Strain field evolution and crack prediction method for all-tailing cemented filling body

Abstract

The invention belongs to the technical field of tailing strain field evolution prediction, and discloses a full-tailing cemented filling body strain field evolution and crack prediction method, which comprises the following steps: preparing a full-tailing cemented filling body CPB sample, and carrying out an expansion degree test in the preparation process of the CPB sample; evaluating the uniaxial compressive strength of the CPB sample, and monitoring the surface change of the sample in the testing process through a digital image related acquisition technology; establishing a computer visual identification model based on an HSV color mode, and analyzing the area proportion and distribution condition of strain regions of different degrees to realize comprehensive analysis of the image; and determining an early warning signal of crack prediction according to an analysis result of the change condition of the strain field. According to the strain field evolution and crack prediction method for the full-tailing cemented filling body, quantitative analysis of strain evolution of the filling body is achieved, prediction and early warning are provided for the damage risk of the filling body, and the method has important significance for ensuring the safety and efficiency of mine operation.

Description

Technical Field

[0001] The present invention relates to the technical field of prediction of the evolution of the strain field of tailings, and particularly to a method for predicting the evolution of the strain field and cracks of a fully tailings cemented filling body. Background Art

[0002] Tailings are an inevitable by-product in the process of mineral resource exploitation. The safe treatment of mine tailings is a key issue faced by the mining industry. In modern underground mines, tailings are usually the main component of cemented paste backfill (CPB). CPB consists of dewatered tailings (70 - 85% of the solid weight), hydraulic binder (3 - 7% of the dry paste weight), and mixed water (fresh or mine-processed). CPB can provide secondary ground support for mining operations, improve the underground working environment, and has significant economic, safety, and environmental protection advantages, thus attracting extensive attention in the mining industry. As a means of controlling mining subsidence, the effect of the filling body in mining is closely related to the integrity and stability of the filling material. Local failures often occur in the filling body, leading to the overall instability of the material. Therefore, studying the strain evolution and failure prediction of CPB under the action of overlying strata loads in structural filling mining is of great practical significance for the stability of the stope.

[0003] In recent years, scholars have conducted extensive research on the mechanical properties of CPB, using various techniques to study the evolution characteristics of pores and microcracks inside rocks and filling materials. To evaluate the deformation of filling samples, the digital image correlation (DIC) method uses a high-speed camera to capture the speckle field images on the surface of the sample, obtaining the corresponding local deformation and instability, which can effectively reflect the damage and failure process of rocks and filling materials. These studies have promoted the understanding of the mechanical properties and deformation evolution of paste backfill materials.

[0004] However, no specific quantitative evolution model of the strain field and crack identification prediction research of fully tailings cemented filling bodies based on digital image correlation has been found. Since the material composition, pore characteristics, and chemical composition of paste backfill materials are different from those of natural geotechnical materials, it is of great significance to study the deformation of paste backfill materials. The present invention uses DIC to observe the fracture evolution and deformation response of CPB specimens during the uniaxial compression test, and proposes a computer vision recognition (Computer vision recognition of HSV color patterns, HSV-CVR) model based on the strain field data obtained by DIC to analyze the area ratio and distribution of strain regions at different levels; then, by analyzing the changes in the strain field, early warning signals for crack prediction are determined. Summary of the Invention

[0005] The object of the present invention is to provide a method for strain field evolution and crack prediction of full tailings cemented filling body. Based on the computer vision recognition model of HSV color mode, by dividing the principal strain field into 8 strain regions, the quantitative analysis of the strain evolution of the filling body is realized, providing early warnings of high, medium and low levels for the failure risk of the filling body; not only enhancing the understanding of the mechanical behavior of mine filling materials, but also providing a scientific basis for the design, construction monitoring and risk management of mine filling materials, which is of great significance for ensuring the safety and efficiency of mine operations.

[0006] To achieve the above object, the present invention provides a method for strain field evolution and crack prediction of full tailings cemented filling body, including the following steps:

[0007] Step S1, prepare a full tailings cemented filling body CPB specimen and conduct a slump test during the preparation process of the CPB specimen;

[0008] Step S2, use a fully automatic flexural and compressive testing machine to evaluate the uniaxial compressive strength of the CPB specimen, and at the same time monitor the surface changes of the specimen during the test through digital image correlation DIC image acquisition technology;

[0009] Step S3, according to the strain field data obtained by DIC, establish a computer vision recognition HSV-CVR model based on the HSV color mode to analyze the area ratio and distribution of different degrees of strain regions, and realize the comprehensive analysis of the image;

[0010] Step S4, determine the early warning signal for crack prediction according to the analysis result of the strain field change situation.

[0011] Preferably, the full tailings cemented filling body specimen is made by mixing and pouring cement, full tailings and tap water, and its preparation process is as follows:

[0012] First, use a cement paste mixer to prepare the slurry. Pour the mixing water into the mixing bucket, and then add the uniformly mixed solid materials full tailings and cement. Stir at low speed for 120 s first, and then at high speed for 120 s;

[0013] Then, inject the slurry into the mortar slump tester for slump test;

[0014] Finally, pour the slurry into a standard triple mold, wrap it with plastic wrap, and place it in a constant temperature and humidity curing box for curing for 28 days to prepare the CPB specimen.

[0015] Preferably, the specifications of the mortar slump tester are 50mm*100mm*150mm; the specifications of the standard triple mold are 70.7mm*70.7mm*70.7mm; the temperature of the constant temperature and humidity curing box is controlled at 20±2°C, and the humidity is greater than 90%.

[0016] Preferably, in step S2, the application of the DIC technique involves a digital image acquisition system and digital image processing software, including a loading device, a light source, a high-resolution digital camera, and a computer;

[0017] The DIC image acquisition system consists of a CCD camera, a ring light source, and MV Viewer software; before loading, the DIC system is calibrated, the angle and focal length of the camera are adjusted, and the camera has a resolution of 4000×3000 pixels and a frame rate of 9 frames per second.

[0018] Preferably, in step S2, a uniaxial compression test is performed on the CPB specimen, and the specific process is as follows:

[0019] Step S21: After the CPB specimen reaches the specified age, first evenly apply a layer of white primer on its surface, and then randomly sprinkle black spots to form a speckle pattern with high contrast;

[0020] Step S22: Adjust the light source of the DIC digital image acquisition system to obtain gray-scale data during the loading process;

[0021] Step S23: Set the acquisition frequency of the camera to capture the image of the specimen coated with artificial speckles;

[0022] Step S24: Store the collected image data in the computer, and use VIC-2D software to analyze and obtain the surface displacement change and the strain field data.

[0023] Preferably, in step S3, based on the strain field image obtained by the DIC technique, the strain field is segmented, and the specific process is as follows:

[0024] Step S31: First, use the camera to capture the high-definition speckle image of the specimen during the uniaxial compression test;

[0025] Step S32: Subsequently, use VIC-2D software to obtain the strain field data of the research area;

[0026] Step S33: Then, use the HSV-CVR program to process the strain field image data of the region of interest of the CPB specimen;

[0027] Step S34: Finally, through the segmentation of the strain field and the quantitative processing of the visual recognition data, the comprehensive analysis of the image is realized.

[0028] Preferably, the strain field image processed by VIC-2D software is a raster image; the image is composed of different pixel points, and each pixel point contains color information; the RGB color mode is used to record this color information, that is, the information of a pixel point is superimposed with the color information of red R, green G, and blue B;

[0029] The strain field image data of the CPB specimen in the region of interest is processed using the HSV-CVR program, and the specific process is as follows:

[0030] Step S331: Traverse all image files in the specified directory and read the image files using the OpenCV library;

[0031] Step S332: Convert the color space of the image from RGB to HSV to process color information, where H is hue, S is saturation, and V is brightness;

[0032] Step S333: Define the number of hue groups and their corresponding ranges, and classify pixels of different hues;

[0033] Step S334: Traverse each hue group, create a mask for each group, and determine the area ratio of the hue group in the entire image by calculating the number of non-zero pixels within the mask. Retain the pixels belonging to the interval and replace the pixels not belonging to the interval with white pixels, thereby realizing image segmentation;

[0034] Step S335: Integrate the above area ratio data into a table, calculate the ratio of the area of the color block in the interval region to the entire region of interest in the frame images corresponding to different times, and obtain the curve of the change in strain percentage with time at the interval scale.

