CN112861207A - Method and equipment for predicting sedimentation of composite stratum and computer storage medium - Google Patents
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Abstract
The application discloses a method, equipment and a computer storage medium for predicting the settlement of a composite stratum, which relate 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 layer 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 formation according to the weighted mean square error of each first deformation modulus and each second deformation modulus and the second deformation modulus; and predicting the first weighted variation coefficient through a preset sedimentation prediction model to obtain a sedimentation 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 application relates to the field of tunnel engineering, and in particular, to a method, an apparatus, and a computer storage medium for predicting settlement of a composite formation.
Background
In the tunneling process, the composite stratum has complex geological characteristics and geotechnical properties, so that construction safety accidents can be effectively prevented by predicting key factors (such as sedimentation value) influencing engineering construction safety.
At present, a finite element method is commonly used for mechanical analysis of tunnel composite stratum surrounding rock, or a thickness weighted average method is adopted for multi-stratum property parameters to approximate homogeneous layer consideration, and then an analytic method can be adopted. Even if the theoretical calculation method makes the result more accurate, the theoretical calculation predicted value of the ground settlement still has great difference with the construction site monitoring value due to the practical particularity of the composite stratum.
Disclosure of Invention
The present application is directed to solving at least one of the problems in the prior art. Therefore, the method, the equipment and the computer storage medium for predicting the settlement of the composite stratum are provided, and the accuracy of prediction of the settlement predicted value can be improved.
A method of subsidence prediction of a composite formation according to an embodiment of the present application, the method comprising:
acquiring a first deformation modulus and deformation modulus weight of each geological layer of a 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 formation according to the weighted mean square error and the second deformation modulus;
and predicting the first weighted variation coefficient through a preset sedimentation prediction model to obtain a sedimentation prediction value of the composite stratum.
According to the above embodiments of the present application, at least the following advantages are provided: the second deformation modulus of the composite stratum is obtained by respectively carrying out weight processing on each first deformation modulus, and weight mean square error processing is carried out on each first deformation module and each second deformation module, so that the characteristic parameter of the complexity degree of the property change of the composite stratum, namely the first weighted variation coefficient, can be obtained, and the accurate settlement prediction value can be obtained by introducing the first weighted variation parameter into a preset settlement prediction model.
According to the method for predicting the subsidence of the composite stratum, the obtaining of the deformation modulus weight of each geological stratification of the composite stratum to be predicted comprises the following steps:
acquiring the section area of each geological stratification;
obtaining the area ratio of the corresponding geological stratification according to the area of each section;
setting each of the area fractions as the deformation modulus weight of the corresponding geological stratification.
Therefore, by setting the area ratio of the cross-sectional 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 some embodiments of the present disclosure, the calculating a 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 value of each first deformation modulus and each second deformation modulus to obtain the modulus difference corresponding to the geological stratification;
weighting each modulus difference and the corresponding deformation modulus weight to obtain a total modulus difference of the composite stratum;
and setting the square of the mean of the total modulus difference as the weighted mean square error.
Therefore, the deformation modulus weight is introduced in the process of solving the mean square error of the first deformation modulus, so that the obtained weighted variation coefficient can better accord with the actual change.
A method of subsidence prediction of a composite formation according to some embodiments of the present application, the method further comprising:
creating a settlement prediction model, wherein the creating of the settlement prediction model specifically comprises:
acquiring multiple 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 settlement prediction model.
Therefore, by performing fitting processing on the sample data, coefficients corresponding to the independent variables in the relational equation can be obtained, and thus the sedimentation prediction model can be obtained.
According to some embodiments of the method for composite formation subsidence prediction, the sample data further comprises a first shield penetration and a first horizontal compliance coefficient;
the establishing of the relation equation of the actual sedimentation value, the first maximum sedimentation theoretical value and the second weighted variation coefficient includes:
setting the ratio of the actual settlement value to the first shield penetration degree 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 method for predicting the subsidence of the composite formation by using the first weighted variation coefficient through a preset subsidence prediction model to obtain a predicted subsidence value of the composite formation includes:
acquiring the maximum effective thrust of the current shield machine;
processing the maximum effective thrust by a finite layer method to obtain a second maximum settlement theoretical value of the composite stratum and the 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 sedimentation theoretical value, the second horizontal flexibility coefficient, a preset second shield penetration degree and the first weighted variation coefficient into the sedimentation prediction model, and outputting the sedimentation prediction value.
