CN114036831A - Real-time detection method for geotechnical parameters of side slope of engineering field to be detected - Google Patents
Real-time detection method for geotechnical parameters of side slope of engineering field to be detected Download PDFInfo
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Abstract
The invention provides a real-time detection method for geotechnical parameters of an engineering field slope to be detected. The method comprises the following steps: performing on-site reconnaissance on an engineering field to be detected to obtain actual geotechnical parameters of a side slope of the engineering field to be detected; the method comprises the steps that monitoring data of a slope of an engineering field to be detected are obtained by detection equipment and transmitted to a processor, and the processor utilizes a numerical analysis model to continuously trial calculate inversion geotechnical parameters until the deviation between the inversion geotechnical parameters and actual geotechnical parameters meets the precision requirement; inputting the inverted rock-soil mechanical parameters serving as training samples into a BP-GA model for training until the accuracy requirement is met, and obtaining a trained BP-GA model; the processor inputs the monitoring data of the engineering field side slope to be detected, which is received in real time, into the trained BP-GA model, and the BP-GA model outputs real-time geotechnical parameters of the engineering field side slope to be detected. The embodiment of the invention can acquire dynamic rock and soil parameters based on real-time monitoring data.
Description
Technical Field
The invention relates to the technical field of geotechnical parameter analysis, in particular to a real-time detection method for geotechnical parameters of a side slope of an engineering field to be detected.
Background
The rapid construction of the railway and highway is accompanied by the appearance of a large number of high slopes, and whether the high slopes are stable or not directly influences the operation safety of the railway and highway, so that the adoption of a proper reinforcement and treatment scheme for the high slopes is particularly important. The selected treatment measures and the specific design scheme of the treatment measures are based on the mechanical parameters of the rock and soil.
Due to the complexity of rock-soil materials, the method for acquiring rock-soil parameters has certain limitations. One kind of method is mainly indoor test, the method is based on-site investigation sampling, various mechanical tests are carried out in a laboratory to obtain geotechnical parameter data, but the method can not fully consider the problems of size effect, joint crack and the like of the rock for the rock, and has the problems of disturbance and the like for the soil body, and the investigation sampling is limited by cost, the investigation area can be far smaller than the research area range, and the variability of the geotechnical material can not be fully reflected; the other method is mainly based on field test, the biggest problem of the field test is limited by cost, the test cannot be carried out on the whole research area, and the field test may have certain influence on the mechanical property of the rock and soil body.
With the continuous development of monitoring technology and communication technology, a large amount of monitoring is carried out on potential dangerous slopes by a plurality of projects, the monitoring data can reflect the stability of the slopes in real time, and most directly, the monitoring data such as surface displacement and deep displacement can be obtained. And the displacement of the side slope is the most direct embodiment of the deformation of the side slope.
The design of the stability analysis and treatment scheme of the side slope is mainly numerical simulation basically, and the mechanical parameters of the rock-soil body are important input parameters in the numerical simulation, and whether the values of the mechanical parameters of the rock-soil body are accurate or not is directly related to the design of the stability analysis result and the treatment scheme.
At present, methods for obtaining rock mechanics parameters in the prior art include indoor and outdoor tests, engineering analogy, rock grading method, inversion analysis, numerical simulation and the like. In recent years, with the moldWith the rapid development of fuzzy mathematics and neural networks, people gradually begin to adopt neural network methods to perform rock-soil body mechanical parameter inversion, that is, sufficient c (cohesive force) is obtained based on experimental data or numerical simulation analysis data,
and (internal friction angle), E (elastic modulus), mu (Poisson's ratio), k (slope safety coefficient), omega (water content) and other rock soil body parameter data. To be inverted
For output layer, other data are input layer, the nonlinear relation between them is obtained by using neural network algorithm, and the trained neural network is used to analyze similar engineering
The value is obtained.
The method for obtaining rock mass mechanical parameters through indoor and outdoor tests in the prior art has the following defects: although the indoor test method can obtain relatively accurate rock-soil body mechanical parameters, the influence of the rock size effect on the rock-soil body mechanical parameters cannot be accurately considered, and for a soil body, a soil sample may be disturbed, so that the mechanical properties of the soil body in an actual field cannot be accurately represented; the in-situ test can relatively well reflect the natural characteristics of rock and soil mass and reduce the influence of the size effect of the rock mass to a certain extent, but the in-situ test generally consumes long time and is high in cost, and the test result has great discreteness and is generally only applied to important or larger projects.
The engineering geology can obtain the geotechnical parameters quickly, but is limited by the recognition difference of address personnel, different people can have different results on the same field, the influence of subjective factors is large, and the data is basically conservative. Although a plurality of influence factors are considered in the rock mass grading method, the acquisition of the influence factors is limited not only by on-site investigation conditions, but also by subjective cognition of geologists on aspects such as structural surface combination degree, slope self-stability and the like.
The basis of the back analysis method based on the measured data is that rock parameters can be known in advance or reasonably estimated for trial calculation, and the functional relationship between the known data and the required data needs to be reasonably listed, so that the difficulty is relatively high; when the neural network algorithm is used for inverse analysis, the accuracy of the basic data has a great influence on the analysis result, but the acquisition of the basic data is also a difficult problem.
Disclosure of Invention
The embodiment of the invention provides a real-time detection method for geotechnical mechanical parameters of a side slope of an engineering field to be detected, and aims to overcome the problems in the prior art.
In order to achieve the purpose, the invention adopts the following technical scheme.
A real-time detection method for geotechnical parameters of an engineering field side slope to be detected comprises the following steps:
performing on-site reconnaissance on an engineering field to be detected to obtain actual geotechnical parameters of a side slope of the engineering field to be detected;
the method comprises the steps that monitoring data of a slope of an engineering field to be detected are obtained by detection equipment and transmitted to a processor, and the processor utilizes a numerical analysis model to continuously trial calculate inversion geotechnical parameters until the deviation between the inversion geotechnical parameters and the actual geotechnical parameters meets the precision requirement;
inputting the inverted rock-soil mechanical parameters serving as training samples into a BP-GA model for training until the accuracy requirement is met, and obtaining a trained BP-GA model;
the processor inputs the monitoring data of the engineering field side slope to be detected, which is received in real time, into the trained BP-GA model, and the BP-GA model outputs real-time geotechnical parameters of the engineering field side slope to be detected.
Preferably, the performing the on-site reconnaissance on the engineering site to be detected to obtain the actual geotechnical parameters of the slope of the engineering site to be detected includes:
the method comprises the steps of carrying out site survey on an engineering field to be detected to obtain a rock core and a soil sample, and carrying out a triaxial compression test on the rock core and the soil sample indoors to obtain corresponding actual geotechnical mechanical parameters, wherein the actual geotechnical mechanical parameters comprise cohesive force, an internal friction angle, an elastic modulus and compressive strength.
Preferably, the detection device acquires monitoring data of the slope of the engineering field to be detected, transmits the monitoring data to the processor, and the processor performs continuous trial calculation on the inverse geomechanical parameters by using the numerical analysis model until the deviation between the inverse geomechanical parameters and the actual geomechanical parameters meets the precision requirement, including:
arranging detection equipment on a slope of the engineering field to be detected, acquiring monitoring data of the slope of the engineering field to be detected, wherein the monitoring data comprises earth surface displacement and rainfall, and the detection equipment transmits the monitoring data to a processor in real time through a network;
the processor utilizes finite element analysis software Flac3d to establish a numerical analysis model, and utilizes the numerical analysis model to continuously perform trial calculation to invert rock-soil mechanical parameters based on monitoring data and test data determined by indoor tests and outdoor tests by taking displacement as a known quantity;
the processor calculates the deviation according to the following formula:
deviation (inverse rock-soil mechanics parameter-actual rock-soil mechanics parameter)/actual rock-soil mechanics parameter
And judging whether the deviation is smaller than a set judgment threshold value, if so, determining that the inverse geotechnical parameters meet the precision requirement, otherwise, returning to recalculation until the deviation is smaller than the set judgment threshold value, and obtaining trial-computation inverse geotechnical parameters meeting the precision requirement.
Preferably, the method for obtaining the trained BP-GA model by inputting the inverse geomechanical parameters into the BP-GA model for training until the accuracy requirement is met includes:
the processor takes monitoring data meeting the precision requirement and corresponding rock-soil mechanical parameters as training samples, takes a displacement value, rainfall, rock-soil body types, a compression modulus, a Poisson ratio and an elastic modulus as input layer neurons, takes cohesive force and an internal friction angle as output layer neurons, determines the number of the neurons in the middle hidden layer by (the number of the input layers plus the number of the output layers)/2 to (2 times of the number of the neurons in the input layers plus 1), and trains the BP-GA model until the precision requirement is met;
in the training process, the output data of the BP-GA model is the cohesive force and the internal friction angle of each layer of rock-soil body corresponding to the data of the input layer, the training of the BP-GA model takes the root mean square difference smaller than 0.01 as the judgment standard, and if the root mean square difference meets the judgment standard, the model training is finished; if the number of the hidden neurons in the middle layer does not meet the precision requirement, the number of the hidden neurons in the middle layer is adjusted again until the precision requirement is met, and the trained BP-GA model is obtained.
According to the technical scheme provided by the embodiment of the invention, the embodiment of the invention can acquire dynamic rock and soil parameters through a BP (inverse propagation of error) -GA (genetic algorithm) model based on real-time monitoring data. The problems of high cost, low efficiency and discrete result of outdoor tests are solved, and the influence of subjective factors of an engineering similarity method can be avoided.
Additional aspects and advantages of the invention 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 invention.
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In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings needed to be used in the description of the embodiments are briefly introduced below, and it is obvious that the drawings in the following description are only some embodiments of the present invention, and it is obvious for those skilled in the art to obtain other drawings based on these drawings without creative efforts.
Fig. 1 is an implementation schematic diagram of a real-time detection method for geotechnical parameters of an engineering field slope to be detected according to an embodiment of the invention.
Detailed Description
Reference will now be made in detail to embodiments of the present invention, examples of which are illustrated in the accompanying drawings, wherein like 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 accompanying drawings are illustrative only for the purpose of explaining the present invention, and are not to be construed as limiting the present invention.
As used herein, the singular forms "a", "an", "the" and "the" are intended to include the plural forms as well, unless the context clearly indicates otherwise. It will be further understood that the terms "comprises" and/or "comprising," when used in this specification, specify the presence of stated features, integers, steps, operations, elements, and/or components, but do not preclude the presence or addition of one or more other features, integers, steps, operations, elements, components, and/or groups thereof. It will be understood that when an element is referred to as being "connected" or "coupled" to another element, it can be directly connected or coupled to the other element or intervening elements may also be present. Further, "connected" or "coupled" as used herein may include wirelessly connected or coupled. As used herein, the term "and/or" includes any and all combinations of one or more of the associated listed items.
It will be understood by those skilled in the art that, unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the prior art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
For the convenience of understanding the embodiments of the present invention, the following description will be further explained by taking several specific embodiments as examples in conjunction with the drawings, and the embodiments are not to be construed as limiting the embodiments of the present invention.
In order to overcome various defects of the conventional method for acquiring mechanical parameters along the way, particularly to acquire dynamic geotechnical parameters, the embodiment of the invention provides a real-time detection method for the geotechnical parameters of the side slope of the engineering field to be detected. Based on-site monitoring data, the method firstly utilizes a displacement inversion method to obtain the mechanical parameters of the slope rock-soil mass, and then utilizes a BP-GA algorithm to obtain the direct relation between the displacement and the mechanical parameters of the rock-soil mass on the basis of the mechanical parameters, thereby obtaining the more practical mechanical parameters of the rock-soil mass possibly.
The embodiment of the invention provides an implementation schematic diagram of a real-time detection method for geotechnical parameters of an engineering field slope to be detected, which is shown in figure 1 and comprises the following processing steps:
and S1, performing on-site reconnaissance on the engineering field to be detected to obtain a rock core, a soil sample and the like, and performing a triaxial compression test on the rock core and the soil sample indoors to obtain corresponding actual geotechnical mechanical parameters such as cohesive force, internal friction angle, elastic modulus, compressive strength and the like.
In order to make up the problem of the size effect of the indoor test, the mechanical parameters of the rock and soil can be obtained by properly increasing a load test, a field direct shear test and the like so as to be mutually corrected with the indoor test.
Step S2, determining a main picture of a side slope of the engineering field to be detected according to the site survey situation, laying reasonable detection equipment on the side slope site, acquiring monitoring data such as surface displacement and rainfall of the side slope through the detection equipment, and transmitting the monitoring data to a processor in real time through a Global Navigation Satellite System (GNSS) network by the detection equipment.
And S3, establishing a numerical analysis model by the processor through finite element analysis software Flac3d, providing a reasonable parameter trial calculation range through the numerical analysis model based on monitoring data and test data determined by indoor tests and outdoor tests, and continuously trial calculating the inverse geotechnical parameters by taking the displacement as a known quantity.
The deviation is calculated according to the following formula:
deviation (inverse rock-soil mechanics parameter-actual rock-soil mechanics parameter)/actual rock-soil mechanics parameter
And when the deviation is less than 5%, judging that the inverse rock-soil mechanical parameters meet the precision requirement, and otherwise, returning to recalculation until the precision requirement is met. So as to determine corresponding rock-soil mechanical parameters under different displacements.
And S4, taking the inverse rock-soil mechanical parameters obtained in the step S3 as training samples, taking a displacement value, rainfall, a rock-soil body type, a compression modulus, a Poisson' S ratio and an elastic modulus as input layer neurons, taking cohesive force and an internal friction angle as output layer neurons, determining the number of middle hidden neurons by (the number of the input layers + the number of the output layers)/2-2 times of the number of the input layer neurons +1, and inputting the training samples into a BP-GA model for training. The displacement value and rainfall are monitoring data, the type of the rock-soil body is an initial survey conclusion, and the compression modulus, the Poisson's ratio and the elastic modulus are empirical data (reference data is generally provided in a survey).
The output data of BP-GA model in training process is correspondent to the cohesive force and internal friction angle of each layer of rock-soil body of input layer data, and is stored in neuron of output layer.
The BP-GA model is a neural network model improved by genetic algorithm, the genetic algorithm is designed and proposed according to the evolution rule of organisms in the nature, is a calculation model for simulating the natural selection of Darwin biological evolution theory and the biological evolution process of genetic mechanism, is a method for searching an optimal solution by simulating the natural evolution process, and the weight of each input factor can be optimized by optimizing the neural network model through the genetic algorithm so as to be more in line with the reality. And finally, the training model takes the root mean square deviation smaller than 0.01 as a judgment standard, if the root mean square deviation is in accordance with the judgment standard, the model training is finished, and if the root mean square deviation is not in accordance with the judgment standard, the number of the hidden neurons in the middle layer is readjusted until the precision requirement is met, so that the trained BP-GA model is obtained.
Mean Square Error (MSE) is a conventional mathematical definition and is the average of the sum of the squares of the differences of the actual values of the respective data.
XiSample value representing the ith sample, YiRepresenting the true value of the ith sample.
And step S5, the processor inputs the monitoring data received in real time into the trained BP-GA model based on the BP-GA model trained in the step S