CN116680972B - Method for testing and evaluating mechanical parameters of soft ground soil - Google Patents
Method for testing and evaluating mechanical parameters of soft ground soil Download PDFInfo
- Publication number
- CN116680972B CN116680972B CN202310613966.1A CN202310613966A CN116680972B CN 116680972 B CN116680972 B CN 116680972B CN 202310613966 A CN202310613966 A CN 202310613966A CN 116680972 B CN116680972 B CN 116680972B
- Authority
- CN
- China
- Prior art keywords
- soil
- compression bar
- testing
- parameters
- compression
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 239000002689 soil Substances 0.000 title claims abstract description 215
- 238000012360 testing method Methods 0.000 title claims abstract description 70
- 238000000034 method Methods 0.000 title claims abstract description 45
- 238000007906 compression Methods 0.000 claims abstract description 120
- 230000006835 compression Effects 0.000 claims abstract description 114
- 238000004088 simulation Methods 0.000 claims abstract description 52
- 238000002474 experimental method Methods 0.000 claims abstract description 47
- 238000003825 pressing Methods 0.000 claims abstract description 35
- 238000011156 evaluation Methods 0.000 claims abstract description 31
- 238000010801 machine learning Methods 0.000 claims abstract description 23
- 238000004364 calculation method Methods 0.000 claims description 29
- 230000008569 process Effects 0.000 claims description 17
- 238000013528 artificial neural network Methods 0.000 claims description 16
- 238000013461 design Methods 0.000 claims description 11
- 210000002569 neuron Anatomy 0.000 claims description 11
- 238000010998 test method Methods 0.000 claims description 11
- 230000006870 function Effects 0.000 claims description 8
- 238000006073 displacement reaction Methods 0.000 claims description 7
- 239000000463 material Substances 0.000 claims description 7
- 230000003068 static effect Effects 0.000 claims description 7
- 230000004913 activation Effects 0.000 claims description 5
- 238000012545 processing Methods 0.000 claims description 5
- 238000000418 atomic force spectrum Methods 0.000 claims description 3
- 238000003062 neural network model Methods 0.000 claims description 3
- 230000001902 propagating effect Effects 0.000 claims description 3
- 239000002344 surface layer Substances 0.000 abstract description 8
- 238000009434 installation Methods 0.000 abstract description 5
- 239000010410 layer Substances 0.000 description 25
- 239000004927 clay Substances 0.000 description 12
- 230000001364 causal effect Effects 0.000 description 4
- 239000000835 fiber Substances 0.000 description 4
- 238000007373 indentation Methods 0.000 description 3
- 230000001133 acceleration Effects 0.000 description 2
- TZCXTZWJZNENPQ-UHFFFAOYSA-L barium sulfate Chemical compound [Ba+2].[O-]S([O-])(=O)=O TZCXTZWJZNENPQ-UHFFFAOYSA-L 0.000 description 2
- 230000000694 effects Effects 0.000 description 2
- 238000011065 in-situ storage Methods 0.000 description 2
- 238000005259 measurement Methods 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 238000010008 shearing Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 238000013135 deep learning Methods 0.000 description 1
- 230000001687 destabilization Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000010586 diagram Methods 0.000 description 1
- 238000011439 discrete element method Methods 0.000 description 1
- 238000005553 drilling Methods 0.000 description 1
- 230000009977 dual effect Effects 0.000 description 1
- 230000001939 inductive effect Effects 0.000 description 1
- 230000007246 mechanism Effects 0.000 description 1
- 238000010606 normalization Methods 0.000 description 1
- 239000002245 particle Substances 0.000 description 1
- 230000000149 penetrating effect Effects 0.000 description 1
- 230000035515 penetration Effects 0.000 description 1
- 238000005070 sampling Methods 0.000 description 1
- 239000002356 single layer Substances 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 238000003860 storage Methods 0.000 description 1
- 238000005303 weighing Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/27—Design optimisation, verification or simulation using machine learning, e.g. artificial intelligence, neural networks, support vector machines [SVM] or training a model
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N3/00—Investigating strength properties of solid materials by application of mechanical stress
- G01N3/02—Details
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N3/00—Investigating strength properties of solid materials by application of mechanical stress
- G01N3/08—Investigating strength properties of solid materials by application of mechanical stress by applying steady tensile or compressive forces
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/17—Mechanical parametric or variational design
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/23—Design optimisation, verification or simulation using finite element methods [FEM] or finite difference methods [FDM]
-
- G—PHYSICS
- G01—MEASURING; TESTING
- G01N—INVESTIGATING OR ANALYSING MATERIALS BY DETERMINING THEIR CHEMICAL OR PHYSICAL PROPERTIES
- G01N2203/00—Investigating strength properties of solid materials by application of mechanical stress
- G01N2203/02—Details not specific for a particular testing method
- G01N2203/0202—Control of the test
- G01N2203/0212—Theories, calculations
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/10—Numerical modelling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Geometry (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Computer Hardware Design (AREA)
- Chemical & Material Sciences (AREA)
- General Health & Medical Sciences (AREA)
- Pathology (AREA)
- Immunology (AREA)
- Biochemistry (AREA)
- Analytical Chemistry (AREA)
- Life Sciences & Earth Sciences (AREA)
- Health & Medical Sciences (AREA)
- Pure & Applied Mathematics (AREA)
- Mathematical Optimization (AREA)
- Artificial Intelligence (AREA)
- Mathematical Analysis (AREA)
- Computational Mathematics (AREA)
- Software Systems (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Medical Informatics (AREA)
- Investigating Strength Of Materials By Application Of Mechanical Stress (AREA)
Abstract
The invention provides a method for testing and evaluating soft ground soil mechanical parameters, which comprises the following steps: performing compression bar numerical simulation to form a special database for machine learning, and forming a soil strength evaluation reduced-order model through machine learning; obtaining a pressure-depth curve of the soil to be measured through a pressing-in experiment of a pressing rod, inputting a soil strength evaluation reduced-order model, obtaining a cohesive force and an internal friction angle of the soil to be measured, and further determining the bearing capacity of the soil to be measured; and carrying out elastic wave test based on a pressing-in experiment of the pressing rod to obtain the longitudinal wave velocity of the soil to be tested, and determining the elastic modulus of the soil to be tested by combining the density and poisson ratio of the soil. According to the invention, through the pressing experiment, numerical simulation and machine learning of the pressing rod, the bearing capacity of the soil can be accurately evaluated while the soil elasticity parameter is tested, and the problem that the soil mechanical parameter testing method of the near-surface layer of the soft ground can not meet the requirements of trafficability evaluation of the existing engineering vehicle and installation risk evaluation of the engineering device is solved.
