Figure 2 is a schematic diagram of a system for determining characteristic parameters of a building according to the present embodiment. Refer to Figure 2 shown, the system includes: a terminal device 100, a server 200 communicatively connected to the terminal device 100, and a user 300. Among them, the user 300 inputs response parameters measured from the building and associated with the building through the terminal device 100. The response parameters may be, for example, the response frequencies of the building (such as the first vertical frequency f 1 of the building, the second vertical frequency f 2 of the building, and the third vertical frequency f 3 of the building). The terminal device 100 may transmit the response parameters input by the user 300 to the server 200.

[0036] The server 200 is configured to receive the response parameters measured by the user 300 (such as the first vertical frequency f 1 of the building, the second vertical frequency f 2 of the building, and the third vertical frequency f 3 of the building), and calculate the characteristic parameters of the building (such as the elastic modulus x 1 of the building, the density x 2 of the building, and the stiffness x 3 of the building) according to the received response parameters by using a calculation model provided in the server 200.

[0037] Then, the server 200 transmits the calculated characteristic parameters of the building to the terminal device 100, and the terminal device 100 displays the characteristic parameters of the building to the user 300, so that the user 300 can obtain the characteristic parameters of the building, thereby performing a safety assessment and risk assessment on the current state of the building.

[0038] It should be noted that the server 200 in the system may adopt the hardware structure described above. Under the above operating environment, according to the first aspect of the present embodiment, a finite element model updating method based on the response surface method is provided. Figure 3 shows a schematic flowchart of the method. Refer to Figure 3 shown, the method includes:

[0039] S302: Establish an initial finite element model for indicating the mapping relationship between the characteristic parameters and response parameters of a building, where the characteristic parameters include the elastic modulus, density, and stiffness of the building, and the response parameters include the response frequency of the building;

[0040] S304: Establish a first set of characteristic parameter samples of the characteristic parameters, and use the initial finite element model to determine a first set of response parameter samples of the response parameters according to the first set of characteristic parameter samples;

[0041] S306: Establish a first response surface model for indicating the mapping relationship between the characteristic parameters and response parameters, and correct the first response surface model according to the correlation between the characteristic parameters and response parameters to obtain a second response surface model;

[0042] S308: Determine the parameters of the second response surface model according to the first set of characteristic parameter samples and the first set of response parameter samples;

[0043] S310: Measure the response parameters of the target building; and

[0044] S312: Use the second response surface model to determine the characteristic parameters of the building according to the measured response parameters.

[0045] Specifically, the initial finite element model of most buildings can only accurately reflect the relationship between the building response parameters and characteristic parameters in the initial stage of building use. However, as the building use time gets longer and longer, the accuracy of the characteristic parameters calculated using the initial finite element model continuously decreases. Therefore, it is necessary to obtain the characteristic parameters of the building with higher accuracy according to the following method steps.

[0046] First, the user 300 can screen out the characteristic parameters (such as elastic modulus x 1 , density x 2 , and stiffness x 3 ) suitable for the safety assessment and risk prediction of the building by analyzing dynamic response data or static response data, and further determine the measurable response parameters (such as the response frequency of the building) having a mapping relationship with the characteristic parameters.

[0047] Then, the user 300 determines the initial values and value ranges of the characteristic parameters according to the existing data. For example, Figure 4 is a schematic diagram of the building according to the first aspect of Embodiment 1 of the present disclosure. Referring to Figure 4 shown, the building selected by the user 300 is a certain bridge, and the elastic modulus x 1 , density x 2 , and stiffness x 3 of the bridge are selected as the characteristic parameters of the building, and the response frequency f 1, f 2 , f 3 are response parameters. Among them, f 1 is the first vertical frequency of the building, f 2 is the second vertical frequency of the building, and f 3 is the third vertical frequency of the building. For example, after user 300 inputs frequency, elastic modulus, density, and stiffness into the terminal device 100, the terminal device 100 will respond with the frequencies f 1 , f 2 , f 3 , elastic modulus x 1 , density x 2 and stiffness x 3 to the server 200. The server 200 determines the initial values and value ranges of the elastic modulus x 1 , density x 2 and stiffness x 3 based on the existing data. Finally, it is determined that the initial value of the elastic modulus x 1 is 3.7×104 N / mm 2 , and the value range is 2.96~4.44×104 N / mm 2 , density x 2 has an initial value of 2.55×10 9 t / mm3, and the value range is 2~3×10 9 t / mm3, and the initial value of the stiffness x 3 is 6×10 5 N / mm, and the value range is 4~8×10 5 N / mm. The initial values and value ranges of the elastic modulus x 1 , density x 2 and stiffness x 3 are shown in Table 1:

[0048] Table 1

[0049]

[0050] Then, the server 200 establishes an initial finite element model of the building. Among them, the initial finite element model of the building can characterize the mapping relationship between the characteristic parameters and the response parameters (S302). Among them, the initial finite element model of the building is determined using abaqus software. The server 200 can input the characteristic parameters of the building into the abaqus software to obtain the initial finite element model of the building. For example, when establishing the initial finite element model of the building, it is necessary to input the initial value of the elastic modulus x 1 of 3.7×10 4 N / mm 2 , density x 2The initial value of 2.55×10 9 t / mm3, stiffness x 3 The initial value of 6×10 5 N / mm and other parameter values of the building. Then, the abaqus software can obtain the initial finite element model of the building according to the input parameter values. The specific process of establishing the initial finite element model using abaqus is prior art and will not be elaborated here.

[0051] In addition, the server 200 establishes a first set of characteristic parameter samples corresponding to the characteristic parameters, and then substitutes the first set of characteristic parameter samples into the initial finite element model to obtain a first set of response parameter samples (S304). Before obtaining the first set of characteristic parameter samples, the server 200 needs to select sample points of multiple characteristic parameters using the experimental design method and obtain the first set of characteristic parameter samples based on the sample points of multiple characteristic parameters. And among them, the experimental design method can select the central composite design method, the uniform design method, the Box-Behnken design method or the D-optimal design method. After obtaining the first set of characteristic parameter samples, the server 200 modifies the py file of the initial finite element model using the parametric command of the abaqus software and runs it in the abaqus finite element software, so as to calculate and output a first set of response parameter samples corresponding to the first set of characteristic parameter samples. Still taking the above bridge as an example, the server 200 uses the central composite design method for design, and thus obtains 15 different sets of samples (i.e., the first set of characteristic parameter samples): (x 1 (1) , x 2 (1) , x 3 (1) ), (x 1 (2) , x 2 (2) , x 3 (2) )......, (x 1 (15) , x 2 (15) , x 3 (15) ). Among them, (x 1 (i) , x 2 (i) , x 3 (i) ) represents the i-th set of samples; x 1 (1) ~x 1 (15) is the elastic modulus of the bridge, x 2 (1) ~x 2(15) is the density of the bridge, and x 3 (1) ~x 3 (15) is the stiffness of the bridge. Then, the server 200 can obtain 15 sets of corresponding frequencies (i.e., the first response parameter sample set) by using the abaqus software to calculate with the initial finite element model and these 15 sets of different elastic moduli, densities, and stiffnesses: (f 1 (1) , f 2 (1) , f 3 (1) ), (f 1 (2) , f 2 (2) , f 3 (2) ),......, (f 1 (15) , f 2 (15) , f 3 (15) ). Among them, f 1 (1) ~f 1 (15) is the first vertical frequency of the bridge, x 2 (1) ~x 2 (15) is the second vertical frequency of the bridge, and x 3 (1) ~x 3 (15) is the third vertical frequency of the bridge.

