CN114781954A - Method and system for evaluating foundation preloading treatment effect - Google Patents
Method and system for evaluating foundation preloading treatment effect Download PDFInfo
- Publication number
- CN114781954A CN114781954A CN202210722793.2A CN202210722793A CN114781954A CN 114781954 A CN114781954 A CN 114781954A CN 202210722793 A CN202210722793 A CN 202210722793A CN 114781954 A CN114781954 A CN 114781954A
- Authority
- CN
- China
- Prior art keywords
- foundation
- preloading
- settlement
- value
- layer
- 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.)
- Granted
Links
- 238000000034 method Methods 0.000 title claims abstract description 76
- 230000000694 effects Effects 0.000 title claims abstract description 34
- 238000007596 consolidation process Methods 0.000 claims abstract description 67
- 239000011148 porous material Substances 0.000 claims abstract description 52
- 238000005070 sampling Methods 0.000 claims abstract description 50
- 238000004364 calculation method Methods 0.000 claims abstract description 46
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims abstract description 44
- 238000012360 testing method Methods 0.000 claims abstract description 42
- 238000013461 design Methods 0.000 claims abstract description 33
- 238000013210 evaluation model Methods 0.000 claims abstract description 32
- 238000012544 monitoring process Methods 0.000 claims abstract description 30
- 238000013499 data model Methods 0.000 claims abstract description 26
- 230000008569 process Effects 0.000 claims abstract description 21
- 238000010008 shearing Methods 0.000 claims abstract description 19
- 238000011156 evaluation Methods 0.000 claims abstract description 16
- 238000005553 drilling Methods 0.000 claims abstract description 14
- IYLGZMTXKJYONK-ACLXAEORSA-N (12s,15r)-15-hydroxy-11,16-dioxo-15,20-dihydrosenecionan-12-yl acetate Chemical compound O1C(=O)[C@](CC)(O)C[C@@H](C)[C@](C)(OC(C)=O)C(=O)OCC2=CCN3[C@H]2[C@H]1CC3 IYLGZMTXKJYONK-ACLXAEORSA-N 0.000 claims abstract description 7
- IYLGZMTXKJYONK-UHFFFAOYSA-N ruwenine Natural products O1C(=O)C(CC)(O)CC(C)C(C)(OC(C)=O)C(=O)OCC2=CCN3C2C1CC3 IYLGZMTXKJYONK-UHFFFAOYSA-N 0.000 claims abstract description 7
- 238000013507 mapping Methods 0.000 claims description 31
- 239000002689 soil Substances 0.000 claims description 26
- 230000006870 function Effects 0.000 claims description 22
- 238000001514 detection method Methods 0.000 claims description 15
- 238000010276 construction Methods 0.000 claims description 9
- 230000004913 activation Effects 0.000 claims description 8
- 238000004458 analytical method Methods 0.000 claims description 7
- 238000004422 calculation algorithm Methods 0.000 claims description 7
- 238000004062 sedimentation Methods 0.000 claims description 7
- 239000011159 matrix material Substances 0.000 claims description 5
- 238000011895 specific detection Methods 0.000 claims description 4
- 239000000203 mixture Substances 0.000 claims description 3
- 230000002706 hydrostatic effect Effects 0.000 claims description 2
- 230000017105 transposition Effects 0.000 claims description 2
- 238000005056 compaction Methods 0.000 claims 1
- 239000004576 sand Substances 0.000 description 10
- 238000012545 processing Methods 0.000 description 9
- 239000010802 sludge Substances 0.000 description 7
- 238000011161 development Methods 0.000 description 3
- 230000002787 reinforcement Effects 0.000 description 3
- 238000012549 training Methods 0.000 description 3
- 238000004590 computer program Methods 0.000 description 2
- 238000010586 diagram Methods 0.000 description 2
- 239000000463 material Substances 0.000 description 2
- 238000012986 modification Methods 0.000 description 2
- 230000004048 modification Effects 0.000 description 2
- 230000009467 reduction Effects 0.000 description 2
- 238000011160 research Methods 0.000 description 2
- 102000002274 Matrix Metalloproteinases Human genes 0.000 description 1
- 108010000684 Matrix Metalloproteinases Proteins 0.000 description 1
- 230000004075 alteration Effects 0.000 description 1
- 150000001875 compounds Chemical class 0.000 description 1
- 238000007796 conventional method Methods 0.000 description 1
- 238000013527 convolutional neural network Methods 0.000 description 1
- 210000005036 nerve Anatomy 0.000 description 1
- 230000003287 optical effect Effects 0.000 description 1
- 230000035699 permeability Effects 0.000 description 1
- 230000036316 preload Effects 0.000 description 1
- 238000003672 processing method Methods 0.000 description 1
- 238000000926 separation method Methods 0.000 description 1
- 238000005728 strengthening Methods 0.000 description 1
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0639—Performance analysis of employees; Performance analysis of enterprise or organisation operations
- G06Q10/06393—Score-carding, benchmarking or key performance indicator [KPI] analysis
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/13—Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q50/00—Information and communication technology [ICT] specially adapted for implementation of business processes of specific business sectors, e.g. utilities or tourism
- G06Q50/08—Construction
-
- 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)
- Business, Economics & Management (AREA)
- Physics & Mathematics (AREA)
- Human Resources & Organizations (AREA)
- Theoretical Computer Science (AREA)
- General Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Geometry (AREA)
- Economics (AREA)
- General Business, Economics & Management (AREA)
- Development Economics (AREA)
- Entrepreneurship & Innovation (AREA)
- Educational Administration (AREA)
- Tourism & Hospitality (AREA)
- Marketing (AREA)
- Computer Hardware Design (AREA)
- Architecture (AREA)
- Civil Engineering (AREA)
- General Health & Medical Sciences (AREA)
- Computational Mathematics (AREA)
- Health & Medical Sciences (AREA)
- Primary Health Care (AREA)
- Structural Engineering (AREA)
- General Engineering & Computer Science (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Evolutionary Computation (AREA)
- Pure & Applied Mathematics (AREA)
- Game Theory and Decision Science (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Investigating Strength Of Materials By Application Of Mechanical Stress (AREA)
Abstract
The invention relates to a method and a system for evaluating the preloading treatment effect of foundation preloading, comprising S1 monitoring the surface subsidence, layered subsidence and pore water pressure data of the foundation in the preloading treatment process; s2, calculating the settlement consolidation degree value and the pore pressure consolidation degree value of the foundation based on the data in S1; meanwhile, carrying out a foundation drilling sampling test, a cross plate shearing test and a load test on the preloading treatment to obtain each test numerical value; s3, constructing a foundation preloading network data model according to the values obtained in S2, and obtaining the position deviating the most from the design requirement value in the network data model and an isoline plan; s4, constructing an evaluation model and outputting a calculation result; s5, comparing the calculated result with the actual water content, the porosity ratio and the bearing capacity of the foundation to obtain a relative error; if the relative error is within the preset range, outputting a calculation result and generating an evaluation result; if the relative error is not within the preset range, returning to S4, and adjusting the weight of the evaluation model.
