CN114329714B - Rapid structural health monitoring method for whole construction and operation stage of assembled super high-rise structure - Google Patents
Rapid structural health monitoring method for whole construction and operation stage of assembled super high-rise structure Download PDFInfo
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Abstract
The invention discloses a rapid structural health monitoring method for the whole stage of construction and operation of an assembled super high-rise structure, which comprises the following steps: gradually installing data acquisition equipment along with the promotion of the construction progress of engineering projects; collecting and analyzing structural vibration data in the construction process; building a structure finite element model along with the engineering construction progress; according to the difference between the structural actual measurement modal parameter and the calculation model modal parameter, evaluating the safety in the construction process; after construction is finished, correcting the finite element model of the structure according to the integral vibration data of the structure; periodically collecting and analyzing structural vibration data in an operation stage; and comparing and analyzing the structure actual measurement modal parameters in the operation stage with the corrected finite element model modal parameters. The invention can timely monitor the structural state change condition in the whole engineering construction process, realize the rapid safety evaluation of the construction process to guide the actual construction, and timely find the structural potential safety hazard by carrying out the real-time safety state evaluation on the structure in the structural operation process.
Description
Technical Field
The invention relates to a rapid structural health monitoring method for the whole stage of construction and operation of an assembled super high-rise structure, and belongs to the technical field of health monitoring of assembled super high-rise structures.
Background
The assembled super high-rise building is used as a novel structural form advocated by great force in China, and because of the characteristics of huge cost, complex structural form, long service life and the like, and the additional structural accessory installation process is needed, the requirements on construction error control of the structure during construction and structural safety, applicability and durability during operation are more strict. In order to ensure the safety of the construction process of the structure, control the construction error of the structure and ensure the structural safety of the operation process, the structure must be tracked and monitored in real time.
The sensor and the data acquisition equipment are mainly installed step by step along with the construction progress in the construction stage, the main manual operation is that monitoring information is mainly led into the data management system, and after analysis, the monitoring information is compared with the design requirements, measures are timely taken for deviation correction, so that the safety of the whole construction process is ensured; and (3) carrying out state identification and structural safety evaluation on the structure through an environmental vibration test in the using stage of the structure. The monitoring system of the super high-rise structure can simultaneously meet the characteristics of a construction period and an operation period, the monitoring project must simultaneously comprise detection contents of the construction period and the operation period, the monitoring data of the two periods can be in seamless connection, the data during the construction period is guaranteed to serve the operation period, monitoring points are buried during the construction, and the monitoring original paper and the equipment position during the operation period are reserved. The construction stage and the use stage have the same monitoring content, the construction stage is dangerous control, the use stage is dangerous discovery, and the construction stage and the use stage are combined with each other to form a complete safety monitoring system.
The assembled super high-rise structure is used as a newer structural form, compared with the traditional super high-rise structure, the assembly link of the structure is added, the construction progress is faster, and the safety requirement on the assembly construction process is higher. The existing health monitoring method for building structures has the following problems:
(1) Because the traditional structure monitoring system involves manual input and related analysis of environmental vibration data in the construction process, the whole process from data acquisition to structure safety evaluation conclusion is long in time, and the requirement of rapid construction progress of an assembled structure cannot be met;
(2) At present, the method for monitoring the structural health of the assembled super high-rise structure at the whole stage of construction operation is still blank, and the method for monitoring the structural health of the assembled super high-rise structure at the whole stage is not specially used for a short time.
Disclosure of Invention
Based on the above, the invention provides a full-stage rapid structural health monitoring method for the construction operation of an assembled super high-rise structure, which can timely monitor the structural state change condition in the whole engineering construction process, realize rapid safety evaluation of the construction process to guide actual construction, and timely find structural potential safety hazards by carrying out real-time safety state evaluation on the structure in the structural operation process.
The technical scheme of the invention is as follows: a rapid structural health monitoring method for the whole stage of construction and operation of an assembled super high-rise structure comprises the following steps:
(1) Gradually installing data acquisition equipment along with the promotion of the construction progress of engineering projects;
(2) The data acquisition equipment acquires structural vibration data in the construction process, and structural actual measurement modal parameters in the construction stage are obtained through analysis;
(3) Building a structural finite element model along with the engineering construction progress to obtain corresponding calculation model modal parameters;
(4) According to the difference between the actually measured modal parameters of the structure and the modal parameters of the calculation model, evaluating the safety in the construction process and guiding the actual construction process;
(5) After the structure construction is finished, correcting the structure finite element model according to the whole vibration data of the structure, and obtaining the mode parameters of the corrected finite element model;
(6) Periodically acquiring structural vibration data through data acquisition equipment in a structural operation stage, and analyzing to obtain structural actual measurement modal parameters in the operation stage;
(7) And comparing the actually measured modal parameters of the structure in the operation stage with the corrected modal parameters of the finite element model, and timely finding out potential structural hazards according to the real-time state change of the structure to ensure the safety of the structure in the operation stage.
Optionally, in step (1), the data acquisition device includes a three-way acceleration sensor and a matched data analysis device, and the three-way acceleration sensor is installed on the structure along with the construction progress.
Optionally, the modal parameters include modal frequencies and modal shapes.
Optionally, in the step (2), after the structural vibration data is collected, structural mode identification based on a bayesian fast fourier transform method is adopted to obtain structural actual measurement mode parameters of the construction stage.
Optionally, the structural mode identification process includes the following steps:
1) Obtaining structural acceleration response data by a three-way acceleration sensor;
2) Performing Bayesian fast FFT (fast Fourier transform) on the acceleration response data to obtain actual measurement frequency domain information of the structure;
3) Calculating a posterior probability density function of the modal parameter according to the actually measured frequency domain information;
4) Converting the posterior probability density function into an optimized negative log-likelihood function;
5) Setting an initial value of a structural modal parameter, and circularly optimizing a negative log likelihood function until convergence to obtain the most probable value of the structural actual measurement modal parameter;
6) Calculating and optimizing the inverse of the jersey matrix and the matrix of the negative log-likelihood function to obtain a posterior covariance matrix;
7) Calculating a variation coefficient of the modal identification parameter according to the posterior covariance matrix;
8) And when the variation coefficient is within the set deviation, the identification parameter is considered to be effective, the most probable value corresponding to each parameter in the actually measured modal parameters of the structure is taken as the actually measured modal parameters of the structure, otherwise, the mode shape and the initial value of the mode frequency are reset, and the calculation is carried out again until the variation parameter is within the set deviation.
Optionally, in step (5), the process of modifying the structural finite element model includes the steps of:
1) Selecting elastic modulus and density parameters of vertical and transverse members in the structure as parameters to be corrected of the finite element model of the structure;
2) Establishing an objective function between the parameter to be corrected and the actually measured sample based on a Bayesian formula;
3) Obtaining a Markov chain with parameters to be corrected as targets and posterior probability distribution as stable distribution by adopting an MCMC sampling algorithm based on the substep, and finally estimating the parameters to be corrected based on sample points generated by the final substep, thereby obtaining the most probable value of the parameters to be corrected;
4) Obtaining the most probable value of the parameter to be corrected based on the effective sample in the final smoothly distributed Markov chain and quantitatively discussing the uncertainty of the parameter to be corrected according to the posterior covariance matrix;
5) And carrying the correction parameter value obtained by calculating the parameter to be corrected into a finite element model to be used as a finite element model after structural correction.
Optionally, the step 3) includes the steps of:
1) The MH samples and obtains the sample point of the initial substep;
2) The subsequent sub-step takes the core density estimation of the previous sub-step as a suggested distribution;
3) With the continuous progress of the substeps, the sample space is continuously reduced, and finally the sample space is converged to stable distribution;
4) And estimating posterior distribution of the parameter to be corrected by the sample points generated in the final substep to obtain the most probable value and uncertainty of the parameter to be corrected.
The beneficial effects of the invention are as follows: aiming at the assembled super high-rise structure, the structure state monitoring of the whole process from the construction stage to the operation stage is realized, the real-time state information of the whole stage of the structure is mastered, the structure construction process can be effectively guided in the construction stage, the safety and the controllability of the construction process are ensured, the structure abnormality can be timely found in the operation stage, the damage is timely positioned, the related maintenance and repair work is adopted, and the safety of the structure in the whole process is effectively ensured.
And (3) for the assembled super high-rise structure in the construction stage, ensuring the safety of the construction process through the steps (1) to (4). Arranging a three-way acceleration sensor at a key part of the structure to obtain vibration information reflecting the integral state of the structure in a targeted way; because measurement errors are inevitably generated in the actual measurement process, the fast FFT mode identification method based on the Bayesian method adopted in the step (2) not only can rapidly process measurement information to obtain structural mode parameters, but also can effectively reduce the influence of the measurement errors on actual structural vibration data from a theoretical level, and can rapidly and accurately realize the structural mode identification process. In the step (3), a finite element model of the structure is established based on an actual construction drawing, so that the theoretical state of the structure in the construction process can be simulated. Because the actual construction process inevitably has a difference with the construction drawing, the calculation result of the finite element model needs to be analyzed by combining with the actual measurement data of the structure, so as to judge whether the structure is safe or not. Through step (4), according to the continuous change of the actually measured modal parameters in the construction process of the structure, the change condition of the modal parameters of the structure in the whole construction process can be recorded, and meanwhile, the theoretical modal parameter calculation result of the finite element model is combined, so that potential safety hazards can be timely found, the reasons of the abnormal structural modal parameter change in the construction process can be analyzed and timely processed, and the safety of the whole construction process is ensured. And the potential safety hazard in the construction process is found and treated early, so that the corresponding treatment cost can be reduced, and the project cost is effectively controlled.
And (3) ensuring the safety of the operation process of the operation stage after the structure is built through the steps (5) to (7). And (3) carrying out vibration measurement on the built assembled super high-rise structure through the step (5), obtaining the modal parameter data of the whole structure as the modal parameter under the nondestructive condition of the structure, correcting the finite element model of the whole structure based on a Bayesian method, and using the corrected finite element model as the initial condition of the nondestructive condition of the simulated structure. And (3) periodically measuring structural vibration data to master the actual performance state of the structure through the step (6) and guiding maintenance and repair work of the structure in the operation stage. When the actual measurement result and the initial structure data are greatly changed, analyzing the damage condition of the structure and positioning the damage position according to the step (7) through the corrected finite element model, analyzing the real-time safety performance of the structure and providing corresponding guidance for the maintenance of the subsequent structure.
The invention can find the safety problems of the structure in the construction and operation processes in time, eliminate the potential safety hazards of the structure in each process, effectively ensure the safety of the structure, save the subsequent maintenance cost and effectively control the project cost.
Drawings
FIG. 1 is a schematic diagram of the method of the present invention;
FIG. 2 is a schematic diagram of a modality identification process;
FIG. 3 is a flowchart of the steps of the MH sampling algorithm;
fig. 4 is a flow chart of the steps of the adaptive MCMC sampling algorithm based on sub-steps.
Detailed Description
In order that the above objects, features and advantages of the invention will be readily understood, a more particular description of the invention will be rendered by reference to the appended drawings. In the following description, numerous specific details are set forth in order to provide a thorough understanding of the present invention. The invention may be embodied in many other forms than described herein and similarly modified by those skilled in the art without departing from the spirit or scope of the invention, which is therefore not limited to the specific embodiments disclosed below.
Referring to fig. 1 to 4, the method for monitoring the health of a rapid structure in the whole stage of the construction operation of an assembled super high-rise structure according to the present embodiment includes the following steps:
S1, gradually installing data acquisition equipment along with the promotion of the construction progress of engineering projects;
In this embodiment, the data acquisition equipment includes three-way acceleration sensor and supporting data analysis equipment, and three-way acceleration sensor installs on the structure along with the construction progress.
In order to acquire the vibration data of the effective structure, the noise in the construction process of the structure and the error caused by the fact that the components do not reach the use state intensity are avoided, the effectiveness of the sensor data is guaranteed, and the three-way acceleration sensor and the matched data analysis equipment are installed once every construction period.
For sensor site placement, the following principle should be followed:
1. The arrangement positions of the measuring points are more completely described for the integral change of the structure;
2. The structural vibration information measured by the measuring points is sensitive enough to the local change of the structure;
3. the arrangement is optimized as much as possible on the basis of meeting the first two points, so that the number of the sensors is optimized.
The installation time of the three-way acceleration sensor can be considered according to the site construction progress and the molding time of cast-in-place concrete in the construction process, and the total quantity of the installed acceleration sensor is considered. The acceleration sensor can be installed generally once every 3-5 layers are constructed.
For the installation position of the three-way acceleration sensor, for general residential and office building structures, the three-way acceleration sensor can be selectively installed at the position of a stairwell and arranged at four corners of a floor at intervals of certain floors at the same time so as to accurately reflect the state change conditions of the whole and part of the structure.
The data acquisition time of the three-way acceleration sensor is generally selected to be carried out under the environmental condition that no site construction is carried out in noon or evening, and the data acquisition and analysis are required to be carried out after the assembly type components are installed, the floor construction with large structural variation is completed and under extreme weather conditions (such as typhoons, snowy days and the like) so as to ensure the safety of the construction process of the structure.
S2, acquiring structural vibration data in a construction process by data acquisition equipment, and analyzing to obtain structural actual measurement modal parameters in a construction stage;
After the structure vibration information of the three-way acceleration sensor is acquired, taking the influence of measurement errors in the measurement process into consideration, adopting the structure mode identification based on the Bayesian fast Fourier transform method to obtain real-time mode parameter data (frequency, vibration mode, damping ratio and the like) of the structure for subsequent structure construction safety analysis. In this embodiment, the modal parameters are modal frequency and modal shape.
After the structural vibration data is collected, structural mode identification based on a Bayesian fast Fourier transform method is adopted to obtain structural actual measurement mode parameters of a construction stage, please refer to fig. 2 again, and the specific structural mode identification process comprises the following steps:
1) Obtaining acceleration response data according to the structural vibration data;
within the framework of Bayesian theory, acceleration response data measured by an acceleration sensor can be modeled as Wherein the modal parameter θ is composed of modal frequency f, damping ratio ζ, cross-power spectral density S, power spectral density σ 2 of prediction error, and modal shape Φ. The prior distribution of the modal parameters is assumed to follow normal distribution according to the Bayesian theory, and the posterior probability density function of the modal parameters is approximately expressed as a Gaussian probability density function.
2) Performing Bayesian fast FFT (fast Fourier transform) on the acceleration response data to obtain actual measurement frequency domain information of the structure;
Responsive to acceleration data And performing Bayesian fast FFT to obtain the actually measured frequency domain information of the structure. From a frequency domain perspective, it can be expressed as: /(I)Where F k is the real part and G k is the imaginary part.
3) Calculating a posterior probability density function of the modal parameter according to the actually measured frequency domain information;
Calculating a posterior probability density function p (θ|{ Z k }) of the structural modal parameter θ, where
4) Converting the posterior probability density function into an optimized negative log-likelihood function;
Converting the posterior probability density function to an optimized negative log-likelihood function (NLLF): p (θ|{ Z k }) ++exp [ -L (θ) ];
Wherein the optimized negative log-likelihood function can be expressed as: l (f, ζ, S, σ 2)＝-nNfln2+(n-1)Nflnσ2, n is the number of structural degrees of freedom.
5) Setting an initial value of a structural modal parameter, and circularly optimizing a negative log likelihood function until convergence to obtain the most probable value of the structural actual measurement modal parameter;
Setting initial values of structural modal parameters (modal shape and modal frequency), and circularly optimizing a negative log likelihood function until convergence to obtain the Most Probable Value (MPV) of the structural actually-measured modal parameters.
6) Calculating and optimizing the inverse of the jersey matrix and the matrix of the negative log-likelihood function to obtain a posterior covariance matrix;
The jersey matrix (Hesian) of NLLF and the inverse of the matrix are calculated. For the inverse matrix of Hesian, the upper left 4×4 portion is the posterior covariance matrix of the parameters. Wherein:
The Hesian matrix is an (n+4) order square matrix, and the elements in the matrix are the second partial derivatives of the log likelihood function L about the parameters [ f, ζ, S, sigma 2, phi ] and can be derived by an analytic form;
The a posteriori covariance matrix of Hesian matrix is resolved as: where k j is the eigenvalue of the jersey matrix, and b j is the eigenvector corresponding to the eigenvalue k j.
7) Calculating a variation coefficient of the modal identification parameter according to the posterior covariance matrix;
8) And when the variation coefficient is within the set deviation, the identification parameter is considered to be effective, the most probable value corresponding to each parameter in the actually measured modal parameters of the structure is taken as the actually measured modal parameters of the structure, otherwise, the mode shape and the initial value of the mode frequency are reset, and the calculation is carried out again until the variation parameter is within the set deviation.
And calculating a coefficient of variation (COV) of the identification parameter according to the posterior covariance matrix, and quantifying the uncertainty of the identification result so as to evaluate the accuracy of the identification result. The smaller the COV value, the more accurate the parameter identification. If the COV value is in a reasonable range, the identification parameters are considered to be effective, and the MPV value of each corresponding parameter is taken as the actual measurement structural modal parameter.
S3, building a structure finite element model along with the engineering construction progress to obtain corresponding calculation model modal parameters;
And (3) building a structure finite element model according to the engineering real-time construction progress, wherein the model is completely built according to a design drawing and construction site conditions, and if the model is correspondingly simplified, a certain influence is generated on the structure safety evaluation result of the subsequent construction process. And then obtaining the calculated modal parameters (modal frequency and modal shape) of the structure through finite element simulation.
S4, evaluating the safety in the construction process according to the difference between the actually measured modal parameter of the structure and the modal parameter of the calculation model, and guiding the actual construction process;
comparing the actually measured modal parameters (modal frequency and modal shape) of the structure with the calculated modal parameters of the finite element model of the structure, correcting the model of the structure in the case of overlarge parameter phase difference (experience judgment), and positioning the structural parameters with problems according to the calculated parameters to be corrected so as to obtain the specific parts with problems of the structure and guide the subsequent repair process and the next construction.
S5, correcting the finite element model of the structure according to the integral vibration data of the structure after the structure construction is finished, and obtaining the mode parameters of the corrected finite element model;
And after the structure construction process is finished, uniformly carrying out vibration measurement on the whole structure, carrying out structural mode identification, and using the actually measured mode data of the whole structure in the correction process of the finite element model of the structure. In the finite element model building process, because a plurality of uncertain factors exist in the actual construction process, the influence of modeling errors between an actual structure and the finite element model is caused inevitably. In order to reduce the influence of modeling errors between an actual structure and a finite element model, a model correction method based on a Bayesian method is adopted, and model design parameters are corrected according to actual measurement structure modal data, so that the finite element model which is more in line with the actual parameters of the structure is obtained. The modified finite element model is used as a model under the condition that the structure is not damaged and is used for finding structural damage and evaluating the structural safety in the subsequent use operation stage.
The process of modifying the structural finite element model comprises the following steps:
1) Selecting elastic modulus and density parameters of vertical and transverse members in the structure as parameters to be corrected of the finite element model of the structure;
firstly, parameters with larger influence on structural states in a finite element model are selected as correction parameters, and parameters such as elastic modulus E, density rho and the like of vertical and transverse members are generally selected. For the subsequent clear indication of the change of the parameters before and after correction, the ratio of the value of the correction parameter to the initial value is used as the parameter to be corrected (if the elastic modulus is selected as the parameter to be corrected Superscript 1 represents the corrected parameter and superscript 0 represents the initial value).
2) Establishing an objective function between the parameter to be corrected and the actually measured sample based on a Bayesian formula;
Because the mode parameters among different modes of the structure are mutually independent, for the first n-order actual measurement modes, the likelihood function can be regarded as the product of the likelihood functions of all the actual measurement modes: Representing the i-th order measured modal frequency of the structure,/> Representing the i-th order measured mode shape of the structure. For the likelihood function of modal frequencies, there are:
For the likelihood function of the mode shape, there are:
thus, the likelihood function p (d|θ) can be further expressed as:
the function related to the parameter to be corrected in the equation is proposed as an objective function as follows:
the likelihood function may be further expressed as:
The constant term in the above formula is generally difficult to directly obtain, and the maximum value solving process in the above formula, namely the minimum value of the objective function J (theta), is known according to the maximum posterior criterion and the function property of the exponential function;
For practical engineering, the posterior distribution of the parameter θ to be corrected often has the characteristics of high dimensionality, nonstandard property and the like, and is generally solved by a Markov chain Monte Carlo sampling algorithm (MCMC). The method comprises the steps of obtaining a group of Markov chain samples with stable posterior distribution of parameters to be corrected through sampling, and estimating mathematical characteristics of the parameters to be corrected according to a sample point set. The process not only can obtain the Most Probable Value (MPV) of the parameter to be corrected, but also can quantitatively evaluate the uncertainty of the parameter through a coefficient of variation (COV). Where the coefficient of variation is expressed as the ratio of standard deviation to the most likely value.
3) Obtaining a Markov chain with parameters to be corrected as targets and posterior probability distribution as stable distribution by adopting an MCMC sampling algorithm based on the substep, and finally estimating the parameters to be corrected based on sample points generated by the final substep, thereby obtaining the most probable value of the parameters to be corrected;
referring to fig. 3 and 4 again, the method specifically includes the following steps:
sampling by 3.1MH to obtain an initial sub-step sample point;
3.2 the subsequent sub-step uses the kernel density estimate of its previous sub-step as a proposed distribution;
3.3, continuously reducing the sample space along with the continuous progress of the substeps, and finally converging to stable distribution;
and 3.4, estimating posterior distribution of the parameter to be corrected by the sample points generated in the final substep to obtain the most probable value and uncertainty of the parameter to be corrected.
4) Obtaining the most probable value of the parameter to be corrected based on the effective sample in the final smoothly distributed Markov chain and quantitatively discussing the uncertainty of the parameter to be corrected according to the posterior covariance matrix;
And calculating a coefficient of variation (COV) of the identification parameter according to the posterior covariance matrix, and quantifying the uncertainty of the identification result so as to evaluate the accuracy of the identification result. The smaller the COV value, the more accurate the parameter identification. And if the COV value is in a reasonable range, the identification parameters are considered to be effective, and the MPV value of each corresponding parameter is taken as the parameter value to be corrected.
5) And carrying the correction parameter value obtained by calculating the parameter to be corrected into a finite element model to be used as a finite element model after structural correction, and representing the model to an actual structure.
S6, periodically acquiring structural vibration data through data acquisition equipment in a structural operation stage, and analyzing to obtain structural actual measurement modal parameters in the operation stage;
And in the structure use operation stage, carrying out environmental vibration test on the structure at intervals, analyzing to obtain real-time state parameters of the structure, and gradually constructing a real-time structure parameter database of the structure use operation stage, wherein the real-time structure parameter database is used for grasping the real-time performance state of the structure and evaluating the real-time safety performance of the structure.
S7, comparing the structure actual measurement modal parameters in the operation stage with the corrected finite element model modal parameters, and timely finding out structural potential safety hazards according to the real-time state change of the structure to ensure the safety of the structure operation stage.
And (3) carrying out contrast analysis according to the structure real-time modal parameters in the step (6) and the corrected finite element model modal parameters in the step (5), evaluating the structure real-time state change according to the difference of the structure real-time modal parameters and the corrected finite element model modal parameters, grasping the structure real-time damage state according to the state change condition, evaluating the structure safety, and guiding the structure maintenance and subsequent damage repair work of the structure in the using operation stage. The specific comparison analysis process is the same as the step S4, and the calculation of the real-time structural modal parameters is the same as the specific structural modal identification process in the step S2.
The foregoing examples illustrate only a few embodiments of the invention and are described in detail herein without thereby limiting the scope of the invention. It should be noted that it will be apparent to those skilled in the art that several variations and modifications can be made without departing from the spirit of the invention, which are all within the scope of the invention. Accordingly, the scope of protection of the present invention is to be determined by the appended claims.
Claims (7)
1. The rapid structural health monitoring method for the whole stage of construction and operation of the assembled super high-rise structure is characterized by comprising the following steps of:
(1) Gradually installing data acquisition equipment along with the promotion of the construction progress of engineering projects;
(2) The data acquisition equipment acquires structural vibration data in the construction process, and structural actual measurement modal parameters in the construction stage are obtained through analysis;
(3) Building a structural finite element model along with the engineering construction progress to obtain corresponding calculation model modal parameters;
(4) According to the difference between the actually measured modal parameters of the structure and the modal parameters of the calculation model, evaluating the safety in the construction process and guiding the actual construction process;
(5) After the structure construction is finished, correcting the structure finite element model according to the whole vibration data of the structure, and obtaining the mode parameters of the corrected finite element model;
(6) Periodically acquiring structural vibration data through data acquisition equipment in a structural operation stage, and analyzing to obtain structural actual measurement modal parameters in the operation stage;
(7) And comparing the actually measured modal parameters of the structure in the operation stage with the corrected modal parameters of the finite element model, and timely finding out potential structural hazards according to the real-time state change of the structure to ensure the safety of the structure in the operation stage.
2. The rapid structural health monitoring method according to claim 1, wherein in step (1), said data acquisition device comprises a three-way acceleration sensor and a mating data analysis device, said three-way acceleration sensor being mounted on the structure with the progress of construction.
3. The rapid structural health monitoring method of claim 1, wherein said modal parameters comprise modal frequency and modal shape.
4. The method according to claim 2, wherein in the step (2), after the structural vibration data is collected, structural mode identification based on a bayesian fast fourier transform method is adopted to obtain structural actual measurement mode parameters of the construction stage.
5. The rapid structural health monitoring method according to claim 4, wherein said structural modality identification process comprises the steps of:
1) Obtaining structural acceleration response data by a three-way acceleration sensor;
2) Performing Bayesian fast FFT (fast Fourier transform) on the acceleration response data to obtain actual measurement frequency domain information of the structure;
3) Calculating a posterior probability density function of the modal parameter according to the actually measured frequency domain information;
4) Converting the posterior probability density function into an optimized negative log-likelihood function;
5) Setting an initial value of a structural modal parameter, and circularly optimizing a negative log likelihood function until convergence to obtain the most probable value of the structural actual measurement modal parameter;
6) Calculating and optimizing the inverse of the jersey matrix and the matrix of the negative log-likelihood function to obtain a posterior covariance matrix;
7) Calculating a variation coefficient of the modal identification parameter according to the posterior covariance matrix;
8) And when the variation coefficient is within the set deviation, the identification parameter is considered to be effective, the most probable value corresponding to each parameter in the actually measured modal parameters of the structure is taken as the actually measured modal parameters of the structure, otherwise, the mode shape and the initial value of the mode frequency are reset, and the calculation is carried out again until the variation parameter is within the set deviation.
6. The rapid structural health monitoring method according to claim 1, wherein in step (5), the process of modifying the structural finite element model comprises the steps of:
1) Selecting elastic modulus and density parameters of vertical and transverse members in the structure as parameters to be corrected of the finite element model of the structure;
2) Establishing an objective function between the parameter to be corrected and the actually measured sample based on a Bayesian formula;
3) Obtaining a Markov chain with parameters to be corrected as targets and posterior probability distribution as stable distribution by adopting an MCMC sampling algorithm based on the substep, and finally estimating the parameters to be corrected based on sample points generated by the final substep, thereby obtaining the most probable value of the parameters to be corrected;
4) Obtaining the most probable value of the parameter to be corrected based on the effective sample in the final smoothly distributed Markov chain and quantitatively discussing the uncertainty of the parameter to be corrected according to the posterior covariance matrix;
5) And carrying the correction parameter value obtained by calculating the parameter to be corrected into a finite element model to be used as a finite element model after structural correction.
7. The rapid structural health monitoring method according to claim 6, wherein said step 3) comprises the steps of:
1) The MH samples and obtains the sample point of the initial substep;
2) The subsequent sub-step takes the core density estimation of the previous sub-step as a suggested distribution;
3) With the continuous progress of the substeps, the sample space is continuously reduced, and finally the sample space is converged to stable distribution;
4) And estimating posterior distribution of the parameter to be corrected by the sample points generated in the final substep to obtain the most probable value and uncertainty of the parameter to be corrected.
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