Description
Technical Field
The invention belongs to the field of soil parameter testing, and particularly relates to a method for testing and evaluating soft ground soil mechanical parameters.
Background
The soil mechanical parameters of the soft ground near-surface layer directly influence the trafficability and passing efficiency of various engineering vehicles and influence the installation and operation of various engineering devices. Therefore, accurate testing and evaluation of soil mechanical parameters of the near-surface of the soft ground are required.
At present, the testing methods of the soil mechanical parameters comprise two main types, namely an indoor experimental method and a field experimental method. The indoor experiment method can accurately give out basic parameters such as density, elastic modulus, cohesive force, internal friction angle and the like of the soil through a weighing experiment, a wave speed test experiment, a triaxial experiment, a direct shear experiment, a ring shear experiment and the like, and further calculate the bearing capacity of the soil according to a Mohr-Colomb criterion. Although various soil mechanical parameters can be obtained in the indoor experiment, timeliness of data testing is not guaranteed due to the fact that samples are required to be retrieved to a laboratory; in addition, the soil sample is inevitably disturbed in the sampling process and the storage and transportation process of the sample, so that the testing precision of the soil parameters is reduced.
The field experiment method gives various mechanical parameters of soil through an in-situ sound wave experiment, a cross plate shearing experiment, an in-situ direct shearing experiment, a static load experiment, a static sounding experiment, a standard penetration experiment, a side pressure experiment and the like. Although the soil mechanical parameters at different depths can be obtained in the field experiment, the field experiment needs a larger experimental field and experimental equipment, the required test period is longer, and the requirement of quick test cannot be met.
Therefore, the existing test method for the soil mechanical parameters of the soil near-surface layer has the problems of insufficient precision or long test period, and influences the operation efficiency of various engineering devices.
However, for the passage of the engineering vehicle and the operation of the engineering equipment, only the mechanical parameters of the shallow soil need to be grasped. In all the shallow soil mechanical parameters, the soil bearing capacity and the soil elastic modulus are two key mechanical parameters, the soil bearing capacity is mainly used for reflecting the destabilization and damage characteristics of the soil under the overlying load, and the soil elastic modulus is mainly used for reflecting the deformation characteristics under the overlying load. In the aspect of testing the mechanical parameters of shallow soil, the drop hammer type deflection meter can test the elastic modulus of a pavement more accurately, the ground penetrating radar can detect the hollow holes and stratum layering of the shallow soil, the cone index meter can evaluate the passing capacity of vehicles, but the functions of the shallow soil parameter testing equipment are often too single, and the elastic parameters and the bearing capacity of the soil cannot be comprehensively measured.
Disclosure of Invention
The invention provides a method for testing and evaluating the mechanical parameters of soft ground soil, which can accurately evaluate the bearing capacity of soil while testing the elastic parameters of the soil through the pressing experiment, numerical simulation and machine learning of a pressing rod, and solves the problem that the test method of the mechanical parameters of the soil near the surface layer of the soft ground cannot meet the requirements of trafficability evaluation of the existing engineering vehicle and installation risk evaluation of an engineering device.
The invention provides a method for testing and evaluating soft ground soil mechanical parameters, which comprises the following steps:
performing compression bar numerical simulation to form a special database for machine learning, and forming a soil strength evaluation reduced-order model through machine learning;
obtaining a pressure-depth curve of the soil to be measured through a pressing-in experiment of a pressing rod, inputting the pressure-depth curve of the soil to be measured into the soil strength evaluation reduced-order model to obtain cohesive force and internal friction angle of the soil to be measured, and determining bearing capacity of the soil to be measured according to related industry specifications;
and carrying out elastic wave test based on the pressing-in experiment of the pressing rod to obtain the longitudinal wave velocity of the soil to be tested, and determining the elastic modulus of the soil to be tested by combining the density and poisson ratio of the soil.
Further, the number of the compression bars in the compression experiment of the compression bars is 2 or more;
the number of the compression bars in the compression bar numerical simulation is the same as that in the compression bar compression experiment.
