CN103593534A - Shield tunneling machine intelligent model selection method and device based on engineering geology factor relevance - Google Patents

Abstract

The invention relates to a shield tunneling machine intelligent model selection method and device based on engineering geology factor relevance. The shield tunneling machine intelligent model selection method is characterized by including the following steps that firstly, projection data are built, and sample sets of shield tunneling machine model selection and corresponding experience levels yi are generated at random within level ranges of various shield tunneling machine types according to classification standards of shield tunneling machine model selection classification indexes; secondly, the projection direction is formed and projection values are calculated; thirdly, a projection index function is built, variation information of a projection index x, a projection index i and a projection index j is extracted, local projection points of projection values zi are condensed into a plurality of point clusters, and then the standard deviation Sz between the local density Dz of the projection values zi and the projection values zi is determined; fourthly, the projection index function is optimized, and the optimum projection direction a* is estimated by solving the maximization problem of the projection index function; fifthly, a shield tunneling machine model selection classification model is set up; sixthly, engineering geology parameter combinations of engineering are input to the acquired shield tunneling machine model selection classification model, and the most appropriate shield tunneling machine type is acquired. Shield tunneling machine model selection results are quantified, and the shield tunneling machine intelligent model selection device has the particular advantages of being easy to operate, wide in application range, high in function and openness, and rich in network resources.

Description

Shield machine Intelligent Model Selection method and device based on engineering geologic factors relevance

Technical field

The present invention relates to a kind of system of selection of shield machine, be specifically related to a kind of shield machine Intelligent Model Selection method and device based on engineering geologic factors relevance.Be applicable to the type selecting of grid type shield machine, earth pressure balance shield machine, muddy water balancing earth-pressure shielding machine, slurry type shield machine.The special engineering machinery that belongs to tunnel piercing.

Background technology

Shield machine is a kind of special engineering machinery of tunnel piercing, integrate light, mechanical, electrical, liquid, sensing, infotech, there is the excavation cutting soil body, conveying soil quarrel, assembled tunnel-liner, measure the functions such as guiding correction, relate to the multi-door subject technologies such as geology, building, machinery, mechanics, hydraulic pressure, electric, control, measurement, and to carry out manufacturing and designing of " cutting the garment according to the figure " formula according to different geology, reliability requirement is high.Reason due to economy and technology, the urban area being restricted in the selection extremely precious, power transmission line corridor of economically developed urban power load high density, land resource, with cable tunnel, replace local overhead transmission line, become urban electric power and carry one of alternative important way.In recent years, the big city such as China Beijing, Shanghai, Guangzhou, Shenzhen has started fairly large city cable tunnel construction.Shield method affects little feature with it to surrounding enviroment, has become the most frequently used construction method in modernization cable tunnel.

The type selecting of shield machine is one of key link in tunnel construction, and the quality of type selecting is directly connected to the even success or failure of the duration in tunnel, cost.The many factors that shield structure type selecting is considered, wherein engineering geologic factors is topmost factor.Expert selects respectively successively according to the adaptability of each engineering geologic factors, finally may cause same engineering geologic factors to have multiple applicable Shield Machine Selection, at this moment just the main personal experience by expert selects, this selection method is not considered the relevance of each engineering geologic factors, causes the indefinite unification of type selecting result.The method of this Shield Machine Selection is not considered the relevance of each factor, can be because each expert's subjective factor or individual preference cause type selecting result disunity.

Summary of the invention

One of object of the present invention, be in order to overcome the type selecting of shield machine in prior art, be subject to the impact of expert's subjective factor or individual preference, do not consider each engineering geologic factors relevance, cause the indefinite unified defect of type selecting result, a kind of shield machine Intelligent Model Selection method based on engineering geologic factors relevance is provided, this Intelligent Model Selection method has been considered the relevance between each engineering geologic factors, make type selecting result clear and definite, objective, and the method is easy and simple to handle, quick.

Two of object of the present invention is for a kind of device of the shield machine Intelligent Model Selection based on engineering geologic factors is provided.This device simple in structure, easy to operate, has considered the relevance between each geologic agent, makes type selecting result clear and definite, objective.

