CN110223490B - Method for judging rock slope stability based on early warning level - Google Patents
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Abstract
The invention discloses a method for judging rock slope stability based on early warning levels, which comprises the steps of carrying out same-order grade division on importance degrees of different early warning levels for judging landslide occurrence according to different early warning levels, calculating weights of different early warning levels for judging landslide occurrence by adopting an analytic hierarchy process, respectively giving weights according to arrangement positions of monitoring equipment, introducing subjective probability of judging whether the landslide enters a critical slip stage by the weights calculated by the analytic hierarchy process, quantitatively judging the probability of the whole landslide entering the critical slip stage by combining early warning levels of a plurality of monitoring points, determining that the landslide is in the critical slip stage when the probability is more than or equal to 50%, and issuing early warning information by a landslide monitoring and early warning platform. The invention can accurately ignore error data, correctly identify the landslide hazard level, remove the influence of single error data on the landslide hazard level judgment according to the fused quantized landslide judgment index, and prevent the occurrence of the false alarm event by taking the influence as the basis of the alarm information release.
Description
Technical Field
The invention relates to the field of landslide stability monitoring, in particular to a method for judging the stability of a rock slope based on an early warning level.
Background
The system platform can record and analyze data collected by intelligent monitoring equipment installed on site in real time, and performs blue, yellow, orange and red early warning grade division on landslide danger grades according to a monitoring early warning model embedded in a cloud system. In the actual monitoring and early warning application, in order to ensure the reliability and the integrity of monitoring data, more than one displacement monitoring device is installed at a rear edge crack, and the monitoring and early warning platform can record and analyze each group of data transmitted back by the device. However, the existing monitoring and early warning platform does not fuse multiple groups of data, only uses the highest early warning level of a single monitoring point as the overall danger level of the slope, and uses the highest early warning level as the basis for issuing a danger alarm, because the monitoring equipment of the single monitoring point is easily disturbed due to natural factors or human factors in practical engineering application, so that the early warning platform performs false alarm on the danger level of the landslide, the method for judging the overall stability of the slope based on different danger levels of the multiple monitoring points is provided.
The existing monitoring and early warning platform does not fuse multiple groups of data, only takes the highest early warning level of a single monitoring point as the overall danger level of a slope and takes the highest early warning level as the basis for issuing danger alarms, and because monitoring equipment of the single monitoring point is easily disturbed due to natural factors or human factors in practical engineering application, the early warning platform carries out false warning on the danger level of the landslide.
Disclosure of Invention
In order to overcome the problem that early warning information is mistakenly sent because the monitoring deformation rate is increased rapidly due to disturbance of a single monitoring point of a landslide monitoring and early warning platform, the invention provides a method for judging the stability of a rock slope based on early warning grade, which comprises the following steps:
s1, carrying out same-magnitude division on the importance degree of landslide judgment according to different early warning levels;
s2, calculating the weight of different early warning levels for judging landslide by adopting an analytic hierarchy process;
s3, respectively giving weights according to the arrangement positions of the monitoring equipment;
s4, introducing the subjective probability of judging whether the landslide enters the critical-sliding stage or not by the weight calculated by the analytic hierarchy process;
and S5, fusing the early warning levels of the multiple monitoring points to quantitatively judge the probability that the whole landslide enters the critical-slip stage, and when the probability is more than or equal to 50%, determining that the landslide is in the critical-slip stage, wherein the landslide monitoring and early warning platform can issue early warning information according to the probability.
Further, the step S1 includes the following sub-steps:
s11, carrying out uniform gradual increase on the importance degree of different early warning levels for judging the stability of the slope;
and S12, dividing the importance degree of the pair of the two early warning levels for judging whether the landslide enters the critical sliding stage according to the scale of 1-9.
Further, in step S12, the different warning levels include: the system comprises a blue early warning B, a yellow early warning Y, an orange early warning O and a red early warning R, and the importance degree of slope stability judgment is uniformly increased step by step.
