CN110702881B - Prediction method of rock-soil material parameter variability result and application thereof - Google Patents
Prediction method of rock-soil material parameter variability result and application thereof Download PDFInfo
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Abstract
The invention discloses a prediction method of parameter variability results of a rock-soil material and application thereof, and aims to solve the technical problems of large measurement amount, time consumption and labor consumption of the rock-soil material in the prior art in random field probability analysis. The prediction method can be applied to the analysis and the measurement of various geotechnical parameters and engineering structure parameter space variability influence results, realizes the efficient and accurate prediction of countless or maximum measurement results by using a small amount of measurement results with limited times, saves calculation power, is simple, convenient, efficient, time-saving and labor-saving, is accurate and reliable in prediction, and can be applied to various civil engineering and can take safety and effectiveness and economy of engineering construction cost into consideration.
Description
Technical Field
The invention relates to the technical field of geotechnical engineering measurement and analysis, in particular to a method for predicting parameter variability results of geotechnical materials and application thereof.
Background
The numerical analysis method is an important method widely applied to geotechnical engineering measurement, research and analysis design. Traditional numerical analysis is based on isotropic or homogeneous assumption of geotechnical or engineering materials, and does not consider the characteristics of spatial variability of material parameters. In the long-term tectonic movement process of the crust, the geotechnical parameters, joints, fault distribution, temperature fields, seepage fields and the like have anisotropic characteristics and show strong spatial variability, and particularly, the geotechnical material parameters have especially obvious spatial variability when the projects such as foundation pit excavation, tunnels, underground plants, side slopes and the like performed by human beings are generally positioned on the ground surface or buried deep within hundreds of meters or even dozens of meters. If the variability of the measurement and calculation analysis is neglected during the measurement and calculation analysis of the related engineering, a large measurement and calculation error is caused, and further potential safety hazards are brought to the implementation of the subsequent engineering.
Therefore, the uncertainty of the property parameter distribution of the rock and soil is the key point to be researched urgently at present aiming at the characteristic of the space variability of the rock and soil material. In short, the method of probability analysis is adopted to evaluate whether the engineering structure is unstable, and the given answer is not the default 100% stability or instability in the traditional sense, but is converted into a probability problem, such as 95% stability, so that the engineering numerical simulation result is more consistent with the engineering practice.
In the numerical simulation analysis, measurement and calculation research, in order to consider the spatial variability of material parameters and achieve the purpose of probability analysis according to the numerical simulation measurement results, it is generally considered that the studied property parameters obey a certain distribution function (such as a commonly used lognormal distribution function) so as to generate hundreds or even thousands of sets of parameter random numbers, and on the basis, measurement and calculation are performed hundreds or thousands of times (each set of random numbers is calculated once) to obtain corresponding hundreds or thousands of measurement and calculation results, so that the probability analysis is performed on the parameter measurement results.
It is well known that for complex numerical models, especially modern numerical models, are larger and larger, the number of units is larger and larger, the calculation amount is huge, and each measurement and calculation needs one day, one week, one month or even longer. Meanwhile, as described above, the probability analysis requires hundreds of times or thousands of measurements, and until the calculation result is converged, a complete numerical probability analysis calculation consumes a lot of time and calculation power. In addition, the accuracy of the measurement results obtained in a large amount of time is not satisfactory.
Therefore, the problems that the time required by measurement and calculation is shortened, the accuracy of prediction is improved, and the safety and the economical efficiency of engineering implementation are considered are urgently needed to be solved in the current engineering construction.
Disclosure of Invention
The invention aims to solve the technical problems of large measurement amount, time consumption and labor consumption of the rock-soil material in the prior art by probability analysis of random fields, and provides a prediction method of the variation influence result of the parameters (such as elastic modulus, cohesive force, friction angle, strength, joint, fault distribution, temperature field, stress field, seepage field and the like) of the rock-soil material and application thereof.
