CN115270238A - Dynamic load-based bridge static behavior prediction method - Google Patents

Dynamic load-based bridge static behavior prediction method Download PDF

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CN115270238A
CN115270238A CN202210684172.XA CN202210684172A CN115270238A CN 115270238 A CN115270238 A CN 115270238A CN 202210684172 A CN202210684172 A CN 202210684172A CN 115270238 A CN115270238 A CN 115270238A
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卢彭真
李登国
陈扬瑞
武瑛
卢立波
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Zhejiang University of Technology ZJUT
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Abstract

The invention discloses a dynamic load-based bridge static behavior prediction method, which comprises the following steps of: establishing a full-bridge structure analysis model according to the design data of the existing bridge; carrying out dynamic load test on the existing bridge to obtain a dynamic response value of the bridge; carrying out sensitivity analysis on each design parameter in the initial structure analysis model, and acquiring and determining key design parameters to be corrected, which influence the bridge structure; based on a uniform design sampling method, constructing a training sample of the key design parameters to be corrected by utilizing a machine learning intelligent algorithm and establishing an agent prediction model; correcting the initial structure analysis model by using the prediction results of machine learning and intelligent algorithm; predicting the static behavior of the bridge based on the corrected structural analysis model; and (4) introducing an error analysis method to evaluate the prediction result of the static force of the bridge. The beneficial effects of the invention are: the cost of the static load test of the bridge is reduced, the damage to the self structure of the bridge is reduced, and the prediction result precision is high.

Description

Dynamic load-based bridge static behavior prediction method
Technical Field
The invention relates to the technical field of civil engineering, structural engineering, bridge detection and health monitoring, in particular to a static behavior prediction method of a bridge based on dynamic load.
Background
With the rapid development of global traffic construction, the total number of bridges has increased in the world in the century, and according to the statistics in 2019, 87.83 thousands of bridges are available only for Chinese highway bridges. In the face of a huge bridge base number, the method is particularly important for quick performance evaluation, damage identification, intelligent analysis and safety performance control of the existing bridge structure.
At present, an effective method for evaluating the technical state of a bridge structure is a load test, but the bridge load test has the defects of high test cost, long test process, large workload, certain damage to the bridge structure, road closure and serious influence on normal traffic travel. In the face of the huge number of bridges which need to be subjected to rapid bridge technical condition assessment urgently, how to improve the bridge detection efficiency, reduce the bridge detection cost, avoid traffic trip influence caused by road closure, achieve the accuracy and reliability of detection results, realize intelligent identification rapid detection and intelligent analysis assessment decision of bridge structures, and become the problem to be solved in the current research. Although the dynamic load test of the bridge can only identify the dynamic performance of the whole structure of the bridge, compared with the static load test, the dynamic load test has the advantages of relatively low cost, relatively short test time, relatively simple test process and relatively small influence on traffic passage.
Therefore, researchers in the field aim to provide a dynamic load-based bridge static behavior prediction method, and the static load result of the bridge is accurately predicted by using a dynamic load test result which is low in cost, high in efficiency and small in traffic influence.
Disclosure of Invention
In view of the defects and shortcomings of the existing bridge in the aspects of health monitoring, damage identification, intelligent analysis and performance evaluation, the method and the device provided by the invention aim to achieve the aim of quickly predicting the static behavior of the bridge by combining a machine learning intelligent algorithm and a model correction technology based on a dynamic load test result which is simple, low in cost, convenient to detect and efficient.
In order to achieve the purpose, the invention provides a dynamic load-based bridge static behavior prediction method, which is characterized by comprising the following steps of:
step 1, establishing a full-bridge structural analysis model according to design data of an existing bridge; taking the structure analysis model as an initial structure analysis model for subsequent model correction;
step 2, carrying out dynamic load test on the existing bridge to obtain a dynamic response value of the bridge;
step 3, carrying out sensitivity analysis on each design parameter in the initial structure analysis model, and acquiring and determining key design parameters to be corrected, which influence the bridge structure;
step 4, based on a uniform design sampling method, constructing a training sample of the key design parameters to be corrected by using a machine learning intelligent algorithm and establishing a proxy prediction model;
step 5, correcting the initial structure analysis model by using the prediction results of machine learning and intelligent algorithm to obtain a corrected structure analysis model;
step 6, predicting static behavior of the bridge based on the corrected structural analysis model;
7, introducing an error analysis method, and evaluating the prediction result of the static force of the bridge; the error analysis adopts a root mean square error RMSE analysis method, and a root mean square error calculation formula is as follows:
Figure BDA0003697411580000021
wherein the content of the first and second substances,
Figure BDA0003697411580000031
test response value and intelligent prediction model prediction response for ith group of samplesValue of,
Figure BDA0003697411580000032
the average of the experimental response values.
Further, the structural analysis model of step 1 is typically modeled by numerical methods, such as finite elements, boundary elements, discrete elements, and/or infinite elements.
Further, in the step 2, when the dynamic load of the bridge is tested, measuring methods (such as a machine vision measuring method) for measuring the bridge by adopting a contact or non-contact method, a direct method or an indirect method are adopted to obtain dynamic characteristic parameters of the bridge; the parameters of the dynamic characteristics comprise the frequency, the vibration mode, the damping, the impact coefficient, the dynamic deflection, the dynamic strain and the like of the bridge.
And 3, performing sensitivity analysis on each design parameter of the initial structure analysis model of the bridge in the step 1 by adopting a sensitivity analysis method, analyzing sensitivity weight indexes of different design parameters, and determining the key design parameters to be corrected of the bridge. The sensitivity analysis method can also adopt a grey correlation degree method to realize the weight analysis of the key parameters to be corrected.
Further, step 4, constructing training samples of key design parameters to be corrected, which are uniformly and fully distributed in space, by adopting a uniform design sampling method, and establishing a prediction model by combining an intelligent algorithm; the intelligent algorithm comprises a Bayesian theory, a Gaussian process method, a Kriging model, various agent models and other prediction methods.
And step 5, based on the prediction model constructed in the step 4, calling the bridge dynamic load test result obtained in the step 2, predicting each key design parameter to be corrected, and substituting the prediction result of each key design parameter to be corrected into the initial structure analysis model of the bridge constructed in the step 1 to realize the correction of the initial structure analysis model.
And further, step 6, based on the corrected structural analysis model, applying a load working condition in the corrected structural analysis model according to a static load test scheme of the bridge, so as to realize prediction of a static force result of the bridge. The static load prediction result can be deformation, internal force, stress and the like of the whole or local bridge respectively.
And 7, introducing an error analysis method, and evaluating the prediction result of the static load of the bridge. The Error analysis usually adopts a Root Mean square Error RMSE (Root Mean Squared Error) analysis method.
The invention is based on the dynamic load test result of the existing bridge, combines the structure analysis model correction method and the intelligent algorithm technology, realizes the accurate prediction of the static behavior of the existing bridge, and achieves the following technical effects:
(1) The static behavior of the bridge is predicted based on the dynamic load test result of the existing bridge, the cost of the static test of the bridge is reduced to a great extent, the detection efficiency is improved, the problem of traffic closure caused by the static load test is avoided, and the damage to the structure in the loading process of the bridge is reduced;
(2) By adopting an intelligent algorithm and combining a model correction method to predict the static behavior of the existing bridge, the obtained prediction result has higher precision and is more suitable for the actual bridge health condition.
(3) The method can realize rapid static behavior analysis on a large batch of bridges, can carry out all-around safety assessment on the whole structure of the bridge, and provides a new method for health monitoring and operation maintenance of the bridge.
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FIG. 1a is a block flow diagram of the present invention.
FIG. 1b is a flow chart of a dynamic load-based bridge static behavior prediction method of the present invention;
FIG. 2 is a graph of a bridge dynamic load test result (the abscissa is sampling time and the ordinate is amplitude) according to an embodiment of the present invention;
FIG. 3 is a graph of the results of a parameter sensitivity analysis in accordance with an embodiment of the present invention;
FIG. 4 is a flow chart of the combination of analytical model modification and intelligent algorithm of the present invention;
FIG. 5 is a comparison graph of predicted static load results and actual field static load test results for a bridge according to an embodiment of the present invention.
Detailed Description
The following detailed description of embodiments of the invention refers to the accompanying drawings. It should be understood that the detailed description and specific examples, while indicating embodiments of the invention, are given by way of illustration and explanation only, not limitation.
It should be noted that the embodiments and features of the embodiments of the present invention may be combined with each other without conflict.
The invention will be described in detail hereinafter with reference to the drawings and in connection with exemplary embodiments.
Step 1, establishing a bridge initial structure analysis model according to existing bridge design data;
according to the design data of the existing bridge, the values of the geometric shapes, specific sizes and material properties of all parts of the bridge and the forms of boundary conditions are determined, and an initial analysis model of the bridge structure is established by using structural analysis software and serves as a reference model for subsequent model correction. The analysis model is usually a numerical model, such as a finite element model, a boundary element model, a discrete element model, an infinite element model, etc., and the commonly used analysis software includes ANSYS, ABAQUS, midas, etc.
Step 2, carrying out dynamic load test on the existing bridge to obtain a dynamic response value of the bridge;
in order to obtain the dynamic response value of the bridge, in the specific embodiment, the field dynamic load test is carried out on an existing bridge by adopting an environment excitation method in a direct measurement method. And during testing, vibration responses of the bridge under the ground pulsation are obtained through the vibration pickups arranged on the L/8, L/4 and L/2 typical sections of the bridge. Fig. 2 shows the result of the dynamic load test on a bridge in the embodiment, where the abscissa is the test time and the ordinate is the amplitude. And performing Fourier transform on the time domain result to obtain a frequency domain result reflecting the frequency characteristics of the bridge.
The fourier transform equation is:
Figure BDA0003697411580000061
in the formula: j is a virtual unit, j ^2= -1, and no unit is used; t is a period and has a unit of second; x is a primitive function of X; t is time in seconds; ω is the frequency and x (t) is the continuous time signal.
Step 3, carrying out sensitivity analysis on each design parameter in the initial structure analysis model, and acquiring and determining key design parameters to be corrected, which affect the bridge structure:
and (3) selecting design parameters to be corrected on the basis of the initial bridge structure model established in the step (1). And (3) carrying out sensitivity analysis on different design parameters by using a global sensitivity analysis method, changing a change threshold of one design parameter to be 10% each time by using a variable control method, keeping other design parameters unchanged, and calculating the influence degree of the first two-stage frequency response results f1 and f2 of the corresponding bridge structure when the different design parameters are changed so as to determine the sensitivity weight indexes of the different design parameters.
The key parameters comprise design parameters such as structural geometric dimension, concrete material elastic modulus, concrete volume weight and the like. And respectively changing the structural design parameters one by using a sensitivity analysis method, calculating corresponding structural response values, constructing a key parameter sensitivity analysis model, quantifying the specific influence effect of different parameters on the bridge structure, and determining the key design parameters to be corrected. Fig. 3 is a result of parameter sensitivity analysis in an embodiment, it should be noted that the parameters in the embodiment are K1, K2, K3, r1, r2, and r3, respectively, and the corresponding structural response results are the first 2-order frequencies f1 and f2.
Step 4, based on a uniform design sampling method, utilizing a machine learning intelligent algorithm to construct a training sample of the key design parameters to be corrected and establish an agent prediction model:
setting variable thresholds for the parameters respectively according to the key design parameters to be corrected determined in the step 3, writing the variable thresholds into a macro file, reading the macro file of the parameter thresholds by using structural analysis software, sequentially calculating structural response values corresponding to each group of parameters in the thresholds, wherein the response values can be frequencies of each order of the bridge or deflection values of a certain measuring point of the bridge, and the like, generating training samples through limited calculation, and establishing a prediction model by using the training samples. FIG. 4 is a flow chart of the structural analysis model modification and intelligent algorithm combination of the present invention.
Specifically, step 4 is based on a uniform design sampling method, a training sample of design parameters and response results is established by using an intelligent algorithm, and a Kriging prediction model is established:
according to the key design parameters needing to be corrected determined by the sensitivity analysis in the step 3, in the embodiment, K1, K2, K3, r1, r2 and r3 are taken as parameters to be corrected. By uniformly designing the sampling method, the table U is uniformly designedn(mr) Where U denotes a uniform design table, n denotes the number of uniform trials required, m denotes the number of factor levels that can be accommodated, and r denotes the number of factors that can be arranged at maximum.
This embodiment establishes U30(306) I.e. 30 trials, 30 levels, 6 parameters. And the corresponding structure response frequency results f1 and f2 form training samples of the intelligent algorithm, and the specific training samples are shown in table 1.
Substituting the training sample into a Kriging theoretical model:
y(x)＝f(x)Tβ+Z(x) (3)；
in the formula: y (x) is a Kriging model function, and T represents the meaning of transposition; f (x) is a polynomial model and beta is a regression coefficient. Z (x) is a random process called a variation function or a correlation model, and further a Kriging agent prediction model is built.
Step 5, correcting the initial structure analysis model by using the prediction results of machine learning and intelligent algorithm to obtain a corrected structure analysis model;
and (4) inputting the result data obtained in the dynamic load test in the step (2) according to the training sample established in the step (4), predicting an optimal value of a group of parameters to be corrected through a machine learning intelligent algorithm, and substituting the group of predicted values into the initial structure analysis model to realize the correction of the structure analysis model.
The machine learning intelligent algorithm comprises a Kriging model algorithm, a Gaussian process algorithm, a Bayesian algorithm, a random forest algorithm, a cloud theory algorithm, various agent models and the like.
The embodiment adopts a Kriging model algorithm, and firstly the Kriging model comprises a polynomial sumTwo parts are randomly distributed, namely y (x) = f (x)Tβ + Z (x), wherein:
f(x)Tβ＝[f1(x),f2(x),...,fp(x)]β＝f1(x)β1+f2(x)β2+...+fp(x)βp (4)；
f (x) is a polynomial model, p is the number of polynomials, and β is the regression coefficient.
Z (x) is a stochastic process called the variogram or correlation model, and the covariance matrix of Z (x) is:
Figure BDA0003697411580000081
in the formula: cov () is covariance; σ is the standard deviation; theta is a hyperparameter. x is the number ofiAnd xjIs a sample point;
Figure BDA0003697411580000082
for any two of the sample points xiAnd xjIs of the functional form:
Figure BDA0003697411580000083
in the Kriging regression function model prediction process, the problem is converted into the minimum optimization problem, namely
Figure BDA0003697411580000091
An optimal Kriging prediction model can be constructed by solving the minimum optimization problem of a formula to obtain a parameter theta, wherein theta is a hyper-parameter, m is a natural number, and m =1,2,3, \ 8230, m and sigma are standard deviations.
Step 6, predicting static behavior of the bridge based on the corrected structural analysis model;
based on the structure analysis model corrected in the step 5, the numerical model is matched with the performance of the actual bridge, the simulation loading is carried out in the corrected numerical model according to the loading position of the static load test of the actual bridge, and the results of the displacement, the stress and the like of the typical sections of the bridge, such as the span, the pivot and the like, are calculated through structure analysis software. In the embodiment, the deflection W1-W18 of each measuring point in the longitudinal direction of the bridge is predicted under the action of concentrated midspan load of a 3-span continuous bridge and is compared with the actual measurement result for verification.
7, introducing an error analysis method, and evaluating the prediction result of the static force of the bridge; wherein, the error analysis adopts a root mean square error RMSE analysis method;
the invention provides a Root Mean Square Error (RMSE) as an evaluation index of a prediction result.
The root mean square error is calculated as follows:
Figure BDA0003697411580000092
wherein the content of the first and second substances,
Figure BDA0003697411580000093
respectively testing response values and intelligent prediction model prediction response values for the ith group of samples,
Figure BDA0003697411580000094
the average of the experimental response values. RMSE is used for evaluating the precision of the intelligent prediction model, and the closer the value is to 0, the smaller the error between the test response value and the predicted value of the intelligent prediction model is.
TABLE 1U30(306) Design matrix
Figure BDA0003697411580000101
TABLE 2 initial value, measured value, predicted value (mm) of each deflection measuring point
Figure BDA0003697411580000102
TABLE 3 deflection error analysis
Figure BDA0003697411580000103
In the description of the present invention, it is to be understood that the terms "central," "longitudinal," "lateral," "length," "width," "thickness," "upper," "lower," "front," "rear," "left," "right," "vertical," "horizontal," "top," "bottom," "inner," "outer," "clockwise," "counterclockwise," "axial," "radial," "circumferential," and the like are used in the orientations and positional relationships indicated in the drawings for convenience in describing the invention and to simplify the description, and are not intended to indicate or imply that the referenced devices or elements must have a particular orientation, be constructed and operated in a particular orientation, and are therefore not to be considered limiting of the invention.
Furthermore, the terms "first", "second" and "first" are used for descriptive purposes only and are not to be construed as indicating or implying relative importance or to implicitly indicate the number of technical features indicated. Thus, a feature defined as "first" or "second" may explicitly or implicitly include at least one such feature. In the description of the present invention, "a plurality" means at least two, e.g., two, three, etc., unless specifically limited otherwise.
In the present invention, unless otherwise explicitly stated or limited, the terms "mounted," "connected," "fixed," and the like are to be construed broadly, e.g., as being permanently connected, detachably connected, or integral; may be mechanically coupled, may be electrically coupled or may be in communication with each other; they may be directly connected or indirectly connected through intervening media, or they may be connected internally or in any other suitable relationship, unless expressly stated otherwise. The specific meanings of the above terms in the present invention can be understood according to specific situations by those of ordinary skill in the art.
In the present invention, unless expressly stated or limited otherwise, the first feature "on" or "under" the second feature may be directly contacting the second feature or the first and second features may be indirectly contacting each other through intervening media. Also, a first feature "on," "above," and "over" a second feature may be directly on or obliquely above the second feature, or simply mean that the first feature is at a higher level than the second feature. A first feature being "under," "below," and "beneath" a second feature may be directly under or obliquely under the first feature, or may simply mean that the first feature is at a lesser elevation than the second feature.
In the description herein, references to the description of the term "one embodiment," "some embodiments," "an example," "a specific example," or "some examples," etc., mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above are not necessarily intended to refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples. Furthermore, various embodiments or examples and features of different embodiments or examples described in this specification can be combined and combined by one skilled in the art without contradiction.
Although embodiments of the present invention have been shown and described above, it is understood that the above embodiments are exemplary and should not be construed as limiting the present invention, and that variations, modifications, substitutions and alterations can be made to the above embodiments by those of ordinary skill in the art within the scope of the present invention.

Claims (9)

1. The dynamic load-based bridge static behavior prediction method is characterized by comprising the following steps of:
step 1, establishing a full-bridge structural analysis model according to design data of an existing bridge; taking the structure analysis model as an initial structure analysis model for subsequent model correction;
step 2, carrying out dynamic load test on the existing bridge to obtain a dynamic response value of the bridge;
step 3, carrying out sensitivity analysis on each design parameter in the initial structure analysis model, and obtaining and determining key design parameters to be corrected, which influence the bridge structure;
step 4, based on a uniform design sampling method, constructing a training sample of the key design parameters to be corrected by using a machine learning intelligent algorithm and establishing a proxy prediction model;
step 5, correcting the initial structure analysis model by using the prediction results of machine learning and intelligent algorithm to obtain a corrected structure analysis model;
step 6, predicting static behavior of the bridge based on the corrected structural analysis model;
7, introducing an error analysis method, and evaluating the prediction result of the static force of the bridge; wherein, the error analysis adopts a root mean square error RMSE analysis method; the root mean square error is calculated as follows:
Figure FDA0003697411570000011
wherein the content of the first and second substances,
Figure FDA0003697411570000012
respectively testing response values and intelligent prediction model prediction response values for the ith group of samples,
Figure FDA0003697411570000013
the average of the experimental response values.
2. The dynamic load-based bridge static behavior prediction method according to claim 1, characterized in that: the structural analysis model in the step 1 is modeled by adopting a numerical method, wherein the numerical method is a finite element modeling method, a boundary element modeling method, a discrete element modeling method and/or an infinite element modeling method.
3. The dynamic load-based bridge static behavior prediction method according to claim 2, characterized in that: in the step 2, the dynamic performance of the bridge is measured by adopting a contact or non-contact method, a direct method or an indirect method when the dynamic load of the bridge is tested; the parameters of the dynamic characteristics comprise the frequency, the vibration mode, the damping, the impact coefficient, the dynamic deflection and the dynamic strain of the bridge.
4. The dynamic load-based bridge static behavior prediction method according to claim 3, characterized in that: in the step 2, an environmental excitation method in a direct measurement method is adopted to carry out on-site dynamic load test on an existing bridge; during testing, vibration response of the bridge under the ground pulsation is obtained through the vibration pickers arranged on the L/8, L/4 and L/2 typical sections of the bridge, and Fourier transformation is carried out on the obtained time domain result, so that a frequency domain result reflecting the frequency characteristics of the bridge can be obtained.
The fourier transform equation is:
Figure FDA0003697411570000021
in the formula: j is a virtual unit, j ^2= -1, and no unit is used; t is the period and the unit is second; x is a primitive function of X; t is time in seconds; ω is the frequency and x (t) is the continuous time signal.
5. The dynamic load-based bridge static behavior prediction method according to claim 3, characterized in that: and 3, carrying out sensitivity analysis on each design parameter of the initial structure analysis model of the bridge in the step 1 by adopting a sensitivity analysis method, analyzing sensitivity weight indexes of different design parameters, and determining the key design parameters to be corrected of the bridge.
6. The dynamic load-based bridge static behavior prediction method according to claim 5, characterized in that: step 4, constructing training samples of key design parameters to be corrected, which are uniformly and fully distributed in space, by adopting a uniform design sampling method, and establishing a prediction model by combining an intelligent algorithm; wherein the intelligent algorithm comprises Bayesian theory, gaussian process method and/or Kriging model.
7. The dynamic load-based bridge static behavior prediction method according to claim 6, characterized in that: the intelligent algorithm adopted in the step 4 is a Kriging model algorithm;
wherein, the Kriging model comprises two parts of polynomial and random distribution, namely y (x) = f (x)Tβ + Z (x), wherein:
f(x)Tβ＝[f1(x),f2(x),...,fp(x)]β＝f1(x)β1+f2(x)β2+...+fp(x)βp (4)；
f (x) is a polynomial model, p is the number of polynomials, and β is the regression coefficient.
Z (x) is a stochastic process called the variogram or correlation model, and the covariance matrix of Z (x) is:
Figure FDA0003697411570000032
in the formula: cov () is covariance; σ is the standard deviation; theta is a hyper-parameter; x is the number ofiAnd xjIs a sample point;
Figure FDA0003697411570000033
for any two of the sample points xiAnd xjIs of the functional form:
Figure FDA0003697411570000034
in the Kriging regression function model prediction process, the problem is converted into the minimum optimization problem, namely
Figure FDA0003697411570000035
An optimal Kriging prediction model can be constructed by solving the minimum optimization problem of a formula to obtain a parameter theta, wherein theta is a hyper-parameter, m is a natural number, and m =1,2,3, \ 8230, m and sigma are standard deviations.
8. The dynamic load-based bridge static behavior prediction method according to claim 7, characterized in that: and 5, calling the dynamic load test result of the bridge obtained in the step 2 based on the prediction model constructed in the step 4, predicting each key design parameter to be corrected, and substituting the prediction result of each key design parameter to be corrected into the initial structure analysis model of the bridge constructed in the step 1 to realize the correction of the initial structure analysis model.
9. The dynamic load-based bridge static behavior prediction method according to claim 6, characterized in that: and 6, based on the corrected structural analysis model, applying a load working condition in the corrected structural analysis model according to a static load test scheme of the bridge, so as to realize prediction of a static force result of the bridge.
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CN115793516A (en) * 2022-11-02 2023-03-14 安徽源信技术有限公司 Control system and method for intelligent bridge movement detection
CN117332488A (en) * 2023-10-17 2024-01-02 唐山市楹辉公路养护有限公司 Road and bridge live-action model analysis method and system based on BIM technology

Cited By (4)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN115793516A (en) * 2022-11-02 2023-03-14 安徽源信技术有限公司 Control system and method for intelligent bridge movement detection
CN115793516B (en) * 2022-11-02 2023-09-15 安徽源信技术有限公司 Control system and method for intelligent bridge movement detection
CN117332488A (en) * 2023-10-17 2024-01-02 唐山市楹辉公路养护有限公司 Road and bridge live-action model analysis method and system based on BIM technology
CN117332488B (en) * 2023-10-17 2024-04-30 唐山市楹辉公路养护有限公司 Road and bridge live-action model analysis method and system based on BIM technology

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