Therefore, the second maximum sedimentation theoretical value obtained theoretically is corrected, so that the accuracy of theoretical prediction can be improved, and the sedimentation prediction value is more referential.
According to the embodiment of the application, the equipment for predicting the subsidence predicted value of the composite stratum comprises the following components:
at least one processor, and,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions for execution by the at least one processor to cause the at least one processor, when executing the instructions, to implement a method of sedimentation prediction of a composite formation according to any one of the first aspect.
A computer-readable storage medium according to an embodiment of the application stores computer-executable instructions for causing a computer to perform a method of sedimentation prediction of a composite formation according to any of the first aspect.
Additional aspects and advantages of the present 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 present application.
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The above 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 of which:
FIG. 1 is a schematic flow chart of a method for subsidence prediction of a composite formation according to an embodiment of the present disclosure;
FIG. 2 is a schematic flow chart of the first deformation modulus and the weight obtaining of the deformation modulus in the 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 chart of the settlement prediction model acquisition according to the embodiment of the present application;
FIG. 5 is a flow chart illustrating step S620 of a method for predicting subsidence of a composite formation according to an embodiment of the present disclosure;
fig. 6 is a schematic diagram of a flow of obtaining a predicted value of sedimentation in an embodiment of the present application.
Detailed Description
Reference will now be made in detail to embodiments of the present application, examples of which are illustrated in the accompanying drawings, wherein like or similar reference numerals refer to the same or similar elements or elements having the same or similar function throughout. The embodiments described below with reference 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 settlement 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 predicting subsidence of a composite formation according to an embodiment of the present application includes:
s100, obtaining a first deformation modulus and a deformation modulus weight of each geological layer 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 layer has a first deformation modulus, wherein the deformation modulus is a compressibility index obtained through a field load test, namely the ratio of the stress increment to the corresponding strain increment under partial side limit conditions. Thus, the first deformation modulus can be detected by the tool.
And S200, 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 by the corresponding deformation modulus weight and then perform an accumulation process. Thus, a second deformation modulus of the composite formation may be obtained.
Step S300, calculating a weighted mean square error of each of the first deformation modulus and the second deformation modulus.
And S400, obtaining a first weighted variation coefficient of the composite formation according to the weighted mean square error and the second deformation modulus.
It should be noted that the first weighted variation coefficient is used for representing the complexity degree of the property change of the composite formation; the larger the first weighted variation coefficient is, the larger the dispersion of the first deformation modulus of each layer of the profile distribution becomes.
And S500, predicting the first weighted variation coefficient through a preset sedimentation prediction model to obtain a sedimentation prediction value of the composite stratum.
Therefore, each first deformation modulus is subjected to weight processing to obtain a second deformation modulus of the composite stratum, and each first deformation module and each second deformation module are subjected to weight mean square error processing to obtain a characterization parameter of the complexity of the property change of the composite stratum, namely a first weighted variation coefficient, so that a more accurate settlement prediction value can be obtained by introducing the first weighted variation parameter into a preset settlement prediction model.
It is understood that, as shown in fig. 2, the obtaining of the distortion modulus weight in step S100 includes:
and step S110, acquiring the section area of each geological stratification.
And S120, obtaining the area ratio of the corresponding geological stratification according to the area of each section.
Step S130, each area ratio is set as the deformation modulus weight of the corresponding geological layer.
It is assumed that geological stratification is numbered 1 to i in sequence; for the ith geological stratification, the cross-sectional area is Ai. The deformation modulus weight λ of the ith geological stratificationiComprises the following steps:
therefore, by setting the area ratio of the cross-sectional area as the deformation modulus weight, the deformation modulus relationship between each geological layer and the composite formation can be described more accurately.
Need to explainIn this case, the second deformation modulus E is obtained with reference to step S200cThe following were used:
it is understood that, as shown in fig. 3, step S300 includes:
and S310, acquiring the sum of squares of the difference value of each first deformation modulus and each second deformation modulus to obtain the modulus difference of the corresponding geological stratification.
And S320, performing weight processing 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 each modulus difference is multiplied by the corresponding deformation weight, the product is sequentially accumulated to obtain the total modulus difference of the composite bottom layer.
