CN106202908B - A kind of determination method in high slope relaxation area - Google Patents
A kind of determination method in high slope relaxation area Download PDFInfo
- Publication number
- CN106202908B CN106202908B CN201610529007.1A CN201610529007A CN106202908B CN 106202908 B CN106202908 B CN 106202908B CN 201610529007 A CN201610529007 A CN 201610529007A CN 106202908 B CN106202908 B CN 106202908B
- Authority
- CN
- China
- Prior art keywords
- formula
- area
- monitoring
- similarity
- data
- 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.)
- Active
Links
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16Z—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS, NOT OTHERWISE PROVIDED FOR
- G16Z99/00—Subject matter not provided for in other main groups of this subclass
Abstract
A kind of determination method in high slope relaxation area, the characteristics of according to relaxation area's displacement monitoring, in certain deformation range, concurrent movement can approximation regard rigid motion as, therefore in corresponding deformed area, deformation vector corresponding to deformation position should have similar intensity and direction, it proposes on the basis of automatic measured data, Variations similar region is measured by using new method for measuring similarity, and realize the judgement to relaxation area eventually by K means methods, to reduce dependence of the project to expert, improve the precision of loose area's judgement, pass through the area's judgement that most preferably relaxes, when to eliminating relaxation area's hidden danger using engineering means, engineering spending can be reduced.
Description
Technical field
The invention belongs to slope monitoring technical fields, and in particular to a kind of determination method in high slope relaxation area.
Background technology
As Human dried bloodstains scale is growing, the construction projects such as mining, communications and transportation, water conservancy and national defence are big
Amount exploitation, slope become one of the Basic Geological environment of engineering activity, dig up mine and cut landslide form mechanism caused by the human factors such as slope
Sex chromosome mosaicism is more and more prominent.Landslide occurs gently then to increase investment, extends the duration；It is heavy then destroy building, it causes casualties.Side
Slope stabilization is related with the perturbing area that hand excavation is formed, comprehensive by multiple factors such as stress variation, engineering construction, environmental hydrology factors
Group photo is rung, therefore its deformation of side slope has the characteristics that uncertainties mathematics, randomness, ambiguity, changeability in digging process,
Stress deformation evolution process is a typical nonlinear problem, the complexity with height.The relaxation of slopes in digging process
Area is actually the potential most dangerous sliding surface under the coefficient of stability of design, and relaxation area's range is exactly corresponding most dangerous cunning
Kinetoplast.Estimation of stability and the regulation that side slope rationally, is effectively defined for relaxation area's range are significant, for guidance
Engineering practice has positive effect, can minimize the excavated volume of the earthwork under the premise that security is guaranteed, has considerable warp
Ji value.
Since defining for area's range of relaxation is a highly complex nonlinear problem, it is difficult to simple mechanics, mathematics
Model is described.Although the number such as more operating limit balancing methods, finite element analysis, elastic plastic theory in Traditional project practice
It is worth the numerical analysis that analysis method carries out displacement and stress fields, also achieves a large amount of achievement.But in a large amount of engineering reality
In trampling, due to being difficult to obtain accurate on-site parameters, the application of these traditional statistical methods is restricted.
Traditional slope monitoring is realized using manual method, for dividing mainly according to monitoring personnel's for relaxation area
Experience and intuition, the problem of due to data volume and method, it is difficult to be realized by automated method.
Invention content
In order to overcome the disadvantages of the above prior art, the purpose of the present invention is to provide a kind of judgements in high slope relaxation area
Method is realized and is judged landslide position, current form, it can be achieved that being automated.
In order to achieve the above object, the technical solution that the present invention takes is：
A kind of determination method in high slope relaxation area, includes the following steps：
Step 1) enables monitoring point set S={ x for all monitoring positionsi,yi,zi, i=1,2 ..., m, observation point
SiIt is v to correspond to the deflection in some cycles with vector in moment ti=[Δ x, Δ y, Δ z]T, obtain the original of Monitoring of Slope Deformation
Beginning data, pre-process initial data, including noise reduction, smooth and missing data supplement, specially：
1.1) noise spot is filtered by Kalman filtering to initial data, it is assumed that monitoring error is the white Gaussian that variance is 0
Noise obtains noise reduction data；
1.2) noise reduction data are carried out smoothly by using configurable fixed size sliding window, acquiescence smooth window is big
Small is 1 hour, obtains smoothed data；
1.3) missing data supplement is carried out to smoothed data, interpolation is calculated using Newton interpolating method, specific formula for calculation is：
vi=vi-1+(vi+1-vi-1)/(ti+1-ti-1)； (1)
Step 2) is for arbitrary two monitoring points SiAnd Sj, point similarity between is calculated, specially：
2.1) according to formula (2) computational geometry distance, and regularization is carried out using formula (3),
Two monitoring point SiAnd SjCorresponding deformation vector is diAnd dj, geometric distance is：
By di,jIt is mapped onto [- 1,1], and minimum value is made to correspond to -1, maximum value and correspond to 1, be written as with formula
2.2) to the geometric distance after the regularization of calculatingAccording to formula (4) computational geometry Distance conformability degree Sdis；Become
If the intensity of shape amount is more close, the correlation of the two is stronger, is expressed as with formula
2.3) direction similarity s is calculated according to formula (5)dir
2.4) comprehensive similarity is calculated according to formula (6)；
Step 3) calculates similarity grouping, specially：
3.1) comprehensive similarity of calculating is arranged from big to small；
3.2) similarity grouping, packet count 3, for initial center point are calculated using K-Means methods according to formula (7)
Selection be respectively comprehensive similarity minimum value, average value and maximum value, fast implement S using K-means clustering algorithmssub
Solution, SsubPoint value maximum grouping in center is exactly the corresponding grouping in most dangerous loose area, SsubMidpoint is monitored to corresponding
The corresponding geometry of point number is exactly most dangerous loose area,
The beneficial effects of the invention are as follows：
The present invention according to relaxation area's displacement monitoring the characteristics of, in certain deformation range, concurrent movement can approximation regard as
It is rigid motion, therefore in corresponding deformed area, the deformation vector corresponding to deformation position should have similar intensity
The direction and.Traditional slope monitoring is realized using manual method, for dividing mainly according to the warp of monitoring personnel for relaxation area
It tests and intuition, the problem of due to data volume and method, it is difficult to be realized by automated method.The present invention is proposed certainly
On the basis of dynamic measurement data, Variations similar region is measured by using new method for measuring similarity, and finally leads to
It crosses K-means methods and realizes that the judgement to relaxation area improves loose area's judgement to reduce dependence of the project to expert
Precision, by the area's judgement that most preferably relaxes, when to eliminating relaxation area's hidden danger using engineering means, it is possible to reduce engineering is paid wages.
Description of the drawings
Fig. 1 is the algorithm flow chart of high slope relaxation area's judgement.
Specific implementation mode
The present invention is described in detail below in conjunction with the accompanying drawings.
Referring to Fig.1, the determination method in a kind of high slope relaxation area, includes the following steps：
Step 1) enables monitoring point set S={ x for all monitoring positionsi,yi,zi, i=1,2 ..., m, observation point
SiIt is v to correspond to the deflection in some cycles with vector in moment ti=[Δ x, Δ y, Δ z]T, obtain the original of Monitoring of Slope Deformation
Beginning data, pre-process data, including noise reduction, smooth and missing data supplement, specific practice are：
1.1) noise spot is filtered by Kalman filtering to initial data, it is assumed that monitoring error is the white Gaussian that variance is 0
Noise obtains noise reduction data；
1.2) noise reduction data are carried out smoothly by using configurable fixed size sliding window, acquiescence smooth window is big
Small is 1 hour, obtains smoothed data；
1.3) smoothed data missing data is supplemented, interpolation is calculated using Newton interpolating method, specific formula for calculation is：
vi=vi-1+(vi+1-vi-1)/(ti+1-ti-1) (1)
Step 2) is for arbitrary two monitoring points SiAnd Sj, point similarity between is calculated, specially：
2.1) according to formula (2) computational geometry distance, and regularization is carried out using formula (3),
Two monitoring point SiAnd SjCorresponding deformation vector is diAnd dj, geometric distance is：
By di,jIt is mapped onto [- 1,1], and minimum value is made to correspond to -1, maximum value and correspond to 1, be written as with formula
2.2) to the geometric distance after the regularization of calculatingAccording to formula (4) computational geometry Distance conformability degree Sdis；Become
If the intensity of shape amount is more close, the correlation of the two is stronger, is expressed as with formula
2.3) direction similarity s is calculated according to formula (5)dir
2.4) comprehensive similarity is calculated according to formula (6)；
Step 3) calculates similarity grouping, specially：
3.1) comprehensive similarity of calculating is arranged from big to small；
3.2) similarity grouping, packet count 3, in order to ensure that algorithm is steady are calculated using K-Means methods according to formula (7)
Qualitative, the selection for initial center point is minimum value, average value and the maximum value of comprehensive similarity respectively, uses K-means
Clustering algorithm fast implements SsubSolution, SsubPoint value maximum grouping in center is exactly most dangerous corresponding point of loose area
Group, SsubMidpoint corresponding to the geometry of corresponding monitoring point number is exactly most dangerous loose area,
。
Claims (1)
1. a kind of determination method in high slope relaxation area, which is characterized in that include the following steps：
Step 1) enables monitoring point set S={ x for all monitoring positionsi,yi,zi, i=1,2 ..., m, observation point SiWhen
It is v to carve t and correspond to the deflection in some cycles with vectori=[Δ x, Δ y, Δ z]T, obtain the original number of Monitoring of Slope Deformation
According to, initial data is pre-processed, including noise reduction, it is smooth supplemented with missing data, specially：
1.1) noise spot being filtered by Kalman filtering to initial data, it is assumed that monitoring error is the white Gaussian noise that variance is 0,
Obtain noise reduction data；
1.2) noise reduction data are carried out smoothly by using configurable fixed size sliding window, acquiescence smooth window size is
1 hour, obtain smoothed data；
1.3) missing data supplement is carried out to smoothed data, interpolation is calculated using Newton interpolating method, specific formula for calculation is：
vi=vi-1+(vi+1-vi-1)/(ti+1-ti-1)； (1)
Step 2) is for arbitrary two monitoring points SiAnd Sj, point similarity between is calculated, specially：
2.1) according to formula (2) computational geometry distance, and regularization is carried out using formula (3),
Two monitoring point SiAnd SjCorresponding deformation vector is diAnd dj, geometric distance is：
By di,jIt is mapped onto [- 1,1], and minimum value is made to correspond to -1, maximum value and correspond to 1, be written as with formula
2.2) to the geometric distance after the regularization of calculatingAccording to formula (4) computational geometry Distance conformability degree Sdis；Deflection
If intensity it is more close, the correlation of the two is stronger, is expressed as with formula
2.3) direction similarity s is calculated according to formula (5)dir
2.4) comprehensive similarity is calculated according to formula (6)；
Step 3) calculates similarity grouping, specially：
3.1) comprehensive similarity of calculating is arranged from big to small；
3.2) similarity grouping, packet count 3, the choosing for initial center point are calculated using K-Means methods according to formula (7)
Select be respectively comprehensive similarity minimum value, average value and maximum value, fast implement S using K-means clustering algorithmssubAsk
Solution, SsubPoint value maximum grouping in center is exactly the corresponding grouping in most dangerous loose area, SsubMidpoint is compiled by corresponding monitoring point
Number geometry it is corresponding be exactly most dangerous loose area,
。
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610529007.1A CN106202908B (en) | 2016-07-05 | 2016-07-05 | A kind of determination method in high slope relaxation area |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610529007.1A CN106202908B (en) | 2016-07-05 | 2016-07-05 | A kind of determination method in high slope relaxation area |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106202908A CN106202908A (en) | 2016-12-07 |
| CN106202908B true CN106202908B (en) | 2018-10-19 |
Family
ID=57465773
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610529007.1A Active CN106202908B (en) | 2016-07-05 | 2016-07-05 | A kind of determination method in high slope relaxation area |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106202908B (en) |
Families Citing this family (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106595563B (en) * | 2016-12-13 | 2018-12-11 | 青岛理工大学 | A kind of measuring method of Landslide Deformation sliding period conspicuousness parameter |
Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103207952A (en) * | 2013-04-23 | 2013-07-17 | 华北科技学院 | Slope displacement prediction method |
| CN103268420A (en) * | 2013-05-24 | 2013-08-28 | 河海大学 | Method for evaluating risks of high rock slope |
| CN103646181A (en) * | 2013-12-20 | 2014-03-19 | 青岛理工大学 | Method for determining wriggle slide type artificial side slope stability coefficient and early warning criteria |
| CN104881583A (en) * | 2015-06-04 | 2015-09-02 | 唐晓松 | Multi-means, dynamic and whole-process landslide prewarning method |
| CN105043355A (en) * | 2015-05-13 | 2015-11-11 | 西安科技大学 | Side slope micro-deformation monitoring method and side slope micro-deformation monitoring system based on similarity determination criterion |
| CN105046100A (en) * | 2015-09-17 | 2015-11-11 | 水利部南京水利水文自动化研究所 | Novel analytical method of deformation monitoring data of dam slope |
| CN105224815A (en) * | 2015-11-05 | 2016-01-06 | 中国电建集团成都勘测设计研究院有限公司 | Counter-tilt slope Toppling Deformation factor of influence Sensitivity Analysis |
-
2016
- 2016-07-05 CN CN201610529007.1A patent/CN106202908B/en active Active
Patent Citations (7)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN103207952A (en) * | 2013-04-23 | 2013-07-17 | 华北科技学院 | Slope displacement prediction method |
| CN103268420A (en) * | 2013-05-24 | 2013-08-28 | 河海大学 | Method for evaluating risks of high rock slope |
| CN103646181A (en) * | 2013-12-20 | 2014-03-19 | 青岛理工大学 | Method for determining wriggle slide type artificial side slope stability coefficient and early warning criteria |
| CN105043355A (en) * | 2015-05-13 | 2015-11-11 | 西安科技大学 | Side slope micro-deformation monitoring method and side slope micro-deformation monitoring system based on similarity determination criterion |
| CN104881583A (en) * | 2015-06-04 | 2015-09-02 | 唐晓松 | Multi-means, dynamic and whole-process landslide prewarning method |
| CN105046100A (en) * | 2015-09-17 | 2015-11-11 | 水利部南京水利水文自动化研究所 | Novel analytical method of deformation monitoring data of dam slope |
| CN105224815A (en) * | 2015-11-05 | 2016-01-06 | 中国电建集团成都勘测设计研究院有限公司 | Counter-tilt slope Toppling Deformation factor of influence Sensitivity Analysis |
Non-Patent Citations (1)
| Title |
|---|
| "边坡开挖卸荷松弛区的力学性质研究";王浩等;《中国地质灾害与防治学报》;20071215;第18卷(第S0期);第5-9页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106202908A (en) | 2016-12-07 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Huang et al. | Landslide displacement prediction based on multivariate chaotic model and extreme learning machine | |
| Teegavarapu et al. | Geo-spatial grid-based transformations of precipitation estimates using spatial interpolation methods | |
| Ren et al. | Application of wavelet analysis and a particle swarm-optimized support vector machine to predict the displacement of the Shuping landslide in the Three Gorges, China | |
| Li et al. | An approach using random forest intelligent algorithm to construct a monitoring model for dam safety | |
| Li et al. | Using the curve moment and the PSO-SVM method to diagnose downhole conditions of a sucker rod pumping unit | |
| Eke et al. | Coevolution of width and sinuosity in meandering rivers | |
| Yao et al. | Training enhanced reservoir computing predictor for landslide displacement | |
| Kamińska et al. | Comparison of deteministic interpolation methods for the estimation of groundwater level | |
| US20140088875A1 (en) | Pumpjack torque fill estimation | |
| Fortin et al. | Spatial autocorrelation | |
| CN106202908B (en) | A kind of determination method in high slope relaxation area | |
| CN102147934A (en) | Geological interface three-dimensional morphological analysis method based on TIN (triangulated irregular network) | |
| Su et al. | Dam structural behavior identification and prediction by using variable dimension fractal model and iterated function system | |
| CN115600044B (en) | River section flow calculation method, device, equipment and storage medium | |
| Wang et al. | Multi-objective optimization design and control of deviation-correction trajectory with undetermined target | |
| Seyedzadeh et al. | A new analytical method for derivation of infiltration parameters | |
| Shams Ghahfarokhi et al. | Investigating the causes of Lake Urmia shrinkage: climate change or anthropogenic factors? | |
| CN105447319A (en) | Determination method for different geological interface calculation parameters of sliding blocks of arch dam abutment | |
| Chen et al. | An application of Coons patch to generate grid-based digital elevation models | |
| CN117113644A (en) | Slope temporary slip forecasting method, system and medium based on deep monitoring deformation sequence | |
| CN109115228A (en) | A kind of object localization method based on weighted least-squares volume Kalman filtering | |
| Sepahvand et al. | Evaluation of deep machine learning-based models of soil cumulative infiltration | |
| Huo et al. | Chaos-based support vector regression for load power forecasting of excavators | |
| CN104715298A (en) | Enhanced oil recovery potential predicting method based on grey system theory | |
| CN110717290B (en) | Method for drawing dam contour line based on three-time interpolation method |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| C06 | Publication | ||
| PB01 | Publication | ||
| C10 | Entry into substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |