CN117195380B - Method for predicting physical and mechanical parameters of rock mass of current tunnel - Google Patents
Method for predicting physical and mechanical parameters of rock mass of current tunnel Download PDFInfo
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- 238000006073 displacement reaction Methods 0.000 claims abstract description 69
- 238000003062 neural network model Methods 0.000 claims abstract description 34
- 238000004422 calculation algorithm Methods 0.000 claims abstract description 16
- 238000010276 construction Methods 0.000 claims abstract description 14
- 238000012544 monitoring process Methods 0.000 claims abstract description 11
- 238000012360 testing method Methods 0.000 claims abstract description 10
- 238000013461 design Methods 0.000 claims abstract description 7
- 239000011159 matrix material Substances 0.000 claims description 14
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Abstract
In the method, data obtained by geological exploration of a site to be built and pre-built tunnel parameters of the pre-built tunnel are used for building a three-dimensional model of the pre-built tunnel before tunnel construction, then a plurality of groups of rock physical mechanical parameter combinations are built through orthogonal test design, the neural network model to be trained corresponding to each target point is trained through the plurality of groups of rock physical mechanical parameter combinations, a relational expression formed by independent variable relations corresponding to the rock physical mechanical parameters on each target point is built through the trained neural network model corresponding to each target point, the obtained relational expression is used as an objective function of an NSGA II algorithm, rock physical mechanical parameters corresponding to a tunnel surrounding rock displacement monitoring value are preset through the NSGA II algorithm, and when the tunnel is constructed, the rock physical mechanical parameters of the current tunnel can be obtained through the method, so that support is provided for selection and construction of current tunnel support parameters.
Description
Technical Field
The application relates to the technical field of tunnel excavation, in particular to a method for predicting physical and mechanical parameters of a rock mass of a current tunnel.
Background
In tunnel engineering, knowing and evaluating the physical parameters of the rock mass of the tunnel in advance is important for the current design and construction of the tunnel, and the current evaluation method depends on core drilling and laboratory tests, which is time-consuming and labor-consuming, so that a method capable of providing the physical mechanical parameters of the rock mass of the current tunnel is needed.
Disclosure of Invention
In view of this, the embodiments of the present application provide a method for predicting a physical and mechanical parameter of a rock mass of a current tunnel, so as to provide the physical and mechanical parameter of the rock mass of the current tunnel, and support for selecting and constructing a support parameter of the current tunnel.
The embodiment of the application provides a method for predicting rock physical and mechanical parameters of a current tunnel, which comprises the following steps:
acquiring stratum distribution data, underground water distribution data of stratum obtained by geological exploration of a site to be built before tunnel construction, on-site rock physical and mechanical parameters obtained by on-site sampling and testing of the site to be built, and pre-built tunnel parameters of a pre-built tunnel;
inputting the rock stratum distribution data, the underground water distribution data, the physical and mechanical parameters of the field rock mass and the parameters of the pre-built tunnel into numerical simulation software, and establishing a three-dimensional model of the pre-built tunnel;
carrying out orthogonal test design on physical and mechanical parameters of the field rock mass by taking a limited range of the physical and mechanical parameters of the rock mass in an engineering geological manual as a limiting condition to obtain a plurality of groups of physical and mechanical parameter combinations of the rock mass;
inputting the physical and mechanical parameters of the rock mass in the physical and mechanical parameter combination of the rock mass into the three-dimensional model for each group of physical and mechanical parameter combination of the rock mass to obtain pre-built tunnel surrounding rock displacement values of a plurality of target points in a pre-built tunnel surrounding rock displacement field corresponding to the physical and mechanical parameter combination of the rock mass;
for each target point, inputting the physical and mechanical parameters of the rock mass in the physical and mechanical parameter combination of the rock mass as training samples into a neural network model to be trained corresponding to the target point, and obtaining an output tunnel surrounding rock displacement value of an output tunnel surrounding rock displacement field corresponding to the physical and mechanical parameter combination of the rock mass on the target point;
after obtaining the output tunnel surrounding rock displacement values corresponding to the physical and mechanical parameter combinations of each group of rock mass on the target point, calculating the mean square deviation of all the output tunnel surrounding rock displacement values corresponding to the target point relative to the pre-built tunnel surrounding rock displacement values, judging whether the mean square deviation is smaller than a preset value, and if the mean square deviation is not smaller than the preset value, adjusting the weight matrix, the threshold matrix and the number of hidden layer neurons in the neural network model to be trained until the mean square deviation of all the output tunnel surrounding rock displacement values corresponding to the target point output by the neural network model to be trained relative to the pre-built tunnel surrounding rock displacement values is smaller than the preset value;
after obtaining trained neural network models corresponding to all target points, constructing a first relation between a pre-built tunnel surrounding rock displacement value corresponding to each target point and rock physical and mechanical parameters corresponding to the target point by utilizing a weight matrix and a threshold matrix in the trained neural network model corresponding to each target point;
inputting the independent variable relation corresponding to each rock physical and mechanical parameter in the trained neural network model corresponding to the target point into a first relation corresponding to the target point to obtain a tunnel surrounding rock displacement predicted value corresponding to the target point, wherein the tunnel surrounding rock displacement predicted value comprises the independent variable relation corresponding to each rock physical and mechanical parameter;
embedding a displacement monitoring device on target points when constructing a tunnel, and acquiring tunnel surrounding rock displacement monitoring values on each target point acquired on the current tunnel construction site;
for a target point on the same position, firstly calculating a difference value of the tunnel surrounding rock displacement predicted value and the tunnel surrounding rock displacement monitored value corresponding to the target point, and then squaring the difference value to obtain a second relation formed by independent variable relations corresponding to physical and mechanical parameters of each rock mass on the target point;
and after obtaining a second relation corresponding to each target point, predicting rock physical and mechanical parameters corresponding to the tunnel surrounding rock displacement monitoring value by using an NSGA II algorithm, wherein a random function for changing the selection of a child individual from a parent individual is added into an elite retention strategy of the NSGA II algorithm, and the second relation corresponding to each target point is used as an objective function of the NSGA II algorithm.
Optionally, the target point comprises at least one of the following positions:
vault position of the tunnel, left arch position of the tunnel, and right arch position of the tunnel.
Optionally, the preset value is 0.01.
Optionally, the method further comprises:
and if the mean square error is smaller than the preset value, stopping training, and taking the current neural network model as a trained neural network model corresponding to the target point.
The technical scheme provided by the embodiment of the application can comprise the following beneficial effects:
in the method, a three-dimensional model of a pre-built tunnel is built through data obtained by geological exploration of a site to be built and pre-built tunnel parameters of the pre-built tunnel before tunnel construction, then a plurality of groups of rock physical and mechanical parameter combinations are built through orthogonal test design, then the neural network model to be trained corresponding to each target point is trained through the plurality of groups of rock physical and mechanical parameter combinations, then a relational expression formed by independent variable relations corresponding to the rock physical and mechanical parameters on the target point is built through the trained neural network model corresponding to each target point, then the obtained relational expression is used as an objective function of an NSGA II algorithm, rock physical and mechanical parameters corresponding to a displacement monitoring value of a tunnel surrounding rock are preset through the NSGA II algorithm, and when the tunnel is constructed, the rock physical and mechanical parameters of the current tunnel can be obtained through the method, and support is provided for current tunnel support parameter selection and construction.
In order to make the above objects, features and advantages of the present application more comprehensible, preferred embodiments accompanied with figures are described in detail below.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present application, the drawings that are needed in the embodiments will be briefly described below, it being understood that the following drawings only illustrate some embodiments of the present application and therefore should not be considered limiting the scope, and that other related drawings may be obtained according to these drawings without inventive effort for a person skilled in the art.
Fig. 1 is a flow chart of a method for predicting physical and mechanical parameters of a rock mass of a current tunnel according to an embodiment of the present application.
Detailed Description
For the purposes of making the objects, technical solutions and advantages of the embodiments of the present application more clear, the technical solutions in the embodiments of the present application will be clearly and completely described below with reference to the drawings in the embodiments of the present application, and it is apparent that the described embodiments are only some embodiments of the present application, but not all embodiments. The components of the embodiments of the present application, which are generally described and illustrated in the figures herein, may be arranged and designed in a wide variety of different configurations. Thus, the following detailed description of the embodiments of the present application, as provided in the accompanying drawings, is not intended to limit the scope of the application, as claimed, but is merely representative of selected embodiments of the application. All other embodiments, which can be made by those skilled in the art based on the embodiments of the present application without making any inventive effort, are intended to be within the scope of the present application.
Fig. 1 is a flow chart of a method for predicting physical and mechanical parameters of a rock mass of a current tunnel according to an embodiment of the present application, as shown in fig. 1, where the method includes:
step 101, acquiring stratum distribution data of stratum obtained by geological exploration of a site to be built before tunnel construction, underground water distribution data, on-site rock physical and mechanical parameters obtained by on-site sampling of the site to be built and then testing, and pre-built tunnel parameters of a pre-built tunnel.
And 102, inputting the rock stratum distribution data, the underground water distribution data, the physical and mechanical parameters of the on-site rock mass and the parameters of the pre-built tunnel into numerical simulation software, and establishing a three-dimensional model of the pre-built tunnel.
And 103, carrying out orthogonal test design on the physical and mechanical parameters of the field rock mass by taking the limited range of the physical and mechanical parameters of the rock mass in the engineering geological manual as a limiting condition to obtain a plurality of groups of physical and mechanical parameter combinations of the rock mass.
And 104, inputting the physical and mechanical parameters of the rock mass in the physical and mechanical parameter combinations of the rock mass into the three-dimensional model for each group of physical and mechanical parameter combinations of the rock mass to obtain the pre-built tunnel surrounding rock displacement values of a plurality of target points in the pre-built tunnel surrounding rock displacement field corresponding to the physical and mechanical parameter combinations of the rock mass.
And 105, for each target point, inputting the physical and mechanical parameters of the rock mass in the physical and mechanical parameter combination of the rock mass as training samples into a neural network model to be trained corresponding to the target point, and obtaining the output tunnel surrounding rock displacement value of the output tunnel surrounding rock displacement field corresponding to the physical and mechanical parameter combination of the rock mass on the target point.
And 106, after obtaining the corresponding output tunnel surrounding rock displacement values of the physical and mechanical parameter combinations of each group of rock mass on the target point, calculating the mean square error of all the output tunnel surrounding rock displacement values corresponding to the target point relative to the pre-built tunnel surrounding rock displacement values, judging whether the mean square error is smaller than a preset value, and if the mean square error is not smaller than the preset value, adjusting the weight matrix, the threshold matrix and the number of hidden layer neurons in the neural network model to be trained until the mean square error of all the output tunnel surrounding rock displacement values corresponding to the target point output by the neural network model to be trained relative to the pre-built tunnel surrounding rock displacement values is smaller than the preset value.
Step 107, after obtaining the trained neural network model corresponding to each target point, constructing a first relation between the pre-constructed tunnel surrounding rock displacement value corresponding to each target point and the rock physical and mechanical parameters corresponding to each target point by using the weight matrix and the threshold matrix in the trained neural network model corresponding to each target point.
And step 108, inputting the independent variable relation corresponding to the physical and mechanical parameters of each rock body in the trained neural network model corresponding to the target point into a first relation corresponding to the target point to obtain a tunnel surrounding rock displacement predicted value corresponding to the target point, wherein the tunnel surrounding rock displacement predicted value comprises the independent variable relation corresponding to the physical and mechanical parameters of each rock body.
And 109, embedding a displacement monitoring device on the target points when constructing the tunnel, and acquiring the displacement monitoring values of the surrounding rocks of the tunnel on each target point acquired on the current tunnel construction site.
Step 110 is to calculate the difference between the predicted value of the displacement of the surrounding rock of the tunnel and the monitored value of the displacement of the surrounding rock of the tunnel corresponding to the target point, and then to square the difference to obtain a second relation formed by the independent variable relations corresponding to the physical and mechanical parameters of each rock mass at the target point.
And 111, predicting rock physical and mechanical parameters corresponding to the tunnel surrounding rock displacement monitoring values by using an NSGA II algorithm after obtaining a second relation corresponding to each target point, wherein a random function for changing the selection of a parent individual into a child individual is added into an elite retention strategy of the NSGA II algorithm, and the second relation corresponding to each target point is used as an objective function of the NSGA II algorithm.
Specifically, geological exploration is required to be performed on the site to be built before the tunnel is built, so that rock stratum distribution data and underground water distribution data of the bottom layer of the site to be built are obtained, the site to be built is sampled, an indoor test is performed on the sample to obtain physical and mechanical parameters of the site to be built, and as the pre-built tunnel parameters of the pre-built tunnel are designed in advance, the obtained data can be input into numerical simulation software, and simulation is performed after the tunnel is built on the site to be built, so that a three-dimensional model of the pre-built tunnel can be obtained.
In order to simulate a tunnel surrounding rock displacement value by using a three-dimensional model, orthogonal test design is required to be carried out on-site rock physical and mechanical parameters by taking a range defined by rock physical and mechanical parameters in an engineering geological manual as a limiting condition, so that a plurality of groups of rock physical and mechanical parameter combinations can be obtained, the numerical values of the parameters in each group of rock physical and mechanical parameters meet the range defined by corresponding parameters in the engineering geological manual, then the rock physical and mechanical parameter combinations of each group are input into the three-dimensional model, a pre-built tunnel surrounding rock displacement field corresponding to the rock physical and mechanical parameter combinations of each group is obtained, and then the pre-built tunnel surrounding rock displacement value of the position of a target point is selected from the pre-built tunnel surrounding rock displacement field corresponding to the rock physical and mechanical parameter combinations of each group.
For each target point, rock physical and mechanical parameters in the rock physical and mechanical parameter combination are input into the neural network model to be trained corresponding to the target point as training samples, so that output tunnel surrounding rock displacement values of the output tunnel surrounding rock displacement field corresponding to the rock physical and mechanical parameter combination on the target point are obtained, and for the same rock physical and mechanical parameter combination, the output tunnel surrounding rock displacement values on different target points are different, so that after training the neural network model to be trained corresponding to different target points, the obtained trained neural network model is different.
After trained neural network models corresponding to different target points are obtained, a first relation between a pre-built tunnel surrounding rock displacement value corresponding to each target point and rock physical mechanical parameters is constructed according to a weight matrix and a threshold matrix in the trained neural network models corresponding to each target point, then independent variable relations corresponding to the rock physical mechanical parameters in the trained neural network models corresponding to the target point are input into the first relation corresponding to the target point, and a tunnel surrounding rock displacement predicted value corresponding to the target point is obtained, wherein the tunnel surrounding rock displacement predicted value comprises independent variable relations corresponding to the rock physical mechanical parameters, and accordingly the tunnel surrounding rock displacement predicted value corresponding to each target point can be obtained.
When constructing a tunnel, embedding a displacement monitoring device at the position of each target point of the current tunnel construction site to obtain tunnel surrounding rock displacement monitoring values on each target point acquired at the current tunnel construction site, then for the target points on the same position, firstly carrying out difference value calculation on the tunnel surrounding rock displacement predicted values and the tunnel surrounding rock displacement monitoring values corresponding to the target points, then carrying out square calculation on the difference values to obtain a second relation formed by independent variable relations corresponding to physical mechanical parameters of each rock mass on the target points, taking the second relation of each target point as an objective function of an NSGA II algorithm, adding a random function for changing a parent individual to enter a offspring individual in an elite retention strategy of the NSGA II algorithm, and finally predicting the physical mechanical parameters of the rock mass corresponding to the tunnel surrounding rock displacement monitoring values by using the NSGA algorithm.
The physical and mechanical parameters of the rock mass obtained by the method are obtained after the physical and mechanical parameters of the rock mass of the current tunnel are predicted, so that support can be provided for the selection and construction of the current tunnel support parameters.
In one possible embodiment, the target point comprises at least one of the following positions:
vault position of the tunnel, left arch position of the tunnel, and right arch position of the tunnel.
In a possible embodiment, the preset value is 0.01.
In a possible embodiment, when step 106 is performed, if the mean square error is smaller than the preset value, training is stopped, the current neural network model is used as the trained neural network model corresponding to the target point, and a subsequent step is performed.
It should be noted that: like reference numerals and letters in the following figures denote like items, and thus once an item is defined in one figure, no further definition or explanation of it is required in the following figures, and furthermore, the terms "first," "second," "third," etc. are used merely to distinguish one description from another and are not to be construed as indicating or implying relative importance.
Finally, it should be noted that: the foregoing examples are merely specific embodiments of the present application, and are not intended to limit the scope of the present application, but the present application is not limited thereto, and those skilled in the art will appreciate that while the foregoing examples are described in detail, the present application is not limited thereto. Any person skilled in the art may modify or easily conceive of the technical solution described in the foregoing embodiments, or make equivalent substitutions for some of the technical features within the technical scope of the disclosure of the present application; such modifications, changes or substitutions do not depart from the spirit and scope of the corresponding technical solutions. Are intended to be encompassed within the scope of this application. Therefore, the protection scope of the present application shall be subject to the protection scope of the claims.
Claims (4)
1. A method of predicting a physical and mechanical parameter of a rock mass of a current tunnel, the method comprising:
acquiring stratum distribution data, underground water distribution data of stratum obtained by geological exploration of a site to be built before tunnel construction, on-site rock physical and mechanical parameters obtained by on-site sampling and testing of the site to be built, and pre-built tunnel parameters of a pre-built tunnel;
inputting the rock stratum distribution data, the underground water distribution data, the physical and mechanical parameters of the field rock mass and the parameters of the pre-built tunnel into numerical simulation software, and establishing a three-dimensional model of the pre-built tunnel;
carrying out orthogonal test design on physical and mechanical parameters of the field rock mass by taking a limited range of the physical and mechanical parameters of the rock mass in an engineering geological manual as a limiting condition to obtain a plurality of groups of physical and mechanical parameter combinations of the rock mass;
inputting the physical and mechanical parameters of the rock mass in the physical and mechanical parameter combination of the rock mass into the three-dimensional model for each group of physical and mechanical parameter combination of the rock mass to obtain pre-built tunnel surrounding rock displacement values of a plurality of target points in a pre-built tunnel surrounding rock displacement field corresponding to the physical and mechanical parameter combination of the rock mass;
for each target point, inputting the physical and mechanical parameters of the rock mass in the physical and mechanical parameter combination of the rock mass as training samples into a neural network model to be trained corresponding to the target point, and obtaining an output tunnel surrounding rock displacement value of an output tunnel surrounding rock displacement field corresponding to the physical and mechanical parameter combination of the rock mass on the target point;
after obtaining the output tunnel surrounding rock displacement values corresponding to the physical and mechanical parameter combinations of each group of rock mass on the target point, calculating the mean square deviation of all the output tunnel surrounding rock displacement values corresponding to the target point relative to the pre-built tunnel surrounding rock displacement values, judging whether the mean square deviation is smaller than a preset value, and if the mean square deviation is not smaller than the preset value, adjusting the weight matrix, the threshold matrix and the number of hidden layer neurons in the neural network model to be trained until the mean square deviation of all the output tunnel surrounding rock displacement values corresponding to the target point output by the neural network model to be trained relative to the pre-built tunnel surrounding rock displacement values is smaller than the preset value;
after obtaining trained neural network models corresponding to all target points, constructing a first relation between a pre-built tunnel surrounding rock displacement value corresponding to each target point and rock physical and mechanical parameters corresponding to the target point by utilizing a weight matrix and a threshold matrix in the trained neural network model corresponding to each target point;
inputting the independent variable relation corresponding to each rock physical and mechanical parameter in the trained neural network model corresponding to the target point into a first relation corresponding to the target point to obtain a tunnel surrounding rock displacement predicted value corresponding to the target point, wherein the tunnel surrounding rock displacement predicted value comprises the independent variable relation corresponding to each rock physical and mechanical parameter;
embedding a displacement monitoring device on target points when constructing a tunnel, and acquiring tunnel surrounding rock displacement monitoring values on each target point acquired on the current tunnel construction site;
for a target point on the same position, firstly calculating a difference value of the tunnel surrounding rock displacement predicted value and the tunnel surrounding rock displacement monitored value corresponding to the target point, and then squaring the difference value to obtain a second relation formed by independent variable relations corresponding to physical and mechanical parameters of each rock mass on the target point;
and after obtaining a second relation corresponding to each target point, predicting rock physical and mechanical parameters corresponding to the tunnel surrounding rock displacement monitoring value by using an NSGA II algorithm, wherein a random function for changing the selection of a child individual from a parent individual is added into an elite retention strategy of the NSGA II algorithm, and the second relation corresponding to each target point is used as an objective function of the NSGA II algorithm.
2. The method of claim 1, wherein the target point comprises at least one of the following locations:
vault position of the tunnel, left arch position of the tunnel, and right arch position of the tunnel.
3. The method of claim 1, wherein the predetermined value is 0.01.
4. The method of claim 1, wherein the method further comprises:
and if the mean square error is smaller than the preset value, stopping training, and taking the current neural network model as a trained neural network model corresponding to the target point.
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