[0052] Then, the server 200 establishes a first response surface model, where the first response surface model can represent the mapping relationship between the characteristic parameters and the response parameters. Specifically, the first response surface model is, for example, a quadratic response surface model, and this quadratic response surface model is expressed as follows:

[0053]

[0054]

[0055]

[0056] Then, the server 200 further analyzes the correlation between the characteristic parameters and the response parameters, and modifies the first response surface model based on the analysis results to obtain a second response surface model (S306). Among them, the correlation between the response parameters and the characteristic parameters refers to the functional relationship between the response parameters and the characteristic parameters.

[0057] For example, through analysis, the first vertical frequency f of the bridge can be determined 1 and the function relationship with the elastic modulus x 1 is a logarithmic function relationship, and with the density x 2 is a reciprocal function relationship, and with the stiffness x 3 is a quadratic function relationship. The second vertical frequency f 2 and the relationship with the elastic modulus x 1 is a linear function relationship, and with the density x 2 is a reciprocal function relationship, and with the stiffness x 3 is a quadratic function relationship. Moreover, the third vertical frequency f 3 and the relationship with the elastic modulus x 1 is a linear function relationship, and with the density x 2 is a reciprocal function relationship, and with the stiffness x 3 is a quadratic function relationship. According to the above relationships, the first response surface model is corrected to obtain the corrected second response surface model:

[0058]

[0059]

[0060]

[0061] where α 0 ~α 7 , β 0 ~β 7 and θ 0 ~θ 7 are parameters to be determined.

[0062] Then, the server 200 determines the parameters of the second response surface model according to the first characteristic parameter sample set and the first response parameter sample set (S308). The server 200 combines the 15 groups of samples of elastic modulus, density, and stiffness calculated above with the 15 groups of response frequency samples calculated to construct 15 groups of samples for training the response surface model related to the building (x 1 (i) , x 2 (i) , x 3 (i) , f 1 (i) , f 2 (i) , f 3 (i)), where 1 ≤ i ≤ 15. Then, the server 200 substitutes these 15 groups of samples into the second response surface model and uses the lsqcurvefit function to fit the second response surface model, so as to obtain the parameters α 0 ~α 7 、β 0 ~β 7 and θ 0 ~θ 7 . Thus, the second response surface model is determined.

[0063] Then, the user 300 measures the response parameters of the target building (S310). For example, the user 300 measures the response parameters of the target building (for example, the vertical first-order frequency f 1 、vertical second-order frequency f 2 and vertical third-order frequency f 3 ), and inputs the measured response parameters of the target building into the terminal device 100, and the terminal device 100 transmits the response parameters of the target building to the server 200. The server 200 substitutes the measured response parameters into the second response surface model to obtain more accurate characteristic parameters of the target building (for example, the elastic modulus x 1 、density x 2 and stiffness x 3 ) of the target bridge.

[0064] Thus, after obtaining the characteristic parameters of the building, the server 200 obtains the response parameters of the building according to the characteristic parameters of the building. And compares the response parameters of the building with the measured response parameters. If the error between the response parameters of the building and the measured response parameters is within the allowable range, it means that the second response surface model has reached the application standard.

[0065] Finally, the server 200 transmits the characteristic parameters of the building that meet the requirements to the terminal device 100, and the terminal device 100 displays the characteristic parameters of the building to the user 300.

[0066] As described in the background art, the finite element model updating method based on the response surface often has certain problems. For example, the existing finite element model updating method based on the response surface method cannot well simulate the internal relationship between the characteristic parameters to be updated and the response characteristic values, so that the fitting accuracy of the response surface model cannot be improved, and further, the current state of the existing building cannot be safely evaluated and risk predicted. Aiming at the technical problem that the existing response surface model of the building in the above-mentioned prior art cannot well indicate the internal relationship between the characteristic parameters and the response parameters, so that more accurate characteristic parameters cannot be obtained, and thus the building cannot be accurately evaluated for safety and risk predicted, no effective solution has been proposed yet.

[0067] Thus, in view of this, the technical solution of the present disclosure corrects the first response surface model according to the correlation between the characteristic parameters and the response parameters, and obtains the second response surface model. Since the obtained second response surface model can better reflect the mapping relationship between the response parameters and the characteristic parameters, the fitting accuracy of the second response surface model is higher than that of the first response surface model. And by substituting the measured response parameters into the second response surface model, more accurate characteristic parameters can be obtained. Thus, through the above operations, the technical effect of being able to correct the first response surface model by analyzing the correlation between the characteristic parameters and the response parameters, obtain the second response surface model, and use the second response surface model to obtain more accurate characteristic parameters is achieved. Furthermore, the technical problem existing in the prior art that the response surface model of the existing building cannot well indicate the internal relationship between the characteristic parameters and the response parameters, so more accurate characteristic parameters cannot be obtained, and thus the building cannot be accurately evaluated for safety and risk predicted is solved. Optionally, the operation of establishing the first characteristic parameter sample set of the characteristic parameters and using the initial finite element model to determine the first response parameter sample set of the response parameters according to the first characteristic parameter sample set includes: establishing the first characteristic parameter sample set by using the experimental design method; and using the initial finite element model to determine the first response parameter sample set according to the first characteristic parameter sample set.

[0068] Specifically, the server 200 uses the central composite design method to conduct design experiments, thereby obtaining 15 groups of different first characteristic parameter sample sets. Among them, the first characteristic parameter sample set is, for example, the above-mentioned (x 1 (1) , x 2 (1) , x 3 (1) ), (x 1 (2) , x 2 (2) , x 3 (2) ),......, (x 1 (15) , x 2 (15) , x 3 (15) ). Among them, (x 1 (i) , x 2 (i) , x 3 (i) ) represents the i-th group of samples; x 1 (1) ~x 1 (15) is the elastic modulus of the building, x2 (1) ~x 2 (15) is the density of the building, and x 3 (1) ~x 3 (15) is the stiffness of the building. And the first response parameter sample set is the 15 groups of corresponding frequencies described above: (f 1 (1) , f 2 (1) , f 3 (1) ), (f 1 (2) , f 2 (2) , f 3 (2) ),......, (f 1 (15) , f 2 (15) , f 3 (15) ). Where f 1 (1) ~f 1 (15) is the first vertical frequency of the building, f 2 (1) ~f 2 (15) is the second vertical frequency of the building, and f 3 (1) ~f 3 (15) is the third vertical frequency of the building.

