CN105913184A - Real time monitoring data-based mine tailing dam instability risk evaluating method - Google Patents
Real time monitoring data-based mine tailing dam instability risk evaluating method Download PDFInfo
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Abstract
The invention relates to a real time monitoring data-based mine tailing dam instability risk evaluating method and belongs to the technical field of special-purpose digital computation or data processing methods. The method helps overcome defects of technologies of prior art that cannot be applied to determining magnitude of dam slope instability risk, risk sources and the like. According to the real time monitoring data-based mine tailing dam instability risk evaluating method, based on a condition that monitoring data is obtained and a representative instability mode is determined; failure probability and slide area of all instability modes are used for determining instability risk thereof, mine tailing dam instability risk can therefore be quantified, mine tailing dam instability risk can be evaluated mostly based on the monitoring data, mode grouping is realized and the representative instability mode is selected according to correlation of anti-sliding stability safety factors of shallow-layer and deep-layer instability modes, and frequency and a glide plane of the representative instability mode which is regarded as an instability mode with maximum danger are used for evaluating risk in a quantified manner. The real time monitoring data-based mine tailing dam instability risk evaluating method is characterized by scientific property, visual property, reasonability and feasibility; scientific basis is provided for mine tailing dam operation safety management and risk control.
Description
Technical field
The present invention relates to tailing dam unstability risk evaluating method based on Real-time Monitoring Data, belong to and be specially adapted for application-specific
The method and technology field that numerical calculation or data process.
Background technology
China is mining powers in the world, and annual tailings discharging amount is huge, the most even reaches 1,000,000,000 tons more than, wherein most
Mine tailing uses the mode constructing Tailings Dam to store.Pointing out in " Safety of Tailings Dam technical regulation ", so-called Tailings Dam refers to build a dam
Intercept that the mouth of a valley or exclosure constitute in order to store metal or non-metal mine, Ore sort after the place of discharged mine tailing.As tail
Periphery, storehouse, ore deposit dam body structures, the stability of tailing dam is directly connected to the safe operation of Tailings Dam.Tailing dam dam slope instabilty is then
Tailings Dam flowing is caused to have the highest danger, on July 13rd, 1, Daye, hubei Province non-ferrous metal company limited dragon
Hornberg Tailings Dam dam break, causes 30 people dead；On October 18th, 2000, collapsing in Nandan County, Guangxi grand plan plant tailing storehouse, makes
Become 28 people's death, 56 people injured；During JIUYUE in 2010 morning 9 on the 21st, Xinyi City, Guangdong Zijin Mining company limited Xinyi silver
The unexpected avalanche of rock cassiterite Tailings Dam.Accident causes 6 people missing altogether, and 5 people are dead, and 7 people are injured, these Tailings Dams burst accident to
The life of our people and property safety cause massive losses.In view of this, " the mine tailing that on July 1st, 2011 plays execution
Storehouse safety supervision management regulation " chapter 1 Article 8 regulation, " encourage the advanced persons such as production-operation unit application Tailings Dam on-line monitoring suitable
By technology " assess the safe operation situation of Tailings Dam in time.
But, although many Tailings Dams have been mounted with on-line monitoring system the most, the most effectively analyze and utilize its Monitoring Data to comment
Estimate tailing dam unstability risk, the most still annoying industry scholar and engineering staff.Traditionally, Tailings Dam operating administration utilizes
The means such as the trend analysis of Monitoring Data or speed sudden change or phenomenon judge the safe coefficient of Tailings Dam, it is impossible to judge that dam slope loses
The size of steady risk and risk source, therefore in Tailings Dam dam break risk assessment, lack a kind of rational evaluation methodology of system.
Summary of the invention
It is an object of the invention to the shortcoming overcoming prior art to exist, seek a kind of tailing dam unstability risk evaluating method, obtaining
On the basis of on the premise of obtaining Monitoring Data and reasonably determining representative Failure Model, the inefficacy comprehensively utilizing each Failure Model is general
Rate and slide area determine its unstability risk, and then quantify tailing dam unstability risk scientifically and rationally, and final realization is many based on prison
Survey the tailing dam unstability risk assessment of data.
In order to achieve the above object, the present invention evaluates the detailed process of tailing dam unstability risk and is: step one, Monitoring Profiles geometry
Data determine: determined the physical mechanics parameter of rock and soil of the dam material of tailing dam by geotechnical investigation report, and it is true to combine design document
Determine the main geometric profile of tailing dam；
Step 2, the determination of saturation: obtain Real-time Monitoring Data from on-line monitoring system monitoring central server；
Step 3, the generation of tailing dam Failure Model: assuming that the Failure Model of tailing dam, programming automatic generation include that shallow-layer loses
Steady and deep layer unstability is at interior multiple Failure Model, and utilizes the limiting equilibrium method of regulation in specification to calculate each unstability mould
The factor against sliding of formula；
Step 4, dam layer parameter variability and probability distribution determine: on the basis of geotechnical engineering investigation is reported, in conjunction with
Laboratory soil test determines the variability of dam material, and supposes the probability distribution of material parameter on the basis of Literature Consult
And fluctuation range；
Calculation of correlation factor between step 5, each Failure Model: by vertical according to fluctuation range 1/5th of tailing dam dam layer
Spacing carries out discrete, obtains tailing dam virtual dam layer, according to each Failure Model distance length between different virtual dams layer,
Calculate the dependency on factor against sliding between each Failure Model；
The packet of step 6, Failure Model and representative Failure Model screening: all of Failure Model is grouped, after packet,
Often the correlation coefficient between the Failure Model in group is higher than the threshold value set, and selects the conduct that safety coefficient is minimum in each packet
Representative Failure Model；
Step 7, each representative Failure Model determine as the number of times of the most dangerous Failure Model: utilize Monte Carlo method to generate enough
The random sample of quantity, calculates under each random sample, safety coefficient minimum and that mistake less than 1 in representative Failure Model
Steady pattern, as the most dangerous Failure Model, adds up each Failure Model number of times as the most dangerous Failure Model；
Step 8, risk assessment and risk source control: calculate the slide area of representative Failure Model and as the most dangerous mistake
The number of times of steady pattern, utilizes its product ratio with Monte Carlo sample size to represent the unstability risk of this Failure Model；According to
Failure Model value-at-risk carries out descending, can assess tailing dam unstability risk intuitively.
Physical mechanics parameter of rock and soil in step one includes unit weight γ, density p, internal friction angleAnd cohesive strength c.
Main geometric profile in step one takes from 3～5 main two-dimensional geometry sections of tailing dam, and the data of geometric profile include
Slope ratio, packway number.
Real-time Monitoring Data in step 2, including saturation position, reservoir level and osmometer data.
Failure Model in step 3 is utilized x by programi, xoAnd β angle automatically generates, Failure Model include shallow-layer unstability with
And deep layer unstability.
Variability in step 4 can be determined internal friction angle by geotechnical engineering investigation report lookupMaximum, minima
And determine according to 3 σ rules,Standard deviation beSearch the maximum of cohesive strength c, minima cmax、cmin, with
Can determine that the standard deviation of cohesive strength c is for (c samplemax-cmin)/6。
Probability distribution in step 4 is assumed to normal distribution, and vertical fluctuation range lambda can be determined by on-the-spot cone penetration test,
This parameter represents that dam layer material is significant correlation in dam layer λ thickness in vertical direction.
Computing formula in step 5 such as formula (1):
In above formula, ρijRepresent Failure Model SiWith Failure Model SjCorrelation coefficient on factor against sliding, LikAnd Ljk
Represent Failure Model S respectivelyiWith Failure Model SjBeing positioned at the length of kth virtual dam layer, n is the number of plies of virtual dam layer, its number
Value depends on the size of fluctuation range.
Failure Model packet in step 6 is in accordance with the following steps:
(1) randomly choose a Failure Model as group leader, then select a correlation coefficient higher than ρ from remaining Failure Model0
Failure Model enter this group, continue select Failure Model enter this group time, it should be noted that judge intend enter this group Failure Model
And the correlation coefficient come between this group Failure Model is all higher than ρ0；
(2) repeatedly carry out Failure Model packet screening according to (1), be grouped into accordingly until all of Failure Model all comes into
Only, so there are Failure Model group number is p；
(3) from often organizing Failure Model, select the conduct representativeness Failure Model that factor against sliding is minimum, be designated as respectively
S1d、S2d、……、Spd。
In step 7, the sample size N=10 000 000 of Monte-Carlo step.
The invention has the beneficial effects as follows:
Compared with prior art, rationally divide with deep layer Failure Model dependency on factor against sliding according to shallow-layer
Group also carries out the screening of representative Failure Model, fully utilize representative Failure Model as the most dangerous Failure Model number of times with
And sliding surface carrys out its risk of quantitative assessment, shown by instance analysis, tailing dam unstability risk evaluating method proposed by the invention
More science, directly perceived, reasonable, provide scientific basis for the management of Tailings Dam operation security and risk control.
Accompanying drawing explanation
Fig. 1 is the FB(flow block) of the present invention.
Fig. 2 is Monitoring Profiles schematic diagram.
Fig. 3 is tailing dam Failure Model schematic diagram.
Fig. 4 is the dependency diagram between two Failure Models.
Fig. 5 is gold mine tailings dam, Zhaoyuan Monitoring Profiles schematic diagram.
Fig. 6 is that gold mine tailings dam, Zhaoyuan risk source controls schematic diagram.
Detailed description of the invention
The invention will be further described below in conjunction with the accompanying drawings.
As it is shown in figure 1, tailing dam unstability risk evaluating method based on Real-time Monitoring Data of the present invention, the present invention implements
Detailed process be:
Step one: obtained geotechnical engineering investigation report and tailing dam design document by Tailings Dam operating administration, by geotechnical investigation
The physical mechanics parameter of rock and soil of the dam material of tailing dam, such as unit weight γ, internal friction angle are searched in reportAnd cohesive strength c；
Determine 3～5 main two-dimensional geometry sections of tailing dam in conjunction with design document and Tailings Dam on-line monitoring design, typically exist
Monitoring Profiles in line monitoring system can be used as main geometric profile, and each geometric profile data include slope ratio, packway number
Deng.
Step 2: for each Monitoring Profiles, on-line monitoring system monitoring central server obtain corresponding sensing data,
Such as osmometer data, by osmometer data and combine reservoir level data and i.e. can determine that the saturation position of this section, respective description
As shown in Figure 2.
Step 3: assuming that the Failure Model of tailing dam, be generally assumed to be shearing slip pattern, and for tailing dam, circle
The probability that arc Failure Model occurs is relatively big, it is therefore assumed that be circular arc Failure Model, Failure Model schematic diagram is as shown in Figure 3.By journey
Sequence utilizes xi, xo and β angle to automatically generate the multiple Failure Model including shallow-layer unstability and deep layer unstability, and profit
Limiting equilibrium method (Bishop approach or Morgenstern Price method) by " Safety of Tailings Dam technical regulation " upper regulation
Calculate the factor against sliding of each Failure Model.
Step 4: on the basis of geotechnical engineering investigation is reported, determine the variability of dam material, example in conjunction with laboratory soil test
As internal friction angle can be determined by geotechnical engineering investigation report lookupMaximum, minimaAnd it is true according to 3 σ rules
FixedStandard deviation beThe maximum of cohesive strength c, minima c can be searched similarlymax、cmin, similarly can be true
The standard deviation determining cohesive strength c is (cmax-cmin)/6；And suppose on the basis of Literature Consult the probability distribution of material parameter with
And fluctuation range, probability distribution is generally assumed to be normal distribution, and vertical fluctuation range lambda can be true by on-the-spot cone penetration test
Calmly, in this parameter represents dam layer λ thickness in vertical direction, dam layer material is significant correlation.
Step 5: carry out discrete according to the vertical interval of fluctuation range 1/5th by tailing dam dam layer, obtains the virtual dam of tailing dam
Layer, according to each Failure Model distance length between different virtual dams layer, calculates and pacifies at Against Sliding Stability between each Failure Model
Dependency in overall coefficient, schematic diagram as shown in Figure 4, its computing formula such as formula (1)；
In above formula, ρijRepresent Failure Model SiWith Failure Model SjCorrelation coefficient on factor against sliding, LikAnd Ljk
Represent Failure Model S respectivelyiWith Failure Model SjBeing positioned at the length of kth virtual dam layer, n is the number of plies of virtual dam layer, its number
Value depends on the size of fluctuation range.
Step 6: by the multiple Failure Model of generation according to correlation coefficient ρijIt is grouped, first sets correlation coefficient threshold ρ0,
Carry out being taken as 0.5 after preferably according to parameters sensitivity analysis, carry out Failure Model packet in accordance with the following steps:
(1) randomly choose a Failure Model as group leader, then select a correlation coefficient higher than ρ from remaining Failure Model0
Failure Model enter this group, continue select Failure Model enter this group time, it should be noted that judge intend enter this group Failure Model with
The correlation coefficient come between this group Failure Model is all higher than ρ0；
(2) repeatedly Failure Model packet screening is carried out according to (1), till all of Failure Model all comes into corresponding packet,
So there are Failure Model group number is p；
(3) from often organizing Failure Model, select the conduct representativeness Failure Model that factor against sliding is minimum, be designated as S respectively1d、
S2d、……、Spd。
Step 7: according to probability distribution and the variability of its parameter of tailing dam layer, produce the sample of Monte-Carlo step
Value, sample size N=10 000 000.Each sample value is considered as the calculating parameter of tailing dam dam layer, calculates representative unstability
Factor against sliding value F that pattern is corresponding1d、F2d、……、Fpd.Minima is selected to go forward side by side from this p safety coefficient
Row judges, if this minima is less than 1, then its corresponding representative Failure Model is the most dangerous Failure Model, the most repeatedly counts
Calculate, judge, finally can draw p the representative Failure Model number of times Q respectively as the most dangerous Failure Model1d、Q2d、……、
Qpd。
Step 8: calculate slide area A of p representative Failure Model1d、A2d..., Apd, calculate p representative
The unstability risk of Failure Model is A1d×Q1d/N、A2d×Q2d/N、……、Apd×Qpd/N.Each representative Failure Model sum
It is tailing dam unstability risk, the unstability risk of each representative Failure Model is arranged in descending order, thus can intuitive judgment tailing dam
The risk source of unstability risk, and then carry out risk control targetedly.
Below in conjunction with Fig. 5,6 it is illustrated.
Tailing dam dam slope shown in Fig. 5 is gold mine tailings dam, Xia Dian town, Zhaoyuan City, and this Tailings Dam has been installed and runed
Line monitoring system.
The process that the present invention realizes quantitative assessment tailing dam unstability risk is as follows:
Step one, Monitoring Profiles geometric data determine:
Consulting Tailings Dam design document to know, at present this tailing dam height of dam 27 meters, the slope ratio of Monitoring Profiles is followed successively by from bottom to top: 1:2,
6:5,1:1,1:2, there are the packway of a level, distance 6 meters in centre.From exploration report, the ground physical mechanics of dam layer
Parameter has unit weight γ, internal friction angleAnd cohesive strength c.
Step 2, the determination of saturation:
The server of the on-line monitoring system Surveillance center runed by installation reads osmometer data and the storehouse water of a certain monitoring period of time
Bit data, comprehensively determines the saturation position of this Monitoring Profiles, from osmometer 1 to osmometer 5, monitoring central server obtains
The data (degree of depth of the water level i.e. measured to dam crest face) taken are followed successively by 8.1 meters, 8 meters, 7.2 meters, 6.3 meters, 5 meters.Comprehensive raw
The saturation position become is as shown in phantom in Figure 5.
Step 3, the generation of tailing dam Failure Model:
Based on circular arc Failure Model it is assumed that Failure Model slips into point, skids off an xi,xoPossible excursion be [0,60], β angle
Excursion be [0 °, 90 °], firstly generate two random number r between [0,1]1, r2, select in two randoms number less by one
What individual (such as r2 < r1) was mapped to Failure Model skids off point coordinates xo=r2 × 60, what another was mapped to Failure Model slips into point coordinates
xi=r1 × 60, and the particular location of Failure Model is determined according to angle beta.So repeatedly generate multiple shallow-layer and deep layer Failure Model,
Lay the foundation for tailing dam unstability risk assessment.
Step 4, dam layer parameter variability and probability distribution determine:
Unit weight γ=the 20kN/m of dam layer3, do not consider its variability, in the risk assessment of tailing dam unstability, be regarded as definite value.
Internal friction angleMeansigma methods be 30 °,Value be respectively 48 ° and 12 °, therefore according to 3 σ rules, internal friction angle
Standard deviation be 6 °；The meansigma methods of cohesive strength c is 5kPa, it is likewise possible to determine that its standard deviation is 1.5kPa.Internal friction angle
It is assumed to be normal distribution with the probability distribution of cohesive strength.Internal friction angle and cohesive strength fluctuation range in vertical direction is taken as
3m。
Calculation of correlation factor between step 5, each Failure Model:
According to the vertical direction fluctuation range value λ=3m determined, determine that the spacing of dam layer Vertical derivative is 0.6m, be spaced according to this
By the most discrete for this tailing dam dam layer be 45 virtual dam layers, therefore n=45 in formula (1).For each unstability
Mode S i, adds up this Failure Model respectively and falls length L in each virtual dam layeri1, Li2..., Lin, basis at this
On, i.e. may utilize the correlation coefficient that formula (1) calculates between each two Failure Model in safety coefficient.
The packet of step 6, Failure Model and representative Failure Model screening:
Set correlation coefficient threshold ρ0=0.5, the grouping process of Failure Model is described as a example by three Failure Models S1, S2, S3.
If the factor against sliding of three Failure Models is followed successively by 1.4,1.5,1.6, the correlation coefficient ρ between S1, S212=0.8,
Remaining is ρ13=0.7, ρ23=0.4.First randomly choose S1 and enter first group, next judge S2, due to ρ12More than set
Correlation coefficient threshold ρ 0, therefore S2 is also into this group, because ρ23<ρ0, so S3 can not enter this group, so 3 unstabilitys
Pattern is divided into into 2 groups.Include S1 and S2 in the first set, select the S1 that safety coefficient is less as representative Failure Model,
S3 is from becoming one group, so S3 also serves as representative Failure Model.In present example, it is divided into into 14 Failure Model groups, choosing
Select and create 14 representative Failure Models.
Step 7, each representative Failure Model determine as the number of times of the most dangerous Failure Model:
According to dam layer parameter internal friction angle and the probability distribution of cohesive strength, generate abundant Monte-Carlo step sample, often
One sample contains oneValue and c value, the present invention generates 000 000 samples of N=10.Utilize in each sample
Value and c value and the γ-value determined, calculate the value of safety factor value of 14 representative Failure Models, therefrom selects the safety system of minimum
Number judges, if this minima is less than 1, then the Failure Model that this minimum safety factor value is corresponding is the most dangerous Failure Model,
Add up 14 Failure Models number of times Q1d～Q14d as the most dangerous Failure Model, as shown in table 1.
Step 8, risk assessment and risk source control:
Calculate slide area A1d～the A14d of 14 representative Failure Models, calculate each representative unstability in conjunction with Q1d～Q14d
The risk of pattern, result is as shown in table 1.From table 1,14 representative Failure Models, carry out descending according to risk,
It is followed successively by: S8, S4, S5, S3, S2, S1, S9, S10, S7, S6, S12, S14, S11, S13.S11～S14 this
The risk of four representative Failure Models is negligible, and selects the risk source of the first six to be controlled, its Failure Model position
As shown in Figure 6, in conjunction with table 1 and Fig. 6, Tailings Dam operating administration can intuitively, scientifically carry out risk control.
The present invention compared with prior art, is carried out with deep layer Failure Model dependency on factor against sliding according to shallow-layer
Rationally it is grouped and carries out the screening of representative Failure Model, fully utilize representative Failure Model as the most dangerous Failure Model
Number of times and sliding surface carry out its risk of quantitative assessment, are shown by instance analysis, and tailing dam unstability risk proposed by the invention is commented
Valency method more science, directly perceived, reasonable, provide scientific basis for the management of Tailings Dam operation security and risk control.
Table 1 tailing dam unstability risk assessment quantifies table
Certainly, foregoing is only presently preferred embodiments of the present invention, it is impossible to be considered for limiting embodiments of the invention scope.
The present invention is also not limited to the example above, and those skilled in the art are made in the essential scope of the present invention
Impartial change and improvement etc., all should belong in the patent covering scope of the present invention.
Claims (10)
1. a tailing dam unstability risk evaluating method based on Real-time Monitoring Data, it is characterised in that: comprise the steps:
Step one, the geometric data of Monitoring Profiles determine: determined the ground physical force of tailing dam dam material by geotechnical investigation report
Learn parameter, and combine design document and determine the main geometric profile of tailing dam；
Step 2, the determination of saturation: obtain Real-time Monitoring Data from on-line monitoring system monitoring central server；
Step 3, the generation of tailing dam Failure Model: assuming that the Failure Model of tailing dam, programming automatic generation include that shallow-layer loses
Steady and deep layer unstability is at interior multiple Failure Model, and utilizes the limiting equilibrium method of regulation in specification to calculate each unstability mould
The factor against sliding of formula；
Step 4, dam layer parameter variability and probability distribution determine: on the basis of geotechnical engineering investigation is reported, in conjunction with
Laboratory soil test determines the variability of dam material, and supposes the probability distribution of material parameter on the basis of Literature Consult
And fluctuation range；
The calculating of correlation coefficient between step 5, each Failure Model: by vertical according to fluctuation range 1/5th of tailing dam dam layer
Spacing carries out discrete, obtains tailing dam virtual dam layer, according to each Failure Model distance length between different virtual dams layer,
Calculate the dependency on factor against sliding between each Failure Model；
The packet of step 6, Failure Model and representative Failure Model screening: all of Failure Model is grouped, after packet,
Often the correlation coefficient between the Failure Model in group is higher than the threshold value set, and selects the conduct that safety coefficient is minimum in each packet
Representative Failure Model；
Step 7, each representative Failure Model determine as the number of times of the most dangerous Failure Model: utilize Monte Carlo method to generate enough
The random sample of quantity, calculates under each random sample, safety coefficient minimum and that mistake less than 1 in representative Failure Model
Steady pattern, as the most dangerous Failure Model, adds up each Failure Model number of times as the most dangerous Failure Model；
Step 8, risk assessment and risk source control: calculate the slide area of representative Failure Model and as the most dangerous mistake
The number of times of steady pattern, utilizes its product ratio with Monte Carlo sample size to represent the unstability risk of this Failure Model；According to
Failure Model value-at-risk carries out descending.
Tailing dam unstability risk evaluating method based on Real-time Monitoring Data the most according to claim 1, it is characterised in that:
Physical mechanics parameter of rock and soil in step one includes unit weight γ, density p, internal friction angleAnd cohesive strength c.
Tailing dam unstability risk evaluating method based on Real-time Monitoring Data the most according to claim 1, it is characterised in that:
Main geometric profile in step one takes from 3～5 main two-dimensional geometry sections of tailing dam, the data of geometric profile include slope ratio,
Packway number.
Tailing dam unstability risk evaluating method based on Real-time Monitoring Data the most according to claim 1, it is characterised in that:
Real-time Monitoring Data in step 2, including saturation position, reservoir level and osmometer data.
Tailing dam unstability risk evaluating method based on Real-time Monitoring Data the most according to claim 1, it is characterised in that:
Failure Model in step 3 is utilized x by programi, xoAnd β angle automatically generates, Failure Model includes shallow-layer unstability and deep
Layer unstability.
Tailing dam unstability risk evaluating method based on Real-time Monitoring Data the most according to claim 1, it is characterised in that:
Variability in step 4 can be determined internal friction angle by geotechnical engineering investigation report lookupMaximum, minima
And determine according to 3 σ rules,Standard deviation beSearch the maximum of cohesive strength c, minima cmax、cmin, with
Can determine that the standard deviation of cohesive strength c is for (c samplemax-cmin)/6。
Tailing dam unstability risk evaluating method based on Real-time Monitoring Data the most according to claim 1, it is characterised in that:
Probability distribution in step 4 is assumed to normal distribution, and vertical fluctuation range lambda can be determined by on-the-spot cone penetration test, should
Parameter represents that dam layer material is significant correlation in dam layer λ thickness in vertical direction.
Tailing dam unstability risk evaluating method based on Real-time Monitoring Data the most according to claim 1, it is characterised in that:
Computing formula in step 5 such as formula (1):
In above formula, ρijRepresent Failure Model SiWith Failure Model SjCorrelation coefficient on factor against sliding, LikAnd Ljk
Represent Failure Model S respectivelyiWith Failure Model SjBeing positioned at the length of kth virtual dam layer, n is the number of plies of virtual dam layer, its number
Value depends on the size of fluctuation range.
Tailing dam unstability risk evaluating method based on Real-time Monitoring Data the most according to claim 1, it is characterised in that:
Failure Model packet in step 6 is in accordance with the following steps:
(1) randomly choose a Failure Model as group leader, then select a correlation coefficient higher than ρ from remaining Failure Model0
Failure Model enter this group, continue select Failure Model enter this group time, it should be noted that judge intend enter this group Failure Model
And the correlation coefficient come between this group Failure Model is all higher than ρ0；
(2) repeatedly carry out Failure Model packet screening according to (1), be grouped into accordingly until all of Failure Model all comes into
Only, so there are Failure Model group number is p；
(3) from often organizing Failure Model, select the conduct representativeness Failure Model that factor against sliding is minimum, be designated as respectively
S1d、S2d、……、Spd。
Tailing dam unstability risk evaluating method based on Real-time Monitoring Data the most according to claim 1, it is characterised in that:
In step 7, the sample size N=10 000 000 of Monte-Carlo step.
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