CN115018164A - Method, system, equipment and storage medium for predicting sedimentation of slurry balance pipe jacking construction - Google Patents
Method, system, equipment and storage medium for predicting sedimentation of slurry balance pipe jacking construction Download PDFInfo
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- CN115018164A CN115018164A CN202210653815.4A CN202210653815A CN115018164A CN 115018164 A CN115018164 A CN 115018164A CN 202210653815 A CN202210653815 A CN 202210653815A CN 115018164 A CN115018164 A CN 115018164A
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Abstract
The invention discloses a sediment balance pipe jacking construction settlement prediction method, a system, equipment and a storage medium, wherein the method comprises the following steps: acquiring geological information parameters, parameters of a pipe jacking machine and density of injected muddy water in a pipe jacking construction path; collecting the settling amount of the ground and the discharged mud amount in real time during the pipe jacking construction process and after the construction is finished; carrying out centrifugal separation on the discharged slurry, and calculating the mass of soil lost by a soil layer; preprocessing the obtained settlement amount data set, removing abnormal settlement amount data, and obtaining a preprocessed ground settlement amount data set; and establishing a random forest regression model, inputting geological information parameters, parameters of the pipe jacking machine, the preprocessed settlement amount data set and soil layer loss soil mass into the random forest regression model, and obtaining the predicted settlement amount. The settlement prediction method can accurately predict the ground settlement and reduce prediction errors.
Description
Technical Field
The invention relates to the technical field of engineering, in particular to a method, a system, equipment and a storage medium for predicting construction settlement of a slurry balance jacking pipe.
Background
The pipe-jacking construction technology is widely applied to trenchless laying of various pipelines such as urban underground water supply and drainage pipelines, natural gas and petroleum pipelines, communication cables and the like in coastal economically developed areas of China. It can cross roads, railways, bridges, mountains, rivers, straits and any buildings on the ground. However, the construction speed of the jacking pipe is high, so that the original balance of the soil body is inevitably damaged, the ground subsidence is caused, and further, the safety risk exists in the ground roads, buildings and the like.
The mud-water balance jacking pipe can be suitable for most soil layers, the caliber of the jacking pipe is flexibly selected, and the caliber selection range can be 400mm to 4000 mm. At present, the measurement of the soil output amount of the jacking pipe is mostly calculated by directly weighing the discharged soil, but for the mud-water balance jacking pipe, slurry discharged by excavated soil is difficult and inaccurate to measure, and larger calculation errors can be caused by subsequently calculating the soil loss amount and the ground settlement amount by utilizing the measured soil output amount.
Disclosure of Invention
The invention aims to provide a settlement prediction method, a system, equipment and a storage medium for muddy water balance pipe jacking construction.
The technical scheme is as follows:
the invention discloses a method for predicting settlement of muddy water balance pipe jacking construction in one embodiment.
The method for predicting the sedimentation of the muddy water balance pipe jacking construction comprises the following steps:
acquiring geological information parameters, parameters of a pipe jacking machine and density of injected muddy water in a pipe jacking construction path;
collecting the settling amount of the ground and the discharged mud amount in real time in the pipe jacking construction process and after the construction is finished;
carrying out centrifugal separation on the discharged slurry, and calculating the mass of soil lost in a soil layer;
preprocessing the obtained settlement amount data set, removing abnormal settlement amount data, and obtaining a preprocessed ground settlement amount data set;
establishing a random forest regression model, inputting geological information parameters, parameters of a pipe jacking machine, a preprocessed settlement amount data set and soil layer loss soil mass into the random forest regression model, and obtaining predicted settlement amount;
wherein, the collected ground settlement comprises the settlement y of the ground in the advancing process of the pipe jacking machine and the settlement y' of the ground after the construction of the pipe jacking machine is finished.
Further, the discharged mud amount is subjected to centrifugal separation, and the soil mass loss of the soil layer is calculated, and the method specifically comprises the following steps:
carrying out centrifugal separation on the discharged slurry to obtain dry soil and separated mud and water;
respectively measuring the weight and the volume of the obtained dry soil and the separated muddy water;
calculating the soil mass loss of the soil layer according to the following formula:
m decrease in the thickness of the steel ＝m Turbid urine -D Water (I) V+m Dry matter ；
Wherein m is Damage to Is the mass of soil lost in the soil layer, m Turbid urine Is the mass of the separated muddy water obtained after separation of the discharged slurry, V isVolume of mud water obtained after separation of the discharged mud, D Water (W) Is the density of the injected muddy water, m Dry matter Is the dry soil mass obtained after separation of the discharged slurry.