Description
Technical Field
The invention relates to the field of quality detection application research of large-area sludge foundation treatment geotechnical engineering, in particular to a method and a system for evaluating a foundation preloading treatment effect.
Background
According to the development trend of national socioeconomic and urbanization scale, the existing land resources of the coastal cities of southeast can not meet the development requirements of the cities, and more land reclamation by sea filling becomes a new land development form of the coastal cities. The large foundation in the southeast coastal region is mostly a sludge soft foundation, and the sludge is characterized by high water content, large pores, high compressibility, low strength and poor water permeability, and the foundation is often insufficient in bearing capacity and stability due to low strength, cannot meet engineering requirements, and has a large treatment area, which is called as a large-area sludge soft foundation, so that the large-area sludge soft foundation can be used for reinforcement treatment.
The common soft foundation treatment method is preloading consolidation, and the foundation property of the silt soft foundation is improved by performing foundation treatment consolidation on the silt soft foundation, so that the bearing capacity of the silt soft foundation is improved, the stability of the foundation is improved, the compressibility of the foundation is reduced, and the deformation of the foundation is reduced.
However, in the process of preloading and strengthening treatment of the soft silt foundation, instability of the soft silt foundation is usually gradually developed from a local shearing process to a whole shearing and damaging process, so that monitoring and comprehensive analysis are necessarily carried out on a foundation soil body to obtain a foundation preloading treatment effect.
The conventional method for evaluating the preloading treatment effect of the foundation simply lays monitoring points in the foundation to be tested, and an operator analyzes and judges a plurality of items of data obtained by monitoring according to experience, so that the preloading treatment effect of the foundation is evaluated, the workload is high, misjudgment is easy to occur, and the accuracy of an evaluation result is low.
Disclosure of Invention
The invention aims to solve the technical problem of providing a method and a system for evaluating the effect of preloading treatment of foundation pile loading, which can be used for constructing a network data model by fully combining construction conditions and monitoring data in the comprehensive construction process, finding the most unfavorable position and then evaluating foundation treatment through an evaluation model.
In order to solve the technical problem, the invention provides a method for evaluating the effect of preloading treatment on a foundation, which comprises the following steps: s1, monitoring the ground surface settlement data, the layered settlement data and the pore water pressure data of the foundation in the preloading treatment process; s2, calculating the settlement consolidation degree value and the pore pressure consolidation degree value of the foundation based on the data obtained in the S1; simultaneously, drilling and sampling, cross plate shearing test and load test are carried out on the foundation subjected to the surcharge preloading treatment, and a foundation soil layer physical parameter value, a foundation strength value at different depths and a foundation bearing capacity value are obtained; s3, constructing a network data model according to the values obtained in S2, and obtaining a position and an isoline plan which deviate from the design requirement value most in the network data model; detecting the position with the maximum deviation from the design requirement value, wherein the detection work comprises a drilling sampling geotechnical test, a cross plate shearing test and a load test to obtain a specific detection parameter value, judging whether the detection parameter value meets the preset requirement value, if so, carrying out the next step, if not, carrying out the foundation preloading again, and returning to S1; s4, constructing an evaluation model which comprises a convolution layer, a sampling layer, a full connection layer and an output layer; the convolution layer performs convolution operation on the contour plane graph and generates a plurality of feature mapping images in a mapping mode; the sampling layer performs down-sampling operation on the feature mapping image to generate a shrinkage ratio map corresponding to the feature mapping image; the full connection layer combines the scaled images of each of the eigenmap images to form global information; the output layer calculates the global information to obtain a calculation result, wherein the calculation result comprises the water content, the pore ratio and the foundation bearing capacity of the preloading foundation; s5, comparing the calculation result with the actual water content, the porosity ratio and the foundation bearing capacity; if the relative error is within a preset range, outputting the calculation result, and evaluating the preloading treatment effect of the foundation according to the calculation result; and if the relative error is not in the preset range, returning to the step S4, and adjusting the weight of the evaluation model.
Preferably, the method for calculating the settlement consolidation degree value and the pore pressure consolidation degree value of the foundation comprises the following specific steps: establishing a vector cross product formula:
wherein AB is a vector line segment formed on the settlement graph by a vector line segment point A and a vector line segment point B, BC is a vector line segment formed on the settlement graph by a vector line segment point B and a vector line segment point C, CD is a vector line segment formed on the settlement graph by a vector line segment point C and a vector line segment point D,
、
、
、
respectively vector line segment pointsA、B、C、DThe time on the graph of the sedimentation curve,
、
、
、
are respectively vector line segment pointsA、B、C、DThe amount of sedimentation on the sedimentation graph;
obtaining a consolidation degree calculation formula under the hyperbolic fitting method theory based on the vector cross product formula:
；
wherein,aandbrespectively an intercept and a slope, of the signal,
for the time points calculated based on the theory of the hyperbolic fitting method,
is the calculated settlement amount based on the theory of the hyperbolic curve fitting method,
for final settlement of the earth's surfaceData; obtaining a foundation settlement consolidation degree formula and a pore pressure consolidation degree formula based on a consolidation degree calculation formula under the theory of a hyperbolic fitting method:
wherein,U 1in order to settle the consolidation degree of the foundation,
in the process of prepressing the foundationtAccumulating the settlement data of the earth surface monitored at any moment,
the final settlement data of the earth surface is obtained;U 2in order to obtain the pore-pressure consolidation degree,
is the pore water pressure dissipation value of the foundation,
，
is pore water pressure data, namely the total super hydrostatic pressure borne by the foundation,
in the process of prepressing the foundationtPore water pressure data at time.
Preferably, the position deviating the most from the design requirement value is acquired by the following specific method: in the feasible domain of the network data model
Internal generationkInitial point:
，
，……
wherein, in the process,
，nis a natural number; calculating outkAn objective function of the initial point
，
(ii) a Find outkA minimum point in said objective function
And maximum point
And performing convergence judgment, and if there is convergence, determining the minimum point
Output when it is
The position deviating from the design required value most in the foundation preloading is provided; if no convergence is found, step S4 is executed.
Preferably, the objective function is:
wherein,x0，y0，z0 is a coordinate which is a position of the line,
in order to be the density of the mixture,
is the cohesive force of the soil body,
is the internal friction angle of the soil body,
in order to be the depth of the film,
is composed of
The cohesive force of the soil body under the depth,
is composed of
The internal friction angle of the soil mass under the depth,
is composed of
The density of the soil mass under the depth,
is prepared fromx0，y0) Foundation settlement consolidation of coordinate pointsThe degree of the magnetic field is measured,
the shear strength of the cross plate is the shear strength of the cross plate,kthe number of the ground surface settlement monitoring points is,z i for each of the depths of the points, the depth of the point,zas a result of the total depth,
the settlement and consolidation degree of the foundation is set,
the pore pressure consolidation degree is the pore pressure consolidation degree,
the settlement and consolidation degree of the foundation layer by layer,
in order to take the weight coefficient occupied by the geotechnical test on the foundation,
is the weight coefficient occupied by the cross plate shearing test,
in order to monitor the weight coefficient occupied at the monitoring point,
in order to design the characteristic value of the bearing capacity of the foundation,
the consolidation degree is required for the design;
preferably, the convergence judgment is performed based on a convergence criterion function, and the convergence criterion function is as follows:
(ii) a Wherein,
is the most excellentThe number of the small dots is small,
is the maximum point of the point, and the point is,
for a given accuracy.
Preferably, the weight of the evaluation model is adjusted by using an error propagation learning algorithm; the error propagation learning algorithm comprises an error term recursion calculation formula:
the activation function is:
。
preferably, in S4, the sampling layer performs a down-sampling operation on the feature map image, specifically: the sampling layer abstractly represents the feature mapping image output by the convolutional layer so as to reduce the size of the feature mapping image; the feature map image is further mapped using a mean convolution kernel to generate a scaled map corresponding to the feature map image.
Ground base surcharge preloading treatment effect evaluation system, its characterized in that includes: the monitoring module is used for acquiring surface subsidence data, layered subsidence data and pore water pressure data in the foundation preloading treatment process; the analysis and calculation module is used for calculating a foundation settlement consolidation degree value and a pore pressure consolidation degree value based on the surface settlement data, the layered settlement data and the pore water pressure data; meanwhile, carrying out drilling sampling, a cross plate shearing test and a load test on the foundation surcharge preloading to obtain a foundation soil layer physical parameter value, a foundation strength value at different depths and a foundation bearing capacity value; the network data model building module is used for building a network data model according to the values output by the analysis and calculation module and acquiring a position and an isoline plan which deviate from the design requirement value most in the network data model; an evaluation model comprising a convolutional layer, a sampling layer, a fully-connected layer, and an output layer; the convolution layer performs convolution operation on the contour line plane graph, and the contour line plane graph is mapped to generate a plurality of feature mapping images; the sampling layer carries out down-sampling operation on the characteristic mapping image to generate a shrinkage ratio map corresponding to the characteristic mapping image; the full connection layer combines the shrinkage rate maps of the feature map images to form global information; the output layer calculates the global information to obtain a calculation result and generates an evaluation result of the foundation preloading processing scheme; and the weight adjusting module is used for comparing the calculation result output by the evaluation model with the actual water content, the porosity ratio and the foundation bearing capacity of the foundation to obtain a relative error value, and adjusting the weight of the evaluation model according to the relative error value to enable the relative error to be in a preset range.
Preferably, the output layer outputs three calculation results, and the calculation results comprise the water content, the porosity ratio and the foundation bearing capacity of the preloading foundation.
A computer readable storage medium, in which instructions are stored, and when the instructions are executed by a processor, the method for evaluating the effect of the foundation preloading handling is performed.
Compared with the prior art, the invention has the following advantages:
the invention can fully combine the construction condition and the monitoring data in the comprehensive construction process, and more comprehensively make objective evaluation on the preloading treatment effect of the foundation preloading.
According to the invention, by constructing the data model and the evaluation model of the foundation preloading network, the position deviating from the design requirement value most in the foundation preloading can be effectively and accurately measured in time, the foundation treatment evaluation is carried out through the evaluation model again, and the evaluation result is output. The method of the invention is used for effect evaluation, is quicker, simpler and more convenient in operability, and can comprehensively make objective evaluation on the treatment effect of the preloading of the foundation.
Drawings
In order that the present disclosure may be more readily and clearly understood, reference is now made to the following detailed description of the present disclosure taken in conjunction with the accompanying drawings, in which:
FIG. 1 is a schematic flow chart of the method for evaluating the effect of the preloading treatment of the foundation in accordance with the present invention;
FIG. 2 is a schematic representation of an improved hyperbolic model of the present invention;
FIG. 3 is a schematic diagram of an evaluation model according to the present invention;
FIG. 4 is a schematic representation of a surface subsidence consolidation contour in accordance with the present invention;
FIG. 5 is a schematic view of the depth weighted average consolidation contour for stratified sedimentation in accordance with the present invention;
FIG. 6 is a schematic representation of a pore pressure consolidation contour according to the present invention;
FIG. 7 is a schematic view of a cross plate shear contour of the present invention.
