CN112347537A - Calibration method and device for engineering structure numerical model, electronic equipment and medium - Google Patents
Calibration method and device for engineering structure numerical model, electronic equipment and medium Download PDFInfo
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Abstract
The invention relates to a method and a device for calibrating an engineering structure numerical model, electronic equipment and a medium. The method comprises the following steps: acquiring an engineering structure numerical model library of a target engineering; constructing a plurality of response function mathematical models based on the engineering structure numerical model library and a plurality of positions, a plurality of external effect quantities and a plurality of variables of the target engineering; acquiring multi-source monitoring information of the target project; generating a plurality of structural parameter combination sets meeting preset errors based on the plurality of response function mathematical models and the multi-source monitoring information; and calculating the certainty factor indexes and the single certainty factors of the multiple structural parameter combination sets, and evaluating the reliability of the engineering structure numerical model calibration based on the certainty factor indexes and the single certainty factor indexes of the multiple structural parameter combination sets. The method can obtain a numerical simulation model reflecting the completeness of the multisource monitoring information of the actual engineering structure, and further realize accurate and effective prediction, evaluation and diagnosis of the actual engineering.
Description
Technical Field
The invention relates to the field of engineering structure simulation analysis, in particular to a method and a device for calibrating an engineering structure numerical model, electronic equipment and a computer readable medium.
Background
Engineering structures involved in engineering fields such as aerospace, ships, bridges, automobiles, machinery, water conservancy, buildings and the like play a vital role in the development process of the human society. The service life of major engineering structures, such as super-large span bridges, super-large span space structures, super high-rise buildings, large hydraulic engineering, large ocean platform structures, nuclear power station buildings and the like, is as long as dozens of years or even hundreds of years, and the coupling effect of disaster factors, such as environmental erosion, long-term effect of material aging and load, fatigue effect, mutation effect and the like, inevitably causes damage accumulation and resistance attenuation of the structures and systems, namely the performance of the structures continuously evolves along with time, so that the capability of resisting natural disasters and even normal environmental effects tends to decline, and catastrophic accidents are caused under extreme conditions. Therefore, in order to ensure the safety, integrity, applicability, durability and reliability of the engineering structure, the real working state of the engineering structure needs to be grasped timely and accurately.
In a traditional engineering structure system numerical model calibration method, identification of a model or model parameters is often converted into an optimization problem, namely, the parameters to be updated are used as variables, and the minimum sum of squares of cumulative deviations between numerical simulation results and monitoring data is used as a target function. The idea can only obtain a combination of a set of parameters to be calibrated, while it is questionable whether a globally optimal result can be obtained due to the highly nonlinear relationship between the set variables and the objective function. Since only a unique parameter combination result is obtained each time, the results obtained based on different monitoring information are often different, which results in: the evolution process of parameter calibration of different monitoring information cannot be embodied, the value and the effect of newly added monitoring information cannot be embodied, the precision influence of monitoring equipment cannot be embodied, the effectiveness of the monitoring system design cannot be embodied, and the dynamic evolution process of a structural system cannot be embodied.
Therefore, a new calibration method, apparatus, electronic device and computer readable medium for engineering structure numerical model are needed.
The above information disclosed in this background section is only for enhancement of understanding of the background of the invention and therefore it may contain information that does not constitute prior art that is already known to a person of ordinary skill in the art.
Disclosure of Invention
In view of the above, the present invention provides a calibration method, a calibration device, an electronic device, and a computer readable medium for an engineering structure numerical model, which can continuously and accurately obtain a numerical simulation model capable of reflecting the completeness of multi-source monitoring information of an actual engineering structure over time, and further realize actual functional requirements for accurate and effective prediction, evaluation, diagnosis, and the like of an actual engineering based on the calibrated numerical simulation model.
Additional features and advantages of the invention will be set forth in the detailed description which follows, or may be learned by practice of the invention.
According to an aspect of the present invention, a method for calibrating a numerical model of an engineering structure is provided, the method comprising: acquiring an engineering structure numerical model library of a target engineering, wherein the engineering structure numerical model library comprises engineering structure numerical models based on different variables; constructing a plurality of response function mathematical models based on the engineering structure numerical model library and a plurality of positions, a plurality of external effect quantities and a plurality of variables of the target engineering; acquiring multi-source monitoring information of the target project, wherein the multi-source monitoring information comprises at least one of monitoring information at different moments, monitoring information at different spatial positions and monitoring information of different monitoring projects; generating a plurality of structural parameter combination sets meeting preset errors based on the plurality of response function mathematical models and the multi-source monitoring information, wherein the structural parameter combination sets comprise a plurality of groups of structural parameter combinations; and calculating the certainty factor indexes and the single certainty factors of the multiple structural parameter combination sets, and evaluating the reliability of the engineering structure numerical model calibration based on the certainty factor indexes and the single certainty factor indexes of the multiple structural parameter combination sets.
In an exemplary embodiment of the present invention, further comprising: obtaining at least one calibrated engineering structure numerical model corresponding to the target engineering; evaluating the reliability of the target project based on the at least one engineering structure numerical model to generate at least one evaluation result; and generating a reliability index of the target project based on the at least one evaluation result.
In an exemplary embodiment of the present invention, further comprising: determining basic assumption, basic variable quantity, probability distribution type and value range of a basic numerical simulation model of the target engineering based on material parameters and/or structural parameters and/or experimental parameters and/or functional parameters of the target engineering; and constructing the engineering structure numerical model library based on the basic assumption, the number of the basic variables, the probability distribution type and the value range.
In an exemplary embodiment of the present invention, constructing the engineering structure numerical model library based on the basic assumption, the number of the basic variables, the probability distribution type, and the value range includes: sampling random variables in the basic variables based on a multivariate random sampling technology to generate a random variable sample space; processing the basic numerical simulation model based on a parallel text processing technology to generate an engineering structure numerical simulation model sample space; and constructing the engineering structure numerical model library based on a parallel numerical simulation technology, the random variable sample space and the engineering structure numerical simulation model sample space.
In an exemplary embodiment of the present invention, constructing a plurality of response function mathematical models based on the engineering structure numerical model library and a plurality of positions, a plurality of external effects, and a plurality of variables of the target engineering comprises: and constructing the plurality of response function mathematical models based on a precise radial basis function neural network interpolation technology, the engineering structure numerical model library, a plurality of positions, a plurality of external effect quantities and a plurality of variables of the target engineering.
