CN116226974A - Damage identification method and system based on data fusion and self-adaptive sparse regularization - Google Patents
Damage identification method and system based on data fusion and self-adaptive sparse regularization Download PDFInfo
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Abstract
The invention discloses a damage identification method and a damage identification system based on data fusion and self-adaptive sparse regularization, which are used for acquiring and analyzing various structural dynamic response monitoring data of key parts of a civil structure in real time by using multidisciplinary Internet of things and structural dynamics analysis technology, and are a set of damage identification indexes based on a small amount of multi-component heterogeneous data fusion and a self-adaptive sparse regularization solving technology for structural health monitoring and damage identification, so as to solve the problems that multi-source heterogeneous sensing data in a civil structure health monitoring system cannot be fully mined and fused and utilized, and damage identification indexes are not sensitive enough or are easy to be interfered by environmental noise to cause misjudgment. The technical scheme provided by the invention can be deployed in a structural health monitoring system, so that diagnosis and evaluation of engineering structure safety are realized, early warning, early discovery and early treatment of engineering structure safety risk hidden danger are realized, and safe operation of the urban civil engineering structure and efficient response of sudden events are ensured.
Description
Technical Field
The invention relates to the technical field of safety monitoring and structural damage identification of civil engineering structures, in particular to a damage identification method and system based on multi-component heterogeneous data fusion and self-adaptive sparse regularization.
Background
The health condition of the major civil engineering structures such as large-span bridges, super high-rise buildings, large-span space structures, reservoir dams, ocean platforms, high-voltage transmission towers and the like is directly related to the safety of lives and properties of people, and the normal operation of the civil engineering infrastructure is related to the normal operation of national economy. However, in the service process of civil engineering structures for decades or even hundreds of years, the coupling effect of disaster factors such as environmental erosion, material aging and loading effect, artificial or natural mutation effect and the like inevitably leads to the accumulation of damage and the attenuation of resistance of the structure, so that the capability of resisting natural disasters, normal loads and environmental effects is reduced, and disastrous sudden accidents are caused. Tragedy caused by failure to find structural damage in time is not counted. For example, the Silver bridge in west virginia in 1967 collapsed, resulting in 46 losses and 9 injuries; korean first supgsoo Grand bridge collapses in 1994, resulting in 32 loss of life and 17 injuries; the bridge of Guangdong Jiujiang in 2007 is impacted by a sand carrier, so that the bridge deck collapses by about 200 meters and 9 people die; the 'lotus-river-side Jing Yuan' of the Shanghai Min Shenghai region of 6 months 27 days 2009 is connected with the 'lying' of a 13-layer residential building under construction; in 2018, 7.27 Zhejiang tung gallery bridge collapse accidents occur, and the accidents cause death of 8 persons and injury of 3 persons in total; the accident of collapse of the tin-free 312 national road overpass in 2019 causes 3 deaths and 2 injuries altogether; the method comprises the following steps that in the period of 7 months and 8 days in 2019, a Shenzhen city sports center carelessly collapses in the dismantling process, and constructors are buried; the accident of collapse of the Shenzhen building in 2019 8.28 and the strong shaking event of the Siegesbeck building of the Shenzhen super high-rise building in 2021 5.18 cause no casualties but large economic loss. A large number of accidents indicate that damage to critical structural components is accumulated to some extent, if not found and handled in time, the damage will rapidly spread, leading to destruction of the entire structure, and that the overall destruction or large-area failure of the civil structure often originates from local damage to the structure, such as minor fatigue damage cracks or corrosion damage to local functional components, etc. In order to discover potential safety hazards in time and ensure structural safety, research on structural health monitoring (Structure Health Monitoring, SHM) and structural damage identification technologies has important significance and a great deal of application requirements.
In the aspects of structural health monitoring and damage identification methods in civil engineering, structural damage identification based on dynamic characteristics is one of the most widely applied technologies, and rapid development is achieved at home and abroad in the past decades. The core idea of the technology is that the vibration characteristics (such as frequency, vibration mode, etc.) and the dynamic response (such as acceleration, displacement, velocity, strain, and stress) of a structure are functions of the physical parameters (such as mass, stiffness) of the structure; structural damage means a change in a physical parameter of the structure that necessarily causes a change in the vibration characteristics and dynamic response of the structure. The structural damage identification method based on the dynamic characteristics can be classified into a frequency domain method (frequency, vibration mode and derivative function thereof are used as damage identification indexes) and a time domain method (structural dynamic response and derivative function thereof are used as damage identification indexes). Although many vibration-based structural damage methods have been proposed, there are difficulties in application, such as insufficient sensitivity of damage identification index (structural frequency or structural vibration pattern) or damage index being easily interfered by environmental noise to cause erroneous judgment, a limited number of sensors, and a very large number of identified structural parameters. The above-mentioned problems are important reasons for preventing the application of the existing dynamic characteristic-based structural damage identification technology to practice, and still require long-term scientific research and gradual progress of researchers.
In addition, the types of intelligent sensors used by the existing SHM system are quite various, including acceleration, displacement, strain sensors and the like, but most of the existing damage identification indexes and methods only use one sensor data for damage identification, and do not fully utilize the data measured by various sensors and do not combine the respective advantages of different types of data for high-precision structure damage identification. The measured data of various sensors have different characteristics, such as acceleration response can easily obtain measured data with high signal-to-noise ratio, and higher kinetic energy is contained in a high-order vibration mode; in contrast, the displacement response contains more kinetic energy in the low-order vibrational modes; the strain or stress response is then very sensitive to local damage changes close to the sensor, but less sensitive to local damage changes far from the sensor. Because of the different advantages and limitations of these sensors, the combined use of multiple types of sensors enables multiple data advantages to be complemented to improve data quality and damage identification. However, the physical meaning, dimension and various characteristics of the data obtained by the various sensors are different, and how to fuse the data of the multiple types of sensors and use the data for identifying the damage still lacks relevant researches.
In summary, most of the existing structural health monitoring systems can only carry out simple alarm based on the alarm threshold, and can not accurately and effectively realize the positioning and quantitative analysis of structural damage. In addition, many existing structural damage identification methods only adopt data of a single type sensor to carry out structural health assessment, the advantages of the multiple heterogeneous data are not fully fused and utilized to make the best of the advantages and keep short, and damage identification indexes are not sensitive enough or are easy to be interfered by environmental noise to cause misjudgment and the convergence speed of the damage identification solving process is low, so how to provide high-sensitivity damage identification indexes based on the multiple heterogeneous data and the corresponding damage identification method are engineering technical bottlenecks which are urgently needed to be solved in the engineering field.
Disclosure of Invention
First, the technical problem to be solved
Most structural health monitoring systems can only carry out simple alarm based on an alarm threshold value, and cannot realize accurate and effective positioning and quantitative analysis of structural damage. In addition, most structural damage identification methods only adopt data of a single type sensor, multi-component heterogeneous data cannot be fully utilized, damage identification indexes are not sensitive enough or are easy to be interfered by environmental noise to cause misjudgment, and the convergence speed of the damage identification solving process is low, so how to provide high-sensitivity damage identification indexes based on the multi-component heterogeneous data and a corresponding damage identification method are engineering technical bottlenecks which are needed to be solved in the engineering field.
(II) technical scheme
In order to overcome the defects of the prior art, the invention utilizes multidisciplinary Internet of things and structural dynamics analysis technology to collect, transmit, store, analyze and apply structural dynamic response monitoring data of key parts of a civil structure in real time, provides a set of damage identification indexes capable of realizing multi-element heterogeneous data fusion such as acceleration, displacement, strain and the like and a self-adaptive sparse regularization solving technology to carry out structural health monitoring and damage identification, and mainly aims to solve the problems that multi-source heterogeneous sensing data in a civil structure health monitoring system cannot be fully excavated and fused for use, the damage identification indexes are not sensitive enough or the damage indexes are easily interfered by environmental noise to cause misjudgment, and provides a self-adaptive sparse regularization method to solve the problems that misjudgment is easily generated in pathological damage identification inverse problems and the iterative solution convergence speed is slow.
The damage identification method based on data fusion and self-adaptive sparse regularization is characterized in that the method is based on the fact that the data measured by a plurality of sensors with small quantity of optimized arrangement are subjected to multi-element perception data fusion through covariance and solved by adopting the self-adaptive sparse regularization method, can well overcome the pathological problems existing in the damage identification inverse problem and obtain sparse and accurate damage identification results, and specifically comprises the following steps:
s1, carrying out optimal arrangement calculation analysis on measuring points of a structural health monitoring sensor through a finite element model of a preset civil structure to obtain an optimal arrangement scheme of a plurality of sensors of the structural health monitoring;
s2, acquiring the multi-element perception structure power response monitoring data of the target civil structure in real time, and carrying out standardization and dimensionless processing on the structure power response monitoring data to obtain a standardized structure response vector;
s3, performing multi-element heterogeneous data fusion on the response vector of the standardized structure by adopting a covariance function, and according to the multi-element heterogeneous dataConstructing a fusion result to obtain a covariance-based multi-element perception fusion damage identification index V pq The damage identification index based on covariance multi-element perception fusion is sensitive to local damage and insensitive to measurement noise;
s4, calculating to obtain the multi-element perception data fusion damage index vector of the target civil structure containing the damage information according to the covariance-based multi-element perception fusion damage identification index and the actually measured multi-element perception structural power response monitoring data
S5, performing simulation calculation according to the covariance-based multi-element perception fusion damage identification index and a finite element model in a health reference state by adopting the preset civil structure to obtain a multi-element perception data fusion damage index vector based on the finite element model
S6, according to
and
Constructing a damage identification equation, solving the damage identification equation by combining an adaptive sparse regularization technology, updating a model, and finally carrying out iterative solution to obtain a damage identification result, thereby determining the position of the damage and the damage degree.
S7, the damage identification method based on data fusion and self-adaptive sparse regularization is characterized by further comprising the step of automatically early warning the damage identification result in a wireless data communication mode.
A damage identification system based on data fusion and adaptive sparse regularization, configured to perform the sparse regularized damage identification method based on data fusion and adaptive as described above, and comprising:
(1) The data real-time acquisition and transmission module is used for continuously acquiring structural power response data such as structural vibration acceleration, displacement, strain and the like in an unattended manner for 24 hours, transmitting the monitoring data back to the management Fang Yun platform in real time through a 4G/5G or special network, and remotely checking and setting the state and related parameters of the sensor;
(2) The data storage and management module is used for mass multi-source heterogeneous monitoring data generated by a plurality of structural arrays of different types of sensors and comprises: establishing a high-performance database with dynamically expandable storage capacity and dynamically hierarchical data management based on a cloud storage technology;
(3) The data analysis and structure safety evaluation module is used for providing basic data analysis such as data cleaning, data integration, data conversion, data reduction, data integration, spectrum analysis, statistic analysis and the like, and is used for evaluating the structure safety and comprises the following steps: the damage identification analysis algorithm based on covariance multi-element data fusion is embedded, so that automatic analysis of mass data and damage diagnosis based on multi-element perception data fusion are realized, manual intervention is not needed in the process, and automatic and efficient structural state evaluation is realized;
(4) The structural safety early warning and early warning information sending module is used for establishing a structural safety multi-level early warning threshold value index system according to the standard limit value and the structural damage identification evaluation result, and taking the structural safety multi-level early warning threshold value index system as the basis of structural safety early warning;
(5) And the system visualization module is used for providing a B/S architecture-based user-friendly system visualization interface.
(III) beneficial effects
The invention provides a structural damage identification index and a damage identification method based on multi-component perception data fusion, which are sensitive to structural local damage and insensitive to measurement noise. The invention aims to provide an innovative method for monitoring and identifying the health of a civil structure based on multi-element sensing data fusion and self-adaptive regularization technology, so as to solve the defects of the existing structure damage identification method in the background technology, and form a systematic integrated system architecture integrating software, hardware and analysis algorithm for diagnosing the health of the civil structure based on an intelligent sensor, an Internet of things technology, a multi-element sensing data fusion damage identification index and a self-adaptive sparse regularization damage identification solving algorithm.
Aiming at the problem that multisource heterogeneous sensing data cannot be fully utilized in a civil structure health monitoring system, the invention provides a damage identification index based on response covariance multisource heterogeneous sensing data fusion, the index can fuse multisource heterogeneous monitoring data, structural power indexes for identifying structural damage are constructed by commonly extracting components sensitive to structural damage from a plurality of structural response data such as measured acceleration, displacement and strain, and the advantages of a plurality of structural power response monitoring data are fused together to play a role in taking advantage of avoiding shortages; the damage identification index based on response covariance multiple sensing obtained after data fusion is more sensitive to local damage of the structure and insensitive to environmental measurement noise, and finally, by combining a self-adaptive sparse regularization damage solving technology, more accurate damage identification can be realized, namely, the problem that misjudgment is easy to generate and the problem of pathological damage identification inverse problem is well overcome, and a sparse and accurate damage identification result can be obtained with high efficiency; the damage identification index and the damage identification method can be deployed in a structural health monitoring system, can realize diagnosis and evaluation of engineering structure safety, realize early warning, early discovery and early treatment of potential safety hazards of the engineering structure, and ensure safe operation of the urban civil engineering structure and efficient response of emergencies.
Drawings
Fig. 1 is a schematic diagram of an application scenario of a damage identification method and system based on data fusion and adaptive sparse regularization in an embodiment of the present invention;
FIG. 2 is a schematic diagram of a technical route of a method and a system for identifying damage based on data fusion and adaptive sparse regularization in an embodiment of the invention;
FIG. 3 is a schematic diagram of an iterative solution process for damage identification based on multivariate perceptual data fusion and adaptive sparse regularization in an embodiment of the invention;
fig. 4 is a schematic diagram of a result of iterative solution of a damage identification method based on multivariate perceptual data fusion and adaptive sparse regularization in an embodiment of the present invention;
fig. 5 is a technical roadmap of a civil structure safety monitoring and early warning software system of a B/S architecture of cloud end deployment in an embodiment of the invention.
