CN115511850A - Method for identifying stable state of side landslide - Google Patents
Method for identifying stable state of side landslide Download PDFInfo
- Publication number
- CN115511850A CN115511850A CN202211216792.7A CN202211216792A CN115511850A CN 115511850 A CN115511850 A CN 115511850A CN 202211216792 A CN202211216792 A CN 202211216792A CN 115511850 A CN115511850 A CN 115511850A
- Authority
- CN
- China
- Prior art keywords
- landslide
- crack
- identifying
- side landslide
- development
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/10—Segmentation; Edge detection
- G06T7/13—Edge detection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10028—Range image; Depth image; 3D point clouds
Landscapes
- Engineering & Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Quality & Reliability (AREA)
- Pit Excavations, Shoring, Fill Or Stabilisation Of Slopes (AREA)
- Testing Or Calibration Of Command Recording Devices (AREA)
Abstract
The invention discloses a method for identifying a stable state of a side landslide, which comprises the following steps: carrying out fixed-time interval aerial photography on the landslide through unmanned aerial vehicle photogrammetry, establishing a DOM (document object model), a DSM (digital document model) and a three-dimensional point cloud model, carrying out boundary identification of the landslide, and identifying crack development and position distribution; filtering point cloud data by an iCSF method for correcting the gravity direction to obtain a visual displacement field of the side landslide; measuring and recording the crack development condition by adopting a crack monitoring device; carrying out dynamic continuous monitoring on critical position monitoring points and crack development in the edge landslide area, the edge landslide front edge, the edge landslide rear edge and the edge landslide middle part, recording and identifying the crack development and deformation conditions of the edge landslide, and carrying out primary identification on the edge landslide development stage; the obtained side landslide crack development condition and the obtained deformation condition are integrated to further identify the state of the side landslide; the method can timely and accurately capture the side landslide crack and deformation development trend, and has strong adaptability to the terrain complex area.
Description
Technical Field
The invention belongs to the technical field of geotechnical engineering, and particularly relates to a method for identifying a stable state of a side landslide.
Background
The side-landslide steady-state identification consists of two aspects: fracture development and surface deformation. Generally, accurate determination of the stage of landslide development requires numerous data supports from the group consisting of deep deformation, surface fracture development. However, the inclination measuring equipment needs to be drilled and buried for deep deformation acquisition, data are easily influenced by the environment, and the reflection of the landslide evolution stage on the ground surface is more intuitive;
rainfall, strong earthquake and engineering disturbance often cause certain damage to a mountain body, so that a large number of cracks with different scales are generated on the slope body. Along with the gradual accumulation of the damage of the rock mass at the damaged part, the cracks at different parts in the slope body are communicated and merged, and a damage boundary can be formed in the slope body, so that the stability of the slope body is reduced, and the instability of the slope body is accelerated; the crack plays the role of a dominant channel for surface water infiltration in the side landslide forming process, and the start of the side landslide is accelerated. Therefore, identification and monitoring of the cracks at the key positions of the sideslip body are important links for investigation and monitoring of geological disasters.
For the identification of the stable state of the side slope, the traditional measurement technology has the defects of mobility, time and labor consumption, feedback lag and low precision in the traditional shooting means.
Disclosure of Invention
The invention aims to solve the problems that the traditional measurement technology has the defects of mobility, time and labor consumption, feedback lag and low precision in the traditional shooting method in the aspect of identifying the stable state of the side slope.