In an exemplary embodiment of the present invention, constructing a plurality of response function mathematical models based on the engineering structure numerical model library and a plurality of positions, a plurality of external effects, and a plurality of variables of the target engineering further includes: and generating an engineering structure parameter sample space to be calibrated by adopting a multivariate random sampling technology according to the number of the basic variables, the probability distribution type and the value range.
In an exemplary embodiment of the present invention, generating a plurality of structural parameter combination sets satisfying a preset error based on the plurality of response function mathematical models and the multi-source monitoring information includes: substituting the multi-source monitoring information into the plurality of response function mathematical models to generate a plurality of structural parameters; generating a structural parameter combination based on the combination of a plurality of structural parameters which meet monitoring errors in a sample space of any variable in an engineering structure numerical model library; generating the plurality of sets of structural parameter combinations based on a plurality of structural parameter combinations.
In an exemplary embodiment of the invention, substituting the multi-source monitoring information into the plurality of response function mathematical models to generate a plurality of structural parameters further comprises: and rejecting the structural parameters which do not meet the monitoring error in the plurality of structural parameters.
In one exemplary embodiment of the present invention, includes:
wherein, CCI is a certainty factor index, and N is the number of samples which are screened out in a calibration sample space N and meet an error condition of a parameter to be calibrated;
wherein SCI is a single certainty index, xiRepresenting the corresponding parameter variable to be calibrated; max (x)i) Variable x representing satisfaction of the screening conditioniMaximum value of (d); min (x)i) Variable x representing satisfaction of the screening conditioniMinimum value of (d); ub (x)i) Representing variable x in a fundamental numerical simulation modeliUpper limit of (d); ul (x)i) Representing variable x in a fundamental numerical simulation modeliThe lower limit of (2).
In one exemplary embodiment of the present invention, includes: acquiring updated multi-source monitoring information of the target project; and updating the plurality of structural parameter combination sets based on the updated multi-source monitoring information.
According to an aspect of the present invention, there is provided an apparatus for calibrating a numerical model of an engineering structure, the apparatus comprising: the model base module is used for obtaining an engineering structure numerical model base of the target engineering, and the engineering structure numerical model base comprises engineering structure numerical models based on different variables; the response function module is used for constructing a plurality of response function mathematical models based on the engineering structure numerical model library, a plurality of positions, a plurality of external effect quantities and a plurality of variables of the target engineering; the monitoring information module is used for acquiring multi-source monitoring information of the target project, wherein the multi-source monitoring information comprises at least one of monitoring information at different moments, monitoring information at different spatial positions and monitoring information of different monitoring projects; the calibration module is used for generating a plurality of structural parameter combination sets meeting preset errors based on the plurality of response function mathematical models and the multi-source monitoring information, and each structural parameter combination set comprises a plurality of groups of structural parameter combinations; the index calculation module is used for calculating the certainty factor index and the single certainty factor index of the multiple structural parameter combination sets; and the evaluation module is used for evaluating the credibility of the engineering structure numerical model calibration based on the credibility index and the single credibility index of the multiple structure parameter combination sets.
According to an aspect of the present invention, there is provided an electronic apparatus including: one or more processors; storage means for storing one or more programs; when executed by one or more processors, cause the one or more processors to implement a method as above.
According to an aspect of the invention, a computer-readable medium is proposed, on which a computer program is stored which, when being executed by a processor, carries out the method as above.
According to the calibration method, the calibration device, the electronic equipment and the computer readable medium of the engineering structure numerical model, a plurality of response function mathematical models are constructed based on an engineering structure numerical model library and a plurality of positions, a plurality of external effect quantities and a plurality of variables of the target engineering; acquiring multi-source monitoring information of the target project, wherein the multi-source monitoring information comprises at least one of monitoring information at different moments, monitoring information at different spatial positions and monitoring information of different monitoring projects; generating a plurality of structural parameter combination sets meeting preset errors based on the plurality of response function mathematical models and the multi-source monitoring information; and calculating the certainty factor index and the unicity certainty factor of the plurality of structural parameter combination sets, evaluating the credibility of the engineering structure numerical model calibration based on the certainty factor index and the unicity certainty factor index of the plurality of structural parameter combination sets, continuously and accurately obtaining a numerical simulation model capable of reflecting the completeness of the actual engineering structure multi-source monitoring information along with the time lapse, and further realizing the actual functional requirements of accurate and effective prediction, evaluation, diagnosis and the like on the actual engineering based on the calibrated numerical simulation model.
It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory only and are not restrictive of the invention, as claimed.
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The above and other objects, features and advantages of the present invention will become more apparent by describing in detail exemplary embodiments thereof with reference to the attached drawings. The drawings described below are only some embodiments of the invention and other drawings may be derived from those drawings by a person skilled in the art without inventive effort.
FIG. 1 is a flow chart of a method of calibrating a numerical model of an engineered structure.
FIG. 2 is a flow chart of a method of calibrating a numerical model of an engineered structure.
FIG. 3 is a finite element mesh model of a concrete gravity dam.
FIG. 4 is a scatter plot between time and upstream water level.
FIG. 5 is a scatter plot of time versus Poisson's ratio of concrete.
FIG. 6 is a scatter plot between numerical simulation data and radial basis function neural network interpolation data.
FIG. 7 is a scatter plot of concrete compressive strength versus foundation deformation modulus.
FIG. 8 scatterplot matrix of calibrated concrete compressive strength and foundation deformation modulus for case (1).
FIG. 9 scatter plot matrix of calibrated concrete compressive strength and foundation deformation modulus for case (2).
FIG. 10 scatterplot matrix of compressive strength of concrete and modulus of deformation of foundation calibrated for case (3).
FIG. 11 scatterplot matrix of compressive strength of concrete and modulus of deformation of foundation calibrated for case (4).
FIG. 12 is a scatter plot matrix of the calibrated concrete compressive strength and the foundation deformation modulus for case (5).
Fig. 13 is a block diagram of a calibration apparatus for a numerical model of an engineering structure.
FIG. 14 is a block diagram illustrating an electronic device in accordance with an example embodiment.
FIG. 15 is a block diagram illustrating a computer-readable medium in accordance with an example embodiment.
Detailed Description
Example embodiments will now be described more fully with reference to the accompanying drawings. Example embodiments may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey the concept of example embodiments to those skilled in the art. The same reference numerals denote the same or similar parts in the drawings, and thus, a repetitive description thereof will be omitted.
Furthermore, the described features, structures, or characteristics may be combined in any suitable manner in one or more embodiments. In the following description, numerous specific details are provided to provide a thorough understanding of embodiments of the invention. One skilled in the relevant art will recognize, however, that the invention may be practiced without one or more of the specific details, or with other methods, components, devices, steps, and so forth. In other instances, well-known methods, devices, implementations or operations have not been shown or described in detail to avoid obscuring aspects of the invention.
The block diagrams shown in the figures are functional entities only and do not necessarily correspond to physically separate entities. I.e. these functional entities may be implemented in the form of software, or in one or more hardware modules or integrated circuits, or in different networks and/or processor means and/or microcontroller means.
The flow charts shown in the drawings are merely illustrative and do not necessarily include all of the contents and operations/steps, nor do they necessarily have to be performed in the order described. For example, some operations/steps may be decomposed, and some operations/steps may be combined or partially combined, so that the actual execution sequence may be changed according to the actual situation.
FIG. 1 is a flow chart illustrating a method of calibrating a numerical model of an engineering structure, according to an exemplary embodiment. The calibration method 10 for the numerical model of the engineering structure at least includes steps S102 to S112.
As shown in fig. 1, in S102, an engineering structure numerical model library of a target engineering is obtained, where the engineering structure numerical model library includes engineering structure numerical models based on different variables. The relevant contents for constructing the engineering structure numerical model library will be described in the embodiment corresponding to fig. 2.
In S104, a plurality of response function mathematical models are constructed based on the engineering structure numerical model base and a plurality of positions, a plurality of external effects and a plurality of variables of the target engineering. The method comprises the following steps: and constructing the plurality of response function mathematical models based on a precise radial basis function neural network interpolation technology, the engineering structure numerical model library, a plurality of positions, a plurality of external effect quantities and a plurality of variables of the target engineering. Further comprising: and generating an engineering structure parameter sample space to be calibrated by adopting a multivariate random sampling technology according to the number of the basic variables, the probability distribution type and the value range. The method comprises the following specific steps:
1) the set variables are subsets of a basic variable set, and the variables which are not set to be required to be updated need to be assigned with values or adopt default values, wherein the default values are the average values of the basic variables;
2) the size of the sample space is not likely to be too small, suggesting that the size of the sample space should not be less than 10000.
More specifically, the general effects of an engineered structural system include: deformation and temperature of all nodes in the numerical simulation model, stress of all unit centroids, and calculation time of the characteristic model convergence characteristics; carrying out accurate radial basis function neural network interpolation on the effect (component) quantity of each position of the engineering structure system; and forming an accurate radial basis function neural network database of the engineering structure system.
The specific steps can be as follows:
1) setting the effect quantity of an engineering structure system, mainly comprising: deformation and temperature of all nodes in the numerical simulation model, stress of all unit centroids, and calculation time of the characteristic model convergence characteristics;
2) carrying out accurate radial basis function neural network interpolation on the effect (component) quantity of each position of the engineering structure system;
3) and forming an accurate radial basis function neural network database of the engineering structure system.
In S106, multi-source monitoring information of the target project is obtained, where the multi-source monitoring information includes at least one of monitoring information at different times, monitoring information at different spatial positions, and monitoring information of different monitoring projects.
More specifically, the monitoring information may be from an engineering structure monitoring system, or may be monitoring information obtained by manual monitoring, that is, the monitoring information includes monitoring information of an existing monitoring point, or may include monitoring information or items of a newly added monitoring point; it is also possible to set the error limits for different types of monitoring information, for example, depending on the measurement accuracy of the monitoring instrument.
Further, according to the established accurate radial basis function neural network interpolation response function mathematical model, for the monitoring item of each point, a parameter combination meeting the error requirement is screened in a sample space; performing intersection calculation on parameter combination screening result sets corresponding to all monitoring projects at the same moment to obtain possible parameter combinations of the engineering structure system at the moment; and when the engineering structure system is assumed to be homogeneous in a certain time period, performing intersection calculation on the parameter combination set at any time in the time period to obtain the possible parameter combination of the concrete structure system in the time period.
In S108, a plurality of structural parameter combination sets satisfying a preset error are generated based on the plurality of response function mathematical models and the multi-source monitoring information, where the structural parameter combination sets include a plurality of groups of structural parameter combinations. Can include the following steps: substituting the multi-source monitoring information into the plurality of response function mathematical models to generate a plurality of structural parameters; generating a structural parameter combination based on the combination of a plurality of structural parameters which meet monitoring errors in a sample space of any variable in an engineering structure numerical model library; generating the plurality of sets of structural parameter combinations based on a plurality of structural parameter combinations. In one embodiment, further comprising: and rejecting the structural parameters which do not meet the monitoring error in the plurality of structural parameters.
The specific steps of generating the plurality of structural parameter combination sets may be as follows:
1) the selected monitoring information can come from an engineering structure real-time monitoring system, and can also be manual monitoring information, namely the monitoring information of the existing monitoring points, or the monitoring information or items of newly added monitoring points;
2) setting error limits of different types of monitoring information;
3) according to the established mathematical model of the accurate radial basis function neural network interpolation response function, for the monitoring items of each point, screening parameter combinations meeting error requirements in a sample space;
4) performing intersection calculation on parameter combination screening result sets corresponding to all monitoring projects at the same moment to obtain possible parameter combinations of the engineering structure system at the moment;
5) when the engineering structure system is assumed to be homogeneous in a certain time period, performing intersection calculation on the parameter combination set at any time in the time period to obtain possible parameter combinations of the engineering structure system in the time period;
6) repeatedly executing 2) -5) for the monitoring information acquired in real time;
in S110, a certainty factor and a single certainty factor of the plurality of structural parameter combination sets are calculated.
Wherein the content of the first and second substances,
wherein, CCI is a certainty factor index, and N is the number of samples which are screened out in a calibration sample space N and meet an error condition of a parameter to be calibrated; more specifically, when CCI is 1, it indicates that the adopted monitoring information completely determines the value of the numerical simulation model parameter combination; when 0 < CCI < 1, the adopted monitoring information reduces the value range of the parameter combination of the numerical simulation model, but the value of the parameter combination cannot be completely determined.
Wherein SCI is a single certainty index, xiRepresenting the corresponding parameter variable to be calibrated; max (x)i) Variable x representing satisfaction of the screening conditioniMaximum value of (d); min (x)i) Variable x representing satisfaction of the screening conditioniMinimum value of (d); ub (x)i) Expressed in a fundamental numerical simulation modelType medium variable xiUpper limit of (d); ul (x)i) Representing variable x in a fundamental numerical simulation modeliThe lower limit of (2). More specifically, when SCI is 0, it indicates that the employed monitoring information is not useful for determination of the parameter; when SCI is 1, the adopted monitoring information completely determines the value of the parameter; when 0 < SCI < 1, the adopted monitoring information reduces the value range of the parameter, but the value of the parameter can not be completely determined.
In S112, the reliability of the engineering structure numerical model calibration is evaluated based on the certainty factor and the single certainty factor of the plurality of structure parameter combination sets. And respectively calculating a combinability certainty factor index CCI of the engineering structure numerical simulation model and a unicity certainty factor index SCI of each parameter to be calibrated according to the monitoring information of the engineering structure system at any moment so as to represent the variation process of the value certainty degree of the combination of the parameters to be calibrated and the single parameter.
In one embodiment, further comprising: obtaining at least one calibrated engineering structure numerical model corresponding to the target engineering; evaluating the reliability of the target project based on the at least one engineering structure numerical model to generate at least one evaluation result; and generating a reliability index of the target project based on the at least one evaluation result. For the building industry, the reliability of the building structure is improved, faults and accidents can be prevented, particularly catastrophic accidents such as building collapse and the like are avoided, meanwhile, the service life of building facilities can be prolonged, and more benefits are brought. The study of reliability engineering is also an aspect that students of safety engineering professionals must understand. Reliability engineering, particularly the reliability of building structures, can be studied from various aspects by using tools such as mechanics, physics and the like to improve the safety performance of the building structures.
In one embodiment, further comprising: acquiring updated multi-source monitoring information of the target project; and updating the plurality of structural parameter combination sets based on the updated multi-source monitoring information.
More particularly, for multiple sourcesMonitoring information, respectively calculating a combinability certainty index CCI1、CCI2、CCI3In which CCIj-CCIiThe influence of monitoring information from different sources on the parameter combination value of the engineering structure numerical simulation model is measured; for multi-source monitoring information, calculating the single certainty factor index SCI of any parameter respectively1、SCI2、SCI3In which SCI isj-SCIiThe influence of monitoring information from different sources on the value of the parameter is measured.
According to the calibration method of the engineering structure numerical model, a plurality of response function mathematical models are constructed based on an engineering structure numerical model library and a plurality of positions, a plurality of external effect quantities and a plurality of variables of the target engineering; acquiring multi-source monitoring information of the target project, wherein the multi-source monitoring information comprises at least one of monitoring information at different moments, monitoring information at different spatial positions and monitoring information of different monitoring projects; generating a plurality of structural parameter combination sets meeting preset errors based on the plurality of response function mathematical models and the multi-source monitoring information; and calculating the certainty factor index and the unicity certainty factor of the plurality of structural parameter combination sets, evaluating the credibility of the engineering structure numerical model calibration based on the certainty factor index and the unicity certainty factor index of the plurality of structural parameter combination sets, continuously and accurately obtaining a numerical simulation model capable of reflecting the completeness of the actual engineering structure multi-source monitoring information along with the time lapse, and further realizing the actual functional requirements of accurate and effective prediction, evaluation, diagnosis and the like on the actual engineering based on the calibrated numerical simulation model.
The invention provides a general engineering structure numerical simulation model calibration method following an uncertainty reasoning framework aiming at the problems of small information quantity, unclear calibration process, poor interpretability of results and the like of the existing engineering structure model calibration method, and provides a certainty factor measurement index of calibration parameters, which can reflect the dynamic evolution process of parameter calibration, the effectiveness of newly added monitoring information, the influence of monitoring equipment precision, the effectiveness of monitoring system design and the dynamic evolution process of an engineering structure system.
The invention utilizes the advanced multivariate random sampling equal probability sampling technology, thus improving the sample space representation efficiency; the accurate radial basis function neural network interpolation technology in artificial intelligence deep learning is adopted, so that the deviation between a numerical model and a mathematical model is greatly reduced; by adopting a parallel processing technology, the speed of constructing a mathematical model and calibrating parameters is greatly improved; the proposed parameter combination and parameter certainty factor indexes fully reflect the influence of multi-source monitoring information on the calibration of the engineering structure numerical model, and measure the completeness of the multi-source monitoring information and the dependence of any parameter to be calibrated on the monitoring information from different sources; the basic model and the model parameters in the engineering structure numerical model are used as correction objects, the correction objects are calibrated in an uncertainty reasoning frame, a numerical simulation model capable of reflecting the completeness of actual engineering structure multi-source monitoring information is continuously and accurately obtained along with the development of related technologies such as continuous improvement of engineering structure cognition, continuous improvement of model construction and solving technology, gradual enhancement of random sampling efficiency, continuous enrichment and accuracy of monitoring information and the like, and further the actual functional requirements of actual engineering such as accurate and effective prediction, evaluation, diagnosis and the like are realized on the basis of the calibrated numerical simulation model.
It should be clearly understood that the present disclosure describes how to make and use particular examples, but the principles of the present disclosure are not limited to any details of these examples. Rather, these principles can be applied to many other embodiments based on the teachings of the present disclosure.
FIG. 2 is a flow chart illustrating a method of calibrating a numerical model of an engineered structure according to another exemplary embodiment. The process 20 shown in fig. 2 is a supplementary description of the process shown in fig. 1.
As shown in fig. 2, in S202, basic assumptions, a number of basic variables, a probability distribution type, and a value range of a basic numerical simulation model of the target project are determined based on the material parameters and/or the structural parameters and/or the experimental parameters and/or the functional parameters of the target project.
