KR101901352B1 - Predicting System For Seismic Observation - Google Patents

Abstract

Disclosed is a prediction system for seismic observation. The system comprises: a test structure including a plurality of floors, and provided with an acceleration sensor on an upper most layer among the floors; a tuned mass damper (TMD) reducing vibration generated in the test structure; a vibration table supporting the test structure and triggering an earthquake signal to the test structure; and a control portion for controlling a vibration table, wherein the control portion uses an actual measurement value measured through the acceleration sensor and at least one prediction model to calculate a prediction value predicting the actual measurement value, and is able to select the prediction model satisfying a predetermined condition. Accordingly, movement of a building can be easily measured.

Description

{Predicting System For Seismic Observation}

This disclosure relates to a system and method for predicting seismic observations, and more particularly to a system and method for predicting seismic observations using test structures.

In the case of high-rise buildings or bridges, seismic design that can withstand earthquakes and wind is essential. Taking high-rise buildings as an example, devices for suppressing vibration of buildings such as tuned mass damper (TMD) are generally disposed.

Test structures can be used to test the building's earthquake prior to the construction work. It can be useful in designing and constructing dry matter by collecting the behavior data of the monitored test structure when artificial impact and vibration are applied to the test structure.

In this case, there is a demand for a technique for easily predicting the behavior of the test structure and further the behavior of the building.

On the other hand, the above information is only presented as background information to help understand the present invention. No decision has been made as to whether any of the above contents can be applied as the prior art related to the present invention and no claim is made

Patent Registration No. 10-1540190 (Registered on July 22, 2015)

The present invention has been made to solve the above-mentioned problems, and one embodiment of the present invention proposes a prediction system and method for a seismic observation using a test structure.

It is to be understood that both the foregoing general description and the following detailed description are exemplary and explanatory and are not restrictive of the invention, unless further departing from the spirit and scope of the invention as defined by the appended claims. It will be possible.

In order to achieve the above object, there is provided a system for predicting seismic observations according to an embodiment of the present invention, comprising: a test structure including a plurality of floors and having an acceleration sensor on the top of the plurality of floors; A tuned mass damper (TMD) for attenuating vibration generated in the test structure; A vibration table for supporting the test structure and triggering an earthquake signal on the test structure; And a control unit for controlling the vibration table. The control unit calculates a predicted value for predicting the actual measurement value using the actual measurement value measured through the acceleration sensor and at least one prediction model, Can be selected.

According to various embodiments of the present invention, the following effects can be obtained.

First, by providing a prediction system for seismic observations using test structures, the behavior of test structures and real buildings can be predicted more accurately and quickly.

Second, device efficiency and cost savings can be improved by providing a prediction system that accurately predicts the behavior of a test structure mathematically without resorting to measurement alone.

Third, the system can be used to evaluate the seismic performance of already built structures, which can help predict and prevent disasters.

Fourth, when judging the safety performance of a high-rise building or a long span bridge, various situations can be tested when the system is used.

The effects obtained by the present invention are not limited to the above-mentioned effects, and other effects not mentioned can be clearly understood by those skilled in the art from the following description will be.

Figure 1 shows a prediction system for seismic observations using a test structure according to an embodiment.
2 is a view for explaining a TMD disposed in a test structure according to an embodiment.
3 is a block diagram showing a configuration of a prediction system for seismic observation according to an embodiment.
4 (a) to 4 (c) show a NARX model according to an embodiment.
5 is a table showing various prediction models according to the embodiment.
6 is a graph for predicting vibration measurement values according to an embodiment based on a specific prediction model.
7 shows a method of driving the prediction system according to the embodiment.

The following detailed description, which refers to the accompanying drawings, will serve to provide a comprehensive understanding of the various embodiments of the present disclosure, which are defined by the claims and the equivalents of the claims. The following detailed description includes various specific details for the sake of understanding, but will be considered as exemplary only. Accordingly, those skilled in the art will recognize that various changes and modifications of the various embodiments described herein may be made without departing from the scope and spirit of this disclosure. Furthermore, the descriptions of well-known functions and constructions may be omitted for clarity and conciseness.