In order to solve the above problems, the present invention specifically relates to a method for identifying a stable state of a side landslide, including the steps of:
firstly, carrying out fixed-time interval aerial photography on the side landslide through unmanned aerial vehicle photogrammetry, establishing a DOM (document object model), a DSM (surface model) and a three-dimensional point cloud model, and extracting earth surface gradient information based on the DSM and roughness information based on the three-dimensional point cloud model; on the basis, the boundary identification of the side landslide is carried out, and the development and the position distribution of the crack are identified;
filtering point cloud data by an iCSF method for correcting the gravity direction based on the three-dimensional point cloud model obtained in the step one, separating and extracting ground points, checking the relative positions of image control points to obtain monitored relative errors, and performing deformation monitoring on a multi-temporal point cloud model by an M3C2 (multi-scale model-to-model cloud comparison) method to obtain a side landslide visual displacement field;
thirdly, measuring the crack development condition by adopting a crack monitoring device based on the crack position determined in the first step, and periodically reading and recording the crack monitoring device through manual/unmanned aerial vehicle inspection;
fourthly, when the crack develops to a certain stage and a clear landslide boundary is formed, monitoring points are arranged at the positions of the front edge, the rear edge and the like of the side landslide, which reflect the deformation characteristics of the side landslide;
step five: arranging a target prism at a monitoring point, placing a GPS receiver antenna, arranging a reference point in a region outside a side landslide, erecting a GPS receiver and a GPS RTK reference station receiver on the reference point, and periodically monitoring the reference point of a stable region to invert the deformation condition of the monitoring point;
step six, carrying out dynamic continuous monitoring on critical position monitoring points (GPS RTK) and crack development (crack meter) in the front edge, the rear edge and the middle part of the side landslide in the side landslide area, recording and identifying the crack development and deformation conditions of the side landslide in real time, and carrying out primary identification on the development stage of the side landslide;
and step seven, integrating the side landslide crack development condition obtained in the step six and the deformation condition obtained in the step two to further identify the state of the side landslide.
Further, the specific method for identifying the boundary of the open side landslide in the first step and identifying the development and position distribution of the crack comprises the following steps: compared with a local neighborhood, the steep ridge or the crack of the side landslide has a sudden change in the slope, the DSM slope map identifies a large steep ridge, and the DOM model identifies a crack which develops near a main crack at the rear edge of the landslide.
Further, the method for filtering the point cloud data by the irsf method for correcting the gravity direction in the second step is as follows:
the CSF algorithm firstly inverts the 3D point cloud data, then covers the inverted point cloud by simulating 'cloth', and generates an approximate curved surface according to the position of the 'cloth' surface covering the point cloud under the action of gravity; identifying ground points from the original point cloud data by comparing the distances between the points in the original point cloud data and the generated 'cloth' surface, and separating the off-points into further feature extraction;
simulating the cloth according to Newton's second law, wherein the relation between the position of the cloth and the force is determined by the following formula:
wherein X represents the position of the particle at time t; f ext (X, t) represents an external force, which is composed of gravity and a collision force by an obstacle:
F ext (X,t)＝mg+f interact (X,t) (2)
when a particle encounters some object in its direction of motion; f int (X, t) represents the internal force of the particle at position X and time t, the internal force is represented by the point cloud (edge)World) interaction; because both the internal force and the external force vary with time t, the above equation is solved by numerical integration (such as the euler method) in the realization of the cloth simulation;
the method is suitable for gentle surfaces and is not friendly to the characteristics of the landslide terrain, so that F is ext (X, t) the gravity direction is changed from the vertical direction of the traditional CSF method to the direction vertical to the slope surface; this direction is determined by the median of the DSM model slope; the method comprises the following steps:
(1) Obtaining a median alpha of the regional gradient based on a DSM (digital model system) model generated by unmanned aerial vehicle photography technology, and determining a normal vector [ N ] according to the median gradient x ,N y ,N z ]；
(2) The first term gravity term g' = [ N ] on the right side in the equation (2) x ,N y ,N z ]·9.81；
(3) And (5) bringing the corrected gravity term back to the formula (2) to start filtering.
Further, the specific method of the third step is as follows: fixing a steel nail at each of two ends of the crack respectively to ensure the motion synchronism of the steel nail and the soil bodies at two sides, fixing the initial scale end and the free end of the electronic vernier caliper on the two steel nails respectively, and driving the steel nails to move when the crack develops so as to change the reading of the electronic vernier caliper;
further, the reference points in the fifth step are arranged in a stable area which is 30m away from the side slip mass.
Further, the specific method of the sixth step is as follows:
let reference point P1 arrange on the relatively stable side of crack, monitor point P2 arrange on the unstable side of crack. Firstly, the reference point P1 and the monitoring point P2 are positioned by RTK to obtain the world coordinates (X) of the points P1 and P2 10 ,Y 10 ,Z 10 ) (ii) a Then, the reference point P1 and the monitoring point P2 are dynamically and continuously monitored, and the world coordinates (X) of the two points are obtained 1t ,Y 1t ,Z 1t )、(X 2t ,Y 2t ,Z 2t ) The dislocation length l and the crack width d in the crack development process can be obtained by calculationAnd the height h of the slab staggering, assuming that the included angle between the connecting line between the points P1 and P2 and the x axis is
Included angle of dynamic monitoring stage
The solution formula is as follows:
h＝Z 2t -Z 1t
and then, carrying out primary identification on the development stage of the side landslide according to the dislocation length l, the crack width d and the dislocation height h data in the crack development process.
