CN112800530A - Digital data processing method for reinforcing stress strain of broken soft surrounding rock face - Google Patents

Digital data processing method for reinforcing stress strain of broken soft surrounding rock face Download PDF

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CN112800530A
CN112800530A CN202110331346.XA CN202110331346A CN112800530A CN 112800530 A CN112800530 A CN 112800530A CN 202110331346 A CN202110331346 A CN 202110331346A CN 112800530 A CN112800530 A CN 112800530A
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surrounding rock
face
representing
reinforcing
finite element
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CN112800530B (en
Inventor
高军
汤宇
刘德安
林晓
孟国基
王圣
罗红明
王峰
彭学军
杨文国
翁小川
谢晓波
李一萍
杨立云
贾超
高宇馨
王伟
杨文龙
游国平
张晓晓
李行利
黄正凯
张旭东
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Wuhan Institute of Rock and Soil Mechanics of CAS
China State Railway Group Co Ltd
First Engineering Co Ltd of China Railway No 5 Engineering Group Co Ltd
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Wuhan Institute of Rock and Soil Mechanics of CAS
First Engineering Co Ltd of China Railway No 5 Engineering Group Co Ltd
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    • 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
    • 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
    • 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
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V10/00—Arrangements for image or video recognition or understanding
    • G06V10/20—Image preprocessing
    • G06V10/30—Noise filtering
    • G—PHYSICS
    • G06—COMPUTING; CALCULATING OR COUNTING
    • G06V—IMAGE OR VIDEO RECOGNITION OR UNDERSTANDING
    • G06V20/00—Scenes; Scene-specific elements
    • G06V20/10—Terrestrial scenes
    • 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

Abstract

The invention provides a digital data processing method for reinforcing stress strain of a broken soft surrounding rock face, which comprises the following steps: s100, establishing a three-dimensional model of a broken weak surrounding rock face according to project design, and carrying out virtual finite element segmentation to form a three-dimensional finite element model of the face; s200, detecting and storing basic data of a surrounding rock face in a section excavation process; s300, stress-strain analysis is carried out on the face by combining the basic data and the three-dimensional finite element model, and the required bonding strength of the surrounding rock face is predicted by adopting a preset algorithm; s400, selecting a surrounding rock face reinforcing measure according to the bonding required strength, and introducing the reinforcing measure into a three-dimensional finite element model for effectiveness analysis. By the method, the analysis efficiency and effectiveness of the reinforcing stress-strain data of the tunnel face of the broken weak surrounding rock can be improved, the analysis precision is improved, the method is used for guiding the surrounding rock to be reinforced, the reinforcing effect can be guaranteed, the reinforcing failure is avoided, and the reinforcing cost of the surrounding rock is controlled.

Description

Digital data processing method for reinforcing stress strain of broken soft surrounding rock face
Technical Field
The invention relates to the technical field of surrounding rock reinforcement stress data processing, in particular to a digital data processing method for reinforcement stress strain of a broken weak surrounding rock face.
Background
In tunnel construction under the geological condition of broken weak surrounding rocks, one of the most prominent problems in the tunnel construction process of the weak surrounding rocks is the stability problem of the tunnel face, tunnel face reinforcement is often required for maintaining the stability of the tunnel face, but if reinforcement measures are not in place, not only the construction safety is endangered, but also the operation safety is influenced. The reinforcement mode can adopt an advanced pre-reinforcement technology, and the strength of broken soft strata and the stability of surrounding rocks are improved by using a pipe shed and/or an anchor rod, so that the safety is ensured.
However, the reinforcing effect of the surrounding rock face needs to be supported by data, and the existing analysis of the reinforcing stress-strain data of the surrounding rock face is usually carried out manually and judged, which wastes time and labor, and the accuracy of the calculation result is difficult to ensure, so that the problem of accuracy of the stress-strain analysis of the reinforcing of the surrounding rock can be caused, the reinforcing effect is reduced, and the stability of the face is influenced.
Disclosure of Invention
In order to solve the technical problem, the invention provides a digital data processing method for reinforcing stress strain of a broken soft surrounding rock tunnel face, which comprises the following steps:
s100, establishing a three-dimensional model of a broken weak surrounding rock face according to project design, and carrying out virtual finite element segmentation to form a three-dimensional finite element model of the face;
s200, detecting and storing basic data of a surrounding rock face in a section excavation process;
s300, stress-strain analysis is carried out on the face by combining the basic data and the three-dimensional finite element model, and the required bonding strength of the surrounding rock face is predicted by adopting a preset algorithm;
s400, selecting a surrounding rock face reinforcing measure according to the bonding required strength, and introducing the reinforcing measure into a three-dimensional finite element model for effectiveness analysis.
Optionally, in step S100, the three-dimensional finite element model is described by using the following object elements:
Figure 100002_DEST_PATH_IMAGE001
in the above formula, the first and second carbon atoms are,
Figure 100002_DEST_PATH_IMAGE002
representing an object element;
Figure 100002_DEST_PATH_IMAGE003
a presentation evaluation unit;
Figure 100002_DEST_PATH_IMAGE004
to indicate the evaluation unit
Figure 100002_DEST_PATH_IMAGE005
Item influencing factors;
Figure 100002_DEST_PATH_IMAGE006
is shown as
Figure 100002_DEST_PATH_IMAGE007
A magnitude domain of term influence factor number quantization;
Figure 100002_DEST_PATH_IMAGE008
representing the number of influencing factors;
determining grade division of the stability of the surrounding rock, and calculating the material elements according to the following formula
Figure 553738DEST_PATH_IMAGE002
Degree of association with the surrounding rock stability grade H:
Figure 100002_DEST_PATH_IMAGE009
in the above formula, the first and second carbon atoms are,
Figure 100002_DEST_PATH_IMAGE010
representing an object
Figure 629142DEST_PATH_IMAGE002
Correlation degree with surrounding rock stability grade H;
Figure 100002_DEST_PATH_IMAGE011
is shown as
Figure 294609DEST_PATH_IMAGE007
Item influencing factor No
Figure 100002_DEST_PATH_IMAGE012
The term index weight coefficient;
Figure 100002_DEST_PATH_IMAGE013
representing the relevance of each single evaluation index on each grade of the stability of the surrounding rock;
Figure 393146DEST_PATH_IMAGE003
the number of indices representing each influencing factor;
then according to the object element
Figure 661317DEST_PATH_IMAGE002
Degree of correlation with surrounding rock stability grade H
Figure 385035DEST_PATH_IMAGE010
The results were calculated to evaluate the surrounding rock stability.
Optionally, in step S300, the preset algorithm for predicting the sticking patch demand strength is as follows:
Figure 100002_DEST_PATH_IMAGE014
in the above formula, the first and second carbon atoms are,
Figure 100002_DEST_PATH_IMAGE015
is shown as
Figure 100002_DEST_PATH_IMAGE016
The required strength of the wall rock bonding of the finite element units is high;
Figure 100002_DEST_PATH_IMAGE017
is shown as
Figure 34453DEST_PATH_IMAGE016
The cohesive force of the surrounding rock of the finite element units;
Figure 100002_DEST_PATH_IMAGE018
is shown as
Figure 213761DEST_PATH_IMAGE016
The surrounding rock normal stress of the finite element units;
Figure 100002_DEST_PATH_IMAGE019
representing a tangent function;
Figure 100002_DEST_PATH_IMAGE020
is shown as
Figure 692147DEST_PATH_IMAGE016
And (3) the internal friction angle of the surrounding rock normal stress of the finite element unit.
Optionally, if the selected surrounding rock face reinforcement measure adopts an anchor rod and grouting matching mode, calculating the slurry viscosity of grouting by adopting the following formula:
Figure 100002_DEST_PATH_IMAGE021
in the above formula, the first and second carbon atoms are,
Figure 100002_DEST_PATH_IMAGE022
viscosity values representing the time-varying nature of the slurry;
Figure 100002_DEST_PATH_IMAGE023
represents the initial viscosity value of the slurry itself;
Figure 100002_DEST_PATH_IMAGE024
the viscosity time-varying coefficient of the slurry is expressed, and the values of the slurry with different water-cement ratios are different and are measured by a viscoplasticity test;
Figure 100002_DEST_PATH_IMAGE025
the time used in the actual grouting process is represented and belongs to a preset value;
and taking the calculated viscosity of the slurry as data for carrying out effectiveness analysis of the reinforcing measure.
Optionally, the maximum distance of slurry diffusion of the grouting is evaluated by adopting the following formula:
Figure 100002_DEST_PATH_IMAGE026
in the above formula, the first and second carbon atoms are,
Figure 100002_DEST_PATH_IMAGE027
represents the maximum distance of slurry diffusion;
Figure 100002_DEST_PATH_IMAGE028
represents the grouting pressure;
Figure 100002_DEST_PATH_IMAGE029
is the height of the surrounding rock fracture;
Figure 100002_DEST_PATH_IMAGE030
representing the flow rate of the slurry;
Figure 100002_DEST_PATH_IMAGE031
representing the width of the surrounding rock fracture;
Figure 100002_DEST_PATH_IMAGE032
representing the consistency factor of the slurry;
Figure 100002_DEST_PATH_IMAGE033
is the water-cement ratio of the slurry.
Optionally, a BP neural network model is set in the three-dimensional finite element model, and the BP neural network model is used for evaluating the stability of the surrounding rock face; the BP neural network model is obtained through the following method:
constructing a BP neural network, determining a plurality of influence factors of the stability of the tunnel face of the surrounding rock and corresponding evaluation indexes thereof, setting weights for the influence factors, wherein the sum of the weights of the influence factors is 1, performing weight distribution on the evaluation indexes of the influence factors, acquiring data of the influence factors serving as input learning samples, taking the acquired actual stable conditions of the tunnel faces as a target vector, representing the connection of each processing unit in the BP neural network by the weights, correcting the weights by errors between the actual output of the data of the influence factors after sample learning and the target vector, transmitting the change of each weight and deviation in direct proportion to the influence of network errors to each layer of the BP neural network in a back propagation manner, obtaining the success probability and the assembly work probability of each stable level by evaluation calculation, and taking the assembly work probability not less than 80 percent as an expected target, and reversely transmitting the error signals along the original connecting path through the network to modify the weight of each layer of neuron until reaching an expected target, thereby obtaining the BP neural network model.
Optionally, in step S200, the reinforced surrounding rock face is further imaged by using an imaging technique to obtain a face image, and the face image is preprocessed.
Optionally, the preprocessed palm surface image is filtered by using the following formula:
Figure 100002_DEST_PATH_IMAGE034
in the above formula, the first and second carbon atoms are,
Figure 100002_DEST_PATH_IMAGE035
representing a filter function;
Figure 100002_DEST_PATH_IMAGE036
，
Figure 100002_DEST_PATH_IMAGE037
respectively representing the space translation amount on an x axis and a y axis;
Figure 100002_DEST_PATH_IMAGE038
the value of the envelope of the function is represented,
Figure 100002_DEST_PATH_IMAGE039
，
Figure 100002_DEST_PATH_IMAGE040
representing the ratio between the center frequency and the bandwidth,
Figure 100002_DEST_PATH_IMAGE041
represents the center frequency;
Figure 100002_DEST_PATH_IMAGE042
is the wavelength of a sine wave;
Figure 100002_DEST_PATH_IMAGE043
representing the argument of the complex modulated part function;
Figure 100002_DEST_PATH_IMAGE044
represents the aspect ratio of a gaussian function;
Figure 100002_DEST_PATH_IMAGE045
representing an imaginary symbol;
Figure 100002_DEST_PATH_IMAGE046
represents the degree of offset;
and then performing median filtering by adopting the following formula:
Figure 100002_DEST_PATH_IMAGE047
in the above formula, the first and second carbon atoms are,
Figure 100002_DEST_PATH_IMAGE048
representing the palm surface image after median filtering processing;
Figure 100002_DEST_PATH_IMAGE049
representing the palm surface image before median filtering processing;
Figure 100002_DEST_PATH_IMAGE050
representing the coordinate values of the palm surface image;
Figure 100002_DEST_PATH_IMAGE051
a sliding template representing a 5 x 21 matrix region;
and then, carrying out crack identification on the tunnel face image after median filtering.
Optionally, the attribute values of the face image are extracted, and the surrounding rock stability analysis model calculates the face image change rate by using the following formula:
Figure 100002_DEST_PATH_IMAGE052
in the above formula, the first and second carbon atoms are,
Figure 100002_DEST_PATH_IMAGE053
representing the crack rate of change of the face image;
Figure 100002_DEST_PATH_IMAGE054
representing the number of attributes of each palm surface image;
Figure 100002_DEST_PATH_IMAGE055
representing the number of the face images saved in front;
Figure 100002_DEST_PATH_IMAGE056
is shown as
Figure 100002_DEST_PATH_IMAGE057
First of the palm face image
Figure 100002_DEST_PATH_IMAGE058
An item attribute value;
Figure 100002_DEST_PATH_IMAGE059
is shown as
Figure 100002_DEST_PATH_IMAGE060
First of the palm face image
Figure 448137DEST_PATH_IMAGE058
An item attribute value;
Figure 100002_DEST_PATH_IMAGE061
is shown as
Figure 100002_DEST_PATH_IMAGE062
First of the palm face image
Figure 924248DEST_PATH_IMAGE058
An item attribute value;
and if the crack change rate reaches the change threshold value, indicating that the surrounding rock has instability risk, and sending out warning information of the instability risk of the surrounding rock.
Optionally, in step S400, the effectiveness analysis process of the reinforcing measure is as follows:
firstly, calculating the minimum surrounding rock reinforcement thickness of each finite element unit position by adopting the following formula:
Figure 100002_DEST_PATH_IMAGE063
in the above formula, the first and second carbon atoms are,
Figure 100002_DEST_PATH_IMAGE064
is shown as
Figure 100002_DEST_PATH_IMAGE065
Minimum surrounding rock reinforcement thickness of the finite element unit positions;
Figure 100002_DEST_PATH_IMAGE066
representing a safety factor;
Figure 100002_DEST_PATH_IMAGE067
representing the maximum dimension of the face;
Figure 100002_DEST_PATH_IMAGE068
represents the poisson's ratio;
Figure 100002_DEST_PATH_IMAGE069
the tensile strength of the reinforced surrounding rock is shown and is measured through tests;
Figure 100002_DEST_PATH_IMAGE070
is shown as
Figure 338656DEST_PATH_IMAGE065
Pressure at the location of the finite element;
if the reinforcing thickness of the surrounding rock face is not smaller than the calculated minimum reinforcing thickness of the surrounding rock, the surrounding rock face after the reinforcing measure is implemented meets the stability requirement, otherwise, the reinforcing thickness of the surrounding rock face needs to be increased.
The method for processing the reinforcing stress-strain digital data of the broken soft surrounding rock face introduces finite element analysis to the surrounding rock face, performs stress-strain analysis by combining basic data detected in section excavation and a three-dimensional finite element model, and predicts the bonding required strength of the surrounding rock face by adopting a preset algorithm; selecting a surrounding rock face reinforcing measure according to the bonding and repairing required strength, and introducing the reinforcing measure into a three-dimensional finite element model for effectiveness analysis; by the method, the analysis efficiency and effectiveness of the reinforcing stress-strain data of the tunnel face of the broken weak surrounding rock can be improved, the analysis precision is improved, the method is used for guiding the surrounding rock to be reinforced, the reinforcing effect can be guaranteed, the reinforcing failure is avoided, and the reinforcing cost of the surrounding rock is controlled.
Additional features and advantages of the invention will be set forth in the description which follows, and in part will be obvious from the description, or may be learned by practice of the invention. The objectives and other advantages of the invention will be realized and attained by the structure particularly pointed out in the written description and claims hereof as well as the appended drawings.
The technical solution of the present invention is further described in detail by the accompanying drawings and embodiments.
Drawings
The accompanying drawings, which are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate embodiments of the invention and together with the description serve to explain the principles of the invention and not to limit the invention. In the drawings:
fig. 1 is a flow chart of a method for processing digitized data of reinforcing stress strain of a broken soft surrounding rock working face in the embodiment of the invention.
Detailed Description
The preferred embodiments of the present invention will be described in conjunction with the accompanying drawings, and it will be understood that they are described herein for the purpose of illustration and explanation and not limitation.
As shown in fig. 1, an embodiment of the present invention provides a method for processing digitized data of reinforcing stress and strain of a rock face of a broken weak surrounding rock, including the following steps:
s100, establishing a three-dimensional model of a broken weak surrounding rock face according to project design, and carrying out virtual finite element segmentation to form a three-dimensional finite element model of the face;
s200, detecting and storing basic data of a surrounding rock face in a section excavation process;
s300, stress-strain analysis is carried out on the face by combining the basic data and the three-dimensional finite element model, and the required bonding strength of the surrounding rock face is predicted by adopting a preset algorithm;
s400, selecting a surrounding rock face reinforcing measure according to the bonding required strength, and introducing the reinforcing measure into a three-dimensional finite element model for effectiveness analysis.
The working principle and the beneficial effects of the technical scheme are as follows: according to the scheme, finite element analysis is introduced into the surrounding rock face, stress-strain analysis is carried out by combining basic data detected in section excavation and a three-dimensional finite element model, and the required bonding strength of the surrounding rock face is predicted by adopting a preset algorithm; selecting a surrounding rock face reinforcing measure according to the bonding and repairing required strength, and introducing the reinforcing measure into a three-dimensional finite element model for effectiveness analysis; by the method, the analysis efficiency and effectiveness of the reinforcing stress-strain data of the tunnel face of the broken weak surrounding rock can be improved, the analysis precision is improved, the method is used for guiding the surrounding rock to be reinforced, the reinforcing effect can be guaranteed, the reinforcing failure is avoided, and the reinforcing cost of the surrounding rock is controlled.
In one embodiment, in step S100, the following object element descriptions are used in the three-dimensional finite element model:
Figure 100002_DEST_PATH_IMAGE071
in the above formula, the first and second carbon atoms are,
Figure 100002_DEST_PATH_IMAGE072
representing an object element;
Figure 100002_DEST_PATH_IMAGE073
a presentation evaluation unit;
Figure 100002_DEST_PATH_IMAGE074
to indicate the evaluation unit
Figure 100002_DEST_PATH_IMAGE075
Item influencing factors;
Figure DEST_PATH_IMAGE076
is shown as
Figure 761678DEST_PATH_IMAGE075
A magnitude domain of term influence factor number quantization;
Figure DEST_PATH_IMAGE077
representing the number of influencing factors;
determining grade division of the stability of the surrounding rock, and calculating the material elements according to the following formula
Figure 197339DEST_PATH_IMAGE072
Degree of association with the surrounding rock stability grade H:
Figure DEST_PATH_IMAGE078
in the above formula, the first and second carbon atoms are,
Figure DEST_PATH_IMAGE079
representing an object
Figure 38825DEST_PATH_IMAGE072
Correlation degree with surrounding rock stability grade H;
Figure DEST_PATH_IMAGE080
is shown as
Figure 927146DEST_PATH_IMAGE075
Item influencing factor No
Figure DEST_PATH_IMAGE081
The term index weight coefficient;
Figure DEST_PATH_IMAGE082
representing the relevance of each single evaluation index on each grade of the stability of the surrounding rock;
Figure 544073DEST_PATH_IMAGE073
the number of indices representing each influencing factor;
then according to the object element
Figure 303081DEST_PATH_IMAGE072
Degree of correlation with surrounding rock stability grade H
Figure 776788DEST_PATH_IMAGE079
The results were calculated to evaluate the surrounding rock stability.
The working principle and the beneficial effects of the technical scheme are as follows: according to the scheme, the following object element description and analysis are adopted in the three-dimensional finite element model, the relevance quantification of the evaluation index of the influence factor and the stability grade of the surrounding rock is realized, the understanding of the relation between the influence factor and the stability of the surrounding rock can be improved, the accurate evaluation of the stability degree of the surrounding rock is facilitated, and the reasonable selection of the reinforcing measures of the tunnel face of the surrounding rock is better facilitated.
In one embodiment, in step S300, the preset algorithm for predicting the sticking requirement strength is as follows:
Figure DEST_PATH_IMAGE083
in the above formula, the first and second carbon atoms are,
Figure DEST_PATH_IMAGE084
is shown as
Figure DEST_PATH_IMAGE085
The required strength of the wall rock bonding of the finite element units is high;
Figure DEST_PATH_IMAGE086
is shown as
Figure 496613DEST_PATH_IMAGE085
The cohesive force of the surrounding rock of the finite element units;
Figure DEST_PATH_IMAGE087
is shown as
Figure DEST_PATH_IMAGE088
The surrounding rock normal stress of the finite element units;
Figure DEST_PATH_IMAGE089
representing a tangent function;
Figure DEST_PATH_IMAGE090
is shown as
Figure 524088DEST_PATH_IMAGE085
And (3) the internal friction angle of the surrounding rock normal stress of the finite element unit.
The working principle and the beneficial effects of the technical scheme are as follows: according to the scheme, the required bonding strength of the surrounding rock face is calculated, the required bonding strength of the surrounding rock is used as prediction data and is used as a quantitative analysis basis for selecting the reinforcement measures subsequently, the effectiveness of reinforcement measure selection can be guaranteed, and the situation that the stability of the reinforced surrounding rock cannot be guaranteed is avoided.
In one embodiment, if the selected surrounding rock face reinforcement measure adopts an anchor rod and grouting matching mode, calculating the slurry viscosity of grouting by adopting the following formula:
Figure DEST_PATH_IMAGE091
in the above formula, the first and second carbon atoms are,
Figure DEST_PATH_IMAGE092
viscosity values representing the time-varying nature of the slurry;
Figure DEST_PATH_IMAGE093
represents the initial viscosity value of the slurry itself;
Figure DEST_PATH_IMAGE094
the viscosity time-varying coefficient of the slurry is expressed, and the values of the slurry with different water-cement ratios are different and are measured by a viscoplasticity test;
Figure DEST_PATH_IMAGE095
the time used in the actual grouting process is represented and belongs to a preset value;
and taking the calculated viscosity of the slurry as data for carrying out effectiveness analysis of the reinforcing measure.
The working principle and the beneficial effects of the technical scheme are as follows: the formula of the relation between the grouting time and the slurry viscosity is utilized, the time change of the grouting process can be intuitively and quantitatively reflected, the effectiveness analysis of grouting reinforcement is carried out, in practice, the method can be used for improving the grouting efficiency by changing the water-cement ratio of the slurry, and the grouting efficiency is improved under the condition of ensuring the slurry viscosity and the grouting effect.
In one embodiment, the following formula is used to calculate the maximum distance of slurry diffusion for evaluating a slurry slip:
Figure DEST_PATH_IMAGE096
in the above formula, the first and second carbon atoms are,
Figure DEST_PATH_IMAGE097
represents the maximum distance of slurry diffusion;
Figure DEST_PATH_IMAGE098
represents the grouting pressure;
Figure DEST_PATH_IMAGE099
is the height of the surrounding rock fracture;
Figure DEST_PATH_IMAGE100
representing the flow rate of the slurry;
Figure DEST_PATH_IMAGE101
representing the width of the surrounding rock fracture;
Figure DEST_PATH_IMAGE102
representing the consistency factor of the slurry;
Figure DEST_PATH_IMAGE103
in the form of a slurryWater to cement ratio.
The working principle and the beneficial effects of the technical scheme are as follows: the grouting maximum distance of slurry diffusion of grouting is calculated through real-time detection grouting pressure, the influence range of grouting is quantized, grouting reinforcement requirements formed by analyzing surrounding rock data are combined, grouting intervals and arrangement modes can be obtained through analysis, and therefore the optimized grouting scheme is selected, efficiency is improved, and grouting cost is saved.
In one embodiment, a BP neural network model is arranged in the three-dimensional finite element model, and the BP neural network model is used for evaluating the stability of the surrounding rock tunnel face; the BP neural network model is obtained through the following method:
constructing a BP neural network, determining a plurality of influence factors of the stability of the tunnel face of the surrounding rock and corresponding evaluation indexes thereof, setting weights for the influence factors, wherein the sum of the weights of the influence factors is 1, performing weight distribution on the evaluation indexes of the influence factors, acquiring data of the influence factors serving as input learning samples, taking the acquired actual stable conditions of the tunnel faces as a target vector, representing the connection of each processing unit in the BP neural network by the weights, correcting the weights by errors between the actual output of the data of the influence factors after sample learning and the target vector, transmitting the change of each weight and deviation in direct proportion to the influence of network errors to each layer of the BP neural network in a back propagation manner, obtaining the success probability and the assembly work probability of each stable level by evaluation calculation, and taking the assembly work probability not less than 80 percent as an expected target, and reversely transmitting the error signals along the original connecting path through the network to modify the weight of each layer of neuron until reaching an expected target, thereby obtaining the BP neural network model.
The working principle and the beneficial effects of the technical scheme are as follows: according to the scheme, the BP neural network model is arranged in the three-dimensional finite element model to evaluate the stability of the surrounding rock face, so that the accuracy of evaluation of the stability of the surrounding rock face is improved; the acquisition of the BP neural network model is realized by determining the influence factors of the stability of the surrounding rock face and the corresponding evaluation indexes thereof, setting the weight, and performing the basic formation of weight optimization adjustment through sample learning and expectation comparison on the basis that the weight value represents the initial BP neural network of the connection of each processing unit, thereby ensuring the applicability and reliability of the BP neural network model to the stability evaluation of the surrounding rock face and improving the reliability of the evaluation result.
In one embodiment, in step S200, an imaging technique is further used for imaging the reinforced surrounding rock working face to obtain a working face image, and the working face image is preprocessed; filtering the preprocessed palm surface image by adopting the following formula:
Figure DEST_PATH_IMAGE104
in the above formula, the first and second carbon atoms are,
Figure DEST_PATH_IMAGE105
representing a filter function;
Figure DEST_PATH_IMAGE106
，
Figure DEST_PATH_IMAGE107
respectively representing the space translation amount on an x axis and a y axis;
Figure DEST_PATH_IMAGE108
the value of the envelope of the function is represented,
Figure DEST_PATH_IMAGE109
，
Figure DEST_PATH_IMAGE110
representing the ratio between the center frequency and the bandwidth,
Figure DEST_PATH_IMAGE111
represents the center frequency;
Figure DEST_PATH_IMAGE112
is the wavelength of a sine wave;
Figure DEST_PATH_IMAGE113
representing the argument of the complex modulated part function;
Figure DEST_PATH_IMAGE114
represents the aspect ratio of a gaussian function;
Figure DEST_PATH_IMAGE115
representing an imaginary symbol;
Figure DEST_PATH_IMAGE116
represents the degree of offset;
and then performing median filtering by adopting the following formula:
Figure DEST_PATH_IMAGE117
in the above formula, the first and second carbon atoms are,
Figure DEST_PATH_IMAGE118
representing the palm surface image after median filtering processing;
Figure DEST_PATH_IMAGE119
representing the palm surface image before median filtering processing;
Figure DEST_PATH_IMAGE120
representing the coordinate values of the palm surface image;
Figure DEST_PATH_IMAGE121
a sliding template representing a 5 x 21 matrix region;
and then, carrying out crack identification on the tunnel face image after median filtering.
The working principle and the beneficial effects of the technical scheme are as follows: the tunnel face image after pretreatment is filtered, the accuracy of identifying the cracks possibly existing in the tunnel surrounding rock tunnel face is improved, the crack identification efficiency is improved, a good foundation is provided for the surrounding rock tunnel face crack inclusion evaluation, the surrounding rock stability evaluation can be achieved by the surrounding rock face crack inclusion, the evaluation is more comprehensive, the accuracy is higher, and the improvement of the crack identification efficiency prevents misjudgment caused by the crack identification error.
In one embodiment, the attribute values of the tunnel face image are extracted, and the surrounding rock stability analysis model calculates the change rate of the tunnel face image by adopting the following formula:
Figure DEST_PATH_IMAGE122
in the above formula, the first and second carbon atoms are,
Figure DEST_PATH_IMAGE123
representing the crack rate of change of the face image;
Figure DEST_PATH_IMAGE124
representing the number of attributes of each palm surface image;
Figure DEST_PATH_IMAGE125
representing the number of the face images saved in front;
Figure DEST_PATH_IMAGE126
is shown as
Figure DEST_PATH_IMAGE127
First of the palm face image
Figure DEST_PATH_IMAGE128
An item attribute value;
Figure DEST_PATH_IMAGE129
is shown as
Figure DEST_PATH_IMAGE130
First of the palm face image
Figure 78216DEST_PATH_IMAGE045
An item attribute value;
Figure DEST_PATH_IMAGE131
is shown as
Figure DEST_PATH_IMAGE132
First of the palm face image
Figure DEST_PATH_IMAGE133
An item attribute value;
and if the crack change rate reaches the change threshold value, indicating that the surrounding rock has instability risk, and sending out warning information of the instability risk of the surrounding rock.
The working principle and the beneficial effects of the technical scheme are as follows: according to the scheme, various attribute values of the face image are extracted, the crack change rate of the face image is calculated according to the formula, and the change of the face image is inevitably brought by the crack change of the surrounding rock, so that the instability risk condition of the surrounding rock can be indirectly reflected through the crack change rate of the face image, the change trend can be known through the analysis of the face images continuously collected at different times, the risk is predicted in advance, the response is made in time, and the risk is eliminated by taking reinforcement measures.
In one embodiment, in step S400, the effectiveness analysis process of the reinforcement measure is as follows:
firstly, calculating the minimum surrounding rock reinforcement thickness of each finite element unit position by adopting the following formula:
Figure DEST_PATH_IMAGE134
in the above formula, the first and second carbon atoms are,
Figure DEST_PATH_IMAGE135
is shown as
Figure DEST_PATH_IMAGE136
Minimum surrounding rock reinforcement thickness of the finite element unit positions;
Figure DEST_PATH_IMAGE137
representing a safety factor;
Figure DEST_PATH_IMAGE138
representing the maximum dimension of the face;
Figure DEST_PATH_IMAGE139
represents the poisson's ratio;
Figure DEST_PATH_IMAGE140
the tensile strength of the reinforced surrounding rock is shown and is measured through tests;
Figure DEST_PATH_IMAGE141
is shown as
Figure 629150DEST_PATH_IMAGE136
Pressure at the location of the finite element;
if the reinforcing thickness of the surrounding rock face is not smaller than the calculated minimum reinforcing thickness of the surrounding rock, the surrounding rock face after the reinforcing measure is implemented meets the stability requirement, otherwise, the reinforcing thickness of the surrounding rock face needs to be increased.
The working principle and the beneficial effects of the technical scheme are as follows: according to the scheme, the minimum surrounding rock reinforcement thickness is calculated through the formula, and the effectiveness of the reinforcement measure is judged according to the comparison condition between the planned or implemented reinforcement measure reinforced surrounding rock tunnel face reinforcement thickness and the calculation result; the tensile strength of the reinforced surrounding rock in the formula is directly related to the characteristics of the surrounding rock and the reinforcing scheme; the formula is simple to calculate and easy to operate, and can correctly guide and implement the reinforcing scheme in construction, so that the stability of the surrounding rock face after reinforcement is improved, evaluation errors are avoided, and safety risks are reduced.
It will be apparent to those skilled in the art that various changes and modifications may be made in the present invention without departing from the spirit and scope of the invention. Thus, if such modifications and variations of the present invention fall within the scope of the claims of the present invention and their equivalents, the present invention is also intended to include such modifications and variations.

Claims (10)

1. A digital data processing method for reinforcing stress strain of a broken soft surrounding rock face is characterized by comprising the following steps:
s100, establishing a three-dimensional model of a broken weak surrounding rock face according to project design, and carrying out virtual finite element segmentation to form a three-dimensional finite element model of the face;
s200, detecting and storing basic data of a surrounding rock face in a section excavation process;
s300, stress-strain analysis is carried out on the face by combining the basic data and the three-dimensional finite element model, and the required bonding strength of the surrounding rock face is predicted by adopting a preset algorithm;
s400, selecting a surrounding rock face reinforcing measure according to the bonding required strength, and introducing the reinforcing measure into a three-dimensional finite element model for effectiveness analysis.
2. The digital data processing method for reinforcing stress-strain of the rock face of the broken weak surrounding rock as claimed in claim 1, wherein in step S100, the three-dimensional finite element model is described by using the following object elements:
Figure DEST_PATH_IMAGE001
in the above formula, the first and second carbon atoms are,
Figure DEST_PATH_IMAGE002
representing an object element;
Figure DEST_PATH_IMAGE003
a presentation evaluation unit;
Figure DEST_PATH_IMAGE004
to indicate the evaluation unit
Figure DEST_PATH_IMAGE005
Item influencing factors;
Figure DEST_PATH_IMAGE006
is shown as
Figure 787960DEST_PATH_IMAGE005
A magnitude domain of term influence factor number quantization;
Figure DEST_PATH_IMAGE007
representing the number of influencing factors;
determining grade division of the stability of the surrounding rock, and calculating the material elements according to the following formula
Figure 970680DEST_PATH_IMAGE002
Degree of association with the surrounding rock stability grade H:
Figure DEST_PATH_IMAGE008
in the above formula, the first and second carbon atoms are,
Figure DEST_PATH_IMAGE009
representing an object
Figure DEST_PATH_IMAGE010
Correlation degree with surrounding rock stability grade H;
Figure DEST_PATH_IMAGE011
is shown as
Figure DEST_PATH_IMAGE012
Item influencing factor No
Figure DEST_PATH_IMAGE013
The term index weight coefficient;
Figure DEST_PATH_IMAGE014
representing the relevance of each single evaluation index on each grade of the stability of the surrounding rock;
Figure DEST_PATH_IMAGE015
the number of indices representing each influencing factor;
then according to the object element
Figure 701482DEST_PATH_IMAGE010
Degree of correlation with surrounding rock stability grade H
Figure 831112DEST_PATH_IMAGE009
The results were calculated to evaluate the surrounding rock stability.
3. The digital data processing method for reinforcing stress and strain of the tunnel face of broken soft surrounding rock as claimed in claim 1, wherein in step S300, the preset algorithm for predicting the sticking requirement strength is as follows:
Figure DEST_PATH_IMAGE016
in the above formula, the first and second carbon atoms are,
Figure DEST_PATH_IMAGE017
is shown as
Figure DEST_PATH_IMAGE018
The required strength of the wall rock bonding of the finite element units is high;
Figure DEST_PATH_IMAGE019
is shown as
Figure 192954DEST_PATH_IMAGE018
The cohesive force of the surrounding rock of the finite element units;
Figure DEST_PATH_IMAGE020
is shown as
Figure 953100DEST_PATH_IMAGE018
The surrounding rock normal stress of the finite element units;
Figure DEST_PATH_IMAGE021
representing a tangent function;
Figure DEST_PATH_IMAGE022
is shown as
Figure 892237DEST_PATH_IMAGE018
And (3) the internal friction angle of the surrounding rock normal stress of the finite element unit.
4. The digital data processing method for the reinforcing stress strain of the rock face of the broken weak surrounding rock as claimed in claim 1, wherein if the selected reinforcing measure of the rock face of the surrounding rock adopts a mode of matching an anchor rod with grouting, the slurry viscosity of the grouting is calculated by adopting the following formula:
Figure DEST_PATH_IMAGE023
in the above formula, the first and second carbon atoms are,
Figure DEST_PATH_IMAGE024
viscosity values representing the time-varying nature of the slurry;
Figure DEST_PATH_IMAGE025
represents the initial viscosity value of the slurry itself;
Figure DEST_PATH_IMAGE026
the viscosity time-varying coefficient of the slurry is expressed, and the values of the slurry with different water-cement ratios are different and are measured by a viscoplasticity test;
Figure DEST_PATH_IMAGE027
the time used in the actual grouting process is represented and belongs to a preset value;
and taking the calculated viscosity of the slurry as data for carrying out effectiveness analysis of the reinforcing measure.
5. The digital data processing method for the reinforcing stress strain of the tunnel face of the broken weak surrounding rock as claimed in claim 4, wherein the maximum distance of slurry diffusion of the evaluation grouting is calculated by adopting the following formula:
Figure DEST_PATH_IMAGE028
in the above formula, the first and second carbon atoms are,
Figure DEST_PATH_IMAGE029
represents the maximum distance of slurry diffusion;
Figure DEST_PATH_IMAGE030
represents the grouting pressure;
Figure DEST_PATH_IMAGE031
is the height of the surrounding rock fracture;
Figure DEST_PATH_IMAGE032
representing the flow rate of the slurry;
Figure DEST_PATH_IMAGE033
representing the width of the surrounding rock fracture;
Figure DEST_PATH_IMAGE034
representing the consistency factor of the slurry;
Figure DEST_PATH_IMAGE035
is the water-cement ratio of the slurry.
6. The digital data processing method for the reinforcing stress strain of the rock face of the broken weak surrounding rock as claimed in claim 1, wherein a BP neural network model is arranged in the three-dimensional finite element model, and the BP neural network model is used for evaluating the stability of the rock face of the surrounding rock; the BP neural network model is obtained through the following method:
constructing a BP neural network, determining a plurality of influence factors of the stability of the tunnel face of the surrounding rock and corresponding evaluation indexes thereof, setting weights for the influence factors, wherein the sum of the weights of the influence factors is 1, performing weight distribution on the evaluation indexes of the influence factors, acquiring data of the influence factors serving as input learning samples, taking the acquired actual stable conditions of the tunnel faces as a target vector, representing the connection of each processing unit in the BP neural network by the weights, correcting the weights by errors between the actual output of the data of the influence factors after sample learning and the target vector, transmitting the change of each weight and deviation in direct proportion to the influence of network errors to each layer of the BP neural network in a back propagation manner, obtaining the success probability and the assembly work probability of each stable level by evaluation calculation, and taking the assembly work probability not less than 80 percent as an expected target, and reversely transmitting the error signals along the original connecting path through the network to modify the weight of each layer of neuron until reaching an expected target, thereby obtaining the BP neural network model.
7. The digital data processing method for reinforcing stress-strain of the rock face of the broken weak surrounding rock as claimed in claim 1, wherein in step S200, the image of the rock face is obtained by imaging the reinforced rock face of the surrounding rock by an imaging technique, and the image of the rock face is preprocessed.
8. The digital data processing method for the reinforcing stress strain of the rock face of the broken soft surrounding rock as claimed in claim 7, wherein the preprocessed rock face image is filtered by adopting the following formula:
Figure DEST_PATH_IMAGE036
in the above formula, the first and second carbon atoms are,
Figure DEST_PATH_IMAGE037
representing a filter function;
Figure DEST_PATH_IMAGE038
，
Figure DEST_PATH_IMAGE039
respectively representing the space translation amount on an x axis and a y axis;
Figure DEST_PATH_IMAGE040
the value of the envelope of the function is represented,
Figure DEST_PATH_IMAGE041
，
Figure DEST_PATH_IMAGE042
representing the ratio between the center frequency and the bandwidth,
Figure DEST_PATH_IMAGE043
represents the center frequency;
Figure DEST_PATH_IMAGE044
is the wavelength of a sine wave;
Figure DEST_PATH_IMAGE045
representing the argument of the complex modulated part function;
Figure DEST_PATH_IMAGE046
represents the aspect ratio of a gaussian function;
Figure DEST_PATH_IMAGE047
representing an imaginary symbol;
Figure DEST_PATH_IMAGE048
represents the degree of offset;
and then performing median filtering by adopting the following formula:
Figure DEST_PATH_IMAGE049
in the above formula, the first and second carbon atoms are,
Figure DEST_PATH_IMAGE050
representing the palm surface image after median filtering processing;
Figure DEST_PATH_IMAGE051
representing the palm surface image before median filtering processing;
Figure DEST_PATH_IMAGE052
representing the coordinate values of the palm surface image;
Figure DEST_PATH_IMAGE053
a sliding template representing a 5 x 21 matrix region;
and then, carrying out crack identification on the tunnel face image after median filtering.
9. The digital data processing method for the reinforcing stress strain of the working face of the broken weak surrounding rock as claimed in claim 8, wherein various attribute values of the working face image are extracted, and the surrounding rock stability analysis model calculates the change rate of the working face image by adopting the following formula:
Figure DEST_PATH_IMAGE054
in the above formula, the first and second carbon atoms are,
Figure DEST_PATH_IMAGE055
representing the crack rate of change of the face image;
Figure DEST_PATH_IMAGE056
representing the number of attributes of each palm surface image;
Figure DEST_PATH_IMAGE057
representing the number of the face images saved in front;
Figure DEST_PATH_IMAGE058
is shown as
Figure DEST_PATH_IMAGE059
First of the palm face image
Figure DEST_PATH_IMAGE060
An item attribute value;
Figure DEST_PATH_IMAGE061
is shown as
Figure DEST_PATH_IMAGE062
First of the palm face image
Figure DEST_PATH_IMAGE063
An item attribute value;
Figure DEST_PATH_IMAGE064
is shown as
Figure DEST_PATH_IMAGE065
First of the palm face image
Figure DEST_PATH_IMAGE066
An item attribute value;
and if the crack change rate reaches the change threshold value, indicating that the surrounding rock has instability risk, and sending out warning information of the instability risk of the surrounding rock.
10. The digital data processing method for reinforcing stress strain of the tunnel face of the broken weak surrounding rock as claimed in claim 1, wherein in the step S400, the effectiveness analysis process of the reinforcing measure is as follows:
firstly, calculating the minimum surrounding rock reinforcement thickness of each finite element unit position by adopting the following formula:
Figure DEST_PATH_IMAGE067
in the above formula, the first and second carbon atoms are,
Figure DEST_PATH_IMAGE068
is shown as
Figure DEST_PATH_IMAGE069
Minimum surrounding rock reinforcement thickness of the finite element unit positions;
Figure DEST_PATH_IMAGE070
representing a safety factor;
Figure DEST_PATH_IMAGE071
representing the maximum dimension of the face;
Figure DEST_PATH_IMAGE072
represents the poisson's ratio;
Figure DEST_PATH_IMAGE073
the tensile strength of the reinforced surrounding rock is shown and is measured through tests;
Figure DEST_PATH_IMAGE074
is shown as
Figure DEST_PATH_IMAGE075
Pressure at the location of the finite element;
if the reinforcing thickness of the surrounding rock face is not smaller than the calculated minimum reinforcing thickness of the surrounding rock, the surrounding rock face after the reinforcing measure is implemented meets the stability requirement, otherwise, the reinforcing thickness of the surrounding rock face needs to be increased.
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