And step S330, setting the square of the mean value of the total modulus difference as the weight mean square error.
It is to be noted that the first deformation modulus is assumed to be EiThe second deformation modulus is Ec(ii) a The first weighted coefficient of variation COVEComprises the following steps:
wherein λ isiThe deformation modulus weight of the ith geological layer. n represents the number of geologic strata in the composite formation.
Therefore, the deformation modulus weight is introduced in the process of solving the mean square error of the first deformation modulus, so that the obtained weighted variation coefficient can better accord with the actual change.
It can 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, 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.
And S620, establishing a relation equation of the actual sedimentation value, the first maximum sedimentation theoretical value and the second weighted variation coefficient.
And S630, fitting a plurality of groups of sample data through a relational equation to obtain a settlement prediction model.
Therefore, by performing fitting processing on the sample data, coefficients corresponding to the independent variables in the relational equation can be obtained, and thus the sedimentation prediction model can be obtained.
It is understood that the sample data further includes a first shield penetration and a first horizontal compliance coefficient.
It should be noted that the first horizontal compliance coefficient is a ratio of an effective thrust applied by the shield tunneling machine in units to a generated horizontal displacement.
Step S620, as shown in fig. 5, includes:
and step S621, setting the ratio of the actual settlement value to the first shield penetration degree as a dependent variable.
Step S622, setting the ratio of the first maximum sedimentation theoretical value to the first horizontal compliance coefficient and the second weighted variation coefficient as arguments.
And step S623, establishing a relation equation according to the dependent variable and the independent variable.
It is to be noted that, assuming the dependent variable is
Two independent variables are respectively
And CIWherein w issRepresenting the actual sedimentation value, f representing the first shield penetration, δ representing the first horizontal compliance factor, wmRepresenting a first theoretical maximum sedimentation value; cIRepresenting a second weighted coefficient of variation; the relationship equation is as follows:
wherein a and b are independent variables
And independent variable CIAfter fitting processing, a determined value of a and b can be obtained; therefore, when f, δ and w arem、CICan be obtained according to the measurement data of the composite bottom layer to be measured, thereby reversely deducing wsAt this time, wsThe predicted value of sedimentation was calculated as a dependent variable.
It is understood that, at this time, the predicted value of the subsidence is obtained with reference to step S400 based on the subsidence prediction model in which the independent variable coefficients are obtained. As shown in fig. 6, step S500 includes:
and S510, obtaining the maximum effective thrust of the current shield machine.
It should be noted that the total thrust for tunneling by the shield tunneling machine can be obtained through a shield tunnel mechanics empirical formula, so that the effective thrust can be obtained.
And S520, processing the maximum effective thrust by a finite layer method to obtain a second maximum settlement 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 amount generated by the maximum effective thrust of the shield machine application unit.
S530, obtaining a second horizontal flexibility coefficient of the shield tunneling machine according to the horizontal displacement and the maximum effective thrust;
it should be noted that, assuming δ 'to represent the second horizontal compliance coefficient, the maximum effective thrust is F', and the horizontal displacement is u; then δ 'is u/F'.
And S540, inputting the second maximum sedimentation theoretical value, the second horizontal flexibility coefficient, the preset second shield penetration degree and the first weighted variation coefficient into a sedimentation prediction model, and outputting a sedimentation prediction value.
It is to be noted that let w bes'represents the predicted value of settlement, f' represents the penetration of the second shield, wm' represents the second theoretical maximum sedimentation value; COVERepresenting a first weighted coefficient of variation; then, the predicted value w of the sedimentation is knowns' and f ', delta ', wm'、COVEHas the following relationship:
therefore, the second maximum sedimentation theoretical value obtained theoretically is corrected, so that the accuracy of theoretical prediction can be improved, and the sedimentation prediction value is more referential.
According to the embodiment of the application, the equipment for predicting the settlement prediction value of the composite stratum comprises the following components:
at least one processor, and,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions for execution by the at least one processor to cause the at least one processor, when executing the instructions, to implement a method of settlement prediction of a composite formation as in any one of the first aspects.
A computer-readable storage medium according to an embodiment of the application stores computer-executable instructions for causing a computer to perform a method of sedimentation prediction of a composite formation as in any one of the first aspect.
It should be 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 accessed by a computer.
It should be noted that all or some of the steps of the methods disclosed above can be implemented as software, firmware, hardware and suitable combinations thereof, as would be understood by one of ordinary skill in the art.
The method for predicting the subsidence of a composite formation according to an embodiment of the present application is described in detail with reference to fig. 1 to 6 as a specific example. It is to be understood that the following description is illustrative only and is not intended to be in any way limiting.
As shown in fig. 1, referring to step S100, a first deformation modulus and a deformation modulus weight for each geological layer are obtained.
Specifically, referring to steps S110 to S140 shown in fig. 2, a deformation modulus weight for each geological 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 variation coefficient of the composite formation is obtained
Further, referring to step S600, multiple sets of sample data are obtained to obtain a settlement prediction model
Further, referring to step S400, the relevant parameters such as the first weighted variation coefficient are processed by the sedimentation prediction model to obtain a sedimentation prediction value.
In this case, as shown in step S410 to step 440, the method comprises
Obtaining the predicted value w of the settlements'。
In the description herein, reference to the description of the terms "one embodiment," "some embodiments," "an illustrative embodiment," "an example," "a specific example," or "some examples" or the like 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 application. In this specification, the schematic representations of the terms used above 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.
While 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 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 (8)
1. A method of sedimentation prediction of a composite formation, the method comprising:
acquiring a first deformation modulus and deformation modulus weight of each geological layer of a 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 formation according to the weighted mean square error and the second deformation modulus;
and predicting the first weighted variation coefficient through a preset sedimentation prediction model to obtain a sedimentation prediction value of the composite stratum.
2. The method of sedimentation prediction of a composite formation according to claim 1,
the obtaining of the deformation modulus weight of each geological stratification of the composite formation to be predicted comprises:
acquiring the section area of each geological stratification;
obtaining the area ratio of the corresponding geological stratification according to the area of each section;
setting each of the area fractions as the deformation modulus weight of the corresponding geological stratification.
3. The method of sedimentation prediction of a composite 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 difference value of each first deformation modulus and each second deformation modulus to obtain the modulus difference corresponding to the geological stratification;
weighting each modulus difference and the corresponding deformation modulus weight to obtain a total modulus difference of the composite stratum;
and setting the square of the mean of the total modulus difference as the weighted mean square error.
4. The method of sedimentation prediction of a composite formation of claim 1, further comprising:
creating a settlement prediction model, wherein the creating of the settlement prediction model specifically comprises:
acquiring multiple 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 settlement prediction model.
5. The method of sedimentation prediction of a composite formation according to claim 4,
the sample data also comprises a first shield penetration degree and a first horizontal flexibility coefficient;
the establishing of the relation equation of the actual sedimentation value, the first maximum sedimentation theoretical value and the second weighted variation coefficient includes:
setting the ratio of the actual settlement value to the first shield penetration degree 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.
6. The method of sedimentation prediction of a composite formation according to claim 5,
the predicting the first weighted variation coefficient through a preset sedimentation prediction model to obtain the sedimentation prediction value of the composite stratum comprises the following steps:
acquiring the maximum effective thrust of the current shield machine;
processing the maximum effective thrust by a finite layer method to obtain a second maximum settlement theoretical value of the composite stratum and the 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 sedimentation theoretical value, the second horizontal flexibility coefficient, a preset second shield penetration degree and the first weighted variation coefficient into the sedimentation prediction model, and outputting the sedimentation prediction value.
7. An apparatus for prediction of subsidence of a composite formation, comprising:
at least one processor, and,
a memory communicatively coupled to the at least one processor; wherein,
the memory stores instructions for execution by the at least one processor to cause the at least one processor, when executing the instructions, to implement a method of sedimentation prediction of a composite formation according to any one of claims 1 to 6.
8. A computer-readable storage medium having stored thereon computer-executable instructions for causing a computer to perform a method of sedimentation prediction of a composite formation according to any one of claims 1 to 6.
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| CN113642812A (en) * | 2021-10-15 | 2021-11-12 | 西南交通大学 | Beidou-based micro-deformation prediction method, device, equipment and readable storage medium |
| CN113642812B (en) * | 2021-10-15 | 2022-02-08 | 西南交通大学 | Beidou-based micro-deformation prediction method, device, equipment and readable storage medium |
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