CN111259605A - Soil landslide monitoring, early warning and evaluating method - Google Patents
Soil landslide monitoring, early warning and evaluating method Download PDFInfo
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Abstract
The invention discloses a soil landslide monitoring, early warning and evaluating method, which comprises the following steps of S1: acquiring topographic and geological data of a landslide to be monitored, and acquiring a rock and soil parameter test value through an indoor test; s2: establishing a landslide geological model, carrying out limit analysis, determining a landslide damage mode and carrying out landslide monitoring; s3: carrying out uniform design parameter combination on the rock-soil parameter test values, carrying out finite element calculation on all parameter combinations to obtain a displacement field calculated by each parameter combination, and recording the displacement of each monitoring point to obtain a parameter combination-displacement neural network model; s4: carrying out rock-soil parameter inversion correction according to landslide real-time accumulated displacement monitoring data and the 'parameter combination-displacement' neural network model, obtaining rock-soil real-time strength parameters, and carrying out precision inspection; s5: and performing limit analysis by adopting real-time strength parameters, establishing a relation curve of real-time accumulated displacement-safety coefficient, establishing a landslide monitoring three-level early warning grade evaluation system, and evaluating the monitored landslide.
Description
Technical Field
The invention relates to the technical field of landslide monitoring, in particular to a soil landslide monitoring, early warning and evaluating method.
Background
The landslide monitoring technology is an application technology for dynamically testing the stability state and the change rule of a rock-soil body or an engineering structure by adopting a certain technical means to install or embed instrument equipment for researching the stability and the safety of a landslide body and a retaining structure engineering related to the landslide body. The purpose and task of landslide monitoring and early warning are to monitor landslide time-space domain evolution information, induction factors and the like, obtain continuous spatial deformation data to the maximum extent, and apply stability evaluation, prediction and prevention and control engineering effect evaluation of geological disasters.
Because the most obvious characteristic of the landslide is motion, the observation of the deformation of the landslide (the deformation of a slope surface and the deformation of the interior of a slope body) is always emphasized, and the landslide monitoring system plays an important role.
However, in practical engineering, the following problems exist in landslide monitoring and early warning based on deformation monitoring data: 1. the landslide monitoring points are arranged randomly, so that not only can the practical problem not be solved, but also the practical state of the landslide is difficult to effectively reflect and waste is caused; 2. the data of the landslide monitoring points are difficult to establish a corresponding relation with the stability of the landslide, and the stability of the landslide cannot be evaluated through real-time monitoring data; 3. the landslide early warning threshold is given by experience and is difficult to determine.
A method for evaluating the stability of the landslide by using the displacement data of the surface of the landslide is urgently needed to be developed so as to better serve the real-time monitoring of the landslide.
Disclosure of Invention
The invention aims to: the method can determine a slope monitoring point arrangement scheme according to a landslide damage mode, carry out post-processing according to displacement monitoring data, is compatible with landslide surface displacement monitoring data and deep displacement data, establishes a relational expression between the actually measured real-time accumulated displacement of each monitoring point and the landslide safety coefficient, provides a general expression of 'safety coefficient-accumulated displacement', determines an accumulated displacement grading threshold value according to a relation curve change rule, and finally establishes a landslide monitoring grading early warning quantitative evaluation method.
In order to achieve the purpose, the invention adopts the technical scheme that:
a soil landslide monitoring and early warning assessment method comprises the following steps:
s1: obtaining landslide terrain and geological data to be monitored, and obtaining rock and soil parameter test values including test value c of cohesive force of rock and soil mass through indoor test0Test value of internal friction angle of rock-soil body
Modulus of elasticity E of rock-soil mass0；
S2: establishing a landslide geological model, carrying out limit analysis, determining a landslide damage mode, and judging a cut-out area and a trailing edge spalling area; selecting surface displacement monitoring or deep layer displacement monitoring to monitor landslide, and arranging monitoring points in a landslide displacement deformation sensitive area (an area with larger displacement deformation of a landslide body determined according to a landslide limit analysis result) to obtain real-time accumulated displacement monitoring data of the landslide; the landslide surface monitoring displacement and the deep monitoring displacement are both suitable for the method, so that the monitoring means is more flexible and diversified.