Further, the compression bar numerical simulation process is as follows:
1) Carrying out the design of a compression bar numerical simulation scheme and determining the number of numerical simulation groups;
2) Establishing a three-dimensional calculation model of soil and a compression bar, and dividing a calculation grid for the three-dimensional calculation model by adopting a numerical simulation method;
3) Applying constitutive models and material parameters to the three-dimensional calculation model;
4) Applying displacement boundary conditions to the three-dimensional computing model;
5) Applying a vertical downward quasi-static speed boundary condition on the top of the compression bar;
6) Carrying out numerical calculation to obtain a drill rod pressure-depth curve in the pressing process of the pressing rod;
7) Storing the numerical simulation result into a simulation result database;
8) Judging whether the set number of numerical simulation groups is reached;
9) If the set number of the numerical simulation groups is not reached, adjusting the soil material parameters and the compression bar parameters, and repeating the steps 2) to 8);
10 If the set number of the numerical simulation groups is reached, processing simulation results to form a special database for machine learning;
11 The neural network model is optimized, and the number of hidden layers, the number of neurons of each layer and the activation function are designed;
12 Developing intelligent learning and checking of the neural network based on the special machine learning database;
13 After learning and checking, forming a soil strength evaluation reduced model based on the neural network.
Further, in the three-dimensional calculation model of the soil and the compression bar, the four sides and the bottom of the soil model are normal constraints, and the characteristic size of the soil model is more than 2 times of the depth of the compression bar.
Further, the simulation result database comprises soil parameters, compression bar parameters, and result data between the soil parameters and the compression bar parameters;
wherein the soil parameters comprise the number of soil layers and the thickness of each layer, and the density, the elastic modulus, the poisson ratio, the cohesive force and the internal friction angle of each layer; the compression bar parameters comprise compression bar diameter, interval between compression bars and cone head height; the result data comprise a compression bar pressure-depth curve, a ground surface bulge height, a vertical displacement cloud picture and a plastic strain cloud picture.
Further, in the soil strength evaluation reduced-order model, input parameters are compression bar pressure-depth curves actually measured on site, and output parameters are cohesive force and internal friction angle of soil to be measured.
Further, the pressing experiment of the pressing rod comprises the following steps:
14 Developing a basic frame and a power system design of the compression bar testing device;
15 Pressure sensors are arranged on the compression bars, and vibration sensors are arranged on any at least two compression bars;
16 Testing an axial force curve on the compression bar in the static force pressing process of the compression bar;
17 After the compression bar reaches the set compression depth, stopping the compression bar process.
Further, in the elastic wave testing process, a certain compression bar provided with a vibration sensor is selected as a transmitting bar, a pulse vibration signal is applied, and the other compression bar provided with the vibration sensor is selected as a receiving bar, so that the pulse vibration signal is received; and determining the longitudinal wave velocity of the elastic wave propagating in the soil through the time difference of the pulse vibration signals and the distance between the two rods.
Further, the diameter of the pressure rod is between 10mm and 50mm, the height of the conical head of the pressure rod is larger than the diameter of the pressure rod, and the distance between the two pressure rods for transmitting pulse vibration signals is larger than 1m.
Further, the method is suitable for testing the mechanical parameters of the soft ground shallow soil, including testing the mechanical parameters of the soil of the homogeneous soil layer and testing the mechanical parameters of the soil of the layered soil layer.
Compared with the prior art, the invention has the following beneficial effects:
the invention provides a method for testing and evaluating the soil mechanical parameters of soft ground, which can be used for comprehensively testing the soil mechanical parameters of the shallow surface layer of the soft ground in the open air, and can give out the elastic modulus of the soil while measuring the bearing capacity of the soil, thereby providing an evaluation basis for the safe passing of engineering vehicles and the safe use of engineering devices.
Drawings
In order to more clearly illustrate the embodiments of the present invention or the technical solutions in the prior art, the drawings used in the description of the embodiments or the prior art will be briefly described below. It will be apparent to those skilled in the art from this disclosure that the drawings described below are merely exemplary and that other embodiments may be derived from the drawings provided without undue effort.
FIG. 1 is a flow chart of a method for testing and evaluating soft ground soil mechanical parameters in an embodiment of the invention;
FIG. 2 is a schematic diagram of a dual compression bar experiment in accordance with an embodiment of the present invention;
FIG. 3 is a graph for testing soil mechanical parameters of a homogeneous soil layer in an embodiment of the invention;
FIG. 4 is a graph showing the soil mechanical parameters of a layered soil layer according to an embodiment of the present invention;
reference numerals in the drawings:
1-compression bar testing device, 2-data acquisition instrument, 3-tire, 4-power loading device, 5-pressure sensor, 6-compression bar, 7-vibration sensor.
Detailed Description
The following description of the embodiments of the present invention will be made clearly and completely with reference to the accompanying drawings, in which it is apparent that the embodiments described are only some embodiments of the present invention, but not all embodiments. All other embodiments, which can be made by those skilled in the art based on the embodiments of the invention without making any inventive effort, are intended to be within the scope of the invention.
The invention discloses a method for testing and evaluating soft ground soil mechanical parameters, which comprises the following steps:
performing compression bar numerical simulation to form a special database for machine learning, and forming a soil strength evaluation reduced-order model through machine learning;
obtaining a pressure-depth curve of the soil to be measured through a pressing-in experiment of a pressing rod, inputting the pressure-depth curve of the soil to be measured into the soil strength evaluation reduced-order model to obtain cohesive force and internal friction angle of the soil to be measured, and determining bearing capacity of the soil to be measured according to related industry specifications;
and carrying out elastic wave test based on the pressing-in experiment of the pressing rod to obtain the longitudinal wave velocity of the soil to be tested, and determining the elastic modulus of the soil to be tested by combining the density and poisson ratio of the soil.
