Tunnel working face monitoring method

Abstract

To provide a tunnel working face monitoring method capable of achieving mountain tunnel construction having excellent workability, economical efficiency and rationality through the tunnel working face monitoring method with improved measurement accuracy.SOLUTION: A tunnel working face monitoring method includes: in a process of continuously repeating a series of measurement work of scanning a tunnel working face 1 by a 3D laser scanner 2 to collect three-dimensional point group data, transferring the three-dimensional point group data, and converting it into mesh data formed by dividing the tunnel working face 1 into a plurality of meshes, adopting, for each of the series of measurement work, a subsequent measurement point in which a plane distance from an initial measurement point in the three-dimensional point group data is within a preset allowable range and that is closest to the initial measurement point; calculating a difference between extrusion amounts of the initial measurement point and the subsequent measurement point; and foreseeing rock falls, falling and collapse of the working face by excluding initial measurement points whose subsequent measurement points are not located within the preset allowable range, from a calculation object.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a tunnel face monitoring method, and more particularly to a tunnel face monitoring method that constantly monitors the tunnel face in order to predict skin collapse, collapse, or collapse during construction of a mountain tunnel, and is capable of grasping the condition of the tunnel face in real time. [Background technology]

[0002] In the construction of mountain tunnels, many tasks require workers to perform in close proximity to the tunnel face, such as excavating rock with heavy machinery, loading explosives into drilled blast holes, erecting steel supports to support the rock, and driving rock bolts into the rock after spraying concrete. As a result, there are frequent industrial accidents caused by rock falls, collapses, and collapses from the tunnel face (hereinafter referred to as "rock fall accidents"). As an example of the occurrence of the aforementioned skin-fall accidents, 6% of the accidents resulted in death, and 42% resulted in workers being absent from work for one month or more, indicating that the severity of the accidents is high when they occur (Ministry of Health, Labour and Welfare: Guidelines for Measures to Prevent Skin-Fall Accidents at the Face of Mountain Tunnel Construction, January 2018).

[0003] Therefore, the present applicants have recently developed a tunnel face monitoring method that can predict such skin-fall disasters in a timely manner (see Patent Document 1). The technology disclosed in Patent Document 1 constantly monitors the tunnel face to predict skin-fall, collapse, or collapse using a face behavior grasping means that uses a laser rangefinder or the like, and immediately notifies those involved in the tunnel construction of the extrusion behavior of the ground occurring at the tunnel face when a preset management standard value is exceeded, thereby making it possible to grasp the condition of the tunnel face in real time, thereby contributing to the prevention of such skin-fall disasters (see the description of Claim 1, etc.). [Prior art documents] [Patent documents]

[0004] [Patent Document 1] Japanese Patent Application Publication No. 2019-199708 Summary of the Invention [Problem to be solved by the invention]

[0005] As described above, the tunnel face monitoring method according to Patent Document 1 is a very useful technology because it uses a face behavior detection means using a laser rangefinder or the like to constantly monitor the tunnel face to predict skin collapse, collapse, or collapse, and can grasp the condition of the tunnel face in real time.

[0006] However, when a 3D laser scanner is used as the means for grasping the tunnel face behavior, the performance of the 3D laser scanner makes it impossible to aim and irradiate (scan) the same location over time, which results in errors in the amount of extrusion of the tunnel face, and the amount of extrusion that is used when the laser is irradiated (mistakenly) on heavy machinery or equipment in front of the tunnel face.The applicants have recognized this problem and have determined that there is still room for improvement. If the measurement accuracy (measurement processing accuracy) of the 3D laser scanner could be further improved by solving the above problems, it would be possible to grasp the behavior of the face (extrusion amount, etc.) more accurately, and it would clearly be an extremely useful technology, as it would not only be possible to prevent the above-mentioned skin-fall accidents but also to improve the work efficiency of mountain tunnel construction.

[0007] Specifically, for example, with conventional measurement methods using 3D laser scanners, workers would stop or halt work near the tunnel face because they had determined that the face had reached the control reference time even though it had not actually been reached.However, by more accurately understanding the behavior of the face, it is possible to accurately determine that the control reference time has not yet been reached, and to continue the work in question, thereby improving the work efficiency of mountain tunnel construction.Therefore, a tunnel face monitoring method with improved measurement accuracy (measurement processing accuracy) can be used to realize mountain tunnel construction that is easy to work with, economical, and rational.

[0008] The present invention was devised in view of the problems of the background art described above, and its purpose is to provide a tunnel face monitoring method that can realize mountain tunnel construction that is excellent in workability, economy, and rationality through a tunnel face monitoring method with improved measurement accuracy by improving measurement accuracy (measurement processing accuracy) by adding an extra step to the conventional measurement method using a 3D laser scanner. [Means for solving the problem]

[0009] As a means for solving the above-mentioned problems, the tunnel face monitoring method according to the invention described in claim 1 is a tunnel face monitoring method in which a tunnel face during tunnel construction is constantly monitored by a 3D laser scanner and the state of the tunnel face is grasped in real time, In the process of continuously repeating a series of measurement operations, the 3D laser scanner is used to scan the tunnel face to collect 3D point cloud data, the 3D point cloud data is transferred, and the tunnel face is converted into mesh data in which the tunnel face is divided into multiple meshes. For each series of measurement operations, an initial or subsequent measurement point whose planar distance from the initial measurement point in the three-dimensional point cloud data is within a preset tolerance range and which is closest to the initial measurement point is adopted, a difference in extrusion amount between the initial measurement point and the initial or subsequent measurement point is calculated, and the initial measurement point whose initial or subsequent measurement point is not within the preset tolerance range is excluded from the calculation target. The method is characterized in that it predicts the collapse, fall, or destruction of the tunnel face based on the average value of the calculated differences in the extrusion amounts for multiple initial measurement points located within each mesh of the mesh data.

[0010] The tunnel face monitoring method according to the invention described in claim 2 is a tunnel face monitoring method for constantly monitoring a tunnel face during tunnel construction using a 3D laser scanner and grasping the status of the tunnel face in real time, In the process of continuously repeating a series of measurement operations, the 3D laser scanner is used to scan the tunnel face to collect 3D point cloud data, the 3D point cloud data is transferred, and the tunnel face is converted into mesh data in which the tunnel face is divided into multiple meshes. For each series of measurement operations, an initial or subsequent measurement point whose planar distance from the initial measurement point of the three-dimensional point cloud data is within a preset tolerance range and which is closest to the initial measurement point is adopted, a difference in extrusion amount between the initial measurement point and the initial or subsequent measurement point is calculated, the initial measurement point whose initial or subsequent measurement point is not within the preset tolerance range is excluded from the calculation target, and the initial measurement point whose extrusion direction distance from the initial measurement point of the three-dimensional point cloud data is not within the preset tolerance range is also excluded from the calculation target. The method is characterized in that it predicts the collapse, fall, or destruction of the tunnel face based on the average value of the calculated differences in the extrusion amounts for multiple initial measurement points located within each mesh of the mesh data.

[0011] The invention described in claim 3 is characterized in that, in the tunnel face monitoring method described in claim 1 or 2, the predetermined tolerance range from the initial measurement point in the planar distance is set, for example, within a radius of 25 mm from the initial measurement point.

[0012] The invention described in claim 4 is characterized in that, in the tunnel face monitoring method described in claim 2, the predetermined tolerance range from the initial measurement point in the extrusion direction distance is set to a range of approximately ±100 mm from the initial measurement point.

[0013] The invention described in claim 5 is characterized in that, in the tunnel face monitoring method described in any one of claims 1 to 4, measurements of the tunnel face using the 3D laser scanner are performed under measurement conditions of a measurement distance of 30m to 70m, a measurement point interval of 4cm to 10cm, and a measurement range angle of 80° to 90°.

[0014] The invention described in claim 6 is characterized in that, in the tunnel face monitoring method described in any one of claims 1 to 4, measurements of the tunnel face using the 3D laser scanner are performed under measurement conditions of a measurement distance of 30 to 70 m, a measurement point interval of 5 cm, and a measurement range angle of 80°. [Effects of the Invention]

[0015] According to the tunnel face monitoring method of the present invention, in the process of continuously repeating a series of measurement operations, the tunnel face is scanned with the 3D laser scanner to collect three-dimensional point cloud data, the three-dimensional point cloud data is transferred, and the tunnel face is converted into mesh data in which the tunnel face is divided into a plurality of meshes, the series of measurement operations is performed by selecting a subsequent measurement point whose planar distance from the initial measurement point of the three-dimensional point cloud data is within a predetermined tolerance range and is closest to the initial measurement point, calculating the difference in the amount of extrusion between the initial measurement point and the subsequent measurement point, excluding the initial measurement point whose subsequent measurement point is not located within the predetermined tolerance range from the calculation target, and predicting the collapse, fall, or collapse of the tunnel face based on the average value of the calculated differences in the amount of extrusion for the multiple initial measurement points located within each mesh of the mesh data. Therefore, compared to the conventional technology in which the 3D point cloud data is averaged directly within the mesh without excluding all of the data, the measurement accuracy (measurement processing accuracy) of the amount of extrusion of the tunnel face can be greatly improved. Therefore, with the conventional measurement method using a 3D laser scanner, workers would be forced to stop or halt work near the tunnel face, assuming that they had reached the management reference time even though they had not actually done so. However, by more accurately grasping the behavior of the face, it is possible to accurately determine that the management reference time has not yet been reached, and the work in question can be continued, thereby improving the work efficiency of mountain tunnel construction. Therefore, through a tunnel face monitoring method with improved measurement accuracy, it is possible to realize mountain tunnel construction that is excellent in workability, economy, and rationality. [Brief explanation of the drawings]

[0016] [Figure 1] 1 is an explanatory diagram showing a schematic overview of measurement implementation of a tunnel face monitoring method according to the present invention; FIG. [Figure 2] 1 is a flowchart showing an example of a tunnel face monitoring method according to the present invention. [Figure 3] 1 is a schematic diagram for explaining one step of a tunnel face monitoring method according to the present invention. FIG. [Figure 4] 1 is a schematic diagram for explaining one step of a tunnel face monitoring method according to the present invention. FIG. [Figure 5] 1 is a schematic diagram for explaining one step of a tunnel face monitoring method according to the present invention. FIG. [Figure 6] 1 is mesh data illustrating the measurement processing results of the tunnel face monitoring method according to the present invention. [Figure 7] 1 is a table for explaining the basis of parameters (numerical values) employed in the tunnel face monitoring method according to the present invention. [Figure 8] 10 is mesh data illustrating the measurement processing results of a conventional measurement method. [Figure 9] This information data was obtained by scanning the tunnel face with a 3D laser scanner. [Figure 10] This information data was obtained by scanning the tunnel face with a 3D laser scanner. BEST MODE FOR CARRYING OUT THE INVENTION

[0017] Next, an embodiment of a tunnel face monitoring method according to the present invention will be described with reference to the drawings.

[0018] The tunnel face monitoring method according to the present invention is a tunnel face monitoring method in which a tunnel face during tunnel construction is constantly monitored by a 3D laser scanner and the status of the tunnel face is grasped in real time, In the process of continuously repeating a series of measurement operations, the 3D laser scanner is used to scan the tunnel face to collect 3D point cloud data, the 3D point cloud data is transferred, and the tunnel face is converted into mesh data in which the tunnel face is divided into multiple meshes. For each series of measurement operations, an initial or subsequent measurement point whose planar distance from the initial measurement point in the three-dimensional point cloud data is within a preset tolerance range and which is closest to the initial measurement point is adopted, a difference in extrusion amount between the initial measurement point and the initial or subsequent measurement point is calculated, and the initial measurement point whose initial or subsequent measurement point is not within the preset tolerance range is excluded from the calculation target. The method is characterized in that it predicts the collapse, fall, or destruction of the tunnel face based on the average value of the calculated differences in the extrusion amounts for multiple initial measurement points located within each mesh of the mesh data. Incidentally, in this specification, the "planar distance" refers to the shortest distance between a point (initial measurement point) and a point (post-initial measurement point) when the tunnel face is viewed two-dimensionally from the front direction.

[0019] That is, the tunnel face monitoring method of the present invention is based on the basic premise that a series of measurement operations are continuously repeated, in which the tunnel face is scanned with the 3D laser scanner to collect three-dimensional point cloud data, the three-dimensional point cloud data is transferred, and the tunnel face is converted into mesh data in which the tunnel face is divided into a plurality of meshes. While the series of measurement operations are being performed, the following steps (processes) are repeated for each of the series of measurement operations. (First step) A step of adopting a subsequent measurement point whose planar distance from the initial measurement point in the three-dimensional point cloud data is within a preset allowable range and is closest to the initial measurement point. (Second step) A step of calculating a difference in the amount of push-out between the initial measurement point and the measurement point after the initial point. (Third step) A step of excluding the initial measurement points from the calculation targets, where the measurement points after the initial point are not located within a preset tolerance range. (Fourth step) A step of excluding from the calculation the initial measurement point whose extrusion direction distance from the initial measurement point of the three-dimensional point cloud data is not within a preset tolerance range. (Fifth step) A step of finding an average value of the differences in the calculated push-out amounts for the plurality of initial measurement points located within each mesh of the mesh data. (Sixth step) A step of predicting the collapse, fall or collapse of the tunnel face based on the calculated average value.

[0020] Therefore, first, the series of measurement operations that form the basis of the tunnel face monitoring method according to the present invention will be explained, and then each of the steps will be explained. The outline of the tunnel configuration according to this embodiment is as follows, by way of example only. (Outline of tunnel configuration, etc.) The tunnel length (L) is 305m, and the excavation cross-sectional area (tunnel face area) is approximately 102-105m. 2 The excavation width is 15m, the excavation height is 7.2m, and approximately 93% of the total length is covered with a small soil cover of 1.5D or less.

[0021] <Explanation of the series of measurement operations> The series of measurement operations in the tunnel face monitoring method of the present invention is, as shown schematically in Figure 1, the operation of scanning the tunnel face 1 with a 3D laser scanner 2 to collect three-dimensional point cloud data, transferring the three-dimensional point cloud data to a terminal or the like (see Figures 9 and 10), and converting the tunnel face 1 into mesh data in which the tunnel face 1 is divided into multiple meshes.

[0022] Measurement using the 3D laser scanner 2 is preferably performed under the following measurement conditions: a measurement distance (L) of 30 m to 70 m, a measurement point interval (H) of 4 cm to 10 cm, and a measurement range angle (θ) of 80° to 90°. In this example, the condition (goal) was to complete a series (one measurement) of measurements within 5 minutes (scan time within 2.5 minutes), and the measurement conditions were a measurement distance (L) of 30 m to 70 m, a measurement point interval (H) of 5 cm, and a measurement range angle (θ) of 80°. The rationale for adopting these measurement conditions is shown in FIG. 7. A detailed explanation of the rationale is omitted here; however, it was taken into consideration that reducing the measurement point interval (H) from 5 cm to 4 cm nearly doubled the 3D point cloud data (number of acquired points) from approximately 22,000 to approximately 41,000, and also doubled the scan time from 137 seconds to 272 seconds. In FIG. 1, the symbol CL indicates the center line of the tunnel, and the symbol SL indicates the spring line of the tunnel.

[0023] There are various variations of the mesh data, which need not be explained in detail, but in this embodiment, as shown in Figure 6, the data is obtained by dividing the tunnel face 1 into multiple (numerous) mesh-like structures (for example, blocks of 0.5m x 0.5m to 1.0m x 1.0m).

[0024] (Initial measurement work) During the series of measurement operations, the initial measurement points (3D point cloud data) obtained by the first measurement are saved as initial value data that will serve as a reference for comparison with subsequent measurement points taken consecutively. Specifically, as shown in Figure 2, initial value measurements were first performed using the 3D laser scanner 2 (see F1). Next, the 22,000 or so initial measurement points (3D point cloud data) collected by scanning (irradiating) the tunnel face 1 with the 3D laser scanner 2 were divided into a number of blocks (mesh regions), and the average value of the extrusion amount (TD) in the extrusion direction (TD direction) was calculated for each block (see F2). Next, as shown schematically in Figure 3, values ​​outside the allowable range in the TD direction (e.g., ±100 mm (see symbol B) from the average value (block average value)) (see x in Figure 3) were treated as misidentified (see F3) and were not adopted as invalid data (see F0). Next, the valid data (three-dimensional point cloud data) of the TD amount excluding the numerical values ​​determined to be invalid data is averaged for each block (division mesh region) and saved as initial value data (see F4).

[0025] (Initial measurement work) <Explanation about (Step 1)> Next, the 3D laser scanner is used to measure displacement to obtain measurement points after the initial stage in order to carry out the first step (see F5). This measurement process is included in the series of repeated measurement steps mentioned above, so a detailed explanation of it will be omitted. As a result of the measurement operation, a first step is performed in which a subsequent measurement point whose planar distance from the initial measurement point in the three-dimensional point cloud data is within a preset tolerance range and is closest to the initial measurement point is adopted. Specifically, as shown schematically in Figure 4, the initial or subsequent measurement point that is closest to the initial measurement point in terms of planar distance (coordinates) is searched for among the large number (approximately 22,000) of initial or subsequent measurement points (see F6), and it is determined whether or not the closest initial or subsequent measurement point (nearby point) is located within the preset tolerance range (for example, within a radius of 25 mm from the initial measurement point) (see F7).

[0026] <Explanation about (Step 2)> Next, for the initial and subsequent measurement points located within the allowable range, the difference between the extrusion amount (TD) of the initial measurement point and the extrusion amount (TD) of the subsequent measurement point is calculated (see F8).

[0027] <Explanation about (Step 3)> Next, the measurement points after the initial stage that are not located within the tolerance range are not adopted as invalid data (F0). In other words, the initial measurement points that are not located within the preset tolerance range are excluded from the calculation.

[0028] <Explanation about (4th step)> At the same time as step 3, as illustrated in FIG. 5, if the extrusion direction distance from the initial measurement point of the 3D point cloud data is not within a preset tolerance (for example, ±100 mm (see symbol C) from the average value of the extrusion amount (TD) for each block), the initial measurement point after the initial point is also excluded from the calculation (see the X mark in FIG. 5) (see F9). Even without carrying out this fourth step, it is possible to collect measurement results with a sufficiently higher degree of accuracy (precision) than conventionally (the invention described in claim 1).

[0029] <Explanation about (5th step)> Next, the valid data (three-dimensional point cloud data) of the TD amount excluding the numerical values ​​excluded in the fourth step is averaged for each block (divided mesh region) (see F10).

[0030] <Explanation about (Step 6)> While the processes from the first to fifth steps (one rotation is 5 minutes) are continuously performed, the skin dropping, collapse, or collapse of the tunnel face is predicted based on the averaged average value. Specifically, the push-out amount is calculated by subtracting the saved initial value data (see F4) from the average value calculated for each block related to the mesh data, and this is saved and displayed on a terminal or the like as mesh data (see Figure 6) (see F11). Based on this information, it is possible to predict the skin dropping, collapse, or collapse of the tunnel face.

[0031] Incidentally, by carrying out the above-mentioned measurement processing, it is possible to measure the displacement of the tunnel face 1 with an error of within ±3 mm, as can be seen from Figure 6. Meanwhile, Figure 8 shows the measurement processing results using conventional technology, in which the 3D point cloud data is averaged within the mesh without excluding any of it. Although there is a slight difference in the mesh size (1.0m x 1.0m compared to 0.5m x 0.5m) according to the measurement processing results using the conventional technology, there is an error of about ±13 mm even when excluding abnormal values ​​on the periphery, and the difference from the measurement processing results according to the present invention is clear.

[0032] Then, when a skin drop, collapse, or collapse of the tunnel face 1 is predicted, an email is sent as needed from a terminal device such as a smartphone, tablet, or PC to notify (everyone involved in the construction) of the status of the tunnel face 1 using video data and / or image data. It should be noted that this type of publicly known means is explained in more detail in the aforementioned Patent Document 1, which was filed by the applicants and has already been made public.

[0033] Although the embodiments of the present invention have been described above with reference to the drawings, it should be noted that the present invention is not limited to the illustrated examples and includes the range of design modifications and application variations that would normally be made by a person skilled in the art, provided that they do not deviate from the technical concept of the present invention. For example, the 3D laser scanner 2 used was Leica's multi-station Nova MS60 (1000 pts / sec, measurement range 300 m, scan range 1000 m), but of course it is not limited to this and any 3D laser scanner will do. In addition, the installation location of the 3D laser scanner 2 is preferably at the top of the tunnel face where there is a good view of the face and where heavy machinery working on the tunnel face 1 is avoided as much as possible. [Explanation of symbols]

[0034] 1 Tunnel face 2. 3D laser scanner 3. Heavy machinery

Claims

1. A tunnel face monitoring method for constantly monitoring a tunnel face during tunnel construction using a 3D laser scanner and grasping the state of the tunnel face in real time, In the process of continuously repeating a series of measurement operations of scanning the tunnel face with the 3D laser scanner to collect three-dimensional point cloud data, transferring the three-dimensional point cloud data, and converting the tunnel face into mesh data in which the tunnel face is divided into a plurality of meshes, For each series of measurement operations, an initial or subsequent measurement point is adopted whose planar distance from an initial measurement point in the three-dimensional point cloud data is within a preset tolerance range and is closest to the initial measurement point, a difference in extrusion amount between the initial measurement point and the initial or subsequent measurement point is calculated, and the initial measurement point whose initial or subsequent measurement point is not within a preset tolerance range is excluded from the calculation. A tunnel face monitoring method, characterized in that it predicts skin collapse, collapse or destruction of the tunnel face based on the average value of the calculated differences in the extrusion amounts for multiple initial measurement points located within each mesh of the mesh data.

2. A tunnel face monitoring method for constantly monitoring a tunnel face during tunnel construction using a 3D laser scanner and grasping the state of the tunnel face in real time, In the process of continuously repeating a series of measurement operations of scanning the tunnel face with the 3D laser scanner to collect three-dimensional point cloud data, transferring the three-dimensional point cloud data, and converting the tunnel face into mesh data in which the tunnel face is divided into a plurality of meshes, For each series of measurement operations, an initial or subsequent measurement point whose planar distance from an initial measurement point in the three-dimensional point cloud data is within a preset tolerance range and is closest to the initial measurement point is adopted, a difference in extrusion amount between the initial measurement point and the initial or subsequent measurement point is calculated, the initial measurement point whose initial or subsequent measurement point is not located within the preset tolerance range is excluded from the calculation target, and the initial measurement point whose extrusion direction distance from the initial measurement point of the three-dimensional point cloud data is not located within the preset tolerance range is also excluded from the calculation target. A tunnel face monitoring method, characterized in that it predicts skin collapse, collapse or destruction of the tunnel face based on the average value of the calculated differences in the extrusion amounts for multiple initial measurement points located within each mesh of the mesh data.

3. 3. A tunnel face monitoring method according to claim 1 or 2, characterized in that the preset tolerance range from the initial measurement point in the planar distance is set within a radius of 25 mm from the initial measurement point.

4. A tunnel face monitoring method according to claim 2, characterized in that the preset tolerance range from the initial measurement point in the extrusion direction distance is set within a range of approximately ±100 mm from the initial measurement point.

5. The measurement of the tunnel face by the 3D laser scanner is performed under measurement conditions of a measurement distance of 30 m to 70 m, a measurement point interval of 4 cm to 10 cm, and a measurement range angle of 80 ° to 90 °. A tunnel face monitoring method according to claim 1 or 2.

6. The measurement of the tunnel face by the 3D laser scanner is performed under measurement conditions of a measurement distance of 30 to 70 m, a measurement point interval of 5 cm, and a measurement range angle of 80°. A tunnel face monitoring method according to claim 1 or 2.

Images