Detailed Description
In order to make the technical problems, technical schemes and beneficial effects to be solved more clear, the invention is further described in detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are for purposes of illustration only and are not intended to limit the scope of the invention.
A damage identification method based on data fusion and adaptive sparse regularization and a system for executing the method are applied to a scene shown in fig. 1. Among them, an acceleration sensor, a displacement sensor, a strain sensor are deployed in a civil engineering structure, but the deployable sensor is not limited thereto. The sensor is communicated with a cloud server through a 4G/5G network, the cloud server identifies damage of a civil engineering structure through an algorithm library, and early warning information is sent to staff by utilizing a visual software front-end platform based on a B/S architecture.
The structural damage identification method of the covariance-based multi-element perception data fusion index in the algorithm library comprises four main sub-algorithms: data standardization and dimensionless, multi-element perception data fusion calculation, structural damage positioning and quantitative analysis based on self-adaptive sparse regularization technology, structural health assessment and auxiliary decision making. Its function and implementation referring to fig. 2, mainly comprises the following steps:
(1) Combining a finite element model of the civil engineering structure, and carrying out optimal arrangement calculation analysis on measuring points of various structural health monitoring sensors such as acceleration, displacement, strain and the like, namely, optimizing an arrangement scheme comprises obtaining optimal arrangement positions and quantity of various sensors;
(2) And (3) carrying out standardization and dimensionless treatment on different types of measurement data, so that data with different dimensions and change characteristics, which are measured by various sensors, can be subjected to data fusion. The data to be standardized and dimensionless processed mainly comprise structural dynamic response monitoring data recorded by intelligent sensors such as acceleration, displacement and strain sensors, and the like, which are commonly recorded in an observation vector, and the original data acquired by various sensors are divided by respective standard deviations to be standardized and dimensionless.
(3) And adopting a covariance function to perform multi-element heterogeneous data fusion and constructing damage identification indexes of multi-element perception data fusion sensitive to local damage. Firstly, information fusion of multi-element heterogeneous data can be carried out by calculating a cross covariance function between any two standardized and dimensionless structural response data, and then, a covariance matrix obtained by calculation is converted into a one-dimensional data vector, so that a multi-element perception fusion damage identification index vector based on covariance can be obtained.
(4) According to the damage structure and the structural dynamic response data such as acceleration response, displacement response and strain response obtained by the corresponding finite element model, damage identification index vectors based on response covariance multivariate perception data fusion are respectively calculated, the damage identification index vectors are expressed as damage identification equations by using a first-order Taylor expansion method, the damage identification equations are further rewritten into linear damage identification equations in an iterative mode by using a time domain iteration method, and on the basis, the adaptive sparse regularization technology is provided to conduct fine damage positioning and quantitative analysis.
(5) Based on the damage identification equation, a damage identification index and sensitivity analysis method based on response covariance multivariate perception data fusion is adopted for damage identification.
Therefore, the multi-element sensing data of the service condition of the feedback structure is collected in real time, and the position and the damage degree of the damage can be effectively judged by combining with the finite element model of the actual engineering structure, the safety of the structure can be effectively evaluated in time, the performance change of the structure is predicted, and the emergency is early warned, so that the stress and the damage evolution rule of the whole process of structure construction and service can be comprehensively mastered, and the service safety of the large engineering structure is ensured.
Specifically, in step S1, a finite element model of the civil structure is preset, and the finite element model has two functions, namely one function is used for optimizing the arrangement of the sensor, and the normalized structural response vector in step S2 is screened to obtain data of sensor measurement based on a plurality of small amount of optimized arrangements; secondly, performing simulation to obtain parameters of a healthy civil engineering structure, namely, fusing the multiple perception data based on the finite element model into a damage index vector
In step S2, sensors disposed in the target civil structure collect data in real time, and let the structural dynamic response monitoring data be y (t), including acceleration, displacement, strain, and the like, be measurement data of multiple isomers, and be represented by formula (1):
y(t)＝C c x(t)+D c f(t) (1)
wherein ,
wherein ,
for the observation of the vector, ε (t) is the strain response time course, z (t) is the displacement response time course, +.>
Is the acceleration response time course;
For the state vector, the power equation is satisfied:
wherein ,
wherein ψ=bgl d Φ is a strain modal matrix; l (L) d Is a selection matrix of node displacement matching strain calculation; the matrix G is a coordinate transformation matrix from global coordinates to local coordinates; vector B defines the local strain-displacement relationship; phi is a modal matrix; omega and xi are diagonal matrixes formed by the natural frequency and damping ratio of the structure respectively; q and
respectively the displacement and the speed under the modal coordinates; f (t) is the external excitation vector.
The method is characterized in that a multi-element sensing data fusion method is adopted, structural dynamic indexes sensitive to structural damage are jointly extracted from various structural dynamic response data such as acceleration, displacement and strain, and the quality of the structural dynamic index data can be effectively improved by utilizing different characteristics of various data, so that the damage positioning and quantitative analysis with sensitivity and high precision are realized.
Normalized and dimensionless processing is performed on the structural dynamic response monitoring data y (t), namely, the original structural response data is divided by the respective standard deviation to obtain a normalized structural response vector
By formula (5):
wherein ,yp (t) is raw structural dynamic response monitoring data,
is y and y p (t) the corresponding standard deviation, i.e., dividing the original structural response data by the respective standard deviation.
In step S3, the covariance-based multivariate perception data is adopted to fuse the damage identification index, the index can effectively fuse the multivariate heterogeneous data, the sensitivity of the damage identification index to structural damage can be improved, and the influence of measurement noise on damage identification is reduced.
And adopting covariance function to fuse the multi-element heterogeneous data and reduce measurement noise, and calculating a cross covariance function formula (6) of any two standardized structure responses:
wherein ,yp For the structural response recorded by the sensor p,
for normalized structural response, +.>
A standard deviation representing the structural dynamic response of sensor p recorded on a sound structure; e denotes the expected variable τ is the time interval, the subscript p, q denotes the response calculated from the sensor p, q;
after the cross covariance function of the structural response is obtained according to the formula (6), the structural response can be further assembled into a damage identification index vector V based on covariance multi-element perception data fusion pq The calculation method is as shown in formula (7):
wherein ,pi ∈[p 1 ,p s ],q j ∈[q 1 ,q s ]Subscript s represents the total number of selected sensors; nt is the total number of time intervals selected for lesion recognition.
In steps S4 and S5, V is obtained according to step S3, respectively pq Calculating to obtain multi-element perception data fusion damage index direction of target civil structureMeasuring amount
And a multivariate perception data fusion damage index vector based on a finite element model>
In S5, based on the finite element model and the damage identification of the sensitivity analysis, a simulation calculation is performed on the finite element model of the preset civil structure, and the objective is to identify the position and degree of the structural rigidity reduction caused by the local structural damage. For quantitative structural damage identification, the stiffness matrix of the damaged structure can be expressed as a mathematical formula:
wherein ,0≤αi ≤1，-1≤Δα i ≤0，α i The E alpha is a coefficient corresponding to the ith unit stiffness matrix; Δα i E Δα is the local stiffness variation of the ith cell; ne is the total number of units; alpha is a vector of stiffness matrix coefficients; Δα is the vector of the local stiffness change of the damaged cell.
Meanwhile, in S4
Expressed as a lesion recognition equation with a first order taylor expansion:
converting the formula (9) into a linear damage identification equation integrated with an iterative Gaussian-Newton algorithm by adopting a time domain iterative method:
where k=0, 1,2,3,.,
Because the engineering can only be used for structural health monitoring by installing a small number of sensors on a structure in practice, the number of units required for damage positioning and quantitative analysis is far greater than the number of damage identification equations, and therefore the problem of solving the damage identification equation (10) is often a problem of solving a pathological equation. The most widely applied method is a Gibbwhereupon regularization method, which usually solves the problem of loss identification of pathological conditions, but because local damage often only occurs in part of the structure, but the spatial distribution of the damage has sparse characteristics, the Gibbwhereupon regularization method can produce some erroneous judgment on some undamaged units.
To this end, the invention proposes an innovative adaptive sparse regularization method to solve the iterative impairment recognition equation (10) (as shown in fig. 3): in S6, the damage recognition parameter α is expressed by equation (13) and represented by Δα= ΣΔα k+1 Obtaining updated damage parameters, wherein the obtained damage parameters are used for modeling in a finite element model of an engineering structureUpdating, thereby identifying a correspondingly updated structural damage location and severity;
s.t.-1≤Δα i k+1 +∑Δα i k ≤0；λ *k+1 ≥0
wherein ,λ*k+1 Is an adaptive sparse regularization coefficient, ΣΔα k+1 The accumulated damage recognition quantity represents the damage position and the damage degree of the structure;
representing data fidelity;
The solution sparsity constraint is identified on behalf of the impairment.
Equation (13) innovatively considers the prior condition that the damage of the actual engineering structure often only occurs at a small number of spatial positions of the structure, thereby adopting L1 norm to construct sparse regularized constraint terms
L2 regularization term ++L 2 instead of traditional Gibbunov regularization method>
Therefore, the sparse constraint can be automatically carried out in the adaptive sparse canonical-based damage identification solving process, the convergence speed of the solving is higher, and false alarms on a non-damage identification unit are greatly reduced. Therefore, in the process of iteratively solving the equation (10) and the equation (13), a convergence solution (as shown in fig. 4) can be finally obtained, so that an accurate structure damage position and an accurate structure damage degree can be provided, the accurate and reliable diagnosis and evaluation of the engineering structure safety are realized, the early warning, early discovery and early treatment of the engineering structure safety risk hidden danger are realized, and the safe operation and emergency of the urban civil engineering structure are ensuredThe high efficiency of the piece should be dealt with.
Further, in the damage identification solving process based on the adaptive sparse regularization method, the adaptive capacity of the method is derived from the adaptive regularization coefficient lambda in the formula (13) *k+1 . Because formula (13) is an L1 regularized optimization equation, the analytic solution is not available, and lambda is not given in the current research literature *k+1 An expression with a mathematical formula gives a subjective regularization coefficient, mainly empirically or by extensive trial-and-error. Thus, lambda is proposed against the above-mentioned problems *k+1 By assuming that the expression represents the fidelity of the data
And +.about.representing impairment recognition solution sparsity constraint>
Has equal importance in the damage identification process, and a novel calculation method is provided, which comprises the following steps:
wherein ,
the solution of the reference is identified for the damage of the kth step, and the specific calculation formula is as follows:
that is, an optimization algorithm of semi-definite programming is adopted to solve, and the method can efficiently search out the optimal solution in a feasible domain in the process of solving each step.
After the damage and the identification result are obtained, the method further comprises step S7, and early warning is carried out on the damage and the identification result through a wireless data communication mode such as a short message mode, an Email mode and the like.
The invention provides a damage identification system based on data fusion and adaptive sparse regularization, which is used for executing the damage identification method based on data fusion and adaptive sparse regularization, and concretely comprises the following steps as shown in fig. 5:
(1) The data real-time acquisition and transmission module: structural dynamic response data such as structural vibration acceleration, displacement, strain and the like are continuously collected in an unattended mode for 24 hours, monitoring data are transmitted back to a management party in real time through a 4G/5G or special network, states and related parameters of the sensor can be remotely checked and set, a data continuous transmission function is provided when transmission is interrupted, and a person on duty is reminded of paying attention. When an event such as an earthquake, typhoon, impact or explosion occurs, the system automatically triggers the recording of event data.
(2) And the data storage and management module is used for: aiming at mass multi-source heterogeneous monitoring data generated by a plurality of structural arrays of different types of sensors, a high-performance database with dynamically expandable storage capacity and dynamically hierarchical data management is established based on a cloud storage technology, and the problems of limited storage space and low reading efficiency of traditional monitoring data are solved.
(3) Data analysis and structural security assessment module: providing basic data analysis such as cleaning, data integration, data conversion, data reduction, data integration, spectrum analysis and statistical value analysis (including the most value, average value, peak-to-peak value and effective value); aiming at structural safety evaluation, a covariance-based multivariate data fusion damage identification analysis algorithm is embedded, so that automatic analysis of mass data and damage diagnosis based on multivariate perception data fusion are realized, manual intervention is not needed in the process, and automatic and efficient structural state evaluation is realized.
(4) Structural safety early warning and early warning information sending module: and establishing a set of scientific and reasonable structural safety multi-level early warning threshold index system according to the standard limit value and the structural damage identification evaluation result, and taking the system as the basis of structural safety early warning. The early warning information can be automatically pushed to the manager in various forms such as mail, weChat, short message and the like according to the requirement.
(5) And a system visualization module: the system visual interface based on the B/S architecture and friendly to users is provided, and comprehensive functions such as structure information management, sensor management, real-time data and spectrum dynamic visual display, real-time data background automatic analysis and result storage, historical data calling and analysis, structure safety state assessment and early warning information management and release are realized.
The cloud platform-based data transmission mode, model application mode and system integration technology integrate data and analysis algorithms based on different platforms and different interfaces to form a unified data layer, a model layer, an evaluation algorithm layer and an auxiliary decision layer, and finally form a set of software system of a B/S architecture deployed at the cloud end, so that integrated and efficient management of disaster prevention safety intelligent monitoring, evaluation early warning and auxiliary decision of a civil structure is realized.
In conclusion, the multi-disciplinary Internet of things and structural dynamics analysis technology are utilized to collect, transmit, store, analyze and apply structural dynamic response monitoring data of acceleration, displacement, strain and the like of key parts of a civil structure in real time, and the invention provides a method and a system for structural health monitoring and damage identification, which can realize the fusion of multiple heterogeneous data such as acceleration, displacement, strain and the like, and a self-adaptive sparse regularization solving technology, so that health diagnosis, evaluation and early warning of a safety state of the civil structure are realized, early warning information is automatically pushed to related personnel, and scientific basis is provided for disaster prevention safety operation of the civil structure. Aiming at the problems of multistage data transmission, independent monitoring system and auxiliary decision making and the like of the traditional health monitoring system, the invention provides a cloud platform-based data transmission mode, a model application mode and a system integration technology, integrates data and analysis algorithms based on different platforms and different interfaces to form a unified data layer, a model layer, an evaluation algorithm layer and an auxiliary decision making layer, and provides a cloud deployment integrated software platform for realizing integrated and efficient management of disaster prevention safety intelligent monitoring, evaluation early warning and auxiliary decision making of a civil structure.
The sparse regular damage identification method and device based on data fusion and self-adaption are described above, and are used for helping understanding the sparse regular damage identification method and device; however, the embodiments of the present invention are not limited to the above examples, and any changes, modifications, substitutions, combinations, and simplifications that do not depart from the principles of the present invention should be made in the equivalent manner, and are included in the scope of the present invention.
Claims (10)
1. The damage identification method based on data fusion and self-adaptive sparse regularization is characterized in that the method is based on the multi-element perception data fusion of the data measured by a plurality of sensors with small quantity of optimized arrangement through covariance and adopts the self-adaptive sparse regularization method to solve, and specifically comprises the following steps:
s1, carrying out optimal arrangement calculation analysis on measuring points of a structural health monitoring sensor through a finite element model of a preset civil structure to obtain an optimal arrangement scheme of a plurality of sensors of the structural health monitoring;
s2, acquiring the multi-element perception structure power response monitoring data of the target civil structure in real time, and carrying out standardization and dimensionless processing on the structure power response monitoring data to obtain a standardized structure response vector;
s3, performing multi-component heterogeneous data fusion on the standardized structure response vector by adopting a covariance function, and constructing and obtaining a damage identification index V of multi-component perception fusion based on covariance according to the multi-component heterogeneous data fusion result pq The damage identification index based on covariance multi-element perception fusion is sensitive to local damage and insensitive to measurement noise;
s4, calculating to obtain the multi-element perception data fusion damage index vector of the target civil structure containing the damage information according to the covariance-based multi-element perception fusion damage identification index and the actually measured multi-element perception structural power response monitoring data
S5, performing simulation calculation according to the covariance-based multi-element perception fusion damage identification index and a finite element model in a health reference state by adopting the preset civil structure to obtain multi-element perception data fusion damage based on the finite element modelIndex vector
S6, according to
and
Constructing a damage identification equation, solving the damage identification equation by combining the self-adaptive sparse regularization technology, updating a model, and finally carrying out iterative solution to obtain a damage identification result, thereby positioning the position of the damage and the damage degree.
2. The method for identifying damage based on data fusion and adaptive sparse regularization of claim 1, further comprising, after obtaining the normalized structural response vector in S2: and screening out an optimized structural power response data vector sensitive to structural local damage from the standardized structural response vector according to the optimized layout scheme of the structural health monitoring sensor.
3. The method for identifying damage based on data fusion and adaptive sparse regularization of claim 1, wherein the structural dynamic response monitoring data comprises structural response variables: structural dynamic response data such as acceleration, displacement, strain and the like are expressed as follows by a state space equation:
y(t)＝C c x(t)+D c f(t) (1)
wherein ,
wherein ,
for the observation vector, ε (t) is the strain response time courseZ (t) is the displacement response time interval,
is the acceleration response time course;
For the state vector, the power equation is satisfied:
wherein ,
wherein ψ=bgl d Φ is a strain modal matrix; l (L) d Is a selection matrix of node displacement matching strain calculation; the matrix G is a coordinate transformation matrix from global coordinates to local coordinates; vector B defines the local strain-displacement relationship; phi is a modal matrix; omega and xi are diagonal matrixes formed by the natural frequency and damping ratio of the structure respectively; q and
respectively the displacement and the speed under the modal coordinates; f (t) is the external excitation vector.