One of object of the present invention can be achieved through the following technical solutions:

Shield machine Intelligent Model Selection method based on engineering geologic factors relevance, is characterized in that comprising the steps:

1) structure data for projection first according to the criteria for classification of shield structure type selecting classification indicators, produces at random the sample set of Shield Machine Selection in each rate range of each shield machine type

with corresponding experience level y _i(i=1,2 ..., n; J=1,2 ..., p),

and y _ibe respectively each shield machine type and corresponding experience level, n, p is respectively number of samples and index number;

2) form projecting direction and calculate projection value, establishing a={a ₁, a ₂..., a _pbe projecting direction, p dimension data x _ijcomprehensively become with a _jone Dimensional Projection value z for projecting direction _i, projection value is calculated by following expression:

$z_i=\underset{j=1}{\overset{p}{\mathrm{\Σ}}}{a}_j{x}_{\mathrm{ij}},i=\mathrm{1,2},\·\·\·,n---\left(1\right)$

3) set up projection target function, extract projection index x _ijvariation information, by projection value z _ipartial projection point be condensed into several some groups, to determine z _ithe D of local density _zand z _istandard deviation S _z, the expression formula of setting up projection target function is as follows:

Q(a)=S _zD _z (2)

4) optimize projection target function, according to projection target function Q (a) along with the variation of projecting direction a changes, optimal projection direction is exactly the projecting direction that maximum possible exposes high dimensional data category feature, by solving projection target function maximization problems, estimates best projection direction a ^*,

maxQ(a)=S _zD _z (3)

$s.t.\underset{j=1}{\overset{p}{\mathrm{\Σ}}}{a}_j^2=1---\left(4\right)$

Formation is with { a _j| j=1,2 ..., the complex nonlinear that p} is optimized variable is optimized relation;

5) set up shield structure type selecting disaggregated model, the best projection direction a that step 4) is tried to achieve ^*bring the projection value that (2) formula is calculated each sample into

, according to with y _iscatter diagram set up Shield Machine Selection disaggregated model, Shield Machine Selection disaggregated model with logistic curve function representation is:

$y_i^{*}=N/(1+e^{c\left(1\right)-c\left(2\right)\·z_i^{*}})---\left(5\right)$

In formula,

the calculated value of the-the i Shield Machine Selection sample; The maximum classification grade of N-shield machine is the higher limit of this curve;

6) to be incorporated into Shield Machine Selection disaggregated model obtained above be formula (5) to the engineering geology parameters of input engineering, obtains the shield machine type of applicable employing of this project.

One of object of the present invention can also be achieved through the following technical solutions:

Further, abovementioned steps 1) during structure data for projection, set up shield structure type selecting

and y _ibetween nonlinear relationship, in order to eliminate the impact of dimension, use expression formula will

be converted into x _ij.

Further, abovementioned steps 3) set up expression formula Q (a)=S of projection target function _zd _zin, S _zfor projection value z _istandard deviation, represent the overall dispersion of data for projection; D _zfor projection value z _ilocal density, expression formula is as follows:

$S_z=\sqrt{\frac{\underset{i=1}{\overset{n}{\mathrm{\Σ}}}{(z_i-E_z)}^2}{n-1}}---\left(6\right)$

$D_z=\underset{i=1}{\overset{n}{\mathrm{\Σ}}}\underset{j=1}{\overset{n}{\mathrm{\Σ}}}(R-r_{\mathrm{ij}})\·u(R-r_{\mathrm{ij}})---\left(7\right)$

In formula, E _z-projection value z _imean value; r _ij=| z _i-z _j|, be the distance between sample; U (R-r _ij)-unit-step function, works as R>=r _ijtime, its value is 1, works as R<r _ijtime, its value is 0; R is the windows radius of local density, can determine according to test, is generally taken as

While further, abovementioned steps 4) optimizing projection target function, form with { a _j| j=1,2 ..., the complex nonlinear that p} is optimized variable is optimized relation and is solved as follows:

(1) initialization population, arranges number of particles M, maximum iteration time T, gets R=p, according to the criteria for classification of Shield Machine Selection classification indicators, produces at random sample set

and y _i, will

be converted into x _ij, and determine at random initial position and the speed of each particle;

(2) calculate projection value and the fitness of each particle, according to fitness, determine the individual optimal value p of each particle _i,jglobal optimum p with colony _g,j;

(3) enter major cycle, upgrade speed and the position ，Bing unitization projecting direction of a particle according to formula (8), (9), expression formula is as follows

v _i,j(t+1)=wv _i,j(t)+c ₁r ₁[p _i,j-x _i,j(t)]+c ₂r ₂[p _g,j-x _i,j(t)] （8）

x _i,j(t+1)=x _i,j(t)+v _i,j(t+1),j=1,2,…,d （9）

In formula: v _i,j(t)-particle rapidity during the t time iteration; x _i,j(t)-particle position during the t time iteration; W-Inertia weight factor; c ₁, c ₂-positive study the factor; r ₁, r ₂equally distributed random number between-0 to 1, wherein:

$\left\{\begin{array}{c}w=\mathrm{\&mu;}+\mathrm{sigma}*N\left(\mathrm{0,1}\right)\\ \mathrm{\&mu;}={\mathrm{\&mu;}}_{\min }+({\mathrm{\&mu;}}_{\max }-{\mathrm{\&mu;}}_{\min })*\mathrm{rand}\left(\mathrm{0,1}\right)\end{array}\right.---\left(10\right)$

In formula: the random number of N (0,1)-standardized normal distribution; Random number between rand (0,1)-0 to 1; μ _max-random weight galassing mean-max; μ _min-random weight galassing mean-min; The variance of sigma-random weight,

(4) calculate projection value and the fitness of the t time iteration, upgrade the individual optimal solution p of each particle _i,j(t), upgrade globally optimal solution p _g,j(t),

(5) judge whether to meet error condition, if do not met error requirements and not reaching maximum iteration time, proceed to (3) step, otherwise the optimal location of end loop output population, i.e. optimum solution.

While further, abovementioned steps 5) setting up shield structure type selecting disaggregated model, the c in logistic curve function expression (1) and c (2) are undetermined parameter, solve, by following minimization problem

$\mathrm{mniF}(c\left(1\right),c\left(2\right))=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{(y_i^{*}-y_i)}^2---\left(11\right)$

To solving of expression formula (11), adopt random weight heavy particle group algorithm RandWPSO to solve above-mentioned minimization problem, method is by solving best projection direction a ^*step.

Further, described shield machine comprises grid type shield machine, earth pressure balance shield machine, muddy water balancing earth-pressure shielding machine and slurry type shield machine.

The inventive method, first according to the criteria for classification of Shield Machine Selection classification indicators, produces at random the sample set of Shield Machine Selection and corresponding experience level, thereby has constructed data for projection; Then by RandWPSO, optimize projecting direction, the projecting direction obtaining according to optimization and PP method calculate optimum projection value; The optimum projection value of last substitution obtains Shield Machine Selection disaggregated model, and the undetermined parameter in its Shield Machine Selection disaggregated model is optimized and obtained by RandWPSO.The engineering geology parameters of input engineering is incorporated into Shield Machine Selection disaggregated model obtained above, can obtain the shield machine type of applicable employing of this project.

Two of object of the present invention can be achieved through the following technical solutions:

The device of the shield machine Intelligent Model Selection based on engineering geologic factors, its design feature is: comprise central controller, man-machine interface and power module, described central controller comprises data acquisition module, data processing module and data memory module, the output terminal of described man-machine interface is connected with the input end of data acquisition module, man-machine interface has input message end and control signal input end, and data acquisition module is by man-machine interface gathering project geologic parameter; The I/O port connection data acquisition module of described data processing module, the information of sending into according to data acquisition module produces the sample set of Shield Machine Selection

with corresponding experience level data for projection y _i, obtain and set projecting direction a ^*, and setting projecting direction a ^*basis on construct shield structure type selecting disaggregated model, in conjunction with the information of data collecting module collected, form the parameter value of Shield Machine Selection sample, according to described parameter value, obtain engineering and be applicable to the shield machine type adopting; Parameter when described data memory module is used for storing shield structure type selection calculation, and export corresponding parameter according to the request of data processing module; Power module is central controller and man-machine interface power supply.

Two of object of the present invention can also be achieved through the following technical solutions:

Further, the data processing module of central controller comprises controlled processing unit, receiving element, operation processing unit, transmitting element, storage element and I/O port.

Further, described data processing module, first according to the criteria for classification of Shield Machine Selection classification indicators, produces the sample set of Shield Machine Selection at random with corresponding experience level y _i, set up projection target function, adopt random weight heavy particle group algorithm to solve and optimize projection target function maximization problems, obtain best projection direction a ^*thereby, obtain optimum projection value

; Then pass through with y _iscatter diagram set up Shield Machine Selection disaggregated model; Finally, by the engineering geology parameters combinatorial input Shield Machine Selection disaggregated model of data acquisition module, obtain this project shield machine type of applicable employing.

Further, described shield machine comprises grid type shield machine, earth pressure balance shield machine, muddy water balancing earth-pressure shielding machine and slurry type shield machine.

