Method for detecting abnormality of support structure

Abstract

To obtain a low-cost and highly real-time method for detecting an abnormality of a support structure included in a road structure in service, in order to prevent serious accidents in advance.SOLUTION: The method for detecting an abnormality of a support structure includes: a measurement step in which a sensor arranged for a support structure measures a physical quantity, and an abnormality diagnosis step in which an abnormality caused by deterioration of the support structure is detected based on the measurement result. The abnormality diagnosis step includes: a first step of storing, in a storage unit, the measured physical quantity obtained as the measurement result as measurement information; a second step of calculating a reference response surface in a normal state; a third step of calculating a diagnostic response surface during monitoring; and a fourth step of performing a statistical equivalence test between the two response surfaces by means of an F-test, and executing abnormality detection of the support structure.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present disclosure relates to a method for detecting abnormalities in support structures, which detects abnormalities associated with deterioration of support structures by performing statistical processing on physical quantities detected by sensors attached to the support structures of road structures in use. [Background technology]

[0002] For example, bridges on which vehicles pass or road information boards installed around roads on which vehicles pass gradually deteriorate due to aging caused by the passage of vehicles. Note that in this disclosure, structures such as bridges and road information boards that are in use will be referred to as road structures in use.

[0003] Many road structures currently in use were constructed in large quantities in response to the rapid expansion of road traffic demand during the period of high economic growth. Accidents caused by deterioration of road structures in use often cause severe damage, so it is extremely important to detect deterioration before they break down.

[0004] However, the main method for inspecting the deterioration state of road structures in service is still to carry out periodic inspections by inspectors, either visually or using some kind of measuring instrument.

[0005] In this situation, a conventional technique has been disclosed that allows for simple and rapid quantitative inspection of structures inside tunnels, which are the subject of diagnosis of deterioration, for cracks that develop over time (see, for example, Patent Document 1).

[0006] In Patent Document 1, a fluorescent dye that emits light when excited by ultraviolet light or blue visible light is mixed in advance into the structure inside the tunnel that is the target of the deterioration diagnosis. Then, a light source that emits ultraviolet light or blue visible light is irradiated onto the structure, and the occurrence of cracks is quantitatively determined by visual inspection or by analyzing images captured by a CCD camera or the like, thereby diagnosing the deterioration state. [Prior art documents] [Patent documents]

[0007] [Patent Document 1] Japanese Patent Application Laid-Open No. 2013-83493 Summary of the Invention [Problem to be solved by the invention]

[0008] Although Patent Document 1 makes it possible to quantitatively diagnose the state of deterioration, it is still based on periodic inspections by inspectors. Furthermore, Patent Document 1 requires that fluorescent dye be mixed into the structure to be diagnosed for deterioration beforehand. Therefore, it is difficult to apply the technology of Patent Document 1 to the diagnosis of the state of deterioration of existing road structures that are in use.

[0009] In recent years, there has been a demand for a method that can detect abnormalities in road structures in use at intervals shorter than regular inspections, without the need for inspectors, and without human intervention.

[0010] In particular, it is important to prevent serious damage caused by deterioration of the support structures used on bridges over which vehicles pass, or road information boards installed around roads over which vehicles pass, and these structures are found all over the country.

[0011] Therefore, a method is desired that can detect abnormalities due to deterioration of support structures included in road structures in service in real time and at low cost.

[0012] The present invention has been made to solve the above-mentioned problems, and aims to provide a low-cost, real-time method for detecting abnormalities in support structures included in road structures in service, in order to prevent serious accidents from occurring. [Means for solving the problem]

[0013] The method for detecting abnormalities in support structures disclosed herein includes a measurement step of measuring physical quantities that serve as index values ​​for detecting abnormalities in the support structure using a sensor with one or more measurement axes placed on a support structure included in a road structure in service, and an abnormality diagnosis step of detecting abnormalities due to deterioration of the support structure based on the measurement results from the sensor.The abnormality diagnosis step includes a first step of storing the physical quantities obtained as measurement results continuously or periodically from the sensor as measurement information in a memory unit, a second step of calculating a reference response surface by applying response surface methodology to the results of one-axis or multiple-axis calculations on the measurement information when the support structure is normal, a third step of calculating a diagnostic response surface by applying response surface methodology to the results of one-axis or multiple-axis calculations on the measurement information during monitoring, and a fourth step of performing statistical equivalence testing for the two response surfaces consisting of the reference response surface and the diagnostic response surface using an F-test, and determining that the support structure is normal if equivalence is adopted as the test result, and determining that there is an abnormality in the support structure if equivalence is rejected, thereby performing abnormality detection in the support structure. In addition, the method for detecting abnormalities in support structures disclosed herein is a method for detecting abnormalities in support structures, comprising: a measurement step of measuring physical quantities that serve as index values ​​for detecting abnormalities in the support structure using sensors placed on the support structure included in a road structure in service; and an abnormality diagnosis step of detecting abnormalities due to deterioration of the support structure based on the measurement results by the sensors.The abnormality diagnosis step comprises a first step of storing physical quantities obtained continuously or periodically as measurement results from sensors having multiple measurement axes as measurement information in a memory unit; a second step of calculating a composite vector from the measurement information included in the multiple measurement axes and calculating a reference response surface by applying response surface methodology from the calculation results for the composite vector; a third step of calculating a diagnostic response surface by applying response surface methodology from the calculation results for the measurement information of any one of the measurement information of the multiple measurement axes; and a fourth step of testing the statistical equivalence of the two response surfaces consisting of the reference response surface and the diagnostic response surface using an F-test, and determining that the support structure is normal if equivalence is adopted as the test result, and determining that there is an abnormality in the support structure if equivalence is rejected, thereby performing abnormality detection in the support structure. Furthermore, the method for detecting abnormalities in support structures disclosed herein is a method for detecting abnormalities in support structures, comprising: a measurement step of measuring physical quantities that serve as index values ​​for detecting abnormalities in the support structure using sensors placed on the support structure included in a road structure in service; and an abnormality diagnosis step of detecting abnormalities due to deterioration of the support structure based on the measurement results by the sensors, wherein the abnormality diagnosis step comprises a first step of storing physical quantities obtained continuously or periodically as measurement results from sensors having multiple measurement axes as measurement information in a memory unit; a second step of calculating a reference response surface by applying response surface methodology to the results of calculation processing on the measurement information of a first axis included in the multiple measurement axes; a third step of calculating a diagnostic response surface by applying response surface methodology to the results of calculation processing on the measurement information of a second axis different from the first axis included in the multiple measurement axes; and a fourth step of testing the statistical equivalence of the two response surfaces formed by the reference response surface and the diagnostic response surface using an F-test, and determining that the support structure is normal if equivalence is adopted as the test result, and determining that there is an abnormality in the support structure if equivalence is rejected, thereby performing abnormality detection in the support structure. [Effects of the Invention]

[0014] According to the present disclosure, a low-cost, real-time method for detecting abnormalities in support structures included in road structures in service can be obtained to prevent serious accidents. [Brief explanation of the drawings]

[0015] [Figure 1] 1 is a functional block diagram of an anomaly detection system that executes a support structure anomaly detection method according to a first embodiment of the present disclosure. [Figure 2] 4 is a flowchart illustrating a series of processes executed by a sensor and a controller as a method for detecting an abnormality in a support structure according to the first embodiment of the present disclosure. [Figure 3] FIG. 2 is an explanatory diagram showing a specific arrangement of sensors as a first arrangement example when an F-type information board is used as a road structure in use in the first embodiment of the present disclosure. [Figure 4] FIG. 10 is an explanatory diagram showing a specific arrangement of sensors as a second arrangement example when an I-shaped information board is used as a road structure in use in the first embodiment of the present disclosure. [Figure 5] FIG. 10 is an explanatory diagram showing a specific arrangement of sensors as a third arrangement example when an I-shaped information board is used as a road structure in use in the first embodiment of the present disclosure. [Figure 6] FIG. 10 is an explanatory diagram showing a specific arrangement of sensors as a fourth arrangement example when a gate-shaped information board is used as a road structure in use in the first embodiment of the present disclosure. [Figure 7] FIG. 10 is an explanatory diagram showing a specific arrangement of sensors as a fifth arrangement example when a bridge is used as a road structure in service in the first embodiment of the present disclosure. [Figure 8] FIG. 10 is an explanatory diagram showing a specific arrangement of sensors as a sixth arrangement example when a bridge is used as a road structure in service in the first embodiment of the present disclosure. [Figure 9] FIG. 11 is an explanatory diagram showing a specific arrangement of sensors as a seventh arrangement example when a bridge is used as a road structure in service in the first embodiment of the present disclosure. [Figure 10] 10 is a flowchart illustrating a series of processes executed by a sensor having multiple measurement axes and a controller as a method for detecting an abnormality in a support structure according to a second embodiment of the present disclosure. [Figure 11] 11 is a flowchart illustrating a series of processes executed by a sensor having multiple measurement axes and a controller as a method for detecting an abnormality in a support structure according to a third embodiment of the present disclosure. DETAILED DESCRIPTION OF THE INVENTION

[0016] Hereinafter, preferred embodiments of the support structure anomaly detection method of the present disclosure will be described with reference to the drawings. The method for detecting abnormalities in support structures disclosed herein has the technical features of installing sensors with one or more measurement axes to measure physical quantities that serve as index values ​​for detecting deterioration abnormalities on the support structures of road structures in service, applying the SI-F method to the measurement data, generating a reference response surface and a diagnostic response surface based on the measurement data, testing the statistical equivalence of the two generated response surfaces using an F-test, and detecting abnormalities in the support structures based on the test results.

[0017] In the following, road information boards and bridges will be used as specific examples of road structures in use, and detailed explanations will be given, including the placement of sensors.

[0018] Embodiment 1 1 is a functional block diagram of an anomaly detection system that executes a support structure anomaly detection method according to a first embodiment of the present disclosure. The anomaly detection system shown in FIG. 1 includes a sensor 10 and a controller 20.

[0019] The sensor 10 is placed on a support structure included in a road structure in service, and measures a physical quantity that serves as an index value for detecting abnormalities in the support structure. The number of sensors may be one or more, and FIG. 1 illustrates a case where the sensor 10 is configured as N sensors 10(1) to 10(N). In the following description, when there is no particular need to distinguish between the multiple sensors 10(1) to 10(N), they will be simply referred to as sensor 10.

[0020] Examples of road structures in use include bridges on which vehicles pass, or road information boards installed in the vicinity of roads on which vehicles pass. Specific installation locations of the sensor 10 relative to bridges or road information boards will be described later with reference to FIGS. 3 to 9.

[0021] The controller 20 performs abnormality diagnosis to detect abnormalities due to deterioration of the support structure based on the measurement results from the sensor 10 having one or more measurement axes. The controller 20 includes an index value data collection unit 21, a response surface calculation unit 22, and an abnormality determination processing unit 23.

[0022] 2 is a flowchart illustrating a series of processes executed by the sensor 10 and the controller 20 as a method for detecting an abnormality in a support structure according to the first embodiment of the present disclosure. The function of each component of the controller 20 will also be described with reference to the series of processes illustrated in the flowchart of FIG.

[0023] Step S201 corresponds to a measurement step executed by the sensor 10. Steps S202 to S205 correspond to abnormality diagnosis steps executed by the components of the controller 20, and are roughly divided into four steps, a first step to a fourth step.

[0024] In the measurement step of step S201, the sensor 10 arranged on the support structure measures a physical quantity that is an index value for detecting an abnormality in the support structure. A specific example of the sensor 10 is an acceleration sensor, which is installed on the support structure to measure acceleration data corresponding to vibrations of the support structure as a physical quantity.

[0025] Next, in the first step of the abnormality diagnosis step of step S202, the index value data collection unit 21 in the controller 20 continuously or periodically acquires physical quantities as measurement results from the sensor 10 in sequence, and stores the measurement information as time-series information in a memory unit (not shown).

[0026] Next, in the second step of the abnormality diagnosis step of step S203, the response surface calculation unit 22 applies the response surface methodology to calculate a reference response surface for each sensor from the results of uniaxial or multiaxial calculation processing of the measurement information collected when the support structure is normal.

[0027] Next, in the third step of the abnormality diagnosis step of step S204, the response surface calculation unit 22 applies response surface methodology to calculate a diagnostic response surface for each sensor from the results of uniaxial or multiaxial calculation processing of the measurement information collected during monitoring.

[0028] Next, in the fourth step of the abnormality diagnosis step of step S205, the abnormality judgment processing unit 23 tests the statistical equivalence of the two response surfaces, the reference response surface calculated in step S203 and the diagnostic response surface calculated in step S204, using an F-test.

[0029] Furthermore, if the equivalence between the two response surfaces is adopted as the test result, the abnormality determination processor 23 determines that the support structure is normal. On the other hand, if the equivalence between the two response surfaces is rejected as the test result, the abnormality determination processor 23 determines that the support structure is abnormal.

[0030] Here, the method for detecting abnormalities in a support structure according to the first embodiment has a technical feature in that abnormalities in the support structure are diagnosed by performing statistical processing using the SI-F method (Statistical Structure Integrity Investigation method using the F-test) in the above-mentioned steps S203 to S205.

[0031] In other words, in the method according to the first embodiment, the equivalence between the reference regression equation generated from the measurement information during normal operation and during monitoring (normal operation) using the response surface methodology and the diagnostic regression equation is tested using an F-test, which is a statistical test. Therefore, the abnormality diagnosis using the SI-F method executed by the controller 20 will be further explained below.

