KR101948850B1 - Electrical Impedance DEVICE AND METHOD FOR Damage MONITORING in SELF-SENSING CONCRETE-BASED STRUCTUREs - Google Patents

Abstract

An electrical impedance device for damage monitoring of a structure constructed based on self-sensing concrete includes a receiver for receiving an electrical impedance signal transmitted from a structure, a memory for storing a structure monitoring program, and a processor for executing a structure monitoring program, According to the execution of the structure monitoring program, it is judged whether or not the damage of the structure is diagnosed based on a plurality of collected electrical impedance signals. By applying a nonlinear principal component analysis method to the electrical impedance signal, the influence of external environmental variables such as temperature and humidity is minimized Damage diagnosis of structures is performed based on one principal component data.

Description

TECHNICAL FIELD [0001] The present invention relates to an electrical impedance device and a method for monitoring damage of a structure constructed based on self-sensing concrete,

The present invention relates to an electrical impedance device and method for damage monitoring of a structure built on a self-sensing concrete.

Damage diagnosis and evaluation technology of concrete structures can be classified into contact type and non-contact type in a large category. Ultrasonic techniques are typically used for evaluation of contact damage diagnosis of concrete structures. The ultrasonic technique is a method of evaluating the state by attaching an ultrasonic sensor to a concrete structure and measuring an electro-dynamic signal. The contact type sensor has a high sensitivity and low cost of the sensor. On the other hand, there is a problem that it is difficult to analyze the acquisition signal due to the durability problem of the sensor itself and the environment change.

Ultrasonic testing and evaluation of damage diagnosis using electromagnetic waves are available for the diagnosis of noncontact damage, but it is not easy to apply to concrete structures, and it is difficult to use for the purpose of Structural Health Monitoring (SHM).

In addition to these methods, electrical resistance measurement method using self-sensing concrete has been widely studied for the diagnosis of concrete damage. It is a method to enable self-sensing by adding conductive fiber to concrete.

Therefore, it is possible to measure the response of the structure itself without using a separate sensor. Typically, there is a DC signal measurement and an AC electrical signal measurement, and the measurement of the DC electrical signal takes a considerable time to stabilize the resistance value by accumulating electric charge in the structure. This does not meet the purpose of real-time SHM, and the AC electrical measurement that does not require a relatively stabilization time meets the SHM objective.

In this connection, in Korean Patent No. 10-1551446 (entitled "Remote monitoring system and method of cement composite incorporating conductive fiber"), conductive fiber (or electroconductive) is used as reinforcing fiber for increasing the rigidity of cement composite Discloses a remote monitoring system and method of a cement composite that can incorporate a fiber into a cement composite and remotely monitor the change in stress of the cement composite due to external environmental factors such as load, fire or earthquake from the conductive fiber.

However, in order to utilize the self-sensing concrete material in various studies, it is necessary to develop a real-time measurement system, to minimize misunderstandings due to environmental effects, and to further utilize this material as a structural member It is a situation.

An embodiment of the present invention is to solve the problems of the prior art described above. In order to realize the object of the SHM, the electrical impedance signal measured in real time is processed through a machine learning based damage diagnosis algorithm, We want to provide only damage information excluding environmental impact.

It should be understood, however, that the technical scope of the present invention is not limited to the above-described technical problems, and other technical problems may exist.

According to an aspect of the present invention, there is provided an electrical impedance apparatus for monitoring damage of a structure built on a self-sensing concrete according to an aspect of the present invention includes a receiver for receiving an electrical impedance signal transmitted from a structure, A memory for storing a monitoring program and a processor for executing a structure monitoring program, wherein the processor judges whether or not the damage of the structure is diagnosed based on a plurality of collected electrical impedance signals in accordance with the execution of the structure monitoring program, The nonlinear principal component analysis method is applied to diagnose the damage of the structure based on the principal component data which minimizes the influence of external environment variables such as temperature and humidity.

According to another aspect of the present invention, there is provided an electrical impedance method for damage monitoring of a structure built on a self-sensing concrete, comprising: receiving an electrical impedance signal transmitted from a structure; Generating learning data based on principal component data that minimizes the influence of temperature and humidity, which are external environmental variables, by applying a nonlinear principal component analysis method to the electrical impedance signal by collecting the received electrical impedance signal to generate learning data; And applying a nonlinear principal component analysis method to the newly input electrical impedance signal and then performing a damage diagnosis of the structure based on the comparison result with the learning data.

According to any one of the above-mentioned objects of the present invention, only damage information excluding the influence of the surrounding environment can be finally delivered to the user through the machine learning-based damage diagnosis algorithm. In other words, the SHM of the concrete structure which can not be used can be ultimately realized, and in particular, the self-sensing concrete has an advantage that it is directly used as the structural member.

In addition, there is no need for additional maintenance or maintenance since additional sensors are not required to be buried or attached.

1 is a configuration diagram of an electrical impedance device for damage monitoring of a structure built on a self-sensing concrete according to an embodiment of the present invention.
FIGS. 2A to 2C are flowcharts for explaining a method for monitoring damage of a structure built on a self-sensing concrete according to an embodiment of the present invention.
3 is a diagram for explaining the concept of a principal component analysis method according to an embodiment of the present invention.
4 is a graph showing a resistance value and a reactance value for analyzing a change in electrical impedance according to temperature and humidity according to an embodiment of the present invention.
5 is a graph illustrating a force-displacement curve for a bending test of a structure according to an embodiment of the present invention.
FIG. 6 is a graph showing a resistance value and a reactance value for analyzing the change of the electrical impedance of the structure damage in FIG.
7 is a diagram showing a difference in distance between input data and learning data based on a statistical model generated based on principal component data according to an embodiment of the present invention.
FIG. 8 is a diagram showing a comparison result of the distance difference between the input data and the learning data of FIG. 7 and the threshold value.

Hereinafter, embodiments of the present invention will be described in detail with reference to the accompanying drawings, which will be readily apparent to those skilled in the art. The present invention may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein. In order to clearly illustrate the present invention, parts not related to the description in the drawings are omitted, and like parts are denoted by similar reference numerals throughout the specification. In the following description with reference to the drawings, the same reference numerals will be used to designate the same names, and the reference numerals are merely for convenience of description, and the concepts, features, and functions Or the effect is not limited to interpretation.

Throughout the specification, when a part is referred to as being "connected" to another part, it includes not only "directly connected" but also "electrically connected" with another part in between . Also, when a component is referred to as " comprising ", it is understood that it may include other components as well as other components, But do not preclude the presence or addition of a feature, a number, a step, an operation, an element, a component, or a combination thereof.

Herein, the term " part " or " module " means a unit realized by hardware or software, a unit realized by using both, and a unit realized by using two or more hardware Or two or more units may be realized by one hardware.

1 is a configuration diagram of an electrical impedance device for damage monitoring of a structure built on a self-sensing concrete according to an embodiment of the present invention.

Referring to FIG. 1, an electrical impedance device 1 for damage monitoring of a structure built on a self-sensing concrete includes a receiver 10 for receiving an electrical impedance signal transmitted from the structure 100, a structure monitoring program A memory 20, and a processor 30 for executing a structure monitoring program. The processor 30 determines whether or not the structure 100 is damaged based on the collected plurality of electrical impedance signals according to the execution of the structure monitoring program, and applies a nonlinear principal component analysis method to the electrical impedance signal, The damage diagnosis of the structure 100 can be performed based on the principal component data that minimizes the influence of temperature and humidity.

Accordingly, the electrical impedance device for damage monitoring of a structure constructed based on the self-sensing concrete of the present invention can measure and analyze the electrical impedance in real time without embedding or attaching the sensor. In addition, since the damage information according to the occurrence of cracks is transmitted to the user regardless of the change in the external environment according to temperature and humidity in real time, it is effective to minimize the misunderstanding of the electric impedance device for damage monitoring of the structure.

 First, a method of measuring the electrical impedance will be described.

