CN110489844B - Prediction method suitable for uneven large deformation grade of soft rock tunnel - Google Patents
Prediction method suitable for uneven large deformation grade of soft rock tunnel Download PDFInfo
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Abstract
The method comprises the steps of obtaining geological conditions, construction conditions and deformation data of each existing soft rock tunnel, grading the uneven deformation degree into a basic deformation grade and a deformation uneven grade, classifying and quantifying main influence factors, and establishing an uneven large deformation initial sample database of the soft rock tunnel; calculating subjective and objective weights of main factors by adopting different methods to obtain comprehensive weights, and calculating the association degrees of all influence factors and the basic deformation grade and the deformation uneven grade of the soft rock tunnel by adopting a grey association degree theory respectively, wherein the influence factors with the association smaller than a set value are reduced; constructing a soft rock tunnel uneven large deformation artificial neural network prediction model by taking the reduced influence factor indexes as input parameters and taking the basic deformation grade and the deformation uneven grade as output parameters; and inputting the acquired data of the soft rock tunnel to be predicted into the artificial neural network prediction model to obtain a prediction result.
Description
Technical Field
The disclosure belongs to the field of rock-soil deformation prediction, and relates to a prediction method suitable for large uneven deformation grade of a soft rock tunnel.
Background
The statements in this section merely provide background information related to the present disclosure and may not necessarily constitute prior art.
The soft rock has the characteristics of low strength, large porosity, poor cementation degree, obvious influence by structure and weathering, large amount of expansive clay minerals and the like, and generates obvious plastic deformation and rheology under the interference of engineering construction. Due to the defects of the soft rock structure and strength, and the influences of factors such as ground stress, underground water conditions and construction, the surrounding rock strength and stress distribution difference of the soft rock tunnel generates uneven deformation, so that the supporting structure is locally damaged, and the safety construction of the tunnel is seriously influenced. The tunnel deformation grading prediction can qualitatively predict and evaluate the deformation of the section of the tunnel without excavation, and provides a basis for preventing and treating the deformation and the damage of the tunnel.
According to the knowledge of the inventor, the traditional tunnel deformation grading prediction mainly adopts methods such as empirical formulas and numerical simulation, but the soft rock tunnel is affected by various factors such as engineering geological factors and construction factors, so that the problem is solved in a nonlinear manner, the traditional empirical formulas and numerical simulation methods have certain limitations, and the prediction on the degree of the non-uniformity of the tunnel deformation is difficult.
Disclosure of Invention
The method is suitable for predicting the uneven large deformation grade of the soft rock tunnel, and can accurately predict the uneven large deformation of the soft rock tunnel.
According to some embodiments, the following technical scheme is adopted in the disclosure:
a prediction method suitable for the uneven large deformation grade of a soft rock tunnel comprises the following steps:
acquiring geological conditions, construction conditions and deformation data of each existing soft rock tunnel, classifying the uneven deformation degree into a basic deformation grade and an uneven deformation grade, determining main factors influencing uneven large deformation of the soft rock tunnel from the aspects of geological factors and construction factors, classifying and quantizing the main factors, and establishing an uneven large deformation initial sample database of the soft rock tunnel;
calculating subjective and objective weights of the main factors by adopting different methods to obtain the comprehensive weight of each factor;
based on the comprehensive weight of each main factor, respectively calculating the association degrees of each influence factor and the basic deformation grade and the deformation uneven grade of the soft rock tunnel by adopting a grey association degree theory, and reducing the influence factors of which the association is smaller than a set value;
establishing an optimized sample database by taking the reduced influence factor indexes as input parameters and basic deformation grades and deformation unevenness grades as output parameters to construct an artificial neural network prediction model for the unevenness and large deformation of the soft rock tunnel;
and inputting the acquired data of the soft rock tunnel to be predicted into the artificial neural network prediction model to obtain a prediction result.
And performing protection construction on the area predicted to belong to the deformation uneven grade according to the prediction result.
As possible embodiments, the geological conditions include rock mass integrity factor, rock formation dip angle, rock mass uniaxial saturation, compressive strength, cohesion, deformation modulus, poisson's ratio, tunnel water seepage, tunnel primary principal stress, and tunnel burial depth.
As possible embodiments, the construction conditions include tunnel span, excavation method, tunnel preliminary bracing closing time and bracing strength.
As a possible implementation mode, according to the average relative deformation degree and the abnormal large deformation degree of the tunnel, the deformation of the tunnel is divided into a basic deformation grade and a deformation non-uniform grade, wherein the non-uniform deformation degree is higher when the grade is larger; dividing each influence factor into different grades according to the numerical value or the influence degree on the deformation; and corresponding classification values are given to the deformation grade and the factor grade, so that an initial sample database of the uneven large deformation of the soft rock tunnel is established.
As a possible implementation, the subjective weight of each main factor is calculated by the delphire method.
As a possible implementation mode, the objective weight of each main factor is calculated by adopting a rough set theory.
As a possible implementation manner, according to an initial sample database, an index matrix and a reference sequence are determined, data normalization is performed, the comprehensive weight of each factor is used as the weighted value of the association coefficient, and the association degree of each factor is calculated by adopting a grey association degree theory.
As a possible implementation mode, the reduced influence factor indexes are used as input parameters, the basic deformation level and the deformation unevenness level are used as output parameters, data of a sample database are normalized, a soft rock tunnel unevenness large deformation artificial neural network prediction model is constructed, prediction is carried out by the model, and protection is carried out by the prediction value.
A prediction system suitable for the uneven large deformation grade of a soft rock tunnel comprises:
the sample database construction module is configured to acquire geological conditions, construction conditions and deformation data of each existing soft rock tunnel, classify the uneven deformation degree into a basic deformation grade and an uneven deformation grade, determine main factors influencing uneven large deformation of the soft rock tunnel from the aspects of geological factors and construction factors, classify and quantify the main factors, and establish an uneven large deformation initial sample database of the soft rock tunnel;
the weight setting module is configured to calculate subjective and objective weights of the main factors by adopting different methods to obtain the comprehensive weight of each factor;
the optimization module is configured to calculate the association degrees of the influence factors and the soft rock tunnel basic deformation grade and the deformation uneven grade respectively by adopting a grey association degree theory based on the comprehensive weight of the main factors, and reduce the influence factors of which the association is smaller than a set value;
and the prediction model construction module is configured to establish an optimized sample database by taking the reduced influence factor indexes as input parameters and the basic deformation grade and the deformation unevenness grade as output parameters to construct an artificial neural network prediction model of the unevenness and large deformation of the soft rock tunnel so as to obtain a prediction result.
A computer readable storage medium, wherein a plurality of instructions are stored, the instructions are suitable for being loaded by a processor of a terminal device and executing the prediction method suitable for the soft rock tunnel uneven large deformation level.
A terminal device comprising a processor and a computer readable storage medium, the processor being configured to implement instructions; the computer-readable storage medium is used for storing a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the prediction method suitable for the soft rock tunnel uneven large deformation level.
Compared with the prior art, the beneficial effect of this disclosure is:
1. the method is based on a large number of soft rock tunnel actual projects, geological information, construction data and monitoring measurement data of the actual projects are collected widely, and the established sample database has breadth and representativeness. The comprehensive weight of the influence factors is calculated by adopting a Delphi method-rough set theory, and the method is applied to the reduction of the grey correlation factor, so that the initial sample database is optimized, the database optimization method is scientific and reasonable, and the accuracy of the artificial neural network prediction result is greatly improved. The artificial neural network prediction method has the characteristic of solving nonlinear arbitrary function approximation, has unique superiority and high accuracy of prediction results.
2. When the large uneven deformation of the soft rock tunnel is predicted, the basic deformation grade and the uneven deformation grade of the tunnel can be obtained only by classifying and quantizing the geological conditions and the construction conditions of the tunnel to be predicted and inputting the obtained optimal artificial neural network prediction model as input parameters, and the method is simple and reliable. If the predicted deformation level of the foundation is too large, correspondingly optimizing a tunnel construction scheme according to the level, increasing the strength of the foundation support, and if the predicted deformation uneven level is too large, performing advanced support to the abnormal deformation area to a corresponding degree according to the level, which is favorable for better controlling the deformation and improving the construction safety and efficiency. Therefore, the method has great practical value for the design and construction of the tunnel with uneven and large deformation of soft rock.
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The accompanying drawings, which are included to provide a further understanding of the disclosure, illustrate embodiments of the disclosure and together with the description serve to explain the disclosure and are not to limit the disclosure.
FIG. 1 is a flow chart of steps;
the specific implementation mode is as follows:
the present disclosure is further described with reference to the following drawings and examples.
It should be noted that the following detailed description is exemplary and is intended to provide further explanation of the disclosure. Unless defined otherwise, all technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this disclosure belongs.
It is noted that the terminology used herein is for the purpose of describing particular embodiments only and is not intended to be limiting of example embodiments according to the present disclosure. As used herein, the singular forms "a", "an" and "the" are intended to include the plural forms as well, and it should be understood that when the terms "comprises" and/or "comprising" are used in this specification, they specify the presence of stated features, steps, operations, devices, components, and/or combinations thereof, unless the context clearly indicates otherwise.
The method is suitable for predicting the large uneven deformation grade of the soft rock tunnel by combining a Delphi method, a rough set theory, a grey correlation degree theory and an artificial neural network method on the basis of widely collecting geological conditions, construction conditions and deformation data of the built and under-built soft rock tunnel. The method includes the steps of widely collecting geological conditions, construction conditions and deformation data of the soft rock tunnel which is built and under construction, comprehensively analyzing uneven deformation characteristics of the soft rock tunnel, dividing uneven deformation levels into basic deformation levels and uneven deformation levels, analyzing main factors influencing uneven large deformation of the soft rock tunnel from the aspects of geological factors and construction factors, classifying and quantifying the main factors, and establishing an initial sample database of uneven large deformation of the soft rock tunnel; and calculating subjective weight of each factor by a Delphi method. And calculating the objective weight of the factors by using an initial sample database and adopting a rough set theory. Calculating the comprehensive weight of each factor by combining the subjective and objective weights; respectively calculating the relevance of each influence factor and the basic deformation grade and the deformation uneven grade of the soft rock tunnel by using an initial sample database and combining the comprehensive weight of each factor and adopting a grey relevance theory, reducing the influence factors with small relevance and optimizing the initial sample database; and constructing a soft rock tunnel uneven large deformation artificial neural network prediction model by taking the reduced influence factor indexes as input parameters and taking the basic deformation grade and the deformation uneven grade as output parameters. The method is based on a large number of soft rock tunnel actual projects, geological information, construction data and monitoring measurement data of the actual projects are collected widely, and the established sample database has breadth and representativeness. Combining the Delphi method and the rough set theory, comprehensively considering the subjective and objective weights, calculating the comprehensive weights of the influence factors, applying the comprehensive weights to the grey correlation factor reduction, and optimizing the initial sample database, thereby greatly improving the accuracy of the artificial neural network prediction result. Meanwhile, the characteristic of the artificial neural network prediction method that the nonlinear arbitrary function approximation can be solved provides possibility for accurately solving the problem of uneven and large deformation of the complex soft rock tunnel. The method has the advantages of real sample data, rich and representative information quantity. The database optimization method is scientific and reasonable, the prediction method has unique superiority, and the accuracy of the prediction result is high. The method has important guiding significance for prediction and safe construction of uneven large deformation of soft rock.
As shown in fig. 1, the method specifically includes:
(1) the method comprises the steps of classifying the uneven deformation degree into a basic deformation grade and an uneven deformation grade by collecting geological conditions, construction conditions and deformation data of the soft rock tunnel built and under the soft rock tunnel building, analyzing main factors influencing uneven large deformation of the soft rock tunnel from the aspects of geological factors and construction factors, classifying and quantifying the main factors, and building an uneven large deformation initial sample database of the soft rock tunnel.
(2) And calculating subjective weight of each factor by a Delphi method. And calculating the objective weight of the factors by adopting a rough set theory. And calculating the comprehensive weight of each factor by combining the subjective and objective weights.
(3) Combining the comprehensive weight of each factor, and adopting a grey correlation degree theory to respectively calculate the correlation degree of each influence factor and the basic deformation grade and the deformation uneven grade of the soft rock tunnel, so as to reduce the influence factors with small correlation;
(4) and establishing an optimized sample database by taking the reduced influence factor indexes as input parameters and the basic deformation grade and the deformation unevenness grade as output parameters to construct an artificial neural network prediction model for the unevenness and large deformation of the soft rock tunnel.
Further, in the step (1), the geological conditions include rock integrity coefficient, rock inclination angle, rock uniaxial saturated compressive strength, cohesive force, deformation modulus, poisson's ratio, tunnel water seepage amount, tunnel first main stress and tunnel burial depth. The construction conditions comprise tunnel span, excavation method, tunnel primary support closing time and support strength. The above conditions are used as main influence factors of uneven large deformation of the soft rock tunnel.
Further, in the step (1), the deformation of the tunnel is divided into a basic deformation grade and a deformation non-uniform grade according to the average relative deformation degree and the abnormal large deformation degree of the tunnel, wherein the non-uniform deformation degree is higher when the grade is larger. And dividing each influence factor into different grades according to the numerical value or the influence degree on the deformation. And corresponding classification values are given to the deformation grade and the factor grade (if the grade is 1, the classification value is 1, the grade is 2, the classification value is 2, and the like), so that an initial sample database of the uneven large deformation of the soft rock tunnel is established.
Further, in the step (2), the delphi method adopts an expert scoring method, experts engaged in relevant research are selected, the importance of the influence factors of the soft rock tunnel basic deformation level and the deformation unevenness level is respectively scored, and the subjective weight of each influence factor on the basic deformation level and the deformation unevenness level is respectively calculated through the delphi method. The method mainly comprises the following steps: 1. selecting an expert; 2. designing an evaluation opinion levy sheet; 3. information feedback of expert inquiry and consultation; 4. data processing and weight determination of the results.
Further, in the step (2), according to the initial sample database, taking each influence factor as a condition attribute, respectively taking the basic deformation level and the deformation non-uniformity level as a decision attribute, and respectively calculating the objective weight of each influence factor on the basic deformation level and the deformation non-uniformity level by using a rough set theory. The method mainly comprises the following steps: 1. constructing a condition attribute set and a decision attribute set; 2. calculating the support degree of the decision attribute; 3. calculating the importance degree of each condition attribute to the decision attribute; 4. and (4) calculating the weight.
Further, in the step (2), based on the obtained subjective weight and objective weight, using a formula:
calculating the integrated weight, wherein wiIs the composite weight of the ith influencing factor,
the objective weight of the ith influencing factor found for the coarse-set method,
the subjective weight of the ith influencing factor obtained by the Delphi method, and n is the number of the influencing factors.
Further, in the step (3), according to the initial sample database, the comprehensive weight of each factor is used as a weighted value of the association coefficient, the association degree of each factor is calculated by adopting a grey association degree theory, and the influence factors with small association are reduced. The method mainly comprises the following steps: 1. determining an index matrix and a reference sequence; 2. normalizing the data; 3. calculating a correlation coefficient; 4. and calculating the association degree.
Further, in the step (4), an optimized sample database is established by taking the reduced influence factor indexes as input parameters and the basic deformation grade and the deformation unevenness grade as output parameters, so as to construct the soft rock tunnel unevenness large deformation artificial neural network prediction model. The method mainly comprises the following steps: 1. determining input parameters and output parameters, and establishing a training sample database; 2. normalizing the data; 3. determining an artificial neural network basic architecture: the number and the number of layers of hidden layer neurons, the type of a neural network, a transfer function, a training function, a learning function and the like; 4. and training the soft rock tunnel uneven large-deformation artificial neural network prediction model to obtain an optimal prediction model.
As will be appreciated by one skilled in the art, embodiments of the present disclosure may be provided as a method, system, or computer program product. Accordingly, the present disclosure may take the form of an entirely hardware embodiment, an entirely software embodiment or an embodiment combining software and hardware aspects. Furthermore, the present disclosure may take the form of a computer program product embodied on one or more computer-usable storage media (including, but not limited to, disk storage, CD-ROM, optical storage, and so forth) having computer-usable program code embodied therein.
The present disclosure is described with reference to flowchart illustrations and/or block diagrams of methods, apparatus (systems), and computer program products according to embodiments of the disclosure. It will be understood that each flow and/or block of the flow diagrams and/or block diagrams, and combinations of flows and/or blocks in the flow diagrams and/or block diagrams, can be implemented by computer program instructions. These computer program instructions may be provided to a processor of a general purpose computer, special purpose computer, embedded processor, or other programmable data processing apparatus to produce a machine, such that the instructions, which execute via the processor of the computer or other programmable data processing apparatus, create means for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be stored in a computer-readable memory that can direct a computer or other programmable data processing apparatus to function in a particular manner, such that the instructions stored in the computer-readable memory produce an article of manufacture including instruction means which implement the function specified in the flowchart flow or flows and/or block diagram block or blocks.
These computer program instructions may also be loaded onto a computer or other programmable data processing apparatus to cause a series of operational steps to be performed on the computer or other programmable apparatus to produce a computer implemented process such that the instructions which execute on the computer or other programmable apparatus provide steps for implementing the functions specified in the flowchart flow or flows and/or block diagram block or blocks.
The above description is only a preferred embodiment of the present disclosure and is not intended to limit the present disclosure, and various modifications and changes may be made to the present disclosure by those skilled in the art. Any modification, equivalent replacement, improvement and the like made within the spirit and principle of the present disclosure should be included in the protection scope of the present disclosure.
Although the present disclosure has been described with reference to specific embodiments, it should be understood that the scope of the present disclosure is not limited thereto, and those skilled in the art will appreciate that various modifications and changes can be made without departing from the spirit and scope of the present disclosure.
Claims (9)
1. A prediction method suitable for the uneven large deformation grade of a soft rock tunnel is characterized by comprising the following steps: the method comprises the following steps:
acquiring geological conditions, construction conditions and deformation data of each existing soft rock tunnel, classifying the uneven deformation degree into a basic deformation grade and an uneven deformation grade, determining main influence factors of uneven large deformation of the soft rock tunnel from the aspects of geological factors and construction factors, classifying and quantizing the main influence factors, and establishing an uneven large deformation initial sample database of the soft rock tunnel;
calculating subjective and objective weights of the main influence factors by adopting different methods to obtain the comprehensive weight of each main influence factor;
based on the comprehensive weight of each main influence factor, respectively calculating the correlation degree of each main influence factor and the basic deformation grade and the deformation uneven grade of the soft rock tunnel by adopting a grey correlation degree theory, and reducing the main influence factors of which the correlation degree is smaller than a set value;
establishing an optimized sample database by taking the reduced main influence factor indexes as input parameters and the basic deformation grade and the deformation unevenness grade as output parameters to construct an artificial neural network prediction model for the unevenness and large deformation of the soft rock tunnel;
and inputting the acquired data of the soft rock tunnel to be predicted into the artificial neural network prediction model to obtain a prediction result.
2. The prediction method for the uneven large deformation grade of the soft rock tunnel as claimed in claim 1, wherein: the geological conditions comprise rock integrity coefficient, rock inclination angle, rock uniaxial saturation, compressive strength, cohesive force, deformation modulus, Poisson's ratio, tunnel water seepage, tunnel first main stress and tunnel burial depth.
3. The prediction method for the uneven large deformation grade of the soft rock tunnel as claimed in claim 1, wherein: the construction conditions comprise tunnel span, excavation method, tunnel primary support closing time and support strength.
4. The prediction method for the uneven large deformation grade of the soft rock tunnel as claimed in claim 1, wherein: according to the average relative deformation degree and the abnormal large deformation degree of the tunnel, the deformation of the tunnel is divided into a basic deformation grade and a deformation uneven grade, wherein the uneven deformation degree is higher when the grade is higher; dividing each main influence factor into different grades according to the numerical value or the influence degree on deformation; and corresponding classification values are given to the deformation grade and the factor grade, so that an initial sample database of the uneven large deformation of the soft rock tunnel is established.
5. The prediction method for the uneven large deformation grade of the soft rock tunnel as claimed in claim 1, wherein: calculating subjective weights of all main influencing factors by a Delphi method; and calculating the objective weight of each main influence factor by adopting a rough set theory.
6. The prediction method for the uneven large deformation grade of the soft rock tunnel as claimed in claim 1, wherein: and determining an index matrix and a reference sequence according to an initial sample database, carrying out data normalization, taking the comprehensive weight of each main influence factor as a weighted value of the association coefficient, and calculating the association degree of each main influence factor by adopting a grey association degree theory.
7. The utility model provides a be applicable to inhomogeneous big deformation grade prediction system in soft rock tunnel which characterized by: the method comprises the following steps:
the sample database construction module is configured to acquire geological conditions, construction conditions and deformation data of each existing soft rock tunnel, classify the uneven deformation degree into a basic deformation grade and an uneven deformation grade, determine main influence factors of uneven large deformation of the soft rock tunnel from the aspects of geological factors and construction factors, classify and quantize the main influence factors, and establish an initial sample database of uneven large deformation of the soft rock tunnel;
the weight setting module is configured to calculate subjective and objective weights of the main influence factors by adopting different methods to obtain comprehensive weights of the main influence factors;
the optimization module is configured to calculate the association degrees of the main influence factors and the soft rock tunnel basic deformation grade and the deformation uneven grade respectively by adopting a grey association degree theory based on the comprehensive weight of the main influence factors, and reduce the main influence factors of which the association degrees are smaller than a set value;
and the prediction model construction module is configured to establish an optimized sample database by taking the reduced main influence factor indexes as input parameters and the basic deformation grade and the deformation unevenness grade as output parameters to construct the soft rock tunnel unevenness large deformation artificial neural network prediction model and obtain a prediction result.
8. A computer-readable storage medium characterized by: a plurality of instructions are stored, the instructions are suitable for being loaded by a processor of a terminal device and executing the method for predicting the uneven large deformation level of the soft rock tunnel according to any one of claims 1-6.
9. A terminal device is characterized in that: the system comprises a processor and a computer readable storage medium, wherein the processor is used for realizing instructions; the computer readable storage medium is used for storing a plurality of instructions, and the instructions are suitable for being loaded by a processor and executing the soft rock tunnel nonuniform large deformation grade prediction method in any one of claims 1-6.
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| CN115511001B (en) * | 2022-10-21 | 2023-07-18 | 中铁二院工程集团有限责任公司 | Tunnel surrounding rock grading method and equipment based on comprehensive exploration data of air-ground wells |
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