Detection and prevention methods of soft rock slope instability under stress-hydraulic coupling

Abstract

The present invention relates to a method for detecting and preventing the instability and destruction of soft rock slopes under the action of stress-hydraulic coupling, and the method belongs to the field of slope monitoring technology. The specific steps include: drilling and sampling before the construction of the soft rock mass, identifying the samples using a camera device, and constructing a three-dimensional model of the soft rock mass. After installing a detection device on all the samples taken out, the samples are replaced in the soft rock mass for real-time monitoring. By analyzing the three-dimensional model of the soft rock mass and the data collected in real time, the initial stress state of the soft rock slope can be determined. Based on the three-dimensional model and the material properties of the soft rock, the boundary conditions and key instability areas of the slope instability are analyzed. According to the changes in the monitoring data, corresponding prevention and control measures are taken. This method can observe the changes in the soft rock mass model data in real time, intuitively reflect the changes in the internal structure of the soft rock mass, and provide more accurate data.

Description

Technical Field

[0001] The present invention relates to the technical field of slope monitoring, and in particular to a method for detecting and preventing instability and damage of soft rock slopes under stress-hydraulic coupling. Background Art

[0002] With the development of engineering construction, especially in mountainous areas or areas with complex terrain, the study of slope stability has become particularly important. Existing research on the instability and failure mechanisms of soft rock slopes under the coupled effects of stress and hydraulics is still incomplete, and there is a lack of effective detection methods and prevention and control measures, resulting in frequent slope accidents.

[0003] In my country, reinforcement measures for soft rock slopes typically include techniques such as graded platforms, shotcrete anchor mesh support, or integral concrete support. These techniques have improved the stability of soft rock slopes to a certain extent. However, these support techniques do not fully consider the infiltration of rainwater after slope excavation, the disturbance caused by anchor drilling, and the reduction in rock and soil strength caused by rainwater seeping into the surrounding rock mass. Therefore, these methods cannot accurately detect the specific causes of changes in the internal stability of soft rock slopes, nor can they provide precise prevention and control measures. Given this, there is an urgent need to develop a new method that can detect and prevent the instability of soft rock slopes under the coupled effects of stress and hydraulics. Summary of the Invention

[0004] The purpose of the present invention is to provide a method for detecting and preventing instability and damage of soft rock slopes under stress-hydraulic coupling, so as to solve the technical problem that existing soft rock slope prevention and control measures cannot accurately detect the factors causing instability inside the soft rock mass and cannot provide accurate prevention and control measures.

[0005] In order to achieve the above object, the technical solution adopted by the present invention is as follows:

[0006] A method for detecting and preventing instability of soft rock slopes under stress-hydraulic coupling, the method comprising the following steps:

[0007] Step 1: Drill a number of samples from the soft rock mass before construction and use a camera device to identify them;

[0008] Step 2: Based on the identification data of the obtained samples, a three-dimensional model of the soft rock mass is constructed;

[0009] Step 3: Install monitoring equipment on all removed samples and reinsert the samples into the soft rock mass for continuous monitoring;

[0010] Step 4: Analyze the initial force of the soft rock slope based on the 3D model of the soft rock mass and the real-time collected data;

[0011] Step 5: Analyze the boundary conditions and key areas of slope instability based on the 3D model and the material properties of the soft rock;

[0012] Step 6: Real-time detection of data within the soft rock mass. When the soft rock mass values ​​change and move towards instability, it indicates that the soft rock slope may become unstable.

[0013] Step 7: Take corresponding prevention and control measures according to the changes in the test data until the test data returns to the initial force.

[0014] Furthermore, in step 1, the camera device consists of four cameras, a ring, two sample fixing ends, a telescopic cylinder and a connecting rod. The two sample fixing ends are respectively located at the two ends of the connecting rod and are used to fix the two ends of the sample. The ring surrounds the outside of the sample and can slide on the connecting rod. The four cameras are installed on the side of the ring, facing the east, south, west and north directions of the sample respectively, and are set to align with the sample. The telescopic end of the telescopic cylinder is connected to the ring, and the ring is driven to move back and forth, so that the four cameras can perform peripheral recognition of the sample during movement.

[0015] Furthermore, in step 1, a circular drilling method is used to extract the sample. If the sample breaks during the drilling process, it is bonded and repaired with epoxy resin. Subsequently, the sample is identified using four cameras, and the images captured by the four cameras are spliced ​​together to obtain video data of the sample's periphery. Next, image recognition processing is performed on the video data to identify the soft rock layering within the sample, the soft rock layer materials, and the distribution and size of the pores. Finally, characteristic data related to the soft rock layer materials is automatically retrieved via the internet.

[0016] Furthermore, in step 2, a three-dimensional spatial model is constructed. The model is constructed according to the corresponding proportions based on the size of the slope to be monitored, and the three-dimensional peripheral images of all samples are placed in the three-dimensional spatial model. According to the different sampling positions of each sample, the samples are placed at position points of corresponding proportions in the spatial model. In the three-dimensional spatial model, the position points of the same depth of each sample are placed at the same height. The soft rock model inside the column of each sample is identified, and the three-dimensional model of the soft rock of each sample is obtained by finite element analysis. In the three-dimensional spatial model, points of the same nature or the same rock layer at the same height are connected with lines, and different line types or colors are used to distinguish points at different heights to obtain the image color and void distribution in the sample. The connecting lines of the same height or the same nature are rendered in layers, moving outward from the center of the sample until the entire three-dimensional spatial model is rendered, thereby obtaining a three-dimensional model of the soft rock mass.

[0017] Furthermore, in step 3, multiple humidity sensors, stress sensors, and displacement sensors are installed on the sides of the sample. Transmission lines are connected to an external device equipped with data acquisition equipment to collect data in real time and transmit it back to the data processing system. During the initial acquisition phase, the data collected represents the initial state of the soft rock mass, i.e., its stable state data.

[0018] Furthermore, in step 4, pre-construction data from the soft rock mass is collected, and the rock's self-weight is calculated based on its material properties. These properties include hardness and density. A humidity sensor detects humidity data and identifies the pore structure to determine the structural stress of the soft rock in its initial state. The self-weight and structural stress are superimposed to determine the initial stress state of the soft rock mass. Subsequently, the data related to the initial stress state is integrated with the three-dimensional model of the soft rock mass, allowing the force data of the three-dimensional model to be updated in real time based on changes in the detection data. Users can observe the force conditions of the three-dimensional model of the soft rock mass in real time on the display screen.

[0019] Furthermore, in step 5, based on the material properties of the identified soft rocks at different depths, the strength reduction method is used to gradually reduce the shear strength parameters of the material, which include cohesion and internal friction angle, until the slope reaches a critical state, that is, failure occurs, which is the instability condition of the slope. According to the instability condition, the instability failure boundary condition is applied. The three-dimensional model of the soft rock mass is backed up as a second three-dimensional model of the soft rock mass, and the failure conditions are simulated in the second three-dimensional model of the soft rock mass so that the soft rock mass gradually approaches the critical state, thereby determining the area where instability occurs fastest and marking the key areas of instability. Finally, the simulated key areas of instability are integrated into the original three-dimensional model of the soft rock mass to form a regionally marked three-dimensional model of the soft rock mass.

[0020] Furthermore, in step 6, the collected data is imported into the three-dimensional model of the soft rock mass with regional annotations in real time. Based on the changes in the collected data, it is determined whether the data trend is consistent with the simulated instability direction. If it is consistent, the management personnel are quickly notified and the data that may cause instability is transmitted to the management personnel.

[0021] Furthermore, in step 7, if the instability data indicates an increase in humidity, drilling and pumping measures should be implemented. Specifically, holes are drilled at the bottom to achieve vacuum pumping, while holes are drilled at the top to allow nitrogen to flow in. After the humidity returns to its initial level, the water extraction holes and nitrogen holes should be sealed. If movement or increased stress is detected and it is determined to be loosening caused by vibration or internal oxidation, anchor reinforcement measures should be taken and cement slurry should be injected into the anchor for reinforcement until the subsequently collected data stabilizes within the error range of the initial setting.

[0022] The present invention has the following beneficial effects due to the adoption of the above technical solution:

[0023] The present invention collects samples from different points of soft rock, identifies the samples through graphics, and obtains a three-dimensional soft rock model of the samples through finite element analysis. All samples are combined, and a model of the entire soft rock mass is generated through identification and rendering. The samples are placed back into the soft rock mass and data of the soft rock mass is collected in real time. The collected data is placed in the model of the soft rock mass. Changes in the model data of the soft rock mass can be seen in real time, and structural changes inside the soft rock mass can be seen more directly. The data is more accurate, and corresponding protective measures are taken according to changes in the data. At the same time, the protective process judges the effect of protection based on real-time data to achieve precise protection. BRIEF DESCRIPTION OF THE DRAWINGS

[0024] Figure 1 It is a flow chart of the method of the present invention. DETAILED DESCRIPTION

[0025] To make the objectives, technical solutions, and advantages of the present invention more clearly understood, the present invention is further described in detail below with reference to the accompanying drawings and by way of preferred embodiments. However, it should be noted that many of the details listed in this specification are merely provided to help the reader gain a thorough understanding of one or more aspects of the present invention, and these aspects of the present invention can be practiced even without these specific details.

[0026] like Figure 1 As shown, a method for detecting and preventing instability of soft rock slopes under stress-hydraulic coupling is provided, and the method comprises the following steps:

[0027] Step 1: Drill holes in the soft rock mass before construction and take several samples for identification using a camera device. The camera device consists of four cameras, a circular ring, two sample fixing ends, a telescopic cylinder, and a connecting rod. The two sample fixing ends are located at both ends of the connecting rod and are used to fix the two ends of the sample. The circular ring surrounds the outside of the sample and can slide on the connecting rod. The four cameras are installed on the side of the circular ring, facing the east, south, west, and north directions of the sample, and are set to align with the sample. The telescopic end of the telescopic cylinder is connected to the circular ring, and by driving the circular ring to move back and forth, the four cameras can perform peripheral identification of the sample during the movement. The telescopic movement of the telescopic cylinder drives the four cameras to move slowly and identify the sample in three dimensions, ensuring that 360 degrees of space can be captured from four directions.

[0028] Samples are extracted using a circular drilling method. Any cracks in the sample during drilling are bonded and repaired with epoxy resin. Four cameras are then used to identify the sample, and the images captured by the four cameras are stitched together to generate video data of the sample's periphery. Image recognition processing is then performed on the video data to identify the soft rock layering within the sample, the materials within the soft rock layers, and the distribution and size of the pores. Finally, data related to the material properties of the soft rock layers is automatically retrieved via the internet.

[0029] Step 2: Construct a three-dimensional model of the soft rock mass based on the sample identification data obtained. A three-dimensional spatial model is constructed. This model is constructed according to the corresponding scale based on the slope size to be monitored, and the three-dimensional peripheral images of all samples are placed in the three-dimensional spatial model. Samples are placed at correspondingly scaled points in the spatial model based on their sampling locations. Within the three-dimensional spatial model, points at the same depth for each sample are placed at the same height. The soft rock model within each sample column is identified, and a three-dimensional model of the soft rock for each sample is obtained using finite element analysis. Within the three-dimensional spatial model, points at the same height with the same properties or rock formations are connected with lines. Different line types or colors are used to distinguish points at different heights to obtain the image color and void distribution within the sample. Connecting lines at the same height or with the same properties are rendered as layers, moving outward from the center of the sample until the entire three-dimensional spatial model is rendered, thus obtaining a three-dimensional model of the soft rock mass.

[0030] The size of the 3D model is determined by the size of the slope to be detected, and the corresponding scale is set according to the size of the slope. When the slope is large, the scale will be larger, but the internal details can be viewed by zooming in and out.

[0031] Step 3: Monitoring equipment is installed on all removed samples, and the samples are reinserted into the soft rock mass for continuous monitoring. Multiple moisture sensors, stress sensors, and displacement sensors are installed on the sides of the sampled samples. Transmission lines are connected to an external data collection device, which collects data in real time and transmits it back to the data processing system. During this initial acquisition phase, the data collected represents the initial state of the soft rock mass, i.e., its stable state data.

[0032] Step 4: Analyze the initial forces acting on the soft rock slope based on the 3D model of the soft rock mass and real-time data. Pre-construction internal data of the soft rock mass is collected, and the rock's self-weight is calculated based on its material properties, including hardness and density. A humidity sensor detects humidity data and identifies the pore structure to determine the structural stress of the soft rock in its initial state. The self-weight and structural stresses are superimposed to determine the initial stress state of the soft rock mass. Subsequently, the data related to the initial stress state is integrated with the 3D model of the soft rock mass, allowing the force data of the 3D model to be updated in real time based on changes in the detection data. Users can observe the force conditions in the 3D model of the soft rock mass in real time on the display screen. The collected data is wirelessly transmitted back to the management end of the service system, where it is then transferred from the server to the model.

[0033] Step 5: Analyze the boundary conditions and key areas of slope instability based on the 3D model and the material properties of the soft rock. Based on the identified material properties of the soft rock at different depths, the strength reduction method is used to gradually reduce the shear strength parameters of the material, which include cohesion and internal friction angle, until the slope reaches a critical state, i.e., failure occurs. This is the instability condition of the slope. Based on this instability condition, the instability failure boundary conditions are applied. The 3D model of the soft rock mass is backed up as a second 3D model of the soft rock mass. The failure conditions are simulated in the second 3D model of the soft rock mass so that the soft rock mass gradually approaches the critical state, thereby determining the area where instability occurs the fastest and marking the key areas of instability. Finally, the simulated key areas of instability are integrated into the original 3D model of the soft rock mass to form a regionally labeled 3D model of the soft rock mass.

[0034] Step 6: Real-time monitoring of data within the soft rock mass. When the soft rock mass values ​​change and trend toward instability, it indicates that the soft rock slope may be unstable. The collected data is imported into the regionally labeled 3D model of the soft rock mass in real time. Based on the changes in the collected data, the data trend is determined to be consistent with the simulated instability direction. If so, management personnel are promptly notified and the data that may cause instability is transmitted to them. All data transmission is wireless, making it convenient and fast.

[0035] Step 7: Take corresponding prevention and control measures according to the changes in the detected data until the test data returns to the initial stress. If the instability data indicates an increase in humidity, drilling and water extraction measures should be implemented. Specifically, holes are drilled at the bottom to achieve vacuum water extraction, while holes are drilled at the top to introduce nitrogen. After the humidity returns to the initial level, the water extraction holes and nitrogen holes should be sealed. If movement or stress increase is detected, and it is determined that the loosening is caused by vibration or internal oxidation, anchor reinforcement measures should be taken, and cement slurry should be injected into the anchor for reinforcement until the subsequently collected data stabilizes within the error range of the initial setting again. Different data errors are set according to different scenarios. For those that have not reached the critical state of instability, no processing is required. However, for some highways with traffic volume greater than the set value, smaller error data are set according to demand. When the error data changes, corresponding processing measures need to be taken.

[0036] By collecting samples from different points of the soft rock, identifying the samples through graphics, and analyzing them using the finite element method, a three-dimensional soft rock model of the samples is obtained. All the samples are then combined, and a model of the entire soft rock mass is generated through identification and rendering. The samples are placed back into the soft rock, and data of the soft rock mass is collected in real time. The collected data is placed in the model of the soft rock mass. This allows us to understand changes in the model data of the soft rock mass in real time, more directly grasp the structural changes inside the soft rock mass, and take corresponding protective measures based on changes in the data.

[0037] Matters not covered by the present invention are known technologies.

[0038] The above is only a preferred embodiment of the present invention. It should be pointed out that for ordinary technicians in this technical field, several improvements and modifications can be made without departing from the principles of the present invention. These improvements and modifications should also be regarded as within the scope of protection of the present invention.

Claims

1. A method for detecting and preventing instability of soft rock slopes under stress-hydraulic coupling, characterized by: The method comprises the following steps: Step 1: Drill a number of samples from the soft rock mass before construction and use a camera device to identify them; Step 2: Based on the identification data of the obtained samples, a three-dimensional model of the soft rock mass is constructed; Step 3: Install monitoring equipment on all removed samples and reinsert the samples into the soft rock mass for continuous monitoring; Step 4: Analyze the initial force of the soft rock slope based on the 3D model of the soft rock mass and the real-time collected data; Step 5: Analyze the boundary conditions and key areas of slope instability based on the 3D model and the material properties of the soft rock; Step 6: Real-time detection of data within the soft rock mass. When the soft rock mass value changes and moves towards instability, it indicates that the soft rock slope is unstable. Step 7: Take corresponding prevention and control measures according to the changes in the test data until the test data returns to the initial force; In step 1, a circular drill hole is used to extract the sample. If the sample breaks during the drilling process, it is bonded and repaired with epoxy resin. Subsequently, the sample is identified by four cameras, and the images captured by the four cameras are spliced ​​to obtain video data of the sample periphery. Next, image recognition processing is performed on the video data to identify the stratification of the soft rock in the sample, the soft rock layer material, and the distribution and size of the pores. Finally, the characteristic data related to the soft rock layer material is automatically retrieved through the internet. In step 2, a three-dimensional spatial model is constructed. The model is constructed according to the corresponding proportion based on the size of the slope to be monitored, and the three-dimensional peripheral images of all samples are placed in the three-dimensional spatial model. According to the different sampling positions of each sample, the samples are placed at position points of corresponding proportions in the spatial model. In the three-dimensional spatial model, the position points of the same depth of each sample are placed at the same height, the soft rock model inside the column of each sample is identified, and the three-dimensional model of the soft rock of each sample is obtained by finite element analysis. In the three-dimensional spatial model, points of the same nature or the same rock layer at the same height are connected with lines, and different line types or colors are used to distinguish points at different heights to obtain the image color and gap distribution in the sample, and the connecting lines of the same height or the same nature are rendered as layers, moving from the center of the sample outward until the entire three-dimensional spatial model is rendered, thereby obtaining a three-dimensional model of the soft rock mass; In step 3, multiple humidity sensors, stress sensors, and displacement sensors are installed on the sides of the sample, and transmission lines are connected to the outside. The outside is equipped with a collection device to collect data in real time and transmit it back to the data processing system. In the initial collection stage, the collected data represents the initial state of the soft rock mass, that is, its stable state data; In step 4, the internal data of the soft rock mass before construction is collected, and the self-weight of the soft rock is calculated based on its material properties, including hardness and density. The humidity data is detected by a humidity sensor, and the pore structure is identified to determine the structural stress of the soft rock in its initial state. The self-weight and structural stress are superimposed to obtain the initial stress state of the soft rock mass. Subsequently, the data related to the initial stress state is integrated with the 3D model of the soft rock mass, so that the force data of the 3D model of the soft rock mass can be updated in real time according to changes in the detection data. The user can observe the force status of the 3D model of the soft rock mass in real time through the display screen. In step 5, based on the material properties of the identified soft rock at different depths, the strength reduction method is used to gradually reduce the shear strength parameters of the material, including cohesion and internal friction angle, until the slope reaches a critical state, that is, failure occurs. This is the instability condition of the slope. According to the instability condition, the instability failure boundary condition is applied, and the 3D model of the soft rock mass is backed up as a second 3D model of the soft rock mass. The failure conditions are simulated in the second 3D model of the soft rock mass, so that the soft rock mass gradually approaches the critical state, thereby determining the area where instability occurs fastest, and marking the key areas of instability. Finally, the simulated key areas of instability are integrated into the original 3D model of the soft rock mass to form a regionally marked 3D model of the soft rock mass.

2. The method for detecting and preventing instability and destruction of soft rock slopes under stress-hydraulic coupling according to claim 1 is characterized in that: in step 1, the camera device consists of four cameras, a circular ring, two sample fixing ends, a telescopic cylinder and a connecting rod. The two sample fixing ends are respectively located at the two ends of the connecting rod, and are used to fix the two ends of the sample. The circular ring surrounds the outside of the sample and can slide on the connecting rod. The four cameras are installed on the side of the circular ring, facing the east, south, west and north directions of the sample respectively, and are aligned with the sample. The telescopic end of the telescopic cylinder is connected to the circular ring, and the circular ring is driven to move back and forth, so that the four cameras can perform peripheral identification of the sample during the movement.

3. The method for detecting and preventing instability and damage of soft rock slopes under stress-hydraulic coupling according to claim 1 is characterized in that: in step 6, the collected data is imported in real time into a three-dimensional model of the soft rock mass with regional annotations, and based on the changes in the collected data, it is determined whether the data trend is consistent with the simulated instability direction. If it is consistent, the management personnel are promptly notified and the data causing the instability is transmitted to the management personnel.

4. The method for detecting and preventing instability and damage of soft rock slopes under stress-hydraulic coupling according to claim 1 is characterized in that: in step 7, if the instability data indicates an increase in humidity, drilling and pumping measures should be implemented, with holes drilled at the bottom to achieve vacuum pumping, and holes drilled at the top to introduce nitrogen. After the humidity returns to the initial level, the pumping holes and nitrogen holes should be sealed. If movement or increased stress is monitored and it is determined to be loosening caused by vibration or internal oxidation, anchor reinforcement measures should be taken and cement slurry should be injected into the anchor for reinforcement until the subsequently collected data stabilizes within the initially set error range again.

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