CN107315862A - A kind of method for setting up open-cut foundation ditch engineering investigation and analog parameter relation - Google Patents

A kind of method for setting up open-cut foundation ditch engineering investigation and analog parameter relation Download PDF

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CN107315862A
CN107315862A CN201710438743.0A CN201710438743A CN107315862A CN 107315862 A CN107315862 A CN 107315862A CN 201710438743 A CN201710438743 A CN 201710438743A CN 107315862 A CN107315862 A CN 107315862A
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soil
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CN107315862B (en
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张晋勋
张建全
张雷
乔胜利
李月阳
尹利洁
赵刚
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Beijing Urban Construction Exploration and Surveying Design Research Institute Co Ltd
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Beijing Urban Construction Exploration and Surveying Design Research Institute Co Ltd
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Abstract

The present invention discloses a kind of method for setting up open-cut foundation ditch engineering investigation and analog parameter relation, gathers the Monitoring Data of base pit engineering;Set up suitable soil constitutive model;The original prospecting parameter of soil layer is extracted, and then expands to the sample parameter and test sample parameter of numerical computations;Sample parameter and test sample parameter are subjected to numerical computations respectively;The BP neural network that result of calculation is substituted into genetic algorithm optimization is trained, the mapping relations set up between Soil Parameters and numerical result, and tests neural network model with test sample parameter;The relation that actual monitoring data are substituted into Soil Parameters and result of calculation tries to achieve the corresponding Soil Parameters of measured displacements;Multiple base pit engineering cases are respectively calculated, the Soil Parameters of the prospecting parameter of multiple base pit engineering cases simulation corresponding with the measured displacements that inverting is obtained are substituted into the BP neural network of genetic algorithm optimization, the relation of open-cut foundation ditch engineering investigation and analog parameter is set up.

Description

A kind of method for setting up open-cut foundation ditch engineering investigation and analog parameter relation
Technical field
Open-cut foundation ditch engineering investigation and analog parameter are set up the invention belongs to technical field of civil engineering, more particularly to one kind The method of relation, the analog parameter for obtaining closing to reality operating mode during open-cut foundation ditch engineering excavation by prospecting parameter, enters And more accurately numerical computations are carried out to Practical Project.
Background technology
With the development of modern science and technology, Computer Numerical Simulation is playing increasing work in all trades and professions With.In Geotechnical Engineering field, with large geotechnical structure, high-rise building and underground mining, tunnel over strait, city rail A large amount of constructions of the underground engineerings such as traffic etc. so that application of the numerical simulation technology in Geotechnical Engineering is continued to develop, rock Native engineering numerical is played a greater and greater role during project surveying, design, construction, and base pit engineering numerical simulation It is its important part.
The credibility of numerical simulation calculation result and engineering design, the security of arrangement and method for construction depend primarily on numerical value Calculate the degree of reliability with Rock And Soil parameter in simulation process.Because Rock And Soil is in forming process, mineralogical composition, soil layer are deep The factors such as degree, stress history, water content and density are ever-changing, and the property of Rock And Soil may have bigger difference at each point.At present It is typically with statistical analysis and with reference to empirical data from test data in the rock & soil mechanical parameter needed for geotechnical engineering design It is fixed, with certain arbitrariness.There is random uncertain feature as the test data of sampling, excursion is larger.When When prospecting operation can not provide correct, rational Rock And Soil parameter, uncertainty will be brought to engineering design, construction safety, It is easily caused the generation of engineering accident.
On the 10th national rock-soil mechanics numerical value and analytic method discussion, the one of Zhejiang University professor Gong Xiaonan initiation Investigation on Geotechnical Engineering numerical analysis, respondent be distributed throughout the country be engaged in geotechnical study and engineering All kinds of personnel of application, with certain authority.Professor Gong Xiaonan is classified statistics to investigation result.By investigation result As can be seen that the research of the Geotechnical Engineering of participation investigation and engineers and technicians generally believe numerical simulation technology in Geotechnical Engineering In occupy increasingly consequence, ratio is more than 90%.At the same time, when numerical analysis is used for geotechnical engineering problems, parameter Determine and obtain high attention with constitutive model, be to influence the key factor of Numerical results reliability, numerical analysis side Method includes numerical algorithm, constitutive relation research and selection, parametric measurement, initial and the determination of boundary condition and analysis skill etc. Aspects of contents, in the research and engineers and technicians for participating in investigation, parametric measurement is 114 person-times, occupies 43.02% most At high proportion.In view of the precision of current value analysis is also weak, for how further to improve ground numerical analysis ability needs The investigation of the key issue of solution shows have 112 person-times to think the problem of Parameters of constitutive model measure is most critical, occupy 49.56%, far above other factors, it can be seen that parameter determines the importance in Geotechnical Engineering analysis.
Due to the complexity of rock soil medium property, directly by reconnoitre and test the parameter obtained usually contain it is many artificial and Other error components so that be directly used in calculating analysis and be difficult to obtain and the preferable result of actual coincidence.Under this background, instead Analysis method is arisen at the historic moment.Back analysis is i.e. with the data of the field monitoring such as information such as displacement, strain, stress or load, by building Vertical inverse model, inverse obtains some initial parameters of the system, such as stratum primary stress, the method for Parameters of constitutive model. The purpose of back analysis is to set up a theoretical prediction model closer to field actual measurement results, more correctly to reflect or in advance Survey some mechanical behaviors of geotechnical structure.
At present, with the continuous progress of technological meanses, prospecting and experimental technique and back analysis algorithm obtain certain journey The development of degree, reconnoitres precision getparms and improves constantly, back analysis numerical algorithm is also proposed and improved in succession.However, logical The research for crossing relation between both the above means acquisition prospecting and analog parameter is but usually neglected, and is closed between both parameters The foundation of system can improve the value ability of base pit engineering geotechnical engineering problems parameters for numerical simulation;Reduce Geotechnical Engineering modeling point Analyse parameter and choose complexity, shorten and adjust the ginseng time, improve numerical simulation calculation precision and reliability, be more effectively design of foundation pit There is provided and assess, check;Issuable Geotechnical Problems of constructing are predicted, assessed and early warning, carried by construction simulation process in advance Before adopt an effective measure, probability etc. that effectively reduction (subway) accident occurs.
The content of the invention
Present invention aims at a kind of utilization numerical computations, the BP neural network of genetic algorithm optimization is provided, by actually supervising The calculating parameter that result data inverting obtains the numerical model of closing to reality operating mode is surveyed, genetic algorithm optimization is then reused The method that BP neural network sets up relation between prospecting parameter and analog parameter, closing to reality is can obtain by the prospecting parameter of engineering The analog parameter of operating mode, and then obtain the numerical result with being actually consistent.
To achieve the above object, the present invention is adopted the following technical scheme that:
It is a kind of to set up open-cut foundation ditch engineering investigation and the method for analog parameter relation comprises the following steps:
Step 1, the Monitoring Data for gathering base pit engineering, are fitted to it according to its changing rule, are used as Inversion Calculation Basic data;
Step 2, the mathematical calculation model for setting up standard, choose suitable soil constitutive model;The original of soil layer is extracted to survey Parameter is examined, the initial soil calculation parameter of numerical computations is converted to this;Based on initial soil calculation parameter, number is expanded to It is worth the sample parameter calculated and test sample parameter;Sample parameter and test sample parameter are subjected to numerical computations respectively;
Step 3, the BP neural network that sample parameter and numerical result are substituted into genetic algorithm optimization are trained, and are built Vertical mapping relations between Soil Parameters and numerical result, and test neural network model with test sample parameter;Will Actual monitoring data substitute into Soil Parameters and the relation of result of calculation tries to achieve the corresponding Soil Parameters of measured displacements;To multiple foundation ditches Engineering Projects is respectively calculated, by the prospecting parameter of multiple base pit engineering cases mould corresponding with the measured displacements that inverting is obtained The Soil Parameters of plan substitute into the BP neural network of genetic algorithm optimization, set up the pass of open-cut foundation ditch engineering investigation and analog parameter System.
Preferably, the Monitoring Data in step 1 includes:Pile head settlement, ground settlement, Horizontal Displacement, pile body water Prosposition is moved.
Preferably, the original prospecting parameter in step 1 includes:Cohesive strength, internal friction angle, modulus of compressibility, Poisson's ratio, day Right density, natural moisture content, proportion, void ratio, liquid limit, plasticity index, cake compressibility, swelling index.
Preferably, step 3 specifically includes following steps:
Step 3.1:The result of calculation that step 2 is obtained substitutes into neural network model, by soil body sample parameter (Nai…Nfi) It is used as input layer, its corresponding simulation result of calculation (Sai…Sfi) as output layer, node in hidden layer can useWherein, n It is respectively to input and output layer nodes with m, using 2n+1 initial the number of hidden nodes, Population in Genetic Algorithms scale is set, changed Generation number, intersection and mutation probability, then run neural network model, set up between soil body sample parameter and simulation result of calculation Mapping relations;
Step 3.2:By test sample parameter (nai…nfi) substitute into soil body sample parameter and simulation meter that step 3.1 is set up The mapping relations between result are calculated, the neural network model that operating procedure 3.1 is preserved is calculated, and show that test sample is corresponding Neural computing result (Lai…Lfi), comparative analysis test sample parameter (nai…nfi) corresponding numerical simulation calculation result (sai…sfi) and neural computing result (Lai…Lfi) error, meet require, then carry out next step;
Step 3.3:By actual monitoring data (Sa1…Sf1) substitute into step 3.1 set up neural network model, run and carry out Calculate, and then draw by the Soil Parameters (M of the corresponding simulation of measured dataa1…Mf1), each open-cut foundation ditch engineering is according to upper Flow is stated to be respectively calculated.The prospecting parameter of all base pit engineering cases is (ai…fi), by measured data according to above-mentioned steps Soil Parameters (the M for the simulation that Inversion Calculation is obtainedai…Mfi);
Step 3.4:Will prospecting parameter (ai…fi) as the input layer of neutral net, obtained by measured data Inversion Calculation Simulation Soil Parameters (Mai…Mfi) as the output layer of neutral net, set neutral net and heredity to calculate according to step 3.1 The correlation computations parameter of method, operation neural network model is calculated, and then draws open-cut foundation ditch engineering investigation and analog parameter Between mapping relation.
Beneficial effect
It is of the invention effective using numerical computations, the BP neural network of genetic algorithm optimization and actual monitoring, survey data, The relation set up between prospecting and analog parameter, the value energy of base pit engineering geotechnical engineering problems parameters for numerical simulation is improved with this Power;Reduce Geotechnical Engineering modeling analysis parameter and choose complexity, shorten and adjust the ginseng time, improve numerical simulation calculation precision and reliable Property, more effectively provide assessment, check for design of foundation pit;Construction simulation process, in advance to issuable Geotechnical Problems of constructing It is predicted, assesses and early warning, adopts an effective measure in advance, probability that effectively reduction (subway) accident occurs etc..
Brief description of the drawings:
The various constitutive models of Fig. 1 excavate the schematic diagram of applicability in analysis in foundation ditch numerical value;
Fig. 2 genetic algorithm optimization BP neural network flow charts;
Fig. 3 the inventive method flow charts.
Specific implementation method:
As shown in figure 3, the present invention provides a kind of method for setting up open-cut foundation ditch engineering investigation and analog parameter relation, including Following steps:
1) selection of Engineering Projects, its control condition includes:
1.1) Engineering Projects needed for must include the data such as prospecting, design, third party monitoring and data is complete
The foundation of mathematical calculation model needs soil layer data in exploration report, therefore prospecting parameter is used as the number of all data Being according to one of basis must be complete complete;The foundation of mathematical calculation model, which is removed, needs to know the soil parameters in exploration report Outside, in addition it is also necessary to know the design data of foundation ditch, such as foundation ditch plane design drawing, profile, excavation flow and supporting construction parameter, More accurate so as to the model of foundation, excavating condition more conforms to live actual condition, and the result of calculating more approaches monitoring Data;Third party monitoring data must be complete, is primarily due to parametric inversion and relies primarily on the data of actual monitoring carry out inverting and obtain To the Soil Parameters for more approaching actual state, in addition, according to third party monitoring point floor plan, in numerical computations Corresponding monitoring point is set, makes the displacement of numerical computations with the displacement of actual monitoring there is corresponding locus to sit Mark, makes whole flow process have invertibility.
1.2) engineering project must include generic Rock And Soil
The model parameter otherness of various soil mass is larger, therefore need to set up between prospecting and the analog parameter of the similar soil body Relation, this requires required Engineering Projects to contain generic Rock And Soil simultaneously.
1.3) engineering project site must be areal region
The congener soil body physico-mechanical properties of different regions is there is also certain otherness, therefore it is required that required work Journey case must be areal.
2) processing of pit retaining monitoring data, including:
2.1) error analysis
In the work progress of the Geotechnical Engineerings such as excavation of foundation pit, the Monitoring Data such as displacement generally comprises many factors Influence, obtained displacement measuring value has error, needs to carry out at data it before back analysis is carried out using displacement data Reason.
Key element Producing reason and property are measured according to such as displacement etc., can say that error is divided into systematic error, random error With the class of human error three:Systematic error, random error and human error.
A) systematic error
The reason for systematic error is due to some fixed is caused, and is characterized in regular all the time in whole measurement process Exist, its absolute value and symbol keep it is constant or it is regular vary, be mainly derived from the following aspects:Method Error, instrumental error, environmental error, operating error and subjective error.
Systematic error can be summarized changing rule, take corresponding measure to reduce in the measurements by searching related causes Error, or measurement result is modified in data processing.
B) random error
Random error is caused by some random accidentalia.If carrying out substantial amounts of measurement, certain statistics can be met Rule, it is considered that obedience is just distributed very much.The reason for producing random error has the side such as measuring instrument, measuring method and environmental condition The fluctuation in face, such as supply voltage, environment temperature, the minor fluctuations of humidity and air pressure, magnetic interference, the minor variations of instrument, behaviour Make minute differences on human users etc..Random error is unavoidable in the measurements.It is difficult often area in actual tests Divide random error and systematic error, therefore many errors are all the combinations of this two classes error.
C) human error
Human error is due to that testing crew is careless, the error that reason causes such as does not handle affairs, such as reads by operational procedure Wrong meter dial, record and calculating mistake.Human error prevailing value is larger, and is not often substantially inconsistent with practical work.
In addition, when using Monitoring Data with base pit engineering numerical simulation be combined progress parametric inversion when, if choosing The constitutive relation taken can not embody the substantive characteristics of rock soil medium deformation completely, also lead to result of calculation actual with base pit engineering Deformation values have differences, and we are for the time being referred to as " essential error ";Other numerical model also can do many to Practical Project Simplify, equally numerical simulation result can be made to be had differences with practical distortion value.
2.2) Analysis on monitoring data
Because monitoring weekly does not record detailed excavation of foundation pit progress, i.e., without clearly embodiment excavation of foundation pit depth and prison Survey the corresponding relation of data, it is therefore desirable to careful analysis is carried out to original Monitoring Data, comprehensive considering various effects are arranged Go out can be used for parameters for numerical simulation inverting, the different cutting depths of correspondence Monitoring Data information.
Monitoring Data processing:Because foundation ditch area is big, construction time limit for length, across autumn and winter spring and summer, centre experienced shut-down, day The influence (experience frost heave and for several times heavy rain) of the factors such as gas change, all can produce significant impact to foundation pit deformation.This Outside, due to detecting instrument in itself and the influence that is brought to Monitoring Data of artificial origin is also very important.At the same time, to foundation ditch Engineering carries out numerical simulation and only accounts for influenceing the essential reason of foundation pit deformation, including the engineering geology bar residing for base pit engineering Part, hydrogeologic condition, foundation ditch form, support structure design, digging process etc.;According to this structure selected in numerical procedure Relational model, generally can also ignore the influence that creep, temperature change etc. are produced to foundation pit deformation.Therefore, pit retaining monitoring data are needed Pre-processed, it is impossible to be directly used in numerical simulation back analysis.How Monitoring Data is reasonably handled, external action of forgoing Factor, obtains the data message of reflection foundation pit construction process nature deformation rule, is the problem of needing serious analysis.
2.3) Monitoring Data is handled
Measured data by site operation environment, working procedure, accuracy of instrument, Changes in weather, artificial disturbance etc. it is numerous because The influence of element, and it is to be carried out under preferable environment to simulate, the result that both settle accounts can produce error unavoidably, to control as far as possible Error rejects some singular points, it is necessary to contrasted and screened to Monitoring Data, what condition was met, and measured data can be carried out Fitting, obtains relatively reasonable measured data.
3) mathematical calculation model of standard is set up, following standardisation requirements are met:
3.1) foundation of numerical model
The model of foundation is the part of engineering entity, between numerical model is set up, and the data of monitoring need to be carried out Induction and conclusion, selection monitoring type is more, position that is relatively complete, relatively completing, and the surrounding enviroment at simultaneous selection modeling position are more simple Single, physical form is simple, rule, if being easy to modeling, condition satisfaction, preferably sets up complete mathematical calculation model.
The size of model takes 3~5 times of foundation depth, and model bottom is fixed boundary, and its X, Y, Z-direction position are constrained respectively Move;It is the scope of freedom at the top of model, is not provided with constraint;Model constrains the displacement of the horizontal direction respectively in two horizontal directions.
The model meshes of foundation are divided and also there are certain requirements, and density need to divide appropriate, and structure is employed during modeling Grid at unit, construction unit need to divide comparatively dense some because at most there is a construction unit section among a cell cube Point.While excavating the key positions such as step and foundation ditch bottom when modeling, interface should be set, prevent excavation is not in place from being produced to result of calculation Raw certain influence.
Cast-in-situ bored pile is included in the supporting construction of foundation ditch case, and the stress form of cast-in-situ bored pile and underground are continuous Wall is close, and by the equal principle of bending rigidity, by cast-in-situ bored pile building enclosure, this is counted as certain thickness diaphram wall, Equivalent formula such as formula 1.
In formula:H is equally connects wall thickness, and D is cast-in-situ bored pile diameter, and t is the clear distance of stake.
Ground-connecting-wall uses solid element, and contact surface is set between ground-connecting-wall and the soil body.
3.2) classification of constitutive model
Conventional constitutive model is divided into the bullet of 3 major classes, elastic class model, bullet-ideal plasticity class model and strain hardening class Plasticity model.Elastic model is due to that can not reflect the plastic property of the soil body, preferably simulation active earth pressure and can not be broken ground Pressure is thus not suitable for the analysis of excavation of foundation pit.The MC models and DP models of bullet-ideal plasticity are due to past using single rigidity It is past to cause very big hole bottom resilience, it is difficult to while providing soil deformation after rational deformation of wall and wall.It can consider that bury should Become hardening characteristics, the difference that loading and off-load can be distinguished and its rigidity dependent on stress history and the hardening class model of stress path Such as MCC models and HS models, soil deformation situation after relatively reasonable deformation of wall and wall can be provided simultaneously.Under sensitive environment Design of Excavation Project needs the deformation of the soil body after critical concern wall, from meeting requirement of engineering and convenient and practical angle, It is proposed with the excavation of foundation pit numerical analysis under the sensitive environment of MCC and HS models progress.According to the characteristics of model itself, Ke Yi great Cause to judge applicability of the various constitutive models in foundation ditch numerical value excavates analysis, refer to Fig. 1.
3.3) selection of constitutive model and its determination of initial parameter values
According to step 2.2), mainly using modified Cam-clay (MCC models), sandy gravel uses mole coulomb model (MC Model).Modified Cam-clay is needed in 4 model parameters, i.e. v-lnp' planes in slope λ, the v-lnp' plane of normal consolidation line The slope M of CSL lines, Poisson's ratio v (or shear modulus G) on slope κ, p'-q' faces of resilience line.Wherein λ and κ can be real according to consolidation Test and tried to achieve respectively by formula 2 and formula 3.
C in formulacAnd CsRespectively native cake compressibility and swelling index.M can be obtained according to triaxial compression test by formula 4.
In formulaThe effective angle of inner friction obtained for triaxial test.
In addition modified Cam-clay is still needed 2 state parameters, i.e. initial void ratio e0With preconsolidation pressure p0
The major parameter of mole coulomb model is:Natural density γ, cohesive strength c, internal friction angleModulus of pressure E, Poisson Compare v.
3.4) generation of sample parameter
Obtain after initial parameter, carry out initial numerical computations, and contrast with measured result.It is determined that needing the soil of inverting Layer and inverting Soil Parameters, then on the basis of calculating parameter, with certain multiple extended parameter (parameter of expansion Result of calculation should be able to include the result of actual monitoring), then the extended parameter of inverting soil layer is subjected to permutation and combination, form sample ginseng Number, finally substitutes into computation model by the parameter of sample, is respectively calculated, enters the result of calculation of sample parameter after the completion of calculating Row is arranged.In addition also need to randomly select several groups of test parameters being located between sample parameter (for testing neutral net mould Type), carry out numerical computations and arrange its result.
4) Parameter Inversion Model is set up, Inversion Calculation is carried out, including:
4.1) qualifications that parametric inversion is calculated
The calculating means of parametric inversion use the BP neural network of genetic algorithm optimization, and (its algorithm flow refers to figure 2), there is certain requirement to sample using this inverse model:A) number of species of measured data must be not less than soil body inverted parameters Number of species;B) result that sample parameter is calculated must can cover the data of actual measurement.Therefore during sample is generated, inverting Parameter to choose rationally, meet the requirement of parametric inversion system, otherwise result of calculation can produce larger error.
4.2) calculation process of parametric inversion mainly includes:
A) input, the design of output layer
By step 2.4) numerical result substitute into neural network model, using Soil Parameters as input layer, by the soil body The numerical result that parameter is obtained is as output layer.
B) determination of the number of plies is implied
The determination of the number of hidden nodes does not have clear and definite analytic expression to represent, need to be determined according to practical experience.Because not being bigger It is better, nor the smaller the better, it is therefore desirable to choose a moderate number.For single hidden layer network, in practical application, the upper bound For number of training, lower bound is output unit number.If using pyramid rule, if that is, only one layer hidden layer network, hidden Node layer number can useWherein n and m are respectively input and output layer nodes.Also have using 2n+1 initial hidden nodes Number, specific number is determined by tentative calculation.
C) normalization of training sample
Normalization is using, from tape function mapminmax, the form of this function normalization is with maximum in one group in MATLAB Value subtracts minimum value as denominator, subtracts minimum value as molecule using normalized number is wanted, you can obtain the normalized value of the number. The normalized of data is that all data are converted to number between [0,1], and the purpose is to cancel quantity between each dimension data Level difference, it is to avoid because inputoutput data order of magnitude difference is larger and causes neural network forecast error larger.
D) neutral net is tentatively set up
BP neural network is tentatively set up using the newff functions tool box in MATLAB, concrete form is as follows:
Net=newff (a, b, hiddennum);
A represents the input sample after the normalization of numerical simulation calculation result;
B represents the output sample after the normalization of numerical simulation Soil Parameters;
Hiddennum represents node in hidden layer.
E) genetic algorithm parameter is initialized
The parameter contained in genetic algorithm is initialized, parameter includes:Iterations, population scale (chromosome Number), crossover probability, mutation probability.In Genetic Algorithm Optimized Neural Network problem, chromosome is exactly the weights by neutral net Constituted with threshold value.
F) initialization of population
By each chromosome initialization in population, will every group of weights and threshold value assign initial value, method is used in MATLAB Rands functions initialized.
G) calculating of chromosome fitness function
The initial weight and threshold value of BP neural network are obtained according to individual, is trained with training data and system is predicted after BP neural network System output, using the Error Absolute Value and E between prediction output and desired output as ideal adaptation angle value F, calculation formula is shown in formula 5.
In formula:N is network output node number;yiFor the desired output of i-th of node of BP neural network;OiFor i-th of node Prediction output;K is coefficient.
The chromosome (weights and threshold value) of every group of initialization is assigned the network (net) tentatively set up, after normalization Numerical simulation calculation result carries out the training of network as the Soil Parameters after input sample and normalization as output sample, and Tested, using the systematic error of test as homologue (weights and threshold value) fitness function value, because error is got over It is small better, so the minimum chromosome (weights and threshold value) of systematic error is optimum individual in this generation population.Record fitness letter Number optimal individual and its fitness function.
H) chromosome is selected
Chromosome is selected using wheel disc pronunciation, i.e. the selection strategy based on fitness ratio, each individual i choosing Select probability PiSee formula 6.
In formula:FiFor individual i fitness value, because fitness value is the smaller the better, so to adapting to before individual choice Angle value asks reciprocal;K is coefficient;N is population at individual number.
I) chiasma
Because individual uses real coding, so crossover operation method uses real number interior extrapolation method, k-th of chromosome akWith L chromosome alSee formula 7 in the crossover operation methods of j.
In formula:B is the random number between [0,1].
J) chromosomal variation
Choose j-th of gene a of i-th of individualijEnter row variation, mutation operation method is shown in formula 8.
In formula:amaxFor gene aijThe upper bound;aminFor gene aijLower bound;F (g)=r2(1-g/Gmax)2;r2For one Random number;G is current iteration number of times;GmaxFor maximum evolution number of times;R is the random number between [0,1].
K) chromosome fitness value is updated
The fitness function value of chromosome of new generation is calculated again, and method is as (g) step.Each neozoic dye Optimal individual is compared in the fitness value of colour solid and the previous generation, updates optimum individual and its fitness value.Then circulation changes Generation, until reaching the iterations of setting.
L) assign neutral net by optimal chromosome and carry out neural metwork training
Neutral net finally is assigned using optimal chromosome (weights, threshold value) as the initial weight threshold value of neutral net, and The result of calculation of the numerical simulation after normalization is regard as output as the parameter of the simulation soil body after input sample and normalization Sample carries out the training of network, and then the mapping set up between the data result of numerical simulation calculation and numerical simulation Soil Parameters Relation.
M) neural network model is tested
By step 2.4) in test parameter substitute into the neural network model that step l) sets up and carry out network test, calculate The error amount of each output variable in each test sample.If training result is unsatisfactory for requiring, genetic algorithm ginseng is adjusted again Number, re-starts the optimization of network weight threshold value until meeting error requirements.
N) calculating parameter inverting
Actual monitoring data are normalized, the measured data after normalization is substituted into the nerve that step l) is set up Network model, inverting obtains the corresponding simulation Soil Parameters of measured displacements.
5) relation between prospecting and analog parameter is set up:
Multiple base pit engineerings are respectively calculated according to above-mentioned steps and obtain simulation soil body ginseng corresponding by measured displacements Number, then again using the prospecting parameter of different base pit engineerings as input layer, the simulation Soil Parameters that inverting is obtained as output layer, Neutral net is substituted into be trained, and then the mapping relations reconnoitred between analog parameter, and look for an Engineering Projects to carry out Checking.
Embodiment 1:
The specific embodiment for setting up open-cut foundation ditch engineering investigation and analog parameter relational approach based on the present invention include with Lower step:
Step 1:According to monitoring plan, Monitoring Data (pile head settlement, ground settlement, the stake top of each monitoring point are extracted Horizontal displacement etc.) (Sa1…Sf1), and prospecting parameter (a1…f1), Monitoring Data is analyzed, some singular points are rejected, so Monitoring Data is handled using methods such as fittings afterwards, the Monitoring Data (S of key point is extracteda2…Sf2)。
According to the difference of support form, the appearance form of pile lateral displacement is also different, and such as pile lateral displacement is presented Measured data, can be fitted to exponential function, then take the Monitoring Data at its stake top position by " rim of a cup " type;If measured data is in Existing " bulging " type, can be fitted to just too distribution function, take the Monitoring Data at its maximum displacement position;Which kind of letter be specifically fitted to Number, also needs dynamically to be adjusted and value according to the engineering characteristic of base pit engineering and the changing rule of Monitoring Data.
Step 2:According to the monitoring plan and Monitoring Data of step 1, selection surrounding enviroment are relatively easy, monitoring species phase To more, the Monitoring Data relatively simple position of complete, foundation ditch shape relatively, according to the actual formation condition of foundation ditch, it is based on FLAC3D5.0 numerical softwares set up the excavation of foundation pit model of standard with 3~5 times of foundation depth, and set its boundary condition.
Step 3:Simulation initial parameter (N is drawn by prospecting relevant parameter or experiencea2…Nf2) (including cohesive strength, interior friction Angle, modulus of compressibility, Poisson's ratio, natural density, the slope λ of normal consolidation line, it is elastic expansion line slope κ, constant of friction M, initial Void ratio e0With preconsolidation pressure p0) and calculated, according to result of calculation, around simulation initial parameter (Na2…Nf2) be multiplied by Certain coefficient draws multigroup sample parameter (Nai…Nfi), sample parameter is then substituted into numerical model respectively and calculated, is obtained Go out its result of calculation (Sai…Sfi).Several groups of test sample parameter (n are randomly selected between sample parameterai…nfi), and enter respectively Row numerical computations, draw its result of calculation (sai…sfi)。
FLAC3D numerical computations, (are computed repeatedly, model is constant, only modification soil body ginseng due to needing to calculate multigroup sample Number), therefore the command file " .txt " of batch processing need to be write, and preserve result of calculation " .sav " form text of different sample parameters Part (its name must modify, otherwise will capped), for convenience of subsequent treatment, it would be desirable to result of calculation be output as " .xls " Excel table format files, above file is placed in same file and is named.
Parametric inversion of the present invention calculates the BP neural network for using genetic algorithm optimization, and program code is to be based on Matlab carries " .m " formatted file write in artificial neural network tool box, and the data message such as input and output variable is protected Save as " .mat " formatted file.So if to perform order being fitted without on the computer of matlab softwares, it is necessary first to By matlab software installations on computers, need to open " .m " file in matlab during operation neutral net, adjust With the data in " .mat " file, operation calculates and preserves network, and network trust system is in " .m " file.
Step 4:The result of calculation that step 3 is obtained substitutes into neural network model, by soil body sample parameter (Nai…Nfi) make For input layer, its corresponding simulation result of calculation (Sai…Sfi) as output layer, node in hidden layer can useWherein n and M is respectively input and output layer nodes, can also use 2n+1 initial the number of hidden nodes, and specific number is true by tentative calculation It is fixed.Population in Genetic Algorithms scale, iterations, intersection and mutation probability are set, neural network model is then run, sets up the soil body Mapping relations between sample parameter and simulation result of calculation.
Step 5:By test sample parameter (nai…nfi) substitute into soil body sample parameter and simulation calculating knot that step 4 is set up Mapping relations between fruit, the neural network model that operating procedure 4 is preserved is calculated, and draws the corresponding nerve net of test sample Network result of calculation (Lai…Lfi), comparative analysis test sample parameter (nai…nfi) corresponding numerical simulation calculation result (sai… sfi) and neural computing result (Lai…Lfi) error, meet require, then carry out next step.
Step 6:By actual monitoring data (Sa1…Sf1) substitute into step 4 set up neural network model, run and counted Calculate, and then draw by the Soil Parameters (M of the corresponding simulation of measured dataa1…Mf1), each open-cut foundation ditch engineering is according to above-mentioned Flow is respectively calculated.The prospecting parameter of all base pit engineering cases is (ai…fi), it is anti-according to above-mentioned steps by measured data Drill the Soil Parameters (M for calculating obtained simulationai…Mfi)
Step 7:Will prospecting parameter (ai…fi) as the input layer of neutral net, obtained by measured data Inversion Calculation Soil Parameters (the M of simulationai…Mfi) as the output layer of neutral net, according to step 4, artificial neural network and genetic algorithms are set Correlation computations parameter, operation neural network model is calculated, and then is drawn between open-cut foundation ditch engineering investigation and analog parameter Mapping relation.
Step 8:With a new open-cut foundation ditch Engineering Projects, and the prospecting parameter of the base pit engineering case is extracted, be updated to The open-cut foundation ditch engineering investigation that step 7 is set up show that its is right with being calculated in the neural network model of analog parameter relation The Soil Parameters for the simulation answered, then set up the numerical model of new base pit engineering, and the Soil Parameters of the simulation drawn are substituted into Calculated in numerical model, its result of calculation and measured result are contrasted, analyze whether its error meets requirement.If not It is full to require, then using the method for amendment numerical model, the constitutive model using higher level, amendment neural network parameter etc., again Flow according to above-mentioned steps 2 to step 7 calculate and required until meeting.
If subsequently having new base pit engineering case again, can again it be counted according to the flow of above-mentioned steps 1 to step 8 Calculate, constantly the relation between the set up open-cut foundation ditch engineering investigation of amendment and analog parameter.

Claims (4)

1. a kind of method for setting up open-cut foundation ditch engineering investigation and analog parameter relation, it is characterised in that comprise the following steps:
Step 1, the Monitoring Data for gathering base pit engineering, are fitted to it according to its changing rule, are used as the base of Inversion Calculation Plinth data;
Step 2, the mathematical calculation model for setting up standard, choose suitable soil constitutive model;Extract the original prospecting ginseng of soil layer Number, the initial soil calculation parameter of numerical computations is converted to this;Based on initial soil calculation parameter, numerical value meter is expanded to The sample parameter and test sample parameter of calculation;Sample parameter and test sample parameter are subjected to numerical computations respectively;
Step 3, the BP neural network that sample parameter and numerical result are substituted into genetic algorithm optimization are trained, and set up soil Mapping relations between body parameter and numerical result, and test neural network model with test sample parameter;Will be actual Monitoring Data substitutes into Soil Parameters and the relation of result of calculation tries to achieve the corresponding Soil Parameters of measured displacements;To multiple base pit engineerings Case is respectively calculated, by the simulation corresponding with the measured displacements that inverting is obtained of the prospecting parameter of multiple base pit engineering cases Soil Parameters substitute into the BP neural network of genetic algorithm optimization, set up the relation of open-cut foundation ditch engineering investigation and analog parameter.
2. the method as claimed in claim 1 for setting up open-cut foundation ditch engineering investigation and analog parameter relation, it is characterised in that step Monitoring Data in rapid 1 includes:Pile head settlement, ground settlement, Horizontal Displacement, pile lateral displacement.
3. the method as claimed in claim 1 for setting up open-cut foundation ditch engineering investigation and analog parameter relation, it is characterised in that step Original prospecting parameter in rapid 2 includes:Cohesive strength, internal friction angle, modulus of compressibility, Poisson's ratio, natural density, natural moisture content, Proportion, void ratio, liquid limit, plasticity index, cake compressibility, swelling index.
4. the method as claimed in claim 3 for setting up open-cut foundation ditch engineering investigation and analog parameter relation, it is characterised in that step Rapid 3 specifically include following steps:
Step 3.1:The result of calculation that step 2 is obtained substitutes into neural network model, by soil body sample parameter (Nai…Nfi) conduct Input layer, its corresponding simulation result of calculation (Sai…Sfi) as output layer, node in hidden layer can useWherein, n and m Respectively input and output layer nodes, using 2n+1 initial the number of hidden nodes, set Population in Genetic Algorithms scale, iteration time Number, intersection and mutation probability, then run neural network model, set up reflecting between soil body sample parameter and simulation result of calculation Penetrate relation;
Step 3.2:By test sample parameter (nai…nfi) substitute into soil body sample parameter and simulation result of calculation that step 3.1 is set up Between mapping relations, operating procedure 3.1 preserve neural network model calculated, draw the corresponding nerve net of test sample Network result of calculation (Lai…Lfi), comparative analysis test sample parameter (nai…nfi) corresponding numerical simulation calculation result (sai… sfi) and neural computing result (Lai…Lfi) error, meet require, then carry out next step;
Step 3.3:By actual monitoring data (Sa1…Sf1) substitute into step 3.1 set up neural network model, run and calculated, And then draw by the Soil Parameters (M of the corresponding simulation of measured dataa1…Mf1), each open-cut foundation ditch engineering is according to above-mentioned stream Journey is respectively calculated.The prospecting parameter of all base pit engineering cases is (ai…fi), by measured data according to above-mentioned steps inverting Calculate the Soil Parameters (M of obtained simulationai…Mfi);
Step 3.4:Will prospecting parameter (ai…fi) as the input layer of neutral net, the mould obtained by measured data Inversion Calculation Soil Parameters (the M of planai…Mfi) as the output layer of neutral net, according to step 3.1, artificial neural network and genetic algorithms are set Correlation computations parameter, operation neural network model is calculated, and then is drawn between open-cut foundation ditch engineering investigation and analog parameter Mapping relation.
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Cited By (12)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109870376A (en) * 2019-02-03 2019-06-11 浙江大学 A method of based on nano impress and numerical simulation inverting rock forming mineral parameter
CN111080020A (en) * 2019-12-23 2020-04-28 中山大学 Robustness evaluation method and device for drilling arrangement scheme
CN111159935A (en) * 2019-12-11 2020-05-15 同济大学 BP neural network parameter calibration method based on LHS
CN111705808A (en) * 2020-06-30 2020-09-25 河海大学 Soil body parameter dynamic inversion analysis method suitable for suspended deep foundation pit engineering
CN112347670A (en) * 2020-10-26 2021-02-09 青海大学 Rockfill material creep parameter prediction method based on neural network response surface
CN112459080A (en) * 2020-11-16 2021-03-09 上海市机械施工集团有限公司 Intelligent adjusting method for steel support axial force based on bp neural network
CN113094948A (en) * 2021-03-29 2021-07-09 天津大学 Intelligent early warning cloud system for geotechnical underground engineering
CN113158586A (en) * 2021-05-26 2021-07-23 华能新能源股份有限公司 Wind power plant numerical simulation method and device combined with neural network algorithm and storage medium
CN113378276A (en) * 2021-06-18 2021-09-10 北方工业大学 Composite foundation intelligent design method based on genetic algorithm and gene expression programming
CN113946899A (en) * 2021-11-10 2022-01-18 广东粤海珠三角供水有限公司 Slurry shield tunneling parameter prediction method based on real-time geological information
CN116579255A (en) * 2023-07-12 2023-08-11 西南交通大学 CPTU model test-based intelligent identification method for correcting Cambridge model parameters
CN118133635A (en) * 2024-05-07 2024-06-04 深地科学与工程云龙湖实验室 Tunnel contact channel deformation control method, system, equipment and storage medium

Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8285659B1 (en) * 2009-08-18 2012-10-09 The United States of America as represented by the Administrator of the National Aeronautics & Space Administration (NASA) Aircraft system modeling error and control error
CN103198215A (en) * 2013-03-20 2013-07-10 辽宁工程技术大学 Method for predicting subsidence of soil outside pit based on differential evolution support vector machine
CN103205972A (en) * 2013-03-19 2013-07-17 辽宁工程技术大学 Method for analyzing relationship between deformation of foundation pit and ground subsidence outside of foundation pit
CN104077451A (en) * 2014-07-03 2014-10-01 中铁上海工程局集团有限公司 Deep soft soil metro foundation pit soil body parameter inversion analyzing method
CN104965969A (en) * 2015-04-20 2015-10-07 广西大学 Inversion method for surrounding rock mechanical parameters of large cavern group
CN106400813A (en) * 2016-10-28 2017-02-15 北京城建勘测设计研究院有限责任公司 Foundation pit support system and construction method thereof

Patent Citations (6)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
US8285659B1 (en) * 2009-08-18 2012-10-09 The United States of America as represented by the Administrator of the National Aeronautics & Space Administration (NASA) Aircraft system modeling error and control error
CN103205972A (en) * 2013-03-19 2013-07-17 辽宁工程技术大学 Method for analyzing relationship between deformation of foundation pit and ground subsidence outside of foundation pit
CN103198215A (en) * 2013-03-20 2013-07-10 辽宁工程技术大学 Method for predicting subsidence of soil outside pit based on differential evolution support vector machine
CN104077451A (en) * 2014-07-03 2014-10-01 中铁上海工程局集团有限公司 Deep soft soil metro foundation pit soil body parameter inversion analyzing method
CN104965969A (en) * 2015-04-20 2015-10-07 广西大学 Inversion method for surrounding rock mechanical parameters of large cavern group
CN106400813A (en) * 2016-10-28 2017-02-15 北京城建勘测设计研究院有限责任公司 Foundation pit support system and construction method thereof

Non-Patent Citations (1)

* Cited by examiner, † Cited by third party
Title
宋春晖: "地铁车站深基坑土体参数动态反演分析及变形预测", 《中国优秀硕士学位论文全文数据库工程科技Ⅱ辑》 *

Cited By (17)

* Cited by examiner, † Cited by third party
Publication number Priority date Publication date Assignee Title
CN109870376A (en) * 2019-02-03 2019-06-11 浙江大学 A method of based on nano impress and numerical simulation inverting rock forming mineral parameter
CN109870376B (en) * 2019-02-03 2020-10-23 浙江大学 Rock mineral parameter inversion method based on nano indentation and numerical simulation
CN111159935A (en) * 2019-12-11 2020-05-15 同济大学 BP neural network parameter calibration method based on LHS
CN111080020A (en) * 2019-12-23 2020-04-28 中山大学 Robustness evaluation method and device for drilling arrangement scheme
CN111080020B (en) * 2019-12-23 2023-03-31 中山大学 Robustness evaluation method and device for drilling arrangement scheme
CN111705808A (en) * 2020-06-30 2020-09-25 河海大学 Soil body parameter dynamic inversion analysis method suitable for suspended deep foundation pit engineering
CN112347670B (en) * 2020-10-26 2022-09-27 青海大学 Rockfill material creep parameter prediction method based on neural network response surface
CN112347670A (en) * 2020-10-26 2021-02-09 青海大学 Rockfill material creep parameter prediction method based on neural network response surface
CN112459080B (en) * 2020-11-16 2022-04-15 上海市机械施工集团有限公司 Intelligent adjusting method for steel support axial force based on bp neural network
CN112459080A (en) * 2020-11-16 2021-03-09 上海市机械施工集团有限公司 Intelligent adjusting method for steel support axial force based on bp neural network
CN113094948A (en) * 2021-03-29 2021-07-09 天津大学 Intelligent early warning cloud system for geotechnical underground engineering
CN113158586A (en) * 2021-05-26 2021-07-23 华能新能源股份有限公司 Wind power plant numerical simulation method and device combined with neural network algorithm and storage medium
CN113378276A (en) * 2021-06-18 2021-09-10 北方工业大学 Composite foundation intelligent design method based on genetic algorithm and gene expression programming
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CN118133635A (en) * 2024-05-07 2024-06-04 深地科学与工程云龙湖实验室 Tunnel contact channel deformation control method, system, equipment and storage medium

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