S3: carrying out uniform design parameter combination on the rock-soil parameter test values, establishing a finite element model, carrying out finite element calculation on all parameter combinations to obtain a displacement field calculated by each parameter combination, recording the displacement of each monitoring point as a neural network training sample, introducing a neural network program to carry out training and learning, and obtaining a 'parameter combination-displacement' neural network model;
s4: performing rock-soil parameter inversion correction according to the landslide real-time accumulated displacement monitoring data obtained in the step S2 and the parameter combination-displacement neural network model obtained in the step S3 to obtain rock-soil real-time strength parameters including a real-time value c of cohesive force of a rock-soil bodyiReal-time value of internal friction angle of rock-soil body
Real-time value E of rock-soil body elastic modulusiAnd carrying out precision inspection;
s5: using said real-time intensity parameter ci、
EiPerforming limit analysis, calculating safety coefficients corresponding to different real-time intensity parameter combinations, and establishing a relation curve of real-time accumulated displacement-safety coefficients; and establishing a landslide monitoring three-level early warning grade evaluation system, and evaluating the monitored landslide.
As a preferred scheme of the invention, in the step S2, the slope is monitored in real time by a deformation ground radar scanner and a beidou monitoring instrument, and the real-time accumulated displacement of each monitoring point on the surface of the landslide is obtained; or the real-time accumulated displacement of the landslide deep layer real-time monitoring point is obtained by arranging a vertical deep layer inclinometer.
As a preferable embodiment of the present invention, the step S2 includes:
s21: establishing a ground surface model according to the actually measured terrain data; performing random field inversion according to the geological survey data of the finite point positions, and establishing a stratum model; establishing a fault model according to fault data; if the anti-skid structure exists, establishing a structural model;
s22: according to the upper and lower limit principle of the plastic mechanics limit analysis, judging a landslide damage mode, and analyzing a landslide shear outlet area and a trailing edge fracture area;
s23: selecting surface displacement monitoring or deep layer displacement monitoring, arranging monitoring points in a landslide displacement deformation sensitive area, and laying monitoring point numbers of a, b, a, n to obtain real-time accumulated displacement of the landslide along the main axis direction of the sliding surface and the vertical direction.
As a preferred embodiment of the present invention, the neural network model adopted in step S3 is a BP neural network.
As a preferable embodiment of the present invention, the step S3 includes:
s31: testing values c of rock-soil parameters of various stratums of landslide0、
E0Enlarging parameter range as central value, determining factors and levels according to the stratum rock soil parameters and variation range to be inverted, and carrying out uniform experimental design to obtain N parameter combinations;
s32: establishing a finite element model according to the information of the earth surface and the stratum and dividing grids;
s33: respectively adopting N rock-soil parameter combinations to carry out finite element numerical simulation on the geological model, calculating to obtain a displacement field corresponding to each parameter combination, and recording the displacement of each landslide surface monitoring point a, b, a, N along the main axis direction of the sliding surface and the vertical direction;
s34: introducing a neural network program, and performing neural network training by taking displacement data of each monitoring point as a learning sample when each parameter is combined to obtain a 'parameter combination-displacement' neural network model;
s35: and calculating the precision of the parameter combination-displacement neural network model, and calculating more learning samples if the model precision is insufficient until the neural network model meets the precision requirement.
As a preferable embodiment of the present invention, the step S4 includes:
s41: the landslide obtained in step S2 is introducedAccumulating the displacement monitoring data and the parameter combination-displacement neural network model established in the step S3 in real time; determining analysis time interval according to accumulated displacement recording time to obtain analysis time node t of accumulated displacement1,t2,...,ti；
S42: slide the slope at t1,t2,...,tiReal-time accumulated displacement data of each monitoring point along the main axis direction of the sliding surface and along the vertical direction { (a)x1...nx1),(ax2...nx2),...,(axi...nxi)；(az1...nz1),(az2...nz2),...,(azi...nzi) Respectively inputting 'parameter combination-displacement' neural network model to reversely perform rock and soil at t1,t2,...,tiReal-time intensity parameters of time correspondences
S43: real-time strength parameter of rock soil
And (4) substituting the finite element model in the step S32, calculating displacement simulation values of the corresponding monitoring points, and comparing and analyzing the displacement simulation values with actual displacement values to perform precision inspection on the real-time strength parameters.
As a preferable embodiment of the present invention, the step S5 includes:
step S51: establishing limit analysis model by using rock-soil real-time strength parameters
Solving the interval of the real solution according to the upper and lower limit principle of the plastic mechanics limit analysis, and taking the median value in the interval as the corresponding real-time safety coefficient K of the landslide1,K2,...,Ki；
Step S52: obtaining each monitoring point of the landslide at t1,t2,...,tiCorresponding real-time accumulated displacement data along the main shaft direction of the sliding surface and the vertical direction respectively { (a)x1...nx1),(ax2...nx2),...,(axi...nxi)；(az1...nz1),(az2...nz2),...,(azi...nzi) The safety coefficient K of landslide1,K2,...,KiThe corresponding relationship of (a);
step S53: fitting an 'accumulated displacement-safety factor' relation curve of the monitoring points a, b, a, n in the direction along the main axis of the sliding surface, in the vertical direction and in the total displacement; the total displacement can be determined by
Calculating and drawing; the corresponding relation curve can be y ═ kxaAnd (4) showing.
Step S54: dividing the displacement change of each monitoring point into four stages, namely, stages of slow deformation, accelerated deformation, fast deformation and rapid deformation, from tangent points according to the change trend of a relation curve of 'accumulated displacement-safety factor', and determining boundary points of the four deformation stages as three-stage thresholds of accumulated displacement of the monitoring points a, b,., n; i.e. entering the division point of accelerated deformation from slow deformation as a class III threshold, e.g. nxIII,nzIIII.e. entering the fast deformation cut-off point from the accelerated deformation as a class II threshold, e.g. nxII,nzIIThe division point from the rapid deformation stage into the rapid deformation stage is used as a class I threshold, such as nxI,nzI. The set of displacement thresholds for each monitoring point is as follows:
total accumulated shift threshold set: { (a)I,aII,aIII),(bI,bII,bIII),...,(nI,nII,nIII)}
Cumulative x-direction shift threshold set: { (a)xI,axII,axIII),(bxI,bxII,bxIII),...,(nxI,nxII,nxIII)}
z-direction cumulative shift threshold set: { (a)zI,azII,azIII),(bzI,bzII,bzIII),...,(nzI,nzII,nzIII)}
Step S55: and respectively selecting sensitive monitoring points with deformation in the x direction (along the main axis direction of the sliding surface) and the z direction (along the vertical direction) from the monitoring points a, b. The set measuring points a and b are arranged near the cut-out, and the deformation of the set measuring points a and b in the x direction is sensitive; and the measuring points m and n are arranged near the rear tensile crack opening of the sliding surface, and the deformation of the measuring points m and n in the z direction is sensitive. And averaging the x-direction accumulated displacement threshold of the x-direction deformation sensitive monitoring point to be used as the x-direction accumulated displacement threshold of the side slope, and the z direction is the same.
Grade I threshold of accumulated displacement in x direction of landslide:
grade I threshold of accumulated displacement in the z direction of the landslide:
step S56: when the actual measurement accumulated displacement data is processed, the monitoring points are classified according to the deformation sensitive measuring points in the x direction and the deformation sensitive measuring points in the z direction, and the accumulated displacement average values of the deformation sensitive measuring points in the x direction and the deformation sensitive measuring points in the z direction are respectively calculated, for example:
actual measurement accumulated displacement in the x direction of the landslide:
actually measured accumulated displacement in the z direction of the landslide:
step S57: establishing a landslide accumulated displacement monitoring three-level early warning system, which comprises the following specific contents:
TABLE 1 landslide monitoring three-stage early warning system
Step S58: and evaluating the monitored landslide according to the landslide monitoring, early warning and evaluating system and the landslide displacement monitoring data.
In summary, due to the adoption of the technical scheme, the invention has the beneficial effects that:
1. according to the method, through geological modeling and limit analysis and calculation, the damage modes (traction type, creeping type and the like and composite sliding surface) of the landslide can be judged truly, the cut-out area and the trailing edge fracture area are judged, the key monitoring part is determined, related monitoring schemes are arranged in a targeted mode, the arrangement is reasonable, the actual state of the landslide can be reflected accurately, the randomness and the blindness of the arrangement can be avoided, and the cost of monitoring equipment is saved effectively.
2. The method comprises the steps of introducing a 'rock-soil parameter combination-displacement' neural network model, performing parameter inversion through accumulated displacement of monitoring points, calculating landslide safety coefficients through inversion real-time strength parameters by a limit analysis method, determining key monitoring points, establishing a relation between x and z directions and total displacement data of the key monitoring points and the landslide safety coefficients, and finally fitting a relational expression y, namely kx, of the accumulated displacement data of the monitoring points and the landslide safety coefficientsaThe method fills the blank that the corresponding relation between the accumulated displacement data and the landslide safety coefficient is not established in landslide monitoring at the present stage.
3. The method determines a relation and a curve chart of the accumulated displacement of each monitoring point of the landslide and the landslide safety coefficient through a limit analysis method through inversion of monitoring data parameters, the curve directly establishes the relation between the deformation of each monitoring point and the safety coefficient, four deformation stages are changed through the relation curve, corresponding accumulated displacement three-level threshold values are set, and three-level early warning levels of landslide monitoring are determined according to threshold judgment conditions, so that the false alarm rate can be reduced by 10%.
Drawings
Fig. 1 is a flowchart of a soil landslide monitoring, early warning and evaluating method according to the present invention.
FIG. 2 is a schematic diagram of the neural network model of geotechnical parameters-displacement according to the invention.
FIG. 3 is a graph of the "cumulative displacement-landslide safety factor" relationship of the present invention.
Detailed Description
The present invention will be described in detail below with reference to the accompanying drawings.
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.
Example 1
As shown in fig. 1, a soil landslide monitoring, early warning and evaluating method includes the following steps:
s1: obtaining landslide terrain and geological data to be monitored, and obtaining rock and soil parameter test values including test value c of cohesive force of rock and soil mass through indoor test0Test value of internal friction angle of rock-soil body
Modulus of elasticity E of rock-soil mass0。
S2: establishing a landslide geological model, carrying out limit analysis, determining a landslide damage mode, and judging a cut-out area and a trailing edge spalling area; and selecting surface displacement monitoring or deep layer displacement monitoring, and setting monitoring points in a landslide displacement deformation sensitive area to establish an optimal landslide monitoring scheme. If the slope is monitored in real time through deformation foundation radar scanning and a Beidou monitoring instrument (GNSS), the real-time accumulated displacement of each monitoring point on the surface of the landslide is obtained; the real-time accumulated displacement of the real-time monitoring point of the deep layer of the landslide can be obtained by arranging the vertical deep inclinometer. Both surface displacement and deep level displacement monitoring are suitable for the monitoring classification prediction evaluation method of the invention.
The step S2 specifically includes the following steps:
s21: establishing a ground surface model according to the actually measured terrain data;
s22: performing random field inversion according to the geological survey data of the finite point positions, and establishing a stratum model;
s23: establishing a fault model according to fault data;
s24: if the anti-skid structure exists, establishing a structural model;
s25: according to the upper and lower limit principle of the plastic mechanics limit analysis, judging a landslide damage mode, and analyzing a landslide shear outlet area and a trailing edge fracture area;
s26: selecting surface displacement monitoring or deep layer displacement monitoring according to the landslide condition, arranging monitoring points in a landslide displacement deformation sensitive area, and arranging monitoring point numbers a, b. And setting the displacement as the x direction along the main shaft direction of the sliding surface and the displacement as the z direction along the vertical direction. The landslide displacement monitoring method can be selected as follows:
①, if earth surface displacement monitoring is set, remote monitoring means such as deformation ground radar scanning and a Beidou monitoring instrument (GNSS) can be selected, marks are set at monitoring points, slope monitoring point displacement signals are obtained in real time and transmitted to a user client, a corresponding data post-processing method is adopted for analysis, real-time accumulated deformation data of slope monitoring points a, b, a.
② if deep level displacement monitoring is adopted, a deep level inclinometer means can be selected, monitoring depth and arrangement intervals are determined according to geological conditions, the deep level inclinometer is embedded vertically in a deformation sensitive area, displacement of each depth monitoring point is transmitted to a user client through a signal transmission system, real-time accumulated deformation data of the deep level monitoring points a, b, a.
S3: and uniformly designing parameter combinations of the rock parameter test values, establishing a finite element model, performing finite element calculation on all the parameter combinations to obtain a displacement field calculated by each parameter combination, and recording the displacement of each monitoring point to be used as a BP neural network training sample. And (3) introducing a BP neural network program to train and learn to obtain a BP neural network model of parameter combination-displacement meeting the precision requirement.
The step S3 specifically includes the following steps:
s31: testing values c of rock-soil parameters of each stratum of the landslide0、
E0The parameter range is expanded as the central value and is determined by the number of the stratum rock parameters and the variation range of each stratum to be invertedDetermining factors and levels, and performing uniform test design to obtain N parameter combinations;
s32: establishing a finite element model according to the information of the earth surface and the stratum and dividing grids;
s33: respectively adopting N rock-soil parameter combinations to carry out finite element numerical simulation on the geological model, calculating to obtain a displacement field corresponding to each parameter combination, and recording the displacement of each landslide surface monitoring point a, b, a. When the method is calculated by finite element simulation, the formula is as follows:
① A geometric equation between node displacement and unit strain is established according to the mechanical characteristics of the unit]{δ}e；
② according to the geometric equation and the constitutive model of rock-soil parameters, a physical equation is established, wherein [ sigma ] } is [ D [][B]{δ}e；
③ according to the principle of virtual work by applying external force (gravity), the relation between equivalent node load array and node displacement is established, F (k)]{δ}eWherein [ k ]]Is a matrix of cell stiffness, [ k ]]＝∫∫∫[B]T[D][B]dxdydz；
④, overlapping the unit stiffness matrix and the equivalent node load array respectively to establish an overall balance equation set of [ K ] { delta } ═ F ];
⑤, applying boundary conditions, and solving an integral balance equation set to obtain node displacement and unit stress;
⑥, finally, based on the relationship between the displacement of each point in the cell and the displacement of the node, { f } - [ N ]]{δ}eCalculating the displacement of each point in the unit to finally obtain the displacement field of the whole model;
in the above equation, { f } -the displacement array of any point in the cell; [ N ]]A shape function matrix; { delta }e-an array of unit node displacements; { ε } -the strain matrix for any point in the cell; [ B ]]-a cell strain matrix; { σ } -stress matrix for any point in the cell; [ D ]]-a rock-soil parameter constitutive matrix; [ k ] A]-a matrix of cell stiffness; { F } -element node load array; [ K ]]By [ k ]]An integral rigidity matrix formed by superposition; [ F ]]-a total load array formed by superposition of equivalent nodal forces; { δ } -Overall Displacement column vector;
s34: introducing BP neuralPerforming BP neural network training by using displacement data of each monitoring point during each parameter combination as a learning sample through a network program to obtain a parameter combination-displacement BP neural network model, wherein the schematic diagram is shown in FIG. 2, and W in the diagramij、WjkThe weight value of each layer;
s35: and calculating the precision of the 'parameter combination-displacement' BP neural network model, and if the precision of the model is not enough, calculating more learning samples until the model meets the precision requirement.
S4: performing rock-soil parameter inversion correction according to the landslide real-time accumulated displacement monitoring data acquired in the step S2 and the 'parameter combination-displacement' BP neural network model to acquire rock-soil real-time strength parameters including a real-time value c of rock-soil mass cohesive forceiReal-time value of internal friction angle of rock-soil body
Real-time value E of rock-soil body elastic modulusiAnd carrying out precision inspection.
The step S4 specifically includes the following steps:
s41: introducing landslide real-time accumulated displacement monitoring data obtained in the step S2 and the 'parameter combination-displacement' BP neural network model established in the step S3; determining analysis time interval, such as 0.5 month, according to accumulated displacement recording time to obtain analysis time node t of accumulated displacement1,t2,...,ti；
S42: slide the slope at t1,t2,...,tiCumulative displacement data of each monitoring point in time in the x direction and the z direction { (a)x1...nx1),(ax2...nx2),...,(axi...nxi)；(az1...nz1),(az2...nz2),...,(azi...nzi) Respectively inputting 'parameter combination-displacement' BP neural network model to reversely perform rock and soil at t1,t2,...,tiReal-time intensity parameters of time correspondences
S43: real-time strength parameter of rock soil
And (5) substituting the finite element model in the step S32, calculating displacement simulation values of the corresponding monitoring points, and comparing and analyzing the displacement simulation values with actual displacement values to perform precision inspection on the real-time strength parameters.
S5: carrying out limit analysis by adopting real-time intensity parameters, calculating safety coefficients corresponding to different real-time intensity parameter combinations, and establishing a relation curve of real-time accumulated displacement-safety coefficients; and establishing a landslide monitoring three-level early warning grade evaluation system, and evaluating the monitored landslide.
The step S5 specifically includes the following steps:
step S51: establishing limit analysis model by using rock-soil real-time strength parameters
Solving the interval of the real solution according to the upper and lower limit principle of the plastic mechanics limit analysis, and taking the median value in the interval as the corresponding real-time safety coefficient K of the landslide1,K2,...,Ki。
Step S52: obtaining each monitoring point of the landslide at t1,t2,...,tiCorresponding real-time accumulated displacement data along the x direction and the z direction respectively { (a)x1...nx1),(ax2...nx2),...,(axi...nxi)；(az1...nz1),(az2...nz2),...,(azi...nzi) The safety coefficient K of landslide1,K2,...,KiThe corresponding relationship of (a);
step S53: fitting an accumulated displacement-safety factor relation curve of the monitoring points a, b, n in the x and z directions and the total displacement, and showing the relation curve in the x and z directions in fig. 3, wherein the total displacement can be measured by
Calculating and drawing; the corresponding relation curve can be expressed by the following expression:
y＝kxa
step S54: according to the variation trend of the relation curve of 'accumulated displacement-safety factor', as shown in fig. 3, along with the reduction of the safety factor, if tangents of 30 degrees, 45 degrees and 60 degrees are used to be tangent with the curve, the displacement variation of each monitoring point can be divided into four stages by tangents, namely, slow deformation, accelerated deformation, fast deformation and rapid deformation, the boundary point of the four deformation stages is determined as the accumulated displacement three-stage threshold of the monitoring points a, bxIII,nzIIII.e. entering the fast deformation cut-off point from the accelerated deformation as a class II threshold, n in FIG. 3xII,nzIIThe boundary point of the rapid deformation stage entering the rapid deformation stage is used as a class I threshold value, such as n in FIG. 3xI,nzI. The set of displacement thresholds for each monitoring point is as follows:
total accumulated shift threshold set: { (a)I,aII,aIII),(bI,bII,bIII),...,(nI,nII,nIII)}
Cumulative x-direction shift threshold set: { (a)xI,axII,axIII),(bxI,bxII,bxIII),...,(nxI,nxII,nxIII)}
z-direction cumulative shift threshold set: { (a)zI,azII,azIII),(bzI,bzII,bzIII),...,(nzI,nzII,nzIII)}
Step S55: and respectively selecting monitoring points sensitive to deformation in the x direction and the z direction from the monitoring points a, b. In the embodiment, the measuring points a and b are arranged near the shearing outlet, and the deformation of the measuring points a and b in the x direction is sensitive; and the measuring points m and n are arranged near the rear tensile crack opening of the sliding surface, and the deformation of the measuring points m and n in the z direction is sensitive. And averaging the x-direction accumulated displacement threshold of the x-direction deformation sensitive monitoring point to be used as the x-direction accumulated displacement threshold of the side slope, and the z direction is the same. As in this example:
grade I threshold of accumulated displacement in x direction of landslide:
grade I threshold of accumulated displacement in the z direction of the landslide:
step S56: when the actual measurement accumulated displacement data is processed, the monitoring points are classified according to the deformation sensitive measuring points in the x direction and the deformation sensitive measuring points in the z direction, and the accumulated displacement average values of the deformation sensitive measuring points in the x direction and the deformation sensitive measuring points in the z direction are respectively calculated, for example:
actual measurement accumulated displacement in the x direction of the landslide:
actually measured accumulated displacement in the z direction of the landslide:
step S57: establishing a landslide accumulated displacement monitoring three-level early warning system, which comprises the following specific contents:
TABLE 1 landslide monitoring three-stage early warning system
Step S58: and evaluating the monitored landslide according to landslide monitoring, early warning and evaluating systems and landslide displacement monitoring data.
The above description is only for the purpose of illustrating the preferred embodiments of the present invention and is not to be construed as limiting the invention, and any modifications, equivalents and improvements made within the spirit and principle of the present invention are intended to be included within the scope of the present invention.
Claims (7)
1. A soil landslide monitoring, early warning and evaluating method is characterized by comprising the following steps:
s1: obtaining landslide terrain and geological data to be monitored, and obtaining rock and soil parameter test values including test value c of cohesive force of rock and soil mass through indoor test0Test value of internal friction angle of rock-soil body
Modulus of elasticity E of rock-soil mass0；
S2: establishing a landslide geological model, carrying out limit analysis, determining a landslide damage mode, and judging a cut-out area and a trailing edge spalling area; selecting surface displacement monitoring or deep layer displacement monitoring to carry out landslide monitoring, and setting monitoring points in a landslide displacement deformation sensitive area to obtain real-time accumulated displacement monitoring data of the landslide;
s3: carrying out uniform design parameter combination on the rock-soil parameter test values, establishing a finite element model, carrying out finite element calculation on all parameter combinations to obtain a displacement field calculated by each parameter combination, recording the displacement of each monitoring point as a neural network training sample, introducing a neural network program to carry out training and learning, and obtaining a 'parameter combination-displacement' neural network model;
s4: performing rock-soil parameter inversion correction according to the landslide real-time accumulated displacement monitoring data obtained in the step S2 and the parameter combination-displacement neural network model obtained in the step S3 to obtain rock-soil real-time strength parameters including a real-time value c of cohesive force of a rock-soil bodyiReal-time value of internal friction angle of rock-soil body
Real-time value E of rock-soil body elastic modulusiAnd carrying out precision inspection;
s5: using said real-time intensity parameter ci、
EiPerforming limit analysis, calculating safety coefficients corresponding to different real-time intensity parameter combinations, and establishing a relation curve of real-time accumulated displacement-safety coefficient(ii) a And establishing a landslide monitoring three-level early warning grade evaluation system, and evaluating the monitored landslide.
2. The soil landslide monitoring, early warning and evaluating method according to claim 1, wherein in step S2, real-time monitoring is performed on the slope surface through deformation foundation radar scanning and a beidou monitoring instrument to obtain real-time accumulated displacement of each monitoring point on the landslide surface; or the real-time accumulated displacement of the landslide deep layer real-time monitoring point is obtained by arranging a vertical deep layer inclinometer.
3. The soil landslide monitoring and warning assessment method according to claim 2, wherein said step S2 comprises:
s21: establishing a ground surface model according to the actually measured terrain data; performing random field inversion according to the geological survey data of the finite point positions, and establishing a stratum model; establishing a fault model according to fault data; if the anti-skid structure exists, establishing a structural model;
s22: according to the upper and lower limit principle of the plastic mechanics limit analysis, judging a landslide damage mode, and analyzing a landslide shear outlet area and a trailing edge fracture area;
s23: selecting surface displacement monitoring or deep layer displacement monitoring, arranging monitoring points in a landslide displacement deformation sensitive area, and laying monitoring point numbers of a, b, a, n to obtain real-time accumulated displacement of the landslide along the main axis direction of the sliding surface and the vertical direction.
4. The soil landslide monitoring, early warning and assessment method according to claim 1, wherein the neural network model adopted in step S3 is a BP neural network.
5. The soil landslide monitoring and warning assessment method according to claim 1, wherein said step S3 comprises:
s31: according to the rock-soil parameter test value c of each stratum of the landslide0、
E0Enlarging parameter range as central value, determining factors and levels according to the stratum rock soil parameters and variation range to be inverted, and carrying out uniform experimental design to obtain N parameter combinations;
s32: establishing a finite element model according to the information of the earth surface and the stratum and dividing grids;
s33: respectively adopting N rock-soil parameter combinations to carry out finite element numerical simulation on the geological model, calculating to obtain a displacement field corresponding to each parameter combination, and recording the displacement of each landslide surface monitoring point a, b, a, N along the main axis direction of the sliding surface and the vertical direction;
s34: introducing a neural network program, and performing neural network training by taking displacement data of each monitoring point as a learning sample when each parameter is combined to obtain a 'parameter combination-displacement' neural network model;
s35: and calculating the precision of the parameter combination-displacement neural network model, and calculating more learning samples if the model precision is insufficient until the neural network model meets the precision requirement.
6. The soil landslide monitoring and warning assessment method according to claim 5, wherein said step S4 comprises:
s41: introducing landslide real-time accumulated displacement monitoring data obtained in the step S2 and the parameter combination-displacement neural network model established in the step S3; determining analysis time interval according to accumulated displacement recording time to obtain analysis time node t of accumulated displacement1,t2,...,ti；
S42: slide the slope at t1,t2,...,tiReal-time accumulated displacement data of each monitoring point along the main axis direction of the sliding surface and along the vertical direction { (a)x1...nx1),(ax2...nx2),...,(axi...nxi)；(az1...nz1),(az2...nz2),...,(azi...nzi) Respectively inputting 'parameter combination-displacement' neural network model to reversely perform rock and soil at t1,t2,...,tiTime of day correspondingReal-time intensity parameters
S43: real-time strength parameter of rock soil
And (4) substituting the finite element model in the step S32, calculating displacement simulation values of the corresponding monitoring points, and comparing and analyzing the displacement simulation values with actual displacement values to perform precision inspection on the real-time strength parameters.
7. The soil landslide monitoring and warning assessment method according to claim 6, wherein said step S5 comprises:
step S51: establishing limit analysis model by using rock-soil real-time strength parameters
Solving the interval of the real solution according to the upper and lower limit principle of the plastic mechanics limit analysis, and taking the median value in the interval as the corresponding real-time safety coefficient K of the landslide1,K2,...,Ki；
Step S52: obtaining each monitoring point of the landslide at t1,t2,...,tiCorresponding real-time accumulated displacement data along the main shaft direction of the sliding surface and the vertical direction respectively { (a)x1...nx1),(ax2...nx2),...,(axi...nxi)；(az1...nz1),(az2...nz2),...,(azi...nzi) The safety coefficient K of landslide1,K2,...,KiThe corresponding relationship of (a);
step S53: fitting an 'accumulated displacement-safety factor' relation curve of the monitoring points a, b, n in the direction along the main axis of the sliding surface, in the vertical direction and in the total displacement, and using y as kxaRepresents;
step S54: dividing the displacement change of each monitoring point into four stages, namely, stages of slow deformation, accelerated deformation, fast deformation and rapid deformation, from tangent points according to the change trend of a relation curve of 'accumulated displacement-safety factor', and determining boundary points of the four deformation stages as three-stage thresholds of accumulated displacement of the monitoring points a, b,., n;
step S55: respectively selecting monitoring points sensitive to deformation along the main axis direction of the sliding surface and monitoring points sensitive to deformation along the vertical direction from the monitoring points a, b,. and n, and averaging accumulated displacement thresholds of the sensitive monitoring points sensitive to deformation along the main axis direction of the sliding surface to obtain an average value which is used as an accumulated displacement threshold of the side slope along the main axis direction of the sliding surface; averaging the accumulated displacement threshold values along the vertical direction of the deformation sensitive monitoring points along the vertical direction to serve as the accumulated displacement threshold values along the vertical direction of the side slope;
step S56: establishing a landslide accumulated displacement monitoring three-level early warning system;
step S57: and evaluating the monitored landslide according to the landslide monitoring, early warning and evaluating system and the landslide displacement monitoring data.
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