Structure evaluation system, structure evaluation device, and structure evaluation method

Abstract

To provide a structure evaluation system, a structure evaluation device, and a structure evaluation method that can improve the accuracy of evaluating a deterioration state of a structure when performing long-term monitoring of the structure by using an evaluation value obtained from the inclination of the frequency distribution for each amplitude scale.SOLUTION: A structure evaluation system of an embodiment has a plurality of sensors, an acquisition unit, a calculation unit, and an evaluation unit. The plurality of sensors detect elastic waves generated from a structure. The acquisition unit acquires, over an evaluation target period, detection information in which information on at least the amplitude of the elastic waves detected by the plurality of sensors and information on the time when the elastic waves are detected are associated with each other. The calculation unit calculates, for every predetermined period, an evaluation value that is the inclination of the frequency distribution for each amplitude scale of the elastic wave based on the acquired detection information over the evaluation target period. The evaluation unit evaluates a deterioration state of the structure based on time-series data of the evaluation values calculated in the predetermined periods.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] An embodiment of the present invention relates to a structure evaluation system, a structure evaluation device, and a structure evaluation method. [Background technology]

[0002] In recent years, problems associated with the deterioration of bridges and other structures constructed during the period of rapid economic growth have become apparent. Because the damage caused by an accident to a structure would be immeasurable, various technologies for monitoring the condition of structures have been proposed. For example, one proposed technology for detecting structural damage is the acoustic emission (AE) method, which uses a highly sensitive sensor to detect elastic waves generated by the initiation or propagation of internal cracks. AE is an elastic wave generated by the propagation of cracks in materials. In the AE method, an AE sensor using a piezoelectric element detects the elastic waves as an AE signal (voltage signal). AE signals are detected as a sign of impending material failure. Therefore, the frequency and intensity of AE signals are useful indicators of material integrity. Therefore, research is being conducted on technologies for detecting signs of structural deterioration using the AE method.

[0003] One method for evaluating the integrity of structures based on AE detection is the b-value, derived from an empirical rule known in the field of seismology as the Gutenberg-Richter law. The b-value is an evaluation value obtained from the slope of the frequency distribution by amplitude scale, and is known to correlate with the state of cracks inside the material. The lifespan of structures such as bridges is generally said to be 50 years. Monitoring the integrity of structures over long periods, from several years to several decades, is essential for building a safe and secure society. In particular, it is desirable to detect signs of deterioration inside structures before large-scale damage occurs. Long-term monitoring of structures based on AE detection is considered promising for early detection of signs of deterioration.

[0004] However, when assessing the deterioration state of structures based on the b-value in the AE method is applied to the long-term monitoring of actual bridges, there are cases where the accuracy of the assessment of the deterioration state decreases if there are periods when no data is available. This type of problem is not limited to the b-value, but also occurs when using other assessment values ​​(improved b-values) obtained from the slope of the frequency distribution by amplitude scale. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Application Laid-Open No. 2006-10595 [Patent Document 2] Patent No. 6567268 [Non-patent literature]

[0006] [Non-Patent Document 1] IS Colombo, I. Main, and M. Forde, “Assessing Damage of Reinforced Concrete Beam Using ``b-value'' Analysis of Acoustic Emission Signals,'' Journal of materials in civil engineering, vol. 15, no. 3, pp. 280-286, 2003. Summary of the Invention [Problem to be solved by the invention]

[0007] The problem to be solved by the present invention is to provide a structure evaluation system, a structure evaluation device, and a structure evaluation method that can improve the accuracy of evaluating the deterioration state of a structure when performing long-term monitoring of the structure using an evaluation value obtained from the slope of the frequency distribution by amplitude scale. [Means for solving the problem]

[0008] A structure evaluation system according to an embodiment includes a plurality of sensors, an acquisition unit, a calculation unit, and an evaluation unit. The plurality of sensors detect elastic waves generated from a structure. The acquisition unit acquires detection information for an evaluation period, which associates information about at least the amplitude of each elastic wave detected by each of the plurality of sensors with time information at which each elastic wave was detected. The calculation unit calculates an evaluation value, which is the slope of a frequency distribution by amplitude scale of the elastic waves, for each predetermined period based on the acquired detection information for the evaluation period. The evaluation unit evaluates the deterioration state of the structure based on time-series data of each evaluation value calculated for each predetermined period. The system further includes an estimation unit that estimates a trend component and a seasonal component using time-series data of each evaluation value. The evaluation unit evaluates the deterioration state of the structure for each of the estimated trend component and seasonal component. When the seasonal component fluctuates periodically, the evaluation unit evaluates that there is low-progressive damage inside the structure. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a diagram showing the configuration of a structure evaluation system according to an embodiment. [Figure 2] FIG. 2 is a schematic block diagram illustrating the functions of a signal processing device according to the embodiment. [Figure 3] FIG. 2 is a schematic block diagram illustrating the function of an AFE according to the embodiment. [Figure 4] FIG. 2 is a schematic block diagram illustrating functions of a signal processing unit according to the embodiment. [Figure 5] FIG. 3 is a sequence diagram showing a processing flow relating to accumulation of detection information in the structure evaluation system according to the embodiment. [Figure 6] 3 is a flowchart showing a processing flow of the structure evaluation device according to the embodiment. [Figure 7] FIG. 10 is a diagram showing an example of analysis results using b-value data for the evaluation period. [Figure 8] An example of a contour map interpolated based on the Voronoi diagram. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, a structure evaluation system, a structure evaluation device, and a structure evaluation method according to embodiments will be described with reference to the drawings. (overview) The structure evaluation system in the embodiment is a system that can improve the accuracy of evaluating the deterioration state of a structure when performing long-term monitoring of the structure using an evaluation value (e.g., b-value) obtained from the slope of the frequency distribution by amplitude scale. Specifically, the structure evaluation system first collects elastic waves generated by impacts applied to a structure such as a bridge for at least the period to be evaluated (hereinafter referred to as the "evaluation period"). Next, the structure evaluation system estimates trend components and seasonal components using an additive regression model based on the b-values ​​for each predetermined period obtained from the collected elastic waves for the evaluation period. The structure evaluation system then evaluates the trend of long-term damage (e.g., cracks) to the structure based on the estimated trend components, and evaluates the presence or absence of stable existing cracks based on the seasonal components.

[0011] The impact on a structure is, for example, the impact caused by contact between a vehicle's tires and the road surface when the vehicle passes over the structure. The evaluation period in this embodiment is at least one year or longer, and more preferably two years or longer. The reason for selecting the above evaluation period is explained below. Generally, structural materials expand and contract with temperature changes. Therefore, from a broad perspective, damage (e.g., cracks) that stably exist within a structure tend to undergo a cyclical cycle of opening in the hot summer and closing in the winter. Depending on the direction of the damage, the damage may close in the hot summer and open in the winter. In any case, cyclical annual fluctuations suggest the presence of existing cracks that open and close due to temperature. On the other hand, the long-term trend in the b-value can be considered to be related to the continuous increase or progression of cracks. For example, the b-value tends to decrease as cracks increase or progress. To perform these analyses, it is effective to use measurement data for one year or longer, which reveals periodicity. Furthermore, to more clearly distinguish between seasonality and trends, it is desirable to use measurement data for two years or longer, which reveals repeated periodicity.

[0012] Next, a method for calculating the b value used in this embodiment will be described. In the field of seismology, the relationship between the magnitude, which indicates the size of the earthquake source, and the logarithmic frequency is said to be expressed by the following formula (1), which is known as the Gutenberg-Richter law.

[0013]

number

[0014] In equation (1), N(M) represents the number of events with magnitude M or greater, and a and b represent constants. Normally, magnitude M is the logarithm of amplitude A. The constant b is called the "b value" and is said to reflect structural heterogeneity and stress systems. Numerous attempts have been made to apply this to the AE method and evaluate the soundness of structures based on the b value. When using the amplitude of elastic waves, which is usually expressed in decibels, equation (1) can be expressed as equation (2) below.

[0015]

number

[0016] From the relationship between Equation (1) and Equation (2), in order to be consistent with the b value in seismology, the slope calculated using the amplitude of elastic waves (i.e., b in Equation (2)) * In this embodiment, the value obtained by multiplying the value (value) by 20 is used as the b-value. For verification using a test specimen, the appropriate number of samples n for determining the b-value is n=50. When targeting elastic waves generated by traffic loads, there is a lot of noise, so using a larger number of samples is more robust. Therefore, in this embodiment, taking into account data fluctuations due to the number of samples, a number of samples n=500, which is 10 times the recommended value, is used to determine one b-value. Note that the number of samples is not limited to 500. For example, if it is acceptable to include the influence of noise, the number of samples may be 50 or more, as in verification using a test specimen.

[0017] Next, the estimation of the trend component and the seasonal component performed in this embodiment will be described. Elastic wave measurements have the following characteristics. First, elastic waves are not detected regularly, and elastic wave data may not be obtained for long periods of time. This is because vehicles travel irregularly over structures. Second, there is seasonality (annual fluctuations) due to temperature changes and traffic volume. Third, there is a large range of fluctuation. To extract a trend based on an appropriate b value from elastic wave data with these characteristics, an additive regression model that takes into account piecewise linear trends and seasonality can be used. First, b^(t) is introduced as an estimate of the b value, and a model is assumed as shown in the following equation (3). Note that "^" is placed above b (same below).

[0018]

number

[0019] In equation (3), g(t) represents the trend component, s(t) represents the seasonal component (annual fluctuation), and εt represents the error. The trend component g(t) can be expressed as an interval linear model with S change points, as shown in equation (4) below.

[0020]

number

[0021] In equation (4), k represents the growth rate and m represents the offset parameter. The correction coefficient vector δ∈R S is the change point s j (j=1,…,S) slope correction coefficient δ j The vector a(t)∈{0,1} s and a vector γ∈R that ensures continuity S is defined by the following equation (5).

[0022]

number

[0023] When the data is daily data and the seasonal component is an annual variation, the seasonal component can be expressed as a Fourier series as shown in the following equation (6).

[0024]

number

[0025] In Equation (6), N is the number of parameters, and N = 10 can be used, but is not limited to this. In this embodiment, the b-value is calculated for each of 500 elastic waves, and the calculated b-value is used to convert the data into daily data. Then, any algorithm is used to fit the data to Equation (3) above, thereby enabling the trend component and seasonal component to be separated and extracted. For example, the L-BFGS (Limited Memory Broyden Fletcher Goldfarb Shanno algorithm), a type of quasi-Newton method, is suitable because it can obtain good fitting results with a small memory footprint. While the estimation method is not limited to maximum likelihood estimation, Bayesian estimation, or maximum a posteriori (MAP) estimation, good results can be obtained using MAP estimation even when the amount of observed data is small. A specific configuration of this embodiment will be described below.

[0026] FIG. 1 is a diagram showing the configuration of a structure evaluation system 100 according to an embodiment. The structure evaluation system 100 is used to evaluate the soundness of a structure 11. In the following description, evaluation means determining the degree of soundness of the structure 11, i.e., the state of deterioration of the structure 11, based on a certain standard. In the following description, a bridge made of concrete is used as an example of the structure 11, but the structure 11 is not limited to a bridge. The structure 11 may be any structure that generates elastic waves 13 due to the occurrence or progression of cracks or external impacts (e.g., rain, artificial rain, etc.). For example, the structure 11 may be a bedrock. Note that bridges are not limited to structures built over rivers, valleys, etc., but also include various structures built above ground level (e.g., highway viaducts).

[0027] Damage that affects the evaluation of the deterioration state of the structure 11 includes, for example, damage inside the structure 11 that interferes with the propagation of elastic waves 13, such as cracks, cavities, and sedimentation. Here, cracks include vertical cracks, horizontal cracks, and diagonal cracks. A vertical crack is a crack that occurs in a direction perpendicular to the surface of the structure 11 on which the sensor is installed. A horizontal crack is a crack that occurs in a direction horizontal to the surface of the structure 11 on which the sensor is installed. A diagonal crack is a crack that occurs in a direction other than horizontal or vertical to the surface of the structure 11 on which the sensor is installed. Sedimentation is deterioration in which concrete turns into sediment, mainly at the boundary between the asphalt and the concrete deck. The specific configuration of the structure evaluation system 100 will be described below.

[0028] The structure evaluation system 100 includes a plurality of sensors 10-1 to 10-P (P is an integer of 2 or more), an activation control device 15, a signal processing device 20, and a structure evaluation device 30. Each of the plurality of sensors 10-1 to 10-P and the signal processing device 20 are connected by wire. The activation control device 15 and the signal processing device 20 are connected by wire. The signal processing device 20 and the structure evaluation device 30 are connected by wire or wirelessly. In the following description, when there is no need to distinguish between the sensors 10-1 to 10-P, they will be referred to as sensors 10.

[0029] As shown in Figure 1, when a vehicle 12 passes over a structure 11, a load is applied to the road surface due to contact between the tires of the vehicle 12 and the road surface. Deflection caused by the load generates a large number of elastic waves 13 inside the structure 11. Each sensor 10 installed on the underside of the structure 11 can detect the elastic waves 13 generated inside the structure 11.

[0030] The sensor 10 detects elastic waves 13 generated from inside the structure 11. For example, the sensor 10 detects elastic waves 13 generated when a vehicle 12 passes through the structure 11. The sensor 10 is installed at a position where it can detect the elastic waves 13. For example, the sensor 10 is installed on a surface of the structure 11 different from the surface on which a load is applied. If the surface on which the load is applied is the road surface of the structure 11, the sensor 10 is installed on either the side or bottom surface of the structure 11. The sensor 10 converts the detected elastic waves 13 into an electrical signal (AE signal). In the following explanation, an example will be given in which the sensor 10 is installed on the bottom surface of the structure 11. Here, the multiple sensors 10-1 to 10-P are arranged on the bottom surface of the structure 11 at different intervals in the vehicle traveling axis direction and in a direction perpendicular to the vehicle traveling axis. The area surrounded by the multiple sensors 10-1 to 10-P is the evaluation target area of ​​the structure 11.

[0031] The sensor 10 uses a piezoelectric element having sensitivity in the range of, for example, 10 kHz to 1 MHz. A more suitable sensor 10 is a piezoelectric element having sensitivity in the range of 100 kHz to 200 kHz. The sensor 10 may be of any type, such as a resonance type having a resonance peak within a frequency range or a broadband type with suppressed resonance. The sensor 10 may detect the elastic wave 13 using a voltage output type, a resistance change type, or a capacitance type, and any of these detection methods may be used. The sensor 10 may have a built-in amplifier.

[0032] An acceleration sensor may be used instead of the sensor 10. In this case, the acceleration sensor detects elastic waves 13 generated in the structure 11. The acceleration sensor converts the detected elastic waves 13 into an electrical signal by performing the same processing as the sensor 10.

[0033] Like the sensor 10, the activation control device 15 is installed on a surface different from the surface on which the load is applied to the structure 11. The activation control device 15 is a sensor that detects the approach of the vehicle 12, and an acceleration sensor can be used. The approach of the vehicle can be detected from the acceleration generated by the passage of the vehicle 12. Note that a MEMS (Micro Electro Mechanical System) acceleration sensor is preferable from the viewpoint of low power consumption. When the activation control device 15 detects the approach of the vehicle 12, it outputs an activation signal to the signal processing device 20 to switch the signal processing device 20 to an operation mode. Switching the signal processing device 20 to the operation mode means causing the signal processing device 20 to perform signal processing. In other words, the signal processing device 20 does not perform signal processing on the elastic wave 13 until it receives the activation signal from the activation control device 15. This makes it possible to reduce the power consumption of the signal processing device 20.

[0034] The signal processing device 20 has a plurality of modes, including an active mode and an inactive mode, and transitions to the active mode based on an activation signal output from the activation control device 15. The inactive mode is a mode in which power consumption is reduced compared to the active mode by limiting functions. The signal processing device 20 is in the inactive mode until transitioning to the active mode. The inactive mode may be, for example, a state in which no signal processing is performed even if the device is active, a sleep state, or a stopped state in which the power is turned off.

[0035] When the signal processing device 20 transitions to the operating mode, it receives as input the electrical signal output from the sensor 10. The signal processing device 20 performs signal processing on the input electrical signal. The signal processing performed by the signal processing device 20 includes, for example, noise removal, determination of arrival time, parameter extraction, etc. The signal processing device 20 stores detection information that associates information about the elastic waves 13 obtained by signal processing with time information at which each elastic wave 13 was detected, for at least the evaluation period. Note that the signal processing device 20 may store detection information for a period longer than the evaluation period. For example, upon request from the structure evaluation device 30, the signal processing device 20 divides or collectively transmits the detection information for the requested evaluation period to the structure evaluation device 30 as transmission data.

[0036] The signal processing device 20 is configured using an analog circuit or a digital circuit. The digital circuit is realized by, for example, an FPGA (Field Programmable Gate Array) or a microcomputer. By using a non-volatile FPGA, power consumption during standby can be reduced. The digital circuit may also be realized by a dedicated LSI (Large-Scale Integration). The signal processing device 20 may be equipped with a non-volatile memory such as a flash memory or a removable memory.

[0037] The structure evaluation device 30 evaluates the deterioration state of the structure 11 using the transmission data for the evaluation period transmitted from the signal processing device 20.

[0038] The structure evaluation device 30 includes a communication unit 31, a control unit 32, a storage unit 33, and a display unit .

[0039] The communication unit 31 receives the transmission data for the evaluation period transmitted from the signal processing device 20. For example, the communication unit 31 receives the transmission data for the evaluation period by requesting the transmission data for the evaluation period from the signal processing device 20 in response to an external instruction. The communication unit 31 outputs the received transmission data for the evaluation period to the control unit 32.

[0040] The control unit 32 controls the entire structure evaluation device 30. The control unit 32 is configured using a processor such as a CPU (Central Processing Unit) and a memory. The control unit 32 executes a program to function as an acquisition unit 321, a calculation unit 322, an estimation unit 323, and an evaluation unit 324.

[0041] Some or all of the functional units of the acquisition unit 321, calculation unit 322, estimation unit 323, and evaluation unit 324 may be realized by hardware (including circuitry) such as an ASIC (Application Specific Integrated Circuit), a PLD (Programmable Logic Device), or an FPGA, or may be realized by a combination of software and hardware. The program may be recorded on a computer-readable recording medium. Examples of computer-readable recording media include portable media such as flexible disks, magneto-optical disks, ROMs, and CD-ROMs, and non-transitory storage media such as storage devices built into a computer system, such as a hard disk. The program may be transmitted via a telecommunications line.

[0042] Some of the functions of the acquisition unit 321, calculation unit 322, estimation unit 323, and evaluation unit 324 do not need to be pre-installed in the structure evaluation device 30, and may be realized by installing additional application programs in the structure evaluation device 30.

[0043] The acquisition unit 321 acquires various information. For example, the acquisition unit 321 acquires transmission data for the evaluation period received by the communication unit 31. The acquisition unit 321 stores the acquired transmission data for the evaluation period in the storage unit 33.

[0044] The calculation unit 322 calculates the b value for each predetermined period based on the transmission data for the evaluation period stored in the storage unit 33. The b value is one aspect of the evaluation value.

[0045] The estimation unit 323 estimates the trend component and seasonal component using the time series data of each b value. Specifically, the estimation unit 323 estimates the trend component and seasonal component by applying the additive regression model shown in equation (3) as described above. The estimation unit 323 estimates the trend component using an interval linear model and the seasonal component using a Fourier series.

[0046] The evaluation unit 324 evaluates the deterioration state of the structure 11 based on the time series data of each b value calculated for each predetermined period. Specifically, if the trend component estimated by the estimation unit 323 is on a downward trend, the evaluation unit 324 evaluates that damage is progressing in the structure 11. If the seasonal component estimated by the estimation unit 323 fluctuates periodically, the evaluation unit 324 evaluates that a crack with low propensity to progress exists inside the structure 11. A crack with low propensity to progress is damage that, although it is damage, can be considered not to require immediate action at this time.

[0047] The storage unit 33 stores the transmission data for the evaluation period acquired by the acquisition unit 321 and the sensor position information. The sensor position information includes information on the installation position of the sensor 10 in association with the sensor ID. The sensor position information may be, for example, information on latitude and longitude on the structure 11, or information such as horizontal and vertical distances from a specific position on the structure 11. The storage unit 33 is configured using a storage device such as a magnetic hard disk drive or a semiconductor storage device.

[0048] The display unit 34 displays information under the control of the control unit 32. For example, the display unit 34 displays the evaluation results of the evaluation unit 324. The display unit 34 is an image display device such as a liquid crystal display or an organic EL (Electro Luminescence) display. The display unit 34 may be an interface for connecting an image display device to the structure evaluation device 30. In this case, the display unit 34 generates a video signal for displaying the identification results and outputs the video signal to the image display device connected to the display unit 34.

[0049] 2 is a schematic block diagram showing the functions of a signal processing device 20 according to an embodiment. The signal processing device 20 includes a plurality of AFEs (Analog Front Ends) 21, a startup control unit 22, a power supply unit 23, a power supply unit 24, a clock oscillator 25, a time information generation unit 26, a signal processing unit 27, and a communication unit 28. The AFE 21 performs filtering and analog-to-digital conversion on the AE signal output from the sensor 10. The AFE 21 outputs the signal after filtering and analog-to-digital conversion to the signal processing unit 27.

[0050] When the activation control unit 22 receives the activation signal from the activation control device 15, it puts the signal processing unit 27 into the operating mode.

[0051] The power supply unit 23 covers the power supplied by the power supply unit 24. The power supply unit 23 is any one of a primary battery, a secondary battery, a solar cell, an energy harvester, and the like.

[0052] The power supply unit 24 supplies the power obtained from the power supply unit 23 to the entire signal processing device 20 or to some of the functional units of the signal processing device 20 .

[0053] The clock oscillator 25 generates a clock signal. The clock oscillator 25 is, for example, a crystal oscillator. The clock oscillator 25 outputs the generated clock signal to the time information generating unit 26.

[0054] The time information generating unit 26 receives a clock signal from the clock oscillator 25. The time information generating unit 26 generates time information using the clock signal. The time information generating unit 26 is, for example, a counter with a register. That is, the time information generating unit 26 counts edges of the clock signal and stores the cumulative count value since the signal processing device 20 was powered on in the register as time information. The bit length k of the time information (register) is determined based on the measurement duration y of the structure 11 and the time resolution dt, as an integer k greater than or equal to 1 that satisfies k ≥ y / dt. The time resolution dt is determined based on the propagation velocity v of the elastic wave 13 and the position information resolution dr of the source of the elastic wave 13, as dt = dr / v. This allows the accuracy of locating the source of the elastic wave 13 to be set within any range, allowing the signal processing device 20 to locate the source with sufficient accuracy. For example, if the material of the structure 11 is iron, the propagation velocity v of the elastic wave 13 is 5950 m / s. In this case, if the accuracy of locating the source of the elastic wave 13 is 10 mm, then dt = 1.68 μsec. If the measurement duration is 100 years, then the bit length k of the time information (register) is k ≧ 51 bits. Furthermore, because the transmission packets of a typical wireless module are basically transmitted in byte units, if the communication unit 28 (described below) is implemented using a general-purpose wireless module, the bit length k of the time information (register) must be a multiple of 8. In other words, by determining the bit length k of the time information (register) to 56 bits = 7 bytes, which is the smallest multiple of 8 that satisfies k ≧ 51 bits, it becomes possible to use a general-purpose wireless module to transmit time information.

[0055] The signal processing unit 27 controls the entire signal processing device 20. The signal processing unit 27 is configured using a processor such as a CPU and a memory. For example, the signal processing unit 27 generates detection information based on the signal output from the AFE 21 after filtering and analog-to-digital conversion.

[0056] The communication unit 28 transmits the detection information generated by the signal processing unit 27 to the structure evaluation device 30 at a predetermined timing. When the communication unit 28 transmits the detection information by wireless communication, the wireless frequency band used is the so-called ISM band (Industry, Science, and Medical band), such as 2.4 GHz and 920 MHz band (915 MHz to 928 MHz in Japan).

[0057] As described above, the signal processing device 20 is equipped with an independent power supply, which improves portability and flexibility in installation. For example, the signal processing device 20 equipped with such an independent power supply can be installed on the girder of a bridge, making it suitable for long-term monitoring.

[0058] 3 is a schematic block diagram showing the functions of the AFE 21 according to the embodiment. The AFE 21 includes a receiving unit 211, a first filter 212, an analog-to-digital conversion unit 213, and a second filter 214. The receiving unit 211 receives the AE signal transmitted from the sensor 10. The receiving unit 211 outputs the received AE signal to the first filter 212. It is assumed that the AE signal is provided with time information detected by the sensor 10.

[0059] The first filter 212 removes noise from the AE signal received by the receiving unit 211. For example, the first filter 212 removes frequency bands other than a specific frequency band from the AE signal as noise. The first filter 212 is, for example, a band-pass filter. The first filter 212 outputs the analog signal after noise removal (hereinafter referred to as the "noise-removed analog signal") to the analog-to-digital conversion unit 213.

[0060] The analog-to-digital converter 213 quantizes the noise-removed analog signal output from the first filter 212 to convert the analog signal into a digital signal. The analog-to-digital converter 213 outputs the digital signal to the second filter 214.

[0061] The second filter 214 removes noise from the digital signal output from the analog-to-digital conversion unit 213. The second filter 214 is a filter for removing noise. The second filter 214 outputs the digital signal after the noise removal (hereinafter referred to as the "noise-removed digital signal") to the control unit 32. In the following description, the processing performed in the AFE 21 will be referred to as preprocessing.

[0062] 4 is a schematic block diagram showing the functions of the signal processing unit 27 in this embodiment. The signal processing unit 27 includes a waveform shaping filter 271, a gate generation circuit 272, an arrival time determination unit 273, a feature extraction unit 274, a detection information generation unit 275, and a memory 276.

[0063] The waveform shaping filter 271 removes noise components outside a predetermined band from the input digital signal. The waveform shaping filter 271 is, for example, a digital band-pass filter (BPF). The waveform shaping filter 271 outputs the digital signal after the noise components have been removed (hereinafter referred to as the "noise-removed signal") to the gate generation circuit 272 and the feature extraction unit 274.

[0064] The gate generation circuit 272 receives the noise removal signal output from the waveform shaping filter 271. The gate generation circuit 272 generates a gate signal based on the received noise removal signal. The gate signal indicates whether the waveform of the noise removal signal is sustained.

[0065] The gate generation circuit 272 is realized by, for example, an envelope detector and a comparator. The envelope detector detects the envelope of the noise-removed signal. The envelope is extracted, for example, by squaring the noise-removed signal and performing a predetermined process (for example, processing using a low-pass filter or a Hilbert transform) on the squared output value. The comparator determines whether the envelope of the noise-removed signal is equal to or greater than a predetermined threshold.

[0066] When the envelope of the noise-removed signal becomes equal to or greater than a predetermined threshold, the gate generation circuit 272 outputs a first gate signal indicating that the waveform of the noise-removed signal is being sustained to the arrival time determination unit 273 and the feature extraction unit 274. On the other hand, when the envelope of the noise-removed signal becomes less than the predetermined threshold, the gate generation circuit 272 outputs a second gate signal indicating that the waveform of the noise-removed signal is not being sustained to the arrival time determination unit 273 and the feature extraction unit 274.

[0067] The arrival time determination unit 273 receives as input the time information generated by the time information generation unit 26 and the gate signal output from the gate generation circuit 272. The arrival time determination unit 273 determines the elastic wave arrival time using the time information input while the first gate signal is being input. The arrival time determination unit 273 outputs the determined elastic wave arrival time as time information to the detection information generation unit 275. The arrival time determination unit 273 does not perform any processing while the second gate signal is being input.

[0068] The feature extraction unit 274 receives as input the noise-removed signal output from the waveform shaping filter 271 and the gate signal output from the gate generation circuit 272. The feature extraction unit 274 extracts a feature of the noise-removed signal using the noise-removed signal input while the first gate signal is being input. The feature extraction unit 274 does not perform processing while the second gate signal is being input. The feature is information indicating the characteristics of the noise-removed signal.

[0069] The feature quantity may be, for example, the amplitude [mV] of the waveform, the rise time [usec] of the waveform, the duration [usec] of the gate signal, the number of zero crossing counts [times], the energy [arb.] of the waveform, the frequency [Hz], and the root mean square (RMS) value. The feature quantity extraction unit 274 outputs parameters related to the extracted feature quantities to the detection information generation unit 275. When outputting the parameters related to the feature quantities, the feature quantity extraction unit 274 associates a sensor ID with the parameters related to the feature quantities. The sensor ID represents identification information for identifying the sensor 10 installed in the area (hereinafter referred to as the "evaluation area") to be evaluated for the soundness of the structure 11.

[0070] The waveform amplitude is information about the amplitude of the elastic wave 13, such as the maximum amplitude value in the noise elimination signal. The waveform rise time is, for example, the time T1 from when the gate signal starts rising until the noise elimination signal reaches its maximum value. The gate signal duration is, for example, the time from when the gate signal starts rising until the amplitude becomes smaller than a preset value. The zero cross count is, for example, the number of times the noise elimination signal crosses a reference line that passes through a zero value.

[0071] The waveform energy is, for example, the value obtained by integrating the squared amplitude of the noise-removed signal at each time point over time. Note that the definition of energy is not limited to the above example, and may be approximated using, for example, the envelope of the waveform. The frequency is the frequency of the noise-removed signal. The RMS value is, for example, the value obtained by squaring the amplitude of the noise-removed signal at each time point and taking the square root.

[0072] The detection information generation unit 275 receives a sensor ID, time information, and parameters related to the feature amount as input, and generates detection information including the input sensor ID, time information, and parameters related to the feature amount.

[0073] The memory 276 stores the detection information generated by the detection information generating unit 275. The memory 276 is, for example, a dual port RAM (Random Access Memory). The memory 276 stores detection information for, for example, two years or more.

[0074] Fig. 5 is a sequence diagram showing the flow of processing related to accumulation of detection information in the structure evaluation system 100 of the embodiment. In Fig. 5, the sensors 10-1 to 10-P will be collectively described as a sensor group. At the start of the processing in Fig. 5, it is assumed that the signal processing device 20 is in sleep mode. The activation control device 15 detects the approach of the vehicle 12 (step S101). The activation control device 15 generates an activation signal and outputs the generated activation signal to the signal processing device 20 (step S102).

[0075] The activation control unit 22 of the signal processing device 20 receives the activation signal transmitted from the activation control device 15. The activation control unit 22 activates the signal processing unit 27 in response to the received activation signal. As a result, the signal processing device 20 transitions from the sleep mode to the active mode (step S103). The sensor group detects a plurality of elastic waves 13 (step S104). The sensor group converts the detected elastic waves 13 into electrical signals (AE signals). The sensor group transmits the electrical signals (AE signals) to the signal processing device 20. Note that, every time the sensor group detects an elastic wave 13, it converts the elastic wave 13 into an electrical signal (AE signal) and transmits it to the signal processing device 20.

[0076] The AFE 21 performs preprocessing on the electrical signal (AE signal) transmitted from the sensor group (step S106). Specifically, the AFE 21 performs filtering and analog-to-digital conversion on the electrical signal (AE signal). The AFE 21 outputs the noise-removed digital signal to the signal processing unit 27. The signal processing unit 27 receives the noise-removed digital signal output from the AFE 21 and determines the arrival time of the elastic wave 13 based on the received noise-removed digital signal (step S107). Furthermore, the signal processing unit 27 extracts a feature of the elastic wave 13 based on the received noise-removed digital signal (step S108). Here, the signal processing unit 27 extracts the feature of the elastic wave 13 based on the noise-removed digital signal only when the first gate signal is output. On the other hand, the signal processing unit 27 does not extract a feature from the noise-removed digital signal when the second gate signal is output.

[0077] The signal processing unit 27 generates detection information that associates the arrival time of the elastic wave 13 with the feature amount and the sensor ID (step S109). The generated detection information is stored in the memory 276 (step S110). Note that by continuously performing the process in FIG. 5, the memory 276 stores detection information for at least the evaluation period.

[0078] FIG. 6 is a flowchart showing the flow of processing by the structure evaluation device 30 in the embodiment. The acquisition unit 321 of the structure evaluation device 30 requests detection information for the evaluation period from the signal processing device 20 via the communication unit 31. As a result, transmission data including the detection information for the evaluation period is generated in the signal processing device 20 and transmitted to the structure evaluation device 30. The acquisition unit 321 acquires the transmission data transmitted from the signal processing device 20 (step S201). The acquisition unit 321 stores the detection information for the evaluation period included in the acquired transmission data in the storage unit 33.

[0079] The calculation unit 322 calculates the b-value for each predetermined period (e.g., one day) based on the detection information for the evaluation period stored in the storage unit 33 (step S202). Specifically, the calculation unit 322 first reads out the detection information for a certain day from the storage unit 33. Next, the calculation unit 322 calculates the b-value for each number of samples (e.g., 500) used to calculate the b-value from the read detection information. The calculation unit 322 uses the amplitude value of the waveform included in the detection information when calculating the b-value. The b-value calculated for each number of samples in this manner is referred to as a candidate b-value. If there are 1,500 pieces of detection information for a certain day, the calculation unit 322 will calculate three candidate b-values. The candidate b-value is one aspect of a candidate evaluation value. The greater the number of candidate b-values, the greater the fluctuation in the b-value within a day. Therefore, to suppress daily fluctuations, the calculation unit 322 calculates a statistical value of the calculated candidate b-values ​​to calculate a representative b-value for that day. Note that the statistical value may be a median or an average. The b value obtained in this way as the representative value for that day is referred to as the daily b value.

[0080] The calculation unit 322 performs the above-described process for each day's worth of detection information. As a result, the calculation unit 322 calculates the b-value for the evaluation period for each predetermined period (e.g., one day). However, as described above, since the timing at which the elastic waves 13 are acquired is irregular in the measurement of the elastic waves 13, it is conceivable that the elastic waves 13 may not be acquired on some days, or that even if the elastic waves 13 are acquired, the number of samples used to calculate the b-value may not be sufficient. Therefore, the calculation unit 322 does not calculate a candidate b-value when the number of samples used to calculate the b-value is not sufficient. In this case, the calculation unit 322 does not calculate a daily b-value either. That is, daily b-values ​​may not be available for all days in the evaluation period, and daily b-values ​​may be obtained only on days when detection information is acquired. Assuming that the evaluation period is a week starting on a Sunday, and elastic waves 13 were not acquired on Wednesday and Friday, it is sufficient to obtain daily b-values ​​for five points: Sunday, Monday, Tuesday, Thursday, and Saturday.

[0081] The estimation unit 323 estimates the trend component and the seasonal component using an additive regression model based on the b value for each predetermined period obtained by the calculation unit 322 (step S203). As a specific process, taking the one-week evaluation period (e.g., a period in which five daily b values ​​were obtained) as an example, the estimation unit 323 optimizes the parameters of the additive regression model so that the five daily data points, which are intermittent time-series data, can be expressed by the additive regression model shown in Equation (3), which is expressed as a function of time. If time t is considered to be the number of elapsed days, the five daily b values ​​on Sunday, Monday, Tuesday, Thursday, and Saturday in a certain week starting on Sunday correspond to the daily b values ​​at t = 0, 1, 2, 4, and 6, respectively, and can be expressed as b(t). The parameters of the additive regression model are optimized so that the error between the estimated value b^(t) of the additive regression model at time t and the daily b value b(t) obtained from the detection information is minimized. As a result, the additive regression model makes it possible to obtain an estimated value b^(t) at any time t. In the above example, it becomes possible to obtain estimated values ​​for Wednesday (t=3) and Friday (t=5), for which daily data was not available. Furthermore, as in equation (3), the trend component and seasonal component are expressed as independent terms, so it is possible to obtain estimated values ​​for the trend component and seasonal component independently. It is also possible to obtain estimated values ​​for the future or past outside the measurement period. In the above example, it is also possible to obtain an estimated value for Sunday t=7, one week later. The estimation unit 323 outputs the estimation results to the evaluation unit 324.

[0082] If the trend component estimated by the estimation unit 323 is on a downward trend, the evaluation unit 324 evaluates that damage is progressing in the structure 11. If the seasonal component estimated by the estimation unit 323 fluctuates periodically, the evaluation unit 324 evaluates that a crack with low propagating property exists inside the structure 11 (step S204). The evaluation unit 324 may display the evaluation result on the display unit 34. In this case, the evaluation unit 324 may display graphs of the trend component and seasonal component and the evaluation result on the same screen.

[0083] Next, the effects of this embodiment will be described based on the experimental results of the inventors. FIG. 7 is a diagram showing an example of analysis results using b-value data for an evaluation period. In the example shown in FIG. 7, the evaluation period is set to three years, and analysis results based on elastic waves 13 obtained over that three-year period are shown. Point P1 shown in the upper diagram of FIG. 7 represents the b-value, and dotted line L1 represents the trend component. In FIG. 7, the period from date t0 to date t1 represents the analysis results using b-value data for the first year of the evaluation period, the period from date t1 to date t2 represents the analysis results using b-value data for the second year of the evaluation period, and the period from date t2 to date t3 represents the analysis results using b-value data for the third year of the evaluation period.

[0084] The results of separating the trend and seasonal components from the results shown in the upper graph of Figure 7 are shown in the lower graph of Figure 7. Line L1 in the lower graph of Figure 7 represents the trend component, and line segment L2 represents the seasonal component. As shown by line L1, the trend component (Trend in Figure 7) shows a gradual downward trend. As shown by line segment L2, the seasonal component (Seasonality in Figure 7) tends to increase in summer and decrease in winter. As shown in Figure 7, the inventors' long-term monitoring revealed for the first time seasonal fluctuations within structures. Such seasonal fluctuations are difficult to confirm using conventional evaluation methods using b-values ​​obtained over a short period of time. Therefore, the evaluation method of this embodiment is a novel evaluation method and can obtain analytical results that could not be obtained using conventional methods. The long-term downward trend in b-values ​​indicates a gradual increase in cracks, and the annual fluctuations can be estimated to be due to changes in the shape of existing cracks due to temperature changes.

[0085] Using multiple sensors 10, the long-term trend changes can be depicted as changes in a contour diagram (two-dimensional map), making it possible to visualize areas of advanced deterioration. Therefore, the evaluation unit 324 may generate a contour diagram interpolated based on a Voronoi diagram, using the b-value as a representative value at the sensor location based on the b-value at the date and time selected by the user and the installation location information of the sensor 10, and display the generated contour diagram on the display unit 44. Here, it is desirable to select a b-value related to the trend component. The b-value related to the trend component is, for example, the b-value that overlaps with the line L1 when the line L1 is drawn using the b-value data obtained during the evaluation period, as shown in FIG. 7. The generating point of the Voronoi diagram is the installation location of the sensor 10.

[0086] An example of a contour diagram interpolated based on a Voronoi diagram, with the b value taken as the representative value at the sensor position, is shown in Fig. 8. The black dots in Fig. 8 represent the placement positions of the sensors 10. The left diagram in Fig. 8 shows a contour diagram based on the b value at date and time t0, and the right diagram in Fig. 8 shows a contour diagram based on the b value at date and time t3 (three years after date and time t0). Comparing the left and right diagrams in Fig. 8 reveals that deterioration has expanded in the upper right region over a three-year period.

[0087] The structure evaluation system 100 configured as described above can improve the accuracy of evaluating the deterioration state of the structure 11 when long-term monitoring of the structure 11 is performed using the b-value. Specifically, the structure evaluation system 100 calculates daily b-values ​​for each predetermined period using detection information for the evaluation period, and evaluates the deterioration state of the structure 11 based on the time-series data of each daily b-value calculated for each predetermined period. For example, if the evaluation period is three years, the structure evaluation system 100 calculates daily b-values ​​for each predetermined period using detection information for three years. Even if daily b-values ​​are not calculated for all dates and times within the evaluation period, the structure evaluation system 100 evaluates the deterioration state of the structure using the time-series data of the calculated daily b-values. Therefore, even if elastic waves 13 are not detected during a certain period, the damage trend can be evaluated by observing the change in the b-value over time. Therefore, when long-term monitoring of the structure 11 is performed using the b-value, it is possible to improve the accuracy of evaluating the deterioration state of the structure 11.

[0088] The structure evaluation system 100 calculates multiple candidate b-values ​​for each predetermined period and calculates a representative value of the multiple candidate b-values ​​as the daily b-value, thereby making it possible to suppress a decrease in evaluation accuracy due to variations in the candidate b-values.

[0089] The structure evaluation system 100 can observe periodicity by using detection information from an evaluation period of two years or more. This makes it easier to capture changes in the b-value due to seasonal changes, and improves the accuracy of evaluating the deterioration state of a structure.

[0090] The structure evaluation system 100 evaluates the progress of damage inside the structure 11 based on the tendency of the trend component, and evaluates whether there is low-progression damage inside the structure 11 based on the seasonal component. In this way, the structure evaluation system 100 can capture not only the progress of damage inside the structure 11, but also changes in damage due to seasonal temperature changes. As a result, this can be used for future structure evaluations.

[0091] A modified example of the structure evaluation system 100 will now be described. (Variation 1) In the above-described embodiment, the object of impact on the structure 11 is the vehicle 12, but the object of impact on the structure 11 is not limited to the vehicle 12. The object of impact on the structure 11 may be anything that can apply impact to the structure 11 over a long period of time, even if it is irregular. For example, the object of impact on the structure 11 may be rain, water spraying, or manual blows.

[0092] (Variation 2) By incorporating temperature dependency into the additive regression model, annual fluctuations can be separated into a temperature-dependent component and a temperature-independent seasonal component s(t). In this case, the temperature-independent seasonal component may be, for example, a component resulting from fluctuations in traffic volume throughout the year. Therefore, the estimation unit 323 may be configured to estimate the trend component and seasonal component using an additive regression model shown in the following equation (7), in which the temperature-dependent component is added to the trend component and seasonal component as a multiplication term of the temperature data T(t) and the regression coefficient α.

[0093]

number

[0094] The estimation unit 323 may use humidity data instead of the temperature data T(t). Similarly, the estimation unit 323 may be configured to estimate the trend component and the seasonal component using data x(t) obtained by sensing another physical quantity and an additive regression model shown in the following equation (8) in which a multiplication term of the regression coefficient β is added to the above equation (7).

[0095]

number

[0096] The other physical quantity is, for example, traffic volume. In this way, the estimation unit 323 estimates the trend component and the seasonal component using an additive regression model that adds a term proportional to at least one of temperature, humidity, and traffic volume in addition to the trend component and the seasonal component.

[0097] (Variation 3) In the above-described embodiment, the b-value has been used as an example of an evaluation value obtained from the slope of the frequency distribution by amplitude scale, but the evaluation value obtained from the slope of the frequency distribution by amplitude scale is not limited to this. For example, the evaluation value obtained from the slope of the frequency distribution by amplitude scale may be the improved b-value described in the following reference 1. The improved b-value statistically determines the amplitude range based on the mean μ and standard deviation σ of the amplitude, and is used as a diagnostic index of soundness based on the AE method. (Reference 1: Japanese Patent Application Laid-Open No. 2006-10595)

[0098] (Variation 4) Some of the functional units of the structure evaluation device 30 may be provided in a separate housing. For example, the structure evaluation device 30 may include a communication unit 31, a control unit 32, and a storage unit 33, and the display unit 34 may be provided in a separate housing. In this configuration, the control unit 32 transmits the evaluation results to the separate housing. Then, the display unit 34 provided in the separate housing displays the evaluation results.

[0099] (Variation 5) The signal processing device 20 and the structure evaluation device 30 may be configured as an integrated unit.

[0100] (Variation 6) In the above-described embodiment, a configuration in which the detection information for the evaluation target period is held in the memory 276 is shown. However, the detection information for the evaluation target period may be stored in the storage unit of another device. The storage unit of another device may be, for example, the storage unit of a server provided on the cloud, or the storage unit 33 of the structure evaluation device 30. When storing the detection information for the evaluation target period in another storage unit, the signal processing device 20 transmits it to another device each time the detection information is obtained, or each time the detection information for a certain period is stored in the memory 276. Note that the certain period is shorter than the evaluation target period, for example, one week, one month, etc. After transmitting the detection information to another device, the signal processing device 20 deletes the detection information stored in the memory 276. Thereby, the capacity of the memory 276 can be suppressed. When the acquisition unit 321 of the structure evaluation device 30 stores the detection information for the evaluation target period in a server provided on the cloud, the acquisition unit 321 may acquire the detection information for the evaluation target period from the server.

[0101] (Modification Example 7) In the evaluation based on the trend component, the evaluation unit 324 may be further configured to perform an evaluation taking into account the b-value. Even if the trend component shows a downward trend, when the b-value is a certain large value, it can be considered that the damage is small, that is, urgent repair or the like is not required. Referring to Table 1 of Reference 2 below, it is described that when the b-value is 1.7 or more, although there is a small amount of damage, it is considered that the damage is small. Therefore, when the trend component shows a downward trend and the b-value is greater than 1.7, the evaluation unit 324 evaluates that although the damage is progressing in the structure 11, urgent countermeasures are not required. Further, when the trend component shows a downward trend and 1.2 < b-value < 1.7, the evaluation unit 324 evaluates that although the damage is progressing in the structure 11, urgent countermeasures are not required. Further, when the trend component shows a downward trend and the b-value is less than 1.2, the evaluation unit 324 evaluates that the damage is progressing in the structure 11 and urgent countermeasures are necessary. (Reference 2: IS Colombo, I. Main, and M. Forde, “Assessing Damage of Reinforced Concrete Beam Using ``b-value'' Analysis of Acoustic Emission Signals,'' Journal of materials in civil engineering, vol. 15, no. 3, pp. 280-286, 2003.)

[0102] (Variation 8) The acquisition unit 321 may be configured to further acquire information regarding vehicles 12 that have traveled through the structure 11 (hereinafter referred to as "vehicle information"). The vehicle information includes, for example, vehicle type information of the vehicles 12 that have traveled through the structure 11 during the evaluation period and traffic volume information of the vehicles 12 on the structure 11. The vehicle type information of the vehicles 12 includes at least information on the tread width of the vehicles 12. The traffic volume information of the vehicles 12 is information indicating how many vehicles 12 of what type have passed the structure 11 during the evaluation period. The acquisition unit 321 may acquire the vehicle information through user input or from a server that stores traffic information. In this configuration, the calculation unit 322 may calculate the b-value for each vehicle type for each predetermined period. The estimation unit 323 estimates the trend component and the seasonal component for each vehicle type using the time series data of each b-value calculated for each vehicle type by the calculation unit 322. The evaluation unit 324 evaluates the deterioration state of the structure 11 based on the time series data of each b-value calculated for each predetermined period.

[0103] (Variation 9) In the above-described embodiment, the signal processing device 20 is configured to be equipped with an independent power supply. However, the signal processing device 20 may be configured to operate using a commercial power supply without being equipped with an independent power supply, although this may reduce portability and flexibility in installation. In such a configuration, the signal processing device 20 does not need to include the power supply unit 23.

[0104] (Variation 10) In the above-described embodiment, the detection information generating unit 275 generates detection information including a sensor ID, time information, and parameters related to feature quantities. To calculate the b-value, at least information on the amplitude of the waveform is required. Furthermore, to calculate the b-value for a certain period, information on the amplitude of the waveform and time information on the elastic wave are required. In other words, the detection information generating unit 275 does not need to generate detection information including all parameters related to feature quantities. Therefore, the detection information generating unit 275 may generate detection information including time information and information on the amplitude of the waveform.

[0105] According to at least one of the embodiments described above, by having a plurality of sensors 10 that detect elastic waves generated from a structure, a memory 276 that stores detection information that associates at least information regarding the amplitude of each elastic wave detected by each of the plurality of sensors 10 with information regarding the time at which each elastic wave was detected, a calculation unit 322 that calculates the b value for each predetermined period based on the detection information for the evaluation period stored in the memory 276, and an evaluation unit 324 that evaluates the deterioration state of the structure 11 based on the time series data of each b value calculated for each predetermined period, the accuracy of evaluation of the deterioration state of the structure 11 can be improved when long-term monitoring of the structure 11 is performed using an evaluation value obtained from the slope of the frequency distribution by amplitude scale.

[0106] Although several embodiments of the present invention have been described, these embodiments are presented as examples and are not intended to limit the scope of the invention. These embodiments can be implemented in various other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their modifications are included within the scope and spirit of the invention, as well as within the scope of the invention described in the claims and their equivalents. [Explanation of symbols]

[0107] 10, 10-1 to 10-P... sensor, 15... start-up control device, 20... signal processing device, 30... structure evaluation device, 31... communication unit, 32... control unit, 33... memory unit, 34... display unit, 321... acquisition unit, 322... calculation unit, 323... estimation unit, 324... evaluation unit, 21... AFE, 22... start-up control unit, 23... power supply unit, 24... power supply unit, 25... clock oscillator, 26... time information generation unit, 27... signal processing unit, 28... communication unit, 211... receiving unit, 212... first filter, 213... analog-to-digital conversion unit, 214... second filter, 271... waveform shaping filter, 272... gate generation circuit, 273... arrival time determination unit, 274... feature extraction unit, 275... detection information generation unit, 276... memory

Claims

1. a plurality of sensors for detecting elastic waves generated from the structure; an acquisition unit that acquires detection information for an evaluation period, the detection information associating information about at least the amplitude of each elastic wave detected by each of the plurality of sensors with time information at which each elastic wave was detected; a calculation unit that calculates an evaluation value, which is a slope of a frequency distribution by amplitude scale of elastic waves, for each predetermined period based on the acquired detection information for the evaluation period; an evaluation unit that evaluates a deterioration state of the structure based on time-series data of each evaluation value calculated for each predetermined period; Equipped with The method further includes an estimation unit that estimates a trend component and a seasonal component using time series data of each evaluation value, the evaluation unit evaluates a deterioration state of the structure for each of the estimated trend component and the estimated seasonal component; The structure evaluation system, wherein the evaluation unit evaluates that low-progression damage exists inside the structure if the seasonal component fluctuates periodically.

2. The structure evaluation system according to claim 1 , wherein the calculation unit calculates a plurality of candidate evaluation values ​​for each of the predetermined periods, and calculates a representative value of the plurality of candidate evaluation values ​​as the evaluation value for each of the predetermined periods.

3. The structure evaluation system according to claim 1 , wherein the estimation unit estimates the trend component and the seasonal component by applying an additive regression model.

4. The structure evaluation system according to claim 3 , wherein the estimation unit estimates the trend component using a piecewise linear model and estimates the seasonal component using a Fourier series.

5. The structure evaluation system according to claim 1 , wherein the evaluation unit evaluates that damage to the structure is progressing when the trend component is on a downward trend.

6. 5. The structure evaluation system according to claim 3, wherein the estimation unit estimates the trend component and the seasonal component using an additive regression model in which a term proportional to sensor data of a predetermined physical quantity is added to the trend component and the seasonal component.

7. 7. The structure evaluation system according to claim 6, wherein the estimation unit estimates the trend component and the seasonal component using an additive regression model to which, in addition to the trend component and the seasonal component, a term proportional to at least one of temperature, humidity, and traffic volume is added.

8. The evaluation period is more than two years. The structure evaluation system according to claim 1 , wherein the calculation unit calculates the evaluation value daily based on detection information for at least two years out of the detection information for the two or more years.

9. Further comprising a display unit for displaying information, 9. The structure evaluation system according to claim 1, wherein the evaluation unit generates a two-dimensional map using the evaluation values ​​as representative values ​​at the installation positions of each sensor, and causes the display unit to display the generated two-dimensional map.

10. an acquisition unit that acquires detection information for an evaluation period, the detection information associating information on at least the amplitude of each elastic wave detected by each of a plurality of sensors that detects elastic waves generated from the structure with time information at which each elastic wave was detected; a calculation unit that calculates an evaluation value, which is a slope of a frequency distribution by amplitude scale of elastic waves, for each predetermined period based on the acquired detection information for the evaluation period; an evaluation unit that evaluates a deterioration state of the structure based on time-series data of each evaluation value calculated for each predetermined period; Equipped with The method further includes an estimation unit that estimates a trend component and a seasonal component using time series data of each evaluation value, the evaluation unit evaluates a deterioration state of the structure for each of the estimated trend component and the estimated seasonal component; The evaluation unit is a structure evaluation device that evaluates that low-progression damage exists inside the structure if the seasonal component fluctuates periodically.

11. Obtaining detection information for an evaluation period that associates information about at least the amplitude of each elastic wave detected by each of a plurality of sensors that detects elastic waves generated from the structure with information about the time at which each elastic wave was detected; Calculating an evaluation value, which is the slope of the frequency distribution by amplitude scale of the elastic wave, for each predetermined period based on the acquired detection information for the evaluation period; Evaluating the deterioration state of the structure based on the time series data of each evaluation value calculated for each predetermined period; Using the time series data of each evaluation value, trend and seasonal components are estimated. evaluating the deterioration state of the structure for each of the estimated trend component and the estimated seasonal component; If the seasonal component fluctuates periodically, it is determined that there is low-progression damage inside the structure. Structural evaluation methods.

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