Further, the method for preliminarily identifying the development stage of the side slope according to the dislocation length l, the crack width d and the dislocation height h in the crack development process comprises the following steps:
(1) Judging that the side landslide without the crack is in a stable state;
(2) Judging that the crack is in a stable state when a small amount of cracks are generated but no obvious side landslide boundary is formed;
(3) When the crack continuously develops to form a definite side landslide boundary, judging that the crack is in a relatively unstable state;
(4) And judging that the side slope with the front edge bulging and the rear edge crack dislocation is already in an unstable state, and when the change rates of three parameters, namely dislocation length l, crack width d and dislocation height h, are remarkably improved, early warning is required.
Further, the method for further identifying the state of the side slope by integrating the side slope crack development condition obtained in the sixth step and the deformation condition obtained in the second step in the seventh step is as follows:
when the side landslide area has no cracks and small deformation, judging that the side landslide area is in a stable state; when the area generates partial cracks but no boundary is formed yet and the deformation rate does not exceed 10 mm/month, judging that the side landslide area is in a relatively stable state; when the side landslide area cracks continuously develop on the surface to form a clear landslide boundary and the deformation rate exceeds 10 mm/month, judging that the side landslide area cracks are in a relatively unstable state; when the crack at the rear edge of the landslide in the side landslide area is staggered from top to bottom, the front edge bulges to form a pinnate crack, the deformation rate exceeds 50 mm/month, and the area is judged to be in an unstable state.
In general, compared with the prior art, the above technical solution contemplated by the present invention can achieve the following beneficial effects:
(1) According to the method for identifying the stable state of the side landslide based on the unmanned aerial vehicle technology, the unmanned aerial vehicle can be used for replacing manpower, and all-weather 360-degree dead-corner-free scanning and monitoring of the side landslide form are realized.
(2) The method for identifying the stable state of the side landslide based on the unmanned aerial vehicle technology can timely and accurately capture the development trend of cracks and deformation of the side landslide, and the given result comprehensively and objectively reflects the actual situation of the state of the side landslide.
(3) The method for identifying the stable state of the side landslide based on the unmanned aerial vehicle technology can monitor and evaluate other landslide, railway, highway side landslide and other terrain complex areas, and has strong adaptability.
Drawings
FIG. 1 is a logic flow diagram of a preferred embodiment of the present invention;
FIG. 2 is a side-slip terrain slope map generated based on DSM images in accordance with a preferred embodiment of the invention;
FIG. 3 is a DOM image based on the landslide threshold and crack identification in accordance with a preferred embodiment of the present invention;
FIG. 4 is a graph showing the comparison result between the point cloud of 18 days at 12 months in 2021 and the point cloud of 23 days at 12 months in 2021 for a landslide according to a preferred embodiment of the present invention;
FIG. 5 is a schematic view of crack monitoring of an unmanned aerial vehicle according to a preferred embodiment of the present invention;
FIG. 6 is a schematic diagram of deformation monitoring of critical locations of side landslide in accordance with a preferred embodiment of the present invention;
FIG. 7 is a photograph of a preferred embodiment of the present invention taken by plane in a steady state;
FIG. 8 is a photograph of a preferred embodiment of the present invention taken by plane in a more steady state;
FIG. 9 is a photograph of a preferred embodiment of the present invention taken by plane in a less stable state;
FIG. 10 is a photograph of an aerial image taken in an unstable state in accordance with the preferred embodiment of the present invention;
Detailed Description
In order to make the objects, technical solutions and advantages of the present invention more apparent, the present invention is described in further detail below with reference to the accompanying drawings and embodiments. It should be understood that the specific embodiments described herein are merely illustrative of the invention and are not intended to limit the invention. In addition, the technical features involved in the embodiments of the present invention described below may be combined with each other as long as they do not conflict with each other.
Referring to fig. 1, a method for identifying a steady state of a side landslide includes the following steps:
firstly, carrying out fixed-time interval aerial photography on the side landslide through unmanned aerial vehicle photogrammetry, establishing a DOM (document object model), a DSM (surface model) and a three-dimensional point cloud model, and extracting earth surface gradient information based on the DSM and roughness information based on the three-dimensional point cloud model; on the basis, the boundary identification of the side landslide is carried out, and the development and the position distribution of the crack are identified;
the specific method for identifying the boundary of the open side landslide in the first step and identifying the development and position distribution of the crack comprises the following steps: compared with a local neighborhood, the steep ridge or the crack of the side landslide has a sudden change in the slope, the DSM slope map identifies a large steep ridge, and the DOM model identifies a crack which develops near a main crack at the rear edge of the landslide.
Fig. 2 shows a diagram of slope of landslide terrain generated based on DSM image for recognition of certain landslide boundary (slope in the diagram) and the slope obtained in the research area is distributed between 0 ° and 90 °. The slope calculation result shows that compared with a local neighborhood, cracks and steep slopes on the periphery of the active landslide generally have slopes higher than 50 degrees, and the slopes are related to abrupt changes of surface cracks and steep ridges. Compared with the local neighborhood of the steep bank or the crack, the steep bank or the crack has a sudden change (the sudden change is more than 50 degrees) on the slope, the DOM and the DSM data are combined to cooperatively identify the crack at the rear edge and determine the boundary of the landslide, the DSM slope map can identify a larger steep bank, the DOM model can identify the small cracks which are approximately concentrically arranged on the plane and develop near the main crack at the rear edge of the landslide, and the identification result of the boundary of the landslide is shown in figure 3.
Filtering point cloud data by an iCSF method for correcting the gravity direction based on the three-dimensional point cloud model obtained in the step one, separating and extracting ground points, checking the relative positions of image control points to obtain monitored relative errors, and performing deformation monitoring on the multi-temporal point cloud model by an M3C2 method to obtain a side landslide visual displacement field;
the method for filtering point cloud data by the iCSF method for correcting the gravity direction comprises the following steps:
the CSF algorithm firstly inverts the 3D point cloud data, then covers the inverted point cloud by simulating 'cloth', and generates an approximate curved surface according to the position of the 'cloth' surface covering the point cloud under the action of gravity; identifying ground points from the original point cloud data by comparing the distances between the points in the original point cloud data and the generated 'cloth' surface, and separating the off-points into further feature extraction;
simulating the cloth according to Newton's second law, wherein the relation between the position of the cloth and the force is determined by the following formula:
wherein X represents the position of the particle at time t; f ext (X, t) represents an external force, which is composed of gravity and a collision force by an obstacle:
F ext (X,t)＝mg+f interact (X,t) (2)
when a particle encounters some object in its direction of motion; f int (X, t) represents the internal force of the particle at position X and time t, resulting from interaction with the point cloud (boundary); because the internal force and the external force are all applied at any timeT varies, so the above equation is usually solved by numerical integration (e.g., euler method) in the implementation of the cloth simulation;
the method is suitable for gentle surfaces and is not friendly to the characteristics of landslide terrain, so that the scheme provides a corrected CSF method, and F is compared with F ext (X, t) the gravity direction is changed from the vertical direction of the traditional CSF method to the direction vertical to the slope surface; this direction is determined by the median of the DSM model slope; the method comprises the following steps:
(1) Obtaining a median alpha of the regional gradient based on a DSM (digital model system) model generated by unmanned aerial vehicle photography technology, and determining a normal vector [ N ] according to the median gradient x ,N y ,N z ]；
(2) The right first gravity term g' = [ N ] in the equation (2) x ,N y ,N z ]·9.81；
(3) And (3) bringing the corrected gravity term back to the formula (2) to start filtering.
For deformation monitoring, fig. 4 shows the comparison result of the point cloud of 12/18/2021/23/2021 day on a certain landslide, and the landslide deformation monitoring based on the point cloud provides visual material loss (collapse) or gain (accumulation) condition of the surface change area. Under continuous blasting operation, the exposed position of the fault collapses, so that the strongly weathered soil body on the fault slides, and finally 3 fan-shaped accumulation bodies (# 1, #2, # 3) are accumulated on the bottom platform.
Thirdly, referring to fig. 5-10, based on the crack position determined in the first step, measuring the crack development condition by adopting a crack monitoring device (a combination of an electronic vernier caliper and a steel nail), and periodically reading and recording the crack monitoring device through manual/unmanned aerial vehicle inspection; the specific method comprises the following steps: fixing a steel nail at each of two ends of the crack respectively to ensure the motion synchronism of the steel nail and the soil bodies at two sides, fixing the initial scale end and the free end of the electronic vernier caliper on the two steel nails respectively, and driving the steel nails to move when the crack develops so as to change the reading of the electronic vernier caliper;
fourthly, when the crack develops to a certain stage and a clear landslide boundary is formed, monitoring points are arranged at the positions of the front edge, the rear edge and the like of the side landslide, which reflect the deformation characteristics of the side landslide;
step five: laying a target prism at a monitoring point, placing a GPS receiver antenna, laying a reference point in a region except a side landslide (in the fifth step, the reference point is laid in a stable region which is 30m away from the side landslide body), erecting a GPS receiver and a GPS RTK base station receiver on the reference point, and periodically monitoring the reference point of the stable region to invert the deformation condition of the monitoring point;
step six, carrying out dynamic continuous monitoring on critical position monitoring points (GPS RTK) and crack development (crack meter) in the side landslide area, the front edge, the rear edge and the middle part of the side landslide, recording and identifying the crack development and deformation conditions of the side landslide in real time, and carrying out primary identification on the development stage of the side landslide
The concrete method of the sixth step comprises the following steps:
let reference point P1 arrange on the relatively stable side of crack, monitor point P2 arrange on the unstable side of crack. Firstly, the reference point P1 and the monitoring point P2 are positioned by RTK to obtain the world coordinates (X) of the points P1 and P2 10 ,Y 10 ,Z 10 ) (ii) a Then, the reference point P1 and the monitoring point P2 are dynamically and continuously monitored, and the world coordinates (X) of the two points are obtained 1t ,Y 1t ,Z 1t )、(X 2t ,Y 2t ,Z 2t ) The dislocation length l, the crack width d and the dislocation height h in the crack development process can be obtained by resolving, and an included angle between a connecting line between the P1 point and the P2 point and an x axis is assumed to be
Included angle of dynamic monitoring stage
The solution formula is as follows:
h＝Z 2t -Z 1t
and then, carrying out primary identification on the development stage of the side slope according to the dislocation length l, the crack width d and the dislocation height h in the crack development process. The method for preliminarily identifying the development stage of the side landslide according to the dislocation length l, the crack width d and the dislocation height h data in the crack development process comprises the following steps:
(1) Judging that the side landslide without the crack is in a stable state;
(2) Judging that the crack is in a stable state when a small amount of cracks are generated but no obvious side landslide boundary is formed;
(3) When the crack continuously develops to form a definite side landslide boundary, judging that the crack is in a relatively unstable state;
(4) And judging that the side slope with the front edge bulging and the rear edge crack dislocation is already in an unstable state, and when the change rates of three parameters, namely dislocation length l, crack width d and dislocation height h, are remarkably improved, early warning is required.
Step seven, integrating the side landslide crack development condition obtained in the step six and the deformation condition obtained in the step two to further identify the state of the side landslide; the specific method comprises the following steps:
when the side landslide area has no cracks and small deformation, judging that the side landslide area is in a stable state; when the area generates partial cracks but no boundary is formed yet and the deformation rate does not exceed 10 mm/month, judging that the side landslide area is in a relatively stable state; when the side landslide area cracks continuously develop on the surface to form a clear landslide boundary and the deformation rate exceeds 10 mm/month, judging that the side landslide area cracks are in a relatively unstable state; when the crack at the rear edge of the landslide in the side landslide area is staggered from top to bottom, the front edge bulges to form a pinnate crack, the deformation rate exceeds 50 mm/month, and the area is judged to be in an unstable state.
In summary, the method for identifying different stages of the steady state of the side slope is summarized as follows:
1. and (3) a side slope crack-free stage: on one hand, the joint inspection of the unmanned aerial vehicle and the manual work is carried out regularly to check whether the side landslide has cracks or not; on the other hand, for the side landslide with cracks, a reference control pile is arranged in a relatively stable area, and the coordinates of the control pile are obtained through RTK static measurement. Determining the boundary of the side landslide through the crack position, selecting monitoring points of the front edge, the middle part and the rear edge of the side landslide, and dynamically measuring through RTK to obtain the deformation of the monitoring points of the front edge, the middle part and the rear edge.
2. A crack generation stage: a simple crack monitoring device is adopted, namely, two steel nails are respectively fixed at two ends of a crack, then the initial scale end and the free end of the electronic vernier caliper are respectively fixed on the two steel nails, and the crack develops to drive the steel nails to move, so that the reading of the electronic vernier caliper is changed; on the other hand, the crack is positioned through the DSM model gradient mutation and the DOM image model.
3. A crack development stage: regularly utilize unmanned aerial vehicle to carry on the camera that zooms, shoot the slide caliper who installs in crack department, read slide caliper's electronic reading and obtain the crack width. When the crack develops to a certain degree, the slab staggering occurs at the rear edge of the side landslide, and the slab staggering height and the crack width can be directly obtained by resolving the RTK monitoring point coordinate; on the other hand, crack development is quantitatively extracted through the DSM model gradient mutation and the DOM image model, and the rough range of the landslide is judged.
4. Leading edge bulging and trailing edge dislocation: when the crack develops to a certain degree, the front edge of the side slope bulges, the rear edge of the side slope is staggered, and the horizontal displacement of the bulges and the height of the staggered platform can be directly read through the photography of an unmanned aerial vehicle; and in the aspect of deformation conditions, the deformation conditions are extracted through GPS RTK monitoring and multi-temporal point cloud model change detection.
5. Identifying the development stage of the side landslide: and (3) integrating crack meter readings acquired by the unmanned aerial vehicle, deformation of the three-dimensional point cloud model and deformation trends of key positions of the side landslide acquired by RTK (real-time kinematic), forming visual deformation and damage development trends of the side landslide, and performing preliminary judgment on a development stage of the side landslide.
It will be understood by those skilled in the art that the foregoing is only a preferred embodiment of the present invention, and is not intended to limit the invention, and that any modification, equivalent replacement, or improvement made within the spirit and principle of the present invention should be included in the scope of the present invention.
Claims (8)
1. A method for identifying a stable state of a side landslide is characterized by comprising the following steps:
firstly, carrying out fixed-time interval aerial photography on the side landslide through unmanned aerial vehicle photogrammetry, establishing a DOM (document object model), a DSM (surface model) and a three-dimensional point cloud model, and extracting earth surface gradient information based on the DSM; on the basis, the boundary identification of the side landslide is carried out, and the development and the position distribution of the crack are identified;
filtering point cloud data by an iCSF method for correcting the gravity direction based on the three-dimensional point cloud model obtained in the step one, separating and extracting ground points, checking the relative positions of image control points to obtain monitored relative errors, and performing deformation monitoring on the multi-temporal point cloud model by an M3C2 method to obtain a side landslide visual displacement field;
thirdly, measuring the crack development condition by adopting a crack monitoring device based on the crack position determined in the first step, and periodically reading and recording the crack monitoring device through manual/unmanned aerial vehicle inspection;
fourthly, when the crack develops to a certain stage and a clear landslide boundary is formed, monitoring points are arranged at the positions of the front edge, the rear edge and the like of the side landslide, which reflect the deformation characteristics of the side landslide;
step five: arranging a target prism at a monitoring point, placing a GPS receiver antenna, arranging a reference point in an area outside a side landslide, erecting a GPS receiver and a GPS RTK reference station receiver on the reference point, and periodically monitoring the reference point of a stable area to invert the deformation condition of a crack;
step six, carrying out dynamic continuous monitoring on critical position monitoring points and crack development in the side landslide area, the front edge of the side landslide, the rear edge of the side landslide and the middle part of the side landslide, recording and identifying the crack development and deformation conditions of the side landslide in real time, and carrying out primary identification on the development stage of the side landslide;
and step seven, integrating the side landslide crack development condition obtained in the step six and the deformation condition obtained in the step two to further identify the state of the side landslide.
2. The method for identifying the steady state of the side landslide according to claim 1, wherein the specific method for developing the boundary identification of the side landslide and identifying the crack development and position distribution in the first step is as follows: compared with a local neighborhood, the steep ridge or the crack of the side landslide has a sudden change in the slope, the DSM slope map identifies a large steep ridge, and the DOM model identifies a crack which develops near a main crack at the rear edge of the landslide.
3. The method for identifying a steady state of a side slope according to claim 1, wherein the filtering of point cloud data by the iCSF method for correcting the direction of gravity in the second step is:
the CSF algorithm firstly inverts the 3D point cloud data, then covers the inverted point cloud by simulating 'cloth', and generates an approximate curved surface according to the position of the 'cloth' surface covering the point cloud under the action of gravity; identifying ground points from the original point cloud data by comparing the distances between the points in the original point cloud data and the generated 'cloth' surface, and separating the off-points into further feature extraction;
simulating the cloth according to Newton's second law, wherein the relation between the position of the cloth and the force is determined by the following formula:
wherein X represents the position of the particle at time t; f ext (X, t) represents an external force, which is composed of gravity and a collision force generated by an obstacle:
F ext (X,t)＝mg+f interact (X,t)(2)
when a particle encounters some object in its direction of motion; f int (X, t) represents the internal force of the particle at position X and time t, resulting from interaction with the point cloud (boundary); since both the internal and external forces vary with time t, the above equation is integrated numerically in the implementation of the cloth simulation(e.g., the euler method);
the above method is suitable for gentle surfaces and is not suitable for the characteristics of landslide terrain, therefore, F ext (X, t) the gravity direction is changed from the vertical direction of the traditional CSF method to the direction vertical to the slope surface; this direction is determined by the median of the DSM model slope; the method comprises the following steps:
(1) Obtaining a median alpha of the regional gradient based on a DSM (digital model system) model generated by unmanned aerial vehicle photography technology, and determining a normal vector [ N ] according to the median gradient x ,N y ,N z ]；
(2) The right first gravity term g' = [ N ] in the equation (2) x ,N y ,N z ]·9.81；
(3) And (3) bringing the corrected gravity term back to the formula (2) to start filtering.
4. The method for identifying a stable state of a side landslide according to claim 1, wherein the specific method of the third step is as follows: a steel nail is fixed respectively at the crack both ends, guarantees the synchronism of steel nail and both sides soil body motion, fixes respectively on two steel nails through electronic vernier caliper initial scale end, free end, and the crack development can drive the steel nail and remove to change electronic vernier caliper's reading, patrol through artifical unmanned aerial vehicle and carry out periodic reading and record to crack meter vernier caliper.
5. The method according to claim 1, wherein the reference point in the fifth step is arranged in a stable region spaced apart from the landslide body by 30 m.
6. The method for identifying the steady state of the side landslide according to claim 1, wherein the specific method of the sixth step is as follows:
let reference point P1 arrange on the relatively stable side of crack, monitor point P2 arrange on the unstable side of crack. Firstly, the reference point P1 and the monitoring point P2 are positioned by RTK to obtain the world coordinates (X) of the points P1 and P2 10 ,Y 10 ,Z 10 ) (ii) a Then, the reference point P1 and the monitoring point P2 are dynamically operatedContinuously monitoring to obtain world coordinates (X) of two points 1t ,Y 1t ,Z 1t )、(X 2t ,Y 2t ,Z 2t ) The dislocation length l, the crack width d and the dislocation height h data in the crack development process can be obtained by calculation, and the included angle between the connecting line between the P1 point and the P2 point and the x axis is assumed to be
Included angle of dynamic monitoring stage
The solution formula is as follows:
h＝Z 2t -Z 1t
and then, carrying out primary identification on the development stage of the side landslide according to the dislocation length l, the crack width d and the dislocation height h data in the crack development process.
7. The method for identifying the stable state of the side landslide according to claim 4, wherein the method for preliminarily identifying the development stage of the side landslide according to the dislocation length l, the crack width d and the dislocation height h in the crack development process comprises the following steps:
(1) Judging that the side landslide without the crack is in a stable state;
(2) Judging that the crack which generates a small amount of cracks but does not form an obvious side landslide boundary is in a more stable state;
(3) When the crack continuously develops to form a definite side landslide boundary, judging that the crack is in a relatively unstable state;
(4) Judging that the side slope with the front edge bulging and the rear edge crack staggered is in an unstable state; when the change rates of three parameters, namely the dislocation length l, the crack width d and the dislocation height h, are remarkably improved, early warning is required.
8. The method for identifying the stable state of the side slope according to any one of claims 1 to 7, wherein the method for further identifying the state of the side slope by combining the side slope crack development condition obtained in the sixth step and the deformation condition obtained in the second step in the seventh step comprises the following steps:
when the side landslide area has no cracks and small deformation, judging that the side landslide area is in a stable state; when partial cracks are generated in the area, but no boundary is formed yet and the deformation rate does not exceed 10 mm/month, judging that the side landslide area is in a relatively stable state; when the side landslide area cracks continuously develop on the surface to form a clear landslide boundary and the deformation rate exceeds 10 mm/month, judging that the side landslide area cracks are in a relatively unstable state; when the crack at the rear edge of the landslide in the side landslide area is staggered from top to bottom, the front edge bulges to form a pinnate crack, the deformation rate exceeds 50 mm/month, and the area is judged to be in an unstable state.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211216792.7A CN115511850A (en) | 2022-09-30 | 2022-09-30 | Method for identifying stable state of side landslide |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202211216792.7A CN115511850A (en) | 2022-09-30 | 2022-09-30 | Method for identifying stable state of side landslide |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN115511850A true CN115511850A (en) | 2022-12-23 |
Family
ID=84507844
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202211216792.7A Pending CN115511850A (en) | 2022-09-30 | 2022-09-30 | Method for identifying stable state of side landslide |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN115511850A (en) |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116596508A (en) * | 2023-05-12 | 2023-08-15 | 广东交科检测有限公司 | Slope crack disease development identification recording method |
-
2022
- 2022-09-30 CN CN202211216792.7A patent/CN115511850A/en active Pending
Cited By (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116596508A (en) * | 2023-05-12 | 2023-08-15 | 广东交科检测有限公司 | Slope crack disease development identification recording method |
| CN116596508B (en) * | 2023-05-12 | 2024-03-08 | 广东交科检测有限公司 | Slope crack disease development identification recording method |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN113091599B (en) | Surface three-dimensional deformation extraction method fusing unmanned aerial vehicle DOM and satellite-borne SAR images | |
| Xiang et al. | Open-pit mine geomorphic changes analysis using multi-temporal UAV survey | |
| Carvajal-Ramírez et al. | Virtual reconstruction of damaged archaeological sites based on Unmanned Aerial Vehicle Photogrammetry and 3D modelling. Study case of a southeastern Iberia production area in the Bronze Age | |
| Abellán et al. | Terrestrial laser scanning of rock slope instabilities | |
| WO2010068186A1 (en) | Method of generating a geodetic reference database product | |
| Wang et al. | Discontinuity interpretation and identification of potential rockfalls for high-steep slopes based on UAV nap-of-the-object photogrammetry | |
| Haas et al. | Quantification and analysis of geomorphic processes on a recultivated iron ore mine on the Italian island of Elba using long-term ground-based lidar and photogrammetric SfM data by a UAV | |
| US20230419501A1 (en) | Image analysis for aerial images | |
| Mazzanti et al. | Short-term geomorphological evolution of the Poggio Baldi landslide upper scarp via 3D change detection | |
| CN102224523B (en) | Stereo matching process system, stereo matching process method, and recording medium | |
| Caudal et al. | Analysis of a large rock slope failure on the east wall of the LAB chrysotile mine in Canada: LiDAR monitoring and displacement analyses | |
| CN111707235A (en) | Ground object measuring method based on three-dimensional laser scanning technology | |
| Mirko et al. | Assessing the Impact of the Number of GCPS on the Accuracy of Photogrammetric Mapping from UAV Imagery | |
| CN115511850A (en) | Method for identifying stable state of side landslide | |
| CN115375866A (en) | Method, device, equipment and medium for updating three-dimensional geological model of mining area | |
| Colica et al. | Investigating the use of UAV systems for photogrammetric applications: a case study of Ramla Bay (Gozo, Malta) | |
| Mora-Felix et al. | Effect of photogrammetric RPAS flight parameters on plani-altimetric accuracy of DTM | |
| CN113587834B (en) | Slope deformation monitoring method based on uncontrolled photogrammetry | |
| Chen et al. | Intelligent interpretation of the geometric properties of rock mass discontinuities based on an unmanned aerial vehicle | |
| Metzger et al. | A Platform for Proactive Risk-Based Slope Asset Management-Phase I | |
| Tran et al. | Application of FARO Focus 3D S350 Terrestrial Laser Scanner in Building 3D Models of Potential Areas of Landslides and Rocks—Case Study in Ha Giang Province, Vietnam | |
| Chen et al. | Semi-automatic Identification of Tunnel Discontinuity Based on 3D Laser Scanning | |
| Dolgikh et al. | Definition of the ground surface deformations and constructions in the rock breakage zone | |
| Rowe | An examination of structural constraints on rockfall behaviour using LiDAR data | |
| Redweik et al. | A strategy for detection and measurement of the cliff retreat in the coast of Algarve (Portugal) by photogrammetry |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination |