CN113449879B - Method for integrating osmotic deformation characteristic discrimination and impermeability gradient prediction - Google Patents
Method for integrating osmotic deformation characteristic discrimination and impermeability gradient prediction Download PDFInfo
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- 230000003204 osmotic effect Effects 0.000 title claims abstract description 26
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- 230000008595 infiltration Effects 0.000 claims description 9
- 238000001764 infiltration Methods 0.000 claims description 9
- 238000011156 evaluation Methods 0.000 claims description 5
- 230000007704 transition Effects 0.000 claims description 5
- 230000035699 permeability Effects 0.000 claims description 4
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 claims description 4
- 230000003068 static effect Effects 0.000 claims description 3
- 238000005325 percolation Methods 0.000 claims description 2
- 239000000463 material Substances 0.000 description 9
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Abstract
The invention discloses a method integrating penetration deformation characteristic discrimination and impervious gradient prediction, comprising the following steps of S1, taking the historical data of a seepage test as a training sample, and constructing a seepage test database; s2, carrying out data cleaning on the training sample, determining the average effective stress of the data under the stress-free condition according to the gravity, and obtaining a data set; s3, segmenting the data set, and segmenting the training samples into a training set and a test set according to the proportion; s4, loading pre-training parameters on the basis of an IE deep learning network of a causal model, and constructing an intelligent analysis network model integrating penetration damage type discrimination and impermeability gradient prediction; s5, testing the test set, and optimizing the intelligent analysis network model according to the error probability to obtain an optimized intelligent analysis network model for rapid assessment of the risk of osmotic damage; and S6, rapidly evaluating the seepage damage types of the dam and the earth-rock dam and whether the seepage damage is generated based on the optimized intelligent analysis network model according to the field working condition.
Description
Technical Field
The invention belongs to the technical field of hydraulic engineering, and particularly relates to a method for integrating osmotic deformation characteristic judgment and impermeability gradient prediction.
Background
The osmotic damage is a common damage phenomenon on the structure itself or foundation of a dam, a dam plug body and the like, and the damage types mainly include three types of flowing soil, piping and transition. When the material generates osmotic damage, the corresponding hydraulic gradient is impervious gradient. The method can quickly and accurately determine the permeation damage type and the permeation-resistant gradient of the material in the actual operation environment, and has important engineering significance for dam engineering design, dam and weir plug seepage safety assessment, emergency rescue and the like.
The current experiment and theoretical research shows that the type of osmotic damage and the impervious gradient thereof are influenced by material grading, compactness, stress state and the like, but most of the existing osmotic damage type damage criteria and impervious gradient prediction formulas only consider grain grading and have the following defects: firstly, the influence of stress state, compactness and the like of the material is not considered, so that the universality is insufficient, the material cannot be used for a dam plug body, a loose dam foundation and the like, and the impermeability gradient is difficult to represent the performance of each material in the actual working state; secondly, when the particle grading is considered, the overall characteristics of the particle grading curve are represented by only using individual parameters such as the fine particle content, D15/D85 and the like, but the whole particle grading curve is not considered and is more unilateral; thirdly, the judgment of the type of osmotic damage and the impervious gradient cannot be carried out simultaneously, and different methods are needed for calculation.
In current research and engineering application, seepage damage types and seepage-resistant gradients of different soil bodies are generally determined through seepage tests. For different soil samples, the grading characteristics, the material physical properties, the occurrence stress state and the like of the soil samples all affect the impermeability gradient of the soil samples, and when the permeability characteristics of actual engineering buildings or foundation soil samples are researched, the grading characteristics, the soil material physical properties, the occurrence stress state and the like of the soil samples are difficult to accurately reduce and set in an indoor permeability failure test, so the measured impermeability gradient also has deviation. If under the condition of emergency disposal, the impervious gradient is determined by methods of field sampling, sample transportation and indoor test, the immediate requirement of emergency can not be met.
Disclosure of Invention
The present invention is directed to overcome the above-mentioned deficiencies in the prior art by providing a method for integrating permeation deformation characteristic determination and permeation resistance gradient prediction, so as to solve or improve the above-mentioned problems.
In order to achieve the purpose, the invention adopts the technical scheme that:
a method for integrating osmotic deformation characteristic discrimination and impermeability gradient prediction comprises the following steps:
s1, taking the seepage test historical data as a training sample, and constructing a seepage test database;
s2, carrying out data cleaning on the training sample, determining the average effective stress of the data under the stress-free condition according to the gravity, and obtaining a data set;
s3, segmenting the data set, and segmenting the training samples into a training set and a test set according to the proportion;
s4, loading pre-training parameters on the basis of an IE deep learning network of a causal model, and constructing an intelligent analysis network model integrating penetration damage type discrimination and impermeability gradient prediction;
s5, testing the test set, and optimizing the intelligent analysis network model according to the error probability to obtain an optimized intelligent analysis network model for rapid assessment of the risk of osmotic damage;
and S6, rapidly evaluating the seepage damage types of the dam and the earth-rock dam and whether the seepage damage is generated based on the optimized intelligent analysis network model according to the field working condition.
Further, the infiltration destruction type labels in step S1 include flowing soil, transition, and piping.
Further, step S3 splits the dataset with the penetration damage type label consistent with the penetration damage type label in the test set.
Further, step S4 is to load pre-training parameters based on the IE deep learning network of the causal model, and construct an intelligent analysis network model integrating the discrimination of the type of osmotic damage and the prediction of the impermeability gradient, including the following steps:
s4.1, constructing a network Input-Factor, wherein parameters of an Input layer comprise grain composition, porosity, stress state and seepage direction;
s4.2, constructing a network Output layer Output-IE, wherein Output layer parameters comprise a penetration destruction type and a permeability resistance gradient;
and S4.3, carrying out supervised learning, and adjusting the number of hidden layers and the number of units of each hidden layer for constructing the deep learning network to obtain an intelligent analysis network model.
Further, the particle grading comprises: d10, D20, D30, D40, D50, D60, D70, D80, D90, D100; the stress state comprises a vertical stress and a static soil pressure coefficient; the percolation direction includes vertically downward, vertically upward and horizontally.
Further, step S6 is based on the optimized intelligent analysis network model to quickly evaluate the infiltration damage types of the dam plug body and the earth-rock dam and whether infiltration damage is generated according to the field conditions, and includes the following steps:
s6.1, for the emergency treated weir plug body, obtaining the on-site working condition parameters of the weir plug body, including height H, length L, grain composition, porosity and estimated pressure of the weir plug body;
s6.2, inputting the field working condition parameters of grain composition, porosity and estimated pressure in the step S6.1 into an optimized intelligent analysis network model, quickly estimating the type of osmotic damage and outputting an anti-permeability gradient;
s6.3, calculating the maximum hydraulic ratio drop i before the water level is choked up and the top is overtopped according to the height H and the length L of the weir plug;
i＝H/L
and S6.4, if the output impervious gradient of the step S6.2 is larger than the maximum hydraulic ratio drop i, judging overtopping damage, and if the output impervious gradient is smaller than the maximum hydraulic ratio drop i, judging penetration damage.
The method integrating the characteristics of osmotic deformation discrimination and the prediction of the impermeability gradient provided by the invention has the following beneficial effects:
1. the method combines the relationship between the osmotic damage type and the impermeability gradient and various influence factors to establish an intelligent analysis network model, and utilizes the existing test data and parameters to carry out deep learning training on the model, thereby being capable of simultaneously and rapidly distinguishing and predicting the osmotic damage type and the impermeability gradient of the soil sample in the actual occurrence environment.
2. The invention has wide application range and can be applied to dam plugs, loose covering layer dam foundations, earth and rockfill dam dams, embankment foundations and the like.
3. The consideration is comprehensive, the representativeness is strong, and the evaluation accuracy is high: the whole grading curve can be considered, and dam loading and seepage risk assessment at different positions can be achieved.
4. And simultaneously evaluating multiple risk factors, and realizing judgment of the type of osmotic damage and prediction of an anti-permeability gradient.
5. Convenient to use, quick, intelligent, non professional also can utilize the cell-phone to shoot and assess the infiltration and destroy the risk, can assess fast to actual conditions, satisfies infiltration destruction risk assessment under the emergency condition.
Drawings
Fig. 1 is a flow chart of a method integrating osmotic deformation feature discrimination and impermeability gradient prediction.
FIG. 2 shows the prediction accuracy of the impermeability gradient of the intelligent analysis network.
Detailed Description
The following description of the embodiments of the present invention is provided to facilitate the understanding of the present invention by those skilled in the art, but it should be understood that the present invention is not limited to the scope of the embodiments, and it will be apparent to those skilled in the art that various changes may be made without departing from the spirit and scope of the invention as defined and defined by the appended claims, and all changes that can be made by the invention using the inventive concept are intended to be protected.
In the first embodiment, referring to fig. 1, the method for integrating the judgment of the osmotic deformation characteristics and the prediction of the impermeability gradient in the present embodiment specifically includes:
step S1, taking the historical data of the seepage test as a training sample, and constructing a seepage test database;
step S2, carrying out data cleaning on the training sample, determining the average effective stress of the data under the stress-free condition according to the gravity, and obtaining a data set;
step S3, segmenting a data set, and segmenting the training samples into a training set and a test set according to a proportion;
s4, loading pre-training parameters based on the IE deep learning network of the causal model, and constructing an intelligent analysis network model integrating penetration damage type discrimination and impermeability gradient prediction;
step S5, testing the test set, optimizing the intelligent analysis network model according to the error probability to obtain an optimized intelligent analysis network model for rapid assessment of the risk of infiltration damage;
and step S6, rapidly evaluating the seepage damage types of the dam body and the earth-rock dam and whether the seepage damage is generated based on the optimized intelligent analysis network model according to the field working condition.
According to the method, the influence factors of the penetration damage type and the anti-permeability gradient of different soil bodies when the penetration damage occurs are determined through research, deep learning training is carried out according to the existing test parameters and data conclusions so as to judge the penetration damage type in the actual occurrence environment and predict the anti-permeability gradient, and therefore the rapid evaluation of the penetration damage risk under the emergency condition can be realized.
In the second embodiment, the steps of the first embodiment will be described in detail below.
And step S1, collecting and processing the existing seepage test data at home and abroad to serve as a training sample, wherein the seepage damage type labels comprise three types of flowing soil, transition and piping.
And step S2, performing data cleaning on the training sample data collected in the step S1, and determining the average effective stress of the data under the stress-free condition according to gravity to obtain a data set.
And step S3, segmenting the data set, segmenting the training samples into a training set and a testing set according to the proportion, and ensuring that the penetration damage type labels are consistent with the penetration damage type labels in the testing set in the segmented data set.
S4, building an IE deep learning network based on a causal model, loading pre-training parameters, forming an intelligent analysis network model integrating penetration damage type discrimination and impermeability gradient prediction, and determining a weight value, wherein the concrete steps comprise:
s4.1, constructing a network Input-Factor, wherein 16 Input layer parameters of the deep learning network are respectively particle size distribution (D10, D20, D30, D40, D50, D60, D70, D80, D90 and D100, wherein DN is the corresponding particle size when the content of soil particles smaller than a certain particle size is N%), porosity, stress state (vertical stress and static soil pressure coefficient), and seepage direction (vertical downward, vertical upward and horizontal);
s4.2, constructing a network Output layer Output-IE, wherein 4 Output layer parameters of the deep learning network are respectively a penetration damage type (soil flow, transition and piping) and an anti-permeability gradient;
and S4.3, carrying out supervised learning, and adjusting and determining the hidden layer number and the unit number of each hidden layer of the built deep learning network.
And S5, testing the test set, taking the error probability as an analysis network model optimization standard which integrates the penetration damage type discrimination and the impermeability gradient prediction and is constructed in the step S4, and determining an intelligent model which can be finally used for rapid penetration damage risk assessment, namely the optimized intelligent analysis network model.
And step S6, aiming at emergency treatment of a dam plug body, an earth and rockfill dam and the like, quickly evaluating the type of the osmotic damage and whether the osmotic damage is generated by using the trained deep learning network model according to the actual conditions of the engineering field.
The method specifically comprises the following steps:
step S6.1, aiming at the dam body of emergency disposal and the like, preliminarily determining the geometric dimension (height H and length L), the particle size distribution, the porosity and the estimated pressure of the dam body according to the actual conditions of an engineering site, wherein after the particle size distribution is photographed by adopting a multi-position mobile phone, D10, D20, D30, D40, D50, D60, D70H, D80, D90, D100 and the porosity required by S1 are automatically output through image recognition, and the stress state is estimated by adopting the height and the like;
s6.2, inputting the material gradation, porosity, estimated pressure and seepage direction determined by the site S6.1 by adopting the trained intelligent analysis network model, and quickly estimating the type of osmotic damage and the anti-seepage gradient;
s6.3, calculating the maximum hydraulic ratio drop i before the water level is choked up and the roof is overtopped according to the geometric dimension of the weir dam;
i＝H/L
wherein H is the height of the dam or the dam, and L is the horizontal seepage length.
And S6.4, if the output impervious gradient of the step S6.2 is larger than the maximum hydraulic ratio drop i, judging overtopping damage, and if the output impervious gradient is smaller than the maximum hydraulic ratio drop i, judging penetration damage.
Example three, rapid analysis for a certain damming body. The height of a certain weir plug is about 100 meters, the length of the certain weir plug is about 600 meters, the slope ratio of the upstream surface is 1:2, and the slope ratio of the downstream surface is 1: 3. The burst mode needs to be judged rapidly on site, so that support is provided for the decision of emergency treatment measures. In view of the fact that the indoor test method is accurate but does not meet the requirement of emergency, the method is adopted to carry out rapid evaluation.
The lower limit of the impermeability gradient determined in steps S6.1 and S6.2 is 0.5.
In step S6.3, the hydraulic pressure drop of the weir plug is estimated as i ═ H/L ═ 100/(600-2 × 100) ═ 0.25
And S6.4 and S6.2, outputting an anti-permeability gradient of 0.5, judging that the overtopping is damaged if the estimated value i of the maximum hydraulic ratio drop is greater than 0.25, and performing emergency treatment by adopting modes such as slotting and the like.
While the embodiments of the invention have been described in detail in connection with the accompanying drawings, it is not intended to limit the scope of the invention. Various modifications and changes may be made by those skilled in the art without inventive work within the scope of the appended claims.
Claims (4)
1. A method integrating permeation deformation characteristic discrimination and impermeability gradient prediction is characterized by comprising the following steps:
s1, taking the seepage test historical data as a training sample, and constructing a seepage test database;
s2, carrying out data cleaning on the training sample, determining the average effective stress of the data under the stress-free condition according to the gravity, and obtaining a data set;
s3, segmenting the data set, and segmenting the training samples into a training set and a test set according to the proportion;
s4, loading pre-training parameters based on the deep learning network of the causal model, and constructing an intelligent analysis network model integrating penetration damage type discrimination and impermeability gradient prediction, wherein the intelligent analysis network model comprises the following steps:
s4.1, constructing a network Input layer Input-Factor, wherein parameters of the Input layer comprise particle grading, porosity, stress state and seepage direction, and the stress state comprises vertical stress and static soil pressure coefficient;
s4.2, constructing a network Output layer Output-IE, wherein Output layer parameters comprise a penetration destruction type and a permeability resistance gradient;
s4.3, carrying out supervised learning, and adjusting the number of hidden layers and the number of units of each hidden layer for constructing the deep learning network to obtain an intelligent analysis network model;
s5, testing the test set, and optimizing the intelligent analysis network model according to the error probability to obtain an optimized intelligent analysis network model for rapid evaluation of the risk of osmotic damage;
s6, rapidly evaluating the infiltration damage types of the dam body and the earth-rock dam and whether infiltration damage is generated or not based on an optimized intelligent analysis network model according to the field working conditions, wherein the evaluation comprises the following steps:
s6.1, for the emergency treatment weir plug body, obtaining the on-site working condition parameters of the weir plug body, including height H, length L, grain composition, porosity and estimated pressure of the weir plug body;
s6.2, inputting the field working condition parameters of grain composition, porosity, estimated stress state and estimated seepage direction in the step S6.1 into an optimized intelligent analysis network model, quickly evaluating the type of osmotic damage and outputting a seepage-resistant gradient;
s6.3, calculating the maximum hydraulic ratio drop i before the water level is choked to be high and the water level is choked to be overtopped according to the height H and the length L of the damming body;
i＝H/L
and S6.4, if the output impervious gradient of the step S6.2 is larger than the maximum hydraulic ratio drop i, judging overtopping damage, and if the output impervious gradient is smaller than the maximum hydraulic ratio drop i, judging penetration damage.
2. The method of claim 1, which integrates osmotic deformation feature discrimination and impermeability gradient prediction, wherein: the infiltration damage type labels in step S1 include flowing soil, transitions, and piping.
3. The method of claim 1, which integrates osmotic deformation feature discrimination and impermeability gradient prediction, wherein: step S3 splits the dataset with the penetration damage type label consistent with the penetration damage type label in the test set.
4. The method of claim 1 integrating osmotic deformation feature discrimination and impermeability gradient prediction, wherein the grading of the particles comprises: d10, D20, D30, D40, D50, D60, D70, D80, D90, D100; the percolation direction includes vertically downward, vertically upward and horizontally.
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