[0032] In the following description, acceleration is measured as the physical quantity. Furthermore, the reference response surface and the reference regression equation are technically synonymous, and the diagnostic response surface and the diagnostic regression equation are technically synonymous.

[0033] That is, the response surface calculation unit 22 can calculate a reference response surface by autoregression based on the correlation distribution of time-series information about measurement information during normal operation or the correlation distribution of time-series information about measurement information on multiple axes during normal operation. Similarly, the response surface calculation unit 22 can calculate a diagnostic response surface by autoregression based on the correlation distribution of time-series information about measurement information during monitoring operation or the correlation distribution of time-series information about measurement information on multiple axes during monitoring operation.

[0034] The realization value F0 of the statistical test quantity F is defined by the following equation (1).

[0035]

number

[0036] SSE0, SSE1, SSE2, and p in the above formula (1) are defined as follows: SSE0: Sum of squares of residuals of the regression equation using both baseline and diagnostic data SSE1: Sum of squares of residuals of the standard regression equation SSE2: Residual sum of squares of diagnostic regression equation p: Degrees of freedom of the regression equation

[0037] The autoregressive equation of acceleration used in the SI-F method in the first embodiment is shown in the following equation (2).

[0038]

number

[0039] x in the above formula (2) t , t, β i (i=1, 2, , p), and e t is defined as follows: x t : Correlation of time series data of acceleration, which is a physical quantity, or time series data of acceleration on multiple axes t: Time series point β i (i=1, 2, , p): Autoregressive coefficient et : A random variable that represents irregular fluctuations that cannot be explained systematically

[0040] In a time series of a variable, a state in which its current value is determined by its past value is called autocorrelation. In this first embodiment, regression is performed using a linear sum. Although this is not completely true in actual measurements due to the influence of errors, etc., when the two autoregressive equations are equivalent, the realized value F0 has the property of generally following a theoretical F distribution with a first degree of freedom of p and a second degree of freedom of n-2p. Therefore, in this first embodiment, this property is utilized to perform an abnormality determination.

[0041] In the first embodiment, a significance level α of the rejection region of the equivalence hypothesis is set for the theoretical F distribution, and if it is rejected, it is determined to be abnormal. The following formula (3) shows the hypothesis rejection region when the significance level is α.

[0042]

number

[0043] Therefore, the response surface calculation unit 22 in the controller 20 can calculate a reference regression equation corresponding to the reference response surface and a diagnostic regression equation corresponding to the diagnostic response surface using the above equation (2).

[0044] Furthermore, the abnormality determination processing unit 23 in the controller 20 uses the above formula (1) to calculate the realized value F0 of the statistical test quantity F, and can use the above formula (3) to test the statistical equivalence of two regression equations, the reference regression equation corresponding to the reference response surface and the diagnostic regression equation corresponding to the diagnostic response surface, by F-test.

[0045] Next, specific installation locations of the sensor 10 on bridges or road information boards will be described in detail with reference to FIGS.

[0046] 3 is an explanatory diagram showing a first example of a specific arrangement of sensors 10 when an F-type information board is used as a road structure in use in the first embodiment of the present disclosure. The F-type information board 100 shown in FIG. 3 has a support structure including an information board 101 and a support pillar 102.

[0047] In this type of support structure, the following three points P31 to P33 shown in FIG. 3 are important positions that correspond to the poles of the vibration resonance frequency or the parts where stress is concentrated. P31: The base of the support 102 P32: Position of the tip of the support 102 P33: Position of the tip of the information board 101 that is the pole in the in-plane direction (the direction in which the display surface of the information board 101 does not shake, i.e., the direction across the road)

[0048] Therefore, in the case of FIG. 3, the sensor 10 is installed at three locations P31 to P33, and abnormality diagnosis is performed at each location by the SI-F method described above.

[0049] 4 is an explanatory diagram showing a second example of a specific arrangement of sensors 10 when an I-shaped information board is used as a road structure in use in the first embodiment of the present disclosure. The I-shaped information board 110 shown in FIG. 4 has a support structure including an information board 111 and a support pillar 112.

[0050] In this type of support structure, the following three points P41 to P43 shown in FIG. 4(A) are important positions that correspond to the poles of the vibration resonance frequency or the parts where stress is concentrated. P41: The base of pillar 112 P42: Position of the tip of support 112 P43: Position of the tip of the information board 111 that is the pole in the in-plane direction (the direction in which the display surface of the information board 111 does not shake, i.e., the direction across the road)

[0051] Therefore, in the case of FIG. 4(A), the sensors 10 are installed at three locations P41 to P43, and abnormality diagnosis is performed at each location by the SI-F method described above.

[0052] However, depending on the situation at the site or the structure of the information board 111, it is also possible to place the sensors 10 at positions P44 and P45 as shown below, instead of positions P42 and P43, as shown in FIG. 4(B). P44: Upper center position of information board 111 P45: Position of the upper tip of the information board 111

[0053] 5 is an explanatory diagram showing a specific arrangement of sensors 10 as a third arrangement example when an I-shaped information board is used as a road structure in use in the first embodiment of the present disclosure. The I-shaped information board 110 shown in FIG. 5 has a support structure including an information board 111 and a support pillar 112, similar to the previous FIG. 4.

[0054] Here, P51(1) and P51(2) shown in FIG. 5(A) and P52(1) and P52(2) shown in FIG. 5(B) correspond to the following positions. P51(1) and P51(2): Positions of the tips symmetrical on either side of the support pole 112 on the underside of the information board 111, which is the pole in the in-plane direction (the direction in which the display surface of the information board 111 does not shake, i.e., the direction across the road)

[0055] P52(1) and P52(2): Positions of the tips of the poles on the upper side of the information board 111 that are symmetrical across the support pole 112 in the in-plane direction (the direction in which the display surface of the information board 111 does not shake, i.e., the direction across the road)

[0056] That is, in the support structure of the I-type information board 110, there are poles of the vibration resonant frequency or areas where stress is concentrated at structurally symmetrical positions, such as the positional relationship between P51(1) and P51(2) shown in Figure 5(A) or the positional relationship between P52(1) and P52(2) shown in Figure 5(B).

[0057] In this way, when there are vibration resonance frequency poles or areas where stress is concentrated at structurally symmetrical positions, it is possible to detect abnormalities not only by comparing the reference response surface and diagnostic response surface of the same sensor 10, but also by comparing the response surfaces of different sensors. This will be further explained by comparing Figures 4 and 5.

[0058] As shown in Fig. 4(B) above, when sensors 10 are installed at positions P44 and P45 relative to information board 111, P44 and P45 are not structurally symmetrical. Therefore, in the case of Fig. 4(B), abnormality diagnosis using the SI-F method is performed separately for the sensor 10 installed at P44 and the sensor 10 installed at P45.

[0059] On the other hand, as shown in Fig. 5(A), when the sensors 10 are installed at positions P51(1) and P51(2) with respect to the information board 111, P51(1) and P51(2) are structurally symmetrical. Therefore, in the case of Fig. 5(A), it is considered that there is a high correlation between the physical quantity measured by the sensor 10 installed at P51(1) and the physical quantity measured by the sensor 10 installed at P51(2).

[0060] Therefore, when there are poles of the vibration resonant frequency or areas where stress is concentrated at structurally symmetrical positions, and sensors 10 are placed at each of these positions, it is possible to detect abnormalities by comparing different sensors using methods 1 to 3 described below.

[0061] In this description, the sensor installed at the position P51(1) in FIG. 5(A) or P52(1) in FIG. 5(B) will be referred to as the first sensor, and the sensor installed at the position P51(2) in FIG. 5(A) or P52(2) in FIG. 5(B) will be referred to as the second sensor.

[0062] Method 1: Statistical equivalence is tested using an F-test using a reference response surface calculated in response to measurement information from a first sensor and a diagnostic response surface calculated in response to measurement information from a second sensor.

[0063] Method 2: Statistical equivalence is tested using an F-test using a reference response surface calculated in response to the measurement information from the second sensor and a diagnostic response surface calculated in response to the measurement information from the first sensor.

[0064] Method 3: A scatter plot of the measurement information from the first sensor and the second sensor corresponding to each time when the support structure is normal, i.e., a correlation distribution, is created, and a reference response surface calculated corresponding to the correlation distribution is created, and a correlation distribution of the measurement information from the first sensor and the second sensor corresponding to each time when monitoring is performed is created, and statistical equivalence is tested using an F-test using a diagnostic response surface calculated corresponding to the correlation distribution.

[0065] In this way, when diagnosing two structurally symmetrical locations, anomaly diagnosis can be performed using the SI-F method by comparing the reference response surface and diagnostic response surface between different sensors, or by comparing the reference response surface and diagnostic response surface calculated from the correlation distribution between different sensors.

[0066] In the case of an F-type information board such as that shown in Figure 3, there are no structurally symmetrical parts in the information board 101, and therefore abnormality diagnosis using the SI-F method based on a comparison between different sensors cannot be performed. However, abnormality diagnosis using the SI-F method based on a comparison of correlation distributions between different sensors is possible.

[0067] 6 is an explanatory diagram showing a specific arrangement of sensors 10 as a fourth arrangement example when a gate-shaped information board is used as a road structure in use in the first embodiment of the present disclosure. The gate-shaped information board 120 shown in FIG. 6 has a support structure including an information board 121 and a support pillar 122.

[0068] 6 has structurally symmetrical portions in the support pillar 122. In the support pillar 122, the base of the support pillar, the shoulder, the center of the horizontal support pillar, and the quartiles correspond to the poles of the vibration resonance frequency or the portions where stress is concentrated.

[0069] The support pillars 122 are divided into horizontal support pillars 122H that are parallel to the information board 121, and vertical support pillars 122V that are provided on both sides of the horizontal support pillar 122H. The placement of the sensor 10 on the shoulder is selected depending on the part of the vertical support pillars 122V and horizontal support pillars 122H that is to be emphasized.

[0070] FIG. 6 illustrates an example in which sensors 10 are arranged at equal intervals at nine locations: P61, P62(1), P62(2), P63(1), P63(2), P64(1), P64(2), P65(1), and P65(2).

[0071] In the example arrangement shown in Figure 6, P62(1) and P62(2), P63(1) and P63(2), P64(1) and P64(2), and P65(1) and P65(2) are all structurally symmetrical parts of the support 122.

[0072] Therefore, the above-mentioned methods 1 to 3 can be applied, and abnormality diagnosis of the support 122 using the SI-F method becomes possible by comparing the reference response surface and diagnostic response surface between different sensors, or by comparing the reference response surface and diagnostic response surface calculated from the correlation distribution between different sensors.

[0073] 3 to 6, a specific example of performing an abnormality diagnosis using the SI-F method on information boards of various shapes as road structures in use has been described. In contrast to this, the following will specifically describe a specific example of performing an abnormality diagnosis using the SI-F method on bridges of various shapes as road structures in use, using FIGS. 7 to 9.

[0074] 7 is an explanatory diagram showing a specific arrangement of sensors 10 as a fifth arrangement example when a bridge is used as a road structure in service in the first embodiment of the present disclosure. Bridge 130 shown in FIG. 7 has a support structure including main girders 131 and a deck slab 132.

[0075] 7 has structurally symmetrical parts in the main girder 131 that supports the deck slab 132. In the main girder 131, the center, ends, and quartiles correspond to the poles of the vibration resonance frequency or the parts where stress is concentrated.

[0076] FIG. 7 illustrates an example in which sensors 10 are arranged at five locations: P71, P72(1), P72(2), P73(1), and P73(2).

[0077] In the example of arrangement shown in FIG. 7, P72(1) and P72(2), and P73(1) and P73(2) are structurally symmetrical parts of the main girder 131.

[0078] Therefore, the above-mentioned methods 1 to 3 can be applied, and in addition to comparing the reference response surface and diagnostic response surface calculated using the same sensor at each individual sensor position, it is also possible to diagnose abnormalities in the main girder using the SI-F method by comparing the reference response surface and diagnostic response surface between different sensors, or by comparing the reference response surface and diagnostic response surface calculated from the correlation distribution between different sensors.

[0079] 8 is an explanatory diagram showing a specific arrangement of sensors 10 as a sixth arrangement example when a bridge is used as a road structure in service in the first embodiment of the present disclosure. Bridge 130 shown in FIG. 8 has a support structure including a main girder 131(1) provided in the center, main girders 131(2) and 131(2) provided on both sides of the main girder 131(1), and a deck slab 132.

[0080] FIG. 8 shows the deck 132 and three main girders 131(1) to 131(3) of the bridge 130 as viewed from below.

[0081] 8, there are structurally symmetrical portions in the main girders 131(1) to 131(3). In each of the main girders 131(1) to 131(3), the center, end, and quartiles correspond to the poles of the vibration resonance frequency or the portions where stress is concentrated.

[0082] Figure 8 shows an example in which sensors 10 are placed at five locations, P81, P82(1), P82(2), P83(1), and P83(2), on main girder 131(1), and sensors 10 are placed at two locations, P84(1) and P84(2), on main girder 131(2) and 131(3).

[0083] In the example arrangement shown in Figure 8, P82(1) and P82(2), and P83(1) and P83(2) are structurally symmetrical parts of the main girder 131(1). Furthermore, P84(1) and P84(2) are structurally symmetrical parts with the main girder 131(1) as the center.

[0084] Therefore, the above-mentioned methods 1 to 3 can be applied, and in addition to comparing the reference response surface and diagnostic response surface calculated using the same sensor at each individual sensor position, it is also possible to diagnose abnormalities in the main girder using the SI-F method by comparing the reference response surface and diagnostic response surface between different sensors, or by comparing the reference response surface and diagnostic response surface calculated from the correlation distribution between different sensors.

[0085] The physical quantities measured by the sensors depend on the environmental temperature, and therefore the reference response surface and the diagnostic response surface calculated based on the measurement results of the physical quantities are also affected by the environmental temperature.

[0086] Therefore, in order to prevent the deterioration of the accuracy of abnormality diagnosis due to the environmental temperature, it is conceivable to measure the environmental temperature at the time when the physical quantity is measured as temperature information, store data correlating the physical quantity and the temperature information as measurement information in a memory unit, and take the environmental temperature into consideration when calculating the response surface.

[0087] Specifically, when collecting data, the index value data collector 21 acquires temperature information as well as physical quantities, and stores data associating the physical quantities with the temperature information in the storage unit as measurement information.

[0088] Furthermore, when calculating the reference response surface, the response surface calculation unit 22 can calculate a reference response surface for each temperature for each environmental temperature by referring to the temperature information included in the measurement information.

[0089] In addition, when the diagnostic response surface is calculated by the response surface calculation unit 22 during monitoring, the abnormality determination processing unit 23 refers to the temperature information included in the measurement information and identifies the environmental temperature corresponding to the calculated diagnostic response surface as the monitoring temperature.

[0090] Furthermore, the abnormality determination processor 23 extracts, from the temperature-specific reference response surfaces stored in the memory unit, the one calculated at the environmental temperature closest to the monitoring temperature as the diagnostic reference response surface.The abnormality determination processor 23 then performs an F-test to test for statistical equivalence using two response surfaces, the diagnostic reference response surface and the diagnostic response surface at the same environmental temperature.As a result, it is possible to suppress the influence of environmental temperature and prevent deterioration in the accuracy of abnormality diagnosis.

[0091] When the response surface calculation unit 22 calculates the reference response surface for each temperature, it is also possible to adopt the following steps 1 to 3. Step 1: The index value data collecting unit 21 sets in advance the range of the environmental temperature when the sensor 10 measures the physical quantity as a plurality of environmental temperature ranges equally divided at predetermined temperature intervals.

[0092] Step 2: When collecting data on physical quantities, the index value data collection unit 21 refers to the temperature information, classifies the physical quantities associated with the temperature information as data for the corresponding environmental temperature range among multiple environmental temperature ranges, and stores the data in the memory unit.

[0093] Step 3: When calculating the reference response surface under normal conditions, the response surface calculation unit 22 calculates a temperature-specific reference response surface for each environmental temperature from the physical quantities classified into multiple environmental temperature ranges and stored in the storage unit.

[0094] In this way, by classifying the collected physical quantities into a plurality of environmental temperature ranges equally divided at predetermined temperature intervals, it is possible to efficiently calculate the reference response surface for each temperature.

[0095] 9 is an explanatory diagram showing a specific arrangement of sensors 10 as a seventh arrangement example when a bridge is used as a road structure in service in the first embodiment of the present disclosure. Bridge 140 shown in FIG. 9 has a support structure including main girders 141, deck slabs 142, and piers 143 provided at the centers of main girders 141 in the longitudinal direction.

[0096] 9 has structurally symmetrical portions in the main girder 141 that supports the deck slab 142. In the left and right parts of the pier 143, the center and end portions correspond to the poles of the vibration resonance frequency or the portions where stress is concentrated.

[0097] FIG. 9 illustrates an example in which sensors 10 are arranged at six locations: P91(1) to P91(4), P92(1), and P92(2).

[0098] In the example of arrangement shown in Figure 9, P91(1) and P91(2), P91(3) and P91(4), and 92(1) and 92(2) are all structurally symmetrical parts with pier 143 at the center. Also, P91(1) and P91(3) are structurally symmetrical parts with P92(1) at the center. Furthermore, P91(2) and P91(4) are structurally symmetrical parts with P92(2) at the center.

[0099] Therefore, the above-mentioned methods 1 to 3 can be applied, and in addition to comparing the reference response surface and diagnostic response surface calculated using the same sensor at each individual sensor position, it is also possible to diagnose abnormalities in the main girders using the SI-F method by comparing the reference response surface and diagnostic response surface between different sensors, or by comparing the reference response surface and diagnostic response surface calculated from the correlation distribution between different sensors.In particular, for P91(1) to P91(4), the sensor positions for calculating the reference response surface and the sensor positions for calculating the diagnostic response surface can be selected as two combinations from four different positions.

[0100] Note that Figures 3 to 9 show first to seventh arrangement examples for multiple sensors 10, but the more sensors 10 installed, the more detailed abnormality diagnosis becomes possible, and the sensors 10 will be arranged appropriately depending on the purpose and application.

[0101] In addition, in order to improve the accuracy of abnormality diagnosis, it is recommended to install multiple sensors 10 so that they are evenly spaced apart and include areas where stress in the support structure is concentrated or positions corresponding to the poles of the resonant frequency of the support structure.

[0102] As described above, according to the first embodiment, physical quantities that serve as indicators for diagnosing deterioration of a support structure are collected as measurement information and analyzed using the SI-F method, thereby making it possible to quantitatively evaluate abnormalities such as deformation of the support structure and loose bolts. By retrofitting sensors to existing road structures that are in use, it is possible to easily detect abnormalities in the support structure, and a low-cost, real-time detection method for detecting abnormalities in support structures can be realized to prevent serious accidents.

[0103] In the first embodiment, the comparison between the reference response surface calculated from the correlation distribution between different sensors and the diagnostic response surface has been described. However, when a sensor having multiple measurement axes is used, the correlation distribution between different axes may be calculated and used instead of the correlation distribution between different sensors.

[0104] Embodiment 2 In the first embodiment, a response surface was calculated using measurement information from past normal times and measurement information from the current monitoring time, and an abnormality in the support structure was detected from the correlation between them. In contrast, in the second embodiment, when a sensor with multiple measurement axes is used, there is no need to collect measurement data from normal times in advance, and an abnormality in the support structure is detected.

[0105] Sensor 10 in the second embodiment is configured as a sensor having two or more measurement axes. For example, an acceleration sensor capable of measuring along three axes, X, Y, and Z, can be used as sensor 10. Below, a specific description will be given of a case in which a reference response surface is calculated using two of the three-axis measurement results, and a diagnostic response surface is calculated using one of the two axes used to calculate the reference response surface.

[0106] FIG. 10 is a flowchart illustrating a series of processes executed by the sensor 10 having multiple measurement axes and the controller 20 as a method for detecting an abnormality in a support structure according to the second embodiment of the present disclosure.

[0107] The components of the controller 20 according to the second embodiment are the same as those in FIG. 1 of the first embodiment, and the functions of the components of the controller 20 will be described with reference to a series of processes shown in the flowchart of FIG.

[0108] Step S1001 corresponds to a measurement step performed by the sensor 10 having multiple measurement axes. Steps S1002 to S1005 correspond to abnormality diagnosis steps performed by each component of the controller 20, and are roughly divided into four steps, Step 1 to Step 4.

[0109] In the measurement step of step S1001, the sensor 10 arranged on the support structure measures a physical quantity that is an index value for detecting an abnormality in the support structure. A specific example of the sensor 10 is an acceleration sensor that can measure in three axes. By being installed on the support structure, the acceleration sensor measures acceleration data corresponding to vibrations of the support structure as physical quantities in three axes, the X-axis, Y-axis, and Z-axis.

[0110] Next, in the first step of the abnormality diagnosis step of step S1002, the index value data collection unit 21 in the controller 20 continuously or periodically acquires three-axis physical quantities as measurement results from the sensor 10 in sequence, and stores the measurement information as time-series information for each axis in a memory unit (not shown).

[0111] Next, in the second step of the abnormality diagnosis step of step S1003, the response surface calculation unit 22 calculates a reference response surface by applying the response surface methodology to the measurement information collected from the sensor 10 having multiple measurement axes.

[0112] Specifically, in the second embodiment, the response surface calculation unit 22 calculates a resultant vector using measurement information on two of the three axes, and calculates a reference response surface using the calculated resultant vector.

[0113] For example, the response surface calculation unit 22 calculates a resultant vector on the XY plane using two of the three axes, the X axis, the Y axis, and the Z axis. Furthermore, the response surface calculation unit 22 calculates a reference response surface using the resultant vector.

[0114] Normal time Next, in the third step of the abnormality diagnosis step of step S1004, the response surface calculation unit 22 calculates a diagnostic response surface by applying response surface methodology to the measurement information collected from the sensor 10 having multiple measurement axes.

[0115] Specifically, in the second embodiment, the response surface calculation unit 22 calculates the diagnostic response surface using measurement information on one of the two axes used to calculate the reference response surface.

[0116] For example, when the reference response surface is calculated based on the composite vector of the XY plane, the response surface calculation unit 22 calculates the diagnostic response surface using the measurement information on the X axis or the Y axis.

[0117] Next, in the fourth step of the abnormality diagnosis step of step S1005, the abnormality judgment processing unit 23 tests the statistical equivalence of the two response surfaces, the reference response surface calculated in step S1003 and the diagnostic response surface calculated in step S1004, using an F-test.

[0118] Furthermore, if the equivalence between the two response surfaces is adopted as the test result, the abnormality determination processor 23 determines that the support structure is normal. On the other hand, if the equivalence between the two response surfaces is rejected as the test result, the abnormality determination processor 23 determines that the support structure is abnormal.

[0119] Here, the support structure anomaly detection method according to the second embodiment has a technical feature in that an anomaly diagnosis is performed by the SI-F method through the processing in steps S1003 to S1005 described above.

[0120] In other words, in the method according to the second embodiment, the equivalence between the reference regression equation generated from the composite vector of two axes using the response surface methodology and the diagnostic regression equation generated from the measurement information of one of the two axes is tested by using an F-test, which is a statistical test. Here, in the abnormality diagnosis by the SI-F method executed by the controller 20, the features unique to the second embodiment will be further explained below.

[0121] The response surface calculation unit 22 in the second embodiment can calculate the reference response surface and the diagnostic response surface based on measurement information collected at the same time or based on measurement information collected at different times.

[0122] Furthermore, the abnormality determination processing unit 23 can perform abnormality diagnosis using the SI-F method by comparing, for example, a reference response surface calculated using a composite vector on the XY plane with either or both of a diagnostic response surface calculated using measurement information on the X axis and a diagnostic response surface calculated using measurement information on the Y axis.

[0123] Furthermore, the abnormality determination processing unit 23 can perform abnormality diagnosis using the SI-F method by comparing the diagnostic response surface calculated using the measurement information on the X-axis with either or both of two reference response surfaces calculated using the resultant vector on the XY plane and the resultant vector on the XZ plane.

[0124] As described above, according to the second embodiment, by using a sensor with multiple measurement axes, it is possible to obtain the same effects as those of the first embodiment. Furthermore, according to the second embodiment, it is not necessary to calculate the reference response surface in advance under normal conditions, and it is possible to obtain the further effect of being able to perform an abnormality diagnosis for each of the multiple axes.

[0125] In this second embodiment, as in the first embodiment, in order to prevent deterioration of the accuracy of abnormality diagnosis due to the environmental temperature, the environmental temperature at the time when the physical quantity is measured may also be measured as temperature information, data correlating the physical quantity with the temperature information may be stored in a storage unit as measurement information, and the environmental temperature may also be taken into consideration when calculating the response surface.

[0126] However, in the second embodiment, when the reference response surface and the diagnostic response surface are calculated based on measurement information collected at the same time, the environmental temperature is the same, so there is no need to take temperature information into consideration.

[0127] Embodiment 3 In this third embodiment, as in the previous second embodiment, a sensor with multiple measurement axes is used to eliminate the need to collect normal measurement data in advance, and an abnormality in the support structure is detected, but a method different from the previous second embodiment will be described.

[0128] Sensor 10 in this third embodiment is configured as a sensor having two or more measurement axes, similar to the second embodiment. For example, an acceleration sensor capable of measuring three axes, X, Y, and Z, can be used as sensor 10. Below, a specific description will be given of a case in which a reference response surface is calculated using one axis (first axis) of the three-axis measurement results, and a diagnostic response surface is calculated using the other axis (second axis).

[0129] FIG. 11 is a flowchart illustrating a series of processes executed by the sensor 10 having multiple measurement axes and the controller 20 as a method for detecting an abnormality in a support structure according to the third embodiment of the present disclosure.

[0130] The components of the controller 20 according to the third embodiment are the same as those in FIG. 1 of the first embodiment, and the functions of the components of the controller 20 will be described with reference to a series of processes shown in the flowchart of FIG.

[0131] Step S1101 corresponds to a measurement step executed by the sensor 10 having multiple measurement axes. Steps S1102 to S1105 correspond to abnormality diagnosis steps executed by each component of the controller 20, and are roughly divided into four steps, Step 1 to Step 4.

[0132] In the measurement step of step S1101, the sensor 10 arranged on the support structure measures a physical quantity that is an index value for detecting an abnormality in the support structure. A specific example of the sensor 10 is an acceleration sensor that can measure in three axes. By being installed on the support structure, the acceleration sensor measures acceleration data corresponding to vibrations of the support structure as physical quantities in three axes, the X-axis, Y-axis, and Z-axis.

[0133] Next, in the first step of the abnormality diagnosis step of step S1102, the index value data collection unit 21 in the controller 20 continuously or periodically acquires three-axis physical quantities as measurement results from the sensor 10 in sequence, and stores the measurement information as time-series information for each axis in a memory unit (not shown).

[0134] Next, in the second step of the abnormality diagnosis step of step S1103, the response surface calculation unit 22 calculates a reference response surface by applying the response surface methodology to the measurement information collected from the sensor 10 having multiple measurement axes.

[0135] Specifically, in the third embodiment, the response surface calculation unit 22 calculates a reference response surface using measurement information on one of the three axes.

[0136] For example, the response surface calculation unit 22 calculates the reference response surface using the X axis as the first axis among the three axes of the X axis, Y axis, and Z axis.

[0137] Normal time Next, in the third step of the abnormality diagnosis step of step S1104, the response surface calculation unit 22 calculates a diagnostic response surface by applying response surface methodology to the measurement information collected from the sensor 10 having multiple measurement axes.

[0138] Specifically, in the second embodiment, the response surface calculation unit 22 calculates the diagnostic response surface using measurement information of the second axis, which is different from the first axis used to calculate the reference response surface, among the three axes.

[0139] For example, when the response surface calculation unit 22 calculates the reference response surface based on the measurement information on the X axis, it calculates the diagnostic response surface using the measurement information on the Y axis or the Z axis.

[0140] Next, in the fourth step of the abnormality diagnosis step of step S1105, the abnormality judgment processing unit 23 tests the statistical equivalence of the two response surfaces, the reference response surface calculated in step S1103 and the diagnostic response surface calculated in step S1104, using an F-test.

[0141] Furthermore, if the equivalence between the two response surfaces is adopted as the test result, the abnormality determination processor 23 determines that the support structure is normal. On the other hand, if the equivalence between the two response surfaces is rejected as the test result, the abnormality determination processor 23 determines that the support structure is abnormal.

[0142] Here, the method for detecting an abnormality in a support structure according to the third embodiment has a technical feature in that an abnormality diagnosis is performed by the SI-F method through the processing in steps S1103 to S1105 described above.

[0143] In other words, in the method according to the third embodiment, the equivalence between the reference regression equation generated from the measurement information of the first axis and the diagnostic regression equation generated from the measurement information of the second axis is statistically tested using an F-test using response surface methodology. Here, the following provides a supplementary explanation of the features specific to the third embodiment in the abnormality diagnosis using the SI-F method executed by the controller 20.

[0144] The response surface calculation unit 22 in the third embodiment can calculate a reference response surface and a diagnostic response surface based on measurement information collected at the same time.

[0145] Furthermore, the abnormality determination processing unit 23 can perform abnormality diagnosis using the SI-F method by comparing, for example, a reference response surface calculated using the X-axis with either or both of a diagnostic response surface calculated using measurement information on the Y-axis and a diagnostic response surface calculated using measurement information on the Z-axis.

[0146] Furthermore, the abnormality determination processing unit 23 can perform abnormality diagnosis using the SI-F method by comparing, for example, a diagnostic response surface calculated using measurement information on the X axis with either or both of a reference response surface calculated using measurement information on the Y axis and a reference response surface calculated using measurement information on the Z axis.

[0147] As described above, according to the third embodiment, by using a sensor with multiple measurement axes, it is possible to obtain the same effects as in the first embodiment. Furthermore, according to the third embodiment, as in the second embodiment, it is not necessary to calculate the reference response surface in advance under normal conditions, and it is possible to obtain the further effect of being able to perform an abnormality diagnosis for each of the multiple axes.

[0148] In addition, in this third embodiment, the measurement values ​​of each axis of a sensor having multiple measurement axes are compared, but abnormality diagnosis can also be performed by comparing the measurement values ​​of sensors arranged in structurally symmetrical positions.

[0149] In this third embodiment, the reference response surface and the diagnostic response surface are calculated based on measurement information for two different axes collected at the same time, so the environmental temperature is always the same and there is no need to consider temperature information.

[0150] In addition, in the above-described first to third embodiments, a case has been described in which a response surface is generated based on time-series information of physical quantities measured by the sensor 10. However, the response surface calculation unit 22 according to the present disclosure can calculate natural frequencies from the physical quantities and generate a response surface based on time-series information of the natural frequencies, instead of using the physical quantities themselves, and can obtain the same effect as when the physical quantities themselves are used.

[0151] Specifically, the response surface calculation unit 22 calculates a frequency spectrum from time-series information of the measured physical quantity using an orthogonal transformation such as a short-time Fourier transform, and plots the peak frequencies in time series, thereby calculating time-series information of the natural frequency. [Explanation of symbols]

[0152] 10 Sensor, 20 Controller, 21 Index value data collection unit, 22 Response surface calculation unit, 23 Abnormality judgment processing unit, 100 F-type information board, 101 Information board, 102 Support, 110 I-type information board, 111 Information board, 112 Support, 120 Gate-type information board, 121 Information board, 122 Support, 122V Vertical support, 122H Horizontal support, 130 Bridge, 131 Main girder, 132 Deck slab, 140 Bridge, 141 Main girder, 142 Deck slab, 143 Pier.

Claims

1. a measurement step of measuring a physical quantity that serves as an index value for detecting an abnormality in a support structure included in a road structure in service using a sensor having one or more measurement axes arranged on the support structure; an abnormality diagnosis step of detecting an abnormality due to deterioration of the support structure based on the measurement results by the sensor; A method for detecting an abnormality in a support structure, comprising: The abnormality diagnosis step includes: a first step of storing the physical quantity acquired continuously or periodically as the measurement result from the sensor in a storage unit as measurement information; a second step of calculating a reference response surface by applying response surface methodology to the results of one-axis or multiple-axis calculations of the measurement information when the support structure is normal; a third step of calculating a diagnostic response surface by applying the response surface methodology to the results of one-axis or multiple-axis calculations of the measurement information during monitoring; a fourth step of detecting an anomaly in the support structure by testing the statistical equivalence of the two response surfaces, the reference response surface and the diagnostic response surface, by an F-test, determining that the support structure is normal if equivalence is adopted as the test result, and determining that there is an anomaly in the support structure if the equivalence is rejected; A method for detecting an abnormality in a support structure, comprising:

2. the measuring step measures the physical quantity and further measures temperature information that is an environmental temperature of the support structure; The abnormality diagnosis step includes: In the first step, the temperature information is acquired together with the physical quantity, and data in which the physical quantity and the temperature information are associated with each other is stored in the storage unit as the measurement information; In the second step, when calculating the reference response surface, a reference response surface for each temperature is calculated for each environmental temperature by referring to the temperature information included in the measurement information, and the reference response surface for each temperature is stored in the storage unit; In the third step, by referring to the temperature information included in the measurement information, an environmental temperature corresponding to the calculated diagnostic response surface is identified as a monitoring temperature; In the fourth step, the reference response surface calculated at the environmental temperature closest to the monitoring temperature is extracted as a diagnostic reference response surface from among the reference response surfaces for each temperature stored in the storage unit, and the two response surfaces, the diagnostic reference response surface and the diagnostic response surface, are tested for statistical equivalence by the F-test, thereby detecting an anomaly in the support structure while taking the environmental temperature into consideration. The method for detecting an abnormality in a support structure according to claim 1 .

3. In the abnormality diagnosis step, when calculating the reference response surface for each temperature in the second step, a plurality of environmental temperature ranges are set in advance by equally dividing the range of environmental temperatures at predetermined temperature intervals when the physical quantity is measured by the sensor, and the temperature information included in the measurement information is referenced to classify the physical quantity associated with the temperature information as data for a corresponding environmental temperature range among the plurality of environmental temperature ranges, and the reference response surface for each temperature is calculated for each environmental temperature from the physical quantities classified into each of the plurality of environmental temperature ranges. The method for detecting an abnormality in a support structure according to claim 2.

4. The measurement information stored in the storage unit is configured as time-series information. The method for detecting an abnormality in a support structure according to any one of claims 1 to 3.

5. The abnormality diagnosis step includes: In the second step, the reference response surface is calculated by autoregression based on a correlation distribution of the time series information regarding the measurement information under normal conditions or the time series information regarding the measurement information between multiple axes or different sensors under normal conditions; In the third step, the diagnostic response surface is calculated by autoregression based on the correlation distribution of the time series information regarding the measurement information during the monitoring, or the time series information regarding the measurement information between multiple axes or different sensors during the monitoring. The method for detecting an abnormality in a support structure according to claim 4.

6. The sensor is configured as a plurality of sensors including a first sensor and a second sensor arranged at structurally symmetrical positions on the road structure, The abnormality diagnosis step includes: In the second step, when calculating the reference response surface, a reference response surface is calculated for each sensor in response to measurement information from each of the plurality of sensors; In the third step, when calculating the diagnostic response surface, a diagnostic response surface is calculated for each sensor in response to measurement information from each of the plurality of sensors; In the fourth step, when detecting an anomaly in the support structure using the first sensor and the second sensor, the statistical equivalence is tested by the F-test using a reference response surface calculated corresponding to the first sensor and a diagnostic response surface calculated corresponding to the second sensor, and using a reference response surface calculated corresponding to the second sensor and a diagnostic response surface calculated corresponding to the first sensor. The method for detecting an abnormality in a support structure according to any one of claims 1 to 3.

7. The sensor is installed at a location where stress is concentrated on the support structure or at a location corresponding to a pole of the resonant frequency of the support structure. The method for detecting an abnormality in a support structure according to any one of claims 1 to 3.

8. The sensor is configured as a plurality of sensors, and is installed so as to include a portion of the support structure where stress is concentrated or a position corresponding to a pole of the resonance frequency of the support structure, and is installed at equal intervals from one another. The method for detecting an abnormality in a support structure according to any one of claims 1 to 3.

9. a measuring step of measuring a physical quantity that serves as an index value for detecting an abnormality in a support structure included in a road structure in service by a sensor arranged on the support structure; an abnormality diagnosis step of detecting an abnormality due to deterioration of the support structure based on the measurement results by the sensor; A method for detecting an abnormality in a support structure, comprising: The abnormality diagnosis step includes: a first step of storing the physical quantities acquired continuously or periodically as the measurement results from the sensor having a plurality of measurement axes in a storage unit as measurement information; a second step of calculating a resultant vector from the measurement information included in the plurality of measurement axes, and calculating a reference response surface by applying response surface methodology to a calculation result of the resultant vector; a third step of calculating a diagnostic response surface by applying the response surface methodology to a calculation result for measurement information of any one of the plurality of measurement axes; a fourth step of detecting an anomaly in the support structure by testing the statistical equivalence of the two response surfaces, the reference response surface and the diagnostic response surface, by an F-test, determining that the support structure is normal if equivalence is adopted as the test result, and determining that there is an anomaly in the support structure if the equivalence is rejected; A method for detecting an abnormality in a support structure, comprising:

10. the measuring step measures the physical quantity and further measures temperature information that is an environmental temperature of the support structure; The abnormality diagnosis step includes: In the first step, the temperature information is acquired together with the physical quantity, and data in which the physical quantity and the temperature information are associated with each other is stored in the storage unit as the measurement information; In the second step, when calculating the reference response surface, a reference response surface for each temperature is calculated for each environmental temperature by referring to the temperature information included in the measurement information, and the reference response surface for each temperature is stored in the storage unit; In the third step, by referring to the temperature information included in the measurement information, an environmental temperature corresponding to the calculated diagnostic response surface is identified as a monitoring temperature; In the fourth step, the reference response surface calculated at the environmental temperature closest to the monitoring temperature is extracted as a diagnostic reference response surface from among the reference response surfaces for each temperature stored in the storage unit, and the two response surfaces, the diagnostic reference response surface and the diagnostic response surface, are tested for statistical equivalence by the F-test, thereby detecting an anomaly in the support structure while taking the environmental temperature into consideration. The method for detecting an abnormality in a support structure according to claim 9.

11. a measuring step of measuring a physical quantity that serves as an index value for detecting an abnormality in a support structure included in a road structure in service by a sensor arranged on the support structure; an abnormality diagnosis step of detecting an abnormality due to deterioration of the support structure based on the measurement results by the sensor; A method for detecting an abnormality in a support structure, comprising: The abnormality diagnosis step includes: a first step of storing the physical quantities acquired continuously or periodically as the measurement results from the sensor having a plurality of measurement axes in a storage unit as measurement information; a second step of calculating a reference response surface by applying response surface methodology to a calculation processing result for measurement information of a first axis included in the plurality of measurement axes; a third step of calculating a diagnostic response surface by applying the response surface methodology to a calculation result for measurement information of a second axis different from the first axis included in the plurality of measurement axes; a fourth step of detecting an anomaly in the support structure by testing the statistical equivalence of the two response surfaces, the reference response surface and the diagnostic response surface, by an F-test, determining that the support structure is normal if equivalence is adopted as the test result, and determining that there is an anomaly in the support structure if the equivalence is rejected; A method for detecting an abnormality in a support structure, comprising:

12. The sensor is an acceleration sensor. The method for detecting an abnormality in a support structure according to any one of claims 1 to 3 and 9 to 11.

13. In the abnormality diagnosis step, instead of using the physical quantity as the measurement information in the first step, a natural frequency calculated from the physical quantity is stored in the storage unit as the measurement information. The method for detecting an abnormality in a support structure according to any one of claims 1 to 3 and 9 to 11.

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