CN110704801B - Bridge cluster structure operation safety intelligent monitoring and rapid detection complete method - Google Patents

Abstract

The invention belongs to the field of bridge structure operation safety monitoring and detection, and particularly relates to a bridge cluster structure operation safety intelligent monitoring and rapid detection complete set method, which mainly comprises four contents: firstly, a bridge cluster monitoring system building technology based on high-measurement-point density sensing equipment; secondly, a bridge structure damage diagnosis method based on temperature-damage stripping; thirdly, a bridge structure safety rapid detection technology based on the deflection influence surface; and fourthly, a bridge cluster structure damage diagnosis method based on the deflection influence surface. The first two contents belong to bridge intelligent monitoring technology and are applied to bridge structure operation safety diagnosis with a monitoring system in a cluster; the latter two contents belong to a bridge rapid detection technology and are applied to bridge structure safety diagnosis without a monitoring system in a cluster. The method combines the bridge structure operation safety monitoring and the rapid detection technology, and the proposed set of method is suitable for the operation safety diagnosis of all bridge structures in the bridge cluster.

Description

Bridge cluster structure operation safety intelligent monitoring and rapid detection complete method

Technical Field

The invention belongs to the field of monitoring and detecting the operation safety of bridge structures, and particularly relates to a set of intelligent monitoring and rapid detecting method for the operation safety of a bridge cluster structure.

Background

The bridge cluster is as follows: the bridge group is composed of a plurality of bridges in a regional road network or an urban loop. Different from a single large bridge, the cluster data not only contains monitoring information of a bridge with a monitoring system, but also contains detection information of a bridge without the monitoring system, and the data types are multiple; the non-similar bridge structures in the cluster need to be subjected to targeted data processing one by one, and the workload is large; data among similar bridges in the cluster have relevance and strong mineability. Therefore, how to effectively utilize cluster data characteristics to realize damage diagnosis of all bridges is a very challenging problem in the health monitoring field.

In addition, the following problems still exist in the monitoring and detection technology for guaranteeing the operation safety of the bridge cluster structure: firstly, the existing bridge structure health monitoring mainly adopts a point type sensor technology, measuring points of the sensors are distributed discontinuously in space, networking is difficult, and the possibility of missing measurement at key parts of the structure exists; secondly, the operation bridge is often subjected to the coupling effect of various factors such as environmental temperature difference change, material aging and load effect, the influence of the environmental factors on the response monitoring data of the bridge structure cannot be ignored, and the influence often covers the monitoring data change caused by the damage of the bridge structure; thirdly, the existing bridge structure detection technology usually needs to seal traffic for a long time, and has complex test procedures and high test detection cost. For the above problems, on one hand, a distributed sensing technology with a large range, a long distance and a long holding time needs to be combined with a traditional point type sensing technology to jointly construct a bridge cluster structure operation safety monitoring system; on the other hand, a bridge cluster structure detection technology for quickly loading and quickly acquiring structural response is urgently needed, and quick detection and diagnosis of damage of the bridge cluster structure are realized under the condition of closing traffic in a short time.

Disclosure of Invention

The invention aims to provide a complete set of method for intelligently monitoring and quickly detecting the operation safety of a bridge cluster structure in order to realize damage diagnosis of all bridge structures in a bridge cluster.

The intelligent monitoring and rapid detection method for the operation safety of the bridge cluster structure comprises the following steps:

the content one is as follows: the method comprises the steps of building a bridge cluster monitoring system based on high-measurement-point density sensing equipment, wherein the monitoring system is built on a partial bridge in a bridge cluster, and the building of the monitoring system comprises the layout of sensing units based on the high-measurement-point density sensing equipment, the integration of a high-measurement-point density data acquisition and transmission module, the building of a bridge cluster structure safety diagnosis and early warning subsystem, the building of a bridge cluster structure high-measurement-point density central database subsystem and the building of a bridge cluster structure safety monitoring system remote management user subsystem;

wherein:

the sensing unit based on the high-measuring-point density sensing equipment comprises a point type electric signal mechanical sensor, a distributed Brillouin optical fiber sensor and a bridge dynamic weighing system; the point-type electric signal mechanical sensor is mainly arranged on the most unfavorable stress section and the key stress section of the bridge superstructure; the distributed Brillouin optical fiber sensors are arranged along the upper structure and the lower structure of the bridge in a through-length mode, and an optical signal transmission loop is formed; the bridge dynamic weighing system is arranged at the bridge head;

the acquisition and transmission of data of the point-type electric signal mechanical sensor in the high-measuring-point density data acquisition and transmission module adopts a radial sub-network form to acquire the data in an electric signal acquisition system, convert monitoring point signals into standard Ethernet and wireless network digital signals and remotely transmit the standard Ethernet and wireless network digital signals to a server; the distributed optical fiber sensing data in the high-measuring-point density data acquisition and transmission module is acquired and transmitted in a loop mode by adopting an optical fiber signal demodulator, a plurality of optical fiber monitoring loops can be acquired simultaneously, and Brillouin frequency shift data of optical signals are transmitted to a server remotely;

the bridge cluster structure safety diagnosis and early warning subsystem comprises the steps of monitoring data preprocessing, modal analysis, structure damage diagnosis, a multi-level real-time early warning mechanism and structure safety assessment;

the high-measurement-point density center database subsystem of the bridge cluster structure comprises a structural parameter database, an online monitoring data database and an offline processing data database, and is used for finishing filing, inquiring, storing, managing and calling all monitoring/detecting static and dynamic data, information and data in the full life cycle of the bridge;

the remote management user subsystem of the bridge cluster structure safety monitoring system is connected with a database interface, displays various static and dynamic monitoring/detection data, information and data of the whole life cycle of the bridge structure to different users according to user requirements in a classified and graded mode and authorization, and receives control and input of different users to the system according to authorization, so that the functions of remote system management, data analysis and evaluation and data query and statistics are realized;

and II, content II: on the basis of a bridge cluster monitoring system built in the content I, carrying out bridge structure damage diagnosis based on temperature-damage stripping on a bridge with the monitoring system in the cluster;

and thirdly: carrying out a bridge quasi-static load test on a bridge structure without a monitoring system in a bridge cluster, arranging a deflection tester, obtaining deflection influence lines, and forming influence surfaces of a test section under the action of quasi-static load by the influence lines;

and IV, content: and carrying out bridge cluster structure damage diagnosis based on the deflection influence surface on the bridge without the monitoring system in the cluster by using the deflection influence line obtained by the content three detection.

Furthermore, in the first embodiment, the point-type electrical signal mechanical sensor includes one or more of a point-type strain sensor, a deflection sensor, a vibration acceleration sensor, a cable force sensor, an anemoscope, and an ambient temperature and humidity sensor.

Further, in the first content, the distributed brillouin optical fiber sensor adopts a differential pulse pair technology, the highest measuring point density reaches 2cm and one measuring point is arranged at intervals, and the longest monitoring distance reaches 120 km.

Further, in the first content, the preprocessing of the monitoring data includes denoising, filtering, de-trending, and FFT transformation; the modal analysis comprises modal parameter identification and model modification.