The invention provides a method for testing and evaluating the soil mechanical parameters of soft ground, which can be used for comprehensively testing the soil mechanical parameters of the shallow surface layer of the soft ground in the open air, and can give out the elastic modulus of the soil while measuring the bearing capacity of the soil, thereby providing an evaluation basis for the safe passing of engineering vehicles and the safe use of engineering devices.
Wherein the number of the compression bars in the compression experiment of the compression bars is 2 or more; the number of the compression bars in the compression bar numerical simulation is the same as that in the compression bar compression experiment, and the more the number of the compression bars is, the more the measurement data is, so that the more accurate the measurement result is.
In a preferred embodiment, two compression bars are selected for compression experiments, and the specific process of test evaluation is as follows:
1) And (5) carrying out the design of a compression bar numerical simulation scheme and determining the number of numerical simulation groups.
The numerical simulation scheme design comprises the following steps: calculation method selection, calculation software selection, geometric model design, grid model design, calculation step design, result checking design and the like.
The number of the numerical simulation groups should be not less than 1000 groups, and the specific number is comprehensively determined by combining the soil type and occurrence environment of the test site.
2) And establishing a three-dimensional calculation model of the soil and the compression bar, and dividing a calculation grid for the three-dimensional calculation model by adopting a numerical simulation method.
The numerical simulation method comprises the following steps: finite element method, discrete element method, mesh-free method, etc., the calculation of the mesh includes: tetrahedral mesh, triangular prism mesh, pyramid mesh, hexahedral mesh, particle mesh, and the like.
3) And applying a constitutive model and material parameters to the three-dimensional calculation model.
The soil constitutive model comprises: mohr-Coulomb ideal elastoplastic model, drucker-Prager ideal elastoplastic model, etc., and the material parameters include: density, modulus of elasticity, poisson's ratio, cohesion and internal friction angle.
4) And applying displacement boundary conditions to the three-dimensional calculation model.
In the three-dimensional calculation model of the soil and the compression bar, the periphery and the bottom of the soil model are normal constraints, and the characteristic size of the soil model is more than 2 times of the depth of the compression bar.
5) A quasi-static velocity boundary condition is applied vertically downward at the top of the plunger.
The quasi-static speed boundary condition applied to the top of the double compression bars can be evaluated through the unbalance rate of the system or the total kinetic energy of the system; if the unbalance rate of the system or the total kinetic energy of the system is larger than a certain set value under a certain quasi-static speed condition, the value of the quasi-static speed is further reduced.
6) And carrying out numerical calculation to obtain a drilling rod pressure-depth curve in the pressing process of the pressing rod.
7) And storing the numerical simulation result into a simulation result database.
8) It is determined whether the set number of numerical simulation sets is reached.
9) And if the set number of the numerical simulation groups is not reached, adjusting the soil material parameters and the compression bar parameters, and repeating the steps 2) to 8).
10 If the set number of the numerical simulation groups is reached, processing the simulation result to form a special database for machine learning.
The simulation result database comprises soil parameters, compression bar parameters and result data between the soil parameters and the compression bar parameters; wherein the soil parameters comprise the number of soil layers and the thickness of each layer, and the density, the elastic modulus, the poisson ratio, the cohesive force and the internal friction angle of each layer; the compression bar parameters comprise compression bar diameter, interval between compression bars and cone head height; the result data comprise a compression bar pressure-depth curve, a ground surface bulge height, a vertical displacement cloud picture and a plastic strain cloud picture.
In addition, in order to build a machine learning specific database, the digital simulation result database needs to be processed, and specific processing procedures include, but are not limited to: and (3) pixelating a pressure-depth curve, a vertical displacement cloud picture and a plastic strain cloud picture, and normalizing various data.
11 The neural network model is optimized, and the hidden layer number, the neuron number of each layer and the activation function are designed.
The neural network comprises a forward neural network and a feedback neural network, wherein the forward neural network comprises a single-layer sensor, a multi-layer sensor and a BP neural network, and the feedback neural network comprises a Hopfield, hamming, BAM network.
12 Based on the machine learning special database, developing intelligent learning and checking of the neural network.
Aiming at intelligent learning and checking based on a neural network, during specific implementation, samples can be randomly divided into A, B types from a machine learning special database, and class A is used as a learning sample to develop intelligent learning of the neural network; and B, taking the class B as a check sample, and carrying out check on the learning result.
13 After learning and checking, forming a soil strength evaluation reduced model based on the neural network.
The input parameters of the soil strength evaluation reduced-order model are compression bar pressure-depth curves actually measured on site, and the output parameters are comprehensive cohesive force and internal friction angle of shallow surface soil.
14 Developing the basic frame and the power system design of the compression bar testing device.
The diameter of the compression bar in the device is between 10mm and 50mm, the height of the conical head of the compression bar is larger than the diameter of the compression bar, and the distance between the two compression bars transmitting the pulse vibration signals is larger than 1m.
The testing device adopting the double compression bars can be a hand-push type wheel type testing device, can be also hung at a designated position of an engineering vehicle, and can move in different testing areas by means of the engineering vehicle.
The power loading device for pressing the compression bar in the device can adopt: the device comprises a hydraulic driving mode and a motor driving mode, wherein the power loading device is connected with a data acquisition instrument arranged in the device, and acquires detection data of a sensor under corresponding pressure in the static pressing process of a pressing rod.
15 Pressure sensors are arranged on the compression bars, and vibration sensors are arranged on any at least two compression bars.
In this embodiment, a double-pressure lever experiment is preferable, so that a position (only a vibration sensor and a pressure sensor may be arranged) is specified on the double-pressure lever, as shown in fig. 2 and 3.
The pressure sensor type comprises: an electric pressure sensor, a capacitive pressure sensor, a resistive pressure sensor, a fiber grating pressure sensor, and the like.
The vibration sensor types include: piezoelectric vibration sensor, inductive vibration sensor, capacitive vibration sensor, fiber grating vibration sensor, etc.
The pressure sensor is generally arranged at the top of the pressure rod, and as the conical head of the pressure rod is convenient to press into soil, the vibration sensor is generally arranged above the conical head at the middle part of the pressure rod, and a plurality of vibration sensors can be arranged on the pressure rod according to actual needs.
16 Testing the axial force curve on the compression bar in the compression process of the compression bar.
17 After the compression bar reaches the set compression depth, stopping the compression bar process.
18 Inputting the actually measured pressure-depth curve into a soil intensity evaluation reduced-order model, and calculating the cohesive force and the internal friction angle of the soil;
19 According to the calculated soil cohesion and internal friction angle, and combining related industry specifications or literature data, calculating the bearing capacity of the soil;
20 A certain compression bar is selected as a transmitting bar, a pulse vibration signal is applied, and the other compression bar is a receiving bar, and the vibration signal is received.
In a press-in experiment based on a press rod, in the elastic wave test, a press rod provided with a vibration sensor is selected as a transmitting rod, a pulse vibration signal is applied, and the other press rod provided with the vibration sensor is a receiving rod, so that the pulse vibration signal is received.
In this embodiment, a dual-compression bar structure is preferred, and one compression bar may be directly selected as the transmitting bar, and the other compression bar as the receiving bar.
21 The longitudinal wave velocity of the elastic wave propagating in the soil is calculated by the time difference of the vibration signals and the distance between the two rods.
The formula for calculating the wave velocity of the soil longitudinal wave according to the time difference is as follows: c p =d/Δt, where c p The wave velocity of the soil longitudinal wave, d is the distance between the double compression bars, and deltat is the vibration wave arrival time difference.
22 According to the longitudinal wave velocity and the soil density, the elastic modulus of the soil is calculated.
The soil density can be obtained through soil type, an empirical formula or a simple field experiment.
The calculation formula for calculating the soil elastic modulus according to the longitudinal wave velocity and the soil density is as follows:wherein E is the elastic modulus and ρ is the soil density.
In the embodiment, field experiments, numerical simulation and machine learning are combined, a sample database is formed through massive numerical simulation, a soil strength evaluation reduced-order model is formed through machine learning, a pressure-depth curve is obtained through a double-pressure-lever experiment, the reduced-order model is input, the cohesive force and the internal friction angle of soil are calculated, and the bearing capacity of the soil to be measured is calculated according to related industry specifications. Through an elastic wave test experiment between the double rods, the longitudinal wave velocity in the soil is given, and the elastic modulus of the soil is given by combining the density and poisson ratio of the soil.
The embodiment can be suitable for testing mechanical parameters of soft ground shallow soil, wherein the shallow soil comprises surface soil, a core soil layer and bottom soil, and a compression bar can penetrate through the shallow soil and is used for testing the mechanical parameters of the soil comprising a homogeneous soil layer and the mechanical parameters of the soil comprising a layered soil layer and for evaluating the trafficability of engineering vehicles and evaluating the installation risk of surface engineering devices. The specific measuring range of the method for testing and evaluating the soft ground soil mechanical parameters can be determined by the length of the pressing rod, and can generally reach a range of 2m, and the measuring range does not comprise the length of the conical head of the pressing rod.
The method has the advantages that the soil mechanical parameter test of the near-surface layer of the soft ground is not required in the trafficability evaluation of the engineering vehicle and the installation risk evaluation of the surface engineering device, and the method is simple compared with the existing near-surface layer soil mechanical parameter test method through a simple compression bar pressing-in experiment, and the method can be used for field experiments, and is small in experimental device and short in test time.
To demonstrate the effectiveness of the test evaluations provided by the present invention, the following two examples are provided.
Example 1:
test evaluation of the ground soil mechanical parameters was carried out according to the flow of fig. 1.
The numerical simulation of the press-in of the double rods is carried out by adopting a finite element method, the diameter of the double rods is 30mm, the distance is 1m, and the height of the conical head is 50mm. The tetrahedral mesh is adopted to mesh the soil body and the compression bar, and the total mesh is 15.3 ten thousands.
The model considers the condition of homogeneous soil, and adopts a mechanical model of the soil as a Mohr-Coulomb ideal elastoplastic model, and adjusts parameters such as soil density, elastic modulus, poisson's ratio, cohesive force, internal friction angle and the like, so as to develop double-rod indentation numerical simulation and form a numerical simulation database. Wherein, the soil density changes 3 levels, the soil elastic modulus changes 5 levels, the soil poisson ratio changes 3 levels, the soil cohesion and the internal friction angle respectively change 8 levels, and the total is 2880 groups of calculation examples.
And (3) counting simulation results of all calculation examples, giving a causal data set of the correspondence between the mechanical parameters and the double-rod pressure-depth curve, and carrying out normalization processing on the causal data set to form a database special for machine learning.
