KR20240032606A - Method and apparatus for predicting structural reliability of bridge - Google Patents

Abstract

A method for predicting the structural stability of a bridge performed by a computing device is disclosed. This method includes the steps of acquiring measurement data of displacement, strain, and acceleration through a plurality of sensors located at predetermined points on the pillars of the bridge and the girders connecting the pillars when a certain vehicle moves on the bridge, the acquired measurement data A step of processing, a step of calculating a cumulative distribution function (CDF) for each processed measurement data, a step of inputting the calculated cumulative distribution function into a time series-based analysis model, and estimating the stability reliability of the bridge through the analysis model. May include steps. By providing this method, the stability of the bridge can be effectively analyzed.

Description

Method for predicting structural stability of bridge and device therefor {METHOD AND APPARATUS FOR PREDICTING STRUCTURAL RELIABILITY OF BRIDGE}

The present disclosure relates to a method for predicting the structural stability of a bridge and an apparatus therefor.

Generally, a bridge can be divided into an upper and lower structure. The upper structure is the part that directly supports the load of the vehicle and is composed of an upper slab and a girder, and the lower structure is the upper structure. It is a part that transmits the load from the structure to the ground and is composed of abutments, piers, and foundations (extended foundations, pile foundations, well foundations, etc.).

In these bridges, the structural behavior state of the substructure cannot be known unless a special device that can measure the structural behavior (deformation, inclination, settlement, crack, etc.) of the substructure is installed in advance. If not known, there is a problem that factors affecting the safety of the entire bridge structure, such as deformation, inclination, settlement, and cracking, can progress seriously and lead to the collapse of the bridge.

Recently, the frequency and magnitude of earthquakes are increasing both at home and abroad, and cases of damage to bridge structures due to earthquakes are also frequently reported. Because bridge foundations are structures installed under the water or ground, it is impossible to accurately determine their soundness at any time or after an earthquake.

Meanwhile, with the trend toward ultra-large structures, interest in safety is increasing, and equipment is being developed to test the vibration characteristics of structures as one of these safety tests. Tests of these characteristics are called dynamic load characteristics tests.

However, even without installing a special structure on the bridge, a method is needed to analyze the safety and reliability of the bridge using only data from sensors placed on the bridge.

Meanwhile, the above information is presented only as background information to aid understanding of the present invention. No decision has been made, and no claim is made, as to whether any of the above is applicable as prior art with respect to the present invention.

Registered Patent Publication No. 10-1328139 (Registration date: 2013.11.05.)

The purpose of the embodiment disclosed in the present disclosure is to provide a method for analyzing and predicting the structural safety reliability of a bridge using only data from various sensors disposed on the bridge.

The problems to be solved by the present disclosure are not limited to the problems mentioned above, and other problems not mentioned can be clearly understood by those skilled in the art from the description below.

A method for predicting the structural stability of a bridge performed by a computing device according to an aspect of the present disclosure for achieving the above-described technical problem includes, when a predetermined vehicle moves on the bridge, the pillars of the bridge and the girders connecting the pillars. Obtaining measurement data of displacement, strain, and acceleration through a plurality of sensors located at predetermined points on the girder; Processing the obtained measurement data; Calculating a cumulative distribution function (CDF) for each of the processed measurement data; Inputting the calculated cumulative distribution function into a time series-based analysis model; And it may include estimating the stability reliability of the bridge through the analysis model.

The step of acquiring the measurement data uses a strain sensor installed at the top and bottom of the pillar, a strain sensor installed at the bottom of the gunner, a displacement sensor installed at the bottom of the pillar, and an acceleration sensor installed at the top of the gunner. , may include obtaining measurement data of displacement, strain, and acceleration.

The step of processing the acquired measurement data includes processing the displacement measurement data and the strain measurement data to determine semi-static behavior of the bridge; and processing the acceleration measurement data to determine dynamic behavior of the bridge.

Calculating the cumulative distribution function (CDF) may include calculating a cumulative distribution function (CDF) for each of the processed measurement data based on the Kaplan-Meier estimation.

The time series-based analysis model can be implemented as an autoregressive integrated moving average (ARIMA) model.

The analysis model receives the calculated cumulative distribution function (CDF), estimates AR parameters and MA parameters, compares the AR parameters and MA parameters in the normal range and the estimated AR parameters and MA parameters, and calculates the bridge It can be configured to estimate the stability reliability of.

An apparatus for predicting the structural stability of a bridge according to an aspect of the present disclosure for achieving the above-described technical problem includes: a memory storing a time series-based analysis model; And when a predetermined vehicle moves on the bridge, measurement data of displacement, strain, and acceleration are acquired through a plurality of sensors located at predetermined points of the pillars of the bridge and the girders connecting the pillars, and the obtained measurement data It may include a control unit that processes and calculates a cumulative distribution function (CDF) for each of the processed measurement data.

The control unit may be configured to input the calculated cumulative distribution function into a time series-based analysis model and estimate the stability reliability of the bridge through the analysis model.

In addition to this, a computer program stored in a computer-readable recording medium for execution to implement the present disclosure may be further provided.

In addition, a computer-readable recording medium recording a computer program for executing a method for implementing the present disclosure may be further provided.

According to the means for solving the above-described problem of the present disclosure, the safety and reliability of the bridge can be analyzed based on data collected from various sensors placed on the bridge without installing a special structure, so the researcher's analysis convenience can be improved.

The effects of the present disclosure are not limited to the effects mentioned above, and other effects not mentioned may be clearly understood by those skilled in the art from the description below.

1 is a diagram illustrating the application environment of the device for predicting structural stability of a bridge according to the present disclosure;
2 is a block diagram showing the configuration of a structural stability prediction device according to the present disclosure;
3 is a sequence diagram showing a method for predicting the structural stability of a bridge according to the present disclosure;
4(a) and 4(b) are diagrams illustrating a structure for simulating damage to a bridge in a laboratory according to the present disclosure;
Figure 5 exemplarily shows the sensing values of each sensor installed in the Brit according to the present disclosure,
Figures 6(a) and 6(b) show strain values according to the speed of a truck traveling on a bridge according to the present disclosure,
Figure 7(a) shows acceleration measurements over time and power spectrum according to frequency when the truck according to the present disclosure travels on the bridge at a first speed, and Figure 7(b) shows the power spectrum according to frequency when the truck according to the present disclosure travels on the bridge. When driving at the second speed, it shows acceleration measurements over time and power spectrum according to frequency.
FIG. 8(a) shows measured values in the case of free vibration according to the present disclosure, and FIG. 8(b) shows frequency content according to the present disclosure.
9(a) to 9(c) show the calculated cumulative distribution function (CDF) according to the present disclosure,
10(a) and 10(b) show coefficients of the analysis model according to the present disclosure,
11(a) and 11(b) show the cumulative distribution function (CDF) for strain and acceleration at a certain point of the bridge according to the present disclosure, and
Figures 12(a) and 12(b) show the parameter performance of the analysis model according to the present disclosure.

Like reference numerals refer to like elements throughout this disclosure. The present disclosure does not describe all elements of the embodiments, and general content or overlapping content between the embodiments in the technical field to which the present disclosure pertains is omitted. The term 'unit, module, member, block' used in the specification may be implemented as software or hardware, and depending on the embodiment, a plurality of 'unit, module, member, block' may be implemented as a single component, or It is also possible for one 'part, module, member, or block' to include multiple components.

Throughout the specification, when a part is said to be “connected” to another part, this includes not only direct connection but also indirect connection, and indirect connection includes connection through a wireless communication network. do.

Additionally, when a part "includes" a certain component, this means that it may further include other components rather than excluding other components, unless specifically stated to the contrary.

Throughout the specification, when a member is said to be located “on” another member, this includes not only cases where a member is in contact with another member, but also cases where another member exists between the two members.

Terms such as first and second are used to distinguish one component from another component, and the components are not limited by the above-mentioned terms.

Singular expressions include plural expressions unless the context clearly makes an exception.

The identification code for each step is used for convenience of explanation. The identification code does not explain the order of each step, and each step may be performed differently from the specified order unless a specific order is clearly stated in the context. there is.

Hereinafter, the operating principle and embodiments of the present disclosure will be described with reference to the attached drawings.

In this specification, the 'device for predicting structural stability of a bridge according to the present disclosure' includes various devices that can perform computational processing and provide results to the user. For example, the device for predicting the structural stability of a bridge according to the present disclosure may include all of a computer, a server device, and a portable terminal, or may take the form of any one.

Here, the computer may include, for example, a laptop, desktop, laptop, tablet PC, slate PC, etc. equipped with a web browser.

The server device is a server that processes information by communicating with external devices, and may include an application server, computing server, database server, file server, game server, mail server, proxy server, and web server.

The portable terminal is, for example, a wireless communication device that guarantees portability and mobility, such as PCS (Personal Communication System), GSM (Global System for Mobile communications), PDC (Personal Digital Cellular), PHS (Personal Handyphone System), and PDA. (Personal Digital Assistant), IMT (International Mobile Telecommunication)-2000, CDMA (Code Division Multiple Access)-2000, W-CDMA (W-Code Division Multiple Access), WiBro (Wireless Broadband Internet) terminal, smart phone ), all types of handheld wireless communication devices, and wearable devices such as watches, rings, bracelets, anklets, necklaces, glasses, contact lenses, or head-mounted-device (HMD). may include.

FIG. 1 is a diagram for explaining the application environment of the apparatus 100 for predicting structural stability of a bridge according to the present disclosure, and FIG. 2 is a block diagram showing the configuration of the apparatus 100 for predicting structural stability of a bridge according to the present disclosure.

The bridge (BR) is a certain bridge (eg, Wonhyo Bridge), and the structural stability of the bridge can be predicted from a portion (AA) of the bridge. The structural stability of a bridge may include structural safety, structural reliability, and stability reliability. The structural stability prediction device 100 can predict (estimate) the structural stability of the Brit according to the movement of a vehicle (Tr, for example, a dump truck) having a predetermined weight.

The bridge (BR) may include columns (G1 to G6) and girders (GD1 to 5) connecting the columns. A plurality of axial strain sensors (S1 to S12) may be disposed on the columns (G1 to G6), and displacement sensors (D1 to D6) may be disposed in the lower portion of the columns (G1 to G6). Vertical strain sensors (S13 to S16) may be placed around the girders (GD1 to GD5), and acceleration sensors (ACC1, ACC2) may be placed on the bridge (BR) at a predetermined distance depending on the driving direction. In this specification, girders (GD1 to 5) may be used as a concept including the upper floor surface.

Referring to FIG. 2, the structural stability prediction device 100 can predict the stability reliability of the bridge (BR) based on data collected from sensors, and includes a communication unit 110, an input unit 120, a display 130, It may include a memory 150 and a control unit 190 that store a prediction model learned in advance based on artificial intelligence. The components shown in FIG. 2 are not essential for implementing the structural stability prediction device 100 according to the present disclosure, so the structural stability prediction device 100 described herein has more or more components than those listed above. , or may have fewer components.

Among the components, the communication unit 110 may include one or more components that enable communication with an external device, for example, a broadcast reception module, a wired communication module, a wireless communication module, a short-range communication module, and location information. It may contain at least one of the modules.

The communication unit 110 includes a location information module, and the location information module may include a GPS (Global Positioning System) module or a WiFi (Wireless Fidelity) module. For example, when using a GPS module, a GPS satellite transmits The location of the water depth prediction device 100 can be obtained using the signal. As another example, by using a Wi-Fi module, the location of the device can be obtained based on information from the Wi-Fi module and a wireless AP (Wireless Access Point) that transmits or receives wireless signals.

The input unit 120 is for inputting image information (or signal), audio information (or signal), data, or information input from a user, and includes at least one of at least one camera, at least one microphone, and a user input unit. can do. Voice data or image data collected from the input unit can be analyzed and processed as a user's control command.

The user input unit is for receiving information from the user. When information is input through the user input unit, the control unit can control the operation of the device to correspond to the input information. This user input unit uses hardware-type physical keys (e.g., buttons, dome switches, jog wheels, jog switches, etc. located on at least one of the front, back, and sides of the device) and software-type touch keys. It can be included. As an example, the touch key consists of a virtual key, soft key, or visual key displayed on a touch screen-type display unit through software processing, or is displayed on the touch screen. It may be composed of touch keys placed in other parts. Meanwhile, the virtual key or visual key can be displayed on the touch screen in various forms, for example, graphic, text, icon, video or these. It can be made up of a combination of .

The display 130 displays (outputs) information processed by the device. For example, the display 1300 can display execution screen information of an application (for example, an application) running on the device, or UI (User Interface) and GUI (Graphic User Interface) information according to this execution screen information. there is.

The memory 150 can store data supporting various functions of the device and programs for the operation of the control unit 190, and input/output data (e.g., music files, still images, videos, etc.) can be stored, and a number of application programs (application programs or applications) running on the device, data for operation of the device, and commands can be stored. At least some of these applications may be downloaded from an external server via wireless communication.

The memory 150 includes a flash memory type, a hard disk type, a solid state disk type, an SDD type (Silicon Disk Drive type), and a multimedia card micro type. micro type), card type memory (e.g. SD or XD memory, etc.), random access memory (RAM), static random access memory (SRAM), read-only memory (ROM), EEPROM (electrically erasable) It may include at least one type of storage medium among programmable read-only memory (PROM), programmable read-only memory (PROM), magnetic memory, magnetic disk, and optical disk. Additionally, the memory 150 is separate from the device, but may be a database connected by wire or wirelessly.

The memory 150 may store an analysis model (AM), which may be a probability-based autoregressive integrated moving average (ARIMA) model, but the embodiment is not limited thereto.

The control unit 190 includes a memory that stores data for an algorithm for controlling the operation of components in the device or a program that reproduces the algorithm, and at least one processor that performs the above-described operations using the data stored in the memory ( (not shown) may be implemented. At this time, the memory and processor may each be implemented as separate chips. Alternatively, the memory and processor may be implemented as a single chip.

In addition, the control unit may control any one or a combination of the above-described components in order to implement various embodiments according to the present disclosure described in FIGS. 3 to 12(b) below on the device.

At least one component may be added or deleted in response to the performance of the components shown in FIG. 2. Additionally, it will be easily understood by those skilled in the art that the mutual positions of the components may be changed in response to the performance or structure of the system.

Meanwhile, each component shown in FIG. 2 refers to software and/or hardware components such as Field Programmable Gate Array (FPGA) and Application Specific Integrated Circuit (ASIC).

Figure 3 is a sequence diagram showing a method for predicting the structural stability of a bridge according to the present disclosure. While explaining FIG. 3, the description will be made with reference to FIGS. 4(a) to 12(b). Specifically, FIGS. 4(a) and 4(b) are diagrams illustrating a structure for simulating damage to a bridge in a laboratory according to the present disclosure, and FIG. 5 illustrates sensing of each sensor installed on the bridge according to the present disclosure. The value is shown by way of example, and FIGS. 6(a) and 6(b) show the strain value according to the speed of the truck traveling on the bridge according to the present disclosure, and FIG. 7(a) shows the strain value of the truck according to the present disclosure. When driving on a bridge at a first speed, acceleration measurements over time and a power spectrum according to frequency are shown, and FIG. 7(b) shows acceleration measurements over time when a truck according to the present disclosure travels on a bridge at a second speed. Shows the power spectrum according to the value and frequency, Figure 8(a) shows the measured value in the case of free vibration according to the present disclosure, Figure 8(b) shows the frequency content according to the present disclosure, Figures 9(a) to Figure 9(c) shows the calculated cumulative distribution function (CDF) according to the present disclosure, Figures 10(a) and 10(b) show the coefficients of the analysis model according to the present disclosure, Figures 11(a) and Figure 11(b) shows the cumulative distribution function (CDF) for strain and acceleration at a certain point of the bridge according to the present disclosure, and Figures 12(a) and 12(b) show the analysis model according to the present disclosure. Indicates the parameter performance of .

Referring to FIG. 3, when a certain vehicle moves on a bridge, the control unit 190 measures displacement, strain and Measurement data of acceleration can be obtained through the communication unit 110 (S310, measurement data acquisition step).

The control unit 190 uses a strain sensor installed at the top and bottom of the bridge pillar, a strain sensor installed at the bottom of the gunner, a displacement sensor installed at the bottom of the pillar, and an acceleration sensor installed at the top of the gunner, to measure displacement, strain, and acceleration. Measurement data can be obtained.

The control unit 190 may process the acquired measurement data (S320).

Specifically, the control unit 190 may process displacement measurement data and strain measurement data to determine the semi-static behavior of the bridge. For example, displacement measurement data and strain measurement data may be filtered through a moving average (MA) filter, and the measurement data may be improved.

The control unit 190 may process acceleration measurement data to determine the dynamic behavior of the bridge. For example, the control unit 190 may apply a wavelet denoising process to the measurement data.

Meanwhile, the behavior of the bridge can also be performed in the laboratory. Referring to Figures 4(a) and 4(b), a cantilever is fixed to the floor, and vibration is generated by the force of the finger. Can be sensed by a sensor at the top (eg, an acceleration sensor). Here, a weak damage state (damage in 2 connection areas) and a strong damage state (damage in 4 connection areas) can be simulated depending on the bolt connection state at the bottom of the cantilever. In embodiments, mild damage simulation may be used to determine the semi-static behavior of the bridge, and strong damage simulation may be used to determine the dynamic behavior of the bridge.

Referring to the actual bridge in FIG. 5, in order to determine quasi-static behavior, the bridge's truck can move at low speed (e.g., 20Km or 30Km), and the control unit 190 includes displacement sensors D1 to D6 and strain sensors ( Measurements (S1~S22) can be received. At this time, shear stress and principal stress can be measured in the G5 column and G6 column.

Figure 6(a) shows strain values over time related to quasi-static behavior when the truck is traveling at a speed of 20 Km/h, and the filtered value may be a value related to quasi-static behavior. Figure 6(b) shows that the truck is traveling at a speed of 50 Km/h. Indicates strain values over time related to quasi-static behavior. The higher the speed of the truck, the higher the measured strain value, and the shorter the section in which the inversion of the measured value occurs.

Figure 7(a) shows the amplitude and frequency spectrum of the acceleration sensors A1 and A2 when the truck travels at a speed of 20 Km/h, and Figure 7(b) shows the amplitude and frequency spectrum when the truck travels at a speed of 50 Km/h. , represents the amplitude and frequency spectrum of the acceleration sensor.

FIG. 8(a) shows measured values in the case of free vibration according to the present disclosure, and FIG. 8(b) shows frequency content according to the present disclosure. Acceleration measurements corresponding to normal, mildly damaged, and strongly damaged states can be observed, and damping may be affected.

Returning again to FIG. 3, the control unit 190 may calculate a cumulative distribution function (CDF) for each of the processed measurement data after step S320 (S330).

The control unit 190 may calculate a cumulative distribution function (CDF) for each piece of processed measurement data based on Kaplan-Meier estimation.

Referring to FIGS. 9(a) to 9(c), the CDF for acceleration, CDF for displacement, and autocorrelation function according to acceleration response can be derived, respectively, in the normal state (Undamaged) and slightly damaged state (Damage1). ) and strong damage conditions (Damage2), and measurement data can be collected through a cantilever in the laboratory. The strongly damaged state (Damage2) is more important than the weakly damaged state (Damage1) and the steady state at the 95% confidence level and may be a more obvious feature of the quasi-static behavior. This measurement data can also be applied to bridges.

After step S330, the control unit 190 may input the calculated cumulative distribution function into a time series-based analysis model (S340).

First, structural behavior (R(F)) can be expressed based on probability, and the analysis model can be implemented as an autoregressive integrated moving average (ARIMA) model. The ARIMA model can be a combined model of autoregressive (AR) and moving average (MR).

Figures 10(a) and 10(b) show the coefficients of the analysis model according to the present disclosure. Referring to Figure 10(a), there is little difference in the values of the AR parameters between the normal state and the mildly damaged state, but the strong It can be observed that the AR parameters in the damaged state are sensitive to damage. Referring to Figure 10(b), no significant difference is observed in the MA parameter values in the three states. It can be said that the AR parameter is more sensitive to strong damage than the MA parameter.

The ARIMA model receives the calculated cumulative distribution function (CDF), estimates AR parameters and MA parameters, and compares the AR parameters and MA parameters in the normal range and the estimated AR parameters and MA parameters to determine the stable reliability of the bridge. It can be configured to estimate.

11(a) and 11(b) show cumulative distribution functions (CDF) for strain and acceleration at a certain point of the bridge according to the present disclosure.

The value of the cumulative distribution function (CDF) of strain may be calculated differently depending on the speed of the truck, and the faster the speed, the closer the cumulative distribution function (CDF) may be to 1 first. Additionally, in the case of the cumulative distribution function (CDF) according to the acceleration value, the graph shape may be analyzed somewhat differently only when the truck is moving the slowest.

The control unit 190 may estimate the stability reliability of the bridge through an analysis model (S350).

In an optional embodiment, the analysis model may be implemented as a neural network-based model rather than a probabilistic ARIMA model. In this case, the analysis model can analyze the quasi-static behavior of the bridge by measuring displacement measurement data and strain measurement data to evaluate the quasi-static behavior and then comparing it with pre-stored label measurement data. Acceleration measurement data to evaluate dynamic behavior can be collected and compared with label measurement data to analyze the dynamic behavior of the bridge. Based on this, the analysis model can estimate the stability reliability of the bridge. The estimated stability reliability can be expressed as a percentage or can be expressed as high, medium, or low stability reliability.

Figures 12(a) and 12(b) show the parameter performance of the analysis model according to the present disclosure.

Referring to Figure 12(a), the correlation between AR parameters according to the speed of the truck appears as high as 0.99, and it can be observed that the reliability of the bridge behavior is very high. Numbers 4, 9, and 12 of bridge behavior?? Changes in mode shape can be observed.

Referring to Figure 12(b), the MA parameter is measured lower at the 20Km/h speed than other speeds, and significant load can be observed at the 30Km/h and 40Km/h speeds.

That is, by calculating the AR parameters and MR parameters, the stability reliability of the bridge can be effectively evaluated.

Meanwhile, the disclosed embodiments may be implemented in the form of a recording medium that stores instructions executable by a computer. Instructions may be stored in the form of program code, and when executed by a processor, may create program modules to perform operations of the disclosed embodiments. The recording medium may be implemented as a computer-readable recording medium.

Computer-readable recording media include all types of recording media storing instructions that can be decoded by a computer. For example, there may be Read Only Memory (ROM), Random Access Memory (RAM), magnetic tape, magnetic disk, flash memory, optical data storage device, etc.

As described above, the disclosed embodiments have been described with reference to the attached drawings. A person skilled in the art to which this disclosure pertains will understand that the present disclosure may be practiced in forms different from the disclosed embodiments without changing the technical idea or essential features of the present disclosure. The disclosed embodiments are illustrative and should not be construed as limiting.

100: Bridge structural stability prediction device.

Claims (8)

A method for predicting the structural stability of a bridge performed by a computing device, comprising:
When a vehicle moves on the bridge, measurement of displacement, strain, and acceleration through a plurality of sensors located at predetermined points on the pillars of the bridge and the girder connecting the pillars. acquiring data;
Processing the obtained measurement data;
Calculating a cumulative distribution function (CDF) for each of the processed measurement data;
Inputting the calculated cumulative distribution function into a time series-based analysis model; and
A method for predicting structural stability of a bridge, comprising the step of estimating stability reliability of the bridge through the analysis model. According to paragraph 1,
The step of acquiring the measurement data is,
Measurement data of displacement, strain, and acceleration using a strain sensor installed at the top and bottom of the pillar, a strain sensor installed at the bottom of the gunner, a displacement sensor installed at the bottom of the pillar, and an acceleration sensor installed at the top of the gunner. A method for predicting the structural stability of a bridge, including the step of obtaining. According to paragraph 2,
The step of processing the obtained measurement data is,
Processing the displacement measurement data and strain measurement data to determine semi-static behavior of the bridge; and
A method for predicting structural stability of a bridge, comprising processing the acceleration measurement data to determine dynamic behavior of the bridge. According to paragraph 3,
The step of calculating the cumulative distribution function (CDF) is,
A method for predicting structural stability of a bridge, comprising calculating a cumulative distribution function (CDF) for each of the processed measurement data based on the Kaplan-Meier estimation. According to paragraph 4,
The time series-based analysis model is,
A method for predicting the structural stability of a bridge, which is an autoregressive integrated moving average (ARIMA) model. According to clause 5,
The analysis model is,
Receives the calculated cumulative distribution function (CDF), estimates AR parameters and MA parameters, and compares the AR parameters and MA parameters in the normal range and the estimated AR parameters and MA parameters to estimate the stable reliability of the bridge. A method for predicting the structural stability of a bridge, configured to: A program coupled to a computer and stored in a computer-readable recording medium to execute the method for predicting the structural stability of a bridge according to any one of claims 1 to 6. As a device for predicting the structural stability of a bridge,
Memory to store time series-based analysis models; and
When a certain vehicle moves on the bridge, measurement data of displacement, strain, and acceleration are acquired through a plurality of sensors located at predetermined points of the pillars of the bridge and the girders connecting the pillars, and the obtained measurement data is processed. and a control unit that calculates a cumulative distribution function (CDF) for each of the processed measurement data,
The control unit,
A device for predicting structural stability of a bridge, configured to input the calculated cumulative distribution function into a time series-based analysis model and estimate stability reliability of the bridge through the analysis model.