[0069] Optionally, according to the correlation between the characteristic parameters and the response parameters, the operation of correcting the first response surface model includes: determining the significance coefficient of the characteristic parameters relative to the response parameters according to the first characteristic parameter sample set and the first response parameter sample set; screening out the significant characteristic parameters corresponding to the response parameters according to the significance coefficient; determining the correlation between the screened significant characteristic parameters and the corresponding response parameters; and correcting the first response surface model according to the correlation between the screened significant characteristic parameters and the corresponding response parameters.

[0070] Specifically, when the server 200 determines that the elastic modulus x 1 , the density x 2 , and the stiffness x 3 are used as the characteristic parameters of the building, and the first vertical frequency f 1 , the second vertical frequency f 2 , and the third vertical frequency f 3When used as a response parameter, the server 200 further determines the elastic modulus x 1 , the density x 2 , and the stiffness x 3 with respect to the vertical first-order frequency f 1 respectively; the elastic modulus x 1 , the density x 2 , and the stiffness x 3 with respect to the vertical second-order frequency f 2 respectively; and the elastic modulus x 1 , the density x 2 , and the stiffness x 3 with respect to the vertical third-order frequency f 3 respectively. The significance coefficient is used to characterize the significance of each characteristic parameter with respect to each response parameter.

[0071] Among them, for example, the server 200 can use multi-factor analysis of variance (or multi-factor F-test), that is, according to the samples (x 1 (1) , x 2 (1) , x 3 (1) ), (x 1 (2) , x 2 (2) , x 3 (2) )......, (x 1 (15) , x 2 (15) , x 3 (15) ) and (f 1 (1) , f 2 (1) , f 3 (1) ), (f 1 (2) , f 2 (2) , f 3 (2) )......, (f 1 (15) , f 2 (15) , f 3 (15) ), analyze multiple factors including the elastic modulus x 1 , the density x 2 , and the stiffness x 3 . Determine each elastic modulus x 1 , the density x2 and stiffness x 3 with respect to the vertical first-order frequency f 1 of the significance coefficient; each elastic modulus x 1 and density x 2 and stiffness x 3 with respect to the vertical second-order frequency f 2 of the significance coefficient; and each elastic modulus x 1 and density x 2 and stiffness x 3 with respect to the vertical third-order frequency f 3 of the significance coefficient.

[0072] Thus, according to the significance coefficient, from the elastic modulus x 1 and density x 2 and stiffness x 3 to determine the characteristic parameters that are significant with respect to the vertical first-order frequency f 1 from the characteristic elastic modulus x 1 and density x 2 and stiffness x 3 to determine the characteristic parameters that are significant with respect to the vertical second-order frequency f 2 and from the characteristic elastic modulus x 1 and density x 2 and stiffness x 3 to determine the characteristic parameters that are significant with respect to the vertical third-order frequency f 3 of significance.

[0073] Specifically, the technical solution of the present disclosure can, for example, use multi-factor analysis of variance (or multi-factor F-test) to determine the significance coefficient. Among them, multi-factor analysis of variance or multi-factor F-test is a common means of analysis of variance in the art and will not be elaborated here.

[0074] Among them, Figure 5A shows the significance coefficient of each factor with respect to the vertical first-order frequency f 1 , Figure 5B shows the significance coefficient of each factor with respect to the vertical second-order frequency f 2 , Figure 5C shows the significance of each factor with respect to the vertical third-order frequency f 3 . Among them, the P-value represents the credibility of the result. The lower the P-value, the higher the significance. When the P-value is less than 0.05, it indicates that the characteristic parameter has a significant impact on the response parameter.

[0075] Taking Figure 5A as an example and referring to Figures 5A to 5C it can be seen that the elastic modulus x 1 and density x 2 are relative to the vertical first-order frequency f1 , the vertical second-order frequency f 2 and the vertical second-order frequency f 3 are both relatively significant characteristic parameters. And the stiffness x 3 is only a relatively significant characteristic parameter with respect to the vertical third-order frequency f 3 . Thus, in the process of obtaining the second response surface model by correcting the first response surface model, when correcting the formulas for the vertical first-order frequency f 1 and the vertical second-order frequency f 2 (i.e., formulas (1) and (2)), only the terms for the elastic modulus x 1 and the density x 2 need to be considered for correction. And when correcting the formula for the vertical third-order frequency f 3 , the terms for the elastic modulus x 1 , the density x 2 and the stiffness x 3 need to be considered for correction.

[0076] . Thus, the above operations can reduce the calculation time. Specifically, in the process of determining the second response surface model according to the first response surface model, if there are too many characteristic parameters, the calculation process will be very complex, and it is very time-consuming for the user to analyze the correlation between the characteristic parameters that have little effect on the response parameters and the response parameters. Therefore, the technical solution of the present disclosure first analyzes the significance of each characteristic parameter with respect to the response parameter. After analyzing the significance of the characteristic parameters, instead of analyzing the correlation between the characteristic parameters that have little effect on the response parameter and the response parameter, only the correlation between the characteristic parameters that have a significant effect on the response parameter and the response parameter is analyzed. Thus, the above operations achieve the technical effects of being able to avoid ineffective calculation processes, reduce calculation time, and save time and economic costs.

[0077] Optionally, the operation of determining the correlation between the selected significant characteristic parameters and the corresponding response parameters includes: selecting one significant characteristic parameter from the significant characteristic parameters as a reserved parameter, and determining a plurality of fitting polynomials for characterizing the correlation between the reserved parameter and the corresponding response parameter; and determining the correlation between the reserved parameter and the corresponding response parameter according to the function form with the highest fitting accuracy with respect to the plurality of fitting polynomials.

[0078] Specifically, as described above, in the technical solution of the present disclosure, the determined significant characteristic parameters corresponding to the vertical first-order frequency f 1 include the elastic modulus x 1 and the density x 2 ; the significant characteristic parameters corresponding to the vertical second-order frequency f 2 include the elastic modulus x 1and density x 2 ; and the significant characteristic parameters corresponding to the vertical third-order frequency f 3 include elastic modulus x 1 , density x 2 and stiffness x 3 .

[0079] On this basis, the server 200 determines the correlation between the significant characteristic parameters x 1 and x 2 and the vertical first-order frequency f 1 as follows:

[0080] First, the server 200 sets the elastic modulus x 1 as a reserved parameter, and then determines multiple fitting polynomials for characterizing the correlation between the elastic modulus x 1 and the vertical first-order frequency f 1 . Then, from these multiple fitting polynomials, the polynomial with the highest fitting accuracy is selected, and the correlation between the elastic modulus x 1 and the vertical first-order frequency f 1 is determined according to the polynomial with the highest fitting accuracy. 1

[0081] Among them, for example, the multiple fitting polynomials can be multiple fitting polynomials in different forms (such as logarithmic form, reciprocal form, quadratic function form, and linear form). Among them, through calculation, it can be determined that among the multiple fitting polynomials, the fitting accuracy of the quadratic function fitting polynomial is 0.88, and the fitting accuracy of the logarithmic function fitting polynomial is 0.96. Since the fitting accuracy of the logarithmic function fitting polynomial is the highest, it can be determined that the relationship between the elastic modulus x 1 and the vertical first-order frequency f 1 is a logarithmic relationship.

[0082] Then, the server 200 sets the density x 2 as a reserved parameter, and then determines multiple fitting polynomials for characterizing the correlation between the density x 2 and the vertical first-order frequency f 2 . Then, from these multiple fitting polynomials, the polynomial with the highest fitting accuracy is selected, and the correlation between the density x 1 and the vertical first-order frequency f 2 is determined according to the polynomial with the highest fitting accuracy. 1

[0083] Among them, through calculation, it can be determined that among the multiple fitting polynomials, the fitting accuracy of the quadratic function fitting polynomial is 0.71, and the fitting accuracy of the reciprocal function fitting polynomial is 0.99. Since the fitting accuracy of the reciprocal function fitting polynomial is the highest, it can be determined that the stiffness x​​2 has an inverse relationship with the vertical first-order frequency f 1 .

[0084] In this way, the server 200 determines the significant feature parameter x 1 and x 2 's correlation with the vertical first-order frequency f 1 . The calculation method for the fitting accuracy of the fitting polynomial will be described in detail later.

[0085] Then, in the same way, the server 200 determines the correlation between the significant feature parameter x 1 and x 2 and the vertical second-order frequency f 2 , and determines the correlation between the significant feature parameter x 1 , x 2 and x 3 and the vertical third-order frequency f 3 .

[0086] Thus, through the above operations, the technical effect of improving the calculation accuracy of the second response surface model can be obtained.

[0087] Optionally, the first response surface model includes multiple polynomials corresponding to respective response parameters, and when the feature parameters are multiple feature parameters, the operation of determining multiple fitting polynomials for characterizing the correlation between the reserved parameter and the corresponding response parameter includes: establishing a second feature parameter sample set of the feature parameters by using an experimental design method, where in each sample of the second feature parameter sample set, other parameters except the reserved parameter are constants; and respectively substituting the samples in the second feature parameter sample set into the polynomials corresponding to the response parameters corresponding to the significant feature parameters to obtain multiple fitting polynomials.

[0088] Specifically, the server 200 first sets the elastic modulus x 1 as the reserved parameter, and then sets other feature parameters except the elastic modulus x 1 (i.e., the density x 2 and the stiffness x 3 ) as constants. Thus, according to different constant values taken by the density x 2 and the stiffness x 3 , multiple groups of samples can be obtained. Among them, different groups of samples take different constant values for the density x 2 and the stiffness x 3 , and in the same group of samples, the density x 2 and the stiffness x 3 take the same constant values.

[0089] For example, the server 200 can obtain the following 4 groups of samples according to the central composite design method:

[0090] The first group of samples, density x 2 and stiffness x 3 respectively take values x 2 a and x 3 a , elastic modulus x 1 take different values;

[0091] The second group of samples, density x 2 and stiffness x 3 respectively take values x 2 b and x 3 b , elastic modulus x 1 take different values;

[0092] The third group of samples, density x 2 and stiffness x 3 respectively take values x 2 c and x 3 c , elastic modulus x 1 take different values; and

[0093] The fourth group of samples, density x 2 and stiffness x 3 respectively take values x 2 c and x 3 c , elastic modulus x 1 take different values.

[0094] Then the server 200 substitutes the above four groups of samples into the polynomial (1) respectively, so as to obtain 4 fitting polynomials that characterize the correlation between x 1 and the vertical first-order frequency f 1 .

[0095] For example, when the density x 2 and the stiffness x 3 respectively take values x 2 a and x 3 a , substitute them into the formula (1) to obtain the first fitting polynomial that characterizes the correlation between x 1 and the vertical first-order frequency f 1 . Thus, the sample point x 1 can be substituted into this polynomial to obtain the vertical first-order frequency f 1 corresponding to the sample point x 1 . Thus, the one used to fit x 1Between the vertical first-order frequency f 1 Multiple sample points of the fitted curve.

[0096] When the density x 2 and stiffness x 3 Take the value x respectively 2 b and x 3 b , substitute it into formula (1) to get the representation x 1 With the vertical first-order frequency f 1 The second fitting polynomial of the correlation between . Thus, the sample point x can be 1 Substituting into the polynomial, we get 1 The corresponding vertical first-order frequency f 1 . Thus, we can get the value used to fit x 1 Between the vertical first-order frequency f 1 Multiple sample points of the fitted curve.

[0097] When the density x 2 and stiffness x 3 Take the value x respectively 2 c and x 3 c , substitute it into formula (1) to get the representation x 1 With the vertical first-order frequency f 1 The third fitting polynomial of the correlation between . Thus, the sample point x can be 1 Substituting into the polynomial, we get 1 The corresponding vertical first-order frequency f 1 . Thus, we can get the value used to fit x 1 Between the vertical first-order frequency f 1 Multiple sample points of the fitted curve.

[0098] When the density x 2 and stiffness x 3 Take the value x respectively 2 d and x 3 d , substitute it into formula (1) to get the representation x 1 With the vertical first-order frequency f 1 The fourth fitting polynomial of the correlation between . Thus, the sample point x can be 1 Substituting into the polynomial, we get 1 The corresponding vertical first-order frequency f 1 . Thus, we can get the value used to fit x 1 Between the vertical first-order frequency f 1 Multiple sample points of the fitted curve.

[0099] In addition, the fitting accuracy of the first polynomial described above can be calculated by the following method:

[0100] First, substitute the first group of samples (i.e., density x 2 and stiffness x 3 with respective values x 2 a and x 3 a ) into formula (1), so as to calculate the calculation results y RS (j) corresponding to each sample calculated using the first polynomial (i.e., the vertical first-order frequency corresponding to each sample calculated according to formula (1)).

[0101] Then, substitute the first group of samples into the initial finite element model, so as to calculate the calculation results y(j) corresponding to each sample in the first group of samples (i.e., the vertical first-order frequency corresponding to each sample calculated according to the initial finite element model).

[0102] Then, calculate the fitting accuracy of the first fitting polynomial according to the following formula:

[0103]

[0104] where R 2 represents the fitting accuracy, and the closer R 2 is to 1, the higher the fitting accuracy of the polynomial. y RS (j) represents the calculation results corresponding to each sample calculated using the polynomial. y(j) represents the calculation results corresponding to each sample calculated using the initial finite element model. represents the mean value of y(j). N represents the number of samples in the sample group.

[0105] If R 2 is closer to 1, it indicates that the fitting accuracy of the polynomial is higher; if R 2 is closer to 0, it indicates that the fitting accuracy of the polynomial is lower.

[0106] Then, referring to the above steps, the fitting accuracies of the second to fourth polynomials can be calculated. Thus, according to the above method, the fitting accuracies of the first to fourth polynomials can be calculated, and then the correlation between the elastic modulus x 1 and the vertical first-order frequency f 1 can be determined based on the polynomial with the highest fitting accuracy.

[0107] where Figure 6A shows the result after polynomial fitting for the generated polynomial using curve fitting software. Where A1, B1, C1, D1 respectively represent according to density x2 and stiffness x 3 The set of sample points determined by the polynomials obtained when taking different values. Where A1 is the density x 2 and stiffness x 3 Take x 2 a and x 3 a The sample points when, B1 is the density x 2 and stiffness x 3 Take x 2 b and x 3 b The sample points when, C1 is the density x 2 and stiffness x 3 Take x 2 c and x 3 c The sample points when, D1 is the density x 2 and stiffness x 3 Take x 2 d and x 3 d The sample points when. Thus, according to Figure 6A It can be seen that the elastic modulus x 1 and the vertical first-order frequency f 1 There is a logarithmic relationship between them.

[0108] Then, in the same way, the server 200 sets the density x 2 as a reserved parameter, and then sets the other characteristic parameters except the density x 2 (i.e., the elastic modulus x 1 and stiffness x 3 ) as constants. Thus, according to the different values taken by the elastic modulus x 1 and stiffness x 3 , multiple fitting polynomials characterizing the correlation between x 2 and the vertical first-order frequency f 1 can be obtained. And referring to the method described above, and using formula (7) to calculate the fitting accuracy of the multiple fitting polynomials. Then, according to the polynomial with the highest fitting accuracy, the correlation between the density x 2 and the vertical first-order frequency f 2 is determined.

[0109] Among them, Figure 6B Shows the result after fitting the generated polynomial using curve fitting software. Where A1, B1, C1, and D1 respectively represent the sets of sample points determined by the polynomials obtained when taking different values according to the elastic modulus x 1 and stiffness x 3 . According toFigure 6B You can see that the density x 2 With the vertical first-order frequency f 1 There is a reciprocal relationship between them.

[0110] In addition, for the vertical second-order frequency f 2 and the vertical third-order frequency f 3 , also refer to the method described above, so as to determine the correlation between each significant characteristic parameter and the corresponding response parameter. Thus, through the above operation, the technical effect of obtaining the fitting polynomial that characterizes the correlation between the characteristic parameter and the response parameter can be achieved.

[0111] Optionally, the operation of determining the parameters of the second response surface model according to the first characteristic parameter sample set and the first response parameter sample set includes: substituting the first characteristic parameter sample set and the first response parameter sample set into the lsqcurvefit function, and fitting the second response surface model using the lsqcurvefit function; and determining the parameters of the second response surface model.

[0112] Specifically, after determining the second response surface model, the server 200 uses the lsqcurvefit function in the Matlab software optimization toolbox to fit the second response surface model and solve the parameters in the second response surface model.

[0113] Therefore, the server 200 uses the lsqcurvefit function to convert the first characteristic parameter sample set (x 1 (i) ,x 2 (i) , x 3 (i) ) and the first response parameter sample set (f 1 (i) ,f 2 (i) ,f 3 (i) ) constructed 15 groups of samples (x 1 (i) ,x 2 (i) ,x 3 (i) ,f 1 (i) ,f 2 (i) ,f 3 (i) ) is substituted into formula (4) to formula (6), thereby determining the parameter α in formula (4) to formula (6): 0 ~α 7 , β 0 ~β 7 and θ0 ~θ 7 Thereby, the second response surface model is determined.

[0114] Optionally, the operation of determining the characteristic parameters of the building according to the measured response parameters by using the second response surface model includes: substituting the measured response parameters into the fmincon algorithm, and performing non-linear optimization on the second response surface model by using the fmincon algorithm to obtain the characteristic parameters of the building.

[0115] Specifically, the server 200 substitutes the measured response parameters into the second response surface model, and performs non-linear optimization on the second response surface model by using the fmincon algorithm. Thereby, higher-precision elastic modulus, density, and stiffness are obtained. The initial values, corrected values, and correction rates of the elastic modulus, the initial values, corrected values, and correction rates of the density, and the initial values, corrected values, and correction rates of the stiffness are shown in Table 2:

[0116] Table 2

[0117]

[0118] As can be seen from Table 2, the corrected values of the elastic modulus, density, and stiffness are all corrected to varying degrees compared with the values to be corrected.

[0119] Thereby, through the above operations, the technical effect of being able to obtain higher-precision characteristic parameters of the building is achieved.

[0120] Optionally, it further includes: substituting the characteristic parameters of the building into the second response surface model to determine the second response parameter sample set; calculating the first deviation between the first response parameter sample set and the measured response parameters; calculating the second deviation between the second response parameter sample set and the measured response parameters; and determining the accuracy of the second response surface model according to the comparison result of the first deviation and the second deviation.

[0121] Specifically, the following formula can be used to test the accuracy of the second response surface model:

[0122]

[0123]

[0124]

[0125] The smaller the EISE value, the higher the accuracy of the second response parameter sample set, and the closer the obtained second response surface model is to the actual situation. If the EISE value is larger, it indicates that the accuracy of the second response parameter sample set is lower, and the experimental design should be redone. If the RMSE value is smaller, it means that the accuracy of the second response parameter sample set is higher, and the second response surface model after regression is closer to the actual situation. If the RMSE value is larger, it indicates that the accuracy of the second response parameter sample set is lower, and the experimental design should be redone.

[0126] If the second response surface models all satisfy the above formulas (7) to (10), it means that the second response surface model is close to the actual situation.

[0127] Then, the server 200 substitutes the characteristic parameters of the building into the second response parameter sample set obtained from the second response surface model. Calculate the first deviation between the first response parameter sample set and the measured response parameters, and then calculate the second deviation between the second response parameter sample set and the measured response parameters. Compare the first deviation and the second deviation to determine the accuracy of the second response surface model.

[0128] Taking the above bridge as an example, the first response parameter sample set, the second response parameter sample set, the measured frequency, the first deviation, and the second deviation are shown in Table 3:

[0129] Table 3

[0130]

[0131] As can be seen from Table 3, the first deviation is 14.84% and the second deviation is 4.38%. It can be seen that the first deviation is much smaller than the second deviation. Therefore, the second response surface model can greatly improve the calculation accuracy, thus verifying the effectiveness of this method.

[0132] Thus, through the above operations, the technical effect of being able to verify the accuracy of the second response surface model is achieved.

[0133] In the embodiments of the present disclosure, first, an initial finite element model is established that can represent the relationship between the characteristic parameters and response parameters of a building. Then, the characteristic parameters to be corrected and the response parameters corresponding to the characteristic parameters are determined. Next, a first set of characteristic parameter samples is established, and the first set of characteristic parameter samples is substituted into the initial finite element model to obtain a first set of response parameter samples. Then, a first response surface model is established that can represent the mapping relationship between the characteristic parameters and response parameters, the correlation between the characteristic parameters and response parameters is analyzed, and the first response surface model is corrected according to the correlation between the characteristic parameters and response parameters to obtain a second response surface model. After that, the parameters of the second response surface model are determined based on the first set of characteristic parameter samples and the first set of response parameter samples. The response parameters of the building are measured and substituted into the second response surface model to obtain the characteristic parameters of the building. Since the corrected second response surface model can well represent the correlation between the characteristic parameters and response parameters, the second response surface model can improve the calculation accuracy of the characteristic parameters of the building. Thus, through the above operations, the technical effect of being able to correct the first response surface model by analyzing the correlation between the characteristic parameters and response parameters, obtaining the second response surface model, and using the second response surface model to obtain more accurate characteristic parameters is achieved. Furthermore, the technical problem in the prior art that the existing response surface model of a building cannot well indicate the internal relationship between the characteristic parameters and response parameters, so more accurate characteristic parameters cannot be obtained, and thus the building cannot be accurately evaluated for safety and risk predicted is solved.

[0134] Figure 7 FIG. shows a schematic flow chart of a method for verifying whether the accuracy of the response parameters of a building meets the standard according to the first aspect of Embodiment 1 of the present disclosure. Refer to Figure 7 As shown, if it is necessary to determine the characteristic parameters of a building, it is necessary to first determine the response parameters and characteristic parameters (S702). After determining the characteristic parameters to be corrected, an initial finite element model is established (S704). After establishing the initial finite element model, a first set of characteristic parameter samples is established. Using the first set of characteristic parameter samples and the initial finite element model, a first set of response parameter samples is determined (S706).

[0135] Meanwhile, a first response surface model is established (S708). A significance analysis is performed on the characteristic parameters, the characteristic parameters that have insignificant influence on the response parameters are removed, and the characteristic parameters that have significant influence on the response parameters are retained (S710). One of the reserved significant characteristic parameters is selected as the reserved parameter, and the remaining characteristic parameters are set as constants. A second set of characteristic parameter samples is established, and the samples in the second set of characteristic parameter samples are substituted into the polynomials corresponding to the significant characteristic parameters to obtain a plurality of fitting polynomials (S712).

[0136] Select the fitting polynomial with the highest fitting accuracy as the functional relationship for characterizing the correlation between the characteristic parameters and the response parameters (S714). Substitute the first characteristic parameter sample set and the first response parameter sample set into the lsqcurvefit function, and use the lsqcurvefit function to fit the second response surface model to determine the parameters of the second response surface model (S716).

[0137] Finally, measure the response parameters of the target building, and substitute the measured response parameters into the second response surface model to obtain the second response parameter sample set (S718). Calculate the first deviation between the first response parameter sample set and the measured response parameters. Calculate the second deviation between the second response parameter sample set and the measured response parameters (S720). Determine the accuracy of the second response surface model according to the comparison result of the first deviation and the second deviation (S722).

[0138] In addition, as shown in Figure 1 According to the third aspect of this embodiment, a storage medium is provided. The storage medium includes a stored program, wherein when the program runs, the method described in any one of the above is executed by a processor.

[0139] Thus, through the above operations, the technical effect of being able to correct the first response surface model by analyzing the correlation between the characteristic parameters and the response parameters, obtain the second response surface model, and use the second response surface model to obtain more accurate characteristic parameters is achieved. Furthermore, it solves the technical problem in the prior art that the existing response surface model of a building cannot well indicate the internal relationship between the characteristic parameters and the response parameters, so more accurate characteristic parameters cannot be obtained, and thus the building cannot be accurately evaluated for safety and risk predicted.

[0140] It should be noted that for the foregoing method embodiments, for the sake of simple description, they are all expressed as a series of action combinations. However, those skilled in the art should know that the present invention is not limited by the described action sequence, because according to the present invention, certain steps can be performed in other sequences or simultaneously. Secondly, those skilled in the art should also know that the embodiments described in the specification are all preferred embodiments, and the actions and modules involved are not necessarily essential to the present invention.

[0141] Through the description of the above embodiments, those skilled in the art can clearly understand that the method according to the above embodiments can be implemented by means of software plus a necessary general hardware platform. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, can be embodied in the form of a software product. The computer software product is stored in a storage medium (such as ROM / RAM, magnetic disk, optical disk), and includes several instructions for causing a terminal device (which can be a mobile phone, computer, server, or network device, etc.) to execute the methods described in various embodiments of the present invention.

[0142] Embodiment 2

[0143] Figure 8 The finite element model correction device 800 based on the response surface method according to the first aspect of the present embodiment is shown. The device 800 corresponds to the method according to the first aspect of Embodiment 1. Refer to Figure 8 As shown, the device 800 includes: a model establishment module 810, configured to establish an initial finite element model for indicating the mapping relationship between the characteristic parameters and response parameters of a building, where the characteristic parameters include the elastic modulus, density, and stiffness of the building, and the response parameters include the response frequency of the building; a first determination module 820, configured to establish a first characteristic parameter sample set of the characteristic parameters, and use the initial finite element model to determine a first response parameter sample set of the response parameters according to the first characteristic parameter sample set; a model correction module 830, configured to establish a first response surface model for indicating the mapping relationship between the characteristic parameters and response parameters, and correct the first response surface model according to the correlation between the characteristic parameters and response parameters to obtain a second response surface model; a undetermined coefficient determination module 840, configured to determine the parameters of the second response surface model according to the first characteristic parameter sample set and the first response parameter sample set; a response parameter measurement module 850, configured to measure the response parameters of the target building; and a parameter solving module 860, configured to determine the characteristic parameters of the building according to the measured response parameters by using the second response surface model.

[0144] Optionally, a characteristic parameter sample set establishment module, configured to establish a first characteristic parameter sample set by using an experimental design method; and a response parameter sample set determination module, configured to use the initial finite element model to determine a first response parameter sample set according to the first characteristic parameter sample set.

[0145] Optionally, a significance coefficient determination module is configured to determine a significance coefficient of a feature parameter relative to a response parameter according to a first feature parameter sample set and a first response parameter sample set; a significant feature parameter screening module is configured to screen out significant feature parameters corresponding to the response parameter according to the significance coefficient; a relevance determination module is configured to determine the relevance between the screened significant feature parameters and the corresponding response parameter; and a correction module is configured to correct the first response surface model according to the relevance between the screened significant feature parameters and the corresponding response parameter.

[0146] Optionally, a first fitting polynomial determination module is configured to select a significant feature parameter from the significant feature parameters as a reserved parameter, and determine a plurality of fitting polynomials for characterizing the relevance between the reserved parameter and the corresponding response parameter; and a first accuracy determination module is configured to determine the relevance between the reserved parameter and the corresponding response parameter according to the function form with the highest fitting accuracy relative to the plurality of fitting polynomials.

[0147] Optionally, a second feature parameter sample set determination module is configured to establish a second feature parameter sample set of the feature parameter by using an experimental design method, where in each sample of the second feature parameter sample set, parameters other than the reserved parameter are constants; and a second fitting polynomial determination module is configured to substitute the samples in the second feature parameter sample set into the polynomials corresponding to the response parameters corresponding to the significant feature parameters respectively to obtain a plurality of fitting polynomials.

[0148] Optionally, a second response surface model fitting module is configured to substitute the first feature parameter sample set and the first response parameter sample set into the lsqcurvefit function, and use the lsqcurvefit function to fit the second response surface model; and a parameter determination module is configured to determine the parameters of the second response surface model.

[0149] Optionally, a feature parameter determination module is configured to substitute the measured response parameter into the fmincon algorithm, and use the fmincon algorithm to perform a nonlinear optimization solution on the second response surface model to obtain the feature parameters of the building.

[0150] Optionally, a second response parameter sample set determination module is configured to substitute the feature parameters of the building into the second response surface model to determine a second response parameter sample set; a first deviation calculation module is configured to calculate a first deviation between the first response parameter sample set and the measured response parameter; a second deviation calculation module is configured to calculate a second deviation between the second response parameter sample set and the measured response parameter; and a second accuracy determination module is configured to determine the accuracy of the second response surface model according to the comparison result between the first deviation and the second deviation.

[0151] Thus, through the above operations, the technical effect of being able to correct the first response surface model by analyzing the correlation between the characteristic parameters and the response parameters, obtaining the second response surface model, and using the second response surface model to obtain more accurate characteristic parameters is achieved. Furthermore, the technical problem in the prior art that the existing response surface model of a building cannot well simulate the correlation between the characteristic parameters and the response parameters, and thus cannot obtain more accurate characteristic parameters is solved.

[0152] Embodiment 3

[0153] Figure 9 Fig. 900 shows a finite element model correction device 900 based on the response surface method according to the first aspect of the present embodiment, and the device 900 corresponds to the method according to the first aspect of Embodiment 1. Refer to Figure 9 As shown, the device 900 includes: a processor 910; and a memory 920, connected to the processor 910, for providing instructions for the processor 910 to perform the following processing steps: establishing an initial finite element model for indicating the mapping relationship between the characteristic parameters and the response parameters of a building, where the characteristic parameters include the elastic modulus, density, and stiffness of the building, and the response parameters include the response frequencies of the building; establishing a first characteristic parameter sample set of the characteristic parameters, and using the initial finite element model to determine a first response parameter sample set of the response parameters according to the first characteristic parameter sample set; establishing a first response surface model for indicating the mapping relationship between the characteristic parameters and the response parameters, and correcting the first response surface model according to the correlation between the characteristic parameters and the response parameters to obtain a second response surface model; determining the parameters of the second response surface model according to the first characteristic parameter sample set and the first response parameter sample set; measuring the response parameters of the target building; and using the second response surface model to determine the characteristic parameters of the building according to the measured response parameters.

[0154] Thus, through the above operations, the technical effect of being able to correct the first response surface model by analyzing the correlation between the characteristic parameters and the response parameters, obtaining the second response surface model, and using the second response surface model to obtain more accurate characteristic parameters is achieved. Furthermore, the technical problem in the prior art that the existing response surface model of a building cannot well indicate the internal relationship between the characteristic parameters and the response parameters, and thus cannot obtain more accurate characteristic parameters, and thus cannot accurately evaluate the safety and predict the risks of the building is solved.

[0155] The above serial numbers of the embodiments of the present invention are only for description and do not represent the advantages or disadvantages of the embodiments.

[0156] In the above embodiments of the present invention, the descriptions of the various embodiments have their own emphases. For the parts not detailed in a certain embodiment, reference may be made to the relevant descriptions of other embodiments.

[0157] In several embodiments provided in the present application, it should be understood that the disclosed technical content can be implemented in other ways. Among them, the device embodiments described above are merely illustrative. For example, the division of the units is only a logical function division. In actual implementation, there may be other division methods. For example, multiple units or components can be combined or integrated into another system, or some features can be ignored or not executed. Another point is that the displayed or discussed couplings or direct couplings or communication connections between each other can be through some interfaces. The indirect couplings or communication connections of the units or modules can be in electrical or other forms.

[0158] The units described as separate components may or may not be physically separated. The components displayed as units may or may not be physical units, that is, they can be located in one place or distributed to multiple network units. Some or all of the units can be selected according to actual needs to achieve the purpose of the solution of this embodiment.

[0159] In addition, in each embodiment of the present invention, the functional units can be integrated in a processing unit, or each unit can exist physically alone, or two or more units can be integrated in one unit. The above-mentioned integrated units can be implemented in the form of hardware or in the form of software functional units.

[0160] If the above-mentioned integrated unit is implemented in the form of a software functional unit and sold or used as an independent product, it can be stored in a computer-readable storage medium. Based on such an understanding, the technical solution of the present invention, in essence, or the part that contributes to the prior art, or all or part of this technical solution, can be embodied in the form of a software product. This computer software product is stored in a storage medium and includes several instructions for causing a computer device (which can be a personal computer, a server, or a network device, etc.) to execute all or part of the steps of the methods described in each embodiment of the present invention. The foregoing storage medium includes: various media such as USB flash drives, read-only memories (ROM, Read-Only Memory), random access memories (RAM, Random Access Memory), mobile hard disks, magnetic disks, or optical discs that can store program codes.

[0161] The above are only the preferred embodiments of the present invention. It should be noted that for those of ordinary skill in the art, without departing from the principle of the present invention, several improvements and refinements can be made, and these improvements and refinements should also be regarded as the protection scope of the present invention.

Claims

1. A method for determining characteristic parameters of a building, characterized in that, comprising: establishing an initial finite element model for indicating the mapping relationship between the characteristic parameters and the response parameters of the building, wherein the characteristic parameters include the elastic modulus, density and stiffness of the building, and the response parameters include the response frequencies of the building; establishing a first set of characteristic parameter samples of the characteristic parameters, and using the initial finite element model to determine a first set of response parameter samples of the response parameters according to the first set of characteristic parameter samples; establishing a first response surface model for indicating the mapping relationship between the characteristic parameters and the response parameters, and correcting the first response surface model according to the correlation between the characteristic parameters and the response parameters to obtain a second response surface model; determining the parameters of the second response surface model according to the first set of characteristic parameter samples and the first set of response parameter samples; measuring the response parameters of the target building; and using the second response surface model to determine the characteristic parameters of the building according to the measured response parameters; the operation of correcting the first response surface model according to the correlation between the characteristic parameters and the response parameters includes: determining the significance coefficient of the characteristic parameters relative to the response parameters according to the first set of characteristic parameter samples and the first set of response parameter samples; screening out the significant characteristic parameters corresponding to the response parameters according to the significance coefficient; determining the correlation between the screened significant characteristic parameters and the corresponding response parameters; correcting the first response surface model according to the correlation between the screened significant characteristic parameters and the corresponding response parameters; the operation of determining the correlation between the screened significant characteristic parameters and the corresponding response parameters includes: selecting one of the significant characteristic parameters as a reserved parameter from the significant characteristic parameters, and determining a plurality of fitting polynomials for characterizing the correlation between the reserved parameter and the corresponding response parameter; and determining the correlation between the reserved parameter and the corresponding response parameter according to the function form with the highest fitting accuracy relative to the plurality of fitting polynomials.

2. The method according to claim 1, characterized in that, the operation of establishing a first set of characteristic parameter samples of the characteristic parameters and using the initial finite element model to determine a first set of response parameter samples of the response parameters according to the first set of characteristic parameter samples includes: establishing the first set of characteristic parameter samples by using an experimental design method; and using the initial finite element model to determine the first set of response parameter samples according to the first set of characteristic parameter samples.

3. The method according to claim 2, characterized in that, the first response surface model includes a plurality of polynomials corresponding to each response parameter respectively, and when the characteristic parameters are multiple characteristic parameters, the operation of determining a plurality of fitting polynomials for characterizing the correlation between the reserved parameter and the corresponding response parameter includes: Using the experimental design method, a second characteristic parameter sample set of the characteristic parameters is established, where in each sample of the second characteristic parameter sample set, other parameters except the reserved parameters are constants; and the samples in the second characteristic parameter sample set are respectively substituted into the polynomials corresponding to the response parameters corresponding to the significant characteristic parameters to obtain the multiple fitting polynomials.

4. The method according to claim 3, wherein, the operation of determining the parameters of the second response surface model according to the first characteristic parameter sample set and the first response parameter sample set includes: substituting the first characteristic parameter sample set and the first response parameter sample set into the lsqcurvefit function, and using the lsqcurvefit function to fit the second response surface model; and determining the parameters of the second response surface model.

5. The method according to claim 4, wherein, the operation of determining the characteristic parameters of the building according to the measured response parameters by using the second response surface model includes: substituting the measured response parameters into the fmincon algorithm, and using the fmincon algorithm to perform non-linear optimization solution on the second response surface model to obtain the characteristic parameters of the building.

6. The method according to claim 1, wherein, further comprising: substituting the characteristic parameters of the building into the second response surface model to determine a second response parameter sample set; calculating a first deviation between the first response parameter sample set and the measured response parameters; calculating a second deviation between the second response parameter sample set and the measured response parameters; and determining the accuracy of the second response surface model according to the comparison result of the first deviation and the second deviation.

7. A storage medium, wherein, the storage medium includes a stored program, wherein when the program runs, the method according to any one of claims 1 to 6 is executed by a processor.

8. An apparatus for determining the characteristic parameters of a building, wherein, comprising: a model establishment module for establishing an initial finite element model for indicating the mapping relationship between the characteristic parameters and the response parameters of the building, wherein the characteristic parameters include the elastic modulus, density and stiffness of the building, and the response parameters include the response frequency of the building; a first determination module for establishing a first characteristic parameter sample set of the characteristic parameters and using the initial finite element model to determine a first response parameter sample set of the response parameters according to the first characteristic parameter sample set; a model correction module for establishing a first response surface model for indicating the mapping relationship between the characteristic parameters and the response parameters, and correcting the first response surface model according to the correlation between the characteristic parameters and the response parameters to obtain a second response surface model; a coefficient to be determined module for determining the parameters of the second response surface model according to the first characteristic parameter sample set and the first response parameter sample set; a response parameter measurement module for measuring the response parameters of the target building; and a parameter solving module, which determines the characteristic parameters of the building according to the measured response parameters by using the second response surface model; The operation of correcting the first response surface model according to the correlation between the characteristic parameters and the response parameters includes: determining a significance coefficient of the characteristic parameters relative to the response parameters according to the first characteristic parameter sample set and the first response parameter sample set; screening out significant characteristic parameters corresponding to the response parameters according to the significance coefficient; determining the correlation between the screened significant characteristic parameters and the corresponding response parameters; correcting the first response surface model according to the correlation between the screened significant characteristic parameters and the corresponding response parameters; The operation of determining the correlation between the screened significant characteristic parameters and the corresponding response parameters includes: selecting one of the significant characteristic parameters as a reserved parameter, and determining a plurality of fitting polynomials for characterizing the correlation between the reserved parameter and the corresponding response parameter; and determining the correlation between the reserved parameter and the corresponding response parameter according to the function form with the highest fitting accuracy relative to the plurality of fitting polynomials.

9. A correction device for determining the characteristic parameters of a building characterized in that it includes: a processor; and a memory, connected to the processor, for providing instructions for the processor to perform the following processing steps: establishing an initial finite element model for indicating the mapping relationship between the characteristic parameters and the response parameters of the building, where the characteristic parameters include the elastic modulus, density and stiffness of the building, and the response parameters include the response frequency of the building; establishing a first characteristic parameter sample set of the characteristic parameters, and using the initial finite element model to determine a first response parameter sample set of the response parameters according to the first characteristic parameter sample set; establishing a first response surface model for indicating the mapping relationship between the characteristic parameters and the response parameters, and correcting the first response surface model according to the correlation between the characteristic parameters and the response parameters to obtain a second response surface model; determining the parameters of the second response surface model according to the first characteristic parameter sample set and the first response parameter sample set; measuring the response parameters of the target building; and determining the characteristic parameters of the building according to the measured response parameters by using the second response surface model; The operation of correcting the first response surface model according to the correlation between the characteristic parameters and the response parameters includes: determining a significance coefficient of the characteristic parameters relative to the response parameters according to the first characteristic parameter sample set and the first response parameter sample set; screening out significant characteristic parameters corresponding to the response parameters according to the significance coefficient; determining the correlation between the screened significant characteristic parameters and the corresponding response parameters; correcting the first response surface model according to the correlation between the screened significant characteristic parameters and the corresponding response parameters; The operation of determining the correlation between the screened significant characteristic parameters and the corresponding response parameters includes: Select one of the significant feature parameters as a reserved parameter, and determine a plurality of fitting polynomials for characterizing the correlation between the reserved parameter and the corresponding response parameter; and determine the correlation between the reserved parameter and the corresponding response parameter according to the functional form with the highest fitting accuracy with respect to the plurality of fitting polynomials.

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