Machine learning is carried out on 2880 groups of examples by adopting a BP neural network, the number of neurons of an input layer is 20 (namely, a pressure-depth curve is discretized into 20 points), the number of neurons of an output layer is 2 (the cohesive force and the internal friction angle are adopted), the number of neurons of a hidden layer is 5, the number of neurons each time is 10, and a tanh function is selected as an activation function. And (3) randomly extracting 2000 groups of examples from 2880 groups of data to learn, constructing a soil strength evaluation reduced order model, checking by using the remaining 880 groups, and analyzing the calculation accuracy of the reduced order model.
A double-compression bar test device was fabricated according to fig. 2 and 3, and the bearing capacity and elastic modulus of the silty clay field were evaluated. The bottom of the double-pressure-rod testing device is provided with wheels, and the wheels are moved in a hand-pushing mode. The pressing power of the pressing rod adopts a hydraulic mode, the diameter of the double rods is 30mm, the distance is 1m, and the height of the conical head is 50mm. The top of the pressure bar is provided with a resistance type pressure sensor, and the middle of the pressure bar is provided with a piezoelectric type acceleration sensor.
The pressure-depth curve was obtained by static indentation at a speed of 1 cm/s. The pressing depth of the test is set to be 1.5m, after the test is pressed to the set depth, the average pressure-depth curve of the double pressing rods obtained by the test is substituted into a reduced order model, so that the average cohesive force of the powdery clay within the depth range of 1.5m is 7.2kPa, the internal friction angle is 20.3 degrees, and the bearing capacity of the powdery clay is 110kPa according to related industry specifications. The double-rod elastic wave propagation test is carried out, the test software automatically interprets to obtain the waveform jump time difference of 9.15ms, the longitudinal wave velocity of the powdery clay is 109m/s according to the distance between the double rods of 1m, and the density of the powdery clay is about 1700kg/m according to engineering experience 3 From this, the elastic modulus of the powdery clay was calculated to be 20.3MPa.
Example 2:
test evaluation of the ground soil mechanical parameters was carried out according to the flow of fig. 1.
The simulation of the double compression bars is carried out by adopting finite difference, the diameter of the double bars is 40mm, the distance is 2m, and the height of the cone head is 70mm. The hexahedral mesh is adopted to mesh the soil and the compression bar, and the total mesh is 15.8 ten thousands.
The model considers the condition of two layers of soil and adopts a Drucker-Prager ideal elastoplastic model for description. Considering that the density of the soil and poisson ratio have little effect on the soil bearing capacity, the effect of these factors is ignored in the present model. In addition, the subsoil is considered to be sufficiently thick in the present model, and therefore the influence of the subsoil thickness is not considered. After simplification, parameters to be adjusted of the model comprise the thickness of surface soil, the elastic modulus of the surface soil, the cohesive force of the surface soil, the internal friction angle of the surface soil, the elastic modulus of the lower soil, the cohesive force of the lower soil and the internal friction angle of the lower soil. Wherein, the surface soil thickness is adjusted by 5 levels, the elastic modulus is adjusted by 3 levels, the cohesion and the internal friction angle are adjusted by 5 levels, and the total is 28125 groups of examples.
After the calculation is completed, the result is subjected to statistical analysis, and a causal relation library between the input parameters and the double-rod average pressure-depth curve is established. And normalizing the data in the causal relation database to form a database special for machine learning.
The BP neural network is adopted for deep learning, the number of neurons of an input layer is 40 (namely, a pressure-depth curve is discretized into 40 points), the number of neurons of an output layer is 5 (namely, the thickness of surface soil, the cohesive force of the surface soil, the internal friction angle of the surface soil, the cohesive force of the lower soil and the internal friction angle of the lower soil), the number of neurons of a hidden layer is 10, the number of neurons each time is 15, and a sigmoid function is selected as an activation function. And extracting 20000 group calculation examples from 28125 groups of data randomly for learning, constructing a soil strength evaluation reduced order model, checking by using the rest 8125 groups, and analyzing the calculation accuracy of the reduced order model.
A double-compression bar test device was fabricated according to fig. 2 and 4, and the bearing capacity and elastic modulus of soil composed of mucky soil and clay soil were evaluated. The bottom of the double-pressure-rod testing device is provided with wheels, and a hanging mechanism is arranged and can be hung at the tail of an engineering vehicle to move. The power of impressing of depression bar adopts motor drive mode, and the diameter of bi-level is 40mm, and the interval is 2m, and the conical head height is 70mm. The top of each rod is provided with 1 fiber grating pressure sensor, and the middle of each pressure rod is provided with 4 fiber grating acceleration sensors at equal intervals.
The pressure-depth curve was obtained by static indentation at a speed of 0.5 cm/s. The pressing depth of the test is set to be 2.2m, after the test is pressed to the set depth, the average pressure-depth curve of the double pressing rods obtained by the test is substituted into a reduced model, so that the thickness of the surface soil is 0.7m, the cohesive force of the surface soil is 6.2kPa, the internal friction angle is 12.6 degrees, and the cohesive force of the deep cohesive soil is 32kPa, and the internal friction angle is 23.6 degrees. According to the relevant industry specifications, the bearing capacity of the silt soil is 82.3kPa, and the bearing capacity of the clay soil is 520kPa. The double-rod elastic wave propagation test is carried out, the test software automatically interprets that the arrival time difference in the silt is 31.0ms, the arrival time difference in the clay is 13.9ms, the longitudinal wave velocity of the silt is 64.6m/s according to the distance between the double rods being 2m, the longitudinal wave velocity of the clay is 144.4m/s, and the density of the silt is about 1510kg/m according to engineering experience 3 The density of the clay is about 1760kg/m 3 From this, the elastic modulus of the mud was calculated to be 6.3MPa, and the elastic modulus of the clay was calculated to be 36.7MPa.
The above embodiments are only exemplary embodiments of the present application and are not intended to limit the present application, the scope of which is defined by the claims. Various modifications and equivalent arrangements may be made to the present application by those skilled in the art, which modifications and equivalents are also considered to be within the scope of the present application.
Claims (9)
1. A method of testing and evaluating a soft ground soil mechanical parameter, the method comprising:
performing compression bar numerical simulation to form a special database for machine learning, and forming a soil strength evaluation reduced-order model through machine learning;
obtaining a pressure-depth curve of the soil to be measured through a pressing-in experiment of a pressing rod, inputting the pressure-depth curve of the soil to be measured into the soil strength evaluation reduced-order model to obtain cohesive force and internal friction angle of the soil to be measured, and determining bearing capacity of the soil to be measured according to related industry specifications;
performing elastic wave test based on the pressing-in experiment of the pressing rod to obtain the longitudinal wave velocity of the soil to be tested, and determining the elastic modulus of the soil to be tested by combining the density and poisson ratio of the soil;
the compression bar numerical simulation process comprises the following steps:
1) Carrying out the design of a compression bar numerical simulation scheme and determining the number of numerical simulation groups;
2) Establishing a three-dimensional calculation model of soil and a compression bar, and dividing a calculation grid for the three-dimensional calculation model by adopting a numerical simulation method;
3) Applying constitutive models and material parameters to the three-dimensional calculation model;
4) Applying displacement boundary conditions to the three-dimensional computing model;
5) Applying a vertical downward quasi-static speed boundary condition on the top of the compression bar;
6) Carrying out numerical calculation to obtain a drill rod pressure-depth curve in the pressing process of the pressing rod;
7) Storing the numerical simulation result into a simulation result database;
8) Judging whether the set number of numerical simulation groups is reached;
9) If the set number of the numerical simulation groups is not reached, adjusting the soil material parameters and the compression bar parameters, and repeating the steps 2) to 8);
10 If the set number of the numerical simulation groups is reached, processing simulation results to form a special database for machine learning;
11 The neural network model is optimized, and the number of hidden layers, the number of neurons of each layer and the activation function are designed;
12 Developing intelligent learning and checking of the neural network based on the special machine learning database;
13 After learning and checking, forming a soil strength evaluation reduced model based on the neural network.
2. A method of testing and evaluating soft ground soil mechanical parameters according to claim 1,
the number of the compression bars in the compression experiment of the compression bars is 2 or more;
the number of the compression bars in the compression bar numerical simulation is the same as that in the compression bar compression experiment.
3. A method of testing and evaluating soft ground soil mechanical parameters according to claim 2,
in the three-dimensional calculation model of the soil and the compression bar, the periphery and the bottom of the soil model are normal constraints, and the characteristic size of the soil model is more than 2 times of the depth of the compression bar.
4. A method of testing and evaluating soft ground soil mechanical parameters according to claim 2,
the simulation result database comprises soil parameters, compression bar parameters and result data between the soil parameters and the compression bar parameters;
wherein the soil parameters comprise the number of soil layers and the thickness of each layer, and the density, the elastic modulus, the poisson ratio, the cohesive force and the internal friction angle of each layer; the compression bar parameters comprise compression bar diameter, interval between compression bars and cone head height; the result data comprise a compression bar pressure-depth curve, a ground surface bulge height, a vertical displacement cloud picture and a plastic strain cloud picture.
5. A method of testing and evaluating soft ground soil mechanical parameters according to claim 2,
in the soil strength evaluation reduced-order model, input parameters are compression bar pressure-depth curves actually measured on site, and output parameters are cohesive force and internal friction angle of soil to be measured.
6. A method of testing and evaluating soft ground soil mechanical parameters according to claim 1,
the pressing experiment process of the pressing rod comprises the following steps:
14 Developing a basic frame and a power system design of the compression bar testing device;
15 Pressure sensors are arranged on the compression bars, and vibration sensors are arranged on any at least two compression bars;
16 Testing an axial force curve on the compression bar in the static force pressing process of the compression bar;
17 After the compression bar reaches the set compression depth, stopping the compression bar process.
7. A method of testing and evaluating soft ground soil mechanical parameters according to claim 6,
in the elastic wave testing process, selecting a certain compression bar provided with a vibration sensor as a transmitting bar, applying a pulse vibration signal, and receiving the pulse vibration signal by using another compression bar provided with the vibration sensor as a receiving bar; and determining the longitudinal wave velocity of the elastic wave propagating in the soil through the time difference of the pulse vibration signals and the distance between the two rods.
8. A method of testing and evaluating soft ground soil mechanical parameters according to claim 7,
the diameter of the pressure lever is between 10mm and 50mm, the height of the conical head of the pressure lever is larger than the diameter of the pressure lever, and the distance between the two pressure levers for transmitting pulse vibration signals is larger than 1m.
