CN109781044A - The synthesis of slope instability gradually approaches method for early warning - Google Patents
The synthesis of slope instability gradually approaches method for early warning Download PDFInfo
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Abstract
The present invention relates to a kind of side slope method for early warning more particularly to a kind of synthesis of slope instability gradually to approach method for early warning, belongs to and determines that curve model is monitored hand excavation's side slope overall collapse and the technical field of prediction using selected-point method.A kind of synthesis of slope instability gradually approaches method for early warning, include the following steps: one, choose side slope elementary exploration and monitoring point, two, monitoring device arrangement and installation, three, deflection monitoring is just handled with monitoring data, four, determine the linear constant speed deformation stage duration of side slope, five, determine slope displacement timing curve prediction model, six, determine slope displacement timing curve prediction model basic parameter, seven, the loop optimization selected-point method of slope displacement Time series forecasting model parameter, eight, the loop optimization of slope displacement time series forecasting knee of curve and unstability pre-warning time determines method.The beneficial effects of the invention are as follows observation and the research discovery Failure of Slopes development process curve destroyed are similar with description biological growth conditional curve.
Description
Technical field
The present invention relates to a kind of side slope method for early warning more particularly to a kind of synthesis of slope instability gradually to approach the pre- police
Method belongs to and determines that curve model is monitored hand excavation's side slope overall collapse and the technology of prediction using selected-point method
Field.
Background technique
Since the traffic engineering such as highway, railway and Mountain Urban Area are built, inevitably need to carry out in mountain area
A large amount of slope excavatings cut slope and cut slope domatic improvement project, and these excavation projects will necessarily side slope slopes structure and slopes
The generations such as intensity and boundary of landslide condition different degrees of disturbance and destruction, to reduce and destroy the stabilization of original side slope
Property, the risk of landslide disaster has been significantly greatly increased.According to incompletely statistics, China has found at new Regressive method nearly 300,000, wherein disaster
Property landslide 1.5 ten thousand at.The direct losses as caused by the disasters such as avalanche are about 20,000,000,000 yuans every year, indirect by its bring
Loss is even more that can not estimate.Therefore, the time of a possibility that hand excavation's Slope hazard occurring and generation, which is made, reasonably comments
The prediction of valence and science is to assuring the safety for life and property of the people and national economic development goes on smoothly and is of great significance.
The estimation of stability of side slope and analysis are one of side slope prediction and the matter of utmost importance of control research and all side slopes
The basis of prediction methods.Comprehensive domestic and international present Research can be seen that manages about the evaluation of side slope and prediction at present
By in method, including Limit equilibrium analysis method and displacement time series predicted method etc..Limit equilibrium theory is a kind of determination of classics
Property analysis method, establish on the basis of Failure Mechanism and specific stress condition.Using limit equilibrium theory as the mechanism type of representative
Model prediction method is generally based on limit equilibrium theory and establishes linear or inearized model, is that the static state of no time parameter is commented
Valence model sets cumbersome constraint condition due to needing to introduce various physical and mechanical parameters (being actually difficult to determine these parameters),
It is but very big by artificial subjective impact to seem objective quantitative method, so that certain evaluation results can be made to be distorted.And side slope
It itself is dynamic nonlinear system, static, linear prediction technique is extremely difficult to ideal effect.Displacement time series predicted method is
According to the displacement versus time sequence that the slope system monitored develops, predicted with displacement, rate of displacement or displacement acceleration
With evaluation slope stability and a kind of method of unstability time.Since displacement (deformation) monitoring has precision height, easily implementation, and should
Class method is the dynamic prediction model comprising time-varying relationship, therefore this method overcomes limit mechanics to a certain extent and puts down
The deficiency of weighing apparatus method, but the parameter that all displacement time series predicted methods are monitored and evaluated is only displacement or rate of displacement and its variation rule
Rule, but monitor and evaluate the reason that slope displacement or rate of displacement change, thus such prediction technique certainly exist it is following
Limitation: (1) what is established is only mathematics apparent model, is not physical mechanism prediction model, can only generally explain the deformation on landslide
Displacement process and rule, and do not explain the formation mechenism and mechanics reason for causing Landslide Deformation and unstability；(2) displacement prediction is joined
Number is without unified INSTABILITY CRITERION, so the time of origin for being difficult side slope disaster makes accurate differentiation and prediction.
Summary of the invention
In order to overcome the limitation and deficiency of traditional Prediction Or Forecast of Landslides, developed according to biological growth stage and slope
One-to-one relationship proposes a kind of to determine that curve model determines that landslide trend displacement is whole with hand excavation's side slope using selected-point method
The method of body unstability early-warning point: the development process that Failure of Slopes destroys is similar with biological growth rule, the slow deformation on slope
The stage of development of biological growth is corresponded to the development of deformation stage；The sharply deformation stage on slope corresponds to the development rank of biological growth
Section；The stage of ripeness that biological growth is corresponded to the stage slowly tended to be steady, therefore curve model occur for the unstable failure on slope
The developing stage and the turning point in the stage of ripeness (inflection point) of middle biological growth are the forecast point of hand excavation's side slope overall collapse.
Relative theory according to real-time monitoring and mathematical statistics proposes a kind of linear constant speed deformation stage duration of judgement side slope
Method, the drastic deformation on landslide is divided by two stages i.e. linear constant speed deformation stage and non-linear acceleration with this and deforms rank
Section；In addition, proposing the parameter to be asked in the new selected-point method loop optimization curve model of one kind: in monitoring data (tn,Yn) in line
Property constant speed deformation stage choose two o'clock, non-linear accelerations deformation stage is chosen a bit, is determined with the mode of cyclic approximation and solves song
Three groups of data (0, Y needed for line model0), Joining in optimization biological growth law curve wait ask
Number；Second derivative is asked to determine different inflection points according to biological growth law curve model, loop optimization side slope turns on this basis
Point and overall collapse early-warning point.
By the deformation on real-time monitoring slope it is for statistical analysis to its data with calculate, determined according to curve model artificial
The overall collapse early-warning point of excavation slope provides foundation for the effective monitoring and warning and science improvement of side slope.
Below with reference to principle and attached drawing, to the overall collapse early-warning point for determining hand excavation's side slope according to biological growth rule
Method is described in detail, and key step is as follows:
Step 1: side slope elementary exploration and monitoring point are chosen
Elementary exploration and mapping are carried out to side slope to be evaluated, the essential characteristics such as side slope distribution and size are determined, on side
Distortion monitoring points are arranged in slope key point: the main sliding face for 1. choosing monitored side slope corresponds to slope surface arrangement monitoring point, according to slope surface reality
Border landform ruptures wall in rear and cuts the equidistant m monitoring point (m >=2) for laying slope surface change in displacement of mouthful slope surface to leading edge；②
Deformation Monitoring Datum point (no less than 3) is selected in stable basement rock or the region without deformation other than monitoring side slope body, forms control
Net guarantees self to check and control slope monitoring point comprehensive monitoring.
Step 2: monitoring device arrangement and installation
Unlimited GPS deformation monitoring equipment is laid in Deformation Monitoring Datum point position and side slope drilling monitoring location.Guarantee
Inbuilt Monitoring of Slope Deformation equipment is combined closely with landslide surface layer, guarantees the level of each monitoring point, the change of vertical displacement
Change value is all effectively monitored.
Step 3: deflection monitoring is just handled with monitoring data
With deformation monitoring equipment, precision carries out real-time monitoring to the deformation of landslide area at a time interval, records simultaneously
Monitoring data are simultaneously transferred to long-range monitoring room by side slope place data-signal collector by deformation measurement data, indoor in monitoring
The pretreatment of data is monitored with the batch processings software such as Excel at regular intervals, obtains horizontal displacement of slope value, vertical position
Shifting value and its resultant displacement value.
Step 4: the determination of the linear constant speed deformation stage duration of side slope
T is monitored the rate of displacement v of slopes monitoring point at a time interval, determines slope displacement rate time sequence
Column: { v1...,...vk,......vn}.To analyze and detecting whether the rate of displacement of slope monitoring point occurs in monitoring time
Mutation or occurrence tendency increase variation, first count and determine that the average value of a certain monitoring point rate of displacement and sequence criteria are poor:
According to its rate of displacement changing rule of the linear constant speed deformation stage of side slope, propose the displacement speed at a certain monitoring moment
The ratio of rate time series standard deviation and average value enters non-linear acceleration by linear constant speed deformation stage as side slope and deforms rank
The criteria parameter of section, i.e.,
According to statistical principle, as coefficient of variation cv<15%, show that data statistics rule is normal, the table of cv>=15%
Bright data statistics rule mutates.Therefore reach 15% as non-linear acceleration is entered using any monitoring point coefficient of variation cv to become
Shape stage criterion, the step of determining the linear constant speed deformation stage duration of side slope according to this, are as follows:
(1) if coefficient of variation cv < 15% of the monitoring point, show that the monitoring point side slope is deformed also in linear constant speed
Stage does not enter non-linear acceleration deformation stage also.
(2) if coefficient of variation cv >=15% of the monitoring point, show that the monitoring point side slope has entered non-linear acceleration
Deformation stage, the point corresponding time point are the turning point of side slope linear constant speed deformation and non-linear acceleration deformation stage,
It is T that the period of monitoring initial stage to the point, which is duration t% and the t% corresponding time point of the linear constant speed deformation stage of side slope,1。
Step 5: the determination of slope displacement timing curve prediction model
Find that side slope experienced slow deformation, development of deformation, sharply deformation and mistake to destruction since deformation by analysis
Steady to destroy four-stage, it is gentle that the growth curve of description biological growth rule often appears as lower part, middle part maximum slope, top
The speed of growth is slack-off so that reaching mature.The developing stage of the two is compared into discovery, slope instability destructive process is similar to life
The development process of object growth, therefore propose to simulate the development process of side slope with biological growth curve model and predict side slope
Overall collapse point, prediction model are as follows:
Wherein: Y is the side slope resultant displacement value (unit: mm) of t moment；
T is monitoring time.
A, b, k are the parameter to be measured calculated by 3 points.
Step 6: the determination of slope displacement timing curve prediction model basic parameter
In order to determine a in slope displacement timing curve prediction model, the size of tri- parameters of b, k introduces selected-point method, it is
In given (ti,Yi) in data, select t at a distance of three pairs of equivalent variables, to estimate a, the side of tri- numerical value of b, k simultaneously
Method.To three data points of selected variable, it has to be possible to represent curve first section, middle section, end position, and three tiValue
At arithmetic series.Three data points are brought into and calculate a in model, tri- parameters of b, k, and specific curve is determined according to a, b, k
Model acquires knee of curve with this.Specific step is as follows, by selected three points (0, Y0),Respectively
Curve model is substituted into obtain:
(5), (6), (7) three equatioies successively make the difference to obtain parameter d1,d2:
It is derived by formula (5) and acquires parameter of curve b:
Joint type (8), (9) two parameter expressions of formula acquire parameter of curve a, k:
By above-mentioned a, tri- parameters of curve of b, k are brought into slope displacement timing curve prediction model and are obtained:
Second derivative is asked to formula (13) curve model, and enables it be equal to 0 and acquires knee of curve:
Step 7: the loop optimization selected-point method of slope displacement Time series forecasting model parameter
It is learnt according to step 6, chooses different feature monitoring data and determine different slope displacement Time series forecasting model ginsengs
Number, and different prediction model parameters also just determine slope displacement Time series forecasting model precision and forecasting accuracy.Based on upper
Principle is stated, proposes to determine that model parameter a, the cyclic approximation of b, k optimize measuring method, the specific steps are as follows:
The selection of (1) three point will represent the state of curve different phase, therefore three points must be dispersed in what step 4 determined
Linear constant speed and non-linear acceleration deformed in two stages.By side slope monitoring data carry out it is a large amount of calculate with analyze three
The optimal choice region of point are as follows: linear constant speed deformation stage chooses two o'clock, and non-linear acceleration deformation stage is chosen a bit.
(2) in the currently monitored data, with initial monitoring point A1It is last point with newest monitoring point C, by A for starting point1C is second-class
Divide and determines midpoint B1And B should be met1<T1, thereby determine that A1,B1, mono- group of characteristic of C.
(3) research discovery data origination A1Two unit lengths are often moved back, midpoint will move back a unit length, therefore
On the basis of above-mentioned steps (2), keeps last point C constant, enable starting point A1Mobile difference △ t is two unit lengths every time, with
An+1=A1+ n △ t (n=1,2,3...) determines point A2,A3..., by An+1C, which is halved, determines midpoint Bn+1And B should be metn+1<T1,
Thereby determine that An+1,Bn+1, C multiple groups characteristic.
(4) according to step 6 determine multi-group data bring into slope displacement Time series forecasting model (formula (4)) acquire it is different
Inflection point, if adjacent comers tnWith tn-1Difference tn-tn-1| it is minimum value, then it is assumed that tnWhat one group of corresponding characteristic acquired
Parameter is the Optimized model parameter at the monitoring moment, and group optimization characteristic is selected to determine slope displacement time series forecasting curve,
And by tnThe spinodal decomposition point found out as current time monitoring and warning model.
Step 8: the loop optimization of slope displacement time series forecasting knee of curve and unstability pre-warning time determines method according to letter
The holographic theory of opinion is ceased, and finds that monitoring data are more by the analysis and research to a large amount of monitoring data, the selection of end point is more leaned on
Nearly newest monitoring data, the overall collapse pre-warning time of side slope are more accurate.It proposes to optimize with cyclic approximation on this basis
Method determines that slope displacement time series forecasting knee of curve and overall collapse pre-warning time, particular content are as follows:
(1) best region that three essential characteristic points of monitoring data are chosen is the same as (1) the step of step 7.
(2) in entire monitoring process, with initial monitoring point AiFor starting point, with monitoring point E1For last point, by AiE1It halves
Determine midpoint D1And D should be met1<T1, thereby determine that Ai,D1,E1One group of characteristic.
(3) starting point A is keptiIt is constant, last point En+1Circulation theory determine A with (3) the step of step 7i,Dn+1,En+1Multiple groups
Characteristic.
(4) the multiple groups characteristic that step (4) determine is brought into slope displacement Time series forecasting model according to step 7 to acquire
Different inflection point ti', if inflection point ti' and tnDifference | ti′-tn|≤5 (d), then show slope monitoring Early-warning Model mistake at this time
Difference is met the requirements, by ti' unstability the early-warning point as the side slope, and pre-warning time is determined according to the unstability early-warning point.
(5) according to the unstability early-warning point t of side slopei' corresponding last point En+1Difference ti′-En+1|, it can be by the entirety of side slope
Unstability is divided into three-level early warning: if 20 (d)≤ti′-En+1|≤30 (d), start level-one early warning, determines that slope stability is kept substantially
It is constant, it need to continue to monitor；If 10 (d)≤| ti′-En+1|≤30 (d), start second level early warning, determines that slope stability is in slow
Decline stage, construction personnel need to pay attention to the problem；If | ti′-En+1|≤10 (d), start three-level early warning, determines slope stability
In the sharply decline stage, construction personnel should withdraw landslide area immediately.
Compared with prior art, the beneficial effects of the present invention are: according to previous test observation and research discovery Failure of Slopes
The development process curve of destruction is similar with description biological growth conditional curve, i.e., when slope is in slowly deformation and development of deformation
Stage, the smaller stage of development corresponding to biological growth of Slope；When slope is in sharply deformation stage, Slope increases
The big developing stage for corresponding to biological growth；When slope is in the unstable failure stage, Slope be increased dramatically so that occurring broken
It is bad slowly to tend to be steady, the stage of ripeness corresponding to biological growth.Therefore it is a pair of according to the one of biological growth stage and slope development
It should be related to that can use selected-point method determines that curve model determines hand excavation's side slope trend displacement and by way of cyclic approximation
Determine the early-warning point of landslide overall collapse.
Detailed description of the invention
Fig. 1 is process flow diagram of the invention,
Fig. 2 is starting point A of the inventioniRound-robin method,
Fig. 3 is last point E of the inventioniRound-robin method,
Fig. 4 is certain landslide and its monitoring point schematic diagram of the invention.
Specific embodiment
The principle and features of the present invention will be described below with reference to the accompanying drawings, and the given examples are served only to explain the present invention, and
It is non-to be used to limit the scope of the invention.
It is described in detail by taking certain landslide as an example below.The shift value on the landslide starts monitoring record in nineteen eighty-three September, directly
It is destroyed to landslide.Specific implementation step are as follows:
Step 1: side slope elementary exploration and monitoring point are chosen
It chooses the main skating area for monitoring and coming down and corresponds to slope surface arrangement monitoring point, according to landslide main skating area west side leading edge and slope foot
Main sliding face be arranged two monitoring point E3、E4。
Step 2: monitoring device arrangement and installation
Unlimited GPS deformation monitoring equipment is laid in Deformation Monitoring Datum point position and side slope drilling monitoring location.Guarantee
Inbuilt Monitoring of Slope Deformation equipment is combined closely with landslide surface layer, the landslide displacement amount and rate of displacement of monitoring criteria point.
Step 3: deflection monitoring is just handled with monitoring data
Carried out data transmission by displacement monitoring data of the side slope place data-signal collector to landslide to long-range monitoring
Room is monitored the pretreatment of data in monitoring room with the batch processings software such as Excel at regular intervals, obtains slope displacement water
Flat vertical displacement value and its resultant displacement value (being shown in Table 1~3).
Certain landslide G3 point moon rate of displacement (unit: the mm/ month) of table 1
Certain landslide G4 point moon rate of displacement (unit: the mm/ month) of table 2
Certain landslide G4 point moon displacement monitoring data (unit: mm) of table 3
Step 4: the determination of the linear constant speed deformation stage duration of side slope
According to unit monitoring time interval, it may be determined that one month as a unit statistical analysis and predetermined period, to slope
The rate of displacement v of body monitoring point G4 is monitored determining slope displacement rate time sequence: 274.6,291.6,320.1,
289.6,313.8,325.8,214.6,263.8,225.7,194.2,223,296.1,333.7,349.6,376.6,450.4 },
To analyze and detecting whether the rate of displacement of slope monitoring point occurs being mutated in monitoring time or occurrence tendency increases variation,
First count and determine that the average value of a certain monitoring point rate of displacement and sequence criteria are poor (being shown in Table 4):
The sequence criteria of certain the landslide G4 point different time of table 4 is poor
According to statistical principle, as coefficient of variation cv<15%, show that data statistics rule is normal, the table of cv>=15%
Bright data statistics rule mutates.Therefore reach 15% as non-linear acceleration is entered using any monitoring point coefficient of variation cv to become
Shape stage criterion, the step of determining the linear constant speed deformation stage duration of side slope according to this, are as follows:
(1) from coefficient of variation cv < 15% of the monitoring point before in June, 1984, show that side slope is also in line at the monitoring point
Property constant speed deformation stage, does not enter non-linear acceleration deformation stage also.
(2) from coefficient of variation cv >=15% after in June, 1984, then show to start in July, 1984 the monitoring point side slope into
Enter non-linear acceleration deformation stage, the point corresponding time point is the linear constant speed deformation of side slope and non-linear acceleration deformation stage
Turning point, monitoring initial stage to the point period be the linear constant speed deformation stage of side slope duration d=9 (moon) and d correspondence
Time point be T1。
Step 5: the determination of slope displacement timing curve prediction model
Find that side slope experienced slow deformation, development of deformation, sharply deformation and mistake to destruction since deformation by analysis
Steady to destroy four-stage, it is gentle that the growth curve of description biological growth rule often appears as lower part, middle part maximum slope, top
The speed of growth is slack-off so that reaching mature.The developing stage of the two is compared into discovery, slope instability destructive process is similar to life
The development process of object growth, therefore propose to simulate the development process of side slope with biological growth curve model and predict side slope
Overall collapse point, prediction model are as follows:
Wherein: Y is the side slope resultant displacement value (unit: mm) of t moment；
T is monitoring time；
A, b, k are the parameter to be measured calculated by 3 points.
Step 6: the determination of slope displacement timing curve prediction model basic parameter
In order to determine a in slope displacement timing curve prediction model, the size of tri- parameters of b, k introduces selected-point method, is supervising
Selection t is at a distance of three pairs of equivalent variables, to estimate a simultaneously, tri- numerical value of b, k in the 12 groups of data (being shown in Table 3) surveyed
Method.To three data points of selected variable, it has to be possible to represent curve first section, middle section, end position, and three ti
It is worth into arithmetic series.Three data points are brought into and calculate a in model, tri- parameters of b, k, and specific song is determined according to a, b, k
Line model acquires knee of curve with this.Specific step is as follows, by selected three points (0, Y0),Point
Not Dai Ru curve model obtain:
Three equatioies successively make the difference to obtain parameter d1,d2:
By formulaDerivation acquires parameter of curve b:
Join d1,d2Two parameter expressions acquire parameter of curve a, k:
By above-mentioned a, tri- parameters of b, k are brought into slope displacement timing curve prediction model and are obtained:
Second derivative is asked to curve model, and enables it be equal to 0 and acquires knee of curve:
Step 7: the loop optimization selected-point method of slope displacement Time series forecasting model parameter
It is learnt according to step 6, chooses different feature monitoring data and determine different slope displacement Time series forecasting model ginsengs
Number, and different prediction model parameters also just determine slope displacement Time series forecasting model precision and forecasting accuracy.Based on upper
Principle is stated, proposes to determine that model parameter a, the cyclic approximation of b, k optimize measuring method, the specific steps are as follows:
The selection of (1) three point will represent the state of curve different phase, therefore three points must be dispersed in what step 6 determined
Linear constant speed and non-linear acceleration deformed in two stages.By side slope monitoring data carry out it is a large amount of calculate with analyze three
The optimal choice region of point are as follows: linear constant speed deformation stage chose two o'clock, non-linear acceleration deformation stage 1984 before in June, 1984
It is chosen a bit after June in year.
(2) with initial monitoring point A1(1,274.6) it is starting point, is last point with newest monitoring data C1 (10,1942), it will
A1C1It halves and determines midpoint B1(5.5,1761.9) and B1In T1Left side thereby determines that A1(1,274.6),B1(5.5,
1761.9),C1(10,1942) one groups of characteristics.
(3) research discovery data origination A1Two unit lengths are often moved back, midpoint will move back a unit length, therefore
On the basis of above-mentioned steps (2), last point C is kept1It is constant, enable starting point A1(1,274.6) mobile difference △ t is two lists every time
Bit length, with An+1=A1+ n △ t (n=1,2,3...) determines point A2(3,640.2),A3(5,1569),A4(7,1502.2), will
An+1C1It halves and determines midpoint B2(6.5,1728.5),B3(7.5,1806.3),B4(8.5,2070.85) and meet B2,B3,B4
In T1Left side thereby determines that (A2,B2,C1),(A3,B3,C1),(A4,B4,C1) three groups of characteristics.
(4) respectively with monitoring data C2(11,2453),C3(12,3553.2),C4(13,4338.1),C6(15,5649),C7
(16,5404.8) determine that multiple groups characteristic is shown in Table 5 with above-mentioned steps six for last point:
The corresponding multiple groups characteristic of the different starting points of the identical end point of table 5
(5) multi-group data determined according to step 6, which brings slope displacement Time series forecasting model into and acquires different inflection points, is shown in Table
6:
Table 6: the corresponding inflection point of different starting point different characteristic data
(6) between adjacent comers, respectively with C6、C7When being put for end, the inflection point difference minimum 0.23 acquired, therefore think t=
The parameter that one group of characteristic acquires corresponding to 16.9 is the Optimized model parameter at the monitoring moment, and the group is selected to optimize feature
Data determine slope displacement time series forecasting curve, and the unstability that t=16.9 is found out as current time monitoring and warning model
Point.
Step 8: the loop optimization of slope displacement time series forecasting knee of curve and unstability pre-warning time determines method
Find that monitoring data are more according to the holographic theory of information theory, and by the analysis and research to a large amount of monitoring data,
Closer to newest monitoring data, the overall collapse pre-warning time of side slope is more accurate for the selection of end point.Fortune is proposed on this basis
Slope displacement time series forecasting knee of curve and overall collapse pre-warning time are determined with cyclic approximation optimization method, and particular content is such as
Under:
(1) best region that three essential characteristic points of monitoring data are chosen is the same as step 7 step (1).
(2) starting point A is keptiIt is constant, last point En+1Circulation theory determine A with step 7 step (3)i,Dn+1,En+1Multiple groups are special
Data are levied, are shown in Table 7:
Table 7: corresponding multiple groups characteristic is put in the different ends of identical starting point
(3) according to step 7 the multi-group data that the step of (3) determines brings slope displacement Time series forecasting model into and acquires difference
Inflection point be shown in Table 8:
Table 8: the corresponding inflection point of different end point different characteristic data
(4) according to step 7 the multiple groups characteristic that the step of (4) determines is brought slope displacement Time series forecasting model into and is acquired
Different inflection point ti', if inflection point ti'=16.8 (moon) and tnThe difference of=16.9 (moons), ti′-tn|=0.1 (moon)=3 (d)≤5
(d), then show that the slope monitoring Early-warning Model error is met the requirements at this time, by tiUnstability of '=16.8 (moon) as the side slope
Early-warning point, and pre-warning time is determined according to the unstability early-warning point.tiThe corresponding end point value E2=16 (moon) in '=16.8 (moon),
At this time 20 (d)≤| ti′-E2|=0.8 (moon)=24 (d)≤30 (d) start level-one early warning, determine that slope stability is kept substantially
It is constant, continue to monitor.
Inventive principle with according to as follows:
Principle 1: being found by the analysis to Slope observational data, and slope experienced slowly since deformation to destruction
Deformation, development of deformation, sharply deformation and unstable failure four-stage.The development process curve and description biology that Failure of Slopes destroys
Law of development curve is similar.In slow deformation and development of deformation stage, the deformation comparison on slope is small, at this moment mainly creep,
The two stages are equivalent to the lower part of biological growth law curve, and curve ratio is more gentle, and slope is smaller, are equivalent to biological growth rule
The stage of development of rule；The sharply deformation stage of Slope, deformation increases, speed is accelerated, and is equivalent to biological growth law curve
Middle part, the slope of curve is bigger, that is, the developing stage of biological growth；The unstable failure stage on slope, Slope are anxious
Sharp increase is subject to destroying, and then tends towards stability, this stage is similar with the middle and upper part of biological growth law curve, quite
In the stage of ripeness of biological growth.This is the separation (inflection point) of curve, is exactly the forecast point required by us.
According to the investigation and research of previous numerous experiments, the general type of the mathematical model of biological growth rule is described
Are as follows:
In formula: k is constant；F (t) is the multinomial of independent variable t, it may be assumed that
F (t)=a0+a1t+....amtm
The case where most common growth curve belongs to an order polynomial for f (t), and one order polynomial coefficient is negative value,
That is:
F (t)=a0+a1t
This multinomial is brought intoIn:
In formula:A=-a1
The foregoing is merely presently preferred embodiments of the present invention, is not intended to limit the invention, it is all in spirit of the invention and
Within principle, any modification, equivalent replacement, improvement and so on be should all be included in the protection scope of the present invention.
Claims (9)
1. a kind of synthesis of slope instability gradually approaches method for early warning, which comprises the steps of: one, selection side slope
Elementary exploration and monitoring point, two, monitoring device arrangement and installation, three, deflection monitoring with monitoring data just handle, four, determine
The linear constant speed deformation stage duration of side slope, five, determine slope displacement timing curve prediction model, six, determine slope displacement timing
Curve prediction model basic parameter, seven, the loop optimization selected-point method of slope displacement Time series forecasting model parameter, eight, slope displacement
The loop optimization of time series forecasting knee of curve and unstability pre-warning time determines method.
2. the synthesis of slope instability according to claim 1 gradually approaches method for early warning, which is characterized in that Step 1: choosing
It takes side slope elementary exploration and the method for monitoring point as follows: 1. choosing the corresponding slope surface of the main sliding face of monitor side slope and arrange that side slope drills
Monitoring point ruptures wall in rear according to slope surface actual landform and cuts the equidistant m for laying slope surface change in displacement of mouthful slope surface to leading edge
A side slope drills monitoring point (m >=2)；2. Deformation Monitoring Datum point (no less than 3) is selected in base stable other than monitoring side slope body
Rock or region without deformation form control net, guarantee self to check and control slope monitoring point comprehensive monitoring.
3. the synthesis of slope instability according to claim 2 gradually approaches method for early warning, which is characterized in that Step 2: prison
Measurement equipment arrangement and the method for installation are as follows: wireless GPS is laid in side slope drilling monitoring location and Deformation Monitoring Datum point position
Deformation monitoring equipment.
4. the synthesis of slope instability according to claim 3 gradually approaches method for early warning, which is characterized in that Step 3: becoming
The method that the monitoring of shape amount and monitoring data are just handled is as follows: using wireless GPS deformation monitoring equipment precision at a time interval
Real-time monitoring is carried out to the deformation of landslide area, while recording deformation measurement data and pass through side slope place data-signal collector will
Monitoring data are transferred to long-range monitoring room, are monitored number with the batch processings software such as Excel at regular intervals in monitoring interior
According to pretreatment, obtain horizontal displacement of slope value, vertical displacement value and its resultant displacement value.
5. the synthesis of slope instability according to claim 4 gradually approaches method for early warning, which is characterized in that Step 4: side
The determination method of the linear constant speed deformation stage duration in slope is as follows: (1) being become according to linear its rate of displacement of constant speed deformation stage of side slope
Law is proposed using the ratio of the rate of displacement time series standard deviation at a certain monitoring moment and average value as side slope by linear
Constant speed deformation stage enters the non-linear criteria parameter for accelerating deformation stage, i.e.,
Wherein,
The rate of displacement of slopes monitoring point when time interval t: ν
Slope displacement rate sequence: { v1...,...vk,......vn,
(2) the linear constant speed deformation stage duration of side slope is determined according to this: 1. if coefficient of variation cv < 15% of the monitoring point, table
The bright monitoring point side slope is in linear constant speed deformation stage, does not enter non-linear acceleration deformation stage；2. if the monitoring point
Coefficient of variation cv >=15% then shows that the monitoring point side slope has entered non-linear acceleration deformation stage, monitoring initial stage to the point
Period be duration t% and t% corresponding time point of the linear constant speed deformation stage of side slope be T1。
6. the synthesis of slope instability according to claim 5 gradually approaches method for early warning, which is characterized in that step 5, really
The method of deckle slope displacement time series curve prediction model is as follows: proposing the development that side slope is simulated with biological growth curve model
Process and the overall collapse point for predicting side slope, prediction model are as follows:
Wherein, t: monitoring time；
The side slope resultant displacement value of t moment: Y (unit: mm)；
A, b, k: the parameter to be measured calculated by 3 points.
7. the synthesis of slope instability according to claim 6 gradually approaches method for early warning, which is characterized in that Step 6: really
The method of deckle slope displacement time series curve prediction model basic parameter is as follows: selected-point method is introduced, by selected three points (0, Y0),Curve model is substituted into respectively to obtain:
Formula (3), (4), (5) three equatioies successively make the difference to obtain parameter d1,d2:
It is derived by formula (3) and acquires parameter of curve b:
Joint type (6), (7) two parameter expressions of formula acquire parameter of curve a, k:
By above-mentioned a, tri- parameters of curve of b, k are brought into slope displacement timing curve prediction model and are obtained:
Second derivative is asked to formula (11) curve model, and enables it be equal to 0 and acquires knee of curve:
8. the synthesis of slope instability according to claim 7 gradually approaches method for early warning, which is characterized in that Step 7: side
The loop optimization selected-point method of slope displacement time series prediction model parameters:
(1) 3 points of optimal choice region is calculated and analyzed and to be obtained by side slope monitoring data according to step 6 are as follows: linearly
Constant speed deformation stage chooses two o'clock, and non-linear acceleration deformation stage is chosen a bit；
(2) in the currently monitored data, with initial monitoring point A1It is last point with newest monitoring point C, by A for starting point1C halves true
Fix midway point B1And B should be met1<T1, thereby determine that A1,B1, mono- group of characteristic of C；
(3) it keeps last point C constant, enables starting point A1Mobile difference △ t is two unit lengths every time, with An+1=A1+ n △ t (n=
1,2,3... point A) is determined2,A3..., by An+1C, which is halved, determines midpoint Bn+1And B should be metn+1<T1, thereby determine that An+1,Bn+1,
C multiple groups characteristic；
(4) multi-group data determined according to step 6 brings slope displacement Time series forecasting model into and acquires different inflection points, if adjacent
Inflection point tnWith tn-1Difference | tn-tn-1| it is minimum value, then it is assumed that tnThe parameter that one group of corresponding characteristic acquires is the prison
The Optimized model parameter for surveying the moment selects group optimization characteristic to determine slope displacement time series forecasting curve, and by tnMake
The spinodal decomposition point found out for current time monitoring and warning model.
9. the synthesis of slope instability according to claim 8 gradually approaches method for early warning, which is characterized in that Step 8: side
Slope displacement time series prediction curve inflection point and the loop optimization of unstability pre-warning time determine that method is as follows:
(1) best region that three essential characteristic points of monitoring data are chosen is the same as (1) the step of step 7；
(2) in entire monitoring process, with initial monitoring point AiFor starting point, with monitoring point E1For last point, by AiE1It halves and determines
Midpoint D1And D should be met1<T1, thereby determine that Ai,D1,E1One group of characteristic；
(3) starting point A is keptiIt is constant, last point En+1Circulation theory determine A with (3) the step of step 7i,Dn+1,En+1Multiple groups feature
Data；
(4) according to step 7 the multiple groups characteristic that the step of (4) determines brings slope displacement Time series forecasting model into and acquires difference
Inflection point t 'iIf inflection point t 'iWith tnDifference | t 'i-tn|≤5 (d), then show that the slope monitoring Early-warning Model error is full at this time
Foot requires, by t 'iPre-warning time is determined as the unstability early-warning point of the side slope, and according to the unstability early-warning point；
(5) according to the unstability early-warning point t ' of side slopeiCorresponding end point En+1Difference | t 'i-En+1|, the entirety of side slope can be lost
Surely it is divided into three-level early warning: if 20 (d)≤| t 'i-En+1|≤30 (d), start level-one early warning, determines that slope stability is kept substantially
It is constant；If 10 (d)≤| t 'i-En+1|≤30 (d), start second level early warning, determines that slope stability is in the slow decline stage；If
|t′i-En+1|≤10 (d), start three-level early warning, determines that slope stability is in the sharply decline stage.
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| CN112326788A (en) * | 2020-10-23 | 2021-02-05 | 江西理工大学 | Monitoring and early warning method and system for instability of tailing dam |
| CN113034855A (en) * | 2021-03-09 | 2021-06-25 | 杭州电子科技大学 | Slope landslide early warning method based on NPR cable slip force monitoring |
| CN113418496A (en) * | 2021-05-26 | 2021-09-21 | 深圳市北斗云信息技术有限公司 | Slope deformation monitoring and early warning method and system and intelligent terminal |
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| CN112326788A (en) * | 2020-10-23 | 2021-02-05 | 江西理工大学 | Monitoring and early warning method and system for instability of tailing dam |
| CN113034855A (en) * | 2021-03-09 | 2021-06-25 | 杭州电子科技大学 | Slope landslide early warning method based on NPR cable slip force monitoring |
| CN113418496A (en) * | 2021-05-26 | 2021-09-21 | 深圳市北斗云信息技术有限公司 | Slope deformation monitoring and early warning method and system and intelligent terminal |
| CN114485788A (en) * | 2022-01-12 | 2022-05-13 | 北京科技大学 | Slope dangerous rock body collapse early warning method and device based on inclination and strong vibration characteristics |
| CN114485788B (en) * | 2022-01-12 | 2022-10-11 | 北京科技大学 | Slope dangerous rock body collapse early warning method and device based on inclination and strong vibration characteristics |
| CN116026267A (en) * | 2022-12-12 | 2023-04-28 | 中铁西北科学研究院有限公司 | Sliding surface position accurate determination method based on multi-sliding-surface B-type deep hole inclinometry curve |
| CN116026267B (en) * | 2022-12-12 | 2023-09-08 | 中铁西北科学研究院有限公司 | Sliding surface position accurate determination method based on multi-sliding-surface B-type deep hole inclinometry curve |
| CN117609742A (en) * | 2024-01-24 | 2024-02-27 | 中建安装集团有限公司 | Side slope construction supervision system and method for realizing intelligent management |
| CN117609742B (en) * | 2024-01-24 | 2024-03-26 | 中建安装集团有限公司 | Side slope construction supervision system and method for realizing intelligent management |
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