Spraying thickness setting system, spraying thickness setting method, and spraying thickness setting program

Abstract

To provide a spraying thickness setting system, a spraying thickness setting method, and a spraying thickness setting program that enable setting of an appropriate spraying thickness when spraying concrete onto an excavation surface in mountain tunnel construction work.SOLUTION: A spraying thickness setting system 1 comprises an image acquisition unit 10 that acquires photographed images of the natural ground that constitutes an excavation surface of a tunnel during tunnel excavation work, an evaluation unit 20 that outputs evaluation results for observation items of the natural ground for the photographed images, and a spraying thickness setting unit 30. A control unit of the spraying thickness setting unit 30 functions as an evaluation result acquisition unit 31A that inputs the photographed images to the evaluation unit 20 and acquires a distribution of evaluation results for the observation items, and a setting processing unit 31B that outputs a distribution of spraying thicknesses of sprayed concrete according to the evaluation results. The observation items include a spring water status item that indicates the amount of spring water from the natural ground.SELECTED DRAWING: Figure 1

Description

[Technical Field]

[0001] The present invention relates to a spraying thickness setting system, a spraying thickness setting method, and a spraying thickness setting program for setting the spraying thickness of concrete sprayed onto an excavation surface in mountain tunnel construction work. [Background technology]

[0002] In the construction of mountain tunnels, after a predetermined length of excavation has been completed, a primary spraying of concrete is carried out on the inner circumferential surface of the tunnel that has been created, and then a second spraying of concrete is carried out on the tunnel face. For example, Patent Document 1 discloses a concrete material spraying device that sprays concrete material on the excavation surfaces such as the inner circumferential surface and the tunnel face.

[0003] When spraying concrete onto an excavation surface, it is necessary to evaluate the geological conditions of the excavation surface in order to set the spray thickness of the primary and secondary spraying depending on the geological conditions of the excavation surface. Conventionally, workers have evaluated the geological conditions of the excavation surface by actually observing the condition of the excavation surface.

[0004] Furthermore, techniques for evaluating the geological conditions of excavation surfaces using image analysis and machine learning are also being considered. For example, Patent Document 2 discloses a technique for obtaining individual evaluation categories by applying learning results to segmented images obtained by dividing the evaluation area of ​​a face image to be evaluated, and predicting the evaluation category of the evaluation area based on the individual evaluation categories. [Prior art documents] [Patent documents]

[0005] [Patent Document 1] Japanese Patent Publication No. 2020-33723 [Patent Document 2] Japanese Patent Application Publication No. 2019-23392 Summary of the Invention [Problem to be solved by the invention]

[0006] Conventionally, the thickness of concrete sprayed onto excavation surfaces, such as mirror spraying and primary spraying, has been determined empirically based on the results of workers' actual observations of the geological conditions of the excavation surface. Therefore, there is a possibility that the thickness of the concrete sprayed onto the excavation surface will vary greatly depending on the level of skill of the workers. Furthermore, Patent Document 2 does not disclose a specific method for utilizing the evaluation results of the face images. [Means for solving the problem]

[0007] A spraying thickness setting system that solves the above-mentioned problems comprises an image acquisition unit that acquires photographed images of the ground that constitutes the excavation surface of a tunnel during tunnel excavation work, an evaluation unit that outputs evaluation results for observation items of the ground for the photographed images, an evaluation result acquisition unit that inputs the photographed images to the evaluation unit and acquires a distribution of the evaluation results for the observation items, and a setting processing unit that outputs a distribution of spraying thickness of concrete sprayed according to the evaluation results, wherein the observation items include a spring water status item that indicates the degree of amount of water springing up from the ground. [Effects of the Invention]

[0008] According to the present invention, an appropriate spraying thickness can be set when spraying concrete onto an excavation surface in mountain tunnel construction work. [Brief explanation of the drawings]

[0009] [Figure 1] FIG. 1 is a block diagram showing a schematic configuration of a spray thickness setting system. [Figure 2] FIG. 2 is an explanatory diagram showing the procedure of the spray thickness setting process. [Figure 3] FIG. 3 is a schematic diagram of a captured image. [Figure 4] FIG. 4 is a schematic diagram of a face image and an inner peripheral surface image cut out from a photographed image. [Figure 5] FIG. 5 is a schematic diagram of a state in which the working face image is divided. [Figure 6] FIG. 6 is a schematic diagram showing the distribution of evaluation results in a face image. [Figure 7] FIG. 7 is a schematic diagram of a heat map image showing the distribution of spray thickness. [Figure 8] FIG. 8 is a schematic diagram showing a modified example of dividing the working face image. DETAILED DESCRIPTION OF THE INVENTION

[0010] Hereinafter, an embodiment of a spray thickness setting system, a spray thickness setting method, and a spray thickness setting program will be described with reference to FIGS. (Spray thickness setting system 1) The spraying thickness setting system 1 shown in FIG. 1 is a system for setting an appropriate spraying thickness for spraying concrete onto an excavation surface during tunnel excavation work. The excavation surface is, for example, the tunnel face or the inner circumferential surface of the tunnel. The concrete spraying may be, for example, a mirror spraying onto the face or a primary spraying onto the inner circumferential surface of the tunnel. In this embodiment, the spraying thickness setting system 1 sets both the mirror spraying thickness and the primary spraying thickness.

[0011] As shown in FIG. 1, the spray thickness setting system 1 includes an image acquisition unit 10, an evaluation unit 20, and a spray thickness setting unit 30. The image acquisition unit 10 acquires photographed images of the natural ground that constitutes the excavation surface of the tunnel. The images acquired by the image acquisition unit 10 include the tunnel face and the inner circumferential surface of the tunnel located near the face. The images acquired by the image acquisition unit 10 are sent to the spraying thickness setting unit 30, and then input from the spraying thickness setting unit 30 to the evaluation unit 20.

[0012] The evaluation unit 20 is a computer system that outputs evaluation results for each observation item of the natural ground for the photographed images acquired by the image acquisition unit 10. The observation items are items that represent the geological conditions of the natural ground, and include, for example, compressive strength, weathering, fracture spacing, fracture condition, strike and dip, spring water condition, and deterioration condition due to spring water.

[0013] The evaluation unit 20 includes a first control unit 21, a teacher data storage unit 22, and a learning result storage unit 23. The first control unit 21 functions as a control means composed of a CPU, RAM, ROM, etc. The first control unit 21 executes an evaluation program to function as an image processing unit 21A, a learning processing unit 21B, an evaluation processing unit 21C, etc.

[0014] The image processing unit 21A performs processing to pre-process the necessary images before learning the image of the excavation surface or evaluating the captured image. For example, the image processing unit 21A performs processing to adjust the size of the images to be processed, such as images for learning or captured images for evaluation, to a fixed value. Then, the image processing unit 21A performs processing to cut out each area of ​​the face and inner peripheral surface from the image to be processed. The image processing unit 21A performs processing to divide the cut-out image into a predetermined reference number of pixels.

[0015] The learning processing unit 21B executes a learning process for evaluating each observation item of the natural ground constituting the excavation surface from the photographed image of the excavation surface by deep learning using teacher data. In this embodiment, the learning processing unit 21B uses, for example, a CNN (Convolutional Neural Network), but is not limited to this.

[0016] The evaluation processing unit 21C outputs an evaluation result for the observation item of the natural ground that constitutes the excavation surface, using the learning result by the learning processing unit 21B. The output evaluation result is transmitted to the spraying thickness setting unit 30.

[0017] As an example, in a CNN, the evaluation processing unit 21C uses information input in the input layer, and performs a final evaluation from the feature values ​​in the fully connected layer via multiple overlapping convolutional layers and pooling layers. The convolutional layer captures the features of local regions of the excavation surface using various filters. The pooling layer replaces the feature points obtained in the convolutional layer with representative numerical values. In this embodiment, the fully connected layer uses a classifier (e.g., SVM: Support Vector Machine) to link the analysis results with a rating according to the feature values ​​output from the convolutional layer and pooling layer, thereby determining a threshold value for analysis.

[0018] The training data storage unit 22 records training data for learning processing for natural ground evaluation by the learning processing unit 21B. The training data is registered before the learning processing unit 21B performs the learning processing. The learning result storage unit 23 stores the learning results obtained by the learning processing unit 21B. The learning results are stored during the learning process. The learning result storage unit 23 holds the learning results that the evaluation processing unit 21C uses to evaluate each observation item on the excavation surface. As an example, the learning result storage unit 23 stores, as the learning result, an evaluation category that the evaluation processing unit 21C uses to evaluate each observation item.

[0019] For example, the evaluation category for compressive strength is divided into four levels from "1" to "4" so that the higher the compressive strength (σ) of the natural ground, the lower the rating. As an example, the compressive strength item is evaluated using ratings from "1" to "4" corresponding to "100MPa ≥ σ", "100MPa > σ ≥ 20MPa", "20MPa > σ ≥ 5MPa", and "5MPa > σ".

[0020] For example, the weathering evaluation categories are divided so that the more advanced the weathering of the ground, the higher the score. For example, the weathering category is evaluated using scores from "1" to "4" corresponding to "generally fresh," "weathering along cracks," "weathering down to the rock core," and "earth and sand, clay, fractured, unconsolidated earth and sand."

[0021] For example, the evaluation category for the gap spacing is divided into four levels from "1" to "4" so that the larger the gap spacing (d), the lower the score. As an example, the gap spacing item is evaluated using scores from "1" to "4" corresponding to "d≧1m", "1m>d≧20cm", "20cm>d≧5cm", and "5cm>d".

[0022] For example, the crack condition is evaluated using four levels from "1" to "4" so that the closer the cracks are to each other, the lower the score. As an example, the crack condition item is evaluated using scores from "1" to "4" corresponding to "close cracks," "some cracks open," "most cracks open," and "clay sandwiched, unconsolidated."

[0023] For example, the strike and inclination evaluation categories are divided into five levels from "1" to "5" so that the more likely a grain is to collapse from the face, the higher the score. As an example, the strike and inclination category is evaluated using scores from "1" to "5" corresponding to "inset grain inclination of 45 degrees or more and 90 degrees or less," "inset grain inclination of 20 degrees or more and less than 45 degrees," "inset grain inclination of 0 degrees or more and less than 20 degrees, or inclination grain inclination of 0 degrees or more and less than 20 degrees," "inclination grain inclination of 20 degrees or more and less than 45 degrees," and "inclination grain inclination of 45 degrees or more and less than 90 degrees."

[0024] For example, the evaluation category for the spring water condition is divided into four levels from "1" to "4" so that the smaller the amount of spring water, the lower the score. In other words, the spring water condition item indicates the degree of the amount of spring water from the ground. As an example, the spring water condition item is evaluated using scores from "1" to "4" corresponding to "no spring water, some seepage," "some dripping water," "concentrated spring water," and "full spring water."

[0025] For example, the assessment of deterioration caused by spring water is divided into four levels from "1" to "4" so that the smaller the degree of deterioration of the ground due to spring water, the lower the score. As an example, the deterioration condition item is assessed using scores from "1" to "4" corresponding to "no deterioration," "loosening," "weakening," and "collapse / washout."

[0026] The learning result storage unit 23 may store learning results corresponding to each region of the tunnel face and inner circumferential surface. As an example, the learning result storage unit 23 stores face learning results for evaluating each observation item for the tunnel face portion in the image to be evaluated, and inner circumferential surface learning results for evaluating each observation item for the tunnel inner circumferential surface portion in the image. The learning result storage unit 23 may store separate face learning results, including a crown learning result, a left shoulder learning result, and a right shoulder learning result, as face learning results.

[0027] The spraying thickness setting unit 30 is a computer system that sets an appropriate spraying thickness for spraying concrete based on the evaluation results by the evaluation processing unit 21C of the evaluation unit 20. The spraying thickness setting unit 30 includes a second control unit 31, a storage unit 32, an input unit 33, and an output unit 34. The second control unit 31 functions as control means composed of a CPU, RAM, ROM, etc. The second control unit 31 executes a spraying thickness setting program to function as an evaluation result acquisition unit 31A, a setting processing unit 31B, etc.

[0028] The evaluation result acquisition unit 31A inputs the captured image transmitted from the image acquisition unit 10 to the evaluation unit 20, thereby executing a process of acquiring the distribution of the evaluation results for each observation item for the captured image.

[0029] The setting processing unit 31B outputs a distribution of appropriate spraying thicknesses for spraying concrete in accordance with the distribution of the evaluation results acquired by the evaluation result acquisition unit 31 A. For example, the setting processing unit 31B performs processing such that the higher the score of the evaluation result for each observation item, the thicker the spraying thickness.

[0030] The storage unit 32 is, for example, a non-volatile memory such as an HDD or SSD, but may also be a cloud server capable of storing various data. The storage unit 32 stores a spraying thickness setting program. The storage unit 32 stores setting information for outputting an appropriate spraying thickness distribution for sprayed concrete from the distribution of evaluation results for each observation item acquired by the evaluation result acquisition unit 31A.

[0031] The setting information is, for example, a lookup table or a function for calculating the spraying thickness using the evaluation score of each observation item as a variable, so that the higher the evaluation score of each observation item, the thicker the spraying thickness of the concrete sprayed. The memory unit 32 may store face setting information for setting the spraying thickness of the mirror spraying on the face, and inner peripheral surface setting information for setting the spraying thickness of the primary spraying on the inner peripheral surface.

[0032] For example, the setting information includes a relational expression that sets an appropriate spraying thickness for concrete spraying by adding up thickness setting parameters corresponding to the scores of each observation item. For example, the desired spraying thickness is T, and the lower limit of the spraying thickness is T. min Let P be the thickness setting parameter corresponding to the score of each observation item, and w be the weighting coefficient according to the observation item. The spraying thickness is T = T min +w1P1+w2P2+w3P3…w n P n If there is no lower limit on the spray thickness, T min Just substitute =0cm.

[0033] As an example, the setting information stores thickness setting parameters of "0 cm," "5 cm," "10 cm," and "15 cm" corresponding to compressive strength ratings of "1" to "4." The same correspondence between ratings and thickness setting parameters can be used when the observation items are weathering, crack spacing, crack condition, and deterioration due to spring water. When the observation item is strike and dip, the setting information stores thickness setting parameters such that the higher the rating, the larger the corresponding value of the rating from "1" to "5."

[0034] For example, when the observation item is a spring water state, the thickness setting parameter is set to "0 cm" for ratings of "1" and "2." When the observation item is a spring water state, the thickness setting parameters are set to "10 cm" and "15 cm" for ratings of "3" and "4." In other words, when spring water is clearly visible in the image, such as when the rating is "3" or higher, the spray thickness is increased.

[0035] The setting processing unit 31B acquires thickness setting parameters according to the scores of each observation item, and then adds them together. At this time, the setting processing unit 31B may perform weighting according to the observation item, as represented by the weighting coefficient in the relational expression.

[0036] In addition, when setting the spray thickness, it is not necessary to use the scores for all observation items, and the evaluation results of any observation item can be used. In this embodiment, a thickness setting parameter corresponding to at least the score for the spring water condition is used to set the spray thickness.

[0037] Furthermore, the setting processing unit 31B may output the distribution of sprayed thickness using other information in addition to the distribution of the evaluation results by the evaluation unit 20. For example, instead of or in addition to the distribution of the evaluation results of the compressive strength by the evaluation unit 20, the setting processing unit 31B may use the distribution of drilling data measured when a drilling machine drills an excavation surface for the purpose of installing rock bolts, etc. The drilling data measured by the drilling machine is, for example, data on the strength of the excavation surface. The data on the strength of the excavation surface may be, for example, a normalized drilling speed ratio converted from the drilling speed and feed pressure, or the compressive strength measured by the drilling machine.

[0038] In this case, the spray thickness setting unit 30 receives the distribution of drilling data from the drilling machine and stores the received distribution of drilling data in the memory unit 32. The memory unit 32 also stores correspondence information such as functions and lookup tables for converting the drilling data measured by the drilling machine into thickness setting parameters. The setting processing unit 31B uses the correspondence information stored in the memory unit 32 to convert the drilling data received from the drilling machine into thickness setting parameters.

[0039] The input unit 33 is a device that allows an operator to input various instructions and information to the spray thickness setting unit 30, and includes, for example, a keyboard or a pointing device. The output unit 34 is a device that outputs various information handled by the spray thickness setting unit 30, and is, for example, a display. The input unit 33 and the output unit 34 may be a touch panel display or the like.

[0040] (Spray thickness setting process) The spray thickness setting process using the evaluation unit 20 and the spray thickness setting unit 30 will be described below with reference to the flow shown in FIG. 2 and with reference to FIGS.