[0035] Preferably, in step S4, according to the analysis results of the change of the strain field, early warnings of three levels, high, medium, and low, are provided for the failure risk of the filling body, and the early warning signals for crack prediction are determined.

[0036] Therefore, the present invention adopts the above method for strain field evolution and crack prediction of a full-tailings cemented filling body, and the beneficial effects are as follows:

[0037] (1) In the present invention, the DIC and HSV-CVR models effectively reveal the deformation field and crack propagation law of the CPB specimen under uniaxial pressure load;

[0038] (2) The HSV-CVR model proposed by the present invention intuitively identifies and quantifies the area ratio of different strain intervals, so as to more accurately understand the strain evolution characteristics of the filling body during the loading process, especially the strain concentration phenomenon during the crack formation and propagation stages;

[0039] (3) The method proposed by the present invention can quantitatively identify the precursor characteristics of crack propagation and penetration. Based on the values and percentage changes in different strain intervals in the specimen strain field, crack prediction is carried out. Three early warning signals of high strain, medium strain, and low strain are set. By analyzing the changes in the strain intervals, the failure risk of the filling body is evaluated, providing a scientific basis for mine filling engineering and helping engineers make more reasonable design decisions before construction.

[0040] The technical solution of the present invention will be further described in detail below with reference to the accompanying drawings and embodiments. Description of the Drawings

[0041] Figure 1 is a flowchart of a method for strain field evolution and crack prediction of a full-tailings cemented filling body;

[0042] Figure 2 is the particle size distribution diagram of the tailings of the present invention;

[0043] Figure 3 is the preparation process of the CPB specimen of the present invention;

[0044] Figure 4 is the DIC system equipment of the present invention;

[0045] Figure 5 is the flowchart of the main strain deformation field region segmentation of the present invention;

[0046] Figure 6 is the flowchart of processing the strain field image data of the CPB specimen by the HSV-CVR program of the present invention;

[0047] Figure 7 is the stress-time curve and deformation field distribution image during the loading process of the present invention; wherein, A, B, C, and D are the marked points on the stress-time curve;

[0048] Figure 8 is the strain ratio-time change and deformation field region segmentation display diagram of the CPB specimen of the present invention; wherein, (a) is the diagram of the ratio of the 8 strain intervals of the CPB specimen changing with time; (b) is the display diagram of the 8 regions obtained by segmenting the strain fields of the four observation points A, B, C, and D based on the HSV-CVR model;

[0049] Figure 9 is the first derivative distribution diagram of the 8 partitions of the main strain field of the CPB specimen of the present invention;

[0050] Figure 10 is the first derivative distribution diagram of the main strain interval percentage curve and the early warning signal of the present invention. Detailed Embodiments

[0051] The technical solution of the present invention will be further described below with reference to the accompanying drawings and embodiments.

[0052] As Figure 1 shown, a method for strain field evolution and crack prediction of a full tailings cemented filling body includes the following steps:

[0053] Step S1: Prepare a full tailings cemented filling body (CPB) specimen and conduct a slump test during the preparation of the CPB specimen;

[0054] Step S2: Use a fully automatic flexural and compressive testing machine to evaluate the uniaxial compressive strength of the CPB specimen, and simultaneously monitor the surface changes of the specimen during the test through digital image correlation (DIC) image acquisition technology;

[0055] Step S3: According to the strain field data obtained by DIC, establish a computer vision recognition (HSV-CVR) model based on the HSV color model to analyze the area ratio and distribution of strain regions at different levels, and achieve comprehensive analysis of the image;

[0056] Step S4: Determine the warning signal for crack prediction based on the analysis results of the strain field change.

[0057] Example 1

[0058] Step S1: Prepare a full tailings cemented filling body (CPB) specimen and conduct a slump test during the preparation of the CPB specimen.

[0059] The full tailings cemented filling body specimen is made by mixing and pouring cement, the full tailings of a tungsten mine in Jiangxi Province, and tap water. Among them, the particle size curve of the full tailings is as Figure 2 shown, and the cement type is 42.5 OPC.

[0060] Use a cement paste mixer to prepare the slurry. First, pour the mixing water into the mixing bucket, and then add the uniformly mixed solid materials (full tailings, cement). To ensure the uniformity of the slurry, stir at low speed for 120 s first, then stir at high speed for 120 s, and inject the slurry into a mortar slump tester (50 mm * 100 mm * 150 mm) for slump test.

[0061] Then, pour the slurry into a pre-prepared standard triple mold (70.7 mm * 70.7 mm * 70.7 mm), wrap it with plastic wrap, and place it in a constant temperature and humidity curing box (temperature controlled at (20 ± 2) °C, humidity controlled above 90%) for curing for 28 days to prepare the CPB specimen, as Figure 3 shown.

[0062] Step S2: Use a fully automatic flexural and compressive strength testing machine to evaluate the uniaxial compressive strength of the CPB specimen, and simultaneously monitor the surface changes of the CPB specimen during the test through digital image correlation (DIC) image acquisition technology to obtain strain field data.

[0063] The present invention uses a DYE-300-10S model fully automatic flexural and compressive strength testing machine to evaluate the uniaxial compressive strength of CPB concrete specimens, and simultaneously monitors the surface changes of the specimens during the test through DIC image acquisition technology. The application of DIC technology involves a digital image acquisition system and digital image processing software, including a loading device, a light source, a high-resolution digital camera, and a computer, as Figure 4 shown.

[0064] The DIC image acquisition system consists of a CCD camera, a ring light source, and MV Viewer software. Before loading, calibrate the DIC system, adjust the angle and focal length of the camera to ensure that the entire surface of the specimen can be clearly and comprehensively captured. The camera has a resolution of 4000×3000 pixels and a frame rate of 9 frames per second. Finally, use VIC-2D software to analyze the continuous speckle image sequence captured during the test to obtain the evolution process of the surface strain field of the specimen.

[0065] The specific process of conducting a uniaxial compression test on the CPB specimen is as follows:

[0066] Step S21: After the CPB specimen reaches the specified age, first evenly apply a layer of white primer on its surface, and then randomly sprinkle black spots to form a speckle pattern with high contrast.

[0067] Step S22: Adjust the light source of the DIC digital image acquisition system to ensure appropriate and stable brightness in order to obtain reliable gray-scale data during loading.

[0068] Step S23: Set the acquisition frequency of the camera to capture images of the specimen coated with artificial speckles.

[0069] Step S24: Store the collected image data in the computer and use VIC-2D software to analyze and obtain the displacement changes on the surface to obtain strain field data.

[0070] Step S3: Based on the strain field data obtained by DIC, establish a computer vision recognition (HSV-CVR) model based on the HSV color model to analyze the area ratio and distribution of strain regions at different levels, and achieve comprehensive analysis of the images.

[0071] The basic principle of DIC is an optical measurement method that compares two digital images recorded before and after deformation. By calculating the gray levels of the speckle images on the surface of the specimen before and after deformation, parameters such as the displacement and deformation of the object under study can be obtained. To simplify the visual recognition and image segmentation process of physical images, DIC technology is used to obtain the strain field image, and the segmentation process of its strain field is as Figure 5 shown.

[0072] Step S31: First, use a camera to capture the high-definition speckle image of the specimen during the uniaxial compression test.

[0073] Step S32: Subsequently, use VIC-2D software to obtain the maximum principal strain field data in the study area.

[0074] Here, the principal strain field ε1 data is exported in the form of a high-definition video and frame extraction is performed to obtain the high-definition image of the region of interest (ROI).

[0075] Step S33: Then, use the HSV-CVR program to process the strain field image data of the ROI of the CPB specimen.