Detailed Description
The present invention is further described below in conjunction with the following figures and specific examples so that those skilled in the art may better understand the present invention and practice it, but the examples are not intended to limit the present invention.
The invention relates to the field of quality detection application research of large-area sludge foundation treatment geotechnical engineering in water conservancy and water transportation port engineering, in particular to an evaluation method of foundation preloading treatment effect. To achieve the above object, referring to fig. 1, the technical solution of the present invention comprises the steps of:
step one, monitoring point arrangement work is carried out, monitoring item monitoring points are arranged according to the distance requirements of the monitoring points, monitoring items comprise surface settlement, layered settlement and pore water pressure, and therefore surface settlement data, layered settlement data, pore water pressure data and the like of the foundation in the preloading treatment process are monitored.
And step two, calculating the settlement consolidation degree value and the pore pressure consolidation degree value of the foundation based on the data obtained in the step one.
In the method, an improved hyperbolic model is adopted for settlement consolidation calculation of the foundation, namely, a vector cross product theory is introduced on the traditional hyperbolic method. High precision of oblique axis parabolic smooth interpolation generation "SComparing the smooth curve with the smooth interpolation of positive axis parabola, and finding out the optimum inflection point, i.e. the zero point in the hyperbolic model and the settlement curve, by the vector cross product method and the oblique axis parabola method "S"features are adapted, refer to fig. 2, which is a schematic diagram of an improved hyperbolic model constructed in the method of the present invention.
The settlement consolidation degree numerical value and the pore pressure consolidation degree numerical value of the foundation are calculated, and the specific method comprises the following steps:
establishing a vector cross product formula:
wherein, as shown in fig. 2, AB is a vector line segment formed on the settlement graph by a vector line segment point a and a vector line segment point B, BC is a vector line segment formed on the settlement graph by a vector line segment point B and a vector line segment point C, CD is a vector line segment formed on the settlement graph by a vector line segment point C and a vector line segment point D,
、
、
、
are respectively vector line segment pointsA、B、C、DThe time on the graph of the sedimentation curve,
、
、
、
are respectively vector line segment pointsA、B、C、DThe settlement amount on the settlement graph;
obtaining a consolidation degree calculation formula under the theory of a hyperbolic fitting method based on a vector cross product formula:
wherein,aandbrespectively an intercept and a slope, of the signal,
for the time points calculated based on the theory of the hyperbolic fitting method,
is the calculated settling amount based on the theory of the hyperbolic fitting method,
final settlement data of the earth surface;
obtaining a foundation settlement consolidation degree formula and a pore pressure consolidation degree formula based on a consolidation degree calculation formula under the theory of a hyperbolic fitting method:
wherein,U 1the settlement and consolidation degree of the foundation is set,
in the process of prepressing the foundationtThe accumulated surface subsidence data at the moment of monitoring,
final settlement data of the earth surface;U 2the pore pressure consolidation degree is the pore pressure consolidation degree,
is the pore water pressure dissipation value of the foundation,
，
is pore water pressure data, namely the total super-static water pressure borne by the foundation,
in the process of prepressing the foundationtPore water pressure data at time.
And meanwhile, carrying out a drilling sampling test, a cross plate shearing test and a load test on the foundation subjected to the surcharge preloading treatment to obtain a foundation soil layer physical parameter value, a strength numerical value of the foundation at different depths and a foundation bearing capacity numerical value.
And step three, constructing a network data model according to the numerical values obtained in the step two, carrying out contour processing on the network data model, interpolating and fitting data by adopting curved surface processing and a distance weighted least square method processing method, forming a contour plane graph of the network data model and acquiring the position which is most deviated from the design required value.
4-7, the contour plan includes surface subsidence consolidation contour, pore pressure consolidation contour, cross plate shear contour, and layered subsidence depth-weighted average consolidation contour.
Acquiring the position which deviates most from the design requirement value, wherein the specific method comprises the following steps of:
(a) In the feasible domain of the network data model
Internal generationkInitial point:
，
，……
wherein
，nis a natural number;
(b) calculating outkAn objective function of an initial point
，
；
(c) Find outkMinimum (good) point in an objective function
And maximum (bad) point
And performing convergence judgment, and if there is convergence, point-counting
Output as the optimal (worst) point, when
The position deviating from the design required value most in the foundation preloading is provided; if no convergence exists, executing the step four.
Wherein the objective function is:
x0，y0，z0 is a coordinate of the number of pixels,
in order to be the density of the mixture,
is the cohesive force of the soil body,
is the internal friction angle of the soil body,
in order to be the depth of the film,
is composed of
The cohesive force of the soil mass under the depth,
is composed of
The internal friction angle of the soil body under the depth,
is composed of
The density of the soil mass under the depth,
is prepared fromx0，y0) The settlement and consolidation degree of the foundation of the coordinate point,
the shear strength of the cross plate is the shear strength of the cross plate,kthe number of the ground surface settlement monitoring points is,z i for each of the depths of the points, the depth of the point,zas a result of the total depth,
in order to settle the consolidation degree of the foundation,
in order to obtain the pore-pressure consolidation degree,
the settlement and consolidation degree of the foundation layer by layer,
in order to take the weight coefficient occupied by the geotechnical test on the foundation,
is the weight coefficient occupied by the cross plate shearing test,
in order to monitor the weight coefficient occupied at the monitoring point,
in order to design the characteristic value of the bearing capacity of the foundation,
the consolidation degree is required for the design;
the convergence judgment processing is completed based on a convergence criterion function which is:
(d) Acquisition remover
Geometric center point of other points
If, if
Meet preset design requirements and calculate the relation
Mapping point of
If at all
If the preset design requirement is not met, the method is carried out
As a starting point, the method comprises the following steps of,
and (c) reconstructing the compound line as the end point, and returning to the step (b).
