JP5535837B2 - Processing method of measurement data in crack deformation monitoring of structures - Google Patents

Description

  The present invention provides a method for setting an appropriate measurement frequency when measuring equipment is installed on a structure, a track, or the like, and the influence of fluctuations and errors caused by temperature changes on the accumulated data. In particular, the present invention relates to a method for processing measurement data in monitoring deformation of a structure for cracks.

  Conventionally, there are cases in which the state of a structure, a track, or the like is monitored in the case where there is a concern that the structure is deformed such as a crack and is advanced, or in the case where close construction is performed. When the automatic measurement technique is used for such monitoring, the data itself can be taken at intervals of 1 to several minutes, and thus a large amount of measurement data is accumulated.

  Conventionally, a measurement technique for monitoring the state of a structure, a track, and the like and a method for automatically measuring this have already been developed. For example, strain measurement using an optical fiber and crack measurement using a conductive paint have been proposed.

  In addition, a measurement system for the tunnel lining behavior at the time of an earthquake has been proposed (see Patent Document 1 below), in which data on the lining behavior of the tunnel at the time of an earthquake is grasped in real time.

  On the other hand, at present, a technique for processing a huge amount of data obtained by automatic measurement has not been sufficiently established.

JP 2009-300343 A

  However, measurement data such as those described above are affected by fluctuations and errors due to temperature changes and the like, but techniques for appropriately processing this have not been proposed, and in many cases an average value of 1 to several hours is obtained. At present, the vast amount of measurement data cannot be effectively utilized, such as looking at trends.

  In particular, in the monitoring of cracks in structures, control reference values are generally set and compared with measured data. However, the measured data itself fluctuates due to temperature changes, etc. Even if the control standard value is exceeded, it is impossible to instantly determine whether it is due to the progress of deformation, and the response may be delayed.

  In view of the above situation, an object of the present invention is to provide a method for processing measurement data in monitoring the deformation of a crack in a structure, which can accurately process enormous data obtained by automatic measurement.

In order to achieve the above object, the present invention provides
[1] lowpass filter is regarded as a time history of the waveform changes over time data for crack width of structure creation, there the processing method of the measurement data in the Deformation monitoring cracks in structure to eliminate variations due to temperature changes The low-pass filter is a two-day or one-week low-pass filter, and by applying the low-pass filter, a daily change in temperature in the measurement data is removed .

  ADVANTAGE OF THE INVENTION According to this invention, in the deformation | transformation monitoring of the crack of a structure, the enormous data obtained by automatic measurement can be processed accurately.

It is a drawing substitute photograph which shows the condition of the automatic measurement using the wireless sensor which concerns on this invention. It is a figure which shows the example (near a wellhead) of the automatic measurement result of the crack width change which concerns on this invention. It is a figure which shows the result of the Fourier-transform which concerns on this invention. It is a figure which shows the thinning-out of the measurement data based on this invention. It is a figure which shows the Fourier spectrum at the time of changing the sampling interval which concerns on this invention. 6 is a data processing flowchart in consideration of the influence of fluctuation error according to the present invention. It is a figure which shows the example (the 1) of the temperature change process which concerns on this invention. It is a figure which shows the example (the 2) of the temperature change process which concerns on this invention.

Lowpass filter considers aging data Crack Width of structure creation of the present invention the time history of the waveform, a processing method of the measurement data in the Deformation monitoring cracks in structure to eliminate variations due to temperature changes The low-pass filter is a two-day or one-week low-pass filter. By applying this low-pass filter, the daily change in temperature in the measurement data is removed.

  Hereinafter, embodiments of the present invention will be described in detail.

  When automatic measurement technology is used for monitoring crack deformation in a tunnel, a large amount of measurement data is accumulated, but it cannot be fully utilized due to fluctuations and errors caused by temperature changes and the like. Therefore, a practical data processing method that considers fluctuations and errors caused by temperature changes and the like was examined.

  First, automatic measurement in tunnel crack deformation monitoring will be described.

  FIG. 1 is a drawing-substituting photograph showing the state of automatic measurement using a wireless sensor according to the present invention.

  FIG. 1A shows a state in which a strain-type crack width meter 1 and a slave unit 2 are attached to a crack in a railway tunnel, and FIG. 1B shows a master unit 3 disposed at a wellhead of the railway tunnel. The wiring 4 connected to the parent device 3 and connected to a personal computer (not shown) is shown.

  In this way, the strain-type crack width meter 1 is attached to a crack in the railway tunnel 5 so that automatically measured data is transmitted to the wellhead using radio.

  FIG. 2 is a diagram showing an example (near the wellhead) of an automatic measurement result of crack width change according to the present invention.

  In this figure, a indicates the crack width and b indicates the temperature.

  As shown in FIG. 2, the automatic measurement data includes fluctuations and errors due to temperature changes. Therefore, in order to effectively use such data, it is necessary to select an appropriate sampling interval and perform an appropriate process for removing the influence of the temperature change.

  Next, frequency analysis of measurement data will be described.

  A summary of the sample data is shown in Table 1.

FIG. 3 is a diagram showing the result of Fourier transform according to the present invention, FIG. 3 (a) is a characteristic diagram of a crack width meter (1) (ID7, near a wellhead, 10 minutes interval), and FIG. 3 (b) is a crack. Characteristic diagram of width meter (2) (ID5, 15 m from wellhead, 1 minute interval), FIG. 3 (c) shows characteristic diagram of crack width meter (3) (ID6, 87 m from wellhead, 1 minute interval).

  As shown in FIG. 3, the sample data was regarded as a time history waveform, and frequency analysis was performed by Fourier transform.

  As a result, it was found that there was a peak at 24 hours, and that the closer to the wellhead, the more easily affected by the daily change in temperature, and the peak at 24 hours tended to be large.

  Next, the measurement interval of crack width change was examined.

  In the sample data shown in Table 1 and FIG. 3, the measurement interval is set to 1 minute or 10 minutes. However, data obtained by thinning sampling as shown in FIG. 4 was created, and frequency analysis was similarly performed by Fourier transform.

  FIG. 5 is a diagram showing a Fourier spectrum when the sampling interval according to the present invention is changed.

  FIG. 5 (a) is a diagram showing a comparison between a 10 minute interval and a 1 minute interval, FIG. 5 (b) is a diagram showing a comparison between a 1 hour interval and a 1 minute interval, and FIG. 5 (c) is a 4 hour interval. FIG. 5D is a diagram showing a comparison with a 1 minute interval, and FIG. 5D is a diagram showing a comparison with a 12 hour interval and a 1 minute interval.

  From these figures, the spectrum of 10-minute or 1-hour interval data is almost the same as that of 1-minute interval data, but there is a difference in 4-hour or 12-hour interval data.

  From the above, it is considered reasonable to set the sampling interval of measurement data to about one hour.

  Next, we examined how to deal with daily temperature changes.

FIG. 6 is a data processing flowchart in consideration of the influence of fluctuation errors according to the present invention. To eliminate the effects of daily temperature changes,
(1) Fourier transform the data of the crack width over time (step S1),
(2) Apply a filter in the frequency domain (for simplicity, use a rectangular filter) (step S2),
(3) A process of returning to time history data by inverse Fourier transform (step S3) was performed.

  FIG. 7 is a diagram showing an example (part 1) of temperature change processing according to the present invention. FIG. 7 (a) shows a measurement data obtained by a crack width meter (1) (ID7) with a low pass filter for two days or one week. FIG. 7B is a diagram showing an example of processing by applying a low-pass filter for two days or one week by a crack width meter (2) (ID5).

  As apparent from FIG. 7, when the low-pass filter was applied for two days or one week, the influence due to the daily change in temperature could be eliminated.

  FIG. 8 is a diagram showing an example (part 2) of the temperature change processing according to the present invention, and is a diagram excerpting 2000 data composed of 6000 to 8000 data in FIG. 7A.

  As is apparent from FIG. 8, the raw data a, the data b obtained by applying a low pass filter for two days, and the data c obtained by applying a low pass filter for one week are clearly shown. It can be seen that it has been removed.

  As described above, according to the present invention, automatic measurement is performed by determining an appropriate sampling interval, and further, by removing the influence of daily temperature variation from the data, a large amount of automatic measurement data is accurately processed and utilized. can do.

  In the above-described embodiment, the measurement data processing method for monitoring the crack deformation of the tunnel is described. However, this processing method can be widely used as a measurement data processing method for structures, tracks, and the like.

  Further, the present invention is not limited to the above-described embodiments, and various modifications can be made based on the spirit of the present invention, and these are not excluded from the scope of the present invention.

  The processing method of measurement data in the monitoring of deformation of a structure according to the present invention is a processing method of measurement data in monitoring of deformation of a structure that can accurately process a huge amount of data obtained by automatic measurement. Is available as

1 Strain-type crack width meter 2 Slave unit 3 Master unit 4 Wiring 5 Railway tunnel

Claims (1)

A method for processing measurement data in the crack monitoring of a structure to remove a variation caused by a temperature change by considering a time-dependent change data of a crack width of a structure as a waveform of a time history and applying a low pass filter , The low-pass filter is a two-day or one-week low-pass filter, and the diurnal change in temperature in the measurement data is removed by applying the low-pass filter. How to process the data.