[0076] The strain field image processed by VIC-2D software is a raster image. The image is composed of different pixel points, and each pixel point contains color information. This color information is recorded using the RGB color model, that is, the information of a pixel point is superimposed with the color information of red (R), green (G), and blue (B).

[0077] As Figure 6 shown, use the HSV-CVR program to process the strain field image data of the ROI of the CPB specimen. The specific process is as follows:

[0078] Step S331: Traverse all image files in the specified directory and use the OpenCV library to read the image files;

[0079] Step S332: Convert the color space of the image from RGB to HSV (hue, saturation, brightness) to process color information more accurately.

[0080] Step S333: Define the number of hue groups and their corresponding ranges to facilitate the classification of pixels with different hues;

[0081] Step S334: Traverse each hue group, create a mask for each group, and determine the area ratio of the hue group in the entire image by calculating the number of non-zero pixels within the mask. Retain the pixels belonging to the interval and replace the pixels not belonging to the interval with white pixels, thereby realizing image segmentation.

[0082] Step S335: Integrate the above area ratio data into a table, calculate the ratio of the colored blocks in the interval area to the entire ROI in the frame images corresponding to different times, and obtain the curve of the strain percentage at this interval scale changing with time, laying a foundation for subsequent data analysis and visualization.

[0083] Step S34: Finally, through the segmentation of the strain field and the quantitative processing of the visual recognition data, comprehensive analysis of the image is achieved.

[0084] Here, the data of the maximum principal strain ε1 is mainly analyzed, and its strain scale range is set from 0 to 0.03. By the uniform equal division method, the strain scale is divided into 8 intervals for more detailed analysis of the strain evolution pattern. These intervals are:

[0085] S1(26.25~30.00×10 ^-3 );S2(22.50~26.25×10 ^-3 );S3(18.75~22.50×10 ^-3 );S4(15.00~18.75×10 ^-3 );S5(11.25~15.00×10 ^-3 );S6(7.50~11.25×10 ^-3 );S7(3.75~7.50×10 ^-3 );S8(0.00~3.75×10 ^-3 )。

[0086] This process not only improves the efficiency of color analysis, but also provides strong data support for in-depth understanding of the characteristics of color distribution in the image.

[0087] Step S4: Determine the warning signal for crack prediction according to the analysis result of the change situation of the strain field.

[0088] Example 2

[0089] In this example, uniaxial compression tests and spread tests were carried out on the full tailings cemented filling slurry with the slurry filling concentration and ash-sand ratio as the main variables. The slurry mass concentration of this filling sample is 76%, the ash-sand ratio is 1:8, the compressive strength at 28 days is 2.4 MPa, and the spread is 300 mm.

[0090] Taking the image captured at the beginning of the uniaxial compression test as the reference image, and the other images captured during the experiment as the deformed images. As Figure 7As shown, four points (A - D) are marked on the stress - time curve, and the corresponding strain fields are determined. According to the strength values of the CPB specimens cured for 28 days, the position with a strength of 0.5 MPa is selected as point A, which serves as the initial loading stage for comparative analysis. The starting point of the continuously appearing S7 deformation region (low - strain region) is taken as point B (Low - Range Strain Point, LRSP); the starting point of the continuously appearing S1 deformation region (high - strain region) is taken as point C (High - Range Strain Point, HRSP); the stress peak point is taken as point D. It should be noted that points B and C here are rough positioning analysis points because the starting point of the S4 middle - strain region (Mid - Range Strain Point, MRSP) cannot be accurately distinguished by the naked eye. Therefore, no observation points are set for the middle - strain region, and the starting time of the specific deformation value region will be quantitatively analyzed using the HSV - CVR algorithm and subsequent first - derivatives.

[0091] Next, based on the HSV - CVR model, the strain evolution characteristics of the CPB specimens at different loading stages were quantitatively analyzed. By monitoring the percentage change of the maximum principal strain ε1 level (S1 to S8) over time, the strain evolution characteristics of the specimens during the entire experiment were revealed.

[0092] As Figure 8 shown, the strain evolution of the control - group specimens with a filling concentration of 76% and a ash - sand ratio of 1:8 is presented. As the stress increases, the percentage of the minimum strain interval S8 gradually decreases, while the percentages of the other level strain intervals gradually increase. These changes are not linear but are accompanied by certain fluctuations, reflecting the complex deformation behavior experienced by the specimens during the loading process. For example, some regions of the specimens may return to the initial state after experiencing elastic strain, while other regions may undergo plastic strain. Throughout the entire test, the percentages of the low - strain intervals S7 and S8 always remain at a relatively high level, indicating that these intervals are relatively active throughout the test. Over time, the percentages of the higher - strain intervals gradually increase, especially in the later stage of the test, due to irreversible plastic strain, there is a sudden increase in the proportion of the high - strain intervals.

[0093] Specifically, the curve in the S7 strain interval showed an obvious evolution pattern throughout the test: starting from a relatively stable state, it increased rapidly, then slowed down slightly, and finally increased rapidly again near crack coalescence. This pattern indicates that the compressible deformation regions inside the specimen responded rapidly to the increasing stress in the initial stage, and then the deformation in these regions further increased and transformed into other strain intervals. Finally, in the crack coalescence stage, the strain value increased rapidly, resulting in a sudden increase in the percentage of the S7 strain interval. For the CPB specimen, in the initial loading stage (point A), the strain was mainly concentrated in the lowest strain interval S8, accounting for as high as 99.95%, indicating that the specimen hardly underwent significant deformation. As the loading continued, the strain began to shift to higher intervals. At point B, the proportion of the S7 interval increased to 3.97%, while the S8 interval decreased to 96.03%. At point C, the strain distribution became more dispersed, strains began to appear in the S1 to S6 intervals, the proportion of the S7 interval increased significantly to 14.23%, and the S8 interval decreased to 75.86%. Near the peak stress (point D), the strain distribution was more extensive, the proportions of the S1 to S6 intervals all increased, the proportion of the S7 interval reached 30.26%, and the S8 interval decreased to 29.26%.

[0094] As Figure 9 shown, the percentage curve of the specimen's ε1 strain interval S1 - S8 (first derivative) change rate can be used to predict the crack development. From the analysis results of the ε1 strain interval, it can be seen that as the stress increases, the strain concentration phenomenon develops from low strain intervals to high strain intervals. In particular, the significant increase in the S7 interval indicates that greater strains are about to occur, and even macroscopic coalescence cracks may appear. The increase in the percentage of the S7 strain interval indicates that more and more strain intervals below the S7 level are developing towards the S7 level, which further confirms that the rapid growth of low strain intervals is an important feature of crack coalescence. For major mine filling projects with strict crack control requirements, it is crucial to pay attention to the change in the percentage growth rate of low strain intervals. Paying attention to the S7 low strain area has important reference value for crack coalescence prediction in actual mine filling projects. As the stress increases, the strain concentration phenomenon develops from low strain intervals to high strain intervals. In the range of S7 - S1, crack coalescence is accompanied by a rapid increase in strain.

[0095] Specifically, the significant increase in the proportion of the S1 strain interval at point C indicates that the strain concentration phenomenon in these intervals of the specimen is the most obvious, which may be the direct result of crack coalescence and propagation. It can be determined that when the strain of S1 increases rapidly, it is caused by crack coalescence. Since the S4 strain interval can reflect the behavior of the CPB specimen at medium strain levels, selecting the S4 strain interval as the key monitoring point can provide important information for the early identification and prevention of cracks. In addition, the significant change in the S4 interval may indicate the intermediate stage of crack propagation, which is of great significance for evaluating material stability and taking timely engineering measures.

[0096] As Figure 10 shown, by comprehensively monitoring the change rates of the strain level curves in the S1, S4, and S7 intervals, growth points with relatively large first derivative values are found as key monitoring points. Here, the first derivative value of S1 is set to 0.01 as the HRSP, the first derivative value of S4 is set to 0.03 as the MRSP, and the first derivative value of S7 is set to 0.2 as the LRSP. This selection is based on a detailed analysis of the strain evolution data of the CPB specimen during the loading process to more accurately predict and evaluate the failure risk of the backfill.

[0097] Below the LRSP, the first derivative curve of the percentage of the S7 strain interval shows fluctuations, which may reflect the non-uniformity of the internal stress distribution of the material in the initial stage of loading or minor adjustments during the experiment. These fluctuations are not sufficient to represent the stable strain response of the material. However, above the LRSP, the strain in the S7 interval begins to increase significantly and continuously, which marks the beginning of the strain concentration phenomenon. In addition, the strain growth after the LRSP is more obvious and continuous, indicating that the material begins to experience more significant strain concentration. The obviousness of this change makes the LRSP a suitable reference point for evaluating the strain evolution and crack development of the material. Similarly, the MRSP and HRSP are selected.

[0098] Since the HRSP indicates that the filling structure is about to reach its maximum bearing capacity, the first derivative value of the S7 region is set relatively small. The MRSP indicates that cracks begin to appear or crack propagation occurs inside the backfill, and the LRSP indicates that the backfill is under peripheral pressure and the strain concentration area begins to increase continuously.

[0099] In practical applications, these mutation points can be used as warning signals to help engineers or researchers predict the failure or breakdown of materials, and can also be further adjusted according to specific mine filling requirements. Selecting these three key points, namely HRSP, MRSP, and LRSP, helps to avoid the interference of experimental noise, focus on analyzing the strain growth stage with engineering significance, and provide key indicators for crack identification and prediction in practical engineering applications.

[0100] The method proposed by the present invention provides a new idea for crack prediction, which helps to improve the engineering stability and safety protection level. When the data of the key parts of the filling structure exceeds the MRSP, remedial measures should be taken to avoid potential hazards. When it exceeds the HRSP, the macroscopic cracks in the mine filling body need to be dealt with in time to ensure the safety and stability of the mine. By monitoring the change of the derivative value in these strain intervals, the failure risk of the filling body can be predicted and evaluated more accurately, providing important reference value for the mine filling project.

[0101] Therefore, the present invention adopts the above-mentioned strain field evolution and crack prediction method for the full-tailings cemented filling body, and a computer vision recognition model based on the HSV color model. By dividing the principal strain field into 8 strain regions, the quantitative analysis of the strain evolution of the filling body is realized, providing early warnings of high, medium, and low levels for the failure risk of the filling body; it not only enhances the understanding of the mechanical behavior of mine filling materials, but also provides a scientific basis for the design, construction monitoring, and risk management of mine filling materials, which is of great significance for ensuring the safety and efficiency of mine operations.

[0102] Finally, it should be noted that the above embodiments are only used to illustrate the technical solutions of the present invention and are not intended to limit them. Although the present invention has been described in detail with reference to the preferred embodiments, those of ordinary skill in the art should understand that they can still modify or equivalently replace the technical solutions of the present invention, and these modifications or equivalent replacements cannot make the modified technical solutions deviate from the spirit and scope of the technical solutions of the present invention.

Claims

1. A strain field evolution and crack prediction method for a full tailings cemented filling body, characterized in that: The following steps are involved: Step S1, preparing a CPB sample of a full tailings cemented filling body, and performing an expansion test during the CPB sample preparation process; Step S2, using a fully automatic flexural and compression testing machine to evaluate the uniaxial compressive strength of the CPB sample, and monitoring the surface changes of the sample during the test by using digital image correlation (DIC) image acquisition technology; Step S3, according to the strain field data obtained by DIC, a computer vision recognition HSV-CVR model based on the HSV color mode is established to analyze the area proportion and distribution of strain areas of different degrees, so as to realize comprehensive analysis of the image; Step S4: determining an early warning signal for crack prediction according to the analysis result of the strain field change.

2. The method for strain field evolution and crack prediction of a full tailings cemented filling body according to claim 1, characterized in that: The full tailings cemented filling body sample is made by mixing and pouring cement, full tailings and tap water. The preparation process is as follows: First, use a cement slurry mixer to prepare the slurry. Pour the mixing water into the mixing bucket, then add the evenly mixed solid materials, tailings and cement, and stir at a low speed for 120 seconds, then at a high speed for 120 seconds. Then, the slurry is injected into a mortar expansion tester to conduct an expansion test; Finally, the slurry was poured into a standard triple test mold, wrapped with plastic wrap, and placed in a constant temperature and humidity curing box for 28 days to prepare the CPB specimen.

3. The method for strain field evolution and crack prediction of a full tailings cemented filling body according to claim 2, characterized in that: The specifications of the mortar expansion tester are 50mm*100mm*150mm; the specifications of the standard triple test mold are 70.7mm*70.7mm*70.7mm; the temperature of the constant temperature and humidity curing box is controlled at 20±2℃, and the humidity is greater than 90%.

4. The method for strain field evolution and crack prediction of a full tailings cemented filling body according to claim 1, characterized in that: In step S2, the application of DIC technology involves a digital image acquisition system and digital image processing software, including a loading device, a light source, a high-resolution digital camera, and a computer; The DIC image acquisition system consists of a CCD camera, a ring light source, and MV Viewer software. Before loading, the DIC system is calibrated to adjust the angle and focal length of the camera. The camera has a resolution of 4000×3000 pixels and a frame rate of 9 frames per second.

5. The method for strain field evolution and crack prediction of a full tailings cemented filling body according to claim 4, characterized in that: In step S2, a uniaxial compression test is performed on the CPB sample, and the specific process is as follows: Step S21, after the CPB sample reaches a specified age, firstly, a layer of white primer is uniformly applied on its surface, and then black spots are randomly sprinkled on it to form a spot pattern with high contrast; Step S22, adjusting the light source of the DIC digital image acquisition system to obtain grayscale data during the loading process; Step S23, setting the acquisition frequency of the camera to capture the sample image coated with artificial spots; Step S24, storing the collected image data in a computer, and using VIC-2D software to analyze the displacement change of the surface to obtain strain field data.

6. The method for strain field evolution and crack prediction of a full tailings cemented filling body according to claim 1, characterized in that: In step S3, based on the strain field image obtained by DIC technology, the strain field is segmented. The specific process is as follows: Step S31, first, using a camera to capture a high-definition speckle image of the sample during the uniaxial compression test; Step S32: Subsequently, the strain field data of the study area is obtained using VIC-2D software; Step S33, then, using the HSV-CVR program to process the strain field image data of the CPB sample region of interest; Step S34: Finally, the comprehensive analysis of the image is achieved through the segmentation of the strain field and the quantitative processing of the visual recognition data.

7. The method for strain field evolution and crack prediction of a full tailings cemented filling body according to claim 6, characterized in that: The strain field image processed by VIC-2D software is a raster image; the image is composed of different pixels, each of which contains color information; the color information is recorded using the RGB color mode, that is, the information of a pixel is superimposed with the red R, green G, and blue B color information; The HSV-CVR program is used to process the strain field image data of the CPB specimen area of ​​interest. The specific process is as follows: Step S331, traverse all image files in the specified directory and use the OpenCV library to read the image files; Step S332, converting the color space of the image from RGB to HSV to process color information; wherein H is hue, S is saturation, and V is brightness; Step S333, defining the number of tone groups and their corresponding ranges, and classifying pixels of different tones; Step S334, traverse each tone group, create a mask for each group, and determine the area proportion of the tone group in the entire image by calculating the number of non-zero pixels in the mask, retain the pixels belonging to the interval, and replace the pixels not belonging to the interval with white pixels, thereby achieving image segmentation; Step S335 , integrating the above area percentage data into a table, calculating the ratio of the color blocks in the interval area in the frame images corresponding to different times to the entire region of interest, and obtaining a curve of the change of the strain percentage at the interval scale over time.

8. The method for strain field evolution and crack prediction of a full tailings cemented filling body according to claim 1, characterized in that: In step S4, according to the analysis results of the strain field changes, three levels of early warning are provided for the damage risk of the filling body, namely, high, medium and low, to determine the early warning signal for crack prediction.

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