CN112861207A - Method and equipment for predicting sedimentation of composite stratum and computer storage medium - Google Patents

Abstract

The present application discloses a method, equipment and computer storage medium for subsidence prediction of a composite stratum, and relates to the field of tunnel engineering. The method includes obtaining a first deformation modulus and a deformation modulus weight of each geological layer of a composite stratum to be predicted ; Perform weighting processing on each of the first deformation modulus and the corresponding deformation modulus weight to obtain the second deformation modulus of the composite formation; calculate each of the first deformation modulus and the The weighted mean square error of the second deformation modulus; and the first deformation modulus of the composite formation is obtained according to the weighted mean square error of each of the first deformation modulus and the second deformation modulus, and the second deformation modulus Weighted coefficient of variation; predicting the first weighted coefficient of variation through a preset subsidence prediction model to obtain a subsidence prediction value of the composite stratum. By introducing the first weighted variation parameter into the preset settlement prediction model, a more accurate settlement prediction value is obtained.

Description

Method, device and computer storage medium for subsidence prediction of composite formation

technical field

The present application relates to the field of tunnel engineering, and in particular, to a method, device and computer storage medium for subsidence prediction of composite strata.

Background technique

In the process of tunnel excavation, due to the complex geological characteristics and geotechnical properties of the composite stratum, the occurrence of construction safety accidents can be effectively prevented by predicting the key factors (such as settlement value) that affect the safety of engineering construction.

At present, the finite element method is commonly used in the mechanical analysis of the surrounding rock of the tunnel composite stratum, or the thickness-weighted average method is used to approximate the property parameters of multiple strata to be considered as a homogeneous layer, and then the analytical method can be used. Even if the theoretical calculation method itself makes the results more accurate, due to the practical particularity of the composite strata, there is still a big difference between the theoretical calculation prediction value of land subsidence and the construction site monitoring value.

SUMMARY OF THE INVENTION

The present application aims to solve at least one of the technical problems existing in the prior art. To this end, the present application provides a method, device and computer storage medium for subsidence prediction of composite strata, which can improve the accuracy of subsidence prediction value prediction.

A method for subsidence prediction of a composite formation according to an embodiment of the present application, the method includes:

obtaining the first deformation modulus and the deformation modulus weight of each geological layer of the composite formation to be predicted;

weighting each of the first deformation modulus and the corresponding deformation modulus weight to obtain a second deformation modulus of the composite formation;

calculating the weighted mean square error of each of the first deformation modulus and the second deformation modulus;

obtaining the first weighted coefficient of variation of the composite formation according to the weighted mean square error and the second deformation modulus;

The first weighted coefficient of variation is predicted by a preset subsidence prediction model to obtain a subsidence prediction value of the composite formation.

According to the above-mentioned embodiments of the present application, at least the following beneficial effects are obtained: the second deformation modulus of the composite formation is obtained by weighting each first deformation modulus, and each first deformation module and the second deformation module are weighted. The weighted mean square error processing can obtain a parameter representing the complexity of the property change of the composite formation, that is, the first weighted variation coefficient, so that a more accurate settlement prediction value can be obtained by introducing the first weighted variation parameter into the preset settlement prediction model. .

According to the method for subsidence prediction of composite strata according to some embodiments of the present application, the obtaining the deformation modulus weight of each geological layer of the composite stratum to be predicted includes:

obtaining the cross-sectional area of each of said geological layers;

According to each of the cross-sectional areas, the area ratio of the corresponding geological layer is obtained;

Each of the area proportions is set as the deformation modulus weight of the corresponding geological layer.

Therefore, by setting the area ratio of the section area as the deformation modulus weight, the deformation modulus relationship between each geological layer and the composite formation can be described more accurately.

According to the method for subsidence prediction of a composite formation according to some embodiments of the present application, the calculating the weighted mean square error of each of the first deformation modulus and the second deformation modulus includes:

obtaining the sum of squares of the difference between each of the first deformation modulus and the second deformation modulus, to obtain the modulus difference corresponding to the geological layer;

Perform weighting processing on each of the modulus differences and the corresponding deformation modulus weights to obtain the total modulus difference of the composite formation;

The square root of the mean of the total modulus differences is set as the weighted mean square error.

Therefore, in the process of obtaining the mean square error of the first deformation modulus, the weight of the deformation modulus is introduced, so that the weighted coefficient of variation can be obtained more in line with the actual change.

According to the method for subsidence prediction of a composite formation according to some embodiments of the present application, the method further includes:

Creating a settlement prediction model, wherein the creating a settlement prediction model specifically includes:

Acquiring multiple sets of sample data, wherein the sample data includes an actual sedimentation value, a first maximum sedimentation theoretical value, and a second weighted coefficient of variation;

establishing the relationship equation between the actual settlement value, the first maximum settlement theoretical value, and the second weighted coefficient of variation;

The multiple sets of the sample data are fitted through the relational equation to obtain the settlement prediction model.

Therefore, by fitting the sample data, the coefficients corresponding to the independent variables in the relational equation can be obtained, so that the settlement prediction model can be obtained.

According to the method for subsidence prediction of a composite formation according to some embodiments of the present application, the sample data further includes a first shield penetration degree and a first horizontal compliance coefficient;

The establishment of the relationship equation between the actual settlement value, the first maximum settlement theoretical value, and the second weighted coefficient of variation includes:

Setting the ratio of the actual settlement value to the penetration degree of the first shield as a dependent variable;

Setting the ratio of the first maximum settlement theoretical value to the first horizontal compliance coefficient and the second weighted coefficient of variation as independent variables;

According to the dependent variable and the independent variable, the relational equation is established.

According to the method for subsidence prediction of a composite stratum according to some embodiments of the present application, the first weighted coefficient of variation is predicted by a preset subsidence prediction model to obtain a subsidence predicted value of the composite stratum, including:

Get the maximum effective thrust of the current shield machine;

The maximum effective thrust is processed by the finite layer method to obtain the second theoretical maximum settlement value of the composite stratum and the horizontal displacement of the shield tunneling machine;

Obtain the second horizontal compliance coefficient of the shield machine according to the horizontal displacement and the maximum effective thrust;

Input the second maximum settlement theoretical value, the second horizontal compliance coefficient, the preset second shield penetration degree and the first weighted variation coefficient into the settlement prediction model, and output the settlement Predictive value.

Therefore, by revising the theoretically obtained second maximum settlement theoretical value, the accuracy of the theoretical prediction can be improved, thereby making the settlement prediction value more reference.

A device for predicting the subsidence prediction value of a composite formation according to an embodiment of the present application, the device includes:

at least one processor, and,

a memory communicatively coupled to the at least one processor; wherein,

The memory stores instructions, the instructions are executed by the at least one processor, so that when the at least one processor executes the instructions, the method for subsidence prediction of a composite formation according to any one of the first aspects is implemented.

A computer-readable storage medium according to an embodiment of the present application, where the computer-readable storage medium stores computer-executable instructions, where the computer-executable instructions are used to cause a computer to execute the compound according to any one of the first aspects Methods for subsidence prediction of formations.

Additional aspects and advantages of the present application will be set forth, in part, from the following description, and in part will become apparent from the following description, or may be learned by practice of the present application.

Description of drawings

The above and/or additional aspects and advantages of the present application will become apparent and readily understood from the following description of embodiments in conjunction with the accompanying drawings, wherein:

Fig. 1 is the method flow schematic diagram of the subsidence prediction of the composite formation of the embodiment of the present application;

FIG. 2 is a schematic flowchart of obtaining the first deformation modulus and the weight of the deformation modulus according to an embodiment of the present application;

3 is a schematic flowchart of the weight mean square error acquisition according to an embodiment of the present application;

Fig. 4 is the schematic flow chart of the settlement prediction model acquisition of the embodiment of the present application;

5 is a schematic flowchart of step S620 of the method for subsidence prediction of a composite formation according to an embodiment of the present application;

FIG. 6 is a schematic diagram of a flow chart of obtaining a settlement prediction value according to an embodiment of the present application.

Detailed ways

The following describes in detail the embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein the same or similar reference numerals refer to the same or similar elements or elements having the same or similar functions throughout. The embodiments described below with reference to the accompanying drawings are exemplary and are only used to explain the present application, but should not be construed as a limitation on the present application.

The method, apparatus and computer storage medium for subsidence prediction of the composite formation of the present application will be described below with reference to FIGS. 1 to 6 .

As shown in FIG. 1 , according to an embodiment of the present application, a method for predicting the settlement of a composite formation, the method includes:

Step S100: Obtain the first deformation modulus and deformation modulus weight of each geological layer of the composite formation to be predicted.

It should be noted that the composite formation is composed of two or more different formations; each geological layer has a first deformation modulus, wherein the deformation modulus is the compressibility index obtained through the field load test, That is, the ratio of the stress increment to the corresponding strain increment under partial confinement conditions. Therefore, the first deformation modulus can be detected by the tool.

Step S200: Perform weighting processing on each first deformation modulus and the corresponding deformation modulus weight to obtain a second deformation modulus of the composite formation.

It should be noted that, the weighting process is to perform an accumulation process after multiplying each first deformation modulus by the corresponding deformation modulus weight. Therefore, the second deformation modulus of the composite formation can be obtained.

Step S300: Calculate the weighted mean square error of each of the first deformation modulus and the second deformation modulus.

Step S400 , obtaining the first weighted coefficient of variation of the composite formation according to the weighted mean square error and the second deformation modulus.

It should be noted that the first weighted coefficient of variation is used to represent the complexity of the property change of the composite formation; the larger the first weighted coefficient of variation, the greater the dispersion of the first deformation modulus of each layer distributed in the profile.

Step S500: Predict the first weighted coefficient of variation through a preset subsidence prediction model to obtain a subsidence prediction value of the composite stratum.

Therefore, the second deformation modulus of the composite formation is obtained by weighting each first deformation modulus, and the weight mean square error processing is performed on each first deformation module and the second deformation module to obtain the property change of the composite formation. The characterization parameter of the complexity degree, namely the first weighted variation coefficient, so that a more accurate settlement prediction value can be obtained by introducing the first weighted variation parameter into the preset settlement prediction model.

It can be understood that, as shown in FIG. 2 , the acquisition of the deformation modulus weight in step S100 includes:

Step S110, acquiring the cross-sectional area of each geological layer.

Step S120: Obtain the area ratio of the corresponding geological layer according to the area of each section.

Step S130, setting each area ratio as the deformation modulus weight of the corresponding geological layer.

It should be noted that it is assumed that the geological layers are sequentially numbered from 1 to i; for the i-th geological layer, its section area is A _i . Then the deformation modulus weight λ _i of the i-th geological layer is:

Therefore, by setting the area ratio of the section area as the deformation modulus weight, the deformation modulus relationship between each geological layer and the composite formation can be described more accurately.

It should be noted that, at this time, referring to step S200 to obtain the second deformation modulus E _c as follows:

It can be understood that, as shown in FIG. 3 , step S300 includes:

Step S310: Obtain the sum of the squares of the difference between each first deformation modulus and the second deformation modulus to obtain the modulus difference corresponding to the geological layer.

Step S320: Perform weighting processing on each modulus difference and the corresponding deformation modulus weight to obtain the total modulus difference of the composite formation.

It should be noted that the total modulus difference of the composite bottom layer can be obtained by multiplying each modulus difference and the corresponding deformation weight, and then accumulating them in sequence.

Step S330, setting the square root of the mean value of the total modulus difference as the weighted mean square error.

It should be noted that, assuming that the first deformation modulus is E _i and the second deformation modulus is E _c ; then the first weighted coefficient of variation COV _E is:

Among them, λ _i is the deformation modulus weight of the ith geological layer. n represents the number of layers of geological layers in the composite formation.

Therefore, in the process of obtaining the mean square error of the first deformation modulus, the weight of the deformation modulus is introduced, so that the obtained weighted coefficient of variation can be more in line with the actual change.

It can be understood that, as shown in FIG. 4 , before step S500, the method for predicting the settlement of the composite stratum further includes creating a settlement prediction model, wherein the creation of the settlement prediction model specifically includes:

Step S610: Acquire multiple sets of sample data, wherein the sample data includes the actual subsidence value, the first maximum subsidence theoretical value, and the second weighted coefficient of variation.

Step S620, establishing a relationship equation between the actual settlement value, the first maximum settlement theoretical value, and the second weighted coefficient of variation.

Step S630 , performing fitting processing on multiple sets of sample data through relational equations to obtain a settlement prediction model.

Therefore, by fitting the sample data, the coefficients corresponding to the independent variables in the relational equation can be obtained, so that the settlement prediction model can be obtained.

It can be understood that the sample data also includes the first shield penetration and the first horizontal compliance coefficient.

It should be noted that the first horizontal compliance coefficient is the ratio of the effective thrust per unit applied by the shield machine to the generated horizontal displacement.

Step S620, as shown in Figure 5, includes:

Step S621, setting the ratio of the actual settlement value to the penetration degree of the first shield as the dependent variable.

Step S622: Set the ratio of the first theoretical maximum settlement value to the first horizontal compliance coefficient and the second weighted coefficient of variation as independent variables.

Step S623, establishing a relational equation according to the dependent variable and the independent variable.

两个自变量分别为

和C_I，其中，w_s表示实际沉降值，f表示第一盾构贯入度，δ表示第一水平柔度系数，w_m表示第一最大沉降理论值；C_I表示第二加权变异系数；则关系等式如下：It should be noted that it is assumed that the dependent variable is

The two independent variables are

and C _I , where _ws represents the actual settlement value, f represents the first shield penetration, δ represents the first horizontal compliance coefficient, w _m represents the first theoretical maximum settlement value; C _I represents the second weighted coefficient of variation ; then the relational equation is as follows:

和自变量C_I的系数，当进行拟合处理后，可以得到一个确定的a、b值；因此当f、δ、w_m、C_I可以根据待测的复合底层的测量数据得到，从而反推出w_s的值，此时，w_s作为因变量计算沉降预测值。where a and b are independent variables, respectively

and the coefficient of the independent variable C _I , after fitting, a definite value of a and b can be obtained; therefore, when f, δ, w _m , and C _I can be obtained according to the measurement data of the composite bottom layer to be measured, thus inversely The value of _ws is derived. At this time, _ws is used as the dependent variable to calculate the predicted value of settlement.

It can be understood that, at this time, according to the settlement prediction model for which the independent variable coefficients have been obtained, the settlement prediction value is obtained with reference to step S400. As shown in Figure 6, step S500 includes:

S510. Obtain the maximum effective thrust of the current shield machine.

It should be noted that the total thrust of the shield tunneling machine can be obtained through the empirical formula of shield tunnel mechanics, so that the effective thrust can be obtained.

S520, the maximum effective thrust is processed by the finite layer method to obtain the second maximum subsidence theoretical value of the composite stratum and the horizontal displacement of the shield tunneling machine.

It should be noted that the horizontal displacement is the horizontal displacement generated by the maximum effective thrust of the shield machine applied unit.

S530. Obtain the second horizontal compliance coefficient of the shield machine according to the horizontal displacement and the maximum effective thrust;

It should be noted that, assuming that δ' represents the second horizontal compliance coefficient, the maximum effective thrust is F', and the horizontal displacement is u; then δ'=u/F'.

S540. Input the second maximum settlement theoretical value, the second horizontal compliance coefficient, the preset second shield penetration degree and the first weighted variation coefficient into the settlement prediction model, and output the settlement prediction value.

It should be noted that it is assumed that _ws ' represents the predicted value of settlement, f' represents the second shield penetration, w _m ' represents the second maximum theoretical value of settlement; _COVE represents the first weighted coefficient of variation; it can be seen that the predicted settlement of The value ws ' has the following relationship with _f ', δ', w _m ', _COVE :

Therefore, by revising the theoretically obtained second maximum settlement theoretical value, the accuracy of the theoretical prediction can be improved, thereby making the settlement prediction value more reference.

A device for predicting the subsidence prediction value of a composite stratum according to an embodiment of the present application, the device includes:

at least one processor, and,

a memory communicatively coupled to the at least one processor; wherein,

The memory stores instructions, which are executed by the at least one processor, so that when the at least one processor executes the instructions, the method for subsidence prediction of a composite formation according to any one of the first aspects is implemented.

According to a computer-readable storage medium according to an embodiment of the present application, the computer-readable storage medium stores computer-executable instructions, and the computer-executable instructions are used to make a computer execute the method for subsidence prediction of a composite formation according to any one of the first aspect .

It should be noted that the term computer storage media includes volatile and nonvolatile, removable, removable storage media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. removable and non-removable media. Computer storage media include, but are not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital versatile disk (DVD) or other optical disk storage, magnetic cartridges, magnetic tape, magnetic disk storage or other magnetic storage devices, or may Any other medium used to store desired information and which can be accessed by a computer.

It should be noted that, those of ordinary skill in the art can understand that all or some of the steps in the methods disclosed above can be implemented as software, firmware, hardware, and appropriate combinations thereof.

The method for subsidence prediction of a composite formation according to an embodiment of the present application will be described in detail below with reference to FIGS. 1 to 6 with a specific embodiment. It is to be understood that the following description is illustrative only and not specific to the application.

As shown in FIG. 1 , referring to step S100 , the first deformation modulus and the deformation modulus weight of each geological layer are obtained.

Specifically, referring to steps S110 to S140 shown in FIG. 2 , the deformation modulus weight of each geological layer is obtained

Further, referring to step S200, the second deformation modulus is obtained

Further, referring to steps S310 to S330 and S400, the first weighted coefficient of variation of the composite formation is obtained

Further, referring to step S600, multiple groups of sample data are obtained to obtain a settlement prediction model

Further, referring to step S400, the relevant parameters such as the first weighted coefficient of variation are processed through the settlement prediction model to obtain the settlement prediction value.

得到沉降预测值w_s'。At this time, as shown in steps S410 to 440, by

The settlement prediction value _ws ' is obtained.

In the description of this specification, reference to the terms "one embodiment," "some embodiments," "exemplary embodiment," "example," "specific example," or "some examples", etc., is meant to incorporate the embodiments A particular feature, structure, material, or characteristic described by an example or example is included in at least one embodiment or example of the present application. In this specification, schematic representations of the above terms do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.

Although the embodiments of the present application have been shown and described, it will be understood by those of ordinary skill in the art that various changes, modifications, substitutions and alterations can be made in these embodiments without departing from the principles and spirit of the present application, The scope of the application is defined by the claims and their equivalents.

Claims (8)

1. a method for subsidence prediction of composite formation, characterized in that the method comprises: obtaining the first deformation modulus and the deformation modulus weight of each geological layer of the composite formation to be predicted; weighting each of the first deformation modulus and the corresponding deformation modulus weight to obtain a second deformation modulus of the composite formation; calculating the weighted mean square error of each of the first deformation modulus and the second deformation modulus; obtaining the first weighted coefficient of variation of the composite formation according to the weighted mean square error and the second deformation modulus; The first weighted coefficient of variation is predicted by a preset subsidence prediction model to obtain a subsidence prediction value of the composite formation. 2. The method for subsidence prediction of composite formation according to claim 1, characterized in that, The obtaining the deformation modulus weight of each geological layer of the composite formation to be predicted includes: obtaining the cross-sectional area of each of said geological layers; According to each of the cross-sectional areas, the area ratio of the corresponding geological layer is obtained; Each of the area proportions is set as the deformation modulus weight of the corresponding geological layer. 3. The method for subsidence prediction of composite strata according to claim 1, characterized in that, The calculating the weighted mean square error of each of the first deformation modulus and the second deformation modulus includes: obtaining the sum of squares of the difference between each of the first deformation modulus and the second deformation modulus, to obtain the modulus difference corresponding to the geological layer; Perform weighting processing on each of the modulus differences and the corresponding deformation modulus weights to obtain the total modulus difference of the composite formation; The square root of the mean of the total modulus differences is set as the weighted mean square error. 4. The method for subsidence prediction of composite strata according to claim 1, characterized in that, further comprising: Creating a settlement prediction model, wherein the creating a settlement prediction model specifically includes: Acquiring multiple sets of sample data, wherein the sample data includes an actual sedimentation value, a first maximum sedimentation theoretical value, and a second weighted coefficient of variation; establishing the relationship equation between the actual settlement value, the first maximum settlement theoretical value, and the second weighted coefficient of variation; The multiple sets of the sample data are fitted through the relational equation to obtain the settlement prediction model. 5. The method for subsidence prediction of composite strata according to claim 4, characterized in that, The sample data further includes a first shield penetration and a first horizontal compliance coefficient; The establishment of the relationship equation between the actual settlement value, the first maximum settlement theoretical value, and the second weighted coefficient of variation includes: Setting the ratio of the actual settlement value to the penetration degree of the first shield as a dependent variable; Setting the ratio of the first maximum settlement theoretical value to the first horizontal compliance coefficient and the second weighted coefficient of variation as independent variables; According to the dependent variable and the independent variable, the relational equation is established. 6. The method for subsidence prediction of composite strata according to claim 5, characterized in that, Predicting the first weighted coefficient of variation through a preset settlement prediction model to obtain a settlement prediction value of the composite stratum, including: Get the maximum effective thrust of the current shield machine; The maximum effective thrust is processed by the finite layer method to obtain the second maximum subsidence theoretical value of the composite stratum and the horizontal displacement of the shield tunneling machine; Obtain the second horizontal compliance coefficient of the shield machine according to the horizontal displacement and the maximum effective thrust; Input the second maximum settlement theoretical value, the second horizontal compliance coefficient, the preset second shield penetration degree and the first weighted variation coefficient into the settlement prediction model, and output the settlement Predictive value. 7. A device for predicting the subsidence of composite strata, characterized in that it comprises: at least one processor, and, a memory communicatively coupled to the at least one processor; wherein, The memory stores instructions that are executed by the at least one processor to enable the at least one processor to implement the subsidence prediction of the composite formation as claimed in any one of claims 1 to 6 when the at least one processor executes the instructions. method. 8. A computer-readable storage medium, characterized in that the computer-readable storage medium stores computer-executable instructions, the computer-executable instructions being used to cause a computer to execute any one of claims 1 to 6 A method for subsidence prediction of composite formations.

Images