1 shows a prediction system 100 for seismic observations using a test structure 10 in accordance with an embodiment of the present invention.

The system 100 may include a test structure 10, a vibration table 110, a vibration control box 120, and a control computer (PC).

The test structure 10 includes three layers 11-1 to 11-3 and accelerometers 130-1 to 130-3 are disposed for each layer 11-1 to 11-3 . However, since the vibration of the acceleration sensor 130-1 of the uppermost layer 11-1 of the acceleration sensor is observed most largely on the arrangement of the test structure 10, it can be used as the most important measurement sensor, Do not.

The floor 11 is flat and the height of the test structure 10 is 930 mm and each floor 11-1 to 11-3 is 300 mm X A size of 300 mm and a size of 3.4 Kg, and columns 15-1 to 15-4 of each layer may be 0.2 Kg. In addition, the test structure 10 includes a vibration table 110 and four contacts 13-1 to 13-4. The test structure 10 may be a structure simulating a building to be actually constructed, but the embodiment is not limited thereto.

The test structure 10 may be disposed on the vibration table 110. The vibration table 110 is moved to the left and right by the forces F ₁ and F ₂ . The force may be generated in the control box 120 under the control of the control computer (PC). Then, the test structure 10 is also moved by the force. In this way, the system 100 can produce an artificial earthquake occurrence situation.

A tuned mass damper (TMD) 20 may be disposed on top of the test structure 10, which will be described in more detail with reference to FIG.

Here, a passive vibration controller (TMD) 20, a tuned mass damper, may be disposed on the test structure 10. The TMD 20 may be used to attenuate vibrations of the structure. The TMD 20 includes a weight M of a somewhat heavy mass and the weight M may be connected to a spring or the like 22 or 24 fixed to the wall 21. The weight M may be 1.80 or 2.39 Kg. The weight of the weight M may be varied depending on the structure of the test structure 10. The TMD 20 shown in FIG. 2 is illustrated by way of example and can be configured in various shapes and shapes.

Hereinafter, the configuration of the prediction system 100 for seismic observation will be described with reference to the block diagram of FIG.

The system 100 may include a vibration table 110, a vibration controller 120, an acceleration sensor 130, a storage unit 140, and a controller 150. The components shown in FIG. 3 are not required to implement the system 100, so that the system 100 described herein may have more or fewer components than the components listed above

The vibration table 110 may be controlled by the vibration controller 120. The vibration table 110 can be vibrated laterally according to the required current provided from the vibration controller 120. [ The test structure 10 on the vibration table 110 is also vibrated together.

The vibration controller 120 receives the encoded feedback signal from the vibration table 110 and transmits the feedback signal to the controller 150. The control unit 150 is a control module including the vibration controller 120. In the following description, the controller 150 includes the vibration controller 120. [

The acceleration sensor 130 can measure the acceleration of the moving object or the intensity of the output. The acceleration sensor 130 may be disposed on each layer of the test structure 10 or on the uppermost layer to sense an earthquake (vibration) signal of the test structure 10.

The storage unit 140 may store various information under the control of the controller 150. [ The storage unit 140 may store a plurality of application programs driven by the system 100, data for operation of the system 100, and commands. At least some of these applications may be downloaded from an external server via wireless communication.

The control unit 150 is a component that controls the system 100 as a whole. The control unit 150 may generate a vibration signal to be tested and transmit the generated vibration signal to the vibration table 110 through the vibration controller 120. The control unit 150 may collect the measurement values received from the acceleration sensor 130 wirelessly or by wire.

Hereinafter, a description will be made in detail of the prediction of the seismic observation according to the embodiment of the present invention.

The system 100 may model the behavior of the test structure 10. Here, the behavior means that the test structure 10 moves according to an earthquake (vibration) signal. The system 100 may measure a vibration signal generated in the test structure 10 through an acceleration sensor and provide a prediction model that predicts how accurately the measured vibration signal is mathematically using the measured vibration signal have.

That is, the system 100 can model the behavior of the test structure 10, and can derive the behavior of the actual test structure 10 and the predictive behavior of the test structure 10 mathematically predicted. Further, the system 100 can predict the behavior of the actual structure through the test structure 10.

In this case, the behavior of the actual structure can be observed while estimating the behavior of the test structure 10 mathematically without evaluating the behavior of the test structure 10 through measurement each time.

Two system predictor / identification models can be used, referring to "Identification of the Response to a Controlled Building Structure Subjected to Seismic Load by Using Nonlinear System Models", published Oct. 18, 2016. have. The model includes nonlinear autoregresive with exogenous inputs (NARX) and an adaptive neuro-fuzzy inference system (ANFIS). With reference to the above paper, since the NARX model has better discrimination against the behavior of the test structure 10, this specification uses the NARX model to predict the system behavior, but the ANFIS model may also be used.

The parameters of the NARX model can include input values and output values. Will be described with reference to Figs. 4 (a) to 4 (c). A (t) is a measurement value measured by an acceleration sensor, and a (t-1) is a measurement value of a time delayed one time , and a (t-2) denotes the measurement value delayed twice. Here, the measurement value delayed one time is a measurement value in which the first earthquake signal is delayed from the measurement values measured by the acceleration sensor, and the measurement value delayed twice is the measurement value in which the first and second earthquake signals are delayed.

According to Fig. 4 (a), the model is a single input single output (SISO) with one input parameter and one output parameter. 4 (b) and 4 (c) show MISO (multiple input single output), which has a plurality of input parameters and one output parameter.

The predictive model of NARX can be used as the predictive behavior of the structure as shown in Equation 1 below.

<Formula 1>

Where f is the nonlinear function, u (t) and y (t) are the input and output sequences, _nu and _ny are the maximum number of input and output measurements, and _nk is the input delay. y (t) corresponds to a nonlinear function applying the neural network algorithm. The NARX prediction model can be applied to the NARX prediction model by variously extracting the input and output parameters through the neural network algorithm.

As described in the paper, sigmoid neurons and wavelet neurons can be applied, but the sigmoid function can be used to evaluate nonlinear model designs using sigmoid neurons with good evaluation results.

Figure 5 shows the statistical parameters used in the evaluation of the NARX model.

As shown in the article, the model number can be from 1 to 9, and all of the models can be selected as NARX. NARX (3) is MISO and input is S (t), a (t-1), NARX (1) is SISO and NARX (2) is MISO and input is S ) and a (t-2), respectively.

The NARX algorithm can be used to identify and adjust model parameters. Identifying the model parameters includes i) parameter estimation and ii) structure detection, which can be divided into model order selection and applying a selected model order. Model order selection can be judged as part of structural detection. Determining the model order can be determined by the trail and order method.

The model order can be defined by Equation 2 below.

<Formula 2>

The terms _nu and _ny represent the maximum number of input and output measurements, and _nk represents the input delay. In particular, the method of determining the degree of correlation with measured values by model is to calculate R ² , mean absolute error (MAE), and root mean square error (RMSE). As shown in Equation 3 below, the correlation coefficient (R2) provides prior dependence information between observed values and predicted values.

<Formula 3>

Where y and y _s are observed values, n is the number of observations, and the performance predicted from time t = 1 to t = n appears. Y and y _s , including double dots, are the average of the observations.

The MAE (mean absolute error, Equation 4 below) is an index representing the average magnitude of the error, and represents the average size of the difference between the actual measured value and the calculated predicted value.

<Formula 4>

RMSE (root mean square error, Equation 5) represents an average magnitude that gives a large weight to a large error. The RMSE represents an average size giving different weights according to the difference between the actual measured value and the calculated predicted value.

&Lt; EMI ID =

At this time, the higher the value of R2, the lower the values of MAE and RMSE, the more predicted values similar to the measured values are calculated.

As shown in Fig. 5, the predicted value of NARX (3) of model 8 forms an approximate value to the measured value. In this manner, the accuracy of the seismic observation can be predicted while imparting an earthquake signal to the test structure 10. FIG. 6 is a graph showing this.

Referring to FIG. 6, the X-axis represents the measured value of the vibration signal to be measured, and the Y-axis represents the predicted value using the predictive model (NARX model). The NARX predicted value can be used to accurately predict the behavior of the test structure 10 as shown in FIG.

By using the above algorithm, it is possible to predict the various parameters of the test structure even without testing until the test structure is collapsed by deriving the measured value and the predicted value of the building most effectively and continuously increasing the vibration value to the test structure.

7 shows a method of driving the prediction system according to the embodiment.

The simulation steps S710 to S750 and the test steps S760 to S780 can be performed. The simulation step is a step in which a TMD with a lower mass M is used and a test step is a step in which a TMD with a higher mass M is used. The behavior of the test structure 10 and further the actual building can be evaluated by gradually increasing the weight.

The simulation step will be described first.

An input, which is an earthquake signal for 1.8 Kg mass and an acceleration sensor measurement value, is selected (S710).

The 1.8 Kg may be set differently depending on the specifications of the test structure 10.

Next, the system 100 controls the noise of the signal (S720).

Thereby, the noise of the observed measurement value can be removed.

Thereafter, parameters and orders of the model are input (S730), and the parameters of the model are adjusted (S740).

In this step, calculations are performed on a plurality of prediction models according to a plurality of parameters, and it is possible to determine what is the most optimal parameter and which is the most optimal prediction model.

If there is an improvement (S750), the simulation step is completed and the process proceeds to the test step (S760S to S780).

In the test phase, the structure output for 2.3 Kg of TMD mass is tested (S760).

If there is an improvement (S770), the optimum model is selected (S780).

R ² , MAE, and RMSE may be used to determine the improvement, but the embodiments are not limited thereto.

The present invention described above can be embodied as computer-readable codes on a medium on which a program is recorded. The computer readable medium includes all kinds of recording devices in which data that can be read by a computer system is stored. Examples of the computer readable medium include a hard disk drive (HDD), a solid state disk (SSD), a silicon disk drive (SDD), a ROM, a RAM, a CD-ROM, a magnetic tape, a floppy disk, , And may also be implemented in the form of a carrier wave (e.g., transmission over the Internet). The computer may also include a controller 150 of the system 100. Accordingly, the above description should not be construed in a limiting sense in all respects and should be considered illustrative. The scope of the present invention should be determined by rational interpretation of the appended claims, and all changes within the scope of equivalents of the present invention are included in the scope of the present invention.

Claims (6)

A prediction system for seismic observations,
A test structure including a plurality of floors and having an acceleration sensor on the top of the plurality of floors;
A tuned mass damper (TMD) for attenuating vibration generated in the test structure;
A vibration table for supporting the test structure and triggering an earthquake signal on the test structure; And
And a control unit for controlling the vibration table,
Wherein,
Calculating a predictive value for predicting the actual measurement value using the actual measurement value measured through the acceleration sensor and at least one predictive model, selecting a predictive model satisfying a predetermined condition,
The predetermined condition is that,
A mean square error (MAE) value indicating a mean magnitude of the difference between the actual measured value and the calculated predicted value or a mean square error (MAE) value giving a different weight according to the difference between the actual measured value and the calculated predicted value root mean square error (RMSE) is the minimum,
The predictive model that satisfies the predetermined condition,
At least one of a first delay measurement value of the actual measurement value and a second delay measurement value of the actual measurement value as an input value, S (t) representing the seismic signal, a predicted behavior of the acceleration sensor behavior) value as an output value,
The predictive model that satisfies the predetermined condition,
We use nonlinear autoregressive with exogenous inputs (NARX)
The weight of the weight included in the TMD is applied differently in the simulation step and the test step, and when the optimal prediction model is determined in the simulation step, the weight of the weight is increased in the test step. Prediction system. delete delete delete delete delete

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