Further, the second content specifically includes the following steps:

s21: acquiring a bridge structure strain monitoring data set matrix X, and if n strain sensor monitoring points exist in the monitoring system and the sampling point number of each monitoring point is m, acquiring a structure strain monitoring data matrix X

The definition of (A) is as follows,

X＝[X₁,X₂,…,X_i,…,X_n]_m×n,i∈(1,2,…,n) (1)

s22: constructing a structural strain monitoring data covariance matrix X^TX, recording as a matrix A; singular value decomposition is performed on the construction matrix A to obtain a matrix decomposition result as follows,

A＝US_nV^T(2)

in the formula, U is a left singular vector matrix of the matrix A; v is a right singular vector matrix of the matrix A; v^TIs the transpose of matrix V; s_nIs a diagonal matrix formed by singular values of the matrix A in descending order, and is defined as shown in the following formula,

S_n＝diag(δ₁,δ₂,…,δ_i,…,δ_n),i∈(1,2,…,n) (3)

s23: arranging the singular values from big to small, replacing the smaller singular value representing the test noise by a 0 value, selecting the first r non-zero singular values and the corresponding left and right singular value matrixes thereof, and reconstructing the matrix A to further obtain a reconstruction matrix for weakening noise interference

Of formula (II) S'_nSingular values of a reconstruction matrix A'; s_rForming a diagonal matrix after reducing the rank of the singular value of the reconstruction matrix A';

s24: calculating a reconstructed structural strain monitoring data set X', and effectively improving the signal-to-noise ratio of original structural strain monitoring data;

X′＝(X^T)^-*A′ (5)

in the formula (X)^T)^-*Is a pair matrix X^TThe pseudo-inverse is calculated out,

s25: the strain monitoring data after being denoised is processed by centralization, namely the strain monitoring data is calculated by adopting the following formula,

in the formula (I), the compound is shown in the specification,

strain monitoring data column vector X 'for ith monitoring point after noise interference attenuation'_iThe mean vector of (2);

monitoring data column vector T for ambient temperature of ith monitoring point_iThe mean vector of (2); x'_c,iStrain monitoring data column vectors after centralization; t is_c,iA centralized ambient temperature data column vector;

s26: the centralized monitoring data is used for constructing a matrix R by adopting the following formula,

R＝[ωT_c,iX′_c,i]_m×2(8)

where ω is a weight value of the structural matrix R, and the value of ω needs to be relatively large, so that ω T_c,iVariance of is far greater than X'_c,iSo that the primary axis direction during PCA decomposition represents the ambient temperature load and the secondary axis direction represents the structural strain response;

s27: constructing a covariance matrix C of the matrix R, performing eigenvalue decomposition on the covariance matrix C,

C＝PΛP^T(9)

the eigenvector matrix P is also the projection vector matrix of the construction matrix R, with which the jth component of the construction matrix R is Y_j，

Y_j＝RP_j(10)

In the formula, P_jIs the jth column vector of the feature vector matrix P; y is_jTo construct the jth principal component of the matrix R. Column vector Y₁Is the first principal component of the construction matrix R, which represents the omega-fold expanded ambient temperature monitoring data, Y₁And Y₂Are orthogonal to each other, Y₂A traffic load trend term representing the strain response monitoring data from which the ambient temperature trend is removed; for newly-built bridges, structural strain monitoring data Y₂The method comprises two parts, namely strain monitoring data under the unstable state of traffic load and under the stable state of traffic load;

s28: for matrix

PCA projection is carried out to obtain a matrix after projection as Y,

Y＝[Y₁Y₂]＝[y₁,y₂,…,y_p,…,y_m]^T,p∈(1,2,…,m) (11)

in the formula, y_pIs the p-th vector of the projection matrix Y. Defining a central point set in a K-mean clustering analysis algorithm as omega ═ c₁c₂]^TVector of center point c₁，

In the formula, N₁Is the 1 st class omega₁The number of samples of (a); n is a radical of₂Is the 2 nd class omega₂The number of samples of (a); by the same token, the vector c of the center point can be determined₂；

S29: adopting an optimization solving mode to perform cluster division on each element in the matrix Y, taking the class 1 as an example, constructing the following optimization objective function,

where dist (y)_p,c₁) Is the p-th element Y of the matrix Y_pVector to center point c₁The Euclidean distance of (c);

s210: solving an objective function formula by using a centroid selection method to respectively obtain the 1 st class omega₁And class 2 Ω₂(ii) a Comparing the monitoring time corresponding to each element in the two categories, the category of the time series before is judged to be unsteady data, the category of the time series after is judged to be a steady data reference model,

in the formula (I), the compound is shown in the specification,

is X'_iSteady state monitoring data of;

is X'_iIs the phi element, phi e (m-N)₂+1,…,m)；

On the basis, g strain monitoring points of the bridge structure section are collected to obtain a steady-state data set at any phi moment

S211: taking a certain section of the bridge structure as a unit, performing damage diagnosis by using monitoring data of g structural strains of the section, and defining a damage diagnosis factor gamma based on a steady-state data reference model at phi moments_φCan be obtained by the same wayInjury diagnostic factor gamma in the state to be diagnosed_d，

In the formula, C_GSteady state data set

The covariance matrix of (a) is determined,

is composed of

Is a matrix of

Phi-th row vector of (1); further obtaining a damage diagnosis factor vector gamma based on a steady-state data reference model_φ，

S212: γ from step S211_φA structural damage diagnostic threshold η is defined,

η＝β·γ_φ，0.95(19)

wherein β is a guarantee coefficient, which is determined according to the monitoring data of the specific bridge structure, and is usually 1.2;. gamma.)_φ，0.95Diagnosis of factor vector gamma for structural damage_φTaking the median of 95% confidence probability;

s213: after the damage diagnosis threshold value of the reference state and the damage diagnosis factor of the state to be diagnosed are respectively obtained, the damage discrimination factor of the bridge structure can be obtained,

in the formula, Z_dWhen the value of the bridge structure damage discrimination factor is 1, marking the bridge structure damage as 1; when the value is 0, the bridge structure is healthy.

Further, the third content comprises the following steps:

s31: calculating the load efficiency lambda of the bridge quasi-static load test to be between 0.85 and 1.05

In the formula, S_aThe maximum calculation effect value of the internal force or displacement of the loading control section corresponding to a certain loading test working condition under the action of test load; s is corresponding to S under the action of control load_aThe calculated worst effect of the force or displacement within the same loading control section is calculated.

S32: determining the weight and the number of the loaded vehicles: firstly, determining the weight and the number of the loaded vehicles by referring to the test load calculated in the step S31 under the loading efficiency; on this basis, the reference weight is adjusted to the final test load by evaluating the actual technical condition of the bridge.

S33: determination of loading vehicle speed: selecting the speed of the loading vehicle according to the fundamental frequency of different bridge structures; in order to ensure that the excitation frequency of the loading vehicle is lower than the basic frequency of the bridge structure, the speed of the loading vehicle is controlled within the range of 5 km/h-10 km/h.

S34: the vehicle weight, the axle weight, the wheel base and the wheel weight of a loaded vehicle are recorded in detail before the bridge quasi-static load test, and a roadster route is planned.

S35: arranging a deflection tester, wherein each girder is longitudinally provided with three testing sections, the measuring points of the testing sections are transversely arranged to fully reflect the transverse deflection distribution characteristics of the bridge, the measuring points of the integral section are transversely arranged to be not less than 3, and the measuring points of the multi-beam section are transversely arranged one by one.

S36: and after the contact type or non-contact type deflection tester is installed, debugging the system, and carrying out stable observation for not less than 15 minutes.

S37: under the condition of closed traffic, slowly passing a test loading vehicle at a constant speed along the longitudinal direction of the bridge, keeping the same speed and performing repeated tests along the same loading lane for multiple times to obtain a test deflection influence line; and the deflection influence lines jointly form an influence surface of the test section under the action of the quasi-static load.

Further, in step S35, the deflection tester is a contact deflection tester or a non-contact deflection tester; when the contact type deflection tester is adopted, the connection mode of the contact type deflection tester and the bridge is determined according to the field conditions, the contact type deflection tester is ensured to be tightly connected with the bridge structure, the length of the cable is controlled at the same time, and the signal precision is prevented from being interfered due to overlarge resistance of the cable; when the non-contact deflection tester is adopted, the firm and reliable inspection work of the non-contact deflection tester support is carried out.

Further, the fourth content comprises the following steps:

s41: when one deflection influence line acquisition test is carried out on one bridge, all data can form an m multiplied by n matrix Y, the expression is as follows,

in the formula, m is the number of measuring points of each influence line; n is the number of sensors.

S42, calculating the included angle between two displacement influence lines according to a certain determined cross section of the bridge, and taking any two column vectors and calculating the included angle β of the two vectors_i

In the formula, ω_jIs any column vector in the matrix Y, j belongs to (1,2, …, n); omega_kFor any column vector in matrix Y, k is equal to (1,2, …, n), i | | · | | is the vector norm, β_iAssembly, a matrix β is obtained,

β＝sort([β₁,β₂,…,β_i,…,β_s]) (24)

wherein sort (. cndot.) is arranged from small to large, and s is an included angle β_iThe number of (2).

S43, arranging the vectors β according to the form of a Hankel matrix, and further constructing an H matrix

Wherein p is the number of lines of the Hankel matrix, q is the number of columns of the Hankel matrix (p < q), the number s of elements forming the vector β has the following relationship with the parameters p and q of the Hankel matrix

s＝p+q-1 (26)

S44, taking the test data as the reference state once under the bridge health condition, and under the reference state, forming a Hankel matrix H by a vector β_rCalculating H_rOf the null-space matrix N_r

N_r＝column(null(H_r)) (27)

Where null (-) is any column of null space of the orientation quantity; column (·) is any column of the matrix.

S45, taking another M test results, recording Hankel matrix H formed by vector β in the c test_cBy N_rAs a comparison to the reference state, each trial was multiplied by H at the reference state_rRight null space N_rTo obtain a residual vector α_c

α_c＝H_cN_r,(α_c∈R^p×1,c∈(1,2,…,M)) (28)

S46: taking the norm of the residual error in step S45 as the structural damage diagnosis factor of the c test

Under the reference state of the bridge structure, counting a plurality of times (assumed to be K times of tests) of different test residual vectorsα_cThe average value of (a) of (b),

s47: establishing a bridge structure damage diagnosis factor gamma 'based on zero space by using Mahalanobis distance'_c

Where Θ is the residual vector α_cCovariance matrix of

S48: calculating a bridge structure damage diagnosis threshold lambda 'under a reference state'

λ′＝[γ′₁,γ′₂,…,γ′_c,…,γ′_K]_0.95(33)

In the formula [ ·]_0.95Vector of structural damage diagnosis factor [ gamma'₁,γ′₂,…,γ′_c,…,γ′_K95% confidence probability value of.

S49 residual vector α for the d-th bridge loading test under the state to be diagnosed_dAnd calculating to obtain the structural damage diagnosis factor,

s410: through comparison of gamma'_dAnd judging whether the bridge generates structural damage or not according to the value of the lambda'.

The invention has the beneficial effects that:

the invention relates to a bridge cluster structure operation safety intelligent monitoring and rapid detection complete set method, which builds a monitoring system for a part of bridges in a cluster, thereby intelligently monitoring bridges with monitoring systems in the cluster, and in the aspect of intelligent monitoring, combines a high measuring point density optical fiber sensing technology with large range, long distance and long duration with a traditional point type sensing technology, overcomes the defects of discontinuous arrangement of measuring points on a space and missing measurement, and realizes that the highest measuring point density of a bridge structure reaches 2cm and the longest monitoring distance reaches 120 km. Meanwhile, on the basis of the built monitoring system, a bridge structure damage diagnosis method based on temperature-damage stripping is adopted, the influence of noise on the bridge structure strain monitoring data is effectively weakened, the traffic load trend item in the bridge structure strain monitoring data is successfully extracted, and on the basis, the monitoring data are accurately classified into two categories, namely a stable state and an unstable state through K-mean cluster analysis. Compared with the traditional method, the provided damage diagnosis factor is more sensitive to damage, can effectively diagnose the damage of the bridge structure, and does not generate misjudgment on the health state of the bridge structure.

The method for rapidly detecting the bridge without the monitoring system in the cluster is adopted, and in the aspect of rapid detection, the problem that the conventional bridge structure damage detection method needs to seal traffic for a long time is solved through the provided technology for rapidly detecting the bridge structure without the monitoring system in the cluster based on the deflection influence surface, and the collection and analysis of the bridge cluster structure loading response information are realized under the condition of sealing traffic for a short time. The bridge cluster structure damage diagnosis method based on the deflection influence surface has good robustness and better anti-noise capability compared with the traditional method.

The invention realizes the damage diagnosis of all bridges in the bridge cluster by using a complete set of method combining intelligent monitoring and rapid detection.

Drawings

FIG. 1 is a block diagram of the present invention.

Fig. 2 is a schematic diagram of a third embodiment of the present disclosure.

Fig. 3 is a layout diagram of two three-span prestressed continuous box girder bridge strain sensors in a bridge cluster monitoring system selected in the first embodiment of the present invention.

FIG. 4 shows the centralized processing result of the temperature monitoring data of the #8 measuring point structure in the first embodiment of the present invention.

FIG. 5 shows the centralized processing result of the denoised structural strain monitoring data of the #8 measuring point in the first embodiment of the present invention.

FIG. 6 is a structural strain monitoring data of point #8 with environmental temperature effect removed according to a first embodiment of the present invention.

FIG. 7 shows the cluster analysis result of #8 station monitoring data in the first embodiment of the present invention.

Fig. 8 shows the damage diagnosis results of the monitoring points #6 to #10 in the first embodiment of the present invention by the algorithm of the present invention with the monitoring data before the vehicle is not on as the reference data.

Fig. 9 shows the damage diagnosis results of the #6 to #10 monitoring points in the first embodiment of the present invention by using the monitoring data before the vehicle is not on as the reference data through the raw data.

FIG. 10 shows the results of the lesion diagnosis by the algorithm of the present invention using the steady state data as the reference data for the #6- #10 monitoring points in the first embodiment of the present invention.

FIG. 11 is a schematic diagram of a finite element numerical model of a concrete simply supported beam according to a second embodiment of the present invention.

FIG. 12 is a graph showing the results of diagnosis of lesions in the second embodiment of the present invention.

Detailed Description

The invention provides a bridge cluster structure operation safety intelligent monitoring and rapid detection complete set of method aiming at the problem of how to realize the intelligent monitoring and rapid detection of the operation safety of all bridge structures in a cluster. The invention combines the intelligent bridge monitoring technology with the rapid detection technology. Firstly, a bridge cluster monitoring system construction technology based on high-point density sensing equipment is provided, and the defects of a traditional point type sensing system are overcome. And then, a bridge structure damage diagnosis method based on temperature-damage stripping is provided on the basis, denoising of bridge structure strain monitoring data is completed by using a matrix singular value decomposition and reconstruction technology, traffic load trend items of the strain monitoring data are further extracted, damage diagnosis of a bridge with a monitoring system in a bridge cluster structure is realized, and the influence of operation environment change on a data reference model is effectively eliminated. Meanwhile, a rapid detection technology of the bridge structure without the monitoring system in the cluster based on the deflection influence surface is provided, and the defect that the conventional bridge structure damage detection method needs to close traffic for a long time is overcome. And finally, by utilizing a matrix zero space theory, providing a bridge cluster structure damage diagnosis method based on the deflection influence surface, and having good robustness and noise resistance. And finally, the damage diagnosis of all bridge structures in the cluster is realized.

The intelligent monitoring and rapid detection method for the operation safety of the bridge cluster structure comprises the following steps as shown in the attached figure 1:

the method mainly comprises two parts, namely, the construction of a monitoring system and the damage diagnosis of a structure are carried out aiming at a bridge which can establish the monitoring system in a bridge cluster; and secondly, carrying out detection test and structural damage diagnosis on the bridge without the monitoring system in the bridge cluster.

The first major part is to perform damage diagnosis on a bridge structure in which a monitoring system can be established in a bridge cluster, and the method comprises the following two aspects:

the content one is as follows: a bridge cluster monitoring system construction technology based on high-measurement-point density sensing equipment;

and II, content II: a bridge structure damage diagnosis method based on temperature-damage stripping.

The second most part is to carry out damage diagnosis on a bridge structure without a monitoring system in a bridge cluster, and the damage diagnosis comprises the following two aspects:

and thirdly: a bridge structure safety rapid detection technology based on a deflection influence surface;

and IV, content: a bridge cluster structure damage diagnosis method based on a deflection influence surface is disclosed.

The following is detailed by way of example:

for the first most part, firstly, a bridge cluster monitoring system of high-measurement-point density sensing equipment is built, and the method specifically comprises the following steps:

s1: bridge cluster monitoring system construction based on high-measurement-point density sensing equipment

The monitoring system is only built on a part of bridges by considering actual and cost factors, and the monitoring system is arranged in key bridges in the bridge cluster, such as higher bridges, cross-road bridges, cross-railway bridges, cross-river bridges and the like, and common bridges can also be partially installed.

The bridge cluster monitoring system of the invention is constructed by the following steps: the method comprises the steps of arranging sensing units based on high-measurement-point density sensing equipment, integrating high-measurement-point density data acquisition and transmission modules, building a bridge cluster structure safety diagnosis and early warning subsystem, building a high-measurement-point density center database subsystem of the bridge cluster structure, and building a remote management user subsystem of the bridge cluster structure safety monitoring system.

The sensing unit based on the high-measuring-point density sensing equipment mainly comprises a point type electric signal mechanical sensor, a distributed Brillouin optical fiber sensor and a bridge dynamic weighing system.

The point type electric signal mechanical sensors are mainly arranged on the most unfavorable stress section and the key stress section of the bridge superstructure, and include but are not limited to point type strain sensors, deflection sensors, vibration acceleration sensors, cable force sensors, anemometers, environment temperature and humidity sensors and the like.

The distributed Brillouin optical fiber sensors are arranged along the upper structure and the lower structure of the bridge in a through manner to form an optical signal transmission loop, the distributed Brillouin optical fiber sensors are based on a differential pulse pair technology (DPP-BOTDA), the density of the highest measuring points can reach 2cm at intervals of one measuring point, and the longest monitoring distance can reach 120 km.

The bridge dynamic weighing system is arranged at the bridge head.

The acquisition and transmission of data of the point type electric signal mechanical sensor in the high-measuring-point density data acquisition and transmission module adopts a radial sub-network form to acquire the data in an electric signal acquisition system, convert monitoring point signals into standard Ethernet and wireless network digital signals and remotely transmit the standard Ethernet and wireless network digital signals to a server; the acquisition and transmission of distributed optical fiber sensing data in the high-measurement-point density data acquisition and transmission module are loop acquisition by adopting an optical fiber signal demodulator, a plurality of optical fiber monitoring loops can be acquired simultaneously, and Brillouin frequency shift data of optical signals are remotely transmitted to a server.

The bridge cluster structure safety diagnosis and early warning subsystem mainly comprises but is not limited to preprocessing (denoising, filtering, trend removing, FFT (fast Fourier transform) and the like) of monitoring data, modal analysis (modal parameter identification, model correction and the like), structure damage diagnosis, a multi-level real-time early warning mechanism and structure safety assessment.

The high-measurement-point density central database subsystem of the bridge cluster structure completes the filing, query, storage, management and call of all monitoring (detection) static and dynamic data, information and data of the whole life cycle of the bridge. The system mainly comprises a structural parameter database, an online monitoring data database and an offline processing data database.

The remote management user subsystem of the bridge cluster structure safety monitoring system is connected with a database interface, displays static and dynamic data, information and data of various monitoring (detection) of the whole life cycle of the bridge structure to different users according to user requirements in a classified and graded mode and receives control and input of the system by different users according to authorization. The functions of remote system management, data analysis and evaluation, data query and statistics and the like are realized.

S2: bridge structure damage diagnosis based on temperature-damage stripping

On the basis of the built monitoring system, the bridge structure damage diagnosis based on temperature-damage stripping is carried out on the bridge with the monitoring system built in the bridge cluster by utilizing the monitoring data obtained by the monitoring system, and the method specifically comprises the following steps:

s21: acquiring a bridge structure strain monitoring data set matrix X, and if n strain sensor monitoring points exist in the monitoring system and the sampling point number of each monitoring point is m, acquiring a structure strain monitoring data matrix X

The definition of (A) is as follows,

X＝[X₁,X₂,…,X_i,…,X_n]_m×n,i∈(1,2,…,n) (1)

s22: constructing a structural strain monitoring data covariance matrix X^TX, is denoted as matrix A. Singular value decomposition is performed on the construction matrix A to obtain a matrix decomposition result of the following formula，

A＝US_nV^T(2)

In the formula, U is a left singular vector matrix of the matrix A; v is a right singular vector matrix of the matrix A; v^TIs the transpose of matrix V; s_nIs a diagonal matrix formed by singular values of the matrix A in descending order, and is defined as shown in the following formula,

S_n＝diag(δ₁,δ₂,…,δ_i,…,δ_n),i∈(1,2,…,n) (3)

s23: arranging the singular values from big to small, replacing the smaller singular value representing the test noise by a 0 value, selecting the first r non-zero singular values and the corresponding left and right singular value matrixes thereof, and reconstructing the matrix A to further obtain a reconstruction matrix for weakening noise interference

Of formula (II) S'_nSingular values of a reconstruction matrix A'; s_rAnd reducing the rank of the singular value of the reconstruction matrix A' to form a diagonal matrix.

S24: and calculating a reconstructed structural strain monitoring data set X', and effectively improving the signal-to-noise ratio of the original structural strain monitoring data.

X′＝(X^T)^-*A′ (5)

In the formula (X)^T)^-*Is a pair matrix X^TThe pseudo-inverse is calculated out,

s25: the strain monitoring data after being denoised is processed by centralization, namely the strain monitoring data is calculated by adopting the following formula,

in the formula (I), the compound is shown in the specification,

strain monitoring data column vector X after noise interference attenuation for ith monitoring point_i' the mean vector;

monitoring data column vector T for ambient temperature of ith monitoring point_iThe mean vector of (2); x'_c,iStrain monitoring data column vectors after centralization; t is_c,iIs a column vector of centered ambient temperature data.

S26: the centralized monitoring data is used for constructing a matrix R by adopting the following formula,

R＝[ωT_c,iX′_c,i]_n×2(8)

where ω is a weight value of the structural matrix R, and the value of ω needs to be relatively large, so that ω T_c,iVariance of is far greater than X'_c,iSuch that the primary axis direction at PCA decomposition represents ambient temperature loading and the secondary axis direction represents structural strain response.

S27: constructing a covariance matrix C of the matrix R, performing eigenvalue decomposition on the covariance matrix C,

C＝PΛP^T(9)

the eigenvector matrix P is also the projection vector matrix of the construction matrix R, with which the jth component of the construction matrix R is Y_j，

Y_j＝RP_j(10)

In the formula, P_jIs the jth column vector of the feature vector matrix P; y is_jTo construct the jth principal component of the matrix R. Column vector Y₁Is the first principal component of the construction matrix R, which represents the omega-fold expanded ambient temperature monitoring data, Y₁And Y₂Are orthogonal to each other, Y₂Strain response monitoring number representing removed ambient temperature trendAccording to the traffic load trend. For newly-built bridges, structural strain monitoring data Y₂The method comprises two parts, namely strain monitoring data under the unstable state of traffic load and under the stable state of traffic load.

S28: for matrix

PCA projection is carried out to obtain a matrix after projection as Y,

Y＝[Y₁Y₂]＝[y₁,y₂,…,y_p,…,y_m]^T,p∈(1,2,…,m) (11)

in the formula, y_pIs the p-th vector of the projection matrix Y. Defining a central point set in a K-mean clustering analysis algorithm as omega ═ c₁c₂]^TVector of center point c₁，

In the formula, N₁Is the 1 st class omega₁The number of samples of (a); n is a radical of₂Is the 2 nd class omega₂The number of samples. By the same token, the vector c of the center point can be determined₂。

S29: adopting an optimization solving mode to perform cluster division on each element in the matrix Y, taking the class 1 as an example, constructing the following optimization objective function,

where dist (y)_p,c₁) Is the p-th element Y of the matrix Y_pVector to center point c₁The euclidean distance of (c).