Further, preprocessing the obtained settlement amount data set, removing abnormal settlement amount data, and obtaining a preprocessed ground settlement amount data set, which specifically comprises the following steps:
introducing a parameter S, wherein S is [0,1], 0 represents a settlement amount data set after the construction of the pipe jacking machine is finished, and 1 represents a settlement amount data set in the continuous advancing process of the pipe jacking machine;
and detecting and removing abnormal settlement data in the settlement data set by adopting a Lauda criterion to obtain the preprocessed ground settlement data set.
Further, establishing a random forest regression model specifically comprises:
a. randomly selecting n data points from a training sample data set S;
b. constructing a regression tree based on the n data points;
c. repeating the steps a and b to obtain K subtrees;
d. constructing and forming a random forest regression model through K subtrees;
e. and taking the average value as output, wherein the output average value is the final predicted ground settlement amount.
Further, the process of establishing the random forest regression model further comprises training and optimizing the random forest regression model, and specifically comprises the following steps:
carrying out hyper-parameter optimization by adopting cross validation, training and optimizing in the established random forest regression model by taking average RMSE as a control score, and specifically comprising the following steps:
taking 80% of the original data set as training data and 20% of the original data set as verification data, and repeating the cross verification five times;
and the geological information parameters, the parameters of the pipe jacking machine, the parameters S and the soil mass loss of the soil layer are used as input prediction factors, and the settlement y of the ground in the construction advancing process of the pipe jacking machine and the settlement y' of the pipe jacking machine after the construction is finished are used as output.
Furthermore, the geological information parameters comprise an internal friction angle, an elastic modulus, a Poisson ratio, a cohesive force and an underground water level, and the parameters of the pipe jacking machine comprise a pipe jacking center depth, a pipe jacking machine caliber, a thrust force and a cutter head torque.
Further, in the mud separation process, a screen with the size of 0.007mm is adopted to filter mud and water.
The invention discloses a sediment prediction system for muddy water balance pipe jacking construction in another embodiment.
This mud-water balance push pipe construction settlement prediction system includes:
the acquisition module is used for acquiring geological information parameters, parameters of a pipe jacking machine and density of injected muddy water in a pipe jacking construction path, and acquiring settling amount of the ground and discharged muddy water amount in real time in the pipe jacking construction process and after the construction is finished;
the calculation and data processing module is used for calculating the soil mass loss of the soil layer, preprocessing the obtained sedimentation amount data set, removing abnormal sedimentation amount data and obtaining a preprocessed ground sedimentation amount data set;
the output module is used for establishing a random forest regression model, inputting geological information parameters, parameters of a pipe jacking machine, a preprocessed settlement amount data set and soil layer loss soil mass into the random forest regression model, and obtaining predicted settlement amount;
the ground settlement comprises a settlement y of the ground in the construction advancing process of the pipe jacking machine and a settlement y' of the ground after the construction of the pipe jacking machine is finished.
The invention discloses, in another embodiment, an electronic device comprising a memory for storing a computer program and a processor for executing the computer program to implement the steps of the subsidence prediction method as described in any one of the above.
The invention discloses in another embodiment a computer readable storage medium for storing a computer program which, when executed by a processor, performs the steps of the method of sedimentation prediction as defined in any one of the above.
The following illustrates the advantages or principles of the invention:
1. in the settlement prediction method, the discharged slurry is subjected to centrifugal separation, so that the soil mass loss of the soil layer can be more accurately predicted, and the prediction error is reduced. Due to the fact that the predicted soil layer loss soil mass error is small, and the settlement amount data set is preprocessed, the ground settlement amount data predicted through the random forest regression model is more accurate.
2. The ground settlement caused by pipe-jacking construction is divided into a construction settlement stage and a soil body re-consolidation settlement stage, and the ground settlement collected by the invention comprises the settlement of the ground in the pipe-jacking construction process and the settlement of the soil body consolidation stage after the construction is finished. The prediction result is more accurate through the collected ground settlement amount.
3. According to the method, the ground settlement amount is predicted by adopting a random forest algorithm, and the prediction accuracy and the balance error can be maintained even if a characteristic loss and unbalanced data set exists.
Drawings
Fig. 1 is a general flow chart of the method for predicting the sedimentation during the muddy water balanced pipe jacking construction according to the embodiment.
Detailed Description
The following detailed description of the preferred embodiments of the present invention, taken in conjunction with the accompanying drawings, will make the advantages and features of the invention easier to understand by those skilled in the art, and thus will clearly and clearly define the scope of the invention.
As shown in fig. 1, the present invention discloses a method for predicting settlement of construction of a slurry balance pipe jacking in an embodiment.
The method for predicting the sedimentation of the muddy water balance pipe jacking construction comprises the following steps:
s1: and acquiring geological information parameters in the pipe jacking construction path, parameters of a pipe jacking machine and density of the injected muddy water.
The geological information parameters comprise an internal friction angle, an elastic modulus, a Poisson ratio, a cohesive force and an underground water level, and the parameters of the pipe jacking machine comprise the central depth of a pipe jacking machine, the caliber of the pipe jacking machine, a thrust force and a cutter head torque. The data can be obtained through the file data such as a geological survey report, a construction file, a construction log and the like.
S2: and collecting the settling amount of the ground and the discharged mud amount in real time during the pipe jacking construction process and after the construction is finished. The collected ground settlement comprises the settlement y of the ground in the construction advancing process of the pipe jacking machine and the settlement y' of the ground after the construction of the pipe jacking machine is finished.
S3: and carrying out centrifugal separation on the discharged slurry, and calculating the mass of soil lost in a soil layer.
Further, the discharged mud amount is subjected to centrifugal separation, and the soil mass loss of the soil layer is calculated, and the method specifically comprises the following steps:
performing centrifugal separation on the discharged slurry to obtain dry soil and separated mud water, and filtering the mud water by using a 0.007mm screen in the separation process, wherein the separated mud water is high-turbidity mud water;
respectively measuring the weight and the volume of the obtained dry soil and the separated muddy water;
calculating soil mass loss of the soil layer according to the following formula:
m damage to ＝m Turbid urine -D Water (I) V+m Dry matter ；
Wherein m is Decrease in the thickness of the steel Is the mass of soil lost in the soil layer, m Turbid urine Is the mass of the separated muddy water obtained after separation of the discharged slurry, V is the volume of the muddy water obtained after separation of the discharged slurry, D Water (W) Is the density of the injected muddy water, m Dry food Is the mass of the dry soil mass obtained after the separation of the discharged slurry.
S4: and preprocessing the obtained settlement amount data set, removing abnormal settlement amount data, and obtaining a preprocessed ground settlement amount data set.
Further, preprocessing the obtained settlement amount data set, removing abnormal settlement amount data, and obtaining a preprocessed ground settlement amount data set, which specifically comprises the following steps:
introducing a parameter S, wherein S is [0,1], 0 represents a settlement amount data set after the construction of the pipe jacking machine is finished, and 1 represents a settlement amount data set in the continuous advancing construction of the pipe jacking machine;
and detecting and removing abnormal settlement data in the settlement data set by adopting a Lauda criterion to obtain the preprocessed ground settlement data set. The method specifically comprises the following steps: when y i -y > 3 σ, removing y i . Wherein, y i Is the sedimentation amount of the sedimentation amount data set, i ═ 1, 2, 3, …, n; y is y i σ is the standard deviation.
S5: and establishing a random forest regression model, inputting geological information parameters, parameters of the pipe jacking machine, the preprocessed settlement amount data set and soil layer loss soil mass into the random forest regression model, and obtaining the predicted settlement amount.
Further, establishing a random forest regression model specifically comprises:
a. randomly selecting n data points from a training sample data set S;
b. constructing a regression tree based on the n data points;
c. repeating the steps a and b to obtain K subtrees;
d. constructing and forming a random forest regression model through K subtrees;
e. taking the mean value
As output, average value of output
I.e. the final predicted ground settlement.
The formula for establishing the random forest regression model is as follows:
f i (x i ) Is the output of the regression tree, K is the number of regression trees,
the estimation process for each regression tree is completely independent for the final output ground settlement.
Further, the process of establishing the random forest regression model further comprises training and optimizing the random forest regression model, and specifically comprises the following steps:
and performing hyperparametric optimization by adopting cross validation, and training and optimizing in the established random forest regression model by taking the average RMSE as a control score. Preferably, the 5-fold-CV method is adopted for cross validation, and specifically comprises the following steps:
80% of the original data set was used as training data and 20% as validation data. Preferably, the original data set is randomly divided into 100 sub-data sets, 80 sub-data sets being training data, and 20 of them being validation data.
And taking geological information parameters, parameters of the pipe jacking machine, parameters S and soil mass loss as input prediction factors. The method is characterized in that 11 parameters of an internal friction angle, an elastic modulus, a Poisson ratio, a cohesive force, an underground water level, a push pipe center depth, a push pipe machine caliber, a thrust force, a cutter head torque, a parameter S and a soil mass loss mass are used as input prediction factors. And taking the settlement y on the ground in the construction advancing process of the pipe jacking machine and the settlement y' after the construction of the pipe jacking machine as output.
In this process, the cross validation process is repeated five times, and the random forest regression model can evaluate the performance of the model through the average prediction error of 100 sub-data sets. The 5-fold-CV method can take full advantage of the data because each part of the original data set is randomly segmented and can be used for both training and testing. Reuse of
Wherein the content of the first and second substances,
to predict the data set, y i Is the actual data set. RMSE with positive and small values indicates that the correlation error between the actual data set and the predicted data set is small and the model prediction accuracy is high.
After the training, optimization and establishment of the random forest regression model are completed, inputting a new data set of internal friction angle, elastic modulus, Poisson' S ratio, cohesive force, underground water level, center depth of a pipe jacking machine, caliber of the pipe jacking machine, thrust, cutter torque, parameters S and soil layer loss soil mass, and obtaining final output ground settlement.
The invention discloses a sediment prediction system for muddy water balance pipe jacking construction in another embodiment.
This mud-water balance push pipe construction settlement prediction system includes:
the acquisition module is used for acquiring geological information parameters, parameters of a pipe jacking machine and density of injected muddy water in a pipe jacking construction path, and acquiring settling amount of the ground and discharged muddy water amount in real time in the pipe jacking construction process and after the construction is finished;
the calculation and data processing module is used for calculating the soil mass loss of the soil layer, preprocessing the obtained sedimentation amount data set, removing abnormal sedimentation amount data and obtaining a preprocessed ground sedimentation amount data set;
and the output module is used for establishing a random forest regression model, inputting geological information parameters, parameters of the pipe jacking machine, the preprocessed settlement amount data set and soil layer loss soil mass into the random forest regression model, and obtaining the predicted settlement amount.
Wherein, the collected ground settlement comprises the settlement y of the ground in the advancing process of the pipe jacking machine and the settlement y' of the ground after the construction of the pipe jacking machine is finished.
The present invention discloses, in another embodiment, an electronic device comprising a memory for storing a computer program and a processor for executing the computer program to implement the steps of the method for predicting subsidence as described above.
The invention discloses in a further embodiment a computer readable storage medium for storing a computer program which, when executed by a processor, carries out the steps of the method for sedimentation prediction as described above.
Those skilled in the art will appreciate that all or part of the steps in the methods of the above embodiments may be implemented by associated hardware instructed by a program, which may be stored in a computer-readable storage medium, and the storage medium may include: flash disks, read-only memories (ROMs), Random Access Memories (RAMs), magnetic or optical disks, and the like.
The embodiments of the present invention are not limited thereto, and according to the above-mentioned contents of the present invention, the present invention can be modified, substituted or combined in other various forms without departing from the basic technical idea of the present invention.
Claims (10)
1. The method for predicting the settlement of the slurry balance pipe jacking construction is characterized by comprising the following steps of:
acquiring geological information parameters, parameters of a pipe jacking machine and density of injected muddy water in a pipe jacking construction path;
collecting the settling amount of the ground and the discharged mud amount in real time during the pipe jacking construction process and after the construction is finished;
carrying out centrifugal separation on the discharged slurry, and calculating the mass of soil lost in a soil layer;
preprocessing the obtained settlement amount data set, removing abnormal settlement amount data, and obtaining a preprocessed ground settlement amount data set;
establishing a random forest regression model, inputting geological information parameters, parameters of a pipe jacking machine, a preprocessed settlement amount data set and soil layer loss soil mass into the random forest regression model, and obtaining predicted settlement amount;
wherein, the collected ground settlement comprises the settlement y of the ground in the advancing process of the pipe jacking machine and the settlement y' of the ground after the construction of the pipe jacking machine is finished.
2. The method for predicting the sedimentation of the mud-water balance pipe jacking construction as claimed in claim 1, wherein the method comprises the following steps of performing centrifugal separation on the amount of the discharged mud, and calculating the mass of soil lost in a soil layer:
performing centrifugal separation on the discharged slurry to obtain dry soil and separated mud and water;
respectively measuring the weight and the volume of the obtained dry soil and the separated muddy water;
calculating the soil mass loss of the soil layer according to the following formula:
m decrease in the thickness of the steel ＝m Turbid urine -D Water (W) V+m Dry food ；
Wherein m is Decrease in the thickness of the steel Is the mass of soil lost in the soil layer, m Turbidity to the body Is the mass of the separated muddy water obtained after the separation of the discharged slurry, V is the volume of the muddy water obtained after the separation of the discharged slurry, D Water (W) Is the density of the injected muddy water, m Dry matter Is the dry soil mass obtained after separation of the discharged slurry.
3. The method for predicting the sedimentation of the mud-water balanced pipe jacking construction according to claim 1, wherein the method for preprocessing the obtained sedimentation amount data set, removing abnormal sedimentation amount data and obtaining a preprocessed ground sedimentation amount data set specifically comprises the following steps:
introducing a parameter S, wherein S is [0,1], 0 represents a settlement amount data set after the construction of the pipe jacking machine is finished, and 1 represents a settlement amount data set in the continuous advancing process of the pipe jacking machine;
and detecting and removing abnormal settlement data in the settlement data set by adopting a Lauda criterion to obtain the preprocessed ground settlement data set.
4. The method for predicting the sedimentation of the mud-water balance pipe jacking construction, according to claim 3, wherein the establishing of the random forest regression model specifically comprises the following steps:
a. randomly selecting n data points from a training sample data set S;
b. constructing a regression tree based on the n data points;
c. repeating the steps a and b to obtain K subtrees;
d. constructing and forming a random forest regression model through K subtrees;
e. and taking the average value as output, wherein the output average value is the final predicted ground settlement amount.
5. The method for predicting the sedimentation of the mud-water balance pipe jacking construction, according to claim 4, wherein the process of establishing the random forest regression model further comprises the steps of training and optimizing the random forest regression model, and specifically comprises the following steps:
carrying out hyper-parameter optimization by adopting cross validation, training and optimizing in the established random forest regression model by taking average RMSE as a control score, and specifically comprising the following steps:
taking 80% of the original data set as training data and 20% of the original data set as verification data, and repeating the cross verification five times;
and the geological information parameters, the parameters of the pipe jacking machine, the parameters S and the soil mass loss are used as input prediction factors, and the settlement y of the ground in the construction advancing process of the pipe jacking machine and the settlement y' of the pipe jacking machine after the construction is finished are used as output.
6. The method for predicting the sedimentation of the mud-water balanced pipe jacking construction as claimed in claim 1, wherein the geological information parameters comprise an internal friction angle, an elastic modulus, a Poisson ratio, a cohesive force and an underground water level, and the parameters of the pipe jacking machine comprise a pipe jacking center depth, a pipe jacking machine caliber, a thrust force and a cutter head torque.
7. The method for predicting the sedimentation of the mud-water balance pipe jacking construction as claimed in claim 2, wherein a screen of 0.007mm is adopted to filter mud and water in the mud-water separation process.
8. Mud water balance push pipe construction settlement prediction system, its characterized in that includes:
the acquisition module is used for acquiring geological information parameters, parameters of a pipe jacking machine and density of injected muddy water in a pipe jacking construction path, and acquiring settling amount of the ground and discharged muddy water amount in real time in the pipe jacking construction process and after the construction is finished;
the calculation and data processing module is used for calculating the soil mass loss of the soil layer, preprocessing the obtained sedimentation amount data set, removing abnormal sedimentation amount data and obtaining a preprocessed ground sedimentation amount data set;
the output module is used for establishing a random forest regression model, inputting geological information parameters, parameters of the pipe jacking machine, the preprocessed settlement volume data set and soil layer loss soil mass into the random forest regression model, and obtaining predicted settlement volume;
the ground settlement comprises a settlement y of the ground in the construction advancing process of the pipe jacking machine and a settlement y' of the ground after the construction of the pipe jacking machine is finished.
9. An electronic device, comprising a memory for storing a computer program and a processor for executing the computer program to implement the steps of the sedimentation prediction method as claimed in any one of claims 1 to 7.
10. A computer-readable storage medium for storing a computer program which, when executed by a processor, performs the steps of the subsidence prediction method of any one of claims 1 to 7.
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