Wherein the geometric center point
The formula of (1) is as follows:
；
mapping point
The formula of (1) is:
in the formula (I), the reaction is carried out,αare the mapping coefficients.
(e) If it is
Satisfying the preset design requirement, comparing the mapping point with the objective function value of the worst point, if so
Then to
Instead of the former
And returning to (c); if it is
Then, it will be 0.5αTo giveα，
Up to
Until now. If it is
（
Is a very small positive number, e.g. 10-5) Time of flight，
If not, the process is exited and the worst point mapping fails. If it is
Not meeting the design requirements, will be 0.5αTo giveαUp to
And (4) the method is feasible.
(f) And finding out the most unfavorable position for detection work, including a drilling sampling geotechnical test, a cross plate shearing test and a load test, obtaining a specific detection parameter value, judging whether the detection value meets a design requirement value, if so, carrying out the next step, otherwise, carrying out secondary foundation treatment, and returning to the step one.
Step four, constructing an evaluation model, and referring to fig. 3, the evaluation model comprises: the convolution layer, the sampling layer, the full connection layer and the output layer are included in the evaluation modelnA convolution layer andna plurality of sampling layers are arranged in the sampling layer,nnot less than 1. The specific operation method comprises the following steps:
an input operation: the above-described contour plane map is input into the evaluation model.
And (3) convolutional layer operation: the convolution layer performs a convolution operation on the contour plane map and maps to generate a plurality of eigen-map images. Specifically, the convolution kernel performs convolution processing on the contour plane map from left to right and from top to bottom in sequence according to a specific step length, the contour plane map is mapped into a plurality of different abstract images, the convolution layer is activated by utilizing a tanh activation function, a plurality of new feature mapping images are obtained after operation processing is finished, and the plurality of feature mapping images are used as input of the sampling layer.
Wherein, the convolution formula of the convolutional layer is as follows:
fis the activation function of the convolutional layer(s),iandjis the row and column index of the eigenmap image,kis a convolution kernel element which is a function of the convolution kernel,bin order to be a term of the offset,lin order to input the number of layers,sfor the weight component, the activation function of the convolutional layer is:
。
sampling layer operation: the sampling layer performs down-sampling operation on the feature map image to generate a scaling map corresponding to the feature map image. Specifically, the sampling layer further abstractly represents the feature map image output by the convolutional layer, performs down-sampling operation, reduces the size of the feature map image to reduce the number of fully-connected nerve clouds, and maps the input feature map image by using a mean value convolutional kernel to generate a scaling map corresponding to the feature map image, thereby achieving the purpose of scaling down the feature map image.
Full connection layer: which is used to combine the scaled maps of the eigen-map images output by the sampling layer to form global information.
And (3) an output layer: and the output layer calculates the global information and obtains three calculation results, wherein the calculation results comprise the water content, the pore ratio and the foundation bearing capacity of the preloading foundation.
And step five, comparing the water content, the pore ratio and the foundation bearing capacity which are calculated by the evaluation model with the actual water content, the pore ratio and the foundation bearing capacity to obtain a relative error value. If the relative error is in a preset range, outputting a calculation result, and generating an evaluation result of the foundation preloading processing scheme according to the calculation result, if the relative error is not in the preset range, then: and adjusting the weight of the evaluation model, and returning to the fourth step.
And adjusting the weight of the evaluation model by using an error propagation learning algorithm, wherein the error propagation learning algorithm comprises an error item recursion calculation formula:
in another preferred embodiment, two convolutional layers and two sampling layers are determined, as shown in fig. 3, wherein the structure of the evaluation model is: input-convolutional layer-sampling layer-full-link layer-output layer. This example determines that the convolution kernel size of all convolution layers of the evaluation model is 3 x 3, the size of the sampling layer is 2 x 2, and the input is the contour planar graph.
The image sizes of each of the convolutional layers-sampling layer-convolutional layer-sampling layer were 30 × 30mm, 28 × 28mm, 24 × 24mm, 20 × 20mm, and 16 × 16mm, respectively, and the number of feature maps of the first convolutional layer was set to 16, and the number of feature maps of the second convolutional layer was set to 8. The output layer outputs 3 results: water content, porosity ratio and foundation bearing capacity of the surcharge preloading foundation.
In another preferred embodiment:
taking a new airport land-building project as an example, the airport land-building project belongs to sea reclamation project, and the area is 3822474 m2Wherein the functional region has an area of 3254231m2The common area A is 568243m2The foundation soil is mainly marine silt, the foundation treatment method adopts a preloading foundation reinforcement mode, and the preloading material is blown-filled sand by a marine cutter suction dredger. The foundation treatment mode is preloading foundation reinforcement, sand is firstly blown and filled to +2.0m in the site, then sand is filled to +5.0m through a small-sized mechanical sand filling cushion layer or a blown and filled sand cushion layer, the thickness of the sand cushion layer is 0.5m, plastic drainage plates are arranged, the distance is 1.0m, the arrangement of squares is realized, the depth is 15m, then sand is blown and filled to +5.0m, a hydraulic fill separation weir is constructed, sand is blown and filled to +9.0m, sand is loaded to the final loading thickness by reverse transportation, the full-load preloading time is designed for about 6-8 months, the actual full-load time is determined by calculating the consolidation degree of the foundation through settlement observation data, the pre-load sand is unloaded to the working face of the foundation after pre-loading is finished, and the surface of the site is leveled. Presetting design requirements, setting rate not more than 0.5mm/d, calculating foundation consolidation degree not less than 90%, unloadingThe shear strength of the front cross plate is not less than 20kPa, the water content of the soil is not more than 65%, the porosity ratio is not more than 1.70, and the bearing capacity of the foundation is required to be 120 kPa. And monitoring points are distributed according to the arrangement requirement.
The processing area of the engineering site is about 382 ten thousand meters2The method has the advantages that the area is large, the treated foundation is silt soil, the foundation treatment mode is preloading, the foundation treatment effect evaluation is carried out on the preloading foundation of the large-area silt soft foundation, firstly, the construction area is large, secondly, the monitoring period is long, then, the workload is large, and all the steps are combined.