In order to solve the technical problems, the invention adopts the following technical scheme:
the method for predicting the parameter variability result of the geotechnical material comprises the following steps:
(1) establishing a corresponding finite element numerical model according to the properties of the geotechnical materials and the engineering precision requirement;
(2) substituting the coordinates of the central point of each unit of the finite element numerical model into an autocorrelation function to obtain a covariance autocorrelation matrix according to a covariance matrix decomposition method, and then performing Cholesky decomposition to obtain multiplication of an upper triangle matrix and a lower triangle matrix;
(3) based on a standard normal distribution rule, generating a standard normal distribution random field by the lower triangular matrix obtained in the previous step; transforming the standard normal distribution random field to a mean value of μVariance isσ 2Normal random field of (a);
mapping parameters in the normal random field obtained by transformation and characteristic rock-soil material property parameters to each unit in a finite element numerical model in a one-to-one correspondence mode respectively, and realizing conversion from the random field model to a numerical analysis model;
(4) according to the Monte Carlo strategy, repeatMSub-step (3), thereby obtainingMCalculating results of group random numbers;
(5) will be provided withMThe group random number measurement results are divided into known quantity groupsAAnd group to be predictedB(ii) a When grouping, the reasonable prediction results can be obtained, such as equal or uniform grouping;
(6) from the group to be predictedBExtracting a set of random numbersgCalculating a random numbergCorresponding standard deviationS(ii) a Taking the initial value of standard errori%=kPercent =0, the range of the standard deviation is calculatedU=[S(1- i%)～S(1+ i%)]；
(7) From a set of known quantitiesAThe standard difference value is screened out within the rangeUInside ofnGroup random numbers to form a new large groupC；
(8) Is provided withC′Is a big groupCThe random field matrix of (a) is,B′for the group to be predictedBAnd calculating a random field matrix ofD=C′-B′From the group to be predictedBRandom number of middle drawgAnd the major groupCIn (1)NRespectively calculating matrix by group random numbersDF norm of |)D‖ F Get itnThe smallest of the calculated results |)D‖ F The corresponding random number calculation result is used as a corresponding prediction result;
(9) Repeating the step (5) to the step (8) to obtain the group to be predictedBComparing the predicted result with the actual calculated result to calculate the average errorSS；
(10) Setting the standard deviation error in the step (6)i% increase per calculation ofj% of the total amount of the componentsNThe sub-calculated standard deviation error range takes the value ofi%=k%+M×jPercent; repeating the steps (6) to (9)NThen, obtainNAn average error; will be provided withNThe standard error range corresponding to the minimum value of the average error takes the value asiPercent, the best value of the standard error range is obtainedii%；
(11) Taking the random number to be measured from the random field in the step (3), and adopting the optimal standard error range to take valueiiAnd (6) repeating the steps (6) to (8) until the calculation result is converged and stable, and obtaining a prediction result corresponding to the repetition times.
Preferably, in the step (2), the autocorrelation function is selected as follows:
in the formula (i), the reaction mixture is,x、y、zis the coordinate direction of the model;δ x 、δ y 、δ z are respectively asx、y、zThe relative distance of the directions;τ x 、τ y 、τ z are respectively any two sheetsBetween the elementsx、y、zThe coordinate difference of the direction.
In step (2) above, the covariance autocorrelation matrix can be expressed by Cholesky decomposition as:
C=LU=LL T -formula (ii);
In the formula (ii), the reaction mixture is,L、Urespectively a lower triangular matrix and an upper triangular matrix,L T is a matrixLThe transposing of (1).
In the step (10) of the method, j%=0.05%～0.1%。
the characteristic parameters of the rock-soil material are parameters representing any one of the properties of the strength, the joint, the fault distribution, the temperature field, the stress field, the seepage field and the like of the rock-soil material.
The property parameters of the rock-soil material are any one of elastic modulus, cohesive force, friction angle and the like.
The prediction method can be widely applied to the analysis and calculation of geotechnical engineering support force, displacement settlement, joint, fault distribution, temperature field, seepage field and the like.
Compared with the prior art, the invention has the main beneficial technical effects that:
the prediction method can be applied to the analysis and the measurement of various geotechnical parameters and engineering structure parameter space variability influence results, realizes the efficient and accurate prediction of countless or maximum measurement results by using a small amount of measurement results with limited times, saves calculation power, is simple, convenient, efficient, time-saving and labor-saving, has accurate and reliable prediction results, and can be applied to various civil engineering and can take account of the safety of the engineering and the economical efficiency of the construction cost.
Drawings
FIG. 1 is a schematic diagram of a process for measuring random numbers.
FIG. 2 is a schematic diagram of a cubic elastic constitutive model;
FIG. 3 is a diagram of a cube finite element numerical model;
FIG. 4 is a diagram of a cubic random field numerical model of elastic modulus;
FIG. 5 is a graph comparing the results of the random number complete calculation with the predicted results of a in FIG. 4;
FIG. 6 is a graph comparing the results of the random number complete calculation with the predicted results of b in FIG. 4;
fig. 7 is a graph comparing the results of the random number complete calculation with the predicted results of c in fig. 4.
FIG. 8 is a flowchart illustrating the determination of the optimal standard deviation error range according to the present invention.
FIG. 9 is a flow chart of the improved random number estimation process according to the present invention.
FIG. 10 is a graph comparing the results of the random number complete calculation of FIG. 4 a with the improved prediction results of the present invention;
FIG. 11 is a graph comparing the results of the random number complete calculation of FIG. 4 b with the improved prediction results of the present invention;
FIG. 12 is a graph comparing the results of the random number complete calculation of c in FIG. 4 with the improved prediction results of the present invention.
FIG. 13 is a photograph of an internal scene at the front end of a shield tunneling machine in construction of a certain tunnel project;
fig. 14 is a photograph of an internal scene after a tunnel project is completed.
FIG. 15 is a schematic diagram of the geological profile of the location of a tunnel project.
FIG. 16 is a finite element numerical model of a tunnel project.
Fig. 17 is a graph of support stress versus displacement of the center of the face for a tunnel project.
FIG. 18 is a diagram of a tunnel cohesion random field numerical model;
FIG. 19 is a comparison graph of a random number complete calculation result of a tunnel cohesion random field numerical model and a prediction result before and after improvement.
FIG. 20 is a diagram of a numerical model of a random field at a tunnel friction angle;
FIG. 21 is a comparison graph of a complete calculation result of random numbers of a numerical model of a tunnel friction angle random field and a prediction result before and after improvement.
Detailed Description
The following examples are intended to illustrate the present invention in detail and should not be construed as limiting the scope of the present invention in any way.
The analysis software referred to in the following examples is conventional application software unless otherwise specified; the procedures involved, unless otherwise specified, are conventional.
The following examples and test examples are described by taking 600 random numbers as examples, unless otherwise specified. Experimental example 3 is illustrated using 500 sets of random numbers.
The first embodiment is as follows: establishment of soil body parameter space variability random field model
Based on FLAC3DAnd the numerical analysis software combines the random field with numerical analysis in a random generation mode to generate the random field. The method comprises the following specific steps:
(1) According to the objects to be measured (such as geotechnical structures, engineering structures and the like) and relevant engineering precision requirements, utilizing the finite difference software FLAC3DEstablishing a finite element numerical model, and setting the established numerical model to containnThen, the coordinate information of the central point of each unit in the model is output by adopting the fish language built in the software (the central point of the two-dimensional model output unit)x、yOf centre points of output units of coordinate, three-dimensional modelsx、y、zCoordinates) and stored in the text file, thereby completing the first step of random field generation.
(2) And (3) finishing the second step of generating the random field by utilizing the coordinate information of the central point of the numerical model unit output in the step (1) and combining an autocorrelation function according to a covariance decomposition theory.
The autocorrelation function is used as shown in formula (i):
in the formula (i), the reaction mixture is,δ x 、δ y 、δ z are respectively asx、y、zThe relative distance of the directions;τ x 、τ y 、τ z respectively between any two unitsx、y、zDifference in coordinates of directions, forxoyTwo-dimensional numerical model of a plane, which can be consideredτ z Is always equal to zero.
And (2) substituting the unit center point coordinate information output in the step (1) into an equation (i), and calculating an autocorrelation coefficient by using the equation (i) relative to the center point coordinates (including the unit center point coordinates) of other units. The result of the autocorrelation coefficient calculation of the single unit can be obtained nA matrix of x 1, the matrix of which,nthe unit is calculated by analogy in turn to obtainn×nOf the covariance autocorrelation matrixC n n×. Using Cholesky decomposition willC n n×Expressed as the multiplication of the upper triangular and lower triangular matrices, as shown in equation (ii):
C=LU=LL T -formula (ii);
in the formula (ii), the reaction mixture is,L、Urespectively a lower triangular matrix and an upper triangular matrix,L T is a matrixLThe transposing of (1).
(3) Utilizing the lower triangular matrix obtained by calculation in the step (2)LGenerating a standard normal distribution random field based on a standard normal distribution function;
setting upYIs thatnCombining the column vectors which are independent from each other and obey the standard normal distribution with the lower triangular matrix obtained by the calculation in the step (2)LObtaining standard normal distribution random fieldZThe formula (iv) is shown in formula (iii):
Z=LY-formula (iii);
i.e. using random column vectorsYObtaining different standard normal distribution random fieldsZ。
(4) (iv) Standard Normal distribution random field calculated using equation (iii)ZGenerating a mean value ofμVariance isσ 2Target normal random field ofZSThe formula (iv) is shown as the following formula:
ZS=σZ+μ-formula (iv).
Geotechnical parameters (such as elastic modulus, cohesive force, friction angle, strength, joint, fault distribution, temperature field, stress field, seepage field and the like) in nature are subjected to lognormal distribution in many ways, and the mean value of the lognormal distribution is set as μ 1nVariance isσ 2 1nBy converting the formula (v) and the formula (vi) into a mean valueμVariance isσ 2Is normally distributed.
coefficient of variation of parameterCOVRepresented by formula (vii):
(5) Utilizing the built-in language of FLAC3D to generate the random field through the step (4)ZSThe parameters in the model are respectively assigned to units in the model in a one-to-one correspondence mode for calculation, and therefore the parameter variability is achieved in the numerical model.
(6) According to the Monte Carlo strategy (Monte Carlo method), repeatMSubsteps (3) to (5) to thereby obtainMAnd (5) calculating results for probability analysis.
Example two: measurement and calculation of random number in soil body parameter space variability random field
In order to solve the technical problems of large calculation amount and time and labor consumption of probability analysis of random fields in the prior art, the invention adopts a prediction mode of predicting the calculation results of a plurality of random fields by using the calculation results of a limited number of random fields, namely setting two random field matrixesAAndBand calculateD=A-BBy computing a matrixDIs given by equation (viii):
when |)D‖ F The more towards zero the value of (A), theAAndBthe same calculation should be obtained.
1. A corresponding numerical analysis model is established based on the method described in the first embodiment, 600 sets of random numbers are generated, and the calculation results of the remaining 550 are predicted by using the calculation results of 50 sets.
The prediction process is shown in fig. 1, and specifically includes:
(1) randomly drawing a group of random numbers from the 550 groups of random numbers to be predicted without repetitionA。
(2) Random number extracted in the first stepACalculating | using formula (viii) with 50 sets of random numbers of known calculation results respectivelyD‖ F Taking the smallest II among the 50 calculation resultsD‖ F The corresponding random number calculation results are used as the corresponding prediction results.
(3) Repeating the steps (1) and (2) 550 times, thereby obtaining the prediction result of 550 groups of random numbers.
2. And verifying the prediction method by adopting the settlement value of the central point of the three-dimensional cube elastomer, and comparing the prediction result with the actual simulation calculation result to verify the accuracy of the prediction method.
As shown in fig. 2, a 10-meter-side cube cell model is established, the boundary conditions of the model are that normal constraints are applied to the remaining boundaries except for the top surface which is a free surface, and the 10-meter-side cube model in fig. 2 is divided into 2744 cells, 10648 cells and 21952 cells (as shown in a, b and c in fig. 3).
For convenience of explanation of the principle, the models all adopt elastic constitutive, the Poisson ratio is 0.23, and the density is 1800Kg/m3. The elastic modulus is set to follow the logarithmic normal distribution, the mean value is 24MPa, and the coefficient of variationCOVIs 0.3, correlation distanceSeparation deviceδ x =δ y =δ z =2m. The method of embodiment 1 is adopted to generate 600 sets of elastic modulus random numbers for model units a, b and c in fig. 3, respectively, a, b and c in fig. 4 respectively show that a set of random numbers forms a typical elastic modulus spatial distribution diagram, and different unit colors/grayscales represent different elastic modulus values.
Further, a stress of 2MPa is applied to the top surfaces of the models a, b and c in fig. 4 in a vertical downward direction, and the vertical displacement value of the center point of the top surface is monitored.
According to the numerical calculation result, the vertical displacement values of the 600 top surface central points of a, b and c in fig. 4 can be obtained respectively. The above-mentioned prediction method of random numbers (as shown in fig. 1) is then used to predict the calculation results of the remaining 550 sets of random numbers for a, b, and c in fig. 4, respectively, using the calculation results of the first 50 sets of random numbers. And comparing the prediction result with the actual complete calculation result by taking the random number group number as an abscissa and the average value of the vertical displacement values as an ordinate.
The final comparison results for the models a, b, and c in fig. 4 are shown in fig. 5 to 7, respectively.
As can be seen from fig. 5 to 7, the prediction curve obtained by the random number prediction method is approximately consistent with the actual fully calculated curve in trend, but the overall matching degree of the two curves is poor except for individual curves (fig. 6), and the prediction effect is still poor. Therefore, further improvement is needed to obtain more accurate and reliable prediction results.
Example three: improvement of random number measurement and calculation in soil body parameter space variability random field
The inventor finds that in long-term engineering practice and experimental research, whether the calculation results of the two groups of random numbers are the same or not is judged, except that |)D‖ F Similarly, the standard deviation should be related to the variation of the standard deviation, since the standard deviation reflects the variation of a set of parameters and data. On the premise of ensuring that the standard deviation of two groups of data is in a certain error range, the minimum II is foundD‖ F Corresponding random number calculation as predictionAs a result, the accuracy of the prediction can be greatly improved. But the error range value of the standard deviation cannot be too large, otherwise, the constraint action of the standard deviation cannot be embodied; the error range of the standard deviation cannot be too small, otherwise, an ideal prediction result cannot be obtained. Therefore, there is a problem of the optimal error range.
Therefore, the improvement of the invention comprises two steps:
firstly, obtaining an optimal standard error range by using a calculation result of random numbers of a certain sample size (50 groups in the embodiment); the more the sample size is, the higher the accuracy of the random number measurement and calculation result of the random field is;
second, using the optimum error range sum |)D‖ F Two constraints yield the predicted result.
1. Best standard error range value
The optimal standard error range is obtained by adopting the calculation results of 50 groups of known random numbers, the optimal error range value determination process is shown in fig. 8, and the specific steps are as follows:
(1) dividing 50 groups of random numbers of known calculation results into two large groupsA、BEach large group comprises 25 groups of random numbers;
(2) will be divided into two groupsA、BOne of the groups is defined as a known quantity and the other group is defined as a group to be predicted. Assume here that a large groupAIs a known quantity, a large groupBIs a group to be predicted;
(3) from main groupBExtracting a set of random numbersgCalculating a random numbergCorresponding standard deviationS；
(4) Giving a small initial value of standard errori%=k% (assume)k= 0.1) to calculate the range of values of the standard deviationU=[S(1- i%)～S(1+ i%)]；
(5) From main groupAThe standard difference value is screened out within the rangeUInner group random numbers to form a new large group C；
(6) Will be selected from the main groupBRandom number of middle drawgGroup III of the related ArtCInNGroup random numberCalculating II by the equation (viii)D‖ F Get itNThe smallest of the calculated results |)D‖ F The corresponding random number calculation result is used as a corresponding prediction result;
(7) repeating the steps (3) to (6) 25 times to obtain a large groupBThe predicted results of the middle 25 groups of random numbers;
(8) subjecting the large group obtained in step (7)BThe predicted results of the middle 25 groups of random numbers are compared with the corresponding actual calculation results, and the average error is calculatedSS；
(9) Setting the error range of the standard deviation in the step (4)i% increase per calculation ofj% (assume)j=0.1) Then it is firstMThe sub-calculated standard deviation error range takes the value ofi%=k%+M×jPercent; repeating the steps (3) to (8)MThen, obtainMAn average error;
(10) will be provided withMThe standard error range corresponding to the minimum value of the average error takes the value asi% as the best standard deviation error range valueii(in this case,%,ii=i）。
2. random number prediction process
Using the obtained optimum standard error rangeii% and 50 groups of known random number calculation results, predicting the calculation results of the other groups (for example, 550 groups) of random numbers, wherein the prediction process is shown in fig. 9 and comprises the following specific steps:
(1) Extracting a set of random numbers from 550 sets of random numbers to be predictedgCalculating random numbersgCorresponding standard deviationS；
(2) According to the optimal standard error rangeiiPercent, calculating to obtain the value range of the standard deviationU=[S(1- ii%)～S(1+ ii%)]；
(3) Screening out the range of standard deviation from 50 random numbers with known calculation resultsUInside ofNGroup random numbers to form a new large groupC；
(4) Random number to be extracted from the 550 sets of random numbers to be predictedgAnd the major groupCIn (1)NCalculating the respective values of | using the random number set of equations (viii)D‖ F Get itNThe smallest II among the calculated resultsD‖ F The corresponding random number calculation result is used as a corresponding prediction result;
(5) repeating the steps (1) to (4) 550 times to obtain the prediction result of 550 groups of random numbers.
The first test example: validation of improved prediction methods
The results of the calculations of the first 50 sets of random numbers were used to predict the results of the remaining 550 sets of random numbers, using the results of the calculations of the random field model a, b, c of fig. 4, which was a 10-meter cube cell model created in example two, and the predicted results obtained by the methods described in example three and example two were compared with the actual full calculation results.
The results are shown in fig. 10 to fig. 12, and it is obvious that the improved prediction method of the present invention is more consistent with the complete calculation result, which shows that the accuracy of prediction is greatly improved by the method of the present invention.
Test example two: application in tunnel shield engineering
(1) Overview of the engineering
A certain proposed station and an interval tunnel in Zhengzhou city are both located in Huang-Huai Chong Hongshengji plain area, the terrain slightly fluctuates, newly repaired or existing municipal roads and green belts are arranged around the tunnel, more buildings and municipal pipelines are arranged around the tunnel, and the engineering construction environment is more complicated.
The initial mileage of the underground section of the field railway engineering is CK79+195.230, the boundary mileage of the U-shaped groove section and the buried section is CK79+457.235, the boundary mileage of the buried section and the shield shaft is right CK79+624.818, the initial mileage of the shield section is CK79+640.882, and the final mileage of the shield section is CK80+ 469.385; the length of the U-shaped groove is 262.005 m; the length of the buried section is 167.583 m; shield segments 809.162m (short chain 19.341 m).
(2) Shield excavation method
According to the initial exploration data, the minimum buried depth of the line between the engineering regions is about 7.39m, and the maximum buried depth is about 10.0m (without considering artificial filling). According to the successful experience of applying the earth pressure balance shield in the Zhengzhou city rail traffic engineering, the design institute recommends adopting the earth pressure balance shield, and the diameter of a cutter head of the shield machine is 6.3 meters.
The front end scene of the shield tunneling machine in the project construction is shown in fig. 13; the internal scene after the construction is completed is shown in fig. 14.
The starting mileage of the left line shield of the project is CK79+640.882, the burial depth is 7.39m, when the mileage is increased to be left CK79+800.775, the burial depth of the tunnel is 10.0m (without considering artificial filling), and then the underground project is basically buried by about 10.0 m.
(3) Analysis of variability of geotechnical parameters
Through drilling and indoor experimental analysis of the rock mass material, a geological profile structure can be obtained as shown in fig. 15, and values (average values) of mechanical parameters of different soil layers are shown in table 1, wherein the artificial filling is applied to the model as a load in calculation.
TABLE 1 recommended value table (mean value) of mechanical parameters of each soil layer
(4) Tunnel random field construction
The tunnel is excavated by adopting a shield method, and the stability of the front tunnel face is crucial to the smooth progress of the engineering, so a normal stress is usually applied to the tunnel face as a supporting force in the engineering. The smaller the supporting force is, the larger the displacement value of the center of the face is, so that a critical minimum supporting force exists to ensure the stability of the face, and once the supporting force is smaller than the critical value, the unstable displacement of the face can occur to influence the safety of construction.
The Moore coulomb criterion is a yield criterion commonly used in engineering, and the required parameters only comprise two parameters of a friction angle and a cohesive force, which can be obtained through indoor experiments, so that the Moore coulomb is still selected as the yield criterion in numerical analysis.
For the engineering example, in order to study the magnitude of the minimum supporting force, a finite element numerical model of the tunnel is established by the method described in the first embodiment (as shown in fig. 16) ) The diameter of the shallow circular tunnel is 6.3 meters, and the buried depth is 10 meters (which is consistent with the actual project);x、y、zthe lengths in three directions are respectively 26.3m, 35m and 30m, and the whole model is dividedzThe top of the direction is out of the free surface, and the rest boundaries are normal constraints and are divided into 22152 units.
The values of the basic parameters are shown in table 1, the analytical values of the spatial variability of the cohesion and friction angles are the same as the laboratory test results, and for the cohesion: the coefficient of variation of the silty clay is 0.3, and the correlation distance is 2 m; taking the variation coefficient of the silt to be 0.25 and taking the correlation distance to be 2.3 m; for the rubbing angle: the coefficient of variation of the silty clay is 0.2, and the correlation distance is 1.6 m; the coefficient of variation of silt was taken as 0.18 and the correlation distance was taken as 1.3 m.
(5) Determination of minimum supporting force of shield tunnel
As shown in fig. 16, a circular tunnel is excavated, and a shoring normal stress is applied to an excavated plane (i.e., a tunnel face)PSimultaneously monitoring the horizontal displacement of the central point of the tunnel face, and gradually reducing the normal stress of the support in the calculation processPAnd recording the center edge of the palm surfaceYDisplacement value of directionDYA series of stresses can be obtainedPAnd corresponding displacementDYBy the stress valuePAs abscissa, displacement value DYAs an ordinate, a stress versus displacement curve was obtained as shown in fig. 17.
As can be seen from FIG. 17, after the point B, the horizontal displacement follows the supporting stressPThe reduction of the point B is reduced and the point B is considered to be the minimum support stress.
The method for finding the minimum supporting stress is described with reference to fig. 17, and the supporting stress is gradually reduced from 50kPa in the calculation processABSelecting 4 coordinate points at any section, fitting the selected 4 coordinate points as close to the point A as possible, and fitting a linear equation of the support stress and the displacement according to the coordinate values of the selected 4 points by using a least square methody=f(x) And as a straight line equation for the stable segment. The linear equations of the distances between the rest coordinate points in the graph 17 are calculated sequentially from large to small in the supporting stressy=f(x) Is a distance ofDF，DFGreater than a certainAfter the value is calculated (by analyzing the model, the critical value is 0.029 m), the supporting stress of the coordinate point is considered as the minimum supporting stress.
The comparison shows that the minimum support stress calculated by the method is equal to the minimum support stress calculated by the curve of figure 17, and the reliability of the improved method is effectively verified.
The Mokolun yield criterion has two basic parameters, the angle of friction φAnd c, respectively calculating the influence of the randomness of the friction angle and the cohesive force on the supporting stress. Since the calculation result of the minimum supporting force is related to the reduction step length of the supporting stress in the calculation process, the step length is taken to be 40pa which is small for the influence of the maximum reduction step length.
1) Randomness of cohesion
Setting cohesive force to obey logarithmic normal distribution, taking the variation coefficient of the silty clay as 0.3 and taking the correlation distance as 2 m; the coefficient of variation of silt is 0.25, the correlation distance is 2.3m, and the other basic parameters are shown in table 1, 500 groups of random numbers of cohesive force are generated for the model shown in fig. 16 by the method described in the first embodiment, and a group of random numbers is obtained to form a typical spatial distribution diagram of cohesive force, as shown in fig. 18, different unit colors/gray levels of the random numbers correspond to different cohesive force values.
According to the model shown in fig. 18, the balance is calculated under the action of the dead weight, and an original ground stress field is formed. After the tunnel is excavated, the minimum supporting stress corresponding to each group of cohesion random numbers is calculated, and finally 500 minimum supporting stresses corresponding to 500 groups of random numbers can be obtained. The results of the calculations for the remaining 450 sets of random numbers are predicted using the results of the calculations for the first 50 sets of random numbers. The prediction results obtained by the methods described in examples three and two were compared with the actual complete calculation results, respectively, using the random number group number as the abscissa and the minimum support stress average value as the ordinate. The comparison result is shown in fig. 19, and it is obvious that the improved prediction method of the present invention is more consistent with the actual complete calculation result, and the prediction accuracy is greatly improved. Considering the space variability of the cohesive force, the minimum supporting force is converged to about 6.25kPa, namely, aiming at the project, the supporting force of the tunnel face is not less than 6.25kPa in the shield excavation process.
2) Randomness of friction angle
Setting a friction angle to obey logarithmic normal distribution, wherein the coefficient of variation of the silty clay is 0.2, and the correlation distance is 1.6 m; the coefficient of variation of silt was taken as 0.18 and the correlation distance was taken as 1.3 m. The remaining basic parameters are shown in table 1. Using the method described in the first embodiment, 500 sets of random numbers of cohesive force are generated for the model shown in fig. 16, and a typical friction angle spatial distribution map is formed by a set of random numbers, as shown in fig. 20, where different unit color/gray generation corresponds to different friction angle values.
The balance is calculated under the action of the dead weight according to the model shown in FIG. 20, and an original ground stress field is formed. After the tunnel is excavated, the minimum supporting stress corresponding to each group of random numbers of the friction angles is calculated, and finally 500 minimum supporting stresses corresponding to 500 groups of random numbers can be obtained. The results of the calculations for the remaining 450 sets of random numbers are predicted using the results of the calculations for the first 50 sets of random numbers. The prediction results obtained by the methods described in examples three and two were compared with the actual complete calculation results, respectively, using the random number group number as the abscissa and the minimum support stress average value as the ordinate. The comparison result is shown in fig. 21, and it is easy to see that the improved prediction method is more consistent with the complete calculation result, and the prediction accuracy is greatly improved. Considering the space variability of the friction angle, the minimum supporting force is converged to about 6.5kPa, namely, aiming at the project, the supporting force of the tunnel face is not less than 6.5kPa in the shield excavation process.
While the present invention has been described in detail with reference to the drawings and the embodiments, those skilled in the art will understand that various specific parameters in the above embodiments can be changed without departing from the spirit of the present invention, and a plurality of specific embodiments are formed, which are common variation ranges of the present invention, and will not be described in detail herein.
Claims (8)
1. A method for predicting parameter variability results of geotechnical materials is characterized by comprising the following steps:
(1) establishing a corresponding finite element numerical model according to the properties of the geotechnical materials and the engineering precision requirement, and generating n' units;
(2) substituting the coordinates of the central point of each unit in the finite element numerical model into an autocorrelation function to obtain a covariance autocorrelation matrix according to a covariance matrix decomposition method, and expressing the covariance autocorrelation matrix as multiplication of an upper triangular matrix and a lower triangular matrix by using Cholesky decomposition;
(3) based on a standard normal distribution rule, generating a standard normal distribution random field by the lower triangular matrix obtained in the previous step; converting the standard normal distribution random field into a normal random field with the mean value of mu and the variance of sigma 2;
mapping parameters in the normal random field obtained by transformation and characteristic rock-soil material property parameters into each unit of the finite element numerical model in a one-to-one correspondence mode respectively to realize conversion from the random field model to the numerical analysis model;
(4) Repeating the step (3) for M times according to a Monte Carlo strategy, thereby obtaining M groups of random number measurement results;
(5) dividing the M groups of random number measurement and calculation results obtained in the previous step into a known quantity group A and a group B to be predicted;
(6) extracting a group of random numbers g from the group B to be predicted, and calculating a standard deviation S corresponding to the random numbers g; taking an initial value of standard deviation error, i% = k%, setting k =0.1, and calculating a value range U of the standard deviation, wherein the value range U is set to be a value range from S (1-i%) to S (1+ i%);
(7) screening n groups of random numbers with standard difference values within the range U from the known quantity group A to form a new large group C;
(8) c 'is a random field matrix of a large group C, B' is a random field matrix of a group B to be predicted, D = C '-B', the random numbers g extracted from the group B to be predicted and n groups of random numbers in the large group C are respectively used for calculating the F norm | D | F of the matrix D, and the random number calculation result corresponding to the smallest | D | F in the n calculation results is taken as the corresponding prediction result;
(9) repeating the steps (5) to (8) to obtain a prediction result of the random numbers in the group B to be predicted, comparing the prediction result with an actual calculation result, and calculating an average error SS;
(10) Setting the increment value of the standard error i% in the step (6) in each calculation as j%, and setting the standard error range value of the Nth calculation as i% = k% + Nxj%; repeating the steps (6) to (9) for N times to obtain N average errors; taking the standard error range corresponding to the minimum value of the N average errors as i%, and obtaining the optimal standard error range value ii%;
(11) and (4) taking the random number to be measured from the random field in the step (3), adopting the optimal standard error range to take the value ii%, and repeating the steps (6) to (8) until the calculation result is stable in convergence to obtain the prediction result corresponding to the repetition times.
2. The method for predicting the result of variability of geotechnical material parameters according to claim 1, wherein in said step (2), the selected autocorrelation function is:
in the formula (i), x, y and z are coordinate directions of the finite element numerical model; δ x, δ y and δ z are respectively the relevant distances in the x, y and z directions; τ x, τ y, τ z are the coordinate differences in the x, y, z directions between any two units, respectively.
3. The method for predicting the result of variability of parameters of geotechnical materials according to claim 1, wherein in said step (2), the covariance autocorrelation matrix is expressed by Cholesky decomposition as:
C = LU = LLT-formula (ii);
in formula (ii), L, U are lower and upper triangular matrices, L, respectivelyTIs the transpose of the matrix L.
4. The method for predicting the result of variability of geotechnical material parameters according to claim 1, wherein in said step (10), j% =0.05% -0.1%.
5. The method for predicting the result of variability of parameters of geotechnical materials according to claim 1, wherein said parameters of properties of geotechnical materials are parameters characterizing any one of strength, joint, fault distribution, temperature field, stress field, and seepage field of geotechnical materials.
6. The method for predicting the result of variability of parameters of geotechnical materials according to claim 1, wherein said geotechnical material property parameter is any one of elastic modulus, cohesion and friction angle.
7. Use of the method of prediction of the results of variability of parameters of geotechnical materials according to claim 1 in the analysis of structural stability of geotechnical engineering.
8. The use according to claim 7, characterized in that the minimum supporting force of tunnel engineering or the displacement and settlement of the engineering structure are calculated.
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Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107992687A (en) * | 2017-12-06 | 2018-05-04 | 昆明理工大学 | Rock side slope reliability plastic limit analysis upper bound method based on monte carlo method |
| CN109490983A (en) * | 2018-11-02 | 2019-03-19 | 西南石油大学 | A kind of reservoir geology mechanics parameter automatic Matching Method and system |
| CN110362905A (en) * | 2019-07-03 | 2019-10-22 | 中国科学院武汉岩土力学研究所 | A kind of Shield Tunneling face failure probability determines method |
Family Cites Families (1)
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Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107992687A (en) * | 2017-12-06 | 2018-05-04 | 昆明理工大学 | Rock side slope reliability plastic limit analysis upper bound method based on monte carlo method |
| CN109490983A (en) * | 2018-11-02 | 2019-03-19 | 西南石油大学 | A kind of reservoir geology mechanics parameter automatic Matching Method and system |
| CN110362905A (en) * | 2019-07-03 | 2019-10-22 | 中国科学院武汉岩土力学研究所 | A kind of Shield Tunneling face failure probability determines method |
Non-Patent Citations (1)
| Title |
|---|
| 基于蒙特卡罗方法的模型质量控制图;宋明顺等;《系统科学与数学》;20151115(第11期);第1291-1303页 * |
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