4. The damage identification method based on data fusion and adaptive sparse regularization of claim 1, wherein in S2, the structural dynamic response monitoring data is normalized and dimensionless processed to obtain a normalized structural response vector, and the normalized structural response vector is calculated by adopting the following formula:
wherein ,yp (t) is raw structural dynamic response monitoring data,
is y and y p (t) the corresponding standard deviation.
5. The method for identifying the damage based on data fusion and adaptive sparse regularization of claim 1, wherein the step of performing multi-component data fusion on the normalized structural response vector in S3 by using a covariance function includes calculating a cross covariance function of any two normalized structural responses, wherein the formula is as follows:
wherein ,yp For the structural response recorded by the sensor p,
for normalized structural response, +.>
A standard deviation representing the structural dynamic response of sensor p recorded on a sound structure; e denotes the expected variable τ is the time interval, the subscript p, q denotes the response calculated from the sensor p, q;
based on the formula (6), the damage identification index V based on covariance multi-element perception fusion is obtained through the following formula construction pq ：
wherein ,pi ∈[p 1 ,p s ],q j ∈[q 1 ,q s ]Subscript s represents the total number of selected sensors; nt is the total number of time intervals selected for lesion recognition.
6. The method for identifying damage based on data fusion and adaptive sparse regularization as recited in claim 1, wherein performing simulation calculation on the finite element model of the preset civil structure in S5 includes:
assuming that the stiffness matrix of the damaged structure can be expressed by a mathematical formula:
wherein ,0≤αi ≤1，-1≤Δα i ≤0，α i The E alpha is a coefficient corresponding to the ith unit stiffness matrix; Δα i E Δα is the local stiffness variation of the ith cell; ne is the total number of units; alpha is a vector of stiffness matrix coefficients; Δα is the vector of the local stiffness change of the damaged cell.
7. The sparse regular impairment recognition method based on data fusion and adaptation of claim 6, wherein S4 comprises: will be
Expressed as a lesion recognition equation with a first order taylor expansion:
converting the formula (9) into a linear damage identification equation integrated with an iterative Gaussian-Newton algorithm by adopting a time domain iterative method:
where k=0, 1,2,3,.,
the step S6 comprises the following steps: let the damage recognition parameter α be expressed by equation (13) and by Δα= ΣΔα k+1 Obtaining updated damage parameters, wherein the obtained damage parameters are used for model updating in a finite element model of an engineering structure, so that the correspondingly updated structure damage position and severity are identified;
wherein ,λ*k+1 Is an adaptive sparse regularization coefficient, ΣΔα k+1 The accumulated damage recognition quantity represents the damage position and the damage degree of the structure;
representing data fidelity; ||ΣΔα k+1 || 1 The solution sparsity constraint is identified on behalf of the impairment.
8. The sparse regular damage identification method based on data fusion and adaptation of claim 7, wherein
and ||∑Δαk+1 || 1 Of equal importance in the lesion recognition process, said lambda *k+1 Expressed as:
wherein ,
the solution of the damage identification reference in the kth step is shown as follows:
9. the method for identifying damage based on data fusion and adaptive sparse regularization of claim 1, further comprising the steps of:
and S7, automatically early warning the damage identification result through a wireless data communication mode.
10. A data fusion and adaptive sparse regularization based lesion recognition system for performing a data fusion and adaptive sparse regularization based lesion recognition method according to any of claims 1-9, and comprising:
(1) The data real-time acquisition and transmission module is used for continuously acquiring structural power response data such as structural vibration acceleration, displacement, strain and the like in an unattended manner for 24 hours, transmitting the monitoring data back to the management Fang Yun platform in real time through a 4G/5G or special network, and remotely checking and setting the state and related parameters of the sensor;
(2) The data storage and management module is used for mass multi-source heterogeneous monitoring data generated by a plurality of structural arrays of different types of sensors and comprises: establishing a high-performance database with dynamically expandable storage capacity and dynamically hierarchical data management based on a cloud storage technology;
(3) The data analysis and structure safety evaluation module is used for providing basic data analysis such as data cleaning, data integration, data conversion, data reduction, data integration, spectrum analysis, statistic analysis and the like, and is used for evaluating the structure safety and comprises the following steps: the damage identification analysis algorithm based on covariance multi-element data fusion is embedded, so that automatic analysis of mass data and damage diagnosis based on multi-element perception data fusion are realized, manual intervention is not needed in the process, and automatic and efficient structural state evaluation is realized;
(4) The structural safety early warning and early warning information sending module is used for establishing a structural safety multi-level early warning threshold value index system according to the standard limit value and the structural damage identification evaluation result, and taking the structural safety multi-level early warning threshold value index system as the basis of structural safety early warning;
(5) And the system visualization module is used for providing a B/S architecture-based user-friendly system visualization interface.
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