The present invention has following outstanding beneficial effect:

The present invention, first according to the criteria for classification of Shield Machine Selection classification indicators, produces at random the sample set of Shield Machine Selection and corresponding experience level, thereby has constructed data for projection; Then by RandWPSO, optimize projecting direction, the projecting direction obtaining according to optimization and PP method calculate optimum projection value; The optimum projection value of last substitution obtains Shield Machine Selection disaggregated model, undetermined parameter in its Shield Machine Selection disaggregated model is optimized and is obtained by RandWPSO, the engineering geology parameters of finally inputting engineering is incorporated into Shield Machine Selection disaggregated model obtained above, obtains this project shield machine type of applicable employing.The shortcomings such as personal experience that overcome and do not considered each engineering geologic factors relevance in current Shield Machine selection method, rely on merely expert; Innovate the technology of Shield Machine Selection, there is technical characterstic rapidly and efficiently; By each shield structure type selecting result has been carried out to quantification treatment, avoided the subjectivity of expertise type selecting, make type selecting result clearly unified, specifically strong, the open strong and abundant beneficial effect of Internet resources of easy to operate, applied widely, function.

Accompanying drawing explanation

Fig. 1 is the structured flowchart of the specific embodiment of the invention 1.

Fig. 2 is shield machine type Intelligent Model Selection flowage structure figure of the present invention.

Fig. 3 is best projection direction Optimizing Flow structural drawing of the present invention.

Fig. 4 is undetermined parameter Optimizing Flow structural drawing of the present invention.

Embodiment

Below in conjunction with specific embodiment, the present invention is further illustrated.

Specific embodiment 1:

With reference to Fig. 1, the device of the shield machine Intelligent Model Selection based on engineering geologic factors that the present embodiment relates to, comprise central controller 1, man-machine interface 2 and power module 3, described central controller 1 comprises data acquisition module 1-1, data processing module 1-2 and data memory module 1-3, the output terminal of described man-machine interface 2 is connected with the input end of data acquisition module 1-1, man-machine interface 2 has input message end and control signal input end, and data acquisition module 1-1 is by man-machine interface 2 gathering project geologic parameters; The I/O port connection data acquisition module 1-1 of described data processing module 1-2, the information of sending into according to data acquisition module 1-1 produces the sample set of Shield Machine Selection

with corresponding experience level data for projection y _i, obtain and set projecting direction a ^*, and setting projecting direction a ^*basis on construct shield structure type selecting disaggregated model, the information gathering in conjunction with data acquisition module 1-1 forms the parameter value of Shield Machine Selection sample, obtains engineering be applicable to the shield machine type adopting according to described parameter value; Parameter when described data memory module 1-3 is used for storing shield structure type selection calculation, and export corresponding parameter according to the request of data processing module 1-2; Power module 3 is central controller 1 and man-machine interface power supply source.

In the present embodiment:

The data processing module 1-2 of central controller 1 comprises controlled processing unit, receiving element, arithmetic element, transmitting element, storage element and I/O port, described controlled processing unit, receiving element, arithmetic element, transmitting element, storage element and I/O port can adopt conventional singlechip chip, signal receiving chip, Arithmetic Processing Chip, signal sends chip, storage chip and I/O chip, singlechip chip and signal receiving chip, Arithmetic Processing Chip, signal sends chip, the connected mode of storage chip and I/O chip can adopt the connected mode of routine techniques, utilize known singlechip chip, signal receiving chip, Arithmetic Processing Chip, signal sends chip, storage chip and I/O chip structure, function and for the method for technical solution problem, structure has gathering project geologic parameter, the information of sending into according to data acquisition module 1-1 produces the sample set of Shield Machine Selection

with corresponding experience level data for projection y _i, obtain and set projecting direction a ^*, and setting projecting direction a ^*basis on construct shield structure type selecting disaggregated model, the information gathering in conjunction with data acquisition module 1-1 forms the parameter value of Shield Machine Selection sample, according to described parameter value, obtains the shield machine Intelligent Model Selection device based on engineering geologic factors that engineering is applicable to the shield machine type of functionality of employing.

Described data processing module 1-2, first according to the criteria for classification of Shield Machine Selection classification indicators, produces the sample set of Shield Machine Selection at random with corresponding experience level y _i, set up projection target function, adopt random weight heavy particle group algorithm to solve and optimize projection target function maximization problems, obtain best projection direction a ^*thereby, obtain optimum projection value

; Then pass through with y _iscatter diagram set up Shield Machine Selection disaggregated model; Finally, by the engineering geology parameters combinatorial input Shield Machine Selection disaggregated model of data acquisition module 1-1, obtain this project shield machine type of applicable employing.

Parameter when described data memory module 1-3 is used for storing shield structure type selection calculation, and export corresponding parameter according to the request of data processing module 1-2.

Described man-machine interface 2 is for inputting engineering geology parameters to data acquisition module 1-1, and data processing module 1-2 obtains engineering geology parameters from data acquisition module 1-1, and the information of sending into according to data acquisition module 1-1 produces the sample set of Shield Machine Selection

with corresponding experience level data for projection y _i, obtain and set projecting direction a ^*, and setting projecting direction a ^*basis on construct shield structure type selecting disaggregated model, the information gathering in conjunction with data acquisition module 1-1 forms the parameter value of Shield Machine Selection sample, according to described parameter value, obtaining the shield machine type that engineering be applicable to adopt, is the engineering shield machine type of applicable employing.

Power module 3 is central controller 1 and man-machine interface 2 power supplies.

The Shield Machine Selection that the present embodiment is applicable to, comprises grid type shield machine, earth pressure balance shield machine, muddy water balancing earth-pressure shielding machine and slurry type shield machine.

With reference to Fig. 2-Fig. 4, the shield machine Intelligent Model Selection method based on engineering geologic factors relevance that the present embodiment relates to, comprises the steps:

1) structure data for projection

First according to shield structure type selecting, divide the criteria for classification of * class index, the random sample set that produces the raw Shield Machine Selection of * in each rate range of each shield machine type

with corresponding experience level y _i(i=1,2 ..., n; J=1,2 ..., p),

and y _ibe respectively each shield machine type and corresponding experience level, n, p is respectively number of samples and index number;

According to the criteria for classification of Shield Machine Selection classification indicators, produce at random the sample set of Shield Machine Selection

with corresponding experience level y _i, structure data for projection, calculates projection value;

Structure data for projection, first the criteria for classification according to shield structure type selecting classification indicators is shown in Table 1, the random sample set that produces Shield Machine Selection in each rate range of each shield machine type

with corresponding experience level y _i, i=1,2 ..., n; J=1,2 ..., p,

and y _ibe respectively classification indicators and corresponding experience level, n, p is respectively number of samples and index number, sets up the RandWPSO-PP model of shield structure type selecting and sets up exactly

and y _ibetween nonlinear relationship, in order to eliminate the impact of dimension, will

be converted into x _ij, as follows:

$x_{\mathrm{ij}}=\log 10\left(x_{\mathrm{ij}}^{*}\right)---\left(1\right)$

Table 1 is the criteria for classification of Shield Machine Selection classification indicators

2) calculate projection value

If a={a ₁, a ₂..., a _pbe projecting direction, projection Pursuit Method is exactly p dimension data x _ijcomprehensively become with a _jone Dimensional Projection value z for projecting direction _i

$z_i=\underset{j=1}{\overset{p}{\mathrm{\&Sigma;}}}{a}_j{x}_{\mathrm{ij}},i=\mathrm{1,2},\&CenterDot;\&CenterDot;\&CenterDot;,n---\left(2\right)$

Secondly, optimize projecting direction, set up projection target function, adopt random weight heavy particle group algorithm to solve and optimize projection target function maximization problems, obtain best projection direction a ^*;

3) set up projection target function

When comprehensive projection index, need maximum extraction projection index x _ijvariation information, just require projection value z _ipartial projection point intensive as far as possible, be preferably condensed into several some groups, i.e. z _ithe D of local density _zlarge as far as possible, between subpoint group, scatter as far as possible on the whole, i.e. z simultaneously _istandard deviation S _zalso large as far as possible, adopt Friedman-Tukey type projection index (being to adopt projection Pursuit Method), its function expression is as follows:

Q(a)=S _zD _z (3)

In formula: S _zfor projection value z _istandard deviation represent the dispersion that data for projection is overall; D _zfor projection value z _ilocal density, S _zexpression formula as follows:

$S_z=\sqrt{\frac{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{(z_i-E_z)}^2}{n-1}}---\left(4\right)$

In formula, E _z-projection value z _imean value; r _ij=| z _i-z _j|, be the distance between sample;

D _zexpression formula as follows:

$D_z=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}\underset{j=1}{\overset{n}{\mathrm{\&Sigma;}}}(R-r_{\mathrm{ij}})\&CenterDot;u(R-r_{\mathrm{ij}})---\left(5\right)$

U (R-r in formula _ij) be unit-step function, work as R>=r _ijtime, its value is 1, works as R<r _ijtime, its value is 0; R is the windows radius of local density, according to test, determines, is generally taken as

4) optimize projection target function

Projection target function Q (a) changes along with the variation of projecting direction a, and optimal projection direction is exactly the projecting direction that maximum possible exposes high dimensional data category feature, by solving projection target function, maximizes to estimate best projection direction a ^*, that is:

maxQ(a)=S _zD _z (6)

$s.t.\underset{j=1}{\overset{p}{\mathrm{\&Sigma;}}}{a}_j^2=1---\left(7\right)$

Adopt random weight heavy particle group algorithm to solve { a _j| j=1,2 ..., the complex nonlinear optimization problem that p} is optimized variable;

Described random weight heavy particle group algorithm, comprises the steps:

(1) initialization population, arranges number of particles M, maximum iteration time T, gets R=p, according to the criteria for classification of Shield Machine Selection classification indicators, produces at random sample set and y _i, will

be converted into x _ij, and determine at random initial position and the speed of each particle;

(2) calculate projection value and the fitness of each particle, according to fitness, determine the individual optimal value p of each particle _i,jglobal optimum p with colony _g,j;

(3) enter major cycle, according to formula (8), (9), upgrade speed and the position ，Bing unitization projecting direction of a particle.

v _i,j(t+1)=wv _i,j(t)+c ₁r ₁[p _i,j-x _i,j(t)]+c ₂r ₂[p _g,j-x _i,j(t)] （8）

x _i,j(t+1)=x _i,j(t)+v _i,j(t+1),j=1,2,…,d （9）

In formula: v _i,j(t) particle rapidity while being the t time iteration; x _i,j(t) particle position while being the t time iteration; W is Inertia weight factor; c ₁, c ₂for the positive study factor; r ₁, r ₂be equally distributed random number between 0 to 1, wherein:

$\left\{\begin{array}{c}w=\mathrm{\&mu;}+\mathrm{sigma}*N\left(\mathrm{0,1}\right)\\ \mathrm{\&mu;}={\mathrm{\&mu;}}_{\min }+({\mathrm{\&mu;}}_{\max }-{\mathrm{\&mu;}}_{\min }*\mathrm{rand})\left(\mathrm{0,1}\right)\end{array}\right.---\left(10\right)$

In formula: the random number that N (0,1) is standardized normal distribution; Rand (0,1) is the random number between 0 to 1; μ _maxfor random weight galassing mean-max; μ _minfor random weight galassing mean-min; Sigma is the variance of random weight;

(4) calculate projection value and the fitness of the t time iteration, upgrade the individual optimal solution p of each particle _i,j(t), upgrade globally optimal solution p _g,j(t);

(5) judge whether to meet error condition, if do not met error requirements and not reaching maximum iteration time, proceed to (3) step, otherwise the optimal location of end loop output population, i.e. optimum solution.

Then, best projection direction is brought into, adopt projection Pursuit Method to calculate optimum projection value

, by

with y _iscatter diagram can set up Shield Machine Selection disaggregated model:

5) set up shield structure type selecting disaggregated model

The best projection direction a that step 4) is tried to achieve ^*bring the optimum projection value of calculating each sample in formula (2) into

, according to

with y _iscatter diagram set up Shield Machine Selection disaggregated model, adopt logistic curve function (Logistic CurveFunction) simulation,

$y_i^{*}=N/(1+e^{c\left(1\right)-c\left(2\right)\&CenterDot;z_i^{*}})---\left(11\right)$

Undetermined parameter c (1) in formula and c (2), solve by following minimization problem,

$\mathrm{mniF}(c\left(1\right),c\left(2\right))=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{(y_i^{*}-y_i)}^2---\left(12\right)$

Adopt random weight heavy particle group algorithm to solve above-mentioned minimization problem, the method is with solving best projection direction a in step 4 ^*method.

Finally, the engineering geology parameters of step 6, input engineering is incorporated into Shield Machine Selection disaggregated model obtained above, i.e. (11) obtain the shield machine type of applicable employing of this project.

The present embodiment is applicable to the shield machine of type selecting, comprises grid type shield machine, earth pressure balance shield machine, muddy water balancing earth-pressure shielding machine and slurry type shield machine.

Described is existing algorithms most in use by random weight heavy particle group algorithm (RandWPSO) and projection Pursuit Method (PP), and logistic curve function (Logistic Curve Function) and Friedman-Tukey type projection index are prior aries.

The method adopts matlab language compilation calculation procedure to calculate, and system and device and the operation platform of matlab are as follows:

Hardware environment:

CPU:Pentium III and above, AMD Athlon, Athlon XP, Athlon MP;

Disk space: 400MB and more than;

Internal memory: 256MB and more than;

Video card: 16 and more than.

Software environment: the different platforms such as Windows2000/NT4.0/XP/XP SP/7, UNIX, Mac OS X.

Claims (10)

1. the shield machine Intelligent Model Selection method based on engineering geologic factors relevance, is characterized in that comprising the steps:

1) structure data for projection first according to the criteria for classification of shield structure type selecting classification indicators, produces at random the sample set of Shield Machine Selection in each rate range of each shield machine type

with corresponding experience level y _i(i=1,2 ..., n; J=1,2 ..., p),

and y _ibe respectively each shield machine type and corresponding experience level, n, p is respectively number of samples and index number;

2) form projecting direction and calculate projection value, establishing a={a ₁, a ₂..., a _pbe projecting direction, p dimension data x _ijcomprehensively become with a _jone Dimensional Projection value z for projecting direction _i, projection value is calculated by following expression:

$z_i=\underset{j=1}{\overset{p}{\mathrm{\&Sigma;}}}{a}_j{x}_{\mathrm{ij}},i=\mathrm{1,2},\&CenterDot;\&CenterDot;\&CenterDot;,n---\left(1\right)$

3) set up projection target function, extract projection index x _ijvariation information, by projection value z _ipartial projection point be condensed into several some groups, to determine z _ithe D of local density _zand z _istandard deviation S _z, the expression formula of setting up projection target function is as follows:

Q(a)=S _zD _z (2)

4) optimize projection target function, according to projection target function Q (a) along with the variation of projecting direction a changes, optimal projection direction is exactly the projecting direction that maximum possible exposes high dimensional data category feature, by solving projection target function maximization problems, estimates best projection direction a ^*,

maxQ(a)=S _zD _z (3)

$s.t.\underset{j=1}{\overset{p}{\mathrm{\&Sigma;}}}{a}_j^2=1---\left(4\right)$

Formation is with { a _j| j=1,2 ..., the complex nonlinear that p} is optimized variable is optimized relation;

5) set up shield structure type selecting disaggregated model, the best projection direction a that step 4) is tried to achieve ^*bring the projection value that (2) formula is calculated each sample into

, according to

with y _iscatter diagram set up Shield Machine Selection disaggregated model, Shield Machine Selection disaggregated model with logistic curve function representation is:

$y_i^{*}=N/(1+e^{c\left(1\right)-c\left(2\right)\&CenterDot;z_i^{*}})---\left(5\right)$

In formula,

the calculated value of the-the i Shield Machine Selection sample; The maximum classification grade of N-shield machine is the higher limit of this curve;

6) to be incorporated into Shield Machine Selection disaggregated model obtained above be formula (5) to the engineering geology parameters of input engineering, obtains the shield machine type of applicable employing of this project.

2. the shield machine Intelligent Model Selection method based on engineering geologic factors relevance according to claim 1, is characterized in that: during step 1) structure data for projection, set up shield structure type selecting and y _ibetween nonlinear relationship, in order to eliminate the impact of dimension, use expression formula will

be converted into x _ij.

3. the shield machine Intelligent Model Selection method based on engineering geologic factors relevance according to claim 1, is characterized in that: step 3) is set up expression formula Q (a)=S of projection target function _zd _zin, S _zfor projection value z _istandard deviation, represent the overall dispersion of data for projection; D _zfor projection value z _ilocal density, expression formula is as follows:

$S_z=\sqrt{\frac{\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{(z_i-E_z)}^2}{n-1}}---\left(6\right)$

$D_z=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}\underset{j=1}{\overset{n}{\mathrm{\&Sigma;}}}(R-r_{\mathrm{ij}})\&CenterDot;u(R-r_{\mathrm{ij}})---\left(7\right)$

In formula, E _z-projection value z _imean value; r _ij=| z _i-z _j|, be the distance between sample; U (R-r _ij)-unit-step function, works as R>=r _ijtime, its value is 1, works as R<r _ijtime, its value is 0; R is the windows radius of local density, can determine according to test, is generally taken as

4. the shield machine Intelligent Model Selection method based on engineering geologic factors relevance according to claim 1, is characterized in that: when step 4) is optimized projection target function, form with { a _j| j=1,2 ..., the complex nonlinear that p} is optimized variable is optimized relation and is solved as follows:

(1) initialization population, arranges number of particles M, maximum iteration time T, gets R=p, according to the criteria for classification of Shield Machine Selection classification indicators, produces at random sample set

and y _i, will

be converted into x _ij, and determine at random initial position and the speed of each particle;

(2) calculate projection value and the fitness of each particle, according to fitness, determine the individual optimal value p of each particle _i,jglobal optimum p with colony _g,j;

(3) enter major cycle, upgrade speed and the position ，Bing unitization projecting direction of a particle according to formula (8), (9), expression formula is as follows

v _i,j(t+1)=wv _i,j(t)+c ₁r ₁[p _i,j-x _i,j(t)]+c ₂r ₂[p _g,j-x _i,j(t)] （8）

x _i,j(t+1)=x _i,j(t)+v _i,j(t+1),j=1,2,…,d （9）

In formula: v _i,j(t)-particle rapidity during the t time iteration; x _i,j(t)-particle position during the t time iteration; W-Inertia weight factor; c ₁, c ₂-positive study the factor; r ₁, r ₂equally distributed random number between-0 to 1, wherein:

$\left\{\begin{array}{c}w=\mathrm{\&mu;}+\mathrm{isgma}*N\left(\mathrm{0,1}\right)\\ \mathrm{\&mu;}={\mathrm{\&mu;}}_{\min }+({\mathrm{\&mu;}}_{\max }-{\mathrm{\&mu;}}_{\min })*\mathrm{rand}\left(\mathrm{0,1}\right)\end{array}\right.---\left(10\right)$

In formula: the random number of N (0,1)-standardized normal distribution; Random number between rand (0,1)-0 to 1; μ _max-random weight galassing mean-max; μ _min-random weight galassing mean-min; The variance of sigma-random weight,

(4) calculate projection value and the fitness of the t time iteration, upgrade the individual optimal solution p of each particle _i,j(t), upgrade globally optimal solution p _g,j(t),

(5) judge whether to meet error condition, if do not met error requirements and not reaching maximum iteration time, proceed to (3) step, otherwise the optimal location of end loop output population, i.e. optimum solution.

5. the shield machine Intelligent Model Selection method based on engineering geologic factors relevance according to claim 1, it is characterized in that: when step 5) is set up shield structure type selecting disaggregated model, c in logistic curve function expression (1) and c (2) are undetermined parameter, by following minimization problem, solve,

$\min F(c\left(1\right),c\left(2\right))=\underset{i=1}{\overset{n}{\mathrm{\&Sigma;}}}{(y_i^{*}-y_i)}^2---\left(11\right)$

To solving of expression formula (11), adopt random weight heavy particle group algorithm RandWPSO to solve above-mentioned minimization problem, method is by solving best projection direction a ^*step.

6. according to the shield machine Intelligent Model Selection method based on engineering geologic factors relevance described in the arbitrary claim of claim 1-5, it is characterized in that: described shield machine comprises grid type shield machine, earth pressure balance shield machine, muddy water balancing earth-pressure shielding machine and slurry type shield machine.

7. the device of the shield machine Intelligent Model Selection based on engineering geologic factors relevance, it is characterized in that: comprise central controller (1), man-machine interface (2) and power module (3), described central controller (1) comprises data acquisition module (1-1), data processing module (1-2) and data memory module (1-3), the output terminal of described man-machine interface (2) is connected with the input end of data acquisition module (1-1), man-machine interface (2) has input message end and control signal input end, data acquisition module (1-1) is by man-machine interface (2) gathering project geologic parameter, the I/O port connection data acquisition module (1-1) of described data processing module (1-2), the information of sending into according to data acquisition module (1-1) produces the sample set of Shield Machine Selection

with corresponding experience level data for projection y _i, obtain and set projecting direction a ^*, and setting projecting direction a ^*basis on construct shield structure type selecting disaggregated model, the information gathering in conjunction with data acquisition module (1-1) forms the parameter value of Shield Machine Selection sample, obtains engineering be applicable to the shield machine type adopting according to described parameter value, the parameter of described data memory module (1-3) when storing shield structure type selection calculation, and export corresponding parameter according to the request of data processing module (1-2), power module (3) is central controller (1) and man-machine interface power supply (2).

8. the device of the shield machine Intelligent Model Selection based on engineering geologic factors according to claim 7, is characterized in that: the data processing module (1-2) of central controller (1) comprises controlled processing unit, receiving element, operation processing unit, transmitting element, storage element and I/O port.

9. according to the device of the shield machine Intelligent Model Selection based on engineering geologic factors described in claim 7 or 8, it is characterized in that: described data processing module (1-2), first according to the criteria for classification of Shield Machine Selection classification indicators, produces the sample set of Shield Machine Selection at random

with corresponding experience level y _i, set up projection target function, adopt random weight heavy particle group algorithm to solve and optimize projection target function maximization problems, obtain best projection direction a ^*, obtain optimum projection value

; Then pass through

with y _iscatter diagram set up Shield Machine Selection disaggregated model; Finally, by the engineering geology parameters combinatorial input Shield Machine Selection disaggregated model of data acquisition module (1-1), obtain this project shield machine type of applicable employing.

10. according to the device of the shield machine Intelligent Model Selection based on engineering geologic factors described in claim 7 or 8, it is characterized in that: described shield machine comprises grid type shield machine, earth pressure balance shield machine, muddy water balancing earth-pressure shielding machine and slurry type shield machine.

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