CN-120724165-B - Beam bridge structure damage diagnosis method based on line-plane information fusion

CN120724165BCN 120724165 BCN120724165 BCN 120724165BCN-120724165-B

Abstract

The invention discloses a beam bridge structure damage diagnosis method based on line-plane information fusion, which comprises the steps of utilizing distributed optical fibers to collect information of sparse measurement points of a beam bridge, encrypting a key section to obtain a strain data set of an unencrypted and encrypted area, denoising the collected data by adopting a sparse optimization method, introducing a Gaussian process regression combination space kernel mode, expanding and reconstructing monitoring data of the encrypted area by utilizing the optical fiber strain data of the unencrypted area to obtain strain data of the whole monitoring area, calculating strain data correlation coefficients under different time domains after the strain data are obtained, constructing a damage feature matrix, and establishing a structural damage threshold by solving the Frobenius norm of the correlation matrix and adopting a kernel density estimation mode to realize effective early warning of bridge structure risks. The method can improve the monitoring efficiency of the sensor, realize the health monitoring of the light structure and provide effective guarantee for the safe operation and maintenance of the bridge structure in the operation period.

Inventors

QIAO WENTING
LIU YANG
ZHOU XIANG
LIU XINJIN
ZHOU ZHENG

Assignees

内蒙古自治区交通运输科学发展研究院
哈尔滨工业大学
济南轨道交通集团有限公司

Dates

Publication Date: 20260508
Application Date: 20250509

Claims (5)

1. A beam bridge structure damage diagnosis method based on line-plane information fusion is characterized by comprising the following steps: The method comprises the steps that firstly, strain monitoring values in a certain area under a key section of a structure are obtained based on a local encryption optical fiber sensor, the key section is at a span of 1/4 of the span, the strain monitoring values of other measuring points along the longitudinal direction of a beam are obtained through distributed optical fibers, and a strain data set based on distributed optical fiber local encryption is constructed; Step two, cleaning the acquired data by a sparse optimization method to obtain a strain monitoring value after noise is removed; Thirdly, carrying out data expansion and reconstruction on the strain monitoring value of the local encryption area by using the mode of combining Gaussian process regression with a spatial kernel through the monitoring data of a single optical fiber, and expanding to obtain the strain data of the whole monitoring area, wherein the method comprises the following specific steps: Step three, a coordinate system is established, and the denoised strain data and the corresponding distributed optical fiber measuring point coordinate set are extracted : (10) (11) In the formula, Is a strain measurement point coordinate set of an unencrypted area, The method comprises the steps of (1) setting a strain measurement point coordinate set for an encryption area; Step three, selecting p groups of non-encryption area data and q groups of encryption area data, and respectively constructing training sets of measuring point coordinates and strain data: (12) in the formula, Representing a training set of coordinates of known measurement points, And Training data sets respectively representing coordinates of the unencrypted area and the encrypted area; (13) in the formula, For a training set of strain at known points, A training set of strain data representing the unencrypted area, Representing vectorization of the two-dimensional matrix; Training a set for strain data of the dense region; thirdly, constructing a full beam strain prediction network, and assuming that the number of the transverse measurement points of the full beam to be predicted is The number of longitudinal measurement points is And (2) and The predicted measuring points are distributed in a rectangular array, and the predicted measuring points are: (14) in the formula, A measurement point coordinate set which is required to be predicted; Thirdly, constructing a relation between the space coordinates and the strain, and predicting non-measuring points: (15) (16) in the formula, For a gaussian process, a random function distribution is represented, In the form of a spatial kernel, Noise is negligible after denoising; for a set of measured point strains to be predicted, N is training point number; and step four, on the basis of the step three, constructing a damage characteristic matrix by calculating correlation coefficients among strain response data in different time domains, solving a Frobenius norm for the damage diagnosis matrix, and constructing a structural damage threshold by adopting a nuclear density estimation method.
2. The method for diagnosing structural damage of a bridge based on line-plane information fusion according to claim 1, wherein the specific steps of the first step are as follows: The method comprises the steps of constructing a monitoring data set f of bridge structure strain according to bridge structure non-encryption area distributed optical fiber monitoring data: (1) in the formula, M is the total number of sampling points of the monitoring data; For monitoring a certain sampling point of data, each measuring point simultaneously covers a plurality of measuring data, and the sample set of the monitoring data of the f measuring point Is composed of the following components in part by weight, : (2) In the formula, The total sampling number of the measuring points in a certain period of time is set; ; a v sampling point under the measuring point; Step two, constructing a monitoring data set g of bridge structure strain according to the monitoring data of the distributed optical fiber encryption area: (3) wherein n is the number of columns of the encryption zone along the bridge longitudinal measuring points; k is the number of measuring points along the bridge transverse line; The sample set of the measuring point is composed of the following formula, : (4) Wherein w is the total sampling number of a certain measuring point in the encryption area; ; is the u-th sampling point under the measuring point.
3. The method for diagnosing structural damage of a bridge based on line-plane information fusion according to claim 2, wherein in the step two, the measuring points of the encrypted area are rectangular.
4. The method for diagnosing structural damage of a bridge based on line-plane information fusion according to claim 2, wherein the specific steps of the second step are as follows: step two, filtering the original data by using a low-pass filter to enhance the sparsity of the data: (5) wherein, the mask is a low-pass filter; Is a filtered data set; Secondly, denoising the filtered data by adopting a sparse optimization algorithm, and simultaneously obtaining a denoised data set: (6) in the formula, To take out The L1 norm minimum of (2) and eliminating noise; Reconstructing signals, solving vector residual errors, and denoising data: (7) Wherein A is a transformation matrix, Representing the projection of the sparse coefficient z into the signal space; for noise tolerance, filtering random disturbance; s is the de-noised strain field; The distributed optical fiber data after noise removal is expressed as: (8) in the formula, To denoise the unencrypted area distributed fiber monitoring dataset, Taking the sparse coefficient of the minimum L1 norm; and similarly, obtaining a denoised optical fiber encryption area monitoring data set: (9) in the formula, To denoise the encrypted region fiber monitoring dataset, Is a transformation matrix; To take the sparse coefficient of the smallest L1 norm.
5. The bridge structure damage diagnosis method based on line-plane information fusion according to claim 1, wherein the specific steps of the fourth step are as follows: Step four, based on the data predicted in the step three, selecting a certain measuring point at a certain moment, taking the point as a reference, and calculating the correlation coefficient of the reference measuring point at a moment different from other measuring points: (17) in the formula, The strain of the predicted point q at the time p; step four, constructing a strain data correlation matrix of the measuring point at different moments from other measuring points And taking the matrix as a characteristic matrix: (18) in the formula, Strain is a reference predicted point at a certain moment; the strain of the predicted point j at the time of n; step four, solving the Frobenius norms of each feature matrix: (19) in the formula, Representation calculation Square sum and evolution of all elements of (2); fourthly, constructing a Frobenius norm set of the feature matrix at different moments of each predicted point in a health state; Fifthly, constructing a damage threshold by adopting a nuclear density estimation method aiming at the data set of each predicted point: (20) in the formula, K represents a Gaussian kernel function for the estimated probability density function; For the damage index, the internal variability of the data is expressed, H is the bandwidth; Calculating cumulative distribution function : (21) Solving 98.5% quantile and determining damage threshold : (22)。

Description

Beam bridge structure damage diagnosis method based on line-plane information fusion Technical Field The invention relates to a beam bridge structure damage diagnosis method, in particular to a beam bridge structure damage diagnosis method based on line-plane information fusion. Background The bridge is not only a pulse for economic development of a connection area, but also a key infrastructure for guaranteeing public safety, wherein the bridge is dominant due to simple structure and convenient construction, and is widely applied to highway, railway and urban traffic systems. However, in the long-term service process, the bridge structure bears the coupling effect of various complex loads, so that various diseases are easy to generate on the structure, and the service performance and safety of the bridge structure are seriously affected. Currently, bridge structure damage monitoring mainly relies on point sensor technology and distributed sensing technology, such as strain gages, accelerometers, distributed optical fibers, and the like. These sensors are typically mounted in a punctiform arrangement at key locations of the bridge and analyze the structural health by collecting strain, vibration or displacement data from local areas. However, these monitoring techniques are characterized by high cost, limited coverage, and low sensor utilization. In addition, the huge data volume, complex processing and heavy computing resources become difficulties and pain points in the current monitoring technology, the light-weight health monitoring is difficult to realize, and the real-time performance and the economical efficiency are severely limited. How to improve the utilization efficiency of the sensors, remove the influence of complex environmental factors on monitoring data, realize more efficient and light bridge damage diagnosis technology through a smaller number of sensors, realize monitoring of the structural range, and improve the diagnosis precision and robustness is still a challenging subject. Disclosure of Invention In order to solve the problem that the utilization rate of the existing distributed optical fiber and monitoring data thereof is low, the invention provides a beam bridge structure damage diagnosis method based on line-plane information fusion, which can improve the monitoring efficiency of a sensor and realize the health monitoring of a lightweight structure. The invention aims at realizing the following technical scheme: a beam bridge structure damage diagnosis method based on line-plane information fusion comprises the following steps: The method comprises the steps that firstly, strain monitoring values in a certain area under a key section of a structure are obtained based on a locally encrypted optical fiber sensor, the strain monitoring values of other measuring points along the longitudinal direction of a beam are obtained through distributed optical fibers, and a strain data set based on the distributed optical fiber local encryption is constructed; Step two, cleaning the acquired data by a sparse optimization method to obtain a strain monitoring value after noise is removed; Thirdly, carrying out data expansion and reconstruction on the strain monitoring value of the local encryption area by using the mode of combining Gaussian process regression with a spatial kernel through the monitoring data of a single optical fiber, and expanding to obtain the strain data of the whole monitoring area; and step four, on the basis of the step three, constructing a damage characteristic matrix by calculating correlation coefficients among strain response data in different time domains, solving a Frobenius norm for the damage diagnosis matrix, and constructing a structural damage threshold by adopting a nuclear density estimation method. Compared with the prior art, the invention has the following advantages: According to the invention, the structural damage information contained in the bridge structure history detection data is extracted, the utilization efficiency of the monitoring data is improved through the optimized arrangement of the optical fiber scheme, the established structural risk early warning limit value can achieve the purpose of effective early warning of the bridge structure risk, and the lightweight monitoring of the bridge structure is realized, so that effective guarantee is provided for the safe operation and maintenance of the bridge structure in the operation period. Drawings Fig. 1 is a flow chart of a method for diagnosing structural damage of a bridge based on line-plane information fusion. Fig. 2 is a selected monitored continuous beam bridge graph. FIG. 3 is a schematic view of an optical fiber encryption scheme, (a) a schematic view of an optical fiber arrangement cross section, (b) a schematic view of an optical fiber arrangement longitudinal section, and (c) a schematic view of an optical fiber arrangement plan. FIG. 4 is historical strain monitoring data for a mo