Structural evaluation system and structural evaluation method

Abstract

To provide a structure evaluation system and structure evaluation method that can accurately estimate the number of vehicles without being affected by damage inside a structure and evaluate a state of deterioration of the structure.SOLUTION: A structure evaluation system comprises multiple first sensors, one or more second sensors, a vehicle information estimation unit, and an evaluation unit. The multiple first sensors detect elastic waves generated inside a structure over which a vehicle is traveling. The one or more second sensors detect passing of the vehicle without regard to damage inside the structure. The vehicle information estimation unit estimates vehicle information, which includes at least information on the number of vehicles that have passed through the structure, based on the detection results of the one or more second sensors. The evaluation unit evaluates a deterioration state of the structure based on the multiple elastic waves detected by each of the multiple first sensors and the vehicle information estimated by the vehicle information estimation unit.SELECTED DRAWING: Figure 1

Description

Technical Field

[0001] Embodiments of the present invention relate to a structure evaluation system and a structure evaluation method.

Background Art

[0002] By installing sensors on the surface of a structure such as a bridge, elastic waves generated inside the structure can be detected. Further, by installing a plurality of sensors on the surface of the structure, the position of the elastic wave source (hereinafter referred to as "elastic wave source") can be calibrated based on the difference in arrival times of the elastic waves detected by each sensor. Elastic waves are also generated inside the structure when an impact is applied to the surface of the structure from the outside. Even in such a case, the position of the elastic wave source can be calibrated based on the difference in arrival times of the elastic waves detected by each sensor.

[0003] When there is damage in the propagation path of elastic waves inside the structure, the propagation of elastic waves is hindered. When the propagation of elastic waves is hindered due to damage inside the structure, elastic waves cannot be detected by some sensors. As a result, the accuracy of the calibration result of the elastic wave source decreases. When an impact uniformly applied in space, such as the collision of raindrops on the road surface during rainfall, is applied to the surface of the structure and elastic waves are detected by each sensor installed opposite, the density of the elastic wave source is observed to decrease in the region having damage inside. By utilizing such characteristics, the deterioration state (presence or absence of damage inside the structure) of the structure can be evaluated. In particular, damage inside the structure can be detected by using elastic waves generated by vehicles traveling on the road surface.

[0004] Conventionally, various correction methods have been disclosed to improve the accuracy of evaluation results obtained by the above methods. For example, in order to compare measurement results with different traffic volumes, a method has been disclosed that estimates information about vehicles traveling at the measurement target location and corrects the density of elastic wave sources using a correction value based on the ratio of the number of vehicles that passed to the measurement target to the number of vehicles that passed to the comparison target structure. However, in this method, since vehicles are estimated based on whether or not elastic waves are detected, the accuracy of the measurement of the number of vehicles decreases when the damage inside the structure is severe. As a result, the accuracy of the correction value used to improve the accuracy of the evaluation results also decreases, and in some cases the accuracy of the evaluation of the deterioration state of the structure decreases. [Prior art documents] [Patent Documents]

[0005] [Patent Document 1] International Publication No. 2017 / 217034 [Patent Document 2] International Publication No. 2022 / 014004 [Patent Document 3] Japanese Patent Publication No. 2022-70711 [Overview of the project] [Problems that the invention aims to solve]

[0006] The problem that this invention aims to solve is to provide a structural evaluation system and a structural evaluation method that can accurately estimate the number of vehicles regardless of internal damage to the structure and evaluate the deterioration state of the structure. [Means for solving the problem]

[0007] The structural evaluation system of the embodiment comprises a plurality of first sensors, one or more second sensors, a vehicle information estimation unit, and an evaluation unit. The plurality of first sensors detect elastic waves generated inside a structure through which a vehicle travels. One or more second sensors detect the passage of the vehicle regardless of damage inside the structure. The vehicle information estimation unit estimates vehicle information, including at least the number of vehicles that have passed through the structure, based on the detection results of the one or more second sensors. The evaluation unit evaluates the deterioration state of the structure based on the plurality of elastic waves detected by each of the plurality of first sensors and the vehicle information estimated by the vehicle information estimation unit. The structural evaluation system further comprises a position determination unit, a density distribution generation unit, and a correction unit. The position determination unit determines the position of the plurality of elastic wave sources based on the plurality of elastic waves detected by each of the plurality of first sensors. The density distribution generation unit generates an elastic wave source density distribution representing the density distribution of the positions of the plurality of elastic wave sources, based on the determination results of the position determination unit. The correction unit corrects the elastic wave source density distribution generated by the density distribution generation unit based on the vehicle information estimated by the vehicle information estimation unit. The evaluation unit then evaluates the corrected elastic wave source density distribution density of each region in The deterioration state of the structure is evaluated by comparing it with a threshold value. [Brief explanation of the drawing]

[0008] [Figure 1] A diagram showing the configuration of the structural evaluation system in the first embodiment. [Figure 2] A diagram showing an example of the configuration of the signal processing unit in the first embodiment. [Figure 3] A diagram illustrating the arrangement of each sensor in the first embodiment. [Figure 4] A diagram illustrating the method for measuring elastic waves in the first embodiment. [Figure 5] A diagram illustrating the method for detecting vehicle traffic in the second sensor in the first embodiment. [Figure 6]A sequence diagram showing the flow of the deterioration state evaluation process by the structural evaluation system in the first embodiment. [Figure 7] A diagram illustrating the vehicle number estimation process performed by the vehicle information estimation unit in the first embodiment. [Figure 8] A diagram showing the configuration of the structural evaluation system in the second embodiment. [Figure 9] A diagram showing an example of features extracted from the output signal of the second sensor. [Figure 10] A sequence diagram showing the flow of the deterioration state evaluation process by the structural evaluation system in the second embodiment. [Figure 11] A flowchart showing the flow of the vehicle type estimation process performed by the vehicle information estimation unit in the second embodiment. [Figure 12] A diagram showing the configuration of the structural evaluation system in the third embodiment. [Figure 13] A diagram illustrating the method for estimating vehicle speed. [Modes for carrying out the invention]

[0009] The structural evaluation system and structural evaluation method of the embodiment will be described below with reference to the drawings.

[0010] (First Embodiment) Figure 1 shows the configuration of the structural evaluation system 100 in the first embodiment. The structural evaluation system 100 is used to evaluate the soundness of the structure 50. In the following description, evaluation means determining the degree of soundness of the structure 50, that is, the state of deterioration of the structure 50, based on certain criteria.

[0011] In the following description, the case where the structure 50 is a bridge will be described as an example, but the structure 50 does not necessarily have to be limited to a bridge. The structure 50 may be any structure as long as elastic waves 11 are generated due to the occurrence or progress of cracks, or external impacts (such as rain, artificial rain, etc.). Note that a bridge is not limited to a structure erected over a river, valley, etc., and also includes various structures provided above the ground (such as an elevated bridge on a highway).

[0012] Examples of damage that affects the evaluation of the deterioration state of the structure 50 include internal damage to the structure that obstructs the propagation of elastic waves 11, such as cracks, cavities, and soil liquefaction. Here, cracks include vertical cracks, horizontal cracks, and diagonal cracks, etc. A vertical crack is a crack that occurs in a direction perpendicular to the road surface. A horizontal crack is a crack that occurs in a direction horizontal to the road surface. A diagonal crack is a crack that occurs in a direction other than horizontal and vertical with respect to the road surface. Soil liquefaction is deterioration in which concrete changes into a soil-like state mainly at the boundary between asphalt and a concrete slab.

[0013] The structure evaluation system 100 includes a plurality of first sensors 20-1 to n (n is an integer of 2 or more), a second sensor 25, a signal processing unit 30, a vehicle information estimation unit 35, and a structure evaluation device 40. Each of the plurality of first sensors 20-1 to n and the signal processing unit 30 are communicably connected by wire. The second sensor 25 and the vehicle information estimation unit 35 are communicably connected by wire. The signal processing unit 30 and the structure evaluation device 40, and the vehicle information estimation unit 35 and the structure evaluation device 40 are communicably connected by wire or wirelessly. In the following description, when the first sensors 20-1 to n are not distinguished, they will be described as the first sensor 20.

[0014] As shown in FIG. 1, when the vehicle 10 passes over the structure 50, a load is applied to the road surface due to the contact between the running part of the vehicle 10 and the road surface. Due to the deflection caused by the load, a large number of elastic waves 11 are generated inside the structure 50. Each first sensor 20 installed on the lower surface of the structure 50 can detect the elastic waves 11 generated inside the structure 50.

[0015] The first sensor 20 has a piezoelectric element and detects elastic waves 11 generated from inside the structure 50. The first sensor 20 is installed in a position on the surface of the structure 50 that allows it to detect elastic waves 11. For example, the first sensors 20-1 to 20-n are installed on one of the surfaces, such as the road surface, side surface, or bottom surface, at the same or different intervals in the direction of the vehicle travel axis and in the direction perpendicular to the vehicle travel axis. The direction of the vehicle travel axis refers to the direction in which the vehicle travels on the road surface. The direction perpendicular to the vehicle travel axis refers to the direction perpendicular to the direction of the vehicle travel axis. The first sensor 20 converts the detected elastic waves 11 into an electrical signal. In the following explanation, the case in which the first sensor 20 is installed on the bottom surface of the structure 50 will be used as an example.

[0016] The first sensor 20 uses, for example, a piezoelectric element with sensitivity in the range of 10 kHz to 1 MHz. The first sensor 20 can be of any type, such as a resonant type with a resonance peak within the frequency range, or a broadband type with suppressed resonance. The first sensor 20 can detect elastic waves 11 using any of the following methods: voltage output type, resistance change type, or capacitance type.

[0017] An acceleration sensor may be used instead of the first sensor 20. In this case, the acceleration sensor detects the elastic waves 11 generated inside the structure 50. The acceleration sensor then converts the detected elastic waves 11 into an electrical signal by performing the same processing as the first sensor 20.

[0018] Between the first sensor 20 and the signal processing unit 30, for example, an amplifier, a filter, and an analog-to-digital converter (not shown) are provided. The amplifier amplifies the electrical signal output from the first sensor 20. The amplifier amplifies the electrical signal to a degree that can be processed by, for example, the analog-to-digital converter. The amplifier outputs the amplified electrical signal to the filter. The filter removes noise components outside a predetermined bandwidth. The filter is, for example, a band-pass filter (BPF). The electrical signal from which the noise has been removed by the filter is input to the analog-to-digital converter. The analog-to-digital converter quantizes the noise-removed electrical signal and converts it into a digital signal. The analog-to-digital converter outputs the digital signal to the signal processing unit 30.

[0019] The signal processing unit 30 receives the digital signal output from the analog-to-digital converter as input. The signal processing unit 30 performs signal processing on the input digital signal. The signal processing performed by the signal processing unit 30 includes, for example, noise reduction and extraction of elastic wave features. The signal processing unit 30 generates transmission data including the digital signal after signal processing. The signal processing unit 30 outputs the generated transmission data to the structural evaluation device 40.

[0020] The signal processing unit 30 is configured using either an analog circuit or a digital circuit. If the signal processing unit 30 is configured using an analog circuit, an analog-to-digital converter is not required between the first sensor 20 and the signal processing unit 30. In other words, when the signal processing unit 30 is configured using an analog circuit, an electrical signal from which noise has been removed by a filter is input to the signal processing unit 30. The digital circuit can be implemented using, for example, an FPGA (Field Programmable Gate Array) or a microcomputer. The digital circuit may also be implemented using a dedicated LSI (Large-Scale Integration). Furthermore, the signal processing unit 30 may be equipped with non-volatile memory such as flash memory or removable memory. The following description will focus on the case where the signal processing unit 30 is configured using a digital circuit.

[0021] The second sensor 25 detects the passage of the vehicle 10 regardless of internal damage to the structure 50. For example, the second sensor 25 is a magnetic sensor that directly or indirectly detects magnetic field strength or magnetic flux density as an electrical signal. Examples of magnetic sensors include magnetic impedance elements, magnetoresistive elements, Hall elements, fluxgate sensors, GSR (GHz-Spin-Rotation) sensors, coils, etc. The second sensor 25 can be any sensor that has the sensitivity to detect changes in magnetic flux density caused by the vehicle 10 passing over the structure 50 on the surface on which it is installed. The second sensor 25 is installed on the same surface as the first sensor 20. More specifically, the second sensor 25 is installed within the range enclosed by a plurality of first sensors 20-1 to 20-n.

[0022] Since the vehicle 10 typically uses many metal parts, the surrounding geomagnetic field is disturbed when the vehicle 10 passes over the structure 50. As a result, the second sensor 25 can detect the change in magnetic flux density that occurs as the vehicle passes. It is known that when the vehicle 10 travels over the structure 50, a change in magnetic flux density of approximately 100 nT (nano Tesla) or more occurs. Therefore, the second sensor 25 needs a resolution that can detect a change in magnetic flux density that is at least sufficiently smaller than 100 nT. A magnetic impedance element with a resolution on the order of several nT is suitable from the viewpoint of sensitivity. The second sensor 25 outputs the detection result as an output signal to the vehicle information estimation unit 35. The output signal of the second sensor 25 is time-series data showing the change in magnetic flux density.

[0023] Between the second sensor 25 and the vehicle information estimation unit 35, for example, an amplifier, a filter, and an analog-to-digital converter (not shown) are provided. The amplifier amplifies the output signal of the second sensor 25. The amplifier outputs the amplified output signal to the filter. The filter has a predetermined passband (for example, a passband in the range of 0.1 Hz to 10 Hz). As a result, the filter removes noise components outside the predetermined band. The filter is, for example, a band-pass filter (BPF). The output signal from which noise has been removed by the filter is converted into a digital signal by the analog-to-digital converter and input to the vehicle information estimation unit 35. Note that the vehicle information estimation unit 35 may also be equipped with an analog-to-digital converter. In this case, the output signal from which noise has been removed by the filter is input to the vehicle information estimation unit 35 and converted into a digital signal inside the vehicle information estimation unit 35. In the following description, the output signal output from the second sensor 25, or the output signal of the second sensor 25, means the signal from which noise has been removed by the filter and converted into a digital signal by the analog-to-digital converter.

[0024] The vehicle information estimation unit 35 receives an output signal (for example, time-series data of changes in magnetic flux density) output from the second sensor 25 as input. The vehicle information estimation unit 35 estimates vehicle information based on the input output signal. That is, the vehicle information estimation unit 35 estimates vehicle information based on the output signal of the second sensor 25. Vehicle information refers to information about vehicles 10 that have passed through the structure 50, and includes at least information on the number of vehicles 10 that have passed through the structure 50 (hereinafter referred to as "number of vehicles passing information"). The vehicle information may further include any of the vehicle type, length, weight, or speed of the vehicles 10 that have passed through the structure 50. In the first embodiment, the vehicle information includes the number of vehicles passing information. The vehicle information estimation unit 35 outputs the estimation result to the structure evaluation device 40.

[0025] Figure 2 shows an example of the configuration of the signal processing unit 30 in the first embodiment. The signal processing unit 30 includes a waveform shaping filter 301, a gate generation circuit 302, an arrival time determination unit 303, a feature extraction unit 304, a transmission data generation unit 305, a memory 306, and an output unit 307.

[0026] The waveform shaping filter 301 removes noise components outside a predetermined bandwidth from the input digital signal. The waveform shaping filter 301 is, for example, a digital bandpass filter (BPF). The waveform shaping filter 301 outputs the digital signal after noise component removal (hereinafter referred to as the "noise-removed signal") to the gate generation circuit 302 and the feature extraction unit 304.

[0027] The gate generation circuit 302 receives the noise-removed signal output from the waveform shaping filter 301 as input. The gate generation circuit 302 generates a gate signal based on the input noise-removed signal. The gate signal indicates whether or not the waveform of the noise-removed signal is sustained.

[0028] The gate generation circuit 302 is implemented, for example, by an envelope detector and a comparator. The envelope detector detects the envelope of the denoised signal. The envelope is extracted, for example, by squaring the denoised 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 denoised signal is above a predetermined threshold.

[0029] The gate generation circuit 302 outputs a first gate signal to the arrival time determination unit 303 and the feature extraction unit 304, indicating that the waveform of the noise-removed signal is sustained, when the envelope of the noise-removed signal exceeds a predetermined threshold. On the other hand, the gate generation circuit 302 outputs a second gate signal to the arrival time determination unit 303 and the feature extraction unit 304, indicating that the waveform of the noise-removed signal is not sustained, when the envelope of the noise-removed signal falls below a predetermined threshold. Although the gate generation circuit 302 is shown as determining whether or not the waveform of the noise-removed signal is sustained based on the envelope, the gate generation circuit 302 may also perform processing on the noise-removed signal itself or on the signal to which the absolute value is applied. The threshold used for this gate generation is referred to as the measurement threshold.

[0030] The arrival time determination unit 303 receives a clock signal output from a clock source such as a crystal oscillator (not shown) and a gate signal output from the gate generation circuit 302 as input. The arrival time determination unit 303 determines the elastic wave arrival time using the clock signal input while the first gate signal is being input. The arrival time determination unit 303 outputs the determined elastic wave arrival time as time information to the transmission data generation unit 305. The arrival time determination unit 303 does not perform any processing while the second gate signal is being input. Based on the signal from the clock source, the arrival time determination unit 303 generates cumulative time information from the time of power-on. Specifically, the arrival time determination unit 303 can be a counter that counts the clock edges, and the value of the counter's register can be used as the time information. The counter's register is determined to have a predetermined bit length.

[0031] The feature extraction unit 304 receives the noise reduction signal output from the waveform shaping filter 301 and the gate signal output from the gate generation circuit 302 as inputs. The feature extraction unit 304 extracts the features of the noise reduction signal using the noise reduction signal input while the first gate signal is input. The feature extraction unit 304 does not perform any processing while the second gate signal is input. The features are information that indicates the characteristics of the noise reduction signal. That is, the features of the noise reduction signal are the features of the elastic wave detected by the first sensor 20.

[0032] Features include, for example, waveform amplitude [mV], waveform rise time [usec], gate signal duration [usec], zero-cross count [times], waveform energy [arb.], frequency [Hz], and RMS (Root Mean Square) value. The feature extraction unit 304 outputs parameters related to the extracted features to the transmission data generation unit 305. When outputting parameters related to the features, the feature extraction unit 304 associates the sensor ID with the parameters related to the features. The sensor ID represents identification information for identifying the first sensor 20 installed in the area of ​​the structure 50 that is subject to evaluation of its integrity (hereinafter referred to as the "evaluation area").

[0033] The amplitude of the waveform is, for example, the maximum amplitude value in the denoising signal. The rise time of the waveform is, for example, the time T1 from the start of the rising edge of the gate signal until the denoising signal reaches its maximum value. The duration of the gate signal is, for example, the time from the start of the rising edge of the gate signal until the amplitude becomes smaller than a preset value. The zero-crossing count is, for example, the number of times the denoising signal crosses a reference line passing through zero.

[0034] The energy of the waveform is, for example, the time integral of the squared amplitude of the denoised signal at each point in time. Note that the definition of energy is not limited to the above example; it may also be approximated using, for example, the waveform envelope. The frequency is the frequency of the denoised signal. The RMS value is, for example, the value obtained by squaring the amplitude of the denoised signal at each point in time and taking the square root.

[0035] The transmission data generation unit 305 receives the sensor ID, time information, and feature parameters as input. The transmission data generation unit 305 generates transmission data that includes the input sensor ID, time information, and feature parameters.

[0036] Memory 306 stores one or more transmission data generated by the transmission data generation unit 305. Memory 306 is, for example, a dual-port RAM (Random Access Memory).

[0037] The output unit 307 sequentially outputs one or more transmission data stored in the memory 306 to the structural evaluation device 40. For example, if the signal processing unit 30 and the structural evaluation device 40 are connected by a wire, the output unit 307 outputs one or more transmission data stored in the memory 306 to the structural evaluation device 40 via the wired cable. If the signal processing unit 30 and the structural evaluation device 40 are connected wirelessly, the output unit 307 outputs one or more transmission data stored in the memory 306 to the structural evaluation device 40 wirelessly.

[0038] Returning to Figure 1, let's continue the explanation. The structural evaluation device 40 includes a communication unit 41, a control unit 42, a storage unit 43, and a display unit 44.

[0039] The communication unit 41 receives one or more transmission data output from the signal processing unit 30. Furthermore, the communication unit 41 receives the estimation result output from the vehicle information estimation unit 35.

[0040] The control unit 42 controls the entire structure evaluation device 40. The control unit 42 is composed of a processor such as a CPU (Central Processing Unit) and memory. By executing a program, the control unit 42 functions as an acquisition unit 421, an event extraction unit 422, a position determination unit 423, a distribution generation unit 424, a correction unit 425, and an evaluation unit 426.

[0041] Some or all of the functional units of the acquisition unit 421, event extraction unit 422, position determination unit 423, distribution generation unit 424, correction unit 425, and evaluation unit 426 may be implemented by hardware such as an ASIC (Application Specific Integrated Circuit), PLD (Programmable Logic Device), or FPGA, or by the cooperation of software and hardware. The program may be recorded on a computer-readable recording medium. Computer-readable recording media include non-temporary storage media such as portable media such as flexible disks, magneto-optical disks, ROMs (Read Only Memory), and CD-ROMs, and storage devices such as hard disks built into computer systems. The program may be transmitted via a telecommunications line.

[0042] Some of the functions of the acquisition unit 421, event extraction unit 422, position determination unit 423, distribution generation unit 424, correction unit 425, and evaluation unit 426 do not need to be pre-installed in the structural evaluation device 40, and may be realized by installing additional application programs in the structural evaluation device 40.

[0043] The acquisition unit 421 acquires various types of information. For example, the acquisition unit 421 acquires the transmission data and estimation results received by the communication unit 41. The acquisition unit 421 acquires the transmission data for the evaluation period. The acquisition unit 421 stores the acquired transmission data and estimation results in the storage unit 43.

[0044] The event extraction unit 422 extracts transmission data for one event from the transmission data for the evaluation period stored in the storage unit 43. An event represents an elastic wave generation event that occurred at the structure 50. In this embodiment, the elastic wave generation event is the passage of a vehicle 10 over the road surface. When one event occurs, multiple first sensors 20 will detect elastic waves 11 at approximately the same time. That is, the storage unit 43 will store transmission data related to the elastic waves 11 detected at approximately the same time. Therefore, the event extraction unit 422 sets a predetermined time window and extracts all transmission data whose arrival time falls within the time window as transmission data for one event. The event extraction unit 422 outputs the extracted transmission data for one event to the positioning unit 423.

[0045] The time window range Tw may be determined using the elastic wave propagation velocity v in the target structure 50 and the maximum sensor interval dmax, such that Tw ≥ dmax / v. To avoid false detection, it is desirable to set Tw to the smallest possible value, so in practice, Tw = dmax / v can be used. The elastic wave propagation velocity v may be determined in advance.

[0046] The position determination unit 423 determines the position of the elastic wave source based on the sensor position information and the sensor ID and time information contained in each of the multiple transmission data extracted by the event extraction unit 422.

[0047] The sensor position information includes information about the installation location of the first sensor 20, associated with the sensor ID. The sensor position information includes information about the installation location of the first sensor 20, such as latitude and longitude, or horizontal and vertical distances from a reference position of the structure 50. The positioning unit 423 has the sensor position information stored in advance. The sensor position information may be stored in the positioning unit 423 at any time before the positioning unit 423 performs positioning of the elastic wave source.

[0048] Sensor position information may be stored in the memory unit 43. In this case, the position determination unit 423 acquires the sensor position information from the memory unit 43 at the time of position determination. A Kalman filter, least squares method, or the like may be used to determine the position of the elastic wave source. The position determination unit 423 outputs the position information of the elastic wave source obtained during the evaluation period to the distribution generation unit 424.

[0049] The distribution generation unit 424 receives position information of multiple elastic wave sources output from the position determination unit 423 as input. The distribution generation unit 424 uses the input position information of multiple elastic wave sources to generate an elastic wave source distribution. The elastic wave source distribution represents a distribution that indicates the positions of the elastic wave sources. More specifically, the elastic wave source distribution is a distribution in which points indicating the positions of elastic wave sources are shown on a hypothetical data representing the structure 50 to be evaluated, with the horizontal axis representing the distance in the direction of travel and the vertical axis representing the distance in the width direction.

[0050] The distribution generation unit 424 generates an elastic wave source density distribution using the elastic wave source distribution. The elastic wave source density distribution represents a distribution in which the density value is determined according to the number of elastic wave sources contained in each predetermined region of the elastic wave source distribution. Specifically, first the distribution generation unit 424 divides the elastic wave source distribution into multiple regions by dividing it into predetermined sections. Next, the distribution generation unit 424 calculates the density D of each region based on the following equation (1). Then, the distribution generation unit 424 generates the elastic wave source density distribution by assigning the calculated density D value of each region to each region. In this way, the distribution generation unit 424 generates an elastic wave source density distribution by calculating the density D for the evaluation target region. Note that in equation (1), N ev represents the number of elastic wave sources located within the region, and S represents the area of ​​the region.

[0051]

number

[0052] The correction unit 425 corrects the elastic wave source density distribution generated by the distribution generation unit 424 based on the estimation results stored in the storage unit 43. Specifically, the correction unit 425 corrects the elastic wave source density distribution based on the number of vehicles indicated by the estimation results. More specifically, the correction unit 425 calculates the density D of each region based on the following equation (2). Then, the correction unit 425 calculates the density within a predetermined region by dividing the number of elastic wave sources located within the predetermined region by the area of ​​the predetermined region. The correction unit 425 corrects the elastic wave source density distribution by assigning the calculated density D of each region to the corresponding region of the elastic wave source density distribution. Assigning to the corresponding region of the elastic wave source density distribution means overwriting with the calculated density D of each region. In this way, the correction unit 425 corrects the elastic wave source density distribution by newly calculating the density D of each region within the evaluation target region. Note that in equation (2), N car This represents the number of vehicles indicated by the estimated result.

[0053]

number

[0054] The evaluation unit 426 evaluates the deterioration state of the structure 50 using the elastic wave source density distribution corrected by the correction unit 425. For example, the evaluation unit 426 evaluates the region where the density of elastic wave sources is above a threshold in the corrected elastic wave source density distribution as a healthy region, and the region where the density of elastic wave sources is below the threshold as a damaged region.

[0055] The storage unit 43 stores the transmission data and estimation results for the evaluation period acquired by the acquisition unit 421. The storage unit 43 is configured using a storage device such as a magnetic hard disk drive or a semiconductor storage device.

[0056] The display unit 44 displays the evaluation results according to the control of the evaluation unit 426. For example, the display unit 44 may display the corrected elastic wave source density distribution as an evaluation result, or it may display areas considered to be damaged areas in a different display manner from other areas. Furthermore, the display unit 44 displays a group of dashed lines indicating the propagation paths of elastic waves to each first sensor 20, for example, by projection, according to the control of the evaluation unit 426. The display unit 44 is an image display device such as a liquid crystal display or an organic EL (Electro Luminescence) display. The display unit 44 may also be an interface for connecting an image display device to the structural evaluation device 40. In this case, the display unit 44 generates a video signal for displaying the evaluation results and outputs the video signal to the image display device connected to it.

[0057] (Example of sensor placement) Figure 3 is a diagram illustrating the arrangement of each sensor in the first embodiment. Figure 3 shows an example of the arrangement of the first sensor 20 and the second sensor 25 on the bottom surface of the structure 50. For example, in Figure 3, four first sensors 20-1 to 20-4 and one second sensor 25 are arranged on the bottom surface of the structure 50. In the example shown in Figure 3, the area enclosed by the four first sensors 20-1 to 20-4 (the area enclosed by the dotted line in Figure 3) is the area to be evaluated. At least one second sensor 25 should be installed within the area enclosed by the multiple first sensors 20.

[0058] (Method for measuring elastic waves) Figure 4 is a diagram illustrating the elastic wave measurement method in the first embodiment. In Figure 4, as an example of an elastic wave measurement method, we consider evaluating the internal damage of a structure 50 based on elastic waves generated by the running part W (e.g., tires) of a vehicle 10 traveling on the road surface. Here, we will explain using the example where the structure 50 is a road on which the vehicle 10 travels. Note that there are no particular limitations on the vehicle 10, and it may be an ordinary vehicle. By utilizing the elastic waves generated by an ordinary vehicle 10, there is no need for vehicle restrictions for inspection, which contributes to improving convenience for users. When the running part W of the vehicle 10 comes into contact with the road surface, a load is applied to the road surface. This generates elastic waves inside the structure 50. The generated elastic waves propagate inside the structure 50 and are detected by each first sensor 20 installed on a surface different from the road surface (e.g., the bottom surface).

[0059] The structure 50 typically consists of a drivable pavement section 51 and a deck section 52 that supports the pavement section 51. The pavement section 51 is, for example, asphalt, and the deck section 52 is, for example, concrete. The thickness of the pavement section 51 is generally about 80 mm, and the thickness of the deck section 52 is about 180 mm to 230 mm. Elastic waves (amplitude A) are generated by the interaction between the running section W of a vehicle 10 traveling on the road surface at speed V and the surface of the pavement section 51. src The elastic wave propagates toward the underside of the deck slab 52. Along the way, at the interface 53 between the pavement 51 and the deck slab 52, some of the elastic wave is reflected and some is transmitted due to the difference in their respective acoustic impedances. The elastic wave that has further propagated toward the underside of the deck slab 52 reaches the first sensor 20 and is detected by the first sensor 20. Multiple first sensors 20 are installed on the underside of the deck slab 52, and the elastic wave is detected by each first sensor 20 with a different time difference. Based on the time difference at which each sensor detects the wave, the position of the elastic wave source SR is determined.

[0060] (Method for detecting the passage of vehicle 10) Figure 5 is a diagram illustrating the method for detecting the passage of a vehicle 10 using the second sensor 25 in the first embodiment. Figure 5 shows the measurement results of magnetic flux density when the second sensor 25 is installed on the underside of a structure 50 and a vehicle 10 (for example, a large vehicle of the 10m class) passes over the top surface of the structure 50. Referring to Figure 5, it can be seen that when a large vehicle of the 10m class passes over, a change in magnetic flux density of more than 500 nT occurs. Thus, the change in magnetic flux density is detected when a vehicle 10 passes over the top surface of the structure 50 on which the second sensor 25 is installed. Therefore, the passage of a vehicle 10 can be detected by detecting the change in magnetic flux density in the second sensor 25. Note that Figure 5 shows the measurement results of magnetic flux density when one vehicle 10 passes over the top surface of the structure 50, but it is usually assumed that multiple vehicles 10 pass over the top surface of the structure 50 in succession. In this case, the second sensor 25 will detect a time-continuous waveform, and the change in magnetic flux density corresponding to the passing vehicle 10 will be measured.

[0061] Figure 6 is a sequence diagram showing the flow of the deterioration state evaluation process by the structural evaluation system 100 in the first embodiment. The process in Figure 6 is executed in response to the vehicle 10 driving over the structure 50 to be evaluated. In Figure 6, multiple first sensors 20 are collectively referred to as the first sensor group.

[0062] When a vehicle 10 passes over the structure 50 being evaluated, the vehicle's running section comes into contact with the road surface. This generates elastic waves 11 within the structure 50. Each of the multiple first sensors 20 detects the elastic waves 11 generated inside the structure 50 (step S101). Each of the multiple first sensors 20 converts the detected elastic waves 11 into an electrical signal and outputs it to the signal processing unit 30 (step S102). The electrical signals output from each of the multiple first sensors 20 are amplified by an amplifier (not shown). After the amplification, the electrical signals are filtered to remove noise components and then converted into digital signals by an analog-to-digital converter.

[0063] The second sensor 25 measures the magnetic flux density generated in response to the vehicle 10 passing over the structure 50 (step S103). The second sensor 25 outputs an output signal, which is time-series data of the change in the measured magnetic flux density, to the vehicle information estimation unit 35 (step S104). The output signal from the second sensor 25 is amplified by an amplifier (not shown). After amplification, the measurement result has noise components removed by a filter and is converted into a digital signal by an analog-to-digital converter before being input to the vehicle information estimation unit 35.

[0064] The signal processing unit 30 receives the digital signal output from the analog-to-digital converter. The arrival time determination unit 303 of the signal processing unit 30 determines the arrival time of each elastic wave 11 (step S105). Specifically, the arrival time determination unit 303 determines the elastic wave arrival time using the clock input while the first gate signal is input. The arrival time determination unit 303 outputs the determined elastic wave arrival time as time information to the transmission data generation unit 305. The arrival time determination unit 303 performs this process for all input digital signals.

[0065] The feature extraction unit 304 of the signal processing unit 30 extracts features from the denoised signal, which is a digital signal input while the first gate signal is input (step S106). The feature extraction unit 304 outputs parameters related to the extracted features to the transmission data generation unit 305. The transmission data generation unit 305 generates transmission data including the sensor ID, time information, and parameters related to the features (step S107). The transmission data generation unit 305 stores the generated transmission data in the memory 306. The output unit 307 sequentially outputs the transmission data stored in the memory 306 to the structure evaluation device 40 (step S108).

[0066] The vehicle information estimation unit 35 acquires the output signals from the second sensor 25 during the evaluation period. The vehicle information estimation unit 35 performs vehicle count estimation processing using the acquired output signals for the evaluation period (step S109). The vehicle count estimation processing is the process of estimating the number of vehicles 10 that passed through the evaluation area during the evaluation period. The vehicle count estimation processing will be described later. The vehicle information estimation unit 35 estimates the number of vehicles passing through as a result of the vehicle count estimation processing. The vehicle information estimation unit 35 generates estimation results showing the estimated number of vehicles passing through. The vehicle information estimation unit 35 outputs the generated estimation results to the structure evaluation device 40 (step S110).

[0067] The communication unit 41 of the structural evaluation device 40 receives the transmission data output from the signal processing unit 30 and the estimation results output from the vehicle information estimation unit 35 (step S111). Note that the signal processing unit 30 and the vehicle information estimation unit 35 are different devices. Therefore, basically, the communication unit 41 receives the transmission data output from the signal processing unit 30 and the estimation results output from the vehicle information estimation unit 35 at different times. The acquisition unit 421 acquires the transmission data and estimation results received by the communication unit 41. The acquisition unit 421 records the acquired transmission data and estimation results in the storage unit 43. The acquisition unit 421 records all the transmission data received during the evaluation period in the storage unit 43.

[0068] The event extraction unit 422 extracts transmission data for one event from the transmission data for the evaluation period stored in the storage unit 43 after the evaluation period has elapsed, or in response to an external instruction. The event extraction unit 422 outputs the extracted transmission data for one event to the location determination unit 423. The event extraction unit 422 performs the extraction process of transmission data for one event and the output process of the extracted transmission data for one event in chronological order.

[0069] The position determination unit 423 determines the position of the elastic wave source based on the sensor ID and time information included in the transmitted data output from the event extraction unit 422 and the sensor position information that it has previously stored (step S112). Specifically, first the position determination unit 423 calculates the difference in arrival times of the elastic waves 11 to each of the multiple first sensors 20. Next, the position determination unit 423 determines the position of the elastic wave source using the sensor position information and the information on the difference in arrival times.

[0070] The position determination unit 423 executes the process in step S112 each time that transmission data for one event is output from the event extraction unit 422 during the evaluation period. This allows the position determination unit 423 to determine the positions of multiple elastic wave sources that occurred during the evaluation period. The position determination unit 423 then outputs the position information of the multiple elastic wave sources to the distribution generation unit 424.

[0071] The distribution generation unit 424 generates an elastic wave source distribution using the position information of multiple elastic wave sources output from the position determination unit 423. Specifically, the distribution generation unit 424 generates an elastic wave source distribution by plotting the positions of the elastic wave sources indicated by the obtained position information of multiple elastic wave sources onto virtual data. The distribution generation unit 424 generates an elastic wave source density distribution using the generated elastic wave source distribution (step S113). The distribution generation unit 424 outputs the generated elastic wave source density distribution to the correction unit 425.

[0072] The correction unit 425 receives the elastic wave source density distribution output from the distribution generation unit 424 and the estimation results stored in the storage unit 43 as input. The correction unit 425 corrects the elastic wave source density distribution based on the input elastic wave source density distribution and the estimation results (step S114). Specifically, the correction unit 425 corrects the elastic wave source density distribution based on the above equation (2). The correction unit 425 outputs the corrected elastic wave source density distribution to the evaluation unit 426.

[0073] The evaluation unit 426 evaluates the deterioration state of the structure using the corrected elastic wave source density distribution (step S115). The evaluation unit 426 outputs the evaluation results to the display unit 44. The display unit 44 displays the evaluation results output from the evaluation unit 426 (step S116).

[0074] Figure 7 is a diagram illustrating the vehicle number estimation process performed by the vehicle information estimation unit 35 in the first embodiment. The waveform shown in Figure 7(A) is the output signal output from the second sensor 25 during the evaluation period. That is, the waveform shown in Figure 7(A) represents the waveform data input to the vehicle information estimation unit 35. The vehicle information estimation unit 35 samples the input output signal and removes noise using a filter circuit as needed. Specifically, first the vehicle information estimation unit 35 applies a digital filter (for example, a cutoff frequency of 3 Hz) to the output signal to separate each vehicle 10 during continuous driving. As a result, the output signal shown in Figure 7(A) becomes the signal shown in Figure 7(B). Next, the vehicle information estimation unit 35 converts the signal after applying the digital filter into a positive polarity signal using an absolute value circuit. That is, the vehicle information estimation unit 35 converts the signal after applying the digital filter into a positive polarity signal by taking the absolute value of the signal after applying the digital filter. As a result, the signal shown in Figure 7(B) becomes the signal shown in Figure 7(C).

[0075] Subsequently, the vehicle information estimation unit 35 extracts the envelope by passing the positive polarity signal through a low-pass filter (for example, a cutoff frequency of 3 Hz). Note that the envelope extraction may be performed using an analog circuit, or an RMS circuit, Hilbert transform circuit, etc. This process results in the signal shown in Figure 7(C) becoming the signal shown in Figure 7(D). Finally, the vehicle information estimation unit 35 performs peak detection on the extracted envelope. For example, the vehicle information estimation unit 35 sets a predetermined time window Tw1 (here, Tw1 = 0.5 seconds) and extracts the maximum value P within the predetermined time window Tw1. The vehicle information estimation unit 35 performs this peak detection process while shifting the predetermined time window Tw1 in the time direction. As a result, multiple maximum values ​​P are extracted, as shown in Figure 7(E). For example, Figure 7(E) shows an example in which 17 maximum values ​​P have been extracted. The vehicle information estimation unit 35 estimates the number of extracted maximum values ​​P as the number of vehicles that passed through the evaluation area during the evaluation period.

[0076] The vehicle information estimation unit 35 estimates the number of vehicles that passed through the evaluation area during the evaluation period by performing the processing described above. The vehicle information estimation unit 35 includes at least a digital filter, an absolute value circuit, a low-pass filter, a peak detection unit, and a vehicle count estimation unit as functions for estimating the number of vehicles. The absolute value circuit converts the signal to a positive polarity. The peak detection unit detects peaks (maximum values ​​P) in relation to the envelope. The vehicle count estimation unit estimates the number of maximum values ​​P detected by the peak detection unit (the number obtained by accumulating peaks) as the number of vehicles that passed through the evaluation area during the evaluation period.

[0077] The structure evaluation system 100 configured as described above includes a plurality of first sensors 20 for detecting elastic waves, a second sensor 25 for detecting the passage of vehicles 10 regardless of internal damage to the structure 50, a vehicle information estimation unit 35 for estimating vehicle information including at least the number of vehicles passing based on the detection results of the second sensor 25, and an evaluation unit 426 for evaluating the deterioration state of the structure 50 based on a plurality of elastic waves detected by each of the plurality of first sensors 20 and the vehicle information estimated by the vehicle information estimation unit 35.

[0078] Thus, the structural evaluation system 100 is equipped with a second sensor 25 that detects the passage of vehicles 10 regardless of internal damage to the structure 50, in addition to a plurality of first sensors 20 that detect elastic waves. Therefore, it is possible to detect the passage of vehicles 10 even when the internal damage to the structure 50 is severe. The vehicle information estimation unit 35 then estimates the number of vehicles 10 that have passed through the structure 50 based on the detection results of the second sensor 25. As described above, the second sensor 25 detects the passage of vehicles 10 by detecting the change in magnetic flux density that occurs when a vehicle 10 passes, without being affected by internal damage to the structure 50, unlike a magnetic sensor. Therefore, the vehicle information estimation unit 35 can accurately measure the number of vehicles. This solves the problem that the accuracy of measuring the number of vehicles decreases when the internal damage to the structure is severe. In this way, the structural evaluation system 100 does not have the factors that previously caused a decrease in the accuracy of evaluation results. Therefore, it becomes possible to accurately estimate the number of vehicles regardless of internal damage to the structure and to evaluate the deterioration state of the structure.

[0079] The correction unit 425 of the structural evaluation device 40 corrects the elastic wave source density distribution based on the number of vehicles estimated by the vehicle information estimation unit 35. As a result, the elastic wave source density distribution obtained based on multiple elastic waves detected by each first sensor 20, which is affected by internal damage to the structure 50, is corrected by taking into account the number of vehicles estimated based on the output signal of the second sensor 25, which is not affected by internal damage to the structure 50. Therefore, the accuracy of the structural evaluation can be improved.

[0080] (Modified version of the first embodiment) The distribution generation unit 424 of the structural evaluation device 40 may be configured to generate the elastic wave source density distribution by taking into account the number of vehicles estimated by the vehicle information estimation unit 35 when generating the elastic wave source density distribution. In this configuration, the structural evaluation device 40 does not need to include a correction unit 425. Specifically, first the distribution generation unit 424 divides the elastic wave source distribution into multiple regions by dividing it into predetermined sections. Next, the distribution generation unit 424 calculates the density D of each region based on the above-described equation (2). Then, the distribution generation unit 424 generates the elastic wave source density distribution by assigning the calculated density D values ​​of each region to each region. In this way, the distribution generation unit 424 generates the elastic wave source density distribution by calculating the density D for the evaluation target region by taking into account the number of vehicles estimated by the vehicle information estimation unit 35. The evaluation unit 426 evaluates the deterioration state of the structure 50 using the generated elastic wave source density distribution. With this configuration, there is no need to correct the elastic wave source density distribution after it has been generated by the distribution generation unit 424. Therefore, the processing required by the structural evaluation device 40 can be reduced.

[0081] (Second embodiment) In the second embodiment, a configuration is described in which, based on the output signal of the second sensor, the vehicle information estimation unit estimates the number of vehicles as well as the vehicle type, the signal processing unit performs filtering by vehicle type, and the structural evaluation device processes using only information for a specific vehicle type.

[0082] Figure 8 shows the configuration of the structural evaluation system 100a in the second embodiment. The structural evaluation system 100a is used to evaluate the soundness of a structure 50. The structural evaluation system 100a comprises a plurality of first sensors 20-1 to 20-n, a second sensor 25, a signal processing unit 30a, a vehicle information estimation unit 35a, and a structural evaluation device 40. Each of the plurality of first sensors 20-1 to 20-n and the signal processing unit 30a are connected to each other via wired communication. The second sensor 25 and the vehicle information estimation unit 35a are connected to each other via wired communication. The signal processing unit 30a and the structural evaluation device 40, and the vehicle information estimation unit 35a and the structural evaluation device 40 are connected to each other via wired or wireless communication.

[0083] The structural evaluation system 100a differs from the structural evaluation system 100 in that it includes a signal processing unit 30a and a vehicle information estimation unit 35a instead of the signal processing unit 30 and vehicle information estimation unit 35. The structural evaluation system 100a is otherwise identical to the structural evaluation system 100. The following explanation will focus on the differences from the structural evaluation system 100.

[0084] The vehicle information estimation unit 35a receives the output signal from the second sensor 25 as input. Based on the input output signal, the vehicle information estimation unit 35a estimates vehicle information. Specifically, the vehicle information estimation unit 35a estimates the number of vehicles 10 and the type of vehicle 10. The estimation of the type of vehicle 10 by the vehicle information estimation unit 35a is performed each time the passage of one vehicle 10 is detected. The vehicle information estimation unit 35a outputs the estimation result showing the estimated number of passing vehicles to the structural evaluation device 40. Furthermore, the vehicle information estimation unit 35a outputs the estimation result showing the estimated type of vehicle 10 to the signal processing unit 30a. The vehicle information estimation unit 35a also has a timing unit. The timing unit acquires time information.

[0085] The time displayed by the timing unit of the vehicle information estimation unit 35a is the same as the time displayed by the signal processing unit 30a. In other words, the timing unit of the vehicle information estimation unit 35a and the signal processing unit 30a acquire time information that represents the same time. Thus, before measurement, the user may adjust the timing displayed by the timing unit of the vehicle information estimation unit 35a and the time displayed by the signal processing unit 30a to be the same. If high-precision time synchronization is required, time synchronization may be performed between the vehicle information estimation unit 35a and the signal processing unit 30a. The vehicle information estimation unit 35a transmits the estimation result, which includes time information along with the estimated vehicle type information of the vehicle 10, to the signal processing unit 30a. Specifically, if the time at which a certain large vehicle passed is between time T and T+Δt, the vehicle information estimation unit 35a transmits the estimation result, which includes information on the vehicle type of the vehicle 10, time T, and passing time Δt, to the signal processing unit 30a. Thus, in the second embodiment, the vehicle information includes information on the number of vehicles passing and information on the vehicle type.

[0086] The signal processing unit 30a receives the digital signal output from the analog-to-digital converter and the estimation result output from the vehicle information estimation unit 35a as input. The signal processing unit 30a performs signal processing on the input digital signal. The signal processing unit 30a generates transmission data including the digital signal after signal processing. The signal processing unit 30a performs vehicle type filtering based on the estimation result. Here, vehicle type filtering means not outputting transmission data for vehicle types other than a specific vehicle type. For example, the signal processing unit 30a outputs only the transmission data corresponding to the specified vehicle type from the generated transmission data to the structural evaluation device 40. In this way, if the vehicle type information indicated in the estimation result corresponding to the generated transmission data is for a vehicle other than the specified vehicle type, the signal processing unit 30a does not output the generated transmission data to the structural evaluation device 40. On the other hand, if the vehicle type information indicated in the estimation result corresponding to the generated transmission data is for a vehicle type specified, the signal processing unit 30a outputs the generated transmission data to the structural evaluation device 40. Which vehicle type of transmission data to output may be set in advance by the user, or it may be instructed by an external device.

[0087] Vehicle type information is information used to identify the type of vehicle 10 that passed over the structure 50, such as large vehicles and regular vehicles. For the sake of simplicity, in the following explanation, two types of vehicle type information, large vehicles and regular vehicles, will be used as examples, but there may be three or more types of vehicle types. Here, as an example, a large vehicle is defined as a vehicle with a length of 5m or more, and a regular vehicle is defined as a vehicle with a length of less than 5m. When filtering as described above, it is necessary to associate vehicle type information with the digital signal input to the signal processing unit 30a. When associating vehicle type information with a digital signal, for example, the signal processing unit 30a may associate vehicle type information with the transmission data generated based on the digital signal. This is because the digital signal input to the signal processing unit 30a and the transmission data generated based on the digital signal are substantially the same. When associating vehicle type information with transmission data, the signal processing unit 30a associates vehicle type information with the transmission data based on the time information included in the transmission data and the time information included in the estimation result. Specifically, the signal processing unit 30a should associate the vehicle type information included in the estimation result with the transmitted data if the elastic wave arrival time indicated by the time information included in the transmitted data falls within the range indicated by the time information included in the estimation result (the range from time T to time T+Δt). This makes it possible to associate the vehicle type information of vehicle 10 with all transmitted data relating to elastic waves that occurred within the range indicated by the time information (for example, the range from time T to time T+Δt).

[0088] The signal processing unit 30a then determines whether the generated transmission data is for a large vehicle or a regular vehicle based on the vehicle type information associated with the transmission data, and outputs only the transmission data corresponding to the specified vehicle type to the structural evaluation device 40. The transmission data output by the signal processing unit 30a means that the characteristic quantities of elastic waves are output. Similarly, multiple transmission data outputs by the signal processing unit 30a mean that multiple characteristic quantities of elastic waves are output.

[0089] The above method is just one example, and the information about the vehicle type of vehicle 10 may be linked to the transmitted data by other methods. For example, let's consider an example where the vehicle information estimation unit 35a transmits an estimation result, including the information about the vehicle type of vehicle 10 and the time T, to the signal processing unit 30a. When the signal processing unit 30a obtains only the time T at which the passage of a large vehicle was detected (without the information about the passage time Δt), the signal processing unit 30a first obtains the time T included in the estimation result. Next, the signal processing unit 30a determines whether the elastic wave arrival time, indicated by the time information included in the transmitted data, falls within the range of time T + Δt, which is obtained by adding a predetermined fixed time Δt to the acquired time T. If the elastic wave arrival time, indicated by the time information included in the transmitted data, falls within the range of time T + Δt, the signal processing unit 30a links the information about the vehicle type of vehicle 10 included in the estimation result to the transmitted data. On the other hand, if the elastic wave arrival time indicated by the time information included in the transmitted data does not fall within the range of time T + Δt, the signal processing unit 30a does not associate the vehicle type information of the vehicle 10 included in the estimation result with the transmitted data. Basically, when the vehicle 10 passes over the structure 50, the vehicle 10 is detected along with the detection of the elastic wave. Therefore, it is considered that association is not impossible. This method can reduce the load on the vehicle information estimation unit 35a. In the following explanation, we will use the case where the time T and passage time Δt information are included as time information in the estimation result as an example.

[0090] In the above explanation, the signal processing unit 30a was shown to associate vehicle type information with the transmitted data when performing filtering. However, the timing of associating vehicle type information is not limited to the timing after the transmission data has been generated. For example, the signal processing unit 30a may associate vehicle type information with the digital signal before generating the transmission data. In this case, the signal processing unit 30a may associate vehicle type information with the digital signal at any timing after the elastic wave arrival time extracted based on the digital signal has been obtained, but before the transmission data has been generated. In this way, the signal processing unit 30a can associate vehicle type information of the vehicle 10 with the input digital signal at various timings. Furthermore, the signal processing unit 30a does not need to generate transmission data if the vehicle type is not the specified vehicle type. This eliminates the need to generate transmission data that is not to be transmitted. Therefore, the processing load of the signal processing unit 30a can be reduced.

[0091] The signal processing unit 30a is configured using analog or digital circuits. The digital circuit may be implemented by, for example, an FPGA or a microcomputer. The digital circuit may also be implemented by a dedicated LSI. The signal processing unit 30a may also be equipped with non-volatile memory such as flash memory or removable memory.

[0092] (Method for estimating vehicle type) Next, the vehicle type estimation process by the vehicle information estimation unit 35a will be described. The vehicle information estimation unit 35a extracts features using the output signal of the second sensor 25 in order to determine the vehicle type of the vehicle 10. For example, the vehicle information estimation unit 35a extracts one of the following as features: the maximum amplitude, energy (area), or duration of the output signal, as shown in Figure 9. Figure 9 is a diagram showing an example of features extracted using the output signal of the second sensor 25. The vehicle information estimation unit 35a may also extract the centroid frequency or peak frequency obtained by Fourier transforming the output signal as features, or it may use a variable that combines the maximum amplitude, energy (area), duration, centroid frequency, and peak frequency of the output signal as features.

[0093] The vehicle information estimation unit 35a estimates the vehicle type of vehicle 10 using a pre-trained model that has been trained to output vehicle type information by taking one or more features as input. The pre-trained model is a machine learning model in which the learning termination condition has been met. The learning termination condition can be any condition relating to the termination of learning, for example, the learning target may have been updated a predetermined number of times, or the change in the learning target due to the update may be less than a predetermined change. When the learning termination condition is met in the machine learning model, the parameters (weights) used in the pre-trained model are optimized. As the machine learning model, logistic regression models, neural networks, SVMs (Support Vector Machines), decision trees, etc., may be used.

[0094] The following explanation uses a logistic regression model as an example of a machine learning model. When x1 and x2 are explanatory variables, the vehicle class v type A logistic regression model that outputs (0: regular car, 1: large car) is represented by equation (3) below. In equation (3), a0, a1, and a2 represent the weights. The explanatory variables x1 and x2 are inputs of one or more features extracted by the vehicle information estimation unit 35a. When selecting the explanatory variables x1 and x2, it is effective to exclude combinations of variables with high correlation to avoid multicollinearity.

[0095]

number

[0096] Here, we will explain the learning process for obtaining a pre-trained model. The learning target in the learning process for obtaining a pre-trained model is a mathematical model that estimates vehicle types based on one or more features. More specifically, the learning target in the learning process for obtaining a pre-trained model is a mathematical model that classifies vehicle types into multiple classes based on one or more features. Hereafter, a mathematical model that estimates vehicle types based on one or more features will be referred to as a vehicle type estimation pre-trained model. The learning of a vehicle type estimation pre-trained model is, for example, supervised learning. In such cases, the training data used to train the vehicle type estimation pre-trained model includes ground truth data. The training data used to train a vehicle type estimation pre-trained model is a pair of one or more features and a value indicating the vehicle class (e.g., large vehicle, regular vehicle). To avoid multicollinearity, two features, for example, energy / amplitude ratio and centroid frequency, are used as the one or more features to be used in the training data. The energy / amplitude ratio is the value obtained by dividing energy by the amplitude ratio. Note that the combination of one or more features used in the training data is not limited to this.

[0097] A value indicating the vehicle class is used as ground truth data during training. Therefore, one or more features are input into the machine learning model during training. During the training of the machine learning model, the machine learning model estimates a value indicating the vehicle class based on one or more features included in the training data. Subsequently, the machine learning model is trained by updating it until the training completion condition is met, so as to minimize the difference between the vehicle class value estimated by the machine learning model and the vehicle class value indicated by the ground truth data included in the training data. Through this process, a trained model for vehicle estimation is created.

[0098] The training process for the vehicle type estimation trained model may be performed in advance by another device, or it may be performed by the vehicle information estimation unit 35. If the training process for the vehicle type estimation trained model has been performed in advance by another device, the vehicle information estimation unit 35 acquires and holds the vehicle type estimation trained model from the other device.

[0099] Figure 10 is a sequence diagram showing the flow of the deterioration state evaluation process by the structural evaluation system 100a in the second embodiment. The process in Figure 10 is executed in response to the vehicle 10 driving over the structure 50 to be evaluated. In Figure 10, processes similar to those in Figure 6 are denoted by the same reference numerals as in Figure 6 and their explanation is omitted. Note that in Figure 10, multiple first sensors 20 are collectively referred to as the first sensor group.

[0100] When the output signal from the second sensor 25 is obtained in step S104, the vehicle information estimation unit 35a performs vehicle type estimation processing using the output signal from the second sensor 25 (step S201). The vehicle type estimation processing is the process of estimating the vehicle type of the vehicle 10 that passed through the evaluation target area during the evaluation target period. The vehicle type estimation processing will be described later. The vehicle information estimation unit 35a performs vehicle type estimation processing as long as the output signal from the second sensor 25 is input. The vehicle information estimation unit 35a generates an estimation result that includes vehicle type information obtained by the vehicle type estimation processing and time information. The vehicle information estimation unit 35a outputs the generated estimation result to the signal processing unit 30a (step S202). The vehicle information estimation unit 35a performs the processing in step S202 each time it estimates a vehicle type through the vehicle type estimation processing.

[0101] The transmission data generation unit 305 of the signal processing unit 30a generates transmission data that includes the sensor ID, time information, and parameters related to feature quantities (step S203). At this time, if the transmission data generation unit 305 has obtained the estimation result output from the vehicle information estimation unit 35a, it compares the time information included in the transmission data with the time information included in the obtained estimation result and determines whether the elastic wave arrival time indicated by the time information included in the transmission data is included within the range indicated by the time information included in the estimation result. If the (elastic wave arrival time) indicated by the time information included in the transmission data is included within the range indicated by the time information included in the estimation result, the transmission data generation unit 305 associates the vehicle type information of the vehicle 10 included in the estimation result with the transmission data. On the other hand, if the transmission data generation unit 305 has not obtained the estimation result output from the vehicle information estimation unit 35a, it waits until the estimation result is obtained.

[0102] The transmission data generation unit 305 saves the generated transmission data to memory 306 if the vehicle type identified by the vehicle type information of the vehicle 10 associated with the transmission data is the specified vehicle type. On the other hand, the transmission data generation unit 305 discards the generated transmission data if the vehicle type identified by the vehicle type information of the vehicle 10 associated with the transmission data is not the specified vehicle type. The transmission data generation unit 305 may also save the generated transmission data to a memory other than memory 306 if the vehicle type identified by the vehicle type information of the vehicle 10 associated with the transmission data is not the specified vehicle type. When multiple first sensors 20 are connected to one signal processing unit 30a, digital signals based on elastic waves detected at approximately the same time are input to the signal processing unit 30a. Elastic waves detected at approximately the same time are elastic waves generated by vehicles 10 of the same vehicle type. Therefore, the transmission data generation unit 305 considers multiple transmission data with time information at approximately the same time (for example, within a predetermined range) to be transmission data corresponding to the same vehicle type and saves multiple transmission data to memory 306.

[0103] The transmission data generation unit 305 performs the above processing each time a digital signal is input to the signal processing unit 30a. The signal processing unit 30a receives the estimated results indicating vehicle type information each time the vehicle information estimation unit 35a performs vehicle type estimation processing. Therefore, when a new estimation result is input, the transmission data generation unit 305 performs the above processing based on the newly input estimation result. The output unit 307 sequentially outputs the transmission data stored in the memory 306 to the structural evaluation device 40. In this way, the signal processing unit 30a outputs transmission data corresponding to a specific vehicle type to the structural evaluation device 40 (step S204).

[0104] For example, if it is specified that only transmission data for large vehicles should be transmitted, the transmission data generation unit 305 will save the generated transmission data to the memory 306 if the vehicle type identified by the vehicle type information of the vehicle 10 associated with the transmission data is a large vehicle. On the other hand, if the vehicle type identified by the vehicle type information of the vehicle 10 associated with the transmission data is not a large vehicle, the transmission data generation unit 305 will discard the generated transmission data. The output unit 307 will sequentially output the transmission data corresponding to large vehicles stored in the memory 306 to the structural evaluation device 40.

[0105] In the example above, the transmission data generation unit 305 filtered the transmission data to be stored in the memory 306 based on the vehicle type information of the vehicle 10 associated with the transmission data. Alternatively, the output unit 307 may filter the transmission data to be output to the structural evaluation device 40. In this case, the transmission data generation unit 305 stores all transmission data associated with the vehicle type information of the vehicle 10 in the memory 306. The output unit 307 sequentially reads only the transmission data associated with the specified vehicle type information from the memory 306 and outputs it to the structural evaluation device 40.

[0106] The vehicle information estimation unit 35a acquires the output signal from the second sensor 25 during the evaluation period. The vehicle information estimation unit 35 performs vehicle number estimation processing using the acquired output signals for the evaluation period (step S205). The vehicle number estimation processing is the same as in the first embodiment. The vehicle information estimation unit 35a estimates the number of vehicles passing through as a result of the vehicle number estimation processing. The vehicle information estimation unit 35a generates an estimation result showing the estimated number of vehicles passing through. The vehicle information estimation unit 35a outputs the generated estimation result to the structure evaluation device 40 (step S206).

[0107] The communication unit 41 of the structural evaluation device 40 receives the transmission data output from the signal processing unit 30a and the estimation results output from the vehicle information estimation unit 35a (step S207). The acquisition unit 421 acquires the transmission data and estimation results received by the communication unit 41. The acquisition unit 421 records the acquired transmission data and estimation results in the storage unit 43. The acquisition unit 421 records all the transmission data received during the evaluation period in the storage unit 43.

[0108] The event extraction unit 422 extracts transmission data for one event from the transmission data for the evaluation period stored in the storage unit 43 after the evaluation period has elapsed, or in response to an external instruction. The event extraction unit 422 outputs the extracted transmission data for one event to the location determination unit 423. The event extraction unit 422 performs the extraction process of transmission data for one event and the output process of the extracted transmission data for one event in chronological order.

[0109] The position determination unit 423 determines the position of the elastic wave source based on the sensor ID and time information included in the transmitted data output from the event extraction unit 422 and the sensor position information that is held in advance (step S208). The position of the elastic wave source determined by the process in step S208 is the position of the source of elastic waves generated by the passage of a vehicle 10 of a specified type (e.g., a large vehicle). In other words, the position of the source of elastic waves generated by the passage of a vehicle 10 of an unspecified type (e.g., a regular car) is not determined.

[0110] The position determination unit 423 executes the process in step S208 each time that transmission data for one event is output from the event extraction unit 422 during the evaluation period. This allows the position determination unit 423 to determine the positions of multiple elastic wave sources that occurred during the evaluation period. The position determination unit 423 then outputs the position information of the multiple elastic wave sources to the distribution generation unit 424.

[0111] The distribution generation unit 424 generates an elastic wave source distribution using the position information of multiple elastic wave sources output from the position determination unit 423. Specifically, the distribution generation unit 424 generates an elastic wave source distribution by plotting the positions of the elastic wave sources indicated by the obtained position information of multiple elastic wave sources onto virtual data. The distribution generation unit 424 generates an elastic wave source density distribution using the generated elastic wave source distribution (step S209). The distribution generation unit 424 outputs the generated elastic wave source density distribution to the correction unit 425.

[0112] The correction unit 425 receives the elastic wave source density distribution output from the distribution generation unit 424 and the estimation results stored in the storage unit 43 as input. The correction unit 425 corrects the elastic wave source density distribution based on the input elastic wave source density distribution and the estimation results (step S210). The correction unit 425 outputs the corrected elastic wave source density distribution to the evaluation unit 426.

[0113] The evaluation unit 426 evaluates the deterioration state of the structure using the corrected elastic wave source density distribution (step S211). The evaluation unit 426 outputs the evaluation result to the display unit 44. Subsequently, the process in step S116 is executed.

[0114] Figure 11 is a flowchart showing the flow of the vehicle type estimation process performed by the vehicle information estimation unit 35a in the second embodiment. The vehicle information estimation unit 35a performs filtering on the output signal of the second sensor 25 (step S2021). For example, the vehicle information estimation unit 35a performs filtering by passing the output signal of the second sensor 25 through a high-pass filter (for example, a cutoff frequency of 1 Hz). Next, the vehicle information estimation unit 35a extracts signals from the output signal of the second sensor 25 for a period of several seconds before and after the time when the vehicle 10 was detected (for example, 3 seconds before and after) (step S2022). Here, the time when the vehicle 10 was detected is the timing of the rising edge of the waveform in the output signal of the second sensor 25.

[0115] The vehicle information estimation unit 35a applies a window function to the extracted signal (step S2023). Then, the vehicle information estimation unit 35a converts the signal after applying the window function into a frequency domain signal by performing a Fast Fourier Transform (step S2024). The vehicle information estimation unit 35a extracts features using the frequency domain signal (step S2025). For example, the vehicle information estimation unit 35a extracts at least one of the output signal's maximum amplitude, energy (area), duration, centroid frequency, or peak frequency. The vehicle information estimation unit 35a estimates the vehicle type by inputting the extracted feature values, or a combination of any of the extracted features, as explanatory variables into a vehicle type estimation trained model (step S2026). For example, the vehicle information estimation unit 35a estimates the vehicle type by inputting the energy / amplitude ratio value and the centroid frequency value as explanatory variables x1 and x2, respectively, into a vehicle type estimation trained model.

[0116] The vehicle information estimation unit 35a estimates the type of vehicle that passed through the evaluation area during the evaluation period by performing the processing described above. In addition to the function for estimating the number of vehicles, the vehicle information estimation unit 35a includes at least a high-pass filter, an extraction unit, a window function multiplication unit, a Fourier transform unit, a feature extraction unit, a vehicle type estimation trained model, and a vehicle type estimation unit as functions for estimating the type of vehicle.

[0117] The structural evaluation system 100a configured as described above can achieve the same effects as the first embodiment.

[0118] Furthermore, in the structural evaluation system 100a, the signal processing unit 30a does not transmit all transmission data to the structural evaluation device 40 as in the first embodiment, but rather transmits only the transmission data corresponding to a specific vehicle type to the structural evaluation device 40. By performing such filtering, the excitation conditions for elastic waves (e.g., driving vehicle conditions) can be kept constant. As a result, it becomes possible to evaluate the deterioration state with high accuracy.

[0119] (Modification 1 of the second embodiment) The structural evaluation device 40 may be configured, similar to the first embodiment, to generate the elastic wave source density distribution by taking into account the number of vehicles estimated by the vehicle information estimation unit 35a during the stage of generating the elastic wave source density distribution.

[0120] (Modification 2 of the second embodiment) The vehicle information estimation unit 35a may be configured to estimate the number of vehicles 10 of a specified type among the vehicles 10 that passed through the evaluation area during the evaluation period. In this case, the vehicle information estimation unit 35a outputs an estimation result to the structure evaluation device 40 indicating the number of vehicles 10 of a specified type that passed through the evaluation area during the evaluation period.

[0121] (Third embodiment) In the third embodiment, a configuration is described in which, based on the output signal of the second sensor, the vehicle information estimation unit estimates the number of vehicles as well as the vehicle type, and the structural evaluation device performs clustering by vehicle type and processes the data for each group. Here, clustering by vehicle type means dividing the data to be processed into groups according to the vehicle type. The structural evaluation device processes the data for each group using the grouped data.

[0122] Figure 12 shows the configuration of the structural evaluation system 100b in the third embodiment. The structural evaluation system 100b is used to evaluate the soundness of a structure 50. The structural evaluation system 100b comprises a plurality of first sensors 20-1 to 20-n, a second sensor 25, a signal processing unit 30b, a vehicle information estimation unit 35a, and a structural evaluation device 40b. Each of the plurality of first sensors 20-1 to 20-n and the signal processing unit 30b are communicated via wire. The second sensor 25 and the vehicle information estimation unit 35a are communicated via wire. The signal processing unit 30b and the structural evaluation device 40b, and the vehicle information estimation unit 35a and the structural evaluation device 40b are communicated via wire or wireless.

[0123] The structural evaluation system 100b differs from the structural evaluation system 100a in that it includes a signal processing unit 30b and a structural evaluation device 40b instead of the signal processing unit 30a and structural evaluation device 40. The structural evaluation system 100b is otherwise the same as the structural evaluation system 100a. The following explanation will focus on the differences from the structural evaluation system 100a.

[0124] The signal processing unit 30b receives the digital signal output from the analog-to-digital converter and the estimation result output from the vehicle information estimation unit 35a as input. The signal processing unit 30b performs signal processing on the input digital signal. The signal processing unit 30b generates transmission data including the digital signal after signal processing. At this time, the signal processing unit 30b associates the vehicle type information indicated by the estimation result with the transmission data. In other words, the signal processing unit 30b adds the vehicle type information indicated by the estimation result to the transmission data. Note that the method for associating the vehicle type information with the transmission data is the same as in the second embodiment, so a detailed explanation is omitted. Furthermore, the signal processing unit 30b may associate the vehicle type information with the digital signal before generating the transmission data, similar to the second embodiment. This allows the signal processing unit 30b to associate the vehicle type information of the vehicle 10 with the input digital signal at various timings. The signal processing unit 30b outputs all the transmission data associated with the vehicle type information of the vehicle 10 to the structural evaluation device 40b. This allows the structural evaluation device 40b, which acquires the transmitted data, to determine which vehicle model the received transmitted data corresponds to.

[0125] As described above, the signal processing unit 30b does not restrict the transmission data output to the structural evaluation device 40b, compared to the signal processing unit 30a in the second embodiment. In the signal processing unit 30a in the second embodiment, only the transmission data corresponding to the specified vehicle type was output to the structural evaluation device 40. In contrast, the signal processing unit 30b outputs all the transmission data associated with the vehicle type information of the vehicle 10 to the structural evaluation device 40b. The transmission data output by the signal processing unit 30b means that the characteristic quantities of elastic waves are output. Similarly, multiple transmission data output by the signal processing unit 30b means that multiple characteristic quantities of elastic waves are output.

[0126] The signal processing unit 30b is configured using analog or digital circuits. The digital circuit may be implemented using, for example, an FPGA or a microcomputer. The digital circuit may also be implemented using a dedicated LSI. The signal processing unit 30b may also be equipped with non-volatile memory such as flash memory or removable memory.

[0127] The structural evaluation device 40b comprises a communication unit 41, a control unit 42b, a storage unit 43, and a display unit 44. The control unit 42b controls the entire structural evaluation device 40b. The control unit 42b is configured using a processor such as a CPU and memory. By executing a program, the control unit 42b functions as an acquisition unit 421, an event extraction unit 422, a position determination unit 423b, a distribution generation unit 424b, a correction unit 425b, an evaluation unit 426b, and a class selection unit 427.

[0128] Some or all of the functional units of the acquisition unit 421, event extraction unit 422, position determination unit 423b, distribution generation unit 424b, correction unit 425b, evaluation unit 426b, and class selection unit 427 may be implemented by hardware such as ASICs, PLDs, or FPGAs, or by the cooperation of software and hardware. The program may be recorded on a computer-readable recording medium. A computer-readable recording medium is a non-temporary storage medium such as a flexible disk, magneto-optical disk, ROM, CD-ROM, or other portable media, or a hard disk built into a computer system. The program may be transmitted via a telecommunications line.

[0129] Some of the functions of the acquisition unit 421, event extraction unit 422, position determination unit 423b, distribution generation unit 424b, correction unit 425b, evaluation unit 426b, and class selection unit 427 do not need to be pre-installed on the structural evaluation device 40b, and may be realized by installing additional application programs on the structural evaluation device 40b.

[0130] The positioning unit 423b determines the position of the elastic wave source based on the sensor position information and the sensor ID and time information contained in each of the multiple transmission data extracted by the event extraction unit 422. At this time, the positioning unit 423b acquires vehicle type information associated with each of the multiple transmission data used to determine the position of the elastic wave source. The positioning unit 423b adds the acquired vehicle type information to the position information of the elastic wave source obtained as a result of the determination. The positioning unit 423b outputs the position information of the elastic wave source with the vehicle type information added to it to the distribution generation unit 424b. The positioning unit 423b adds vehicle type information to each of the position information of the elastic wave source obtained during the evaluation period and outputs it to the distribution generation unit 424.

[0131] The distribution generation unit 424b receives location information of multiple elastic wave sources output from the position determination unit 423b as input. The distribution generation unit 424b clusters the input location information of multiple elastic wave sources based on vehicle type information. For example, the distribution generation unit 424b clusters the input location information of multiple elastic wave sources into a group of large vehicles and a group of regular vehicles based on vehicle type information. The distribution generation unit 424b generates an elastic wave source distribution corresponding to the group of large vehicles using the location information of multiple elastic wave sources included in the group of large vehicles. Similarly, the distribution generation unit 424b generates an elastic wave source distribution corresponding to the group of regular vehicles using the location information of multiple elastic wave sources included in the group of regular vehicles. In this way, the distribution generation unit 424b generates an elastic wave source distribution for each vehicle class. The distribution generation unit 424b generates an elastic wave source density distribution for each vehicle class using the elastic wave source distribution for each vehicle class.

[0132] The correction unit 425b corrects the elastic wave source density distribution for each vehicle class generated by the distribution generation unit 424b based on the estimation results output from the vehicle information estimation unit 35. The correction unit 425b may store the corrected elastic wave source density distribution for each vehicle class in the storage unit 43.

[0133] The evaluation unit 426b evaluates the deterioration state of the structure 50 using the corrected elastic wave source density distribution for each vehicle class. For example, the evaluation unit 426b may evaluate the deterioration state of the structure 50 using the corrected elastic wave source density distribution corresponding to the large vehicle group, or it may evaluate the deterioration state of the structure 50 using the corrected elastic wave source density distribution corresponding to the regular vehicle group.

[0134] The class selection unit 427 selects a specific class in response to an instruction received from an external source. The class selection unit 427 displays the corrected elastic wave source density distribution corresponding to the selected class on the display unit 44.

[0135] The structural evaluation system 100b configured as described above can achieve the same effects as the first embodiment.

[0136] Furthermore, in the structural evaluation system 100b, the signal processing unit 30b does not transmit transmission data corresponding to a specific vehicle type to the structural evaluation device 40, as in the second embodiment, but rather adds vehicle type information to all transmission data and transmits it to the structural evaluation device 40b. As a result, the structural evaluation device 40b clusters the transmission data based on the vehicle type information added to the transmission data, enabling evaluation based on the transmission data for each vehicle type. Therefore, it is possible to generate an elastic wave source density distribution for each elastic wave excitation condition (e.g., driving vehicle conditions). Then, by selecting an appropriate vehicle class, highly accurate evaluation becomes possible.

[0137] (Modification 1 of the third embodiment) The structural evaluation device 40b may be configured, similar to the second embodiment, to generate the elastic wave source density distribution for each vehicle type by taking into account the number of vehicles estimated by the vehicle information estimation unit 35a during the stage of generating the elastic wave source density distribution.

[0138] (Modification 2 of the third embodiment) The vehicle information estimation unit 35a may be configured to estimate the number of vehicles 10 of each type from among the vehicles 10 that passed through the evaluation area during the evaluation period. In this case, the vehicle information estimation unit 35a outputs an estimation result to the structural evaluation device 40b showing the number of vehicles 10 of each type that passed through the evaluation area during the evaluation period. The structural evaluation device 40b corrects the elastic wave source density distribution using the estimation result showing the number of vehicles 10 of each type. For example, the correction unit 425b corrects the elastic wave source density distribution corresponding to large vehicles generated by the distribution generation unit 424b based on the number of large vehicles shown in the estimation result output from the vehicle information estimation unit 35. For example, the correction unit 425b corrects the elastic wave source density distribution corresponding to ordinary vehicles generated by the distribution generation unit 424b based on the number of ordinary vehicles shown in the estimation result output from the vehicle information estimation unit 35.

[0139] (Modification 1 common to all embodiments) The signal processing units 30, 30a and the vehicle information estimation units 35, 35a may be provided in the structural evaluation devices 40, 40a. In this configuration, in the first embodiment, the structural evaluation device 40 may include the signal processing unit 30 and the vehicle information estimation unit 35 instead of the communication unit 41. The signal processing unit 30 outputs the transmission data obtained by performing signal processing on the elastic waves detected by the first sensor 20 to the control unit 42. The vehicle information estimation unit 35 outputs the number of passing vehicles obtained based on time-series data representing the change in magnetic flux density detected by the second sensor 25 to the control unit 42. In the second embodiment, the structural evaluation device 40 may include the signal processing unit 30a and the vehicle information estimation unit 35a instead of the communication unit 41. The signal processing unit 30a outputs the specified transmission data from the transmission data obtained by performing signal processing on the elastic waves detected by the first sensor 20 to the control unit 42. The vehicle information estimation unit 35a outputs vehicle count information obtained based on time-series data representing the change in magnetic flux density detected by the second sensor 25 to the control unit 42, and outputs vehicle type information obtained based on time-series data representing the change in magnetic flux density to the signal processing unit 30a. In the third embodiment, the structure evaluation device 40b may include a signal processing unit 30b and a vehicle information estimation unit 35a instead of a communication unit 41. The signal processing unit 30b adds vehicle type information to the transmission data obtained by performing signal processing on the elastic waves detected by the first sensor 20 and outputs it to the control unit 42b. The vehicle information estimation unit 35a outputs vehicle count information obtained based on time-series data representing the change in magnetic flux density detected by the second sensor 25 to the control unit 42b, and outputs vehicle type information obtained based on time-series data representing the change in magnetic flux density to the signal processing unit 30b.

[0140] (Modification 2 common to each embodiment) In each of the embodiments described above, a configuration was shown in which multiple first sensors 20-1 to 20-n are connected to one signal processing unit 30, 30a, 30b. The structural evaluation systems 100, 100a, and 100b may include multiple signal processing units 30, 30a, and 30b, and each first sensor 20 may be connected to a different signal processing unit 30, 30a, or 30b.

[0141] (Modification 3 common to each embodiment) Some or all of the functional units of the structural evaluation devices 40 and 40b may be provided in other devices. For example, the display unit 44 of the structural evaluation devices 40 and 40b may be provided in other devices. In this configuration, the structural evaluation devices 40 and 40b transmit the evaluation results to the other device equipped with the display unit 44. The other device equipped with the display unit 44 displays the received evaluation results.

[0142] (Modification 4 common to each embodiment) The vehicle information estimation units 35 and 35a may be configured to further estimate the vehicle length, vehicle weight, lane, or vehicle speed of the vehicle 10 as vehicle information. In this configuration, in the second embodiment, the signal processing unit 30a may perform filtering by vehicle length, vehicle weight, lane, or vehicle speed. Filtering by vehicle length means not transmitting data other than vehicle length within a specific range. The specific range of vehicle length may be A (A is an integer of 1 or more) m or less, Am or more, Am to B (B is an integer of 2 or more), etc. Filtering by vehicle weight means not transmitting data other than vehicle weight within a specific range. The specific range of vehicle weight may be several hundred kg or less, several hundred kg or more, several hundred kg to several thousand kg, etc.

[0143] Lane filtering means that data will not be transmitted for any lane other than a specific lane. Specific lanes include the driving lane and the passing lane. Vehicle speed filtering means that data will not be transmitted for any vehicle speed other than a specific range. Specific speed ranges include speeds of several tens (km / h) or less, several tens (km / h) or more, several tens (km / h) to several tens (km / h), etc. The specific operation is the same as vehicle type filtering in the second embodiment; for example, the vehicle type can be replaced with other vehicle information.

[0144] On the other hand, the vehicle information estimation unit 35a needs to maintain a trained model that has been trained to output vehicle information when the vehicle information to be estimated is vehicle length, vehicle weight, driving lane, or vehicle speed. For example, when the vehicle information estimation unit 35a estimates vehicle length, it needs to maintain a trained model that has been trained to output vehicle length information using one or more features as input. Similarly, a trained model needs to be maintained for other types of vehicle information as well. When estimating a lane, the amplitude of the signal detected differs between the driving lane of vehicle 10 and the passing lane adjacent to the driving lane. Therefore, a trained model for estimating lanes can be created by training a machine learning model with amplitude-related features as input.

[0145] In the third embodiment, the vehicle information estimation unit 35a may determine one of the vehicle length, vehicle weight, lane, or vehicle speed, and the structural evaluation device 40b may perform clustering based on one of the vehicle length, vehicle weight, lane, or vehicle speed, and process each group. Clustering by vehicle length means dividing the data to be processed into groups according to the vehicle length. For example, dividing the data into groups with a vehicle length of less than Am and groups with a vehicle length of Am or more. Clustering by vehicle weight means dividing the data to be processed into groups according to the vehicle weight. For example, dividing the data into groups with a vehicle weight of less than several hundred kg and groups with a vehicle weight of several hundred kg or more.

[0146] Lane clustering means dividing the data to be processed into groups according to the lane. For example, this could involve dividing the data into a group of driving lanes and a group of passing lanes. Vehicle speed clustering means dividing the data to be processed into groups according to the vehicle speed. For example, this could involve dividing the data into a group of vehicles with a speed of less than several tens (km / h) and a group of vehicles with a speed of several tens (km / h) or more. The specific operation is the same as vehicle type clustering in the third embodiment.

[0147] (Method for estimating vehicle speed) The method for estimating vehicle speed will be explained using Figure 13. Figure 13 is a diagram illustrating the method for estimating vehicle speed. When estimating vehicle speed, two or more second sensors 25 are placed along the vehicle's travel axis. Figure 1 shows an example where two second sensors 25-1 and 25-2 are placed. The installation interval d of the second sensors 25-1 and 25-2 is known. As the vehicle 10 travels over the structure, the second sensors 25-1 and 25-2 will detect the vehicle 10 with a time difference corresponding to the vehicle speed. Specifically, after the second sensor 25-1 detects the vehicle 10, the second sensor 25-2 will detect the vehicle 10 after a certain time difference dt. The vehicle information estimation units 35 and 35a estimate the vehicle speed V based on the following equation (4), using the time difference dt measured from the time difference in the detection timing of the output signals of the second sensors 25-1 and 25-2, and the known installation interval d.

[0148]

number

[0149] According to at least one embodiment described above, the deterioration state of the structure can be evaluated with greater accuracy by having a plurality of first sensors 20 that detect elastic waves generated inside the structure 50 on which the vehicle 10 travels, one or more second sensors 25 that detect the passage of the vehicle 10 regardless of damage inside the structure 50, a vehicle information estimation unit 35 that estimates vehicle information including at least the number of vehicles 10 that have passed through the structure 50 based on the detection results of one or more second sensors 25, and an evaluation unit 426 that evaluates the deterioration state of the structure 50 based on a plurality of elastic waves detected by each of the plurality of first sensors 20 and the vehicle information estimated by the vehicle information estimation unit 35.

[0150] While several embodiments of the present invention have been described, these embodiments are presented as examples only and are not intended to limit the scope of the invention. These embodiments can be carried out in a variety of other forms, and various omissions, substitutions, and modifications can be made without departing from the spirit of the invention. These embodiments and their variations are included in the scope and spirit of the invention, as well as in the claims and their equivalents. [Explanation of symbols]

[0151] 20, 20-1~20-n…First sensor, 25…Second sensor, 30, 30a, 30b…Signal processing unit, 35, 35a…Vehicle information estimation unit, 40, 40b…Structural evaluation device, 41…Communication unit, 42, 42b…Control unit, 43…Storage unit, 44…Display unit, 100, 100a, 100b…Structural evaluation system, 301…Waveform shaping filter, 302…Gate generation circuit, 303…Arrival time determination unit, 304…Feature extraction unit, 305…Transmission data generation unit, 306…Memory, 307…Output unit, 421…Acquisition unit, 422…Event extraction unit, 423, 423b…Position determination unit, 424, 424b…Distribution generation unit, 425, 425b…Correction unit, 426, 426b…Evaluation unit

Claims

1. Multiple first sensors that detect elastic waves generated inside the structure through which the vehicle travels, One or more second sensors that detect the passage of the vehicle regardless of damage inside the structure, A vehicle information estimation unit estimates vehicle information, which includes at least the number of vehicles that passed through the structure, based on the detection results of the one or more second sensors. An evaluation unit evaluates the deterioration state of the structure based on a plurality of elastic waves detected by each of the plurality of first sensors and the vehicle information estimated by the vehicle information estimation unit. Equipped with, A positioning unit that determines the position of the multiple elastic wave sources based on the multiple elastic waves detected by each of the multiple first sensors, A density distribution generation unit generates an elastic wave source density distribution representing the density distribution of the locations of the plurality of elastic wave sources based on the location determination results of the location determination unit, A correction unit corrects the elastic wave source density distribution generated by the density distribution generation unit based on the vehicle information estimated by the vehicle information estimation unit, Furthermore, The evaluation unit is a structural evaluation system that evaluates the deterioration state of the structure by comparing the density of each region in the corrected elastic wave source density distribution with a threshold value.

2. The aforementioned one or more second sensors are magnetic sensors that detect changes in magnetic flux density. The vehicle information estimation unit estimates the vehicle information based on the change in magnetic flux density detected by the one or more second sensors. The structural evaluation system according to claim 1.

3. The vehicle information estimation unit estimates the number of vehicles that have passed through the structure by integrating the peaks of the output signals of the one or more second sensors. A structural evaluation system according to claim 1 or 2.

4. The correction unit calculates the density in each region by dividing the number of elastic wave sources located within each region of the elastic wave source density distribution by the area of ​​each region, and corrects the elastic wave source density distribution by dividing the calculated density by the number of vehicles included in the vehicle information. A structural evaluation system according to claim 1 or 2.

5. The system further includes a signal processing unit that extracts characteristic quantities of the plurality of elastic waves by performing signal processing on the plurality of elastic waves output from each of the plurality of first sensors, The vehicle information estimation unit further estimates the vehicle type, length, weight, lane, or speed of the vehicle based on the output signals of the one or more second sensors, and outputs the estimation result to the signal processing unit. The signal processing unit identifies the feature quantities of the plurality of elastic waves obtained from the driving of a vehicle corresponding to one of the vehicle types, lengths, weights, driving lanes, or speeds of the vehicle included in the estimation results output from the vehicle information estimation unit, and outputs the identified feature quantities of the plurality of elastic waves. The evaluation unit evaluates the deterioration state of the structure based on the feature quantities of the plurality of elastic waves output from the signal processing unit and the vehicle information estimated by the vehicle information estimation unit. A structural evaluation system according to claim 1 or 2.

6. The vehicle information estimation unit takes at least one of the following features obtained from the output signals of the one or more second sensors as input: amplitude, duration, envelope area, centroid frequency, or maximum frequency, and uses a trained model that classifies the vehicle into one of several classes based on its type, length, weight, lane, or speed. The structural evaluation system according to claim 5.

7. The system further includes a signal processing unit that extracts characteristic quantities of the plurality of elastic waves by performing signal processing on the plurality of elastic waves output from each of the plurality of first sensors, The vehicle information estimation unit further estimates the vehicle type, length, weight, lane, or speed of the vehicle based on the output signals of the one or more second sensors, and outputs the estimation result to the signal processing unit. The signal processing unit outputs the estimation results from the vehicle information estimation unit, adding the estimation results to the extracted feature quantities of elastic waves and outputting them. The density distribution generation unit generates the elastic wave source density distribution for each class representing a group obtained by clustering each vehicle traveling on the structure by type, length, weight, lane, or speed. A structural evaluation system according to claim 1 or 2.

8. The system further includes a class selection unit that selects a specific class in response to instructions input from an external source. The class selection unit displays the elastic wave source density distribution corresponding to the selected class on the display unit. The structural evaluation system according to claim 7.

9. The above second sensors are multiple second sensors, The aforementioned plurality of second sensors are arranged along the vehicle's travel axis, The vehicle information estimation unit estimates the vehicle speed based on the time difference between the times when the vehicle's passage is detected by each of the multiple second sensors. The structural evaluation system according to claim 5.

10. The one or more second sensors are installed within the range enclosed by the plurality of first sensors. A structural evaluation system according to claim 1 or 2.

11. Based on the detection results of one or more sensors that detect vehicle traffic regardless of internal damage to the structure, vehicle information including at least the number of vehicles that passed through the structure is estimated. Based on the multiple elastic waves detected by each of the multiple first sensors that detect elastic waves generated inside the structure on which the vehicle travels, the location of the multiple elastic wave sources is determined. Based on the localization results, an elastic wave source density distribution is generated, which represents the density distribution of the locations of the multiple elastic wave sources. The generated elastic wave source density distribution is corrected based on the estimated vehicle information. A structural evaluation method for evaluating the deterioration state of a structure by comparing the density of each region in the corrected elastic wave source density distribution with a threshold value.

Images