More specifically, for an engineering structural system, basic premise assumptions adopted need to be made clear so as to facilitate correction, selection and analysis of parameter calibration results. The main basic premise assumption should include the following aspects: 1) presuming a basic mechanical model of the engineering structure system; 2) presuming a geometric scale range of the numerical model; 3) presuming a constitutive model of the engineering structural material; 4) the interaction model assumption of different components in the engineering structure; 5) presuming a load model of the engineering structure; 6) presuming a temperature analysis model of a concrete dam structure system;
the specific setting method comprises the following steps:
1) the number of basic variables is selected according to the basic assumption of the basic model of the engineering structure system, and all the related main variables are required to be selected;
2) when corresponding material experiment results exist, the probability distribution type and the value range of the variable are required to be according to the experiment results;
3) when the material does not have the corresponding material experiment result, the probability distribution type of the variable is set to be normal distribution;
in S204, a random variable in the basic variables is sampled based on a multivariate random sampling technique, and a random variable sample space is generated. The method comprises the following specific steps:
1) considering the mutual independence among all variables of the engineering structure system;
2) determining a corresponding accumulative probability value range according to the variable value range determination and the probability distribution pattern thereof;
3) uniformly sampling in the range of the value of the accumulative probability by adopting a multivariate random sampling technology;
4) determining corresponding variable values according to the accumulated probability sampling values and the probability distribution function;
5) when the number of the variables is not more than 5, the sample space is not less than 500; when the number of the variables is not more than 20, the sample space is not less than 1000; when the number of variables is not more than 100, the sample space should be not less than 2000.
In S206, the basic numerical simulation model is processed based on a parallel text processing technique to generate an engineering structure numerical simulation model sample space. Taking the parallel text processing by using MATLAB software as an example, the specific steps are as follows:
1) setting information needing to be modified in the basic numerical model;
2) setting the line number and the column number of the non-relevant parameters in the basic model text, such as the elastic modulus, the Poisson ratio and the like in the concrete dam structure system;
3) for relevant parameters, such as the dam foundation uplift pressure in a concrete dam structure system, which is related to the upstream and downstream water heads and the uplift pressure reduction coefficient, a unit uplift pressure value program needs to be compiled;
4) the method comprises the steps that a technology of simultaneously running multiple programs is adopted based on MATLAB, and model sample spaces are grouped, for example, each 100 model sample spaces are used as a group;
5) and (4) simultaneously calculating each group, and quickly finishing the construction of a numerical simulation model sample space.
In S208, the engineering structure numerical model library is constructed based on a parallel numerical simulation technique, the random variable sample space, and the engineering structure numerical simulation model sample space. Taking ABAQUS numerical simulation software as an example, the method comprises the following specific steps:
1) compiling an ABAQUS batch processing format generation program;
2) generating a batch file according to the sample name in the numerical simulation sample space, for example: call abaqus jobi ═ jobi _1 int;
3) grouping the numerical simulation sample space, for example, every 100 as a group;
4) simultaneously calculating each group to generate a numerical model database;
5) adopting Python language to program, and extracting a numerical simulation result in the ODB format;
6) and constructing a simulation result database of the effect quantity numerical model of the engineering structure.
In one embodiment, a concrete gravity dam in hydraulic engineering is taken as an example to describe the specific implementation process of the invention.
The basic assumptions set here are as follows: assume that 1: adopting a plane strain mechanics model; assume 2: the scale range of the finite element model is as follows: l1 ═ L2 ═ D ═ 200 m; assume that 3: the dam heel is provided with cracks; assume 4: the dam foundation rock mass is permeable, and the dam concrete is impermeable; assume that 5: treating dam concrete as plain concrete with a plastic damage model; assume 6: homogenizing bedrock, Mohr-Coulomb model; assume 7: the dam foundation contact adopts a Mohr-Coulomb model; assume that 8: adopting Mohr-Coulomb model for heel crack; assume that 9: a transient model is adopted for analyzing the temperature of the dam system; assume that 10: the uplifting pressure adopts a model based on the research of Colorado university; assume that 11: when a gravitational field is applied, initial ground stress balance is adopted; assume 12: the water load is used as a surface load and is applied to a dam surface and a foundation surface which are in contact with the water body; assume 13: the boundaries of the ground base part and the upstream and downstream models are constrained by adopting roller hinges, and the vertical displacement and the horizontal displacement of the ground base part and the upstream and downstream models are respectively constrained.
Wherein, the adopted numerical simulation software can be ABAQUS. The finite element geometric model and the mesh discretization are shown in fig. 3:
the variable number, type, value range, probability distribution type and distribution parameters of the concrete gravity dam numerical simulation model are shown in the following table.
TABLE 1 selected parameters and their distribution characteristics
The selected multivariate random sampling technique is a multivariate Sobol quasi-random number sampling technique, and the sample size is selected to be 1600, wherein a scatter diagram between time and upstream water level and between time and the Poisson's ratio of concrete is shown in figures 4 and 5.
This example forms a total of 1600 sets of numerical simulation model samples using parallel text processing techniques.
By adopting a parallel numerical simulation calculation technology, 1600 groups of numerical simulation model result data are formed in the example, and a corresponding model database is formed.
Adopting an accurate radial basis function neural network interpolation technology to construct a response function mathematical model between different positions and different types of effect quantities and variables of an engineering structure; taking horizontal deformation and vertical deformation of A, B, C points in fig. 3 as an example, a scatter plot between numerical simulation data and radial basis function neural network interpolation data is shown in fig. 6.
In the embodiment, the compressive strength of concrete and the deformation modulus of the foundation are selected as calibration parameters, and the value ranges are shown in table 1; setting other numerical model parameters as mean values; the adopted multivariate random sampling technology is a multivariate Sobol quasi-random number sampling technology, and the sample amount is 20000. A scatter plot of concrete compressive strength versus foundation deformation modulus is shown in fig. 7.
The concrete dam numerical model is used for calibrating two parameters of the concrete compression strength and the foundation deformation modulus in the concrete dam numerical model by taking the following five conditions as examples.