Specifically, the receiving unit 10 may receive the electrical impedance signal transmitted from the structure 100 through the self-sensing circuit 110. [ Here, the structure 100 means a self-sensing concrete that can be utilized as a signal measurement sensor. For example, the combination of the concrete mixture may be mixed in proportion to the amount of cement on a weight basis, and the concrete may be cured by mixing steel fibers to increase the conductivity and stiffness. That is, the steel provided on the cross section of the concrete becomes a channel through which electricity flows as steel fibers, thereby enhancing the conductivity of the concrete and enhancing the rigidity thereof.

In order to measure the electrical impedance of the concrete, the conductivity of the nonconductive adult concrete should be given. 1, the structure 100 may be designed to protrude a steel fiber (e.g., steel or copper) reinforced with a fiber-reinforced cement composite material, The electric impedance can be measured. The input signal used in the measurement utilizes frequency sweeping. Then, by measuring the electrical impedance signal through the input signal, many characteristics and characteristic changes can be confirmed at various frequencies.

Illustratively, a self-sensing scheme is used to measure the capacitance of a fiber-reinforced cementitious composite through a self-sensing circuit 110 and to calculate an electrical response. . The calculation procedure is shown in Equation 1 below.

&Quot; (1) "

C _C, v _c, v _i, v _o, i, C _r represents the capacitance of the capacitance, voltage, input voltage, output voltage, output current, and reference (Reference) of each fiber-reinforced cement composite material, τ is It means a short time term. The differential value of the voltage, the AC voltage, and the angular velocity.

Then, the electrical impedance is measured using an input waveform through the self-sensing scheme obtained through Equation (1). The measurement process is shown in Equation 2 below.

&Quot; (2) "

The processor 30 generates learning data based on the principal component data, applies a nonlinear principal component analysis method to the newly inputted electrical impedance signal, and then performs damage diagnosis of the structure 100 based on the comparison result with the learning data have.

The processor 30 applies a nonlinear transform function for transforming low-dimensional data to a high-dimensional transform for the electrical impedance signal, generates a covariance matrix using the transformed high-dimensional data, constructs an eigenvalue problem based on the covariance matrix, The eigenvalue problem can be solved by applying a predetermined kernel function to obtain eigenvalues, eigenvectors, and nonlinear principal components.

The processor 30 generates a statistical model that conforms to the Generalized Extreme Value Distribution (GEV) based on the inputted principal component data, and compares the difference between the input data and the learning data and the threshold value based on the statistical model It is possible to diagnose whether or not it is damaged.

Specifically, characteristics are extracted from the measured data when there is no damage to the fiber-reinforced cement structure 100, and a statistical model is generated for them that follows the GEV distribution. Then, the cumulative density function (CDF) of the estimated probability model is calculated, and the confidence interval is set using the calculated CDF. At this time, a value having the reliability is set as a threshold value.

In addition, the processor 30 may recognize that a damage has occurred when the damage index of the test data exceeds a threshold value. In this case, the damage index means a difference in distance between input data and training data obtained by transforming principal component data obtained through principal component analysis into higher dimensional data. A detailed description thereof will be described later with reference to equations (12) to (14), (7) and (8).

For example, the processor 30 applies the non-linear principal component analysis method to a plurality of electrical impedance signals transmitted from the structure 100 to generate principal component data minimizing the influence of temperature and humidity, and generates learning data based on the generated principal impedance data can do. Accordingly, when damage occurs to the structure 100, the processor 30 calculates a distance difference between the input data and the learning data, compares the distance difference with the threshold value, and judges that the structure 100 is damaged when the structure 100 is damaged .

Hereinafter, a method for monitoring damage of a structure built on a self-sensing concrete according to another embodiment of the present invention will be described.

The description of the configuration shown in FIG. 1 for performing the same function will be omitted.

FIGS. 2A to 2C are flowcharts for explaining a method for monitoring damage of a structure built on a self-sensing concrete according to an embodiment of the present invention.

Referring to FIG. 2A, a method for monitoring the damage of a structure built on the basis of the self-sensing concrete of the present invention includes receiving an electrical impedance signal transmitted from the structure 100 (S110), receiving the received electrical impedance signal (S120) of generating learning data based on principal component data that minimizes the influence of temperature and humidity, which are external environmental variables, by applying a nonlinear principal component analysis method to the electrical impedance signal, And a step (S130) of performing damage diagnosis of the structure based on the comparison result with the learning data after applying the nonlinear principal component analysis method to the electrical impedance signal.

Referring to FIG. 2B, the step of generating learning data S120 includes applying a nonlinear transform function for transforming low-dimensional data to a high-dimensional transform for the electrical impedance signal (S121), transforming the covariance matrix using the transformed high- (S123) of constructing an eigenvalue problem based on the covariance matrix, and a step (S124) of obtaining eigenvalues, eigenvectors and nonlinear principal components by applying a predetermined kernel function to the eigenvalue problem .

Referring to FIG. 2C, step S 130 of performing damage diagnosis of a structure includes generating a statistical model following a generalized extreme value distribution (GEV) based on principal component data (S 131) and based on a statistical model (S132) diagnosing damage based on a result of comparison between a distance difference between the newly input electrical impedance signal and the learning data and a threshold value.

Hereinafter, a method of excluding electrical signals due to external environmental influences of temperature and humidity by using a principal component analysis method and extracting only electrical changes of a state (crack) change of the structure 100 will be described in detail.

3 is a diagram for explaining the concept of a principal component analysis method according to an embodiment of the present invention.

Here, the principal component analysis method is to reduce a large number of variables to a small number of factors, which means that a small number of changes among a plurality of variables is defined as a principal component.

Electrical impedance varies with the state change, but it also changes with the temperature and humidity change which are environmental influences. The sensitivity of this impedance causes a false alarm in the instantaneous impedance measurement of the actual structure 100. To reduce these false alarms, data normalization can be performed using nonlinear principal component analysis techniques.

That is, as shown in FIG. 3, the principal component analysis technique transforms low-dimensional data into high-dimensional data, and then the average of the transformed data is zero. The measured impedance data that has undergone this process can be assumed to be linear by extending from a nonlinear relationship to a higher dimensional space, and thus can perform principal component analysis.

First, the processor 30 applies the following Equation 3, which is a nonlinear transform function for transforming low-dimensional data to high-dimensional in order to perform principal component analysis.

&Quot; (3) "

Then, the processor 30 generates a covariance matrix as shown in Equation (4) using the converted high-dimensional data.

&Quot; (4) "

At this time, ρ (x _j ) is the nonlinear characteristic vector converted through the transform function, and the covariance matrix M ₂ can be derived using the transformed data.

Next, the processor 30 constructs the eigenvalue problem, as shown in equation (5), based on the covariance matrix.

Equation (5)

Here, lambda and c are eigenvalues and corresponding eigenvectors, respectively. Also, c can be assumed to be linear by extending the above-mentioned high dimensional data, and can be represented by a linear polynomial of p (x _j ) as shown in Equation (6) below. (i = 1, ..., N)

&Quot; (6) "

Here,? _I is an unknown coefficient, and multiplying both sides of Equation (6) by? (X _j ) T yields another eigenvalue problem as shown in Equation (7) below.

&Quot; (7) "

이고, 공분산 행렬

으로 정의된다. here,

, And the covariance matrix

.

Next, the processor 30 may apply a predetermined kernel function to the eigenvalue problem, as shown in equation (8), to obtain eigenvalues, eigenvectors, and nonlinear principal components. The two transformation functions ρ (x _i ) and ρ (x _j ) in Eq. 7 are nonlinear transformations and can easily be calculated by Mercer's theorem by replacing them with kernel functions k (x _i , x _j ) .

&Quot; (8) "

Here, a Gaussian kernel is used as a kernel function, and ρ is a constant that must be defined by a user with a Gaussian width.

Also, an eigenvector corresponding to p eigenvalues is normalized as shown in Equation (9).

&Quot; (9) "

Here, lambda _k and alpha _k are the kth eigenvalue and the eigenvector respectively obtained from Equation (7). Finally, the k-th non-linear principal component element of the feature vector x _i derives the equation (10) by the orthogonal projection x _j to k-th eigenvector.

&Quot; (10) "

Here, KPC _k (x _j ) and ( _k ) _i , c _k mean the kth nonlinear principal component, eigenvalue and eigenvector of the feature vector x _i obtained through kernel principal component analysis, respectively. Also, k (x _i , x _j ) is the selected kernel function. When the nonlinear transform function ρ (.) Is used, the K matrix is more easily calculated by normalizing the average of the transformed data to 0, which can be defined as K ^* as shown in Equation (11). The use of the K ^* matrix instead of the existing K matrix allows more efficient nonlinear principal component analysis.

Equation (11)

The processor 30 then generates a statistical model that follows the Generalized Extreme Value Distribution (GEV) based on the input principal component data.

First, to diagnose damage based on nonlinear principal component analysis, the threshold value should be calculated by using statistical technique. The extreme value distributions and the angular distributions obtained by generalizing the Gumbel, Frechet and Weibull distributions of the extreme value distribution as one distribution are as shown in the following equations (12) and (13).

&Quot; (12) "

&Quot; (13) "

Where k and δ are the location, scale and shape parameters of the extremum distribution, respectively. And the generalized extreme values of μ, σ, and γ represent the position, scale, and shape parameters of the probability distribution, respectively. Gumbel, Frechet, and Weibull distributions can all be represented by a combination of these parameters.

Extracts characteristics from measured data when fiber-reinforced cementitious composites (FRCCs) are not damaged and generates statistical models that follow the GEV distribution for them. Then, the cumulative density function (CDF) of the estimated probability model is calculated, and the confidence interval is set using the calculated CDF. Finally, a value having the reliability is set as a threshold value.

Next, based on the statistical model, the processor 30 can diagnose the damage based on the comparison result of the distance difference between the input data and the learning data and the threshold value.

If the damage index of the input data exceeds the threshold value, it can be recognized that the damage has occurred. As shown in Equation (14), the damage index is defined as the minimum distance between the input data and the training data obtained by vertically transforming the data through the principal component analysis.

&Quot; (14) "

은 i번째 주성분 축 위에 정사영시킨 j번째 입력 데이터, l번째 학습 데이터를 의미한다. 이와 같은 머신 러닝 기반의 신호 처리를 통해 온도, 습도 등 환경영향에 의한 전기적 신호를 배제한 오직 상태변화만의 전기적 변화를 추출한다.here,

Denotes the j-th input data and the l-th learning data orthogonalized on the i-th principal component axis. Through such machine learning based signal processing, only electrical changes of state are extracted, excluding electrical signals due to environmental influences such as temperature and humidity.

Hereinafter, with reference to FIGS. 4 to 8, a method for minimizing the influence of temperature and humidity, which are external environmental variables, by applying a nonlinear principal component analysis method to an impedance signal according to an embodiment of the present invention will be described. This will be described in detail.

Experimental setup consisted of experimental setup to exclude temperature and humidity influences and experimental setup to measure electrical impedance change due to cracking. The self - sensing concrete used in the experiment was the fiber - reinforced cement composite material described above.

In order to analyze the change of electrical impedance according to temperature and humidity, experiments were conducted under the same conditions as in Table 1. The electric impedance value according to the influence of temperature and humidity is represented by a complex number, of which the real part represents the resistance value and the imaginary part represents the reactance value.

<Table 1>

4 is a graph showing a resistance value and a reactance value for analyzing a change in electrical impedance according to temperature and humidity according to an embodiment of the present invention. That is, Fig. 4 (a) shows the change of the resistance value according to the temperature and humidity change, and Fig. 4 (b) shows the change of the reactance value.

In this case, frequency sweeping is used to analyze various frequency responses. In fact, since the change in resistance and reactance varies depending on the frequency of the excitation, it is possible to learn more information in the subsequent machine learning process There are advantages to be able to. As shown in Fig. 4, the impedance varies very sensitively due to temperature and humidity. Particularly, it can be confirmed that a change value of electric impedance due to temperature is dominant rather than a change of electric impedance due to humidity.

5 is a graph illustrating a force-displacement curve for a bending test of a structure according to an embodiment of the present invention. FIG. 6 is a graph showing a resistance value and a reactance value for analyzing the change of the electrical impedance of the structure damage in FIG.

In order to confirm the change of electrical impedance due to the damage, a three - point bending test was carried out using a universal testing machine (UTM).

As shown in FIG. 5, the force-displacement curve for the bending test is divided into four state changes, and the experiment is performed with the elastic section (closed crack), the inelastic section (initial open crack) yielding section (multiple cracking) .

FIG. 6 shows the result of the electrical impedance change according to the damage shown in FIG. 5, and the electrical impedance value according to the damage is the resistance value and the reactance value used for the temperature and humidity change. That is, FIG. 6A shows the change of the resistance value with the state change, and FIG. 6B shows the change of the reactance value.

As a result of the experiment, various electrical impedance values can be confirmed in the interval of 50 to 100 Hz through frequency sweeping as shown in FIG. Particularly, it can be seen that the impedance variation according to temperature is dominant rather than the impedance change due to the crack, and this is an obstacle to extracting only the crack information as described above.

Therefore, a structure monitoring program according to an embodiment of the present invention is applied in order to eliminate environmental influences due to temperature and humidity and extract only the influence of cracks.

7 is a diagram showing a difference in distance between input data and learning data based on a statistical model generated based on principal component data according to an embodiment of the present invention. FIG. 8 is a diagram showing a comparison result of the distance difference between the input data and the learning data of FIG. 7 and the threshold value.

Referring to FIG. 7, data necessary for analysis between input data (test data) and training data (training data) are classified based on statistical models generated based on principal component data, and conditions for temperature and humidity are summarized.

 FIG. 8 shows the result of the reactance value processed according to the above-described structure monitoring program, and it can be seen that the damage index of the input data 31 to 34 exceeds the threshold value. That is, it can be seen that only the input data 31 to 34 exceeds the threshold calculated in real time. This means that the change in impedance due to the rest of the environmental influence due to temperature and humidity is excluded.

Accordingly, the electrical impedance device and method for damage monitoring of a structure built on the basis of the self-sensing concrete of the present invention can measure and analyze the impedance instantly and provide the user with real- It is possible to transmit only the damage information according to the occurrence of cracks. This has the effect of minimizing the misunderstanding of the electrical impedance device for damage monitoring of the structure due to external environmental influences due to temperature and humidity.

The electrical impedance device and method for damage monitoring of a structure built on the basis of the self-sensing concrete according to the embodiment of the present invention described above can be applied to a recording medium including a program including a program module executed by a computer, But may also be implemented in the form of a medium. Computer readable media can be any available media that can be accessed by a computer and includes both volatile and nonvolatile media, removable and non-removable media. In addition, the computer readable medium may include both computer storage media. Computer storage media includes both volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data.

It will be understood by those skilled in the art that the foregoing description of the present invention is for illustrative purposes only and that those of ordinary skill in the art can readily understand that various changes and modifications may be made without departing from the spirit or essential characteristics of the present invention. will be. It is therefore to be understood that the above-described embodiments are illustrative in all aspects and not restrictive. For example, each component described as a single entity may be distributed and implemented, and components described as being distributed may also be implemented in a combined form.

Furthermore, while the methods and systems of the present invention have been described in terms of specific embodiments, some or all of those elements or operations may be implemented using a computer system having a general purpose hardware architecture.

It is intended that the present invention covers the modifications and variations of this invention provided they come within the scope of the appended claims and their equivalents. .

1: Electrical impedance device for structural damage monitoring
10: Receiver
20: Memory
30: Processor
100: Structure
110: self-sensing circuit

Claims (7)

An electrical impedance device for damage monitoring of a structure built on self-sensing concrete,
A receiver for receiving an electrical impedance signal transmitted from the structure,
Structural monitoring program stored memory,
And a processor for executing the structure monitoring program,
Wherein the processor is configured to determine whether to diagnose damage to the structure based on the collected plurality of electrical impedance signals in accordance with execution of the structure monitoring program and to apply a nonlinear principal component analysis method to the electrical impedance signal, And a nonlinear principal component analysis method is applied to a newly inputted electrical impedance signal, and the damage diagnosis of the structure is performed based on the comparison result with the learning data,
The structure includes an electrode formed to protrude to measure a signal of the fiber-reinforced cement composite material,
The receiver measures an electrical impedance through an electrode of the structure through a self-sensing circuit,
An input signal used for the measurement utilizes frequency sweeping to check a measurement signal in various sections and measures the electrical impedance signal through an output signal,
Applying a nonlinear transform function for transforming low dimensional data to high dimensional for the electrical impedance signal, generating a covariance matrix using the transformed high dimensional data, constructing a unique value problem based on the covariance matrix, To obtain an eigenvalue, an eigenvector, and a nonlinear principal component,
Generating a statistical model that follows a generalized extreme value distribution (GEV) based on the measured principal component data when there is no damage to the structure, calculating a distance difference between input data and learning data based on the statistical model, Based on the comparison result with the value,
The threshold value may be calculated by calculating a cumulative density function (CDF) of the statistical model, setting a confidence interval using the calculated cumulative density function, and setting a confidence interval corresponding to the confidence interval However,
When the damage index of the input data exceeds the threshold value, it is determined that damage has occurred to the structure,
Wherein the damage index is defined as a minimum distance between the input data and the learning data obtained by orthogonally transforming the principal component data into a higher dimensional form and defined by Equation (1) below.
&Quot; (1) &quot;

(here,

Is j-th input data and l-th learning data orthogonalized on the i-th principal component axis) delete delete delete An electrical impedance method for damage monitoring of a structure built on self-sensing concrete,
Receiving an electrical impedance signal transmitted from the structure;
And generates learning data by collecting the received electrical impedance signal and applying learning of nonlinear principal component analysis to the electrical impedance signal to generate learning data based on principal component data that minimizes influence of temperature and humidity, step; And
Applying a nonlinear principal component analysis method to a newly input electrical impedance signal and then performing a damage diagnosis of the structure based on a comparison result with the learning data,
The structure includes an electrode formed to protrude to measure a signal of the fiber-reinforced cement composite material,
The step of receiving the electrical impedance signal
And the electrical impedance is measured through the electrode of the structure through the self-sensing circuit,
An input signal used for the measurement utilizes frequency sweeping to identify an output signal in various frequency ranges and measures the electrical impedance signal through an output signal,
The step of generating the learning data
Applying a nonlinear transform function for transforming low dimensional data to high dimensional for the electrical impedance signal, generating a covariance matrix using the transformed high dimensional data, constructing a unique value problem based on the covariance matrix, Eigenvectors, and nonlinear principal components by applying a predetermined kernel function to the eigenvalues, eigenvectors,
The step of performing the damage diagnosis of the structure
Generating a statistical model that follows a generalized extreme value distribution (GEV) based on the measured principal component data when there is no damage to the structure, calculating a distance difference between input data and learning data based on the statistical model, Based on the comparison result with the value,
The threshold value may be calculated by calculating a cumulative density function (CDF) of the statistical model, setting a confidence interval using the calculated cumulative density function, and setting a confidence interval corresponding to the confidence interval However,
When the damage index of the input data exceeds the threshold value, it is determined that damage has occurred to the structure,
Wherein the damage index is defined as a minimum distance between the input data and the learning data orthogonally transformed from the principal component data into the higher dimensional data and defined by Equation 1 below.
&Quot; (1) &quot;

(here,

Is j-th input data and l-th learning data orthogonalized on the i-th principal component axis) delete delete

Images