Further, in step S12, the scales of 1 to 9 are specifically:
scale 1, representing two elements of equal importance compared;
scale 3, indicating that the former is slightly more important than the latter, compared to the two elements;
scale 5, indicating that the former is significantly more important than the latter when compared to the two elements;
scale 7, indicating that the former is more important than the latter, compared to the two elements;
scale 9, indicating that the former is extremely important compared to the latter;
scales 2, 4, 6 and 8, represent intermediate values of the above described adjacent decisions.
Further, in step S12, the result of dividing according to the scale of 1-9 includes:
(R:B)=9; (R:Y)=6; (R:O)=3 ; (O:B)=6; (O:Y)=3; (Y:B)=3;
the above results are available (C)1:C2) Denotes X, i.e. warning class C1And early warning grade C2The degree of importance for the same event is X, and there is (C)2:C1)=1/X；
From this, a decision matrix a is obtained:
further, in step S2, for a consistent decision matrix a, each column of the matrix a is normalized to be a corresponding weight vector, so that the sum method is to use the arithmetic mean of the 4 column vectors as the weight vector, and the specific calculation steps are as follows:
s21, normalizing the elements of the judgment matrix A according to columns;
s22, adding the normalized columns;
s23, dividing the added vector by 4 to obtain a weight vector;
according to the calculation steps, the following steps can be obtained:
whereinw 1 ,w 2 ,w 3 ,w 4And the weights respectively represent the blue early warning B, the yellow early warning Y, the orange early warning O and the red early warning R for judging whether the landslide enters the critical-slip stage.
Further, in step S3, since the sliding body of the rock slope has strong integrity, the displacement change rates monitored by the surface displacers arranged on the same trailing edge fracture are substantially consistent, and there is no special monitoring point, so that each monitoring point may be given the same weight coefficient, that is: c = 1-nWherein C is the weight of the monitoring point location,nthe number of the point positions is monitored.
Further, in step S4, since the early warning event occurs step by step, the criterion of the early warning level is only related to the slope accumulated displacement tangent angle, and thus the early warning level weight may be obtained by stacking:
whereinp b , p y , p o , p r The values are all more than or equal to 0 and less than or equal to 1, and the subjective probability is defined, so that the early warning level can be regarded as the subjective probability of judging that the landslide enters the stage of impending landslide according to the early warning level, namely, the early warning level is quantized.
Further, in step S5, the result of the quantization process in step S4 is combined with the weighting factor of each monitoring point position to obtain:
whereinn 1+n 2+n 3+n 4=nAnd P is the probability of judging the landslide to be instable after multi-source data fusion is carried out according to the early warning level of each monitoring point, when the probability P is greater than or equal to 50%, the landslide can be judged to enter the stage of impending landslide, and the landslide monitoring and early warning platform can issue early warning information according to the probability.
Furthermore, when the weight vectors are sorted under the single criterion, consistency check must be performed, and the specific steps are as follows:
s61, calculating a consistency index C.I.:
wherein the content of the first and second substances,λ max to determine the maximum eigenvalue of the matrix a,mjudging the matrix order of the matrix A;
s62, searching a corresponding average random consistency index R.I according to the order of the judgment matrix A;
s63, calculating a consistency ratio C.R.:
when the consistency ratio c.r. <0.1, the consistency of the judgment matrix is considered acceptable.
The invention has the beneficial effects that: the invention can accurately ignore error data, correctly identify the landslide hazard level, remove the influence of single error data on the landslide hazard level judgment according to the fused quantized landslide judgment index, and prevent the occurrence of the false alarm event by taking the influence as the basis of the alarm information release.
Detailed Description
The embodiment provides a method for judging rock slope stability based on early warning grade, which specifically comprises the following steps:
1. analytic Hierarchy Process (AHP) is a practical multi-attribute decision-making method, which decomposes a complex problem into various component factors, and the components are grouped according to a domination relationship to form a hierarchical structure, the relative importance of the factors in the Hierarchy is determined by means of pairwise comparison, and then the judgment of a decision maker is integrated to determine the total sequence of the relative importance of a decision-making scheme. The whole process embodies the basic characteristics of human decision thinking, namely decomposition and judgment synthesis. In the embodiment, different importance scales are respectively given to different early warning levels according to the importance degree of the landslide entering the critical slip stage.
According to the consistent matrix method, the importance degree of two different early warning levels for judging the stability of the slope can be divided into 9 scales, namely 1-9, wherein:
scale 1, representing two elements of equal importance compared;
scale 3, indicating that the former is slightly more important than the latter, compared to the two elements;
scale 5, indicating that the former is significantly more important than the latter when compared to the two elements;
scale 7, indicating that the former is more important than the latter, compared to the two elements;
scale 9, indicating that the former is extremely important compared to the latter;
the scales 2, 4, 6, and 8 represent intermediate values of the above-described adjacent judgment.
Different early warning grades are gradually and uniformly increased for the importance degree of judging the stability of the slope, and the early warning grades are from low to high: and then dividing the importance degree of each two early warning levels to judging whether the landslide enters the critical slipping stage according to the scale of 1-9 to obtain:
(R:B)=9; (R:Y)=6; (R:O)=3 ; (O:B)=6; (O:Y)=3; (Y:B)=3;
the above results are available (C)1:C2) Denotes X, i.e. warning class C1And early warning grade C2The degree of importance for the same event is X, and there is (C)2:C1)=1/X；
From this, a decision matrix a is obtained:
for a consistent decision matrix a, each column of it is normalized to be the corresponding weight vector. The sum method is therefore used to take the arithmetic mean of the 4 column vectors as the weight vector.
The specific implementation steps are as follows:
the first step is as follows: judging the element normalization of the matrix A according to columns;
the second step is that: adding the normalized columns;
the third step: and dividing the added vector by 4 to obtain a weight vector.
According to the calculation, the following results are obtained:
whereinw 1 ,w 2 ,w 3 ,w 4And the weights respectively represent the blue early warning B, the yellow early warning Y, the orange early warning O and the red early warning R for judging whether the landslide enters the critical-slip stage.
2. A consistency check must also be performed when computing the ordering weight vector under a single criterion. When the matrix deviation consistency is judged to be too large, the reliability degree of the approximate estimation is doubtful. Therefore, the consistency of the judgment matrix needs to be checked, and the specific steps are as follows:
(1) calculate the consistency index c.i.:
wherein the content of the first and second substances,λ max to determine the maximum eigenvalue of the matrix a,mjudging the matrix order of the matrix A;
(2) searching a corresponding average random consistency index R.I. according to the order of the judgment matrix A:
| the matrix order r.i. | 1 0 | 2 0 | 3 0.52 | 4 0.89 | 5 1.12 | 6 1.26 | 7 1.36 |
| The matrix order r.i. | 8 1.41 | 9 1.46 | 10 1.49 | 11 1.52 | 12 1.54 | 13 1.56 | 14 1.58 |
(3) The consistency ratio c.r is calculated.
When c.r. <0.1, the consistency of the judgment matrix is considered acceptable.
And (3) through consistency test:
and the consistency requirement is met.
3. Because the sliding body of the rock slope has stronger integrity, the displacement change rates monitored by the earth surface displacement device arranged on the same trailing edge crack are basically consistent, no special monitoring point exists, and therefore, each monitoring point can be endowed with the same weight coefficient, namely: c = 1-nWherein C is the weight of the monitoring point location,nthe number of the point positions is monitored.
Meanwhile, the weight of the monitoring point position for judging the stability of the slope is given to the mean value, so that the error of the layout point position for measuring the stability of the slope can be reduced as much as possible.
4. Subjective probabilities are introduced according to the weights calculated by the analytic hierarchy process. Because the early warning event happens step by step, the criterion of the early warning level is only related to the slope accumulated displacement tangent angle, and therefore the weight of the early warning level can be superposed to obtain:
whereinp b , p y , p o , p r The values are all more than or equal to 0 and less than or equal to 1, and the subjective probability is defined, so that the early warning level can be regarded as the subjective probability of judging that the landslide enters the stage of impending landslide according to the early warning level, namely, the early warning level is quantized.
Therefore, the quantization result is combined with the position weight coefficient of each monitoring point to be calculated to obtain:
whereinn 1+n 2+n 3+n 4=nAnd P is the probability of judging the landslide to be instable after multi-source data fusion is carried out according to the early warning level of each monitoring point, when the probability is greater than or equal to 50%, the landslide can be judged to enter the stage of impending landslide, and the landslide monitoring and early warning platform can issue early warning information according to the probability.
The foregoing is illustrative of the preferred embodiments of this invention, and it is to be understood that the invention is not limited to the precise form disclosed herein and that various other combinations, modifications, and environments may be resorted to, falling within the scope of the concept as disclosed herein, either as described above or as apparent to those skilled in the relevant art. And that modifications and variations may be effected by those skilled in the art without departing from the spirit and scope of the invention as defined by the appended claims.
Claims (10)
1. A method for judging the stability of a rock slope based on an early warning grade is characterized by comprising the following steps:
s1, carrying out same-magnitude division on the importance degree of landslide judgment according to different early warning levels;
s2, calculating the weight of different early warning levels for judging landslide by adopting an analytic hierarchy process;
s3, respectively giving weights according to the arrangement positions of the monitoring equipment;
s4, introducing the subjective probability of judging whether the landslide enters the critical-sliding stage or not by the weight calculated by the analytic hierarchy process;
and S5, fusing the early warning levels of the multiple monitoring points, namely combining the subjective probability and the weight of the arrangement position of each monitoring device, quantitatively judging the probability that the whole landslide enters the critical-sliding stage, and when the probability is more than or equal to 50%, determining that the landslide is in the critical-sliding stage, wherein the landslide monitoring and early warning platform can issue early warning information according to the probability.
2. The method for judging the stability of the rock slope based on the early warning level as claimed in claim 1, wherein the step S1 comprises the following sub-steps:
s11, carrying out uniform gradual increase on the importance degree of different early warning levels for judging the stability of the slope;
and S12, dividing the importance degree of the pair of the two early warning levels for judging whether the landslide enters the critical sliding stage according to the scale of 1-9.
3. The method for determining the stability of the rock slope based on the early warning level as claimed in claim 2, wherein in the step S12, the different early warning levels include: the system comprises a blue early warning B, a yellow early warning Y, an orange early warning O and a red early warning R, and the importance degree of slope stability judgment is uniformly increased step by step.
4. The method for judging the stability of the rock slope based on the early warning level as claimed in claim 3, wherein in the step S12, the scales of 1-9 are specifically as follows:
scale 1, representing two elements of equal importance compared;
scale 3, indicating that the former is slightly more important than the latter, compared to the two elements;
scale 5, indicating that the former is significantly more important than the latter when compared to the two elements;
scale 7, indicating that the former is more important than the latter, compared to the two elements;
scale 9, indicating that the former is extremely important compared to the latter;
scales 2, 4, 6 and 8, represent intermediate values of the above described adjacent decisions.
5. The method for determining the stability of the rock slope based on the early warning level as claimed in claim 4, wherein the step S12, the dividing result according to the scale of 1-9 includes:
(R:B)=9; (R:Y)=6; (R:O)=3 ; (O:B)=6; (O:Y)=3; (Y:B)=3;
the above results are available (C)1:C2) Denotes X, i.e. warning class C1And early warning grade C2The degree of importance for the same event is X, anIs provided with (C)2:C1)=1/X；
From this, a decision matrix a is obtained:
6. the method as claimed in claim 5, wherein in step S2, for a consistent decision matrix a, each column of the decision matrix a is normalized to be a corresponding weight vector, so that the sum method is to use the arithmetic mean of the 4 column vectors as the weight vector, and the specific calculation steps are as follows:
s21, normalizing the elements of the judgment matrix A according to columns;
s22, adding the normalized columns;
s23, dividing the added vector by 4 to obtain a weight vector;
according to the calculation steps, the following steps can be obtained:
whereinw 1 ,w 2 ,w 3 ,w 4And the weights respectively represent the blue early warning B, the yellow early warning Y, the orange early warning O and the red early warning R for judging whether the landslide enters the critical-slip stage.
7. The method as claimed in claim 6, wherein in step S3, since the sliding body of the rock slope has strong integrity, the displacement change rates monitored by the surface displacers arranged on the same trailing edge fracture are substantially consistent, and there is no special monitoring point, so that each monitoring point can be assigned with the same weight coefficient, that is: c = 1-nWherein C is the weight of the monitoring point location,nthe number of the point positions is monitored.
8. The method for determining stability of a rock slope based on early warning level as claimed in claim 7, wherein in step S4, since the early warning event occurs step by step, the criterion of the early warning level is only related to the slope accumulated displacement tangent angle, so that the weights of the early warning level can be superimposed to obtain:
whereinp b , p y , p o , p r The values are all more than or equal to 0 and less than or equal to 1, and the subjective probability is defined, so that the early warning level can be regarded as the subjective probability of judging that the landslide enters the stage of impending landslide according to the early warning level, namely, the early warning level is quantized.
9. The method as claimed in claim 8, wherein in step S5, the result of the quantization process in step S4 is combined with the position weight coefficients of the monitoring points to calculate:
whereinn 1+n 2+n 3+n 4=nAnd P is the probability of judging the landslide to be instable after multi-source data fusion is carried out according to the early warning level of each monitoring point, when the probability P is greater than or equal to 50%, the landslide can be judged to enter the stage of impending landslide, and the landslide monitoring and early warning platform can issue early warning information according to the probability.
10. The method for judging the stability of the rock slope based on the early warning level as claimed in claim 6, wherein when the weight vectors are sorted under the single criterion, consistency check is also required, and the specific steps are as follows:
s61, calculating a consistency index C.I.:
wherein the content of the first and second substances,λ max to determine the maximum eigenvalue of the matrix a,mjudging the matrix order of the matrix A;
s62, searching a corresponding average random consistency index R.I according to the order of the judgment matrix A;
s63, calculating a consistency ratio C.R.:
when the consistency ratio c.r. <0.1, the consistency of the judgment matrix is considered acceptable.
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| CN112462361B (en) * | 2020-02-26 | 2023-11-21 | 苏州锐思突破电子科技有限公司 | Slope radar early warning algorithm |
| CN112085921B (en) * | 2020-08-20 | 2022-11-11 | 青岛地质工程勘察院(青岛地质勘查开发局) | Landslide comprehensive monitoring and early warning method based on displacement and power multi-parameter |
| CN117198018A (en) * | 2023-10-09 | 2023-12-08 | 长安大学 | Slope stability early warning method based on multisource monitoring data change |
| CN117493833B (en) * | 2023-12-29 | 2024-04-09 | 江西飞尚科技有限公司 | Landslide deformation stage identification method, landslide deformation stage identification system, storage medium and computer |
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| CN201936016U (en) * | 2010-12-13 | 2011-08-17 | 成都理工大学 | Landslide monitoring device |
| KR101270923B1 (en) * | 2011-11-23 | 2013-06-03 | 한국지질자원연구원 | System for monitoring landslide, and positioning sensor used thereto |
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