S210: solving an objective function formula by using a centroid selection method to respectively obtain the 1 st class omega₁And class 2 Ω₂. Comparing the monitoring time corresponding to each element in the two categories, the category of the time series before is judged to be unsteady data, the category of the time series after is judged to be a steady data reference model,

in the formula (I), the compound is shown in the specification,

is X'_iSteady state monitoring data of;

is X'_iIs the phi element, phi e (m-N)₂+1,…,m)。

On the basis, g strain monitoring points of the bridge structure section are collected to obtain a steady-state data set at any phi moment

S211: taking a certain section of the bridge structure as a unit, performing damage diagnosis by using monitoring data of g structural strains of the section, and defining a damage diagnosis factor gamma based on a steady-state data reference model at phi moments_φSimilarly, the damage diagnostic factor gamma in the state to be diagnosed can be obtained_d，

In the formula, C_GSteady state data set

The covariance matrix of (a) is determined,

is composed of

Is a matrix of

Phi-th row vector. Further obtaining a damage diagnosis factor vector gamma based on a steady-state data reference model_φ，

S212: γ from step S211_φA structural damage diagnostic threshold η is defined,

η＝β·γ_φ，0.95(19)

wherein β is a guarantee coefficient, which is determined according to the monitoring data of the specific bridge structure, and is usually 1.2;. gamma.)_φ，0.95Diagnosis of factor vector gamma for structural damage_φThe median of the 95% confidence probabilities was taken.

S213: after the damage diagnosis threshold value of the reference state and the damage diagnosis factor of the state to be diagnosed are respectively obtained, the damage discrimination factor of the bridge structure can be obtained,

in the formula, Z_dWhen the value of the bridge structure damage discrimination factor is 1, marking the bridge structure damage as 1; when the value is 0, the bridge structure is healthy.

The following introduces a damage diagnosis for a bridge structure without a monitoring system in a bridge cluster, wherein the technology for rapidly detecting the bridge structure without the monitoring system in the cluster based on a deflection influence surface comprises the following specific steps:

s31: calculating the load efficiency lambda of the bridge quasi-static load test to be between 0.85 and 1.05

In the formula, S_aThe maximum calculation effect value of the internal force or displacement of the loading control section corresponding to a certain loading test working condition under the action of test load; s is corresponding to S under the action of control load_aThe calculated worst effect of the force or displacement within the same loading control section is calculated.

S32: determining the weight and the number of the loaded vehicles: firstly, determining the weight and the number of the loaded vehicles by referring to the test load calculated in the step S31 under the loading efficiency; on this basis, the reference weight is adjusted to the final test load by evaluating the actual technical condition of the bridge.

S33: determination of loading vehicle speed: selecting the speed of the loading vehicle according to the fundamental frequency of different bridge structures; in order to ensure that the excitation frequency of the loading vehicle is lower than the basic frequency of the bridge structure, the speed of the loading vehicle is controlled within the range of 5 km/h-10 km/h.

S34: the vehicle weight, the axle weight, the wheel base and the wheel weight of a loaded vehicle are recorded in detail before the bridge quasi-static load test, and a roadster route is planned.

S35: the contact type or non-contact type deflection tester is arranged, three testing sections are arranged on each girder along the longitudinal direction, the transverse arrangement of the measuring points of the testing sections fully reflects the transverse deflection distribution characteristics of the bridge, the transverse arrangement of the measuring points of the integral type section is not less than 3, and the transverse arrangement of the measuring points of the multi-girder type (separated type) section is preferably arranged piece by piece for measuring the vertical deflection of the bridge structure.

When the contact type deflection tester is adopted, the connection mode of the contact type deflection tester and the bridge is determined according to the field conditions, the contact type deflection tester is ensured to be tightly connected with the bridge structure, the length of the cable is controlled at the same time, and the signal precision is prevented from being interfered due to overlarge resistance of the cable; when the non-contact deflection tester is adopted, the firm and reliable inspection work of the non-contact deflection tester support is carried out.

S36: and after the contact type or non-contact type deflection tester is installed, debugging the system, and carrying out stable observation for not less than 15 minutes.

S37: and under the condition of closed traffic, slowly passing the test loading vehicle along the longitudinal direction of the bridge at a constant speed. And keeping the same vehicle speed to perform repeated tests along the same loading lane for multiple times so as to obtain a reliable test deflection influence line.

For an actual bridge, considering the transverse distribution of load, each test section of the bridge superstructure has a plurality of deflection influence lines. The test section consists of a plurality of beams, each beam is tested to obtain an influence line, and the influence lines jointly form an influence surface of the test section under the action of quasi-static load.

And performing damage diagnosis by using the deflection influence line obtained by the detection, wherein the concrete steps of the damage diagnosis of the bridge cluster structure based on the deflection influence surface in the fourth content are as follows:

s41: when one deflection influence line acquisition test is carried out on one bridge, all data can form an m multiplied by n matrix Y, the expression is as follows,

in the formula, m is the number of measuring points of each influence line; n is the number of sensors.

S42, calculating the included angle between two displacement influence lines according to a certain determined cross section of the bridge, and taking any two column vectors and calculating the included angle β of the two vectors_i

In the formula, ω_jIs any column vector in the matrix Y, j belongs to (1,2, …, n); omega_kFor any column vector in matrix Y, k is equal to (1,2, …, n), i | | · | | is the vector norm, β_iAssembly, a matrix β is obtained,

β＝sort([β₁,β₂,…,β_i,…,β_s]) (24)

wherein sort (. cndot.) is arranged from small to large, and s is an included angle β_iThe number of (2).

S43, arranging the vectors β according to the form of a Hankel matrix, and further constructing an H matrix

Wherein p is the number of lines of the Hankel matrix, q is the number of columns of the Hankel matrix (p < q), the number s of elements forming the vector β has the following relationship with the parameters p and q of the Hankel matrix

s＝p+q-1 (26)

S44, taking the test data as the reference state once under the bridge health condition, and under the reference state, forming a Hankel matrix H by a vector β_rCalculating H_rOf the null-space matrix N_r

N_r＝column(null(H_r)) (27)

Where null (-) is any column of null space of the orientation quantity; column (·) is any column of the matrix.

S45, taking another M test results, recording Hankel matrix H formed by vector β in the c test_cBy N_rAs a comparison to the reference state, each trial was multiplied by H at the reference state_rRight null space N_rTo obtain a residual vector α_c

α_c＝H_cN_r,(α_c∈R^p×1,c∈(1,2,…,M)) (28)

S46: taking the norm of the residual error in step S45 as the structural damage diagnosis factor of the c test

Under the reference state of the bridge structure, different test residual vectors α are counted for multiple times (assumed to be K tests)_cThe average value of (a) of (b),

s47: establishing a zero-based basis using mahalanobis distanceBridge structural damage diagnosis factor gamma 'of space'_c

Where Θ is the residual vector α_cCovariance matrix of

S48: calculating a bridge structure damage diagnosis threshold lambda 'under a reference state'

λ′＝[γ′₁,γ′₂,…,γ′_c,…,γ′_K]_0.95(33)

In the formula [ ·]_0.95Vector of structural damage diagnosis factor [ gamma'₁,γ′₂,…,γ′_c,…,γ′_K95% confidence probability value of.

S49 residual vector α for the d-th bridge loading test under the state to be diagnosed_dAnd calculating to obtain the structural damage diagnosis factor,

s410: general comparison of gamma_dAnd judging whether the bridge generates structural damage or not according to the values of the 'and the lambda'.

The first calculation example: two three-span prestressed continuous box girder bridges in the bridge cluster monitoring system are selected as an example, and the effectiveness of the bridge structure damage diagnosis method based on temperature-damage stripping is verified.

The span combination of the selected #1 bridge is 30m +30m +30m, the main beam is a widened beam, the width of the widened beam is changed from 26.3m to 28.3m, and the main beam is provided with 25 point type strain sensors. The span combination of the selected #2 bridge is 32m +34m +32m, the width of the girder is changed from 27.3m to 36.4m, and the girder is provided with 24 point type strain sensors. The cross sections of the two bridges both adopt a single-box six-chamber structure form, and the point type strain sensor layout of the two bridges is shown in fig. 3.

In FIG. 3, monitoring points #1 to #5, #11 to #15, #21 to #29, #35 to #39 and #45 to #49 are located on the bridge girder bottom plate, and monitoring points #6 to #10, #16 to #20, #30 to #34 and #40 to #44 are located on the bridge girder top plate. The monitoring system of the whole bridge passes completion acceptance in 2016, 6, 4 and begins to collect data and enter a debugging stage. The whole bridge monitoring system finishes debugging and checking work in 2016, 10 months and 1 day, enters a test run stage, and the sampling frequency of strain is 1 time of acquisition every 10 minutes. The whole bridge monitoring system enters the formal operation stage in 2017, 4, 1.

Taking the #8 monitoring point of the top plate as an example, the structural temperature monitoring data and the denoised structural strain monitoring data are respectively subjected to centralization processing, and the processed results are shown in fig. 4 and 5. According to the monitoring time sequence, unsteady data approximate to an unloaded state are arranged before the time axis, and steady data approximate to a fully loaded state are arranged after the time axis.

As can be seen from fig. 4 and 5, the structural temperature monitoring data after the centering process has a fluctuation range of ± 30 ℃, and the structural strain monitoring data after the centering process has a fluctuation range of ± 100 μ ∈. The fluctuation of the strain response is greater than the temperature data, therefore, when the matrix R is constructed, a weight omega needs to be given to the structural temperature monitoring data to enable the fluctuation of the structural temperature to be greater than the structural strain monitoring data, and the first main direction Y of the matrix R is constructed₁For structural temperature data, a second principal direction Y₂Structural strain monitoring data to remove temperature trends, i.e., strain response induced by vehicle loading. The weight ω here is set to 1000 depending on the actual situation. Y of the calculated construction matrix R₂Axial projection (strain response due to vehicle load effect) as shown in FIG. 6, the first principal direction Y of the matrix R is constructed₁And a second main direction Y₂The K-means clustering results of the scatter distribution are shown in FIG. 7.

As can be seen from FIG. 6, Y of the matrix R is constructed₂Axial projection, namely strain response caused by the load action of the vehicle shows two states and a trend, wherein 1 day in 10 months in 2016 to 1 day in 2017 and 27 days in 4 months in 2017 are the first stable state and are in an approximately no-load state before the vehicle is opened; 27 days in 2017, 4 months and 2017The traffic flow gradually increases in 10 months and 3 days, the load of the vehicle increases after the vehicle is communicated, and the traffic flow is in a second stable state in 2017, 3 days in 10 months and 3 days-2017, 5 months and 1 day in 5 months, and is in a full-load state after the vehicle is communicated. Description of extracted Y₂The axial projection can accurately reflect the action change of the vehicle load. As shown in fig. 7, in a first main direction Y of the construction matrix R₁And a second main direction Y₂In the K-means clustering result of the scatter distribution, the upper part is unsteady data (no-load state and vehicle flow rising stage), and the lower part is steady data (full-load state). Since the vehicle load contribution of the non-steady state data is different from that of the steady state data, along Y₂The upper and lower parts in the axial direction are clearly distinguished. The data can be easily divided into two categories to obtain the steady state data in the lower part. The results of fig. 7 illustrate: the algorithm provided by the invention can effectively find the load trend of the top plate data and extract the steady-state monitoring data.

Taking data before the vehicle is not on the bus from 10 and 3 days in 2016 to 27 days in 4 and 2017 as a reference state; and (4) regarding the bridge structure after traffic is passed as a damage state, namely taking data after traffic is passed from 2017, 4 and 27 days to 2018, 5 and 2 days as a state to be diagnosed. Selecting data of monitoring points #6 to #10 of the top plate, establishing a damage diagnosis factor vector by using structural strain monitoring data in a reference state, and determining a damage diagnosis threshold; then, a damage diagnosis factor vector is calculated by using the structural strain monitoring data of the state to be diagnosed, damage diagnosis is performed by comparing the structural strain monitoring data with a threshold value, and the damage diagnosis result is shown in fig. 8. The abnormal condition of the state to be diagnosed can be obviously judged by the damage diagnosis factor vectors of monitoring points #6 to #10 of the top plate, and the steady-state data section (the full-load state after the vehicle is communicated) from 10 and 3 days in 2017 to 5 and 1 days in 2018 is higher than a threshold value and is obviously different from the non-vehicle-communicated state of the reference data.

In addition, in order to compare the effectiveness of the damage diagnosis method with the results, the strain response data of the monitoring points #6 to #10 of the top plate are directly adopted, and the final damage diagnosis result is shown in fig. 9. Therefore, the abnormity of the state to be diagnosed can not be distinguished without extracting the vehicle load trend item through the algorithm provided by the invention.

In order to verify whether the algorithm provided by the invention can make misjudgment on the damage of the bridge in a healthy state. And (3) taking the steady-state monitoring data as a reference state, namely taking the monitoring data of 7 months between 10 and 3 months in 2017 and 5 and 1 month in 2018 as the reference state. 4-month data from 5 months 1 days in 2018 to 9 months 1 days in 2018 are used as the state to be diagnosed to check the validity of the algorithm. The results of structural damage diagnosis using strain data from #6 to #10 monitoring points of the top plate are shown in fig. 10.

The structural damage diagnosis factor vector of the #6 to #10 monitoring points of the top plate shows that the state to be diagnosed is not abnormal, and the correctness of the structural damage diagnosis algorithm provided by the invention is verified.

Example two: and selecting a concrete simply supported beam finite element model shown in FIG. 11 as a numerical example, and verifying the effectiveness of the provided bridge cluster structure damage diagnosis method based on the deflection influence surface.

The finite element model is divided into 60 units and 61 nodes, each unit is 0.5m long, the full bridge is 30m long, C50 concrete is adopted, and the elastic modulus of the concrete is 3.497 multiplied by 10⁷kN/m²The bending moment of inertia of the hollow plate beam is 2.395 multiplied by 10 in the cross section^-2m⁴。

The method is characterized in that the bending rigidity reduction in a local range is adopted to simulate the damage phenomena such as crack damage or concrete strength reduction and the like which may occur in the structure, a possible damage position is arranged every 3m from 1/10 span, and the single damage condition of damage occurring at different positions is simulated in sequence. The structural damage ranges from 0.5m to the left of the damage point, and the structural damage degree is 5-15%. And the positions of the observation points are also provided with 9 sensors at intervals of 3m from 1/10 span, structural damage identification is carried out by testing the change curve of the deflection of each observation point along with the position of the quasi-static moving load, the quasi-static load is 100kN under the bridge health state, and the quasi-static load is 100kN or 50kN under the bridge damage state.

The numerical simulation adopts Midas Civil software to simulate 405 influence lines together, wherein under the moving load of 100kN, the influence lines are divided into four groups of 0% (healthy), 5%, 10% and 15% according to the damage degree, each group is divided into 9 different damage positions, and deflection influence lines of 9 different observation points are collected under the working condition of each damage position, and 324 influence lines are total. Data 1 set with a damage level of 5% under a 50kN moving load, for a total of 81 influence lines. The method comprises the steps of calculating a vector included angle by adopting the method provided by the invention, constructing a Hankel matrix, and finally calculating damage indexes, wherein the total number of the damage indexes is 45, and the specific result is shown in figure 12, wherein the damage indexes are 1 in each working condition.

From the analysis of FIG. 12, it can be seen that: on one hand, when the damage positions of the bridge structure are the same, the numerical value of the damage diagnosis factor is increased along with the increase of the damage degree; on the other hand, when the structural damage degree is the same, the damage diagnosis factor value changes along with the change of the damage position, namely, the closer the damage position is to the supporting end, the larger the obtained damage characteristic measurement value is. When the damage occurs in the midspan, 4 influence lines of 9 influence lines in the simply-supported beam are the same; and when the damage position is closer to the branch point, the differentiation of the 9 influence lines is larger, which indicates that the displacement influence line at the branch point is more sensitive to the damage. In addition, the damage indicators in the latter two cases are the same, which means that the proposed method is independent of the loading conditions before and after the damage of the bridge structure. The effectiveness of the bridge cluster structure damage diagnosis method based on the deflection influence surface is demonstrated through the numerical calculation example.

The present invention may be embodied in other specific forms without departing from the spirit or essential attributes thereof, and it is intended that all such changes and modifications be considered within the scope of the appended claims.

Claims (8)

1. The intelligent monitoring and rapid detection method for the operation safety of the bridge cluster structure is characterized in that: the method comprises the following steps:

the content one is as follows: the method comprises the steps of building a bridge cluster monitoring system based on high-measurement-point density sensing equipment, wherein the monitoring system is built on a partial bridge in a bridge cluster, and the building of the monitoring system comprises the layout of sensing units based on the high-measurement-point density sensing equipment, the integration of a high-measurement-point density data acquisition and transmission module, the building of a bridge cluster structure safety diagnosis and early warning subsystem, the building of a bridge cluster structure high-measurement-point density central database subsystem and the building of a bridge cluster structure safety monitoring system remote management user subsystem;

wherein:

the sensing unit based on the high-measuring-point density sensing equipment comprises a point type electric signal mechanical sensor, a distributed Brillouin optical fiber sensor and a bridge dynamic weighing system; the point-type electric signal mechanical sensor is mainly arranged on the most unfavorable stress section and the key stress section of the bridge superstructure; the distributed Brillouin optical fiber sensors are arranged along the upper structure and the lower structure of the bridge in a through-length mode, and an optical signal transmission loop is formed; the bridge dynamic weighing system is arranged at the bridge head;

the acquisition and transmission of data of the point-type electric signal mechanical sensor in the high-measuring-point density data acquisition and transmission module adopts a radial sub-network form to acquire the data in an electric signal acquisition system, convert monitoring point signals into standard Ethernet and wireless network digital signals and remotely transmit the standard Ethernet and wireless network digital signals to a server; the distributed optical fiber sensing data in the high-measuring-point density data acquisition and transmission module is acquired and transmitted in a loop mode by adopting an optical fiber signal demodulator, a plurality of optical fiber monitoring loops can be acquired simultaneously, and Brillouin frequency shift data of optical signals are transmitted to a server remotely;

the bridge cluster structure safety diagnosis and early warning subsystem comprises the steps of monitoring data preprocessing, modal analysis, structure damage diagnosis, a multi-level real-time early warning mechanism and structure safety assessment;

the high-measurement-point density center database subsystem of the bridge cluster structure comprises a structural parameter database, an online monitoring data database and an offline processing data database, and is used for finishing filing, inquiring, storing, managing and calling all monitoring/detecting static and dynamic data, information and data in the full life cycle of the bridge;

the remote management user subsystem of the bridge cluster structure safety monitoring system is connected with a database interface, displays various static and dynamic monitoring/detection data, information and data of the whole life cycle of the bridge structure to different users according to user requirements in a classified and graded mode and authorization, and receives control and input of different users to the system according to authorization, so that the functions of remote system management, data analysis and evaluation and data query and statistics are realized;

and II, content II: on the basis of a bridge cluster monitoring system built in the content I, carrying out bridge structure damage diagnosis based on temperature-damage stripping on a bridge with the monitoring system in the cluster;

and thirdly: carrying out a bridge quasi-static load test on a bridge structure without a monitoring system in a bridge cluster, arranging a deflection tester, obtaining deflection influence lines, and forming influence surfaces of a test section under the action of quasi-static load by the influence lines;

and IV, content: and carrying out bridge cluster structure damage diagnosis based on the deflection influence surface on the bridge without the monitoring system in the cluster by using the deflection influence line obtained by the content three detection.

2. The bridge cluster structure operation safety intelligent monitoring and rapid detection complete set method according to claim 1, characterized in that: in the first aspect, the point-type electrical signal mechanical sensor includes one or more of a point-type strain sensor, a deflection sensor, a vibration acceleration sensor, a cable force sensor, an anemoscope, and an environmental temperature and humidity sensor.

3. The bridge cluster structure operation safety intelligent monitoring and rapid detection complete set method according to claim 1, characterized in that: in the first content, the distributed Brillouin optical fiber sensor adopts a differential pulse pair technology, the highest measuring point density reaches 2cm and is separated by one measuring point, and the longest monitoring distance reaches 120 km.

4. The bridge cluster structure operation safety intelligent monitoring and rapid detection complete set method according to claim 1, characterized in that: in the first content, the preprocessing of the monitoring data comprises denoising, filtering, trend removing and FFT (fast Fourier transform); the modal analysis comprises modal parameter identification and model modification.

5. The bridge cluster structure operation safety intelligent monitoring and rapid detection complete set method according to claim 1, characterized in that: the second content specifically comprises the following steps:

s21: acquiring a bridge structure strain monitoring data set matrix X, and if n strain sensor monitoring points exist in the monitoring system and the sampling point number of each monitoring point is m, acquiring a structure strain monitoring data matrix X

The definition of (A) is as follows,

X＝[X₁,X₂,…,X_i,…,X_n]_m×n,i∈(1,2,…,n) (1)

s22: constructing a structural strain monitoring data covariance matrix X^TX, recording as a matrix A; singular value decomposition is performed on the construction matrix A to obtain a matrix decomposition result as follows,

A＝US_nV^T(2)

in the formula, U is a left singular vector matrix of the matrix A; v is a right singular vector matrix of the matrix A; v^TIs the transpose of matrix V; s_nIs a diagonal matrix formed by singular values of the matrix A in descending order, and is defined as shown in the following formula,

S_n＝diag(δ₁,δ₂,…,δ_i,…,δ_n),i∈(1,2,…,n) (3)

s23: arranging the singular values from big to small, replacing the smaller singular value representing the test noise by a 0 value, selecting the first r non-zero singular values and the corresponding left and right singular value matrixes thereof, and reconstructing the matrix A to further obtain a reconstruction matrix for weakening noise interference

Of formula (II) S'_nFor reconstructing singular values of matrix A；S_rForming a diagonal matrix after reducing the rank of the singular value of the reconstruction matrix A';

s24: calculating a reconstructed structural strain monitoring data set X', and effectively improving the signal-to-noise ratio of original structural strain monitoring data;

X′＝(X^T)^-*A′ (5)

in the formula (X)^T)^-*Is a pair matrix X^TThe pseudo-inverse is calculated out,

s25: the strain monitoring data after being denoised is processed by centralization, namely the strain monitoring data is calculated by adopting the following formula,

in the formula (I), the compound is shown in the specification,

strain monitoring data column vector X 'for ith monitoring point after noise interference attenuation'_iThe mean vector of (2);

monitoring data column vector T for ambient temperature of ith monitoring point_iThe mean vector of (2); x'_c,iStrain monitoring data column vectors after centralization; t is_c,iA centralized ambient temperature data column vector;

s26: the centralized monitoring data is used for constructing a matrix R by adopting the following formula,

R＝[ωT_c,iX′_c,i]_m×2(8)

where ω is a weight value of the structural matrix R, and the value of ω needs to be relatively large, so that ω T_c,iHas a large varianceIs greater than X'_c,iSo that the primary axis direction during PCA decomposition represents the ambient temperature load and the secondary axis direction represents the structural strain response;

s27: constructing a covariance matrix C of the matrix R, performing eigenvalue decomposition on the covariance matrix C,

C＝PΛP^T(9)

the eigenvector matrix P is also the projection vector matrix of the construction matrix R, with which the jth component of the construction matrix R is Y_j，

Y_j＝RP_j(10)

In the formula, P_jIs the jth column vector of the feature vector matrix P; y is_jTo construct the jth principal component of the matrix R; column vector Y₁Is the first principal component of the construction matrix R, which represents the environmental temperature monitoring data multiplied by ω, Y₁And Y₂Are orthogonal to each other, Y₂A traffic load trend term representing the strain response monitoring data from which the ambient temperature trend is removed; for newly-built bridges, structural strain monitoring data Y₂The method comprises two parts, namely strain monitoring data under the unstable state of traffic load and under the stable state of traffic load;

s28: for matrix

PCA projection is carried out to obtain a matrix after projection as Y,

Y＝[Y₁Y₂]＝[y₁,y₂,…,y_p,…,y_m]^T,p∈(1,2,…,m) (11)

in the formula, y_pIs the p-th vector of the projection matrix Y; defining a central point set in a K-mean clustering analysis algorithm as omega ═ c₁c₂]^TVector of center point c₁，

In the formula, N₁Is the 1 st class omega₁Sample ofCounting; n is a radical of₂Is the 2 nd class omega₂The number of samples of (a); by the same token, the vector c of the center point can be determined₂；

S29: adopting an optimization solving mode to perform cluster division on each element in the matrix Y, taking the class 1 as an example, constructing the following optimization objective function,

where dist (y)_p,c₁) Is the p-th element Y of the matrix Y_pVector to center point c₁The Euclidean distance of (c);

s210: solving an objective function formula by using a centroid selection method to respectively obtain the 1 st class omega₁And class 2 Ω₂(ii) a Comparing the monitoring time corresponding to each element in the two categories, the category of the time series before is judged to be unsteady data, the category of the time series after is judged to be a steady data reference model,

in the formula (I), the compound is shown in the specification,

is X'_iSteady state monitoring data of;

is X'_iIs the phi element, phi e (m-N)₂+1,…,m)；

On the basis, g strain monitoring points of the bridge structure section are collected to obtain a steady state data set at any phi moment

S211: taking a certain section of the bridge structure as a unit, performing damage diagnosis by using monitoring data of g structural strains of the section, and defining a damage diagnosis factor gamma based on a steady-state data reference model at phi moments_φSimilarly, the damage diagnostic factor gamma in the state to be diagnosed can be obtained_d，

In the formula, C_GFor steady state data sets

The covariance matrix of (a) is determined,

is composed of

Is a matrix of

Phi-th row vector of (1); further obtaining a damage diagnosis factor vector gamma based on a steady-state data reference model_φ，

S212: γ from step S211_φA structural damage diagnostic threshold η is defined,

η＝β·γ_φ，0.95(19)

wherein β is a guarantee coefficient, which is determined according to the monitoring data of the specific bridge structure, and is usually 1.2;. gamma.)_φ，0.95For diagnosing structural damageSubvector γ_φTaking the median of 95% confidence probability;

s213: after the damage diagnosis threshold value of the reference state and the damage diagnosis factor of the state to be diagnosed are respectively obtained, the damage discrimination factor of the bridge structure can be obtained,

in the formula, Z_dWhen the value of the bridge structure damage discrimination factor is 1, marking the bridge structure damage as 1; when the value is 0, the bridge structure is healthy.

6. The bridge cluster structure operation safety intelligent monitoring and rapid detection complete set method according to claim 1, characterized in that: the third content comprises the following steps:

s31: calculating the load efficiency lambda of the bridge quasi-static load test to be between 0.85 and 1.05

In the formula, S_aThe maximum calculation effect value of the internal force or displacement of the loading control section corresponding to a certain loading test working condition under the action of test load; s is corresponding to S under the action of control load_aCalculating the most adverse effect of the force or displacement in the same loading control section;

s32: determining the weight and the number of the loaded vehicles: firstly, determining the weight and the number of the loaded vehicles by referring to the test load calculated in the step S31 under the loading efficiency; on the basis, the loading vehicle weight is adjusted to the final test load by evaluating the actual technical condition of the bridge;

s33: determination of loading vehicle speed: selecting the speed of the loading vehicle according to the fundamental frequency of different bridge structures; controlling the speed of the loading vehicle within the range of 5 km/h-10 km/h in order to ensure that the excitation frequency of the loading vehicle is lower than the fundamental frequency of the bridge structure;

s34: recording the vehicle weight, axle weight, wheel base and wheel weight of a loaded vehicle in detail before the quasi-static load test of the bridge, and planning a roadster route;

s35: arranging a deflection tester, wherein each girder is provided with three testing sections along the longitudinal direction, the measuring points of the testing sections are transversely arranged to fully reflect the transverse deflection distribution characteristics of the bridge, the measuring points of the integral section are transversely arranged to be not less than 3, and the measuring points of the multi-beam section are transversely arranged one by one;

s36: after the contact type or non-contact type deflection tester is installed, debugging the system, and carrying out stable observation for not less than 15 minutes;

s37: under the condition of closed traffic, slowly passing a test loading vehicle at a constant speed along the longitudinal direction of the bridge, keeping the same speed and performing repeated tests along the same loading lane for multiple times to obtain a test deflection influence line; and the deflection influence lines jointly form an influence surface of the test section under the action of the quasi-static load.

7. The bridge cluster structure operation safety intelligent monitoring and rapid detection complete set method according to claim 6, characterized in that: in step S35, the deflection tester is a contact deflection tester or a non-contact deflection tester; when the contact type deflection tester is adopted, the connection mode of the contact type deflection tester and the bridge is determined according to the field conditions, the contact type deflection tester is ensured to be tightly connected with the bridge structure, the length of the cable is controlled at the same time, and the signal precision is prevented from being interfered due to overlarge resistance of the cable; when the non-contact deflection tester is adopted, the firm and reliable inspection work of the non-contact deflection tester support is carried out.

8. The bridge cluster structure operation safety intelligent monitoring and rapid detection complete set method according to claim 6, characterized in that: the fourth content comprises the following steps:

s41: when one deflection influence line acquisition test is carried out on one bridge, all data can form an m multiplied by n matrix Y, the expression is as follows,

in the formula, m is the number of measuring points of each influence line; n is the number of sensors;

s42, calculating the included angle between two displacement influence lines according to a certain determined cross section of the bridge, and taking any two column vectors and calculating the included angle β of the two vectors_i

In the formula, ω_jIs any column vector in the matrix Y, j belongs to (1,2, …, n); omega_kIs any column vector in the matrix Y, k belongs to (1,2, …, n), i | | · | | is vector norm, and β is defined as_iAssembly, a matrix β is obtained,

β＝sort([β₁,β₂,…,β_i,…,β_s]) (24)

wherein sort (. cndot.) is arranged from small to large, and s is an included angle β_iThe number of (2);

s43, arranging the vectors β according to the form of a Hankel matrix, and further constructing an H matrix

Wherein p is the number of lines of the Hankel matrix, q is the number of columns of the Hankel matrix, p < q, the number s of elements forming the vector β and the parameters p and q of the Hankel matrix have the following relationship

s＝p+q-1 (26)

S44, taking the test data as the reference state once under the bridge health condition, and under the reference state, forming a Hankel matrix H by a vector β_rCalculating H_rOf the null-space matrix N_r

N_r＝column(null(H_r)) (27)

Where null (-) is any column of null space of the orientation quantity; column (·) is any column of the matrix;

s45, taking another M test results, recording Hankel matrix H formed by vector β in the c test_cBy N_rAs a comparison to the reference state, each trial was multiplied by H at the reference state_rRight null space N_rTo obtain a residual vector α_c

α_c＝H_cN_r,(α_c∈R^p×1,c∈(1,2,…,M)) (28)

S46: taking the norm of the residual error in step S45 as the structural damage diagnosis factor of the c test

Counting a plurality of times under the reference state of the bridge structure, and assuming K times of tests, wherein different test residual vectors α_cThe average value of (a) of (b),

s47: establishing a bridge structure damage diagnosis factor gamma 'based on zero space by using Mahalanobis distance'_c

Where Θ is the residual vector α_cCovariance matrix of

S48: calculating a bridge structure damage diagnosis threshold lambda 'under a reference state'

λ′＝[γ′₁,γ′₂,…,γ′_c,…,γ′_K]_0.95(33)

In the formula [ ·]_0.95Vector of structural damage diagnosis factor [ gamma'₁,γ′₂,…,γ′_c,…,γ′_K95% confidence probability value of;

s49 residual vector α for the d-th bridge loading test under the state to be diagnosed_dAnd calculating to obtain the structural damage diagnosis factor,

s410: through comparison of gamma'_dAnd judging whether the bridge generates structural damage or not according to the value of the lambda'.

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