9. A method for testing and evaluating a soft ground soil mechanical parameter according to any of the claims 1-8,
the method is suitable for testing the mechanical parameters of the soft ground shallow soil, including the soil mechanical parameters of the homogeneous soil layer and the soil mechanical parameters of the layered soil layer.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310613966.1A CN116680972B (en) | 2023-05-29 | 2023-05-29 | Method for testing and evaluating mechanical parameters of soft ground soil |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202310613966.1A CN116680972B (en) | 2023-05-29 | 2023-05-29 | Method for testing and evaluating mechanical parameters of soft ground soil |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN116680972A CN116680972A (en) | 2023-09-01 |
| CN116680972B true CN116680972B (en) | 2024-02-20 |
Family
ID=87784691
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202310613966.1A Active CN116680972B (en) | 2023-05-29 | 2023-05-29 | Method for testing and evaluating mechanical parameters of soft ground soil |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN116680972B (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102155231A (en) * | 2011-03-18 | 2011-08-17 | 大连海事大学 | Quick feedback analyzing system in tunnel constructing process |
| CN103015390A (en) * | 2013-01-06 | 2013-04-03 | 山东大学 | Method and device for layered simultaneous determination and long-term monitoring of foundation compactness |
| CN114239317A (en) * | 2021-12-27 | 2022-03-25 | 贵州省交通规划勘察设计研究院股份有限公司 | Calculation model for researching performance of soil body after dynamic compaction reinforcement |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP3247837A2 (en) * | 2015-01-21 | 2017-11-29 | Roger Arnold Stromsoe | An impact compactor, compaction system and a method of obtaining soil strength |
-
2023
- 2023-05-29 CN CN202310613966.1A patent/CN116680972B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN102155231A (en) * | 2011-03-18 | 2011-08-17 | 大连海事大学 | Quick feedback analyzing system in tunnel constructing process |
| CN103015390A (en) * | 2013-01-06 | 2013-04-03 | 山东大学 | Method and device for layered simultaneous determination and long-term monitoring of foundation compactness |
| CN114239317A (en) * | 2021-12-27 | 2022-03-25 | 贵州省交通规划勘察设计研究院股份有限公司 | Calculation model for researching performance of soil body after dynamic compaction reinforcement |
Non-Patent Citations (6)
| Title |
|---|
| 三峡库区松散地质体加固声波测试法试验研究;姚洪 等;勘察科学技术(第3期);第61-64页 * |
| 国际冻土工程研究进展――第五届冻土工程国际学术讨论会综述;程国栋, 马巍;冰川冻土(第03期);第65-70页 * |
| 李晓红,靳晓光,亢会明,卢义玉.隧道位移智能化反分析及其应用.地下空间.2001,(第04期),第59-64+99页. * |
| 程国栋,马巍.国际冻土工程研究进展――第五届冻土工程国际学术讨论会综述.冰川冻土.2003,(第03期),第65-70页. * |
| 隧道位移智能化反分析及其应用;李晓红, 靳晓光, 亢会明, 卢义玉;地下空间(第04期);第59-64+99页 * |
| 高置换率挤密砂桩加固软土地基的承载力计算;许增会;水运工程(第6期);第131-135页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN116680972A (en) | 2023-09-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Goktepe et al. | Advances in backcalculating the mechanical properties of flexible pavements | |
| Alwalan et al. | Finite element analysis of helical piles subjected to axial impact loading | |
| CN114139381B (en) | Pile foundation damage census and evaluation method considering pile soil parameter uncertainty | |
| JP2003278171A (en) | Liquefaction phenomenon prediction system | |
| CN104133960A (en) | Improved optimal arranging method of static sensors | |
| Hassanlourad et al. | Dry unit weight of compacted soils prediction using GMDH-type neural network | |
| Jierula et al. | Detection of damage locations and damage steps in pile foundations using acoustic emissions with deep learning technology | |
| CN116680972B (en) | Method for testing and evaluating mechanical parameters of soft ground soil | |
| Chen et al. | Estimation of elastic wave velocity through unsaturated soil slope as function of water content and shear deformation | |
| Wu et al. | A rapid approach to interpretation of SASW results | |
| CN113158315A (en) | Rock-soil body parameter three-dimensional non-stationary condition random field modeling method based on static cone penetration data | |
| Tu et al. | Dynamic multi-point method for evaluating the pile compressive capacity | |
| CN116205515A (en) | Construction evaluation method for driven precast pile based on static sounding and BP neural network | |
| US20110313685A1 (en) | System and method for sand detection | |
| Thiene et al. | Impact location in composite plates using proper orthogonal decomposition | |
| Haque | Field instrumentation and testing to study set-up phenomenon of driven piles and its implementation in LRFD design methodology | |
| Meloche et al. | Influence of slab depth spatial variability on skier-triggering probability and avalanche size | |
| Ponomaryov et al. | The use of express method for determining the modulus of deformation of fill soil | |
| Churkin et al. | Evaluating the Integrity of Slab–Soil Contact with Impulse Response Testing: Insights from Numerical Simulations | |
| Wang et al. | Experimental model updating of slope considering spatially varying soil properties and dynamic loading | |
| Kundu et al. | Numerical studies on the effectiveness of dynamic compaction in loose granular deposits using shear wave velocity profiling | |
| WO2010076295A2 (en) | System and method for sand detection | |
| CN112064617A (en) | Soil-stone mixture foundation quality detection method | |
| Yu et al. | Theoretical basis and numerical simulation of impedance log test for evaluating the integrity of columns and piles | |
| CN115266914B (en) | Pile sinking quality monitoring system and method based on acoustic signal processing |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |