CN117870609A - Soft rock tunnel face deformation monitoring method based on incomplete arch effect - Google Patents
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Abstract
The invention provides a soft rock tunnel face deformation monitoring method based on incomplete arch effect, which comprises the following steps: confirming a soft rock tunnel face to be monitored, acquiring geological information of the soft rock tunnel face to be monitored, analyzing rock mass evaluation indexes of the soft rock tunnel face through the geological information, and arranging a plurality of sensors at positions where the rock mass evaluation indexes reach preset standards; obtaining deformation data, and analyzing the deformation data to obtain deformation characteristics of the tunnel face of the soft rock tunnel to be monitored; evaluating deformation conditions such as deformation trend, deformation range and the like of the tunnel face of the soft rock tunnel according to the obtained deformation characteristics; according to the evaluation result, pre-warning the deformation of the soft rock tunnel face to be monitored, and carrying out mode demonstration on the progress of the deformation to obtain a prediction result of deformation in a future period of time; and giving out corresponding measures to repair or support according to the prediction result. The invention provides guarantee for engineering safety and scientific decision basis.
Description
Technical Field
The invention relates to the technical field of soft rock tunnel face measurement, in particular to a soft rock tunnel face deformation monitoring method based on an incomplete arch effect.
Background
Soft rock tunnels refer to tunnels composed of weak, deformable rocks. Deformation and damage are easy to occur in the construction and operation process due to poor strength and stability of the soft rock, so that deformation monitoring is needed. The incomplete arch effect means that after a soft rock tunnel is excavated, a complete arch structure cannot be formed due to poor strength of a rock mass, but local collapse or deformation occurs at an arch springing, and the deformation can cause instability and damage of the tunnel, so that monitoring and control are required. However, the monitoring means in the prior art have simpler functions, resulting in poor monitoring accuracy.
First, application number: CN202110121749.1 discloses a construction method for controlling large deformation of tunnel face of soft rock tunnel, comprising the following steps: step one, excavating a concave tunnel face; step two, installing an anchor rod; and step three, installing a flexible supporting mechanism. Although the method can prevent and treat large tunnel face deformation disasters in the construction process of the soft rock tunnel, the safety in the tunnel construction process is guaranteed, the construction period is shortened, and the design level and construction technology of the soft rock tunnel are further improved; however, the monitoring and preventing effects cannot be achieved, so that the error of monitoring the deformation of the tunnel face of the soft rock tunnel is large.
Second prior art, application number: CN2016611255212. X discloses a method for monitoring tunnel face deformation along tunnel axial direction, belonging to tunnel excavation monitoring method field, the method for monitoring tunnel face deformation along tunnel axial direction comprises installing reflecting sheets on tunnel face of tunnel excavation, measuring initial distance from each reflecting sheet to laser range finder assembly, then periodically measuring real-time distance from each reflecting sheet to laser range finder assembly, wherein difference between real-time distance and initial distance is deformation of tunnel face along tunnel axial direction, although the deformation size and change trend of tunnel face along tunnel axial direction can be judged according to tunnel face deformation along tunnel axial direction; however, the method is not suitable for deformation monitoring after soft rock tunnel excavation, and the construction quality and safety cannot be effectively improved.
Third, application number: CN202311217450.1 discloses a soft rock tunnel deformation monitoring method, which comprises the following steps: calculating the radius of a plastic region of the rock mass; determining the length of a single-point displacement meter according to the radius of the plastic region; arranging a plurality of single-point displacement meters and a plurality of total stations on the surrounding rock of the tunnel, arranging the anchor head end of the single-point displacement meters on the boundary between an elastic area and a plastic area in the surrounding rock of the tunnel, and arranging the tail end of the single-point displacement meters on the boundary of the free surface of the tunnel; acquiring displacement data of surrounding rock of the tunnel in real time through a single-point displacement meter; collecting settlement data of surrounding rocks of a tunnel in real time through a total station; and carrying out data analysis on the acquired displacement data and settlement data, and determining the deformation of the tunnel according to analysis results. Although a tunnel deformation monitoring method taking the displacement inside the surrounding rock as a main part and vault settlement deformation as an auxiliary part is adopted, the surrounding rock deformation is continuously monitored in real time, and is uploaded to a cloud for data processing and analysis through a data transmission cable, so that the monitoring efficiency and accuracy are improved; however, deformation monitoring under the incomplete arch effect cannot be realized, resulting in poor accuracy of analysis results and incomplete monitoring data.
The invention provides a soft rock tunnel face deformation monitoring method based on an incomplete arch effect, which is used for monitoring and evaluating deformation conditions of the soft rock tunnel face by installing a sensor, collecting deformation data and processing and analyzing.
Disclosure of Invention
In order to solve the technical problems, the invention provides a soft rock tunnel face deformation monitoring method based on a non-complete arch effect, which comprises the following steps:
confirming a soft rock tunnel face to be monitored, acquiring geological information of the soft rock tunnel face to be monitored, analyzing rock mass evaluation indexes of the soft rock tunnel face through the geological information, and arranging a plurality of sensors for acquiring strain and displacement deformation data at the position where the rock mass evaluation indexes reach a preset standard;
obtaining deformation data, analyzing the deformation data to obtain deformation quantity and deformation rate deformation characteristics of the soft rock tunnel face to be monitored, and evaluating deformation trend and deformation range deformation conditions of the soft rock tunnel face according to the obtained deformation characteristics;
According to the evaluation result, pre-warning the deformation of the soft rock tunnel face to be monitored, and carrying out mode demonstration on the progress of the deformation to obtain a prediction result of deformation in a future period of time; and giving out corresponding measures to repair or support according to the prediction result.
Optionally, the process of obtaining the position of the layout sensor includes the following steps:
taking the content of the geological information as a target feature, configuring acquisition programs of different contents; deleting the characteristic values irrelevant to the target characteristics in the process of acquiring the target characteristics to obtain the characteristic values corresponding to the target characteristics, and collecting the target characteristics according to different content attributes; wherein the target features comprise lithology, rock strength, fracture conditions, and hydrogeology;
carrying out format standardization on the collected target features, and inputting a corresponding rock mass evaluation index analysis model to realize analysis of geological information, so as to obtain rock mass evaluation index analysis results related to the geological information;
comparing the rock mass index evaluation analysis result with a corresponding preset standard, dividing the rock mass index evaluation analysis result into a position where the sensor can be arranged and a position where the sensor cannot be arranged, wherein the position where the sensor can be arranged is reached to the preset standard, and the position where the sensor cannot be arranged is not reached to the preset standard; the position is the labeling coordinate of the position for acquiring the geological information in the geological profile.
Optionally, the rock mass evaluation index comprises: rock mass strength: compressive strength, tensile strength and shear strength of the rock mass; rock mass structure: the joint development condition, the joint spacing and the joint inclination angle of the rock mass; rock mass water permeability: permeability, porosity, and fracture characteristics of the rock mass; rock mass stability: rock inclination angle, fault condition and rock deformation of rock mass; weather resistance of rock mass: durability, degree of weathering, and mineral composition of the rock mass.
Optionally, the process of obtaining the deformation characteristic includes the following steps:
acquiring deformation data acquired by a plurality of sensors, filtering the acquired strain and displacement data by adopting a digital filter, aligning the strain data and the displacement data in the deformation data in time, and smoothing the aligned strain data and displacement data by adopting a weighted moving average method to obtain new strain data and displacement data;
determining parameters of a fitting curve by minimizing residual errors between actual strain data points and displacement data points and the fitting curve, and obtaining the fitting curve according to the determined curve parameters; selecting an elastoplastic constitutive model to describe the characteristics of a deformation curve; obtaining a deformation curve, namely an accumulated value of displacement, by integrating the fitted curve;
According to the strain data, corresponding displacement data is obtained through the strain-displacement relation, and the displacement data is integrated to obtain deformation, namely an accumulated value of displacement; deriving deformation data by using a difference method to obtain a deformation rate, namely a displacement change rate; and analyzing the obtained deformation and deformation rate to obtain deformation characteristics, and observing the variation trend and the periodicity characteristics of the deformation and the deformation rate.
Optionally, the process of integrating the fitted curve to obtain the deformation curve includes the following steps:
expressing the fitting curve as a polynomial function, determining the discretization interval of the fitting curve, dividing the whole fitting curve into a plurality of small sections according to the polynomial function form and the discretization interval, and taking a plurality of discrete points in each small section for discretization;
in each small section, calculating the slope of a fitted curve on the small section by adopting a central difference method, namely the derivative of the curve, discretizing the small section on the abscissa to obtain a series of abscissa points, calculating the ordinate value corresponding to each abscissa point according to a polynomial function of the fitted curve, and calculating the slope of each abscissa point by adopting the central difference method; repeatedly calculating the slope on each small segment; multiplying the slope on each small segment by the time step to obtain the displacement increment on the small segment;
And receiving the displacement increment value output by the increment multiple unit, accumulating again by using the multiple time every multiple time of the input increment period to generate an accumulation result value, and accumulating the displacement increment to obtain a deformation curve, namely the accumulation value of the displacement.
Optionally, the calculation formula of the central difference method is as follows: slope = (f (x+h) -f (x-h))/(2 h), where f (x) is the ordinate value of the fitted curve on the abscissa x and h is the discretized step size.
Optionally, the process of obtaining the deformation characteristic according to the deformation amount and the deformation rate comprises the following steps:
analyzing the obtained deformation and deformation rate, calculating the average value, standard deviation, maximum value and minimum value statistical indexes of the deformation and the deformation rate, drawing a scatter diagram of the deformation and the deformation rate according to the statistical indexes, and observing the change trend, the distribution condition and the abnormal value;
periodically analyzing the scatter diagram of the deformation quantity and the deformation rate, collecting time domain data of the deformation quantity or the deformation rate, namely continuous data in a certain time range, discretizing the time domain data, and converting the continuous data into discrete data;
and visually displaying the periodic characteristics and the change trend of the deformation quantity and the deformation rate, and displaying the periodic characteristics and the change trend of the deformation quantity and the deformation rate by using a histogram.
Optionally, fourier transforming the discrete data, transforming the time domain data into frequency domain data, performing amplitude spectrum analysis on the frequency domain data after fourier transforming to obtain amplitude information of different frequency components, namely the contribution degree of each frequency component, identifying the periodic variation by observing the amplitude spectrum analysis result, and determining the main period or frequency component of the deformation or deformation rate according to the amplitude spectrum analysis result by a larger amplitude in the frequency domain to obtain the periodic characteristic and the variation trend of the deformation or deformation rate.
Optionally, the process of evaluating the deformation condition includes the following steps:
acquiring evaluation indexes for evaluating deformation conditions of the tunnel face of the soft rock tunnel, dividing the types of evaluation models corresponding to the tunnel face of the soft rock tunnel according to deformation trends and deformation ranges, respectively establishing a plurality of evaluation models according to the evaluation indexes, the deformation trends and the deformation ranges, and modifying a plurality of configuration parameters of the evaluation models according to the types;
receiving the obtained deformation characteristics, initiating an evaluation request, obtaining configuration parameters of an evaluation model corresponding to the deformation characteristics, inputting the deformation characteristics into the evaluation model for evaluation of deformation trend and deformation range deformation conditions, and sending information with evaluation index results;
And sending information, wherein the information comprises an evaluation index, an evaluation model, configuration parameters and an evaluation result, and simultaneously, independently listing the result which does not accord with the evaluation index.
Optionally, the process of respectively establishing a plurality of evaluation models according to the evaluation index, the deformation trend and the deformation range includes the following steps:
generating a training set according to a plurality of historical deformation trends and deformation ranges, setting the types of the evaluation models according to the deformation trends and the deformation ranges, performing fitting calculation on the evaluation indexes by adopting the deformation trends and the deformation ranges, and verifying the types of the evaluation models;
creating a plurality of structural trees of deformation trends and deformation ranges, building corresponding evaluation models, acquiring evaluation parameters of the evaluation models, training the evaluation models based on the evaluation parameters, inputting one evaluation parameter of the plurality of evaluation parameters as a variable by the evaluation models, taking other evaluation parameters as constants, and obtaining evaluation results of the deformation trends and the deformation ranges based on the evaluation parameters which are input as the variable;
setting the adjustment amounts of the deformation trend and the deformation range, inputting the real-time deformation trend and the real-time deformation range into an evaluation model, acquiring the adjustment amounts, the change results of the deformation trend and the deformation range according to the evaluation model, respectively evaluating the change results, and outputting corresponding evaluation results.
Firstly, confirming a soft rock tunnel face to be monitored, acquiring geological information of the soft rock tunnel face to be monitored, analyzing rock mass evaluation indexes of the soft rock tunnel face through the geological information, and arranging a plurality of sensors for acquiring deformation data such as strain, displacement and the like at the position where the rock mass evaluation indexes reach a preset standard; secondly, obtaining deformation data, analyzing the deformation data to obtain deformation characteristics of the soft rock tunnel face to be monitored, wherein the deformation characteristics comprise: deformation amount, deformation rate, etc.; evaluating deformation conditions such as deformation trend, deformation range and the like of the tunnel face of the soft rock tunnel according to the obtained deformation characteristics; finally, pre-warning is carried out on the deformation of the soft rock tunnel face to be monitored according to the evaluation result, and mode demonstration is carried out on the progress of the deformation to obtain a prediction result of deformation in a future period of time; corresponding measures are given according to the prediction result to repair or support; the scheme monitors and pre-warns the deformation of the tunnel face of the soft rock tunnel, and performs early preparation of repair or supporting measures according to the prediction result. The specific meaning is as follows: security assessment: the stability and the safety of the face of the soft rock tunnel can be evaluated through analysis of rock mass evaluation indexes of the face of the soft rock tunnel, and a reference is provided for subsequent engineering. Deformation characteristic analysis: by analyzing the deformation data, the deformation characteristics of the soft rock tunnel face, including deformation amount, deformation rate and the like, can be known, and a basis is provided for subsequent prediction and evaluation. Deformation trend evaluation: according to the deformation characteristics, the deformation trend and the deformation range of the tunnel face of the soft rock tunnel can be evaluated, the deformation condition can be found timely, the deformation trend of a period of time in the future can be predicted, and early warning can be provided for engineering safety. Repair or support decision: according to the evaluation result and the prediction result, corresponding repair or support measures can be formulated, the preparation is advanced, and accidents and engineering delays caused by deformation are avoided.
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 thereof as well as the appended drawings.
The technical scheme of the invention is further described in detail through the drawings and the embodiments.
Drawings
The accompanying drawings are included to provide a further understanding of the invention and are incorporated in and constitute a part of this specification, illustrate the invention and together with the embodiments of the invention, serve to explain the invention. In the drawings:
FIG. 1 is a flow chart of a soft rock tunnel face deformation monitoring method based on incomplete arch effect in embodiment 1 of the present invention;
FIG. 2 is a diagram showing the process of obtaining the position of the sensor in the embodiment 2 of the present invention;
FIG. 3 is a diagram showing the process of obtaining the deformation characteristic in embodiment 3 of the present invention;
FIG. 4 is a diagram showing the process of integrating the fitted curve to obtain the deformed curve in example 4 of the present invention;
FIG. 5 is a process diagram showing the deformation characteristics according to the deformation amount and the deformation rate in example 5 of the present invention;
FIG. 6 is a diagram showing the evaluation of deformation in example 6 of the present invention;
FIG. 7 is a process diagram of establishing a plurality of evaluation models according to the evaluation index, the deformation trend and the deformation range in embodiment 7 of the present invention;
FIG. 8 is a process diagram of obtaining configuration parameters of an evaluation model corresponding to deformation characteristics in embodiment 8 of the present invention;
FIG. 9 is a diagram showing the processing procedure of the evaluation result in example 9 of the present invention.
Detailed Description
The preferred embodiments of the present invention will be described below with reference to the accompanying drawings, it being understood that the preferred embodiments described herein are for illustration and explanation of the present invention only, and are not intended to limit the present invention.
The terminology used in the embodiments of the application is for the purpose of describing particular embodiments only and is not intended to be limiting of the embodiments of the application. As used in the examples and the appended claims, the singular forms "a," "an," and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It should also be understood that the term "and/or" as used herein refers to and encompasses any or all possible combinations of one or more of the associated listed items.
When the following description refers to the accompanying drawings, the same numbers in different drawings refer to the same or similar elements, unless otherwise indicated. The implementations described in the following exemplary examples are not representative of all implementations consistent with the present application. Rather, they are merely examples of apparatus and methods consistent with some aspects of the present application as detailed in the accompanying claims. In the description of this application, it should be understood that the terms "first," "second," "third," and the like are used merely to distinguish between similar objects and are not necessarily used to describe a particular order or sequence, nor should they be construed to indicate or imply relative importance. The specific meaning of the terms in this application will be understood by those of ordinary skill in the art as the case may be.
Example 1: as shown in fig. 1, the embodiment of the invention provides a soft rock tunnel face deformation monitoring method based on incomplete arch effect, which comprises the following steps:
s100: confirming a soft rock tunnel face to be monitored, acquiring geological information of the soft rock tunnel face to be monitored, analyzing rock mass evaluation indexes of the soft rock tunnel face through the geological information, and arranging a plurality of sensors for acquiring deformation data such as strain, displacement and the like at the position where the rock mass evaluation indexes reach a preset standard;
s200: obtaining deformation data, analyzing the deformation data to obtain deformation characteristics such as deformation quantity and deformation rate of the soft rock tunnel face to be monitored, and evaluating deformation conditions such as deformation trend and deformation range of the soft rock tunnel face according to the obtained deformation characteristics;
s300: according to the evaluation result, pre-warning the deformation of the soft rock tunnel face to be monitored, and carrying out mode demonstration on the progress of the deformation to obtain a prediction result of deformation in a future period of time; corresponding measures are given according to the prediction result to repair or support;
the working principle and beneficial effects of the technical scheme are as follows: firstly, confirming a soft rock tunnel face to be monitored, acquiring geological information of the soft rock tunnel face to be monitored, analyzing rock mass evaluation indexes of the soft rock tunnel face through the geological information, and arranging a plurality of sensors for acquiring deformation data such as strain, displacement and the like at the position where the rock mass evaluation indexes reach a preset standard; secondly, obtaining deformation data, analyzing the deformation data to obtain deformation characteristics of the soft rock tunnel face to be monitored, wherein the deformation characteristics comprise: deformation amount, deformation rate, etc.; evaluating deformation conditions such as deformation trend, deformation range and the like of the tunnel face of the soft rock tunnel according to the obtained deformation characteristics; finally, pre-warning is carried out on the deformation of the soft rock tunnel face to be monitored according to the evaluation result, and mode demonstration is carried out on the progress of the deformation to obtain a prediction result of deformation in a future period of time; corresponding measures are given according to the prediction result to repair or support; the scheme monitors and pre-warns the deformation of the tunnel face of the soft rock tunnel, and performs early preparation of repair or supporting measures according to the prediction result. The specific meaning is as follows: security assessment: the stability and the safety of the face of the soft rock tunnel can be evaluated through analysis of rock mass evaluation indexes of the face of the soft rock tunnel, and a reference is provided for subsequent engineering. Deformation characteristic analysis: by analyzing the deformation data, the deformation characteristics of the soft rock tunnel face, including deformation amount, deformation rate and the like, can be known, and a basis is provided for subsequent prediction and evaluation. Deformation trend evaluation: according to the deformation characteristics, the deformation trend and the deformation range of the tunnel face of the soft rock tunnel can be evaluated, the deformation condition can be found timely, the deformation trend of a period of time in the future can be predicted, and early warning can be provided for engineering safety. Repair or support decision: according to the evaluation result and the prediction result, corresponding repair or support measures can be formulated, the preparation is advanced, and accidents and engineering delays caused by deformation are avoided.
In summary, the meaning of the embodiment is to provide a means for monitoring and early warning the deformation of the tunnel face of the soft rock tunnel, provide a guarantee for engineering safety, and provide a scientific decision basis at the same time, so as to reduce risk and cost.
According to the embodiment, a large deformation instability mechanism of the tunnel face of the broken area of the tunnel structure is analyzed based on a new method and an incomplete arch effect theory, mechanical parameters of tunnel surrounding rocks are obtained based on a GSI surrounding rock grading system, a three-dimensional numerical model of the tunnel is constructed through FLAC3D, a series of working condition tests are carried out, change characteristics of extrusion displacement and pre-convergence displacement of the tunnel face when the tunnel is under the incomplete arch effect are obtained, and influences of pre-constraint and pre-reinforcement measures of the tunnel face on the stability of a large-section soft rock tunnel are analyzed.
The longitudinal displacement of the advanced core soil at the position close to the tunnel face is obviously larger than that of the deep measuring point, and the influence range of the advanced displacement is about 1.2 times of the hole span. The longitudinal displacement of the advanced core soil suddenly drops at the position about 9m from the tunnel face, which indicates that the joint cracks of the surrounding rock at the position 9m develop, and the maximum disturbance range caused by tunnel excavation is about 0.8 times of hole span. The method is characterized in that glass fiber anchor rods (the length of the anchor rods is 18m, the diameter is 32mm, the distance is 2m multiplied by 2 m) are adopted to pre-reinforce surrounding rocks of a tunnel face, an advance pipe shed (the length of the advance pipe shed is 12m, the length of a steel pipe is 12.5m, the lap joint length is 3.5m, the lap joint angle is 5 DEG) is arranged to pre-restrain a tunnel vault, and reserved beams and pre-reinforcement measures enable stress of the surrounding rocks of the tunnel to develop close to a tunnel contour line, arch formation is carried out close to the tunnel contour line, a plastic area around a tunnel chamber is obviously reduced, and pre-convergence displacement and convergence displacement of the surrounding rocks are limited.
Example 2: as shown in fig. 2, on the basis of embodiment 1, the process for obtaining the position of the layout sensor provided in the embodiment of the present invention includes the following steps:
s101: taking the content of the geological information as a target feature, configuring acquisition programs of different contents; deleting the characteristic values irrelevant to the target characteristics in the process of acquiring the target characteristics to obtain the characteristic values corresponding to the target characteristics, and collecting the target characteristics according to different content attributes; wherein the target features include lithology, rock strength, fracture conditions, hydrogeology, and the like;
s102: carrying out format standardization on the collected target features, and inputting a corresponding rock mass evaluation index analysis model to realize analysis of geological information, so as to obtain rock mass evaluation index analysis results related to the geological information; wherein the rock mass evaluation index comprises: rock mass strength: compressive strength, tensile strength, shear strength, etc. of the rock mass; rock mass structure: the joint development condition, joint spacing, joint inclination angle and the like of the rock mass; rock mass water permeability: permeability, porosity, fracture characteristics, etc. of the rock mass; rock mass stability: rock inclination angle, fault condition, rock deformation and the like of the rock mass; weather resistance of rock mass: durability, weathering degree, mineral composition, etc. of the rock mass;
S103: comparing the rock mass index evaluation analysis result with a corresponding preset standard, dividing the rock mass index evaluation analysis result into a position where the sensor can be arranged and a position where the sensor cannot be arranged, wherein the position where the sensor can be arranged is reached to the preset standard, and the position where the sensor cannot be arranged is not reached to the preset standard; the position is a labeling coordinate of the position for acquiring the geological information in the geological profile;
the working principle and beneficial effects of the technical scheme are as follows: firstly, taking the content of geological information as a target feature, configuring acquisition programs of different contents; deleting the characteristic values irrelevant to the target characteristics in the process of acquiring the target characteristics to obtain the characteristic values corresponding to the target characteristics, and collecting the target characteristics according to different content attributes; wherein the target features include lithology, rock strength, fracture conditions, hydrogeology, and the like; secondly, format standardization is carried out on the collected target features, a corresponding rock mass evaluation index analysis model is input to realize analysis of geological information, and rock mass evaluation index analysis results related to the geological information are obtained; wherein the rock mass evaluation index comprises: rock mass strength: compressive strength, tensile strength, shear strength, etc. of the rock mass; rock mass structure: the joint development condition, joint spacing, joint inclination angle and the like of the rock mass; rock mass water permeability: permeability, porosity, fracture characteristics, etc. of the rock mass; rock mass stability: rock inclination angle, fault condition, rock deformation and the like of the rock mass; weather resistance of rock mass: durability, weathering degree, mineral composition, etc. of the rock mass; finally, comparing the rock mass index evaluation analysis result with a corresponding preset standard, dividing the rock mass index evaluation analysis result into a position where the sensor can be arranged and a position where the sensor cannot be arranged, wherein the position where the sensor can be arranged is reached to the preset standard, and the position where the sensor cannot be arranged is not reached to the preset standard; the position is a labeling coordinate of the position for acquiring the geological information in the geological profile; the scheme is used for analyzing and evaluating the geological information and providing guidance for arranging the sensors. The specific meaning is as follows: the geological information analysis efficiency is improved: by configuring acquisition programs with different contents, data acquisition can be performed aiming at different geological features; in the process of acquiring the target feature, deleting the feature value irrelevant to the target feature can reduce the complexity of data processing and improve the analysis efficiency. Comprehensively evaluating rock mass properties: the collected target features are subjected to format standardization, and are analyzed by using a rock mass evaluation index analysis model, so that the characteristics of rock mass such as lithology, rock mass strength, fracture condition and hydrogeology can be comprehensively evaluated, the properties of the rock mass can be comprehensively known, and scientific basis is provided for subsequent engineering design and construction. Determining a sensor arrangement position: by comparing the rock mass index evaluation analysis result with a preset standard, the positions where the sensors can be arranged and the positions where the sensors cannot be arranged can be divided, the arrangement range of the sensors can be determined, and the effectiveness and the reliability of the sensors are improved. Labeling geological profile coordinates: the position for acquiring the geological information is marked with coordinates in the geological section, so that the distribution condition of the geological information can be intuitively displayed, the geological workers can be helped to read and analyze the geological section, and other related personnel can conveniently review and use the geological section.
In summary, the meaning of the embodiment is to improve the analysis efficiency of the geological information, comprehensively evaluate the rock mass property, determine the sensor layout position, and intuitively display the distribution situation of the geological information in the geological section, which is beneficial to scientifically performing engineering design and construction and improving the safety and reliability of engineering.
Example 3: as shown in fig. 3, on the basis of embodiment 1, the process for obtaining the deformation characteristics provided in the embodiment of the present invention includes the following steps:
s201: acquiring deformation data acquired by a plurality of sensors, filtering the acquired strain and displacement data by adopting a digital filter, aligning the strain data and the displacement data in the deformation data in time, and smoothing the aligned strain data and displacement data by adopting a weighted moving average method to obtain new strain data and displacement data; the smoothing formula adopting the weighted moving average method is as follows:
wherein,strain data indicative of time t after smoothing, < ->Indicating the set weight value of the weight,i=；/>indicating time of dayt+iIs a raw strain data of (1);displacement data indicative of time t after smoothing, < ->Indicating the set weight value of the weight,j=；/>indicating time of day t+jIs the original displacement data of the (a);
s202: determining parameters of a fitting curve by minimizing residual errors between actual strain data points and displacement data points and the fitting curve, and obtaining the fitting curve according to the determined curve parameters; selecting an elastoplastic constitutive model to describe the characteristics of a deformation curve; obtaining a deformation curve, namely an accumulated value of displacement, by integrating the fitted curve;
s203: according to the strain data, corresponding displacement data is obtained through the strain-displacement relation, and the displacement data is integrated to obtain deformation, namely an accumulated value of displacement; deriving deformation data by using a difference method to obtain a deformation rate, namely a displacement change rate; analyzing the obtained deformation and deformation rate to obtain deformation characteristics, and observing the characteristics of the deformation and deformation rate such as variation trend and periodicity;
the working principle and beneficial effects of the technical scheme are as follows: firstly, acquiring deformation data acquired by a plurality of sensors, filtering the acquired strain and displacement data by adopting a digital filter, aligning the strain data and the displacement data in the deformation data in time, and smoothing the aligned strain data and displacement data by adopting a weighted moving average method to obtain new strain data and displacement data; the smoothing formula adopting the weighted moving average method is as follows:
Wherein,strain data indicative of time t after smoothing, < ->Indicating the set weight value of the weight,i=；/>indicating time of dayt+iIs a raw strain data of (1);displacement data indicative of time t after smoothing, < ->Indicating the set weight value of the weight,j=；/>indicating time of dayt+jIs the original displacement data of the (a);
secondly, determining parameters of a fitting curve by minimizing residual errors between actual strain data points and displacement data points and the fitting curve, and obtaining the fitting curve according to the determined curve parameters; selecting an elastoplastic constitutive model to describe the characteristics of a deformation curve; obtaining a deformation curve, namely an accumulated value of displacement, by integrating the fitted curve;
finally, according to the strain data, corresponding displacement data is obtained through the strain-displacement relation, and the displacement data is integrated to obtain the deformation, namely the accumulated value of displacement; deriving deformation data by using a difference method to obtain a deformation rate, namely a displacement change rate; analyzing the obtained deformation and deformation rate to obtain deformation characteristics, and observing the characteristics of the deformation and deformation rate such as variation trend, periodicity and the like; the scheme processes and analyzes the deformation data acquired by the sensor, and extracts the characteristics of the deformation curve. Specifically, the method comprises the following steps: and (3) filtering processing by a digital filter: and filtering the acquired strain and displacement data to remove noise and interference and obtain smoother data, thereby reducing errors of subsequent analysis. Time alignment and smoothing: time alignment is carried out on the strain data and the displacement data after the filtering treatment, and smoothing treatment is carried out on the strain data and the displacement data by a weighted moving average method, so that the smoothed strain data and the smoothed displacement data are obtained; and the weighted moving average method performs weighted average on the data at the current moment and the surrounding moments according to the set weight value so as to reduce the fluctuation of the data and better describe the characteristics of the deformation curve. Curve fitting: parameters of the fitted curve are determined by minimizing the residuals between the actual strain data points and displacement data points and the fitted curve. By selecting a suitable curve model (e.g., an elastoplastic constitutive model), the fitted curve can better characterize the deformation curve. Integrating to obtain a deformation curve: and integrating the fitted curve according to the determined curve parameters to obtain a deformation curve, namely an accumulated value of displacement. The deformation curve can reflect the deformation of the object over time. Strain-displacement relationship: and obtaining corresponding displacement data according to the strain data through a strain-displacement relation. And integrating the displacement data to obtain the deformation, namely the accumulated value of the displacement. The deformation amount can quantify the deformation degree of the object. Deformation characteristic analysis: the deformation rate, i.e., the rate of change of displacement, is obtained by deriving the deformation data using a difference method. The obtained deformation amount and deformation rate are analyzed, and the characteristics of the deformation amount and the deformation rate such as the change trend, the periodicity and the like can be observed, so that the deformation characteristics of the object such as the deformation amplitude, the deformation period and the like are revealed.
In general, the present embodiment can better describe and analyze the characteristics of the deformation curve by performing operations such as filtering, time alignment, smoothing, curve fitting, etc. on the acquired deformation data, thereby providing a basis and basis for further deformation analysis and application.
Example 4: as shown in fig. 4, on the basis of embodiment 3, the process of integrating the fitted curve to obtain the deformation curve provided in the embodiment of the present invention includes the following steps:
s2021: expressing the fitting curve as a polynomial function, determining the discretization interval of the fitting curve, dividing the whole fitting curve into a plurality of small sections according to the polynomial function form and the discretization interval, and taking a plurality of discrete points in each small section for discretization;
s2022: in each small section, calculating the slope of a fitted curve on the small section by adopting a central difference method, namely the derivative of the curve, discretizing the small section on the abscissa to obtain a series of abscissa points, calculating the ordinate value corresponding to each abscissa point according to a polynomial function of the fitted curve, and calculating the slope of each abscissa point by adopting the central difference method, wherein the calculation formula of the central difference method is as follows: slope= (f (x+h) -f (x-h))/(2 h), where f (x) is the ordinate value of the fitted curve on the abscissa x and h is the discretized step size; repeating the steps, calculating the slope on each small segment; multiplying the slope on each small segment by the time step to obtain the displacement increment on the small segment;
S2023: receiving the displacement increment value output by the increment multiple unit, accumulating again by the multiple time every multiple time of the input increment period to generate an accumulation result value, and accumulating the displacement increment to obtain a deformation curve, namely the accumulation value of displacement;
the working principle and beneficial effects of the technical scheme are as follows: in the embodiment, a fitting curve is expressed as a polynomial function, the discretization interval of the fitting curve is determined, the whole fitting curve is divided into a plurality of small sections according to the polynomial function form and the discretization interval, and a plurality of discrete points are taken in each small section for discretization; secondly, calculating the slope of a fitted curve on each small segment by adopting a central difference method, namely the derivative of the curve, discretizing the small segment on the abscissa to obtain a series of abscissa points, calculating the ordinate value corresponding to each abscissa point according to a polynomial function of the fitted curve, and calculating the slope of each abscissa point by adopting the central difference method, wherein the calculation formula of the central difference method is as follows: slope= (f (x+h) -f (x-h))/(2 h), where f (x) is the ordinate value of the fitted curve on the abscissa x and h is the discretized step size; repeating the steps, calculating the slope on each small segment; multiplying the slope on each small segment by the time step to obtain the displacement increment on the small segment; finally, receiving the displacement increment value output by the increment multiple unit, accumulating again by using the multiple time every multiple time of the input increment period to generate an accumulation result value, and accumulating the displacement increment to obtain a deformation curve, namely the accumulation value of displacement; the scheme discretizes the fitted curve and calculates the slope of each small segment so as to better understand the variation trend and characteristics of the fitted curve. Specifically, the discretization divides the fitting curve into a plurality of small sections, and a plurality of discrete points are taken in each small section, so that the behavior of the fitting curve in different sections can be observed more finely; calculating the slope of each small segment to obtain the change condition of the slope of the curve on the small segment, namely the derivative of the fitting curve; the slope reflects the change rate of the curve on the small section, and can help us judge the trend and the change speed of the curve.
Further, the present embodiment can obtain the displacement increment on each small segment by discretizing and calculating the slope. The displacement increment represents the displacement variation of the curve over the small segment, which is the result of the slope multiplied by the time step. The accumulation of the displacement increment can obtain the accumulated value of the displacement, namely a deformation curve; the deformation curve reflects the overall displacement condition of the fitting curve under different time steps, and helps to know the overall deformation characteristics of the fitting curve.
In summary, the discretization and slope calculation scheme of the embodiment is helpful for better understanding and analyzing the variation trend, deformation characteristics and displacement accumulation condition of the fitting curve, and has important significance for researching and applying the related problems of the fitting curve.
Example 5: as shown in fig. 5, on the basis of example 3, the process for obtaining deformation characteristics according to the deformation amount and the deformation rate provided by the embodiment of the present invention includes the following steps:
s2031: analyzing the obtained deformation and deformation rate, calculating statistical indexes such as average value, standard deviation, maximum value and minimum value of the deformation and the deformation rate, drawing a scatter diagram of the deformation and the deformation rate according to the statistical indexes, and observing variation trend, distribution condition, abnormal value and the like;
S2032: periodically analyzing the scatter diagram of the deformation quantity and the deformation rate, collecting time domain data of the deformation quantity or the deformation rate, namely continuous data in a certain time range, discretizing the time domain data, and converting the continuous data into discrete data; performing Fourier transform on the discrete data, converting the time domain data into frequency domain data, performing amplitude spectrum analysis on the frequency domain data after Fourier transform to obtain amplitude information of different frequency components, namely the contribution degree of each frequency component, identifying the periodic variation by observing an amplitude spectrum analysis result, wherein a larger amplitude in the frequency domain generally represents a stronger periodic component, and determining the main period or frequency component of the deformation quantity or deformation rate according to the amplitude spectrum analysis result to obtain the periodic characteristics and the variation trend of the deformation quantity or deformation rate;
s2033: the periodic characteristics and the change trend of the deformation quantity and the deformation rate are visually displayed, and a histogram is used for displaying the periodic characteristics and the change trend of the deformation quantity and the deformation rate;
the working principle and beneficial effects of the technical scheme are as follows: the method comprises the steps of firstly analyzing the obtained deformation and deformation rate, calculating statistical indexes such as average value, standard deviation, maximum value and minimum value of the deformation and the deformation rate, drawing a scatter diagram of the deformation and the deformation rate according to the statistical indexes, and observing variation trend, distribution condition, abnormal value and the like; secondly, periodically analyzing a scatter diagram of the deformation quantity and the deformation rate, collecting time domain data of the deformation quantity or the deformation rate, namely continuous data in a certain time range, discretizing the time domain data, and converting the continuous data into discrete data; performing Fourier transform on the discrete data, converting the time domain data into frequency domain data, performing amplitude spectrum analysis on the frequency domain data after Fourier transform to obtain amplitude information of different frequency components, namely the contribution degree of each frequency component, identifying the periodic variation by observing an amplitude spectrum analysis result, wherein a larger amplitude in the frequency domain generally represents a stronger periodic component, and determining the main period or frequency component of the deformation quantity or deformation rate according to the amplitude spectrum analysis result to obtain the periodic characteristics and the variation trend of the deformation quantity or deformation rate; finally, the periodic characteristics and the change trend of the deformation quantity and the deformation rate are visually displayed, and a histogram is used for displaying the periodic characteristics and the change trend of the deformation quantity and the deformation rate; according to the scheme, through a statistical method and periodic analysis, deep analysis is carried out on the deformation quantity and the deformation rate data, so that more information about deformation characteristics and change trends is obtained. The specific meaning includes: knowing statistical indexes such as average value, standard deviation, maximum value and minimum value can help to more intuitively know the overall level and distribution condition of deformation and deformation rate, and find abnormal values or abnormal fluctuation. By drawing a scatter diagram of the deformation amount and the deformation rate, the change trend and the distribution situation of the deformation amount and the deformation rate can be intuitively observed, and the possible regular change or abnormal situation can be found. The fourier transform can transform the time domain data into frequency domain data, and the periodic components existing in the deformation amount and deformation rate can be identified through amplitude spectrum analysis, so as to help determine the main period or frequency component. The periodic characteristics and the change trend of the deformation quantity and the deformation rate are known, and a basis can be provided for further data analysis and prediction. For example, future deformation conditions can be predicted according to periodic characteristics, or health condition assessment and early warning can be performed according to the change trend. Finally, the periodic characteristics and the change trend are visually displayed, so that the change conditions of the deformation quantity and the deformation rate can be more intuitively transmitted, and decision makers and related personnel can conveniently understand and deal with the deformation quantity and the deformation rate.
Example 6: as shown in fig. 6, on the basis of embodiment 1, the process for evaluating deformation conditions provided in the embodiment of the present invention includes the following steps:
s204: acquiring evaluation indexes for evaluating deformation conditions of the tunnel face of the soft rock tunnel, dividing the types of evaluation models corresponding to the tunnel face of the soft rock tunnel according to deformation trends and deformation ranges, respectively establishing a plurality of evaluation models according to the evaluation indexes, the deformation trends and the deformation ranges, and modifying a plurality of configuration parameters of the evaluation models according to the types;
s205: receiving the obtained deformation characteristics, initiating an evaluation request, obtaining configuration parameters of an evaluation model corresponding to the deformation characteristics, inputting the deformation characteristics into the evaluation model for evaluation of deformation conditions such as deformation trend, deformation range and the like, and sending information with evaluation index results;
s206: transmitting information, wherein the information comprises an evaluation index, an evaluation model, configuration parameters and an evaluation result, and independently listing the result which does not accord with the evaluation index;
the working principle and beneficial effects of the technical scheme are as follows: firstly, acquiring evaluation indexes for evaluating deformation conditions of a soft rock tunnel face, dividing types of evaluation models corresponding to the soft rock tunnel face according to deformation trends and deformation ranges, respectively establishing a plurality of evaluation models according to the evaluation indexes, the deformation trends and the deformation ranges, and modifying a plurality of configuration parameters of the evaluation models according to the types; secondly, receiving the obtained deformation characteristics, initiating an evaluation request, obtaining configuration parameters of an evaluation model corresponding to the deformation characteristics, inputting the deformation characteristics into the evaluation model for evaluation of deformation conditions such as deformation trend, deformation range and the like, and sending information with evaluation index results; finally, information is sent, the information comprises an evaluation index, an evaluation model, configuration parameters and an evaluation result, and the results which do not accord with the evaluation index are listed independently; the deformation condition of the tunnel face of the soft rock tunnel is comprehensively evaluated by the scheme, so that the future deformation trend can be effectively predicted; by acquiring deformation characteristics, establishing a plurality of evaluation models and evaluating according to different evaluation indexes, deformation trends and deformation ranges, accurate deformation condition evaluation results can be provided, engineers and decision makers are helped to know the deformation condition of the tunnel face of the soft rock tunnel, so that corresponding maintenance and repair measures are formulated, and safe and reliable operation of the tunnel is ensured. In addition, the results which do not accord with the evaluation indexes are listed independently, so that engineers can be helped to identify the problem areas, and corresponding measures are taken for repairing so as to further improve the stability and safety of the tunnel; in a word, the method and the device are beneficial to improving accuracy and reliability of deformation evaluation of the tunnel face of the soft rock tunnel, and provide important reference for operation and maintenance of tunnel engineering.
Example 7: as shown in fig. 7, based on embodiment 6, the process of establishing a plurality of evaluation models according to the evaluation index, the deformation trend and the deformation range according to the embodiment of the present invention includes the following steps:
s2041: generating a training set according to a plurality of historical deformation trends and deformation ranges, setting the types of the evaluation models according to the deformation trends and the deformation ranges, performing fitting calculation on the evaluation indexes by adopting the deformation trends and the deformation ranges, and verifying the types of the evaluation models;
s2042: creating a plurality of structural trees of deformation trends and deformation ranges, building corresponding evaluation models, acquiring evaluation parameters of the evaluation models, training the evaluation models based on the evaluation parameters, inputting one evaluation parameter of the plurality of evaluation parameters as a variable by the evaluation models, taking other evaluation parameters as constants, and obtaining evaluation results of the deformation trends and the deformation ranges based on the evaluation parameters which are input as the variable; the evaluation parameters include: deformation trend parameter: including the rate of deformation, the direction of the trend, the period, etc., to describe the overall trend and characteristics of the deformation; deformation range parameters: including amplitude, magnitude, range, etc. of the deformation, parameters describing the magnitude and amplitude of the deformation; environmental condition parameters: environmental conditions such as air temperature, humidity, geological conditions and the like are included, so that the deformation can be influenced, and the deformation is considered in a model; monitoring data parameters: the method comprises the steps of monitoring the position, monitoring time, monitoring frequency and the like of the monitoring points, wherein parameters are used for describing the specific condition of monitoring data and are important for training and verifying an evaluation model;
S2043: setting adjustment amounts of a deformation trend and a deformation range, inputting the real-time deformation trend and the real-time deformation range into an evaluation model, acquiring the adjustment amounts, the change results of the deformation trend and the deformation range according to the evaluation model, respectively evaluating the change results, and outputting corresponding evaluation results;
the working principle and beneficial effects of the technical scheme are as follows: firstly, generating a training set according to a plurality of historical deformation trends and deformation ranges, setting the types of the evaluation models according to the deformation trends and the deformation ranges, carrying out fitting calculation on evaluation indexes by adopting the deformation trends and the deformation ranges, and verifying the types of the evaluation models; secondly, a plurality of structural trees of deformation trends and deformation ranges are created, corresponding evaluation models are built, evaluation parameters of the evaluation models are obtained, the evaluation models are trained based on the evaluation parameters, one evaluation parameter of the plurality of evaluation parameters is used as variable input by the evaluation models, other evaluation parameters are used as constants, and evaluation results of the deformation trends and the deformation ranges are obtained based on the evaluation parameters which are used as variable input; the evaluation parameters include: deformation trend parameter: including the rate of deformation, the direction of the trend, the period, etc., to describe the overall trend and characteristics of the deformation; deformation range parameters: including amplitude, magnitude, range, etc. of the deformation, parameters describing the magnitude and amplitude of the deformation; environmental condition parameters: environmental conditions such as air temperature, humidity, geological conditions and the like are included, so that the deformation can be influenced, and the deformation is considered in a model; monitoring data parameters: the method comprises the steps of monitoring the position, monitoring time, monitoring frequency and the like of the monitoring points, wherein parameters are used for describing the specific condition of monitoring data and are important for training and verifying an evaluation model; setting adjustment amounts of a deformation trend and a deformation range, inputting the real-time deformation trend and the real-time deformation range into an evaluation model, acquiring the adjustment amounts, the change results of the deformation trend and the deformation range according to the evaluation model, respectively evaluating the change results, and outputting corresponding evaluation results; according to the scheme, the deformation of the structure is estimated and predicted by establishing an estimation model and using estimation parameters such as deformation trend, deformation range and the like. The specific meaning is as follows: providing early warning and monitoring: through the evaluation model, the deformation trend and range of the structure can be predicted and monitored, the abnormal deformation of the structure can be found in time, and early warning information is provided so as to take corresponding repair or protection measures and avoid disaster accidents. Optimizing maintenance strategies: through the evaluation result of the evaluation model, the deformation condition of the structure can be known, the maintenance strategy can be formulated in a targeted manner, the maintenance work can be reasonably arranged, the maintenance efficiency can be improved, and the service life of the structure can be prolonged. The safety performance is enhanced: the deformation trend and range of the structure are important indexes of the structural safety performance, the safety state of the structure can be timely judged by evaluating the deformation, and necessary measures are taken to ensure the safety performance of the structure. Support for decision-making: the evaluation results of the deformation trend and the range provided by the evaluation model can provide basis for engineering decision, for example, in engineering design and construction process, the deformation trend and the range can be adjusted and optimized according to the deformation condition of the structure, and the engineering quality is improved.
In a word, the embodiment can provide support for safety monitoring and maintenance decision of the structure by evaluating and predicting the deformation trend and the deformation range, thereby effectively improving the safety performance and the service life of the structure.
Example 8: as shown in fig. 8, on the basis of embodiment 6, the process for obtaining the configuration parameters of the deformation feature corresponding evaluation model provided in the embodiment of the present invention includes the following steps:
s2051: retrieving a trained model file of the evaluation model, determining configuration parameters related to the evaluation model according to the type of the evaluation model and the model file, determining the query sequence of the configuration parameters, querying the setting condition of the configuration parameters according to the query sequence, and transmitting the setting condition to a management database of the evaluation model;
s2052: based on the service request of the deformation characteristic and the current configuration parameters, running an evaluation model corresponding to the service request, wherein the current configuration parameters of the evaluation model running for the first time are initial configuration parameters; monitoring the operation resources of each service in the service request to obtain the current operation data of the service request, comparing the numerical value of the current operation data with a preset operation threshold value, and adjusting the current configuration parameters based on the comparison result to obtain new current configuration parameters;
S2053: acquiring initial configuration parameters and new current configuration parameters, screening out configuration parameters which accord with the operation conditions of the evaluation model, and taking a set of all the configuration parameters which accord with the operation conditions as a target configuration parameter set; the output target configuration parameter set comprises an initial state, a change state or a failure state mark;
the working principle and beneficial effects of the technical scheme are as follows: firstly, a trained model file of an evaluation model is called, configuration parameters related to the evaluation model are determined according to the type of the evaluation model and the model file, the query sequence of the configuration parameters is determined, the setting condition of the configuration parameters is queried according to the query sequence, and the setting condition is transmitted to a management database of the evaluation model; secondly, based on the service request of the deformation characteristic and the current configuration parameters, operating an evaluation model corresponding to the service request, wherein the current configuration parameters of the evaluation model operated for the first time are initial configuration parameters; monitoring the operation resources of each service in the service request to obtain the current operation data of the service request, comparing the numerical value of the current operation data with a preset operation threshold value, and adjusting the current configuration parameters based on the comparison result to obtain new current configuration parameters; finally, acquiring initial configuration parameters and new current configuration parameters, screening out configuration parameters which accord with the operation conditions of the evaluation model, and taking a set of all the configuration parameters which accord with the operation conditions as a target configuration parameter set; the output target configuration parameter set comprises an initial state, a change state or a failure state mark; according to the scheme, the trained model file and the relevant configuration parameters of the evaluation model are retrieved, and the configuration parameters of the evaluation model are dynamically adjusted by combining the deformation characteristics and the current operation data, so that the accuracy and the performance of the evaluation model are improved. The specific meaning is as follows: automatic configuration parameter adjustment: by automatically acquiring and adjusting the configuration parameters of the evaluation model, the requirement of manual intervention is reduced, and the operation and maintenance efficiency is improved. Real-time performance optimization: by monitoring the operation resources and the current operation data of the service request, the configuration parameters of the evaluation model can be adjusted in real time so as to adapt to the performance requirements under different loads and environmental conditions, and the real-time performance of the evaluation model is improved. Efficient configuration parameter screening: according to the configuration parameter set conforming to the operation condition of the evaluation model, the configuration parameters suitable for the current situation can be rapidly screened out, unnecessary configuration parameter adjustment and trial-and-error processes are avoided, and the operation efficiency of the evaluation model is improved. Accuracy of the evaluation model is improved: by dynamically adjusting the configuration parameters, the accuracy of the evaluation model can be optimized according to actual conditions, and the prediction and judgment capabilities of the model are improved.
In summary, the significance of this embodiment is to improve the performance and accuracy of the evaluation model, reduce the need for manual intervention, and improve the operation and maintenance efficiency and the capability of real-time performance optimization.
Example 9: as shown in fig. 9, on the basis of embodiment 1, the processing procedure of the evaluation result provided in the embodiment of the present invention includes the following steps:
s301: establishing an early warning model by utilizing historical data and related deformation monitoring indexes; model training is carried out by utilizing historical data, the parameter machine weight of the early warning model is determined, model verification is carried out by utilizing part of the historical data, and the accuracy and reliability of the model are evaluated; if the prediction effect of the model is not ideal, the model is adjusted and optimized;
s302: performing deformation early warning according to the early warning model and the real-time evaluation result, inputting the evaluation result into the early warning model, and calculating an early warning index through the model; triggering early warning and sending out an alarm when the early warning index exceeds a preset threshold value; according to the change trend of the early warning index, carrying out mode demonstration, and displaying the early warning index in the form of a graph and the like through a visualization technology;
s303: based on the early warning model and historical data, predicting deformation in a future period, predicting the deformation in the future period by using the model, obtaining a deformation trend and a possible change range, and formulating corresponding repair or support measures;
The working principle and beneficial effects of the technical scheme are as follows: firstly, utilizing historical data and related deformation monitoring indexes to establish an early warning model; model training is carried out by utilizing historical data, the parameter machine weight of the early warning model is determined, model verification is carried out by utilizing part of the historical data, and the accuracy and reliability of the model are evaluated; if the prediction effect of the model is not ideal, the model is adjusted and optimized; secondly, carrying out deformation early warning according to the early warning model and the real-time evaluation result, inputting the evaluation result into the early warning model, and calculating an early warning index through the model; triggering early warning and sending out an alarm when the early warning index exceeds a preset threshold value; according to the change trend of the early warning index, carrying out mode demonstration, and displaying the early warning index in the form of a graph and the like through a visualization technology; finally, based on the early warning model and the historical data, predicting the deformation in the future period by using the model, obtaining the trend and the possible change range of the deformation, and formulating corresponding repair or support measures; the deformation condition of the underground unit body is monitored and predicted by establishing the early warning model and evaluating in real time, the potential deformation problem of the underground unit body can be found in time and early warning is carried out, so that corresponding repair or supporting measures are adopted to ensure the stability and safety of the underground unit body; through analysis of the early warning model and the historical data, the deformation trend and the possible change range of the underground unit body in a period of time in the future can be predicted, a basis is provided for formulating a proper repair or support scheme, the monitoring and management efficiency of the underground unit body can be greatly improved, the potential risk is reduced, and the safety of personnel and property is protected.
It will be apparent to those skilled in the art that various modifications and variations can be made to the present invention without departing from the spirit or scope of the invention. Thus, it is intended that the present invention also include such modifications and alterations insofar as they come within the scope of the appended claims or the equivalents thereof.
Claims (7)
1. The soft rock tunnel face deformation monitoring method based on the incomplete arch effect is characterized by comprising the following steps of:
confirming a soft rock tunnel face to be monitored, acquiring geological information of the soft rock tunnel face to be monitored, analyzing rock mass evaluation indexes of the soft rock tunnel face through the geological information, and arranging a plurality of sensors for acquiring strain and displacement deformation data at the position where the rock mass evaluation indexes reach a preset standard;
obtaining deformation data, analyzing the deformation data to obtain deformation quantity and deformation rate deformation characteristics of the soft rock tunnel face to be monitored, and evaluating deformation trend and deformation range deformation conditions of the soft rock tunnel face according to the obtained deformation characteristics;
according to the evaluation result, pre-warning the deformation of the soft rock tunnel face to be monitored, and carrying out mode demonstration on the progress of the deformation to obtain a prediction result of deformation in a future period of time; corresponding measures are given according to the prediction result to repair or support;
The acquisition process of the position of the layout sensor comprises the following steps:
taking the content of the geological information as a target feature, configuring acquisition programs of different contents; deleting the characteristic values irrelevant to the target characteristics in the process of acquiring the target characteristics to obtain the characteristic values corresponding to the target characteristics, and collecting the target characteristics according to different content attributes; wherein the target features comprise lithology, rock strength, fracture conditions, and hydrogeology;
carrying out format standardization on the collected target features, and inputting a corresponding rock mass evaluation index analysis model to realize analysis of geological information, so as to obtain rock mass evaluation index analysis results related to the geological information;
comparing the rock mass index evaluation analysis result with a corresponding preset standard, dividing the rock mass index evaluation analysis result into a position where the sensor can be arranged and a position where the sensor cannot be arranged, wherein the position where the sensor can be arranged is reached to the preset standard, and the position where the sensor cannot be arranged is not reached to the preset standard; the position is a labeling coordinate of the position for acquiring the geological information in the geological profile;
a process for evaluating deformation comprising the steps of:
acquiring evaluation indexes for evaluating deformation conditions of the tunnel face of the soft rock tunnel, dividing the types of evaluation models corresponding to the tunnel face of the soft rock tunnel according to deformation trends and deformation ranges, respectively establishing a plurality of evaluation models according to the evaluation indexes, the deformation trends and the deformation ranges, and modifying a plurality of configuration parameters of the evaluation models according to the types;
Receiving the obtained deformation characteristics, initiating an evaluation request, obtaining configuration parameters of an evaluation model corresponding to the deformation characteristics, inputting the deformation characteristics into the evaluation model for evaluation of deformation trend and deformation range deformation conditions, and sending information with evaluation index results;
transmitting information, wherein the information comprises an evaluation index, an evaluation model, configuration parameters and an evaluation result, and independently listing the result which does not accord with the evaluation index;
the process for respectively establishing a plurality of evaluation models according to the evaluation indexes, the deformation trend and the deformation range comprises the following steps:
generating a training set according to a plurality of historical deformation trends and deformation ranges, setting the types of the evaluation models according to the deformation trends and the deformation ranges, performing fitting calculation on the evaluation indexes by adopting the deformation trends and the deformation ranges, and verifying the types of the evaluation models;
creating a plurality of structural trees of deformation trends and deformation ranges, building corresponding evaluation models, acquiring evaluation parameters of the evaluation models, training the evaluation models based on the evaluation parameters, inputting one evaluation parameter of the plurality of evaluation parameters as a variable by the evaluation models, taking other evaluation parameters as constants, and obtaining evaluation results of the deformation trends and the deformation ranges based on the evaluation parameters which are input as the variable;
Setting the adjustment amounts of the deformation trend and the deformation range, inputting the real-time deformation trend and the real-time deformation range into an evaluation model, acquiring the adjustment amounts, the change results of the deformation trend and the deformation range according to the evaluation model, respectively evaluating the change results, and outputting corresponding evaluation results.
2. The soft rock tunnel face deformation monitoring method based on the incomplete arch effect according to claim 1, wherein the rock mass evaluation index comprises: rock mass strength: compressive strength, tensile strength and shear strength of the rock mass; rock mass structure: the joint development condition, the joint spacing and the joint inclination angle of the rock mass; rock mass water permeability: permeability, porosity, and fracture characteristics of the rock mass; rock mass stability: rock inclination angle, fault condition and rock deformation of rock mass; weather resistance of rock mass: durability, degree of weathering, and mineral composition of the rock mass.
3. The soft rock tunnel face deformation monitoring method based on the incomplete arch effect as claimed in claim 1, wherein the process of obtaining the deformation characteristics comprises the following steps:
acquiring deformation data acquired by a plurality of sensors, filtering the acquired strain and displacement data by adopting a digital filter, aligning the strain data and the displacement data in the deformation data in time, and smoothing the aligned strain data and displacement data by adopting a weighted moving average method to obtain new strain data and displacement data;
Determining parameters of a fitting curve by minimizing residual errors between actual strain data points and displacement data points and the fitting curve, and obtaining the fitting curve according to the determined curve parameters; selecting an elastoplastic constitutive model to describe the characteristics of a deformation curve; obtaining a deformation curve, namely an accumulated value of displacement, by integrating the fitted curve;
according to the strain data, corresponding displacement data is obtained through the strain-displacement relation, and the displacement data is integrated to obtain deformation, namely an accumulated value of displacement; deriving deformation data by using a difference method to obtain a deformation rate, namely a displacement change rate; and analyzing the obtained deformation and deformation rate to obtain deformation characteristics, and observing the variation trend and the periodicity characteristics of the deformation and the deformation rate.
4. A soft rock tunnel face deformation monitoring method based on incomplete arch effect as claimed in claim 3, wherein the process of integrating the fitted curve to obtain the deformation curve comprises the following steps:
expressing the fitting curve as a polynomial function, determining the discretization interval of the fitting curve, dividing the whole fitting curve into a plurality of small sections according to the polynomial function form and the discretization interval, and taking a plurality of discrete points in each small section for discretization;
In each small section, calculating the slope of a fitted curve on the small section by adopting a central difference method, namely the derivative of the curve, discretizing the small section on the abscissa to obtain a series of abscissa points, calculating the ordinate value corresponding to each abscissa point according to a polynomial function of the fitted curve, and calculating the slope of each abscissa point by adopting the central difference method; repeatedly calculating the slope on each small segment; multiplying the slope on each small segment by the time step to obtain the displacement increment on the small segment;
and receiving the displacement increment value output by the increment multiple unit, accumulating again by using the multiple time every multiple time of the input increment period to generate an accumulation result value, and accumulating the displacement increment to obtain a deformation curve, namely the accumulation value of the displacement.
5. The soft rock tunnel face deformation monitoring method based on the incomplete arch effect according to claim 4, wherein the calculation formula of the center difference method is as follows: slope = (f (x+h) -f (x-h))/(2 h), where f (x) is the ordinate value of the fitted curve on the abscissa x and h is the discretized step size.
6. A soft rock tunnel face deformation monitoring method based on incomplete arch effect as claimed in claim 3, wherein the process of obtaining deformation characteristics according to deformation amount and deformation rate comprises the following steps:
Analyzing the obtained deformation and deformation rate, calculating the average value, standard deviation, maximum value and minimum value statistical indexes of the deformation and the deformation rate, drawing a scatter diagram of the deformation and the deformation rate according to the statistical indexes, and observing the change trend, the distribution condition and the abnormal value;
periodically analyzing the scatter diagram of the deformation quantity and the deformation rate, collecting time domain data of the deformation quantity or the deformation rate, namely continuous data in a time range, discretizing the time domain data, and converting the continuous data into discrete data;
and visually displaying the periodic characteristics and the change trend of the deformation quantity and the deformation rate, and displaying the periodic characteristics and the change trend of the deformation quantity and the deformation rate by using a histogram.
7. The soft rock tunnel face deformation monitoring method based on the incomplete arch effect according to claim 6, wherein discrete data are subjected to fourier transform, time domain data are converted into frequency domain data, amplitude spectrum analysis is performed on the frequency domain data after the fourier transform to obtain amplitude information of different frequency components, namely the contribution degree of each frequency component, the existing periodic variation is identified by observing the amplitude spectrum analysis result, the amplitude value in the frequency domain generally represents the periodic component, the main period or frequency component of the deformation amount or deformation rate is determined according to the amplitude spectrum analysis result, and the periodic characteristics and the variation trend of the deformation amount or deformation rate are obtained.
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| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118130251A (en) * | 2024-05-06 | 2024-06-04 | 天津滨海新区轨道交通投资发展有限公司 | Method and system for monitoring deformation bearing capacity of tunnel structure |
Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20090106257A (en) * | 2008-04-04 | 2009-10-08 | 한국시설안전공단 | The method to estimate realtime-quantitative stability of the tunnel under construction and it's system |
| CN105937408A (en) * | 2016-06-15 | 2016-09-14 | 中国铁道科学研究院电子计算技术研究所 | Tunnel surrounding rock deformation early-warning method and system |
| JP2017218827A (en) * | 2016-06-09 | 2017-12-14 | 公益財団法人鉄道総合技術研究所 | Estimation method of factor relevant to tunnel deformation due to plastic pressure of natural ground considering three-dimensional geological structure and examination method for comprehending range requiring countermeasure |
| CN112832863A (en) * | 2021-01-19 | 2021-05-25 | 西南交通大学 | Grading method suitable for soft rock tunnel deformation grade under action of ultrahigh ground stress |
| CN115062390A (en) * | 2022-07-07 | 2022-09-16 | 河北工程技术学院 | Deep-buried tunnel extrusion type large deformation grading standard research and analysis method |
| CN116044501A (en) * | 2022-12-20 | 2023-05-02 | 广西交科集团有限公司 | Advanced geological forecast dynamic monitoring and early warning system and method |
-
2024
- 2024-03-13 CN CN202410282771.8A patent/CN117870609B/en active Active
Patent Citations (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| KR20090106257A (en) * | 2008-04-04 | 2009-10-08 | 한국시설안전공단 | The method to estimate realtime-quantitative stability of the tunnel under construction and it's system |
| JP2017218827A (en) * | 2016-06-09 | 2017-12-14 | 公益財団法人鉄道総合技術研究所 | Estimation method of factor relevant to tunnel deformation due to plastic pressure of natural ground considering three-dimensional geological structure and examination method for comprehending range requiring countermeasure |
| CN105937408A (en) * | 2016-06-15 | 2016-09-14 | 中国铁道科学研究院电子计算技术研究所 | Tunnel surrounding rock deformation early-warning method and system |
| CN112832863A (en) * | 2021-01-19 | 2021-05-25 | 西南交通大学 | Grading method suitable for soft rock tunnel deformation grade under action of ultrahigh ground stress |
| CN115062390A (en) * | 2022-07-07 | 2022-09-16 | 河北工程技术学院 | Deep-buried tunnel extrusion type large deformation grading standard research and analysis method |
| CN116044501A (en) * | 2022-12-20 | 2023-05-02 | 广西交科集团有限公司 | Advanced geological forecast dynamic monitoring and early warning system and method |
Non-Patent Citations (5)
| Title |
|---|
| 刘佳楠;陈龙;李志远: "基于非完整拱效应的软岩隧道掌子面变形及其控制研究", 中国地质灾害与防治学报, no. 004, 31 December 2019 (2019-12-31) * |
| 张顶立;台启民;房倩;: "复杂隧道围岩安全性及其评价方法", 岩石力学与工程学报, no. 02, 28 February 2017 (2017-02-28) * |
| 李明领;周全能;陈善雄;许锡昌;: "试论铁路客运专线沉降变形观测评估工作模式", 铁道标准设计, no. 02, 20 February 2010 (2010-02-20) * |
| 汤晓光;陈善雄;许锡昌;秦尚林;: "论铁路客运专线沉降变形评估标准与合理控制", 铁道标准设计, no. 02, 20 February 2010 (2010-02-20) * |
| 王瑜;邵森林;: "软岩公路隧道全断面开挖下拱顶沉降和掌子面挤出变形研究", 施工技术, no. 2, 30 December 2017 (2017-12-30) * |
Cited By (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN118130251A (en) * | 2024-05-06 | 2024-06-04 | 天津滨海新区轨道交通投资发展有限公司 | Method and system for monitoring deformation bearing capacity of tunnel structure |
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