For a 500 × 500m area in the field, 132 surface subsidence monitoring points, 36 sets of layered subsidence monitoring points, 36 sets of pore water pressure monitoring points, 36 sets of cross plate shearing detection points, 36 sets of drilling sampling points and 6 sets of load tests are distributed. And establishing a network data model. According to the method, the worst position is found out for detection, then the drilling sampling geotechnical test, the cross plate shearing test and the load test are carried out, specific detection parameter values are obtained, whether the detection values meet the design requirement values or not is judged, if yes, the next step is carried out, if not, secondary foundation treatment is carried out, and therefore the worst position of the treatment of the large-area sludge soft foundation preloading foundation can be found out for effect evaluation and judgment.
And constructing an evaluation model, taking No. 1-126 settlement points, No. 1-30 layered settlement points, No. 1-30 pore water pressure monitoring points, No. 1-30 cross plate shearing detection points, No. 1-30 drilling sampling detection points and No. 1-3 load tests as training samples, giving test results to the evaluation model for training, and carrying out weight fine adjustment by model training and an error finding propagation algorithm to obtain the optimized model. 127-plus 132 settlement points, 31-36 layered settlement points, 31-36 pore water pressure monitoring points, 31-36 cross plate shearing detection points, 31-36 drilling sampling detection points and 4-6 load tests are used as test samples to test and debug the model.
The test was performed by selecting another 200 × 200m area for comparison, and the comparison results are shown in table 1 below:
TABLE 1 summary of actual parameters and evaluation model calculation results
As can be seen from Table 1, the relative errors are all less than 0.05, which satisfies the requirements. Comparing the output index with the design index, the design index (the design index is that the water content of the soil is not more than 65%, the porosity ratio is not more than 1.70, and the bearing capacity requirement of the foundation is 120 kPa) can be seen to be met. Namely, the method can be applied to the practical application of the project, and the result obtained by the method is more accurate.
The method is faster, simpler and more convenient to operate, the accuracy of the measured result is more accurate, the cost of human resources and material resources is greatly reduced, the construction period is better saved, the construction quality is effectively controlled, the construction of the project is guided, and experience is provided for subsequent related similar projects.
Based on the above method for evaluating the effect of preloading treatment of foundation preloading, the invention also provides a system for evaluating the effect of preloading treatment of foundation preloading, which comprises:
and the monitoring module is used for acquiring surface settlement data, layered settlement data and pore water pressure data in the foundation preloading treatment process.
The analysis and calculation module is used for calculating a foundation settlement consolidation degree value and a pore pressure consolidation degree value based on the surface settlement data, the layered settlement data and the pore water pressure data; and simultaneously, carrying out drilling sampling, a cross plate shearing test and a load test on the foundation preloading to obtain a foundation soil layer physical parameter value, a strength numerical value of the foundation at different depths and a foundation bearing capacity numerical value.
And the foundation preloading network data model building module is used for building a foundation preloading network data model according to the values output by the analysis and calculation module and acquiring the position and the isoline plan which deviate from the design required value most in the network data model.
An evaluation model comprising a convolutional layer, a sampling layer, a fully-connected layer, and an output layer;
the convolution layer performs convolution operation on the position most deviated from the design requirement value in the network data model and the isoline plane graph, a plurality of feature mapping images are generated through mapping, the sampling layer performs down-sampling operation on the feature mapping images to generate a reduction rate graph corresponding to the feature mapping images, and the full connection layer combines the reduction rate graphs of all the feature mapping images to form global information; and the output layer calculates the global information to obtain a calculation result and generates an evaluation result of the foundation preloading processing scheme.
And the weight value adjusting module is used for comparing the calculation result output by the convolutional neural network with the actual water content, the pore ratio and the foundation bearing capacity of the foundation to obtain a relative error value, and adjusting the weight value of the evaluation model according to the relative error value to enable the relative error to be within a preset range.
Further, the present invention also provides a computer readable storage medium, in which instructions are stored, and when the instructions are executed by a processor, the method for evaluating the effect of the foundation preloading handling is performed.
As will be appreciated by one skilled in the art, embodiments of the present application may be provided as a method, system, or computer program product. Accordingly, the present application may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present application may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.
It should be understood that the above examples are only for clarity of illustration and are not intended to limit the embodiments. Various other modifications and alterations will occur to those skilled in the art upon reading the foregoing description. This need not be, nor should it be exhaustive of all embodiments. And obvious variations or modifications therefrom are within the scope of the invention.
Claims (10)
1. The evaluation method for the treatment effect of the preloading of the foundation is characterized by comprising the following steps of:
s1, monitoring the ground surface settlement data, the layered settlement data and the pore water pressure data of the foundation in the preloading treatment process;
s2, calculating the settlement consolidation degree value and the pore pressure consolidation degree value of the foundation based on the data obtained in the S1;
simultaneously, drilling and sampling, cross plate shearing test and load test are carried out on the foundation subjected to the surcharge preloading treatment, and a foundation soil layer physical parameter value, a foundation strength value at different depths and a foundation bearing capacity value are obtained;
s3, constructing a network data model according to the values obtained in S2, and acquiring a position and an isoline plan which deviate from the design requirement value most in the network data model;
detecting the position with the maximum deviation from the design requirement value, wherein the detection work comprises a drilling sampling geotechnical test, a cross plate shearing test and a load test, obtaining specific detection parameter values, judging whether the detection parameter values meet the preset requirement value, if so, carrying out the next step, if not, carrying out the foundation preloading again, and returning to S1;
s4, constructing an evaluation model which comprises a convolution layer, a sampling layer, a full connection layer and an output layer;
the convolution layer performs convolution operation on the contour plane graph and generates a plurality of feature mapping images in a mapping mode;
the sampling layer performs down-sampling operation on the feature mapping image to generate a shrinkage ratio map corresponding to the feature mapping image;
the full connection layer combines the shrinkage rate maps of the feature map images to form global information;
the output layer calculates the global information to obtain a calculation result, wherein the calculation result comprises the water content, the porosity ratio and the foundation bearing capacity of the preloading foundation;
s5, comparing the calculation result with the actual water content, the pore ratio and the foundation bearing capacity; if the relative error is within a preset range, outputting the calculation result, and evaluating the preloading treatment effect of the foundation according to the calculation result; and if the relative error is not in the preset range, returning to the step S4, and adjusting the weight of the evaluation model.
2. The method for evaluating the effect of the foundation preloading treatment according to claim 1, wherein the settlement consolidation degree value and the pore pressure consolidation degree value of the foundation are calculated by the specific method comprising the following steps:
establishing a vector cross product formula:
wherein AB is a vector line segment formed on the settlement graph by a vector line segment point A and a vector line segment point B, BC is a vector line segment formed on the settlement graph by a vector line segment point B and a vector line segment point C, CD is a vector line segment formed on the settlement graph by a vector line segment point C and a vector line segment point D,
、
、
、
respectively vector line segment pointsA、B、C、DThe time on the graph of the sedimentation curve,
、
、
、
are respectively vector line segment pointsA、B、C、DThe settlement amount on the settlement graph;
based on the vector cross product formula, obtaining a consolidation degree calculation formula under the hyperbolic fitting method theory:
wherein,aandbrespectively an intercept and a slope, of the signal,tfor prepressing foundationstAt the time of day, the user may,
for the time points calculated based on the theory of the hyperbolic fitting method,
is the calculated settlement amount based on the theory of the hyperbolic curve fitting method,
the final settlement data of the earth surface is obtained;
obtaining a foundation settlement consolidation degree formula and a pore pressure consolidation degree formula based on a consolidation degree calculation formula under the theory of a hyperbolic fitting method:
wherein,
is a foundationThe degree of sedimentation and consolidation is controlled,
in the process of prepressing the foundationtAccumulating the settlement data of the earth surface monitored at any moment,
the final settlement data of the earth surface is obtained;
the pore pressure consolidation degree is the pore pressure consolidation degree,
is the dissipation value of the pore water pressure of the foundation,
，
is pore water pressure data, namely the total super hydrostatic pressure borne by the foundation,
in the process of prepressing the foundationtPore water pressure data at time.
3. The method for evaluating the effect of foundation preloading treatment according to claim 1, wherein the position deviating the most from the design requirement value in the network data model is obtained by the specific method:
in the feasible domain of the network data model
Internal generationkInitial point:
，
，……
wherein, in the process,
，nis a natural number;
computingkAn objective function of the initial point
，
；
Find outkA minimum point in said objective function
And maximum point
And performing convergence judgment, and if there is convergence, determining the minimum point
Output when it is
The position deviating from the design requirement value most in the foundation preloading is selected; if no convergence is found, step S4 is executed.
4. The method for evaluating the effect of foundation preloading treatment according to claim 3, wherein the objective function is:
wherein,x0，y0，z0 is a coordinate which is a position of the line,
in order to be the density of the mixture,
is the cohesive force of the soil body,
is the internal friction angle of the soil body,
in order to be the depth of the film,
is composed of
The cohesive force of the soil body under the depth,
is composed of
The internal friction angle of the soil body under the depth,
is composed of
The density of the soil mass under the depth,
is prepared from (a)x0，y0) The settlement and consolidation degree of the foundation of the coordinate point,
the shear strength of the cross plate is the shear strength of the cross plate,kthe number of the monitoring points of the surface subsidence,z i for each of the depths of the points, the depth of the point,zas a result of the total depth,
the settlement and consolidation degree of the foundation is set,
in order to obtain the pore-pressure consolidation degree,
the settlement and consolidation degree of the foundation layer by layer,
in order to take the weight coefficient occupied by the geotechnical test on the foundation,
is the weight coefficient occupied by the cross plate shearing test,
in order to monitor the weight coefficient occupied at the monitoring point,
in order to design the characteristic value of the bearing capacity of the foundation,
the degree of consolidation is required for design.
5. The method for evaluating the effect of foundation preloading treatment according to claim 3, wherein the convergence judgment is performed based on a convergence criterion function, the convergence criterion function being:
6. The method for evaluating the effect of foundation preloading treatment according to claim 1, wherein the weight of the evaluation model is adjusted by using an error propagation learning algorithm;
the error propagation learning algorithm comprises an error term recursion calculation formula:
the activation function is:
。
7. the method for evaluating the effect of foundation preloading treatment according to claim 1, wherein in S4, the sampling layer performs a down-sampling operation on the feature map image, specifically:
the sampling layer abstractly represents the characteristic mapping image output by the convolution layer so as to reduce the size of the characteristic mapping image;
the feature map image is further mapped using a mean convolution kernel to generate a scaled map corresponding to the feature map image.
8. Ground surcharge carries pre-compaction treatment effect evaluation system, its characterized in that includes:
the monitoring module is used for acquiring surface subsidence data, layered subsidence data and pore water pressure data in the foundation preloading treatment process;
the analysis and calculation module is used for calculating a foundation settlement consolidation degree value and a pore pressure consolidation degree value based on the surface settlement data, the layered settlement data and the pore water pressure data; simultaneously, carrying out drilling sampling, a cross plate shearing test and a load test on the foundation preloading to obtain a foundation soil layer physical parameter value, a foundation strength value at different depths and a foundation bearing capacity value;
the network data model construction module is used for constructing a network data model according to the values output by the analysis and calculation module and acquiring a position and an isoline plan which deviate from the design requirement value most in the network data model;
an evaluation model comprising a convolutional layer, a sampling layer, a fully-connected layer, and an output layer;
the convolution layer performs convolution operation on the contour line plane graph, and a plurality of feature mapping images are generated through mapping; the sampling layer performs down-sampling operation on the feature mapping image to generate a shrinkage ratio map corresponding to the feature mapping image; the full connection layer combines the shrinkage rate maps of the feature map images to form global information; the output layer calculates the global information to obtain a calculation result, and evaluates the preloading treatment effect of the foundation preloading according to the calculation result;
and the weight value adjusting module is used for comparing the calculation result output by the evaluation model with the actual water content, the pore ratio and the foundation bearing capacity of the foundation to obtain a relative error value, and adjusting the weight value of the evaluation model according to the relative error value to enable the relative error to be within a preset range.
9. The system for evaluating the effect of foundation preloading treatment according to claim 8, wherein the output layer outputs three calculation results, and the calculation results include water content, porosity ratio and foundation bearing capacity of the preloading foundation.
10. A computer-readable storage medium storing instructions which, when executed by a processor, perform the method for evaluating the effect of a ground preloading procedure as defined in any one of claims 1 to 7.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210722793.2A CN114781954B (en) | 2022-06-24 | 2022-06-24 | Method and system for evaluating foundation preloading treatment effect |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210722793.2A CN114781954B (en) | 2022-06-24 | 2022-06-24 | Method and system for evaluating foundation preloading treatment effect |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN114781954A true CN114781954A (en) | 2022-07-22 |
| CN114781954B CN114781954B (en) | 2022-09-13 |
Family
ID=82422356
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202210722793.2A Active CN114781954B (en) | 2022-06-24 | 2022-06-24 | Method and system for evaluating foundation preloading treatment effect |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN114781954B (en) |
Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104697493A (en) * | 2015-02-16 | 2015-06-10 | 中交天津港湾工程研究院有限公司 | Underground preloading settlement monitoring system |
| CN105512390A (en) * | 2015-12-04 | 2016-04-20 | 中冶集团武汉勘察研究院有限公司 | Dynamic prediction method for large raw material storage yard foundation deformation under graded preloading |
| CN109255191A (en) * | 2018-09-20 | 2019-01-22 | 中铁第四勘察设计院集团有限公司 | A method of quantitatively calculating produced settling amount after railway bed piling prepressing |
| CN112948746A (en) * | 2021-01-15 | 2021-06-11 | 中建路桥集团有限公司 | Composite roadbed post-construction settlement prediction method based on fuzzy comprehensive evaluation |
| CN113761630A (en) * | 2021-09-10 | 2021-12-07 | 广东电网有限责任公司 | Foundation settlement prediction method based on timeliness judgment and related device |
-
2022
- 2022-06-24 CN CN202210722793.2A patent/CN114781954B/en active Active
Patent Citations (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104697493A (en) * | 2015-02-16 | 2015-06-10 | 中交天津港湾工程研究院有限公司 | Underground preloading settlement monitoring system |
| CN105512390A (en) * | 2015-12-04 | 2016-04-20 | 中冶集团武汉勘察研究院有限公司 | Dynamic prediction method for large raw material storage yard foundation deformation under graded preloading |
| CN109255191A (en) * | 2018-09-20 | 2019-01-22 | 中铁第四勘察设计院集团有限公司 | A method of quantitatively calculating produced settling amount after railway bed piling prepressing |
| CN112948746A (en) * | 2021-01-15 | 2021-06-11 | 中建路桥集团有限公司 | Composite roadbed post-construction settlement prediction method based on fuzzy comprehensive evaluation |
| CN113761630A (en) * | 2021-09-10 | 2021-12-07 | 广东电网有限责任公司 | Foundation settlement prediction method based on timeliness judgment and related device |
Non-Patent Citations (1)
| Title |
|---|
| 钱彬等: "改进的双曲线模型地基沉降预测", 《工程勘察》 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN114781954B (en) | 2022-09-13 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Narita et al. | Bearing capacity analysis of foudations on slopes by use of log-spiral sliding surfaces | |
| AU2013274604B2 (en) | Digital rock analysis with multiphase flow REV determination | |
| Crawford | State of the art: evaluation and interpretation of soil consolidation tests | |
| Schmertmann | Dilatometer to compute foundation settlement | |
| Olson et al. | One-dimensional consolidation problems | |
| Young et al. | Liquefaction potential of coastal slopes induced by solitary waves | |
| Zhou et al. | Application of an improved multi-temporal InSAR method and forward geophysical model to document subsidence and rebound of the Chinese Loess Plateau following land reclamation in the Yan'an New District | |
| Aghajani et al. | Application of artificial neural network for calculating anisotropic friction angle of sands and effect on slope stability | |
| Duncan et al. | Evaluating reliability in geotechnical engineering | |
| CN115481467A (en) | Complicated geological immersed tunnel foundation trench soil-distribution quality overbreak and underexcavation analysis method and device | |
| CN110377935B (en) | Method for predicting change of excess pore water pressure along depth of soil body around pile in pile sinking process | |
| CN113780741B (en) | Landslide risk evaluation method, system and storage medium based on slope characteristics | |
| CN114781954B (en) | Method and system for evaluating foundation preloading treatment effect | |
| Chen et al. | Modified double-reduction method considering strain softening and equivalent influence angle | |
| CN111239818B (en) | Ancient landform analysis method based on three-dimensional dip angle attribute body correction | |
| Rowe et al. | The behaviour of footings resting on a non-homogeneous soil mass with a crust. Part II. Circular footings | |
| Wanatowski | Strain softening and instability of sand under plane-strain conditions | |
| Abed et al. | Numerical simulation of unsaturated soil behaviour | |
| Lacquement | Recalculating mound volume at moundville | |
| Yang et al. | Modification of phreatic line calculation model for landslide accumulation in the Three Gorges Reservoir area | |
| Sharafutdinov | Clay soil stiffness under consolidated isotropic drained triaxial tests | |
| Kantesaria et al. | Strain energy, yielding and undrained shear characteristics of high plasticity compacted clay subjected to stress anisotropy | |
| Harnpattanapanich et al. | Numerical Laplace–Fourier transform inversion technique for layered soil consolidation problems; II, Gibson soil layer | |
| Sun et al. | Experimental and computational analysis of hydraulic behavior in shallow buried structures in sand | |
| CN114898343A (en) | Soil compaction degree detection method, system and medium based on image recognition 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 |