CN112861207B - Method, apparatus and computer storage medium for subsidence prediction of a composite formation - Google Patents
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Abstract
The application discloses a method, equipment and a computer storage medium for predicting settlement of a composite stratum, and relates to the field of tunnel engineering, wherein the method comprises the steps of obtaining a first deformation modulus and a deformation modulus weight of each geological stratification of the composite stratum to be predicted; weighting each first deformation modulus and the corresponding deformation modulus weight to obtain a second deformation modulus of the composite stratum; calculating the weight mean square error of each first deformation modulus and each second deformation modulus; obtaining a first weighted variation coefficient of the composite stratum according to the weight mean square error of each first deformation modulus and the second deformation modulus; and predicting the first weighted variation coefficient through a preset settlement prediction model to obtain a settlement prediction value of the composite stratum. And introducing the first weighted variation parameter into a preset sedimentation prediction model to obtain a more accurate sedimentation prediction value.
Description
Technical Field
The present disclosure relates to the field of tunnel engineering, and in particular, to a method and apparatus for predicting settlement of a composite stratum, and a computer storage medium.
Background
In the tunneling process, the composite stratum has complex geological characteristics and rock-soil properties, so that the occurrence of construction safety accidents can be effectively prevented by predicting key factors (such as sedimentation values) affecting the construction safety of the restricted engineering.
At present, a finite element method is commonly used for mechanical analysis of tunnel composite stratum surrounding rock, or a method for adopting thickness weighted average to approximate multi-stratum property parameters to be considered as a homogeneous layer, and then an analytic method can be adopted. Even though the theoretical calculation method makes the result more accurate, the theoretical calculation predicted value of the ground subsidence still has a larger difference from the monitoring value of the construction site due to the reality specificity of the composite stratum.
Disclosure of Invention
The present application aims to solve at least one of the technical problems existing in the prior art. Therefore, the application provides a method, equipment and a computer storage medium for predicting the settlement of a composite stratum, which can improve the accuracy of predicting the settlement predicted value.
A method of subsidence prediction for a composite formation according to an embodiment of the present application, the method comprising:
acquiring a first deformation modulus and a deformation modulus weight of each geological stratification of the composite stratum to be predicted;
weighting each first deformation modulus and the corresponding deformation modulus weight to obtain a second deformation modulus of the composite stratum;
calculating the weight mean square error of each first deformation modulus and each second deformation modulus;
obtaining a first weighted variation coefficient of the composite stratum according to the weight mean square error and the second deformation modulus;
and predicting the first weighted variation coefficient through a preset settlement prediction model to obtain a settlement prediction value of the composite stratum.
According to the embodiment of the application, at least the following beneficial effects are achieved: the first deformation modulus is respectively weighted to obtain the second deformation modulus of the composite stratum, and the weight mean square error is processed on each first deformation module and each second deformation module, so that the characterization parameter of the complexity degree of the composite stratum property change, namely the first weighted variation coefficient, can be obtained, and a relatively accurate sedimentation prediction value can be obtained by introducing the first weighted variation parameter into a preset sedimentation prediction model.
According to some embodiments of the present application, the method for obtaining deformation modulus weights of each geological stratification of a composite formation to be predicted includes:
acquiring the cross-sectional area of each geological stratification;
obtaining the area occupation ratio of the corresponding geological stratification according to each section area;
and setting each area ratio as the deformation modulus weight of the corresponding geological stratification.
Therefore, by setting the area ratio of the cross-sectional area to the deformation modulus weight, the deformation modulus relationship between each geologic formation and the composite formation can be described more accurately.
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 differences of the first deformation modulus and the second deformation modulus to obtain a modulus difference corresponding to the geological stratification;
carrying out weight treatment on each modulus difference and the corresponding deformation modulus weight to obtain the total modulus difference of the composite stratum;
and setting the square of the mean value of the total modulus difference as the weight mean square error.
Therefore, the deformation modulus weight is introduced in the process of carrying out the mean square error calculation on the first deformation modulus, so that the obtained weighted variation coefficient can be more in line with the actual variation.
Methods of subsidence prediction for a composite formation according to some embodiments of the present application, the methods further comprising:
creating a settlement prediction model, wherein the creating the settlement prediction model specifically comprises:
obtaining a plurality of groups of sample data, wherein the sample data comprises an actual sedimentation value, a first maximum sedimentation theoretical value and a second weighted variation coefficient;
establishing a relation equation of the actual sedimentation value, the first maximum sedimentation theoretical value and the second weighted variation coefficient;
and fitting a plurality of groups of sample data through the relational equation to obtain the sedimentation prediction model.
Therefore, by fitting the sample data, coefficients corresponding to the independent variables in the relational equation can be obtained, and thus a sedimentation prediction model can be obtained.
Methods of subsidence prediction for a composite formation according to some embodiments of the present application, the sample data further comprising a first shield penetration and a first horizontal compliance coefficient;
the establishing a relation equation of the actual sedimentation value, the first maximum sedimentation theoretical value and the second weighted variation coefficient comprises the following steps:
setting the ratio of the actual sedimentation value to the first shield penetration as a dependent variable;
setting the ratio of the first maximum sedimentation theoretical value to the first horizontal flexibility coefficient and the second weighted variation coefficient as independent variables;
and establishing the relation equation according to the dependent variable and the independent variable.
According to some embodiments of the present application, the predicting the first weighted variation coefficient through a preset settlement prediction model to obtain a settlement prediction value of the composite stratum includes:
obtaining the maximum effective thrust of the current shield tunneling machine;
processing the maximum effective thrust by a finite layer method to obtain a second maximum settlement theoretical value of the composite stratum and horizontal displacement of the shield tunneling machine;
obtaining a second horizontal flexibility coefficient of the shield tunneling machine according to the horizontal displacement and the maximum effective thrust;
and inputting the second maximum settlement theoretical value, the second horizontal compliance coefficient, the preset second shield penetration and the first weighted variation coefficient into the settlement prediction model, and outputting the settlement prediction value.
Therefore, by correcting the theoretical second maximum sedimentation theoretical value obtained in theory, the accuracy of theoretical prediction can be improved, so that the sedimentation predicted value is more reference.
An apparatus for settlement prediction value prediction of a composite formation according to an embodiment of the present application, the apparatus comprising:
at least one processor, and,
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores instructions that are executable by the at least one processor to cause the at least one processor to perform the method of subsidence prediction of a composite formation according to any of the first aspects when the instructions are executed.
A computer-readable storage medium according to an embodiment of the present application stores computer-executable instructions for causing a computer to perform a method of subsidence prediction of a composite formation as set forth in any one of the first aspects.
Additional aspects and advantages of the application will be set forth in part in the description which follows, and in part will be obvious from the description, or may be learned by practice of the application.
Drawings
The foregoing and/or additional aspects and advantages of the present application will become apparent and readily appreciated from the following description of the embodiments, taken in conjunction with the accompanying drawings, wherein:
FIG. 1 is a schematic flow diagram of a method of subsidence prediction for a composite formation in accordance with an embodiment of the present application;
FIG. 2 is a flow chart of a first deformation modulus and deformation modulus weight acquisition according to an embodiment of the present application;
FIG. 3 is a schematic flow chart of weight mean square error acquisition according to an embodiment of the present application;
FIG. 4 is a schematic flow diagram of a settlement prediction model acquisition according to an embodiment of the present application;
FIG. 5 is a flow chart of step S620 of a method of subsidence prediction for a composite formation according to an embodiment of the present application;
fig. 6 is a schematic diagram of a settlement prediction value obtaining flow according to the embodiment of the application.
Detailed Description
Embodiments of the present application are described in detail below, 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 by referring to the drawings are exemplary only for the purpose of explaining the present application and are not to be construed as limiting the present application.
Methods, apparatus, and computer storage media for subsidence prediction of a composite formation of the present application are described below with reference to fig. 1-6.
As shown in fig. 1, a method for subsidence prediction of a composite formation according to an embodiment of the present application includes:
step S100, obtaining a first deformation modulus and a deformation modulus weight of each geological stratification of the composite stratum to be predicted.
It should be noted that the composite stratum is composed of two or more different strata; each geological stratification has a first deformation modulus, wherein the deformation modulus is an index of compressibility obtained by in-situ load testing, i.e. the ratio of the stress increment to the corresponding strain increment under partial lateral conditions. Thus, the first deformation modulus can be detected by the tool.
And step 200, weighting each first deformation modulus and the corresponding deformation modulus weight to obtain a second deformation modulus of the composite stratum.
The weighting process is to multiply each first deformation modulus with the corresponding deformation modulus weight and then to perform the accumulation process. Thus, a second deformation modulus of the composite formation may be obtained.
And step S300, calculating the weight mean square error of each first deformation modulus and each second deformation modulus.
And step 400, obtaining a first weighted variation coefficient of the composite stratum according to the weight mean square error and the second deformation modulus.
It should be noted that the first weighted coefficient of variation is used to characterize the complexity of the change in the properties of the composite formation; the larger the first weighted coefficient of variation is, the larger the first deformation modulus of each layer of the profile distribution is.
And S500, predicting the first weighted variation coefficient through a preset settlement prediction model to obtain a settlement prediction value of the composite stratum.
Therefore, the second deformation modulus of the composite stratum is obtained by respectively carrying out weight treatment on each first deformation modulus, and the characterization parameters of the complexity degree of the property change of the composite stratum, namely the first weight variation coefficient, can be obtained by carrying out weight mean square error treatment on each first deformation module and each second deformation module, so that a relatively accurate settlement prediction value can be obtained by introducing the first weight variation parameter into a 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, the cross-sectional area of each geological stratification is obtained.
And step S120, obtaining the area occupation ratio of the corresponding geological stratification according to each section area.
And step S130, setting the area ratio of each area as the deformation modulus weight of the corresponding geological stratification.
It is to be noted that it is assumed that geologic strata are numbered sequentially from 1 to i; for the ith geologic formation, its cross-sectional area is A i . The deformation modulus weight lambda of the ith geologic formation i The method comprises the following steps:
therefore, by setting the area ratio of the cross-sectional area to the deformation modulus weight, the deformation modulus relationship between each geologic formation and the composite formation can be described more accurately.
At this time, the second deformation modulus E is obtained with reference to step S200 c The following are provided:
it may be understood that, as shown in fig. 3, step S300 includes:
and step S310, obtaining the sum of squares of the difference values of each first deformation modulus and each second deformation modulus, and obtaining the modulus difference of the corresponding geological stratification.
And step 320, carrying out weight treatment on each modulus difference and the corresponding deformation modulus weight to obtain the total modulus difference of the composite stratum.
It should be noted that, after multiplying each modulus difference by the corresponding deformation weight, the modulus differences are sequentially accumulated, so as to obtain the total modulus difference of the composite bottom layer.
Step S330, the evolution of the mean value of the total modulus difference is set as the weight mean square error.
It is to be noted that the first deformation modulus is assumed to be E i A second deformation modulus of E c The method comprises the steps of carrying out a first treatment on the surface of the Then the first weighted coefficient of variation COV E The method comprises the following steps:
wherein lambda is i Deformation modulus weights for the ith geologic formation. n represents the number of layers of geologic layering in the composite formation.
Therefore, in the process of carrying out the mean square error calculation on the first deformation modulus, deformation modulus weight is introduced, so that the obtained weighted variation coefficient can be more in line with actual variation.
It will be understood that, as shown in fig. 4, before step S500, the method for predicting the subsidence of the composite formation further includes creating a subsidence prediction model, where creating the subsidence prediction model specifically includes:
step S610, a plurality of groups of sample data are obtained, wherein the sample data comprise an actual sedimentation value, a first maximum sedimentation theoretical value and a second weighted variation coefficient.
And S620, establishing a relation equation of the actual sedimentation value, the first maximum sedimentation theoretical value and the second weighted variation coefficient.
And step 630, fitting the plurality of groups of sample data through a relation equation to obtain a sedimentation prediction model.
Therefore, by fitting the sample data, coefficients corresponding to the independent variables in the relational equation can be obtained, and thus a sedimentation prediction model can be obtained.
It is understood that the sample data also includes a first shield penetration and a first horizontal compliance coefficient.
The first horizontal compliance coefficient is the ratio of the effective thrust of the shield machine applied unit to the generated horizontal displacement.
Step S620, as shown in fig. 5, includes:
and S621, setting the ratio of the actual sedimentation value to the first shield penetration as a dependent variable.
In step S622, the ratio of the first maximum sedimentation theoretical value to the first horizontal compliance coefficient and the second weighted variation coefficient are set as independent variables.
Step S623, establishing a relation equation according to the dependent variable and the independent variable.
It is to be noted that the dependent variable is assumed to be
Two independent variables are +.>
And C I Wherein w is s Represents the actual sedimentation value, f represents the first shield penetration, delta represents the first horizontal compliance coefficient, w m Representing a first maximum sedimentation theory value; c (C) I Representing a second weighted coefficient of variation; the relationship equation is as follows:
wherein a and b are independent variables respectively
And independent variable C I When fitting is performed, a determined a and b value can be obtained; thus when f, delta, w m 、C I Can be obtained according to the measured data of the composite bottom layer to be measured, thereby reversely deducing w s At this time, w s The sedimentation prediction value was calculated as a dependent variable.
It is understood that in this case, the sedimentation prediction value is obtained with reference to step S400 based on the sedimentation prediction model having the obtained independent coefficient. As shown in fig. 6, step S500 includes:
s510, obtaining the maximum effective thrust of the current shield tunneling machine.
The total thrust of the tunneling of the shield machine can be obtained through a shield tunnel mechanical empirical formula, so that the effective thrust can be obtained.
S520, processing the maximum effective thrust by a finite layer method to obtain a second maximum settlement theoretical value of the composite stratum and horizontal displacement of the shield tunneling machine.
The horizontal displacement is the amount of horizontal displacement generated by the maximum effective thrust per unit applied by the shield machine.
S530, obtaining a second horizontal flexibility coefficient of the shield machine according to the horizontal displacement and the maximum effective thrust;
it should be noted that, assuming δ 'represents the second coefficient of horizontal compliance, the maximum effective thrust is F', and the horizontal displacement is u; delta '=u/F'.
S540, inputting a second maximum settlement theoretical value, a second horizontal compliance coefficient, a preset second shield penetration and a first weighted variation coefficient into a settlement prediction model, and outputting a settlement prediction value.
It is to be noted that let w s 'represents a settlement predicted value, f' represents a second shield penetration, w m ' represents a second maximum sedimentation theory value; COV (chip on board) E Representing a first weighted coefficient of variation; it can be seen that the sedimentation prediction value w s ' and f ', delta ', w m '、COV E Has the following relationship:
therefore, by correcting the theoretical second maximum sedimentation theoretical value obtained in theory, the accuracy of theoretical prediction can be improved, so that the sedimentation predicted value is more reference.
An apparatus for subsidence prediction of a composite formation according to an embodiment of the present application, the apparatus comprising:
at least one processor, and,
a memory communicatively coupled to the at least one processor; wherein,,
the memory stores instructions that are executable by the at least one processor to cause the at least one processor to perform a method of subsidence prediction of a composite formation according to any of the first aspects when the instructions are executed.
According to an embodiment of the present application, a computer-readable storage medium stores computer-executable instructions for causing a computer to perform a method of subsidence prediction of a composite formation according to any one of the first aspects.
It is noted that the term computer storage media includes volatile and nonvolatile, removable and non-removable media implemented in any method or technology for storage of information such as computer readable instructions, data structures, program modules or other data. Computer storage media includes, but is not limited to, RAM, ROM, EEPROM, flash memory or other memory technology, CD-ROM, digital Versatile Disks (DVD) or other optical disk storage, magnetic cassettes, magnetic tape, magnetic disk storage or other magnetic storage devices, or any other medium which can be used to store the desired information and which can be accessed by a computer.
It is to be understood by one of ordinary skill in the art that all or some of the steps of the methods disclosed above may be implemented as software, firmware, hardware, and suitable combinations thereof.
A method of subsidence prediction of a composite formation according to embodiments of the present application is described in detail below with reference to fig. 1-6 in one particular embodiment. It is to be understood that the following description is exemplary only and is not intended to limit the application to the details of construction and the arrangements of the components set forth herein.
As shown in fig. 1, referring to step S100, a first deformation modulus and a deformation modulus weight are obtained for each geologic formation.
Specifically, referring to steps S110 to S140 shown in fig. 2, the deformation modulus weight of each geologic layer is obtained
Further, referring to step S200, a second deformation modulus is obtained
Further, referring to steps S310 to S330 and S400, a first weighted coefficient of variation of the composite formation is obtained
Further, referring to step S600, a plurality of sets of sample data are acquired to obtain a sedimentation prediction model
/>
Further, referring to step S400, the sedimentation prediction value is obtained by processing the relevant parameters such as the first weighted coefficient of variation and the like by the sedimentation prediction model.
At this time, as shown in steps S410 to 440, the process proceeds to
Obtaining a sedimentation predicted value w s '。
In the description of the present specification, reference to the terms "one embodiment," "some embodiments," "illustrative embodiments," "examples," "specific examples," or "some examples," etc., means that a particular feature, structure, material, or characteristic described in connection with the embodiment 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 embodiments or examples. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
While embodiments of the present application have been shown and described, it will be understood by those of ordinary skill in the art that: many changes, modifications, substitutions and variations may be made to the embodiments without departing from the principles and spirit of the application, the scope of which is defined by the claims and their equivalents.
Claims (5)
1. A method of subsidence prediction of a complex formation, the method comprising:
acquiring a first deformation modulus and a deformation modulus weight of each geological stratification of the composite stratum to be predicted;
weighting each first deformation modulus and the corresponding deformation modulus weight to obtain a second deformation modulus of the composite stratum;
calculating the weight mean square error of each first deformation modulus and each second deformation modulus;
obtaining a first weighted variation coefficient of the composite stratum according to the weight mean square error and the second deformation modulus;
predicting the first weighted variation coefficient through a preset sedimentation prediction model to obtain a sedimentation prediction value of the composite stratum;
wherein, the creation of the sedimentation prediction model comprises:
obtaining a plurality of groups of sample data, wherein the sample data comprises an actual sedimentation value, a first maximum sedimentation theoretical value, a second weighted variation coefficient, a first shield penetration and a first horizontal compliance coefficient;
setting the ratio of the actual sedimentation value to the first shield penetration as a dependent variable;
setting the ratio of the first maximum sedimentation theoretical value to the first horizontal flexibility coefficient and the second weighted variation coefficient as independent variables;
establishing a relation equation according to the dependent variable and the independent variable;
fitting a plurality of groups of sample data through the relational equation to obtain the sedimentation prediction model;
the predicting the first weighted variation coefficient through a preset sedimentation prediction model to obtain a sedimentation prediction value of the composite stratum comprises the following steps:
obtaining the maximum effective thrust of the current shield tunneling machine;
processing the maximum effective thrust by a finite layer method to obtain a second maximum settlement theoretical value of the composite stratum and horizontal displacement of the shield tunneling machine;
obtaining a second horizontal flexibility coefficient of the shield tunneling machine according to the horizontal displacement and the maximum effective thrust;
and inputting the second maximum settlement theoretical value, the second horizontal compliance coefficient, the preset second shield penetration and the first weighted variation coefficient into the settlement prediction model, and outputting the settlement prediction value.
2. A method of subsidence prediction for a complex formation according to claim 1,
the obtaining the deformation modulus weight of each geological stratification of the composite stratum to be predicted comprises the following steps:
acquiring the cross-sectional area of each geological stratification;
obtaining the area occupation ratio of the corresponding geological stratification according to each section area;
and setting each area ratio as the deformation modulus weight of the corresponding geological stratification.
3. A method of subsidence prediction for a complex formation according to claim 1,
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 differences of the first deformation modulus and the second deformation modulus to obtain a modulus difference corresponding to the geological stratification;
carrying out weight treatment on each modulus difference and the corresponding deformation modulus weight to obtain the total modulus difference of the composite stratum;
and setting the square of the mean value of the total modulus difference as the weight mean square error.
4. An apparatus for subsidence prediction of a complex formation comprising:
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 cause the at least one processor to perform the method of subsidence prediction for a composite formation of any one of claims 1 to 3 when the instructions are executed.
5. A computer-readable storage medium storing computer-executable instructions for causing a computer to perform the method of sedimentation prediction of a composite formation according to any one of claims 1 to 3.
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