[0041] As shown in FIG. 2, the spray thickness setting process includes steps S1 to S8. In step S1, the image acquisition unit 10 acquires a photographed image of the tunnel excavation surface. In this case, as shown in FIG. 3, the image acquisition unit 10 acquires a photographed image 40 including a face region 41, an inner peripheral surface region 42, and a roadbed region 43. The image acquisition unit 10 then transmits the acquired photographed image 40 to the spraying thickness setting unit 30. The spraying thickness setting unit 30 inputs the photographed image 40 transmitted from the image acquisition unit 10 to the evaluation unit 20.

[0042] The inner peripheral surface region 42 includes a completed section 44 where the secondary spraying has been completed, and an uncompleted section 45 where the natural ground is exposed before the primary spraying is performed. The uncompleted section 45 is located closer to the face region 41 than the completed section 44. In Figures 3 and 4, the face region 41 is indicated by dark dots, and the uncompleted section 45 of the inner peripheral surface region 42 is indicated by light dots.

[0043] In step S2, the image processing unit 21A of the evaluation unit 20 performs a process of cutting out the area to be evaluated from the captured image 40. In detail, as shown in FIG. 4, the image processing unit 21A creates a face image 41A by cutting out the face area 41 from the captured image 40, and an inner periphery image 42A by cutting out the unconstructed section 45 of the inner periphery area 42. The cutting out process can use pattern recognition such as edge extraction and color judgment. Note that in step S2, the roadbed area 43 is not cut out.

[0044] In step S3, the image processing unit 21A of the evaluation unit 20 executes a process of dividing each image cut out in step S2 into pieces of a predetermined size. For example, the image processing unit 21A divides each of the cutting face image 41A and the inner peripheral surface image 42A cut out in step S2 into pieces of a predetermined size.

[0045] 5, in step S3, the image processing unit 21A divides the cutting face image 41A cut out in step S2 into regions each having a predetermined number of pixels, thereby generating a plurality of divided images 40S. Note that the same process is also performed on the inner circumferential surface image 42A.

[0046] Next, the evaluation processing unit 21C of the evaluation unit 20 repeats the evaluation process for each observation item according to the procedures of steps S4 to S6 for each region cut out in step S2. For example, the evaluation processing unit 21C of the evaluation unit 20 repeats the procedures of steps S4 to S6 for each observation item for the working face image 41A, and then repeats the procedures of steps S4 to S6 for each observation item for the inner circumferential surface image 42A.

[0047] First, in step S4, the evaluation processing unit 21C of the evaluation unit 20 executes a process of identifying the learning results to be used for evaluating the observation items. Specifically, the evaluation processing unit 21C acquires the area to be evaluated and the learning results corresponding to the observation items from the learning result storage unit 23. For example, when evaluating the compressive strength of the tunnel face image 41A, the evaluation processing unit 21C acquires the tunnel face learning results for evaluating the compressive strength from the learning result storage unit 23.

[0048] Then, in step S5, the evaluation processing unit 21C of the evaluation unit 20 executes an evaluation process to evaluate the observation items for each divided image 40S included in each region. In detail, the evaluation processing unit 21C inputs the divided image 40S, which is the image to be processed, to the input layer of the trained CNN, and acquires an evaluation result for the divided image 40S in the output layer. In this manner, the evaluation processing unit 21C acquires an evaluation result for each divided image 40S included in the image cut out for each region. That is, in the process of step S5, a score of "1" to "4" or "1" to "5" based on the learning result is assigned to each divided image 40S included in the image cut out for each region.

[0049] Then, in step S6, the evaluation processing unit 21C of the evaluation unit 20 outputs the evaluation results for each divided image 40S in step S5 to the spray thickness setting unit 30 as a distribution of the evaluation results in the images cut out for each region in step S2.

[0050] 6, in step S6, the evaluation processing unit 21C outputs the distribution of the ratings assigned to each divided image 40S by the processing of step S5 in accordance with the arrangement of the divided image 40S. The evaluation processing unit 21C repeats the processing of steps S4 to S6 for each observation item, thereby outputting the distribution of the ratings evaluated for each divided image 40S as shown in FIG. 6 for each observation item for the area cut out in step S2.

[0051] For example, the evaluation processing unit 21C repeats the processes of steps S4 to S6 for each observation item on the working face image 41A cut out in step S2, thereby outputting the distribution of ratings for each observation item on the working face image 41A. Similarly, the evaluation processing unit 21C repeats the processes of steps S4 to S6 for each observation item on the inner circumferential surface image 42A cut out in step S2, thereby outputting the distribution of ratings for each observation item on the inner circumferential surface image 42A.

[0052] Next, in step S7, the setting processing unit 31B of the spraying thickness setting unit 30 sets an appropriate spraying thickness for spraying concrete for each divided image 40S in accordance with the distribution of the evaluation results output by the evaluation processing unit 21C. For example, the setting processing unit 31B sets the spraying thickness required for mirror spraying at the position of the face corresponding to the divided image 40S, based on the evaluation results of each observation item for each divided image 40S in the face image 41A.

[0053] In detail, the setting processing unit 31B acquires thickness setting parameters according to the evaluation score of each observation item based on the setting information stored in the memory unit 32, and sets an appropriate spray thickness for the mirror spray by adding up the thickness setting parameters.

[0054] Through the above procedure, the setting processing unit 31B executes a process of setting an appropriate spraying thickness for the mirror spraying for each divided image 40S included in the working face image 41A. That is, in the process of step S7, an appropriate spraying thickness is assigned to each divided image 40S according to the evaluation result of the evaluation processing unit 21C. Furthermore, through a similar procedure, the setting processing unit 31B executes a process of setting an appropriate spraying thickness for the primary spraying for each divided image 40S included in the inner peripheral surface image 42A.

[0055] In step S7, the setting processing unit 31B may output a spraying thickness distribution using the distribution of drilling data measured by the drilling machine in addition to the distribution of the evaluation results by the evaluation unit 20. That is, the setting processing unit 31B acquires thickness setting parameters corresponding to the scores of each observation item in the divided image 40S and thickness setting parameters corresponding to the drilling data measured by the drilling machine at positions corresponding to the arrangement of the divided image 40S. The setting processing unit 31B then sets the spraying thickness by adding up the thickness setting parameters. By repeating this process, the setting processing unit 31B may output a spraying thickness distribution according to the distribution of the evaluation results by the evaluation unit 20 and the distribution of data related to the strength of the excavation surface measured by the excavator.

[0056] Next, in step S8, the setting processing section 31B of the spray thickness setting section 30 outputs the spray thicknesses set for each divided image 40S in step S7 as a distribution according to the arrangement of each divided image 40S.

[0057] 7, in step S8, the setting processing unit 31B outputs a heat map image 50 showing the distribution of the spray thickness. The setting processing unit 31B causes the output unit 34 of the spray thickness setting unit 30 to display the heat map image 50.

[0058] The heat map image 50 includes a mirror spray thickness distribution 51 and a primary spray thickness distribution 52. In the mirror spray thickness distribution 51, the spray thickness set for each divided image 40S included in the face image 41A in step S7 is displayed according to the arrangement of each divided image 40S. In the primary spray thickness distribution 52, the spray thickness set for each divided image 40S included in the inner surface image 42A in step S7 is displayed according to the arrangement of each divided image 40S. Note that the heat map image 50 may also display a spray thickness according to the average value of an area combining multiple divided images 40S.

[0059] (Effects of the embodiment) (1) The spraying thickness setting system 1 outputs the distribution of the spraying thickness of concrete sprayed onto the excavation surface according to the distribution of the evaluation results for each observation item for the photographed image 40 of the natural ground that constitutes the excavation surface. This allows an appropriate spraying thickness to be set regardless of the worker's level of skill. Furthermore, compared to when the worker sets the spraying thickness by visually checking the excavation surface, this method is preferable in terms of safety management because it does not require the worker to approach the face or nearby heavy machinery.

[0060] (2) The spraying thickness setting system 1 uses the distribution of the evaluation results of the spring water condition when setting the distribution of the spraying thickness of the concrete sprayed. Since it is necessary to make the spraying thickness of the concrete thicker at the location of the excavation surface where spring water occurs, according to this embodiment, it is possible to set an appropriate spraying thickness according to the state of the spring water.

[0061] (3) According to the spraying thickness setting system 1, it is possible to output both the spraying thickness of the mirror spraying on the tunnel face and the spraying thickness of the primary spraying on the inner surface of the tunnel. (4) In this embodiment, the thickness of the concrete sprayed for each of the working face and the inner circumferential surface is set using the photographed image 40 including the working face region 41 and the inner circumferential surface region 42. Therefore, the thickness of the concrete sprayed for each of the working face and the inner circumferential surface can be set from a single photographed image 40 including the working face region 41 and the inner circumferential surface region 42, without preparing images for each of the working face and the inner circumferential surface.

[0062] (5) The setting processing unit 31B can output the spraying thickness distribution using the distribution of drilling data measured when the drilling machine drills the excavation surface, in addition to the distribution of the evaluation results by the evaluation unit 20. In this case, quantitative measurement results of the natural ground can be used to set the spraying thickness as a parameter other than the evaluation results of the evaluation unit 20 for the captured image 40. Therefore, a more appropriate spraying thickness can be set.

[0063] (Example of change) The above embodiment can be modified as follows: The following modifications can be implemented in combination with each other within the scope of technical compatibility.

[0064] 8, in step S2, a top image 41T obtained by cutting out the top edge, a left shoulder image 41L obtained by cutting out the left shoulder, and a right shoulder image 41R obtained by cutting out the right shoulder may be created from the face image 41A cut out from the photographed image 40. In this case, evaluation of the observation items and setting of the spray thickness may be performed separately for the top image 41T, the left shoulder image 41L, and the right shoulder image 41R.

[0065] The setting processing unit 31B may execute a process to output an appropriate support pattern for the inner circumferential surface according to the distribution of the spray thickness of the primary spraying on the inner circumferential surface output in step S8. For example, the setting processing unit 31B outputs an appropriate support pattern for the inner circumferential surface according to a representative value in the distribution of the spray thickness of the primary spraying on the inner circumferential surface output in step S8. The representative value may be the maximum value, minimum value, average value, or median value in the distribution of the spray thickness of the primary spraying. For example, the memory unit 32 stores support pattern setting information indicating the correspondence between the representative value in the distribution of the spray thickness of the primary spraying and an appropriate support pattern. With this configuration, an appropriate support pattern can be set for the inner circumferential surface of the tunnel according to the distribution of the primary spraying thickness that reflects the evaluation results of the observation items.

[0066] For example, in the support pattern setting information, when the representative value of the spray thickness of the primary spray is "3 cm or more but less than 5 cm," the "CI pattern" is stored as the corresponding support pattern. Also, in the support pattern setting information, the "CII pattern," "DI pattern," and "DII pattern" support patterns are stored so that they correspond to representative values ​​of "5 cm or more but less than 7 cm," "7 cm or more but less than 10 cm," and "10 cm or more."

[0067] In the above embodiment, the spray thickness setting system 1 is configured to output both the spray thickness distribution of mirror spraying on the face and the spray thickness distribution of primary spraying on the inner peripheral surface. Alternatively, the spray thickness setting system 1 may be configured to output only the spray thickness distribution of mirror spraying, or only the spray thickness distribution of primary spraying. In other words, the spray thickness setting system 1 may be configured to output at least one of the spray thickness distribution of mirror spraying and the primary spraying thickness distribution.

[0068] In the above embodiment, the spraying thickness setting system 1 sets the spraying thickness of concrete sprayed on each of the tunnel face and the inner peripheral surface using a captured image 40 including the tunnel face region 41 and the inner peripheral surface region 42. However, this is not limiting. For example, separate images may be prepared for outputting the distribution of the spraying thickness of the mirror spraying on the tunnel face and the distribution of the primary spraying on the inner peripheral surface. For example, the image for the tunnel face may be a captured image 40 in which the tunnel face is positioned in front. For example, the image for the inner peripheral surface may be a panoramic image of the inner peripheral surface in the circumferential direction. In this case, since the inner peripheral surface can be photographed with the inner peripheral surface positioned in front, the condition of the natural ground constituting the inner peripheral surface can be evaluated more accurately than when evaluating the inner peripheral surface region 42 included in the captured image 40 in which the tunnel face is positioned in front.

[0069] The setting information may set weighting according to the position in the face image 41A. For example, different weighting coefficients may be set for the center part of the face and the vicinity of the end part. Similarly, the setting information may set weighting according to the position in the inner circumferential surface image 42A.

[0070] In the setting information, weighting may be changed depending on the schedule for the next process. For example, in the case of a face, if there is a long time until the next excavation process, the weighting coefficient may be set so that the spraying thickness is thicker. In the case of an inner peripheral surface, if there is a long time until the construction of support after the first spraying or the second spraying, the weighting coefficient may be set so that the spraying thickness is thicker. [Explanation of symbols]

[0071] 1...spraying thickness setting system, 10...image acquisition unit, 20...evaluation unit, 21...first control unit, 21A...image processing unit, 21B...learning processing unit, 21C...evaluation processing unit, 22...teaching data memory unit, 23...learning result memory unit, 30...spraying thickness setting unit, 31...second control unit, 31A...evaluation result acquisition unit, 31B...setting processing unit, 32...memory unit, 33...input unit, 34...output unit, 40...captured image, 40S...divided image, 41...face area, 41A...face image, 41L...left shoulder image, 41R...right shoulder image, 41T...top image, 42...inner surface area, 42A...inner surface image, 43...roadbed area, 44...constructed section, 45...unconstructed section, 50...heat map image, 51...mirror spraying thickness distribution, 52...primary spraying thickness distribution.

Claims

1. an image acquisition unit that acquires photographed images of the natural ground that constitutes the excavation surface of the tunnel during tunnel excavation work; an evaluation unit that outputs an evaluation result for the observation item of the natural ground for the photographed image; an evaluation result acquisition unit that inputs the captured image to the evaluation unit and acquires a distribution of the evaluation results for the observation items; a setting processing unit that outputs a distribution of sprayed concrete thickness according to the evaluation result; The observation items include a spring water condition item that indicates the amount of spring water from the natural ground. Spray thickness setting system.

2. The excavation surface includes a face of the tunnel and an inner peripheral surface of the tunnel, The evaluation result acquisition unit acquires a distribution of evaluation results for the observation items at the face and a distribution of evaluation results for the observation items at the inner circumferential surface, The setting processing unit outputs a distribution of the spraying thickness of mirror spraying on the face in accordance with the distribution of the evaluation results for the observation items at the face, and outputs a distribution of the spraying thickness of primary spraying on the inner circumferential surface in accordance with the distribution of the evaluation results for the observation items on the inner circumferential surface. The spray thickness setting system according to claim 1 .

3. The setting processing unit outputs the distribution of the spraying thickness according to the distribution of the evaluation results and the distribution of drilling data measured when a drilling machine drills the excavation surface. The spray thickness setting system according to claim 1 or 2.

4. The excavation surface includes an inner circumferential surface of the tunnel, the evaluation result acquisition unit acquires a distribution of evaluation results for the observation items on the inner circumferential surface; The setting processing unit outputting a distribution of the spray thickness of the primary spraying on the inner circumferential surface according to a distribution of the evaluation results for the observation items on the inner circumferential surface; A support pattern for the inner peripheral surface is output according to the distribution of the spray thickness of the primary spray. The spray thickness setting system according to claim 1 .

5. an image acquisition unit that acquires photographed images of the natural ground that constitutes the excavation surface of the tunnel during tunnel excavation work; an evaluation unit that outputs an evaluation result for the photographed image with respect to observation items including a spring water state item that indicates the degree of the amount of spring water from the natural ground; A spraying thickness setting method for setting a spraying thickness of concrete sprayed onto the excavation surface using a control unit, The control unit inputting the captured image into the evaluation unit to obtain a distribution of the evaluation results for the observation items; The distribution of the spray thickness is output according to the evaluation result. How to set the spray thickness.

6. an image acquisition unit that acquires photographed images of the natural ground that constitutes the excavation surface of the tunnel during tunnel excavation work; an evaluation unit that outputs an evaluation result for the photographed image with respect to observation items including a spring water state item that indicates the degree of the amount of spring water from the natural ground; A control unit and a spraying thickness setting program for setting a spraying thickness of concrete sprayed onto the excavation surface using the program, The control unit inputting the captured image into the evaluation unit to obtain a distribution of the evaluation results for the observation items; The device functions as a means for outputting the distribution of the spray thickness in accordance with the evaluation result. Spray thickness setting program.

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