(1) And monitoring information by using horizontal deformation of the point A at a certain moment, wherein the monitoring error is 0.1 mm:
table 2 monitoring information table corresponding to the case (1)
| Time (moon) | Upstream water level (m) | Downstream water level (m) | Horizontal deformation of A point (mm) |
| 3 | 264.065 | 194.986 | 23.8076 |
(2) The horizontal deformation monitoring information at two moments of the point A is utilized, and the monitoring error is 0.1 mm:
table 3 monitoring information table corresponding to the case (2)
| Time (moon) | Upstream water level (m) | Downstream water level (m) | Horizontal deformation of A point (mm) |
| 3 | 264.065 | 194.986 | 23.8076 |
| 9 | 254.524 | 199.808 | 4.0657 |
(3) Monitoring information of horizontal deformation and vertical deformation of a certain moment of the point A is utilized, and a monitoring error is 0.1 mm:
table 4 monitoring information table corresponding to the case (3)
(4) The horizontal deformation monitoring information at a certain moment of A/B/C is utilized, and the monitoring error is 0.1 mm:
table 5 monitoring information table corresponding to the case (4)
(5) And monitoring information by using horizontal deformation of the point A at a certain moment, wherein the monitoring error is 0.05 mm:
table 6 monitoring information table corresponding to the case (5)
| Time (moon) | Upstream water level (m) | Downstream water level (m) | Horizontal deformation of A point (mm) |
| 3 | 264.065 | 194.986 | 23.8076 |
Acquiring a sample point distribution rule meeting a screening condition in a sample space of any variable in the numerical simulation model and a combination rule of any two variable sample points meeting the screening condition by adopting a scatter diagram matrix drawing technology; in this example, the distribution rule of the sample points of the concrete compressive strength and the foundation deformation modulus calibrated in the five cases and the combination rule of the two are shown in fig. 8 to 12.
Measuring the reliability of the combination and the parameter of the parameter to be calibrated based on the combinability certainty factor index and the unicity certainty factor index of the parameter calibration;
TABLE 7 Combined certainty index and unicity certainty index corresponding to the five cases
Based on the certainty factor difference between different multi-source monitoring information, the influence of newly introduced monitoring information on the combination and parameters of the engineering structure to be calibrated is measured.
From table 7, the following monitoring information influence conclusions can be obtained:
(1) compared with the case (2) and the case (1), the utilization of the monitoring data at the point A at the second moment improves the overall certainty factor of the calibration of the concrete compressive strength and the foundation deformation modulus in the concrete dam numerical model by 0.0265; the reliability of the compressive strength of the concrete is improved by 0.4892, and the improvement range is large; the reliability of the foundation deformation modulus is improved 0.1165;
(2) compared with the case (3) and the case (1), the utilization of the vertical deformation monitoring data of the point A improves the integral certainty factor of the calibration of the concrete compressive strength and the foundation deformation modulus in the concrete dam numerical model by 0.02; the reliability of the compressive strength of the concrete is improved 0.3207; the reliability of the foundation deformation modulus is improved by 0.079;
(3) compared with the case (4) and the case (1), the utilization of the horizontal deformation monitoring data of the B/C two points improves the integral certainty factor of the calibration of the concrete compression strength and the foundation deformation modulus in the concrete dam numerical model by 0.0283; the reliability of the compressive strength of the concrete is improved 0.3874; the reliability of the foundation deformation modulus is improved by 0.1026;
(4) compared with the case (5) and the case (1), the monitoring error is reduced, and the overall certainty factor of the calibration of the concrete compressive strength and the foundation deformation modulus in the concrete dam numerical model is improved by 0.0201; the reliability of the concrete compressive strength is improved by 0.0145; the reliability of the foundation deformation modulus is improved by 0.0069.
Those skilled in the art will appreciate that all or part of the steps implementing the above embodiments are implemented as computer programs executed by a CPU. The computer program, when executed by the CPU, performs the functions defined by the method provided by the present invention. The program may be stored in a computer readable storage medium, which may be a read-only memory, a magnetic or optical disk, or the like.
Furthermore, it should be noted that the above-mentioned figures are only schematic illustrations of the processes involved in the method according to exemplary embodiments of the invention, and are not intended to be limiting. It will be readily understood that the processes shown in the above figures are not intended to indicate or limit the chronological order of the processes. In addition, it is also readily understood that these processes may be performed synchronously or asynchronously, e.g., in multiple modules.
The following are embodiments of the apparatus of the present invention that may be used to perform embodiments of the method of the present invention. For details which are not disclosed in the embodiments of the apparatus of the present invention, reference is made to the embodiments of the method of the present invention.
FIG. 13 is a block diagram illustrating a calibration apparatus for a numerical model of an engineered structure, according to an exemplary embodiment. As shown in fig. 13, the calibration apparatus 130 for the numerical model of the engineering structure includes: model library module 1302, response function module 1304, monitoring information module 1306, calibration module 1308, index calculation module 1310, and evaluation module 1312.
The model library module 1302 is configured to obtain an engineering structure numerical model library of the target engineering, where the engineering structure numerical model library includes engineering structure numerical models based on different variables;
the response function module 1304 is used for constructing a plurality of response function mathematical models based on the engineering structure numerical model library and a plurality of positions, a plurality of external effects and a plurality of variables of the target engineering;
the monitoring information module 1306 is configured to obtain multi-source monitoring information of the target project, where the multi-source monitoring information includes at least one of monitoring information at different times, monitoring information at different spatial positions, and monitoring information of different monitoring projects;
the calibration module 1308 is configured to generate a plurality of structural parameter combination sets satisfying a preset error based on the plurality of response function mathematical models and the multi-source monitoring information, where the structural parameter combination sets include a plurality of groups of structural parameter combinations;
the index calculation module 1310 is configured to calculate a certainty factor index and a single certainty factor index of the plurality of structural parameter combination sets;
the evaluation module 1312 is configured to evaluate the credibility of the engineering structure numerical model calibration based on the certainty factor and the single certainty factor of the multiple structural parameter combination sets.
According to the calibration device of the engineering structure numerical model, a plurality of response function mathematical models are constructed based on an engineering structure numerical model library and a plurality of positions, a plurality of external effect quantities and a plurality of variables of the target engineering; acquiring multi-source monitoring information of the target project; generating a plurality of structural parameter combination sets meeting preset errors based on the plurality of response function mathematical models and the multi-source monitoring information; and calculating the certainty factor index and the unicity certainty factor of the multiple structural parameter combination sets, evaluating the credibility of the engineering structure numerical model calibration based on the certainty factor index and the unicity certainty factor index, continuously and accurately obtaining a numerical simulation model capable of reflecting the completeness of the actual engineering structure multi-source monitoring information along with the passage of time, and further realizing the actual functional requirements of accurate and effective prediction, evaluation, diagnosis and the like on the actual engineering based on the calibrated numerical simulation model.
FIG. 14 is a block diagram illustrating an electronic device in accordance with an example embodiment.
An electronic device 1400 according to this embodiment of the invention is described below with reference to fig. 14. The electronic device 1400 shown in fig. 14 is only an example, and should not bring any limitation to the functions and the scope of use of the embodiments of the present invention.
As shown in fig. 14, the electronic device 1400 is embodied in the form of a general purpose computing device. The components of the electronic device 1400 may include, but are not limited to: at least one processing unit 1410, at least one memory unit 1420, a bus 1430 connecting different system components (including the memory unit 1420 and the processing unit 1410), a display unit 1440, and the like.
Wherein the storage unit stores program code that is executable by the processing unit 1410, such that the processing unit 1410 performs the steps according to various exemplary embodiments of the present invention described in this specification. For example, the processing unit 1410 may execute the steps shown in fig. 1 and fig. 2.
The storage unit 1420 may include readable media in the form of volatile memory units, such as a random access memory unit (RAM)14201 and/or a cache memory unit 14202, and may further include a read only memory unit (ROM) 14203.
The storage unit 1420 may also include a program/utility 14204 having a set (at least one) of program modules 14205, such program modules 14205 including, but not limited to: an operating system, one or more application programs, other program modules, and program data, each of which, or some combination thereof, may comprise an implementation of a network environment.
The electronic device 1400 may also communicate with one or more external devices 1400' (e.g., keyboard, pointing device, bluetooth device, etc.) such that a user can communicate with devices with which the electronic device 1400 interacts, and/or any device (e.g., router, modem, etc.) with which the electronic device 1400 can communicate with one or more other computing devices. Such communication can occur via an input/output (I/O) interface 1450. Also, the electronic device 1400 may communicate with one or more networks (e.g., a Local Area Network (LAN), a Wide Area Network (WAN), and/or a public network, such as the internet) via the network adapter 1460. The network adapter 1460 may communicate with other modules of the electronic device 1400 via the bus 1430. It should be appreciated that although not shown in the figures, other hardware and/or software modules may be used in conjunction with the electronic device 1400, including but not limited to: microcode, device drivers, redundant processing units, external disk drive arrays, RAID systems, tape drives, and data backup storage systems, among others.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, as shown in fig. 15, the technical solution according to the embodiment of the present invention may be embodied in the form of a software product, which may be stored in a non-volatile storage medium (which may be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which may be a personal computer, a server, or a network device, etc.) to execute the above method according to the embodiment of the present invention.
The software product may employ any combination of one or more readable media. The readable medium may be a readable signal medium or a readable storage medium. A readable storage medium may be, for example, but not limited to, an electronic, magnetic, optical, electromagnetic, infrared, or semiconductor system, apparatus, or device, or any combination of the foregoing. More specific examples (a non-exhaustive list) of the readable storage medium include: an electrical connection having one or more wires, a portable disk, a hard disk, a Random Access Memory (RAM), a read-only memory (ROM), an erasable programmable read-only memory (EPROM or flash memory), an optical fiber, a portable compact disc read-only memory (CD-ROM), an optical storage device, a magnetic storage device, or any suitable combination of the foregoing.
The computer readable storage medium may include a propagated data signal with readable program code embodied therein, for example, in baseband or as part of a carrier wave. Such a propagated data signal may take many forms, including, but not limited to, electro-magnetic, optical, or any suitable combination thereof. A readable storage medium may also be any readable medium that is not a readable storage medium and that can communicate, propagate, or transport a program for use by or in connection with an instruction execution system, apparatus, or device. Program code embodied on a readable storage medium may be transmitted using any appropriate medium, including but not limited to wireless, wireline, optical fiber cable, RF, etc., or any suitable combination of the foregoing.
Program code for carrying out operations for aspects of the present invention may be written in any combination of one or more programming languages, including an object oriented programming language such as Java, C + + or the like and conventional procedural programming languages, such as the "C" programming language or similar programming languages. The program code may execute entirely on the user's computing device, partly on the user's device, as a stand-alone software package, partly on the user's computing device and partly on a remote computing device, or entirely on the remote computing device or server. In the case of a remote computing device, the remote computing device may be connected to the user computing device through any kind of network, including a Local Area Network (LAN) or a Wide Area Network (WAN), or may be connected to an external computing device (e.g., through the internet using an internet service provider).
The computer readable medium carries one or more programs which, when executed by a device, cause the computer readable medium to perform the functions of: acquiring an engineering structure numerical model library of a target engineering, wherein the engineering structure numerical model library comprises engineering structure numerical models based on different variables; constructing a plurality of response function mathematical models based on the engineering structure numerical model library and a plurality of positions, a plurality of external effect quantities and a plurality of variables of the target engineering; acquiring multi-source monitoring information of the target project, wherein the multi-source monitoring information comprises at least one of monitoring information at different moments, monitoring information at different spatial positions and monitoring information of different monitoring projects; generating a plurality of structural parameter combination sets meeting preset errors based on the plurality of response function mathematical models and the multi-source monitoring information, wherein the structural parameter combination sets comprise a plurality of groups of structural parameter combinations; and calculating the certainty factor and the single certainty factor of the plurality of structural parameter combination sets.
Those skilled in the art will appreciate that the modules described above may be distributed in the apparatus according to the description of the embodiments, or may be modified accordingly in one or more apparatuses unique from the embodiments. The modules of the above embodiments may be combined into one module, or further split into multiple sub-modules.
Through the above description of the embodiments, those skilled in the art will readily understand that the exemplary embodiments described herein may be implemented by software, or by software in combination with necessary hardware. Therefore, the technical solution according to the embodiment of the present invention can be embodied in the form of a software product, which can be stored in a non-volatile storage medium (which can be a CD-ROM, a usb disk, a removable hard disk, etc.) or on a network, and includes several instructions to enable a computing device (which can be a personal computer, a server, a mobile terminal, or a network device, etc.) to execute the method according to the embodiment of the present invention.
Exemplary embodiments of the present invention are specifically illustrated and described above. It is to be understood that the invention is not limited to the precise construction, arrangements, or instrumentalities described herein; on the contrary, the invention is intended to cover various modifications and equivalent arrangements included within the spirit and scope of the appended claims.
Claims (12)
1. A method for calibrating a numerical model of an engineering structure, comprising:
acquiring an engineering structure numerical model library of a target engineering, wherein the engineering structure numerical model library comprises engineering structure numerical models based on different variables;
constructing a plurality of response function mathematical models based on the engineering structure numerical model library and a plurality of positions, a plurality of external effect quantities and a plurality of variables of the target engineering;
acquiring multi-source monitoring information of the target project, wherein the multi-source monitoring information comprises at least one of monitoring information at different moments, monitoring information at different spatial positions and monitoring information of different monitoring projects;
generating a plurality of structural parameter combination sets meeting preset errors based on the plurality of response function mathematical models and the multi-source monitoring information, wherein the structural parameter combination sets comprise a plurality of groups of structural parameter combinations;
calculating a certainty factor index and a unicity certainty factor index of the plurality of structural parameter combination sets;
and evaluating the credibility of the engineering structure numerical model calibration based on the credibility index and the unicity credibility index of the plurality of structure parameter combination sets.
2. The calibration method according to claim 1, further comprising:
determining basic assumption, basic variable quantity, probability distribution type and value range of a basic numerical simulation model of the target engineering based on material parameters and/or structural parameters and/or experimental parameters and/or functional parameters of the target engineering;
and constructing the engineering structure numerical model library based on the basic assumption, the number of the basic variables, the probability distribution type and the value range.
3. The calibration method according to claim 2, wherein constructing the engineering structure numerical model library based on the basic assumptions, the number of basic variables, the probability distribution types, and the value ranges comprises:
sampling random variables in the basic variables based on a multivariate random sampling technology to generate a random variable sample space;
processing the basic numerical simulation model based on a parallel text processing technology to generate an engineering structure numerical simulation model sample space;
and constructing the engineering structure numerical model library based on a parallel numerical simulation technology, the random variable sample space and the engineering structure numerical simulation model sample space.
4. The calibration method according to claim 1, wherein constructing a plurality of response function mathematical models based on the library of engineering structure numerical models and a plurality of positions, a plurality of external effects, and a plurality of variables of the target engineering comprises:
and constructing the plurality of response function mathematical models based on a precise radial basis function neural network interpolation technology, the engineering structure numerical model library, a plurality of positions, a plurality of external effect quantities and a plurality of variables of the target engineering.
5. The calibration method according to claim 2 or 4, wherein constructing a plurality of response function mathematical models based on the engineering structure numerical model library and a plurality of positions, a plurality of external effects, and a plurality of variables of the target engineering, further comprises:
and generating an engineering structure parameter sample space to be calibrated by adopting a multivariate random sampling technology according to the number of the basic variables, the probability distribution type and the value range.
6. The calibration method of claim 1, wherein generating a plurality of sets of combinations of structural parameters satisfying a predetermined error based on the plurality of mathematical response function models and the multi-source monitoring information comprises:
substituting the multi-source monitoring information into the plurality of response function mathematical models to generate a plurality of structural parameters;
generating a structural parameter combination based on the combination of a plurality of structural parameters which meet monitoring errors in a sample space of any variable in an engineering structure numerical model library;
generating the plurality of sets of structural parameter combinations based on a plurality of structural parameter combinations.
7. The calibration method of claim 6, wherein substituting the multi-source monitoring information into the plurality of response function mathematical models generates a plurality of structural parameters, further comprising:
and rejecting the structural parameters which do not meet the monitoring error in the plurality of structural parameters.
8. The calibration method according to claim 1, comprising:
wherein, CCI is a certainty factor index, and N is the number of samples which are screened out in a calibration sample space N and meet an error condition of a parameter to be calibrated;
wherein SCI is a single certainty index, xiRepresenting the corresponding parameter variable to be calibrated; max (x)i) Variable x representing satisfaction of the screening conditioniMaximum value of (d); min (x)i) Variable x representing satisfaction of the screening conditioniMinimum value of (d); ub (x)i) Representing variable x in a fundamental numerical simulation modeliUpper limit of (d); ul (x)i) Representing variable x in a fundamental numerical simulation modeliThe lower limit of (2).
9. The calibration method according to claim 1, further comprising:
acquiring updated multi-source monitoring information of the target project;
and updating the plurality of structural parameter combination sets based on the updated multi-source monitoring information.
10. A calibration device for a numerical model of an engineering structure, comprising:
the model base module is used for obtaining an engineering structure numerical model base of the target engineering, and the engineering structure numerical model base comprises engineering structure numerical models based on different variables;
the response function module is used for constructing a plurality of response function mathematical models based on the engineering structure numerical model library, a plurality of positions, a plurality of external effect quantities and a plurality of variables of the target engineering;
the monitoring information module is used for acquiring multi-source monitoring information of the target project, wherein the multi-source monitoring information comprises at least one of monitoring information at different moments, monitoring information at different spatial positions and monitoring information of different monitoring projects;
the calibration module is used for generating a plurality of structural parameter combination sets meeting preset errors based on the plurality of response function mathematical models and the multi-source monitoring information, and each structural parameter combination set comprises a plurality of groups of structural parameter combinations;
the index calculation module is used for calculating the certainty factor index and the single certainty factor index of the multiple structural parameter combination sets;
and the evaluation module is used for evaluating the credibility of the engineering structure numerical model calibration based on the credibility index and the single credibility index of the multiple structure parameter combination sets.
11. An electronic device, comprising:
one or more processors;
storage means for storing one or more programs;
when executed by the one or more processors, cause the one or more processors to implement the method of any one of claims 1-9.
12. A computer-readable medium, on which a computer program is stored, which, when being executed by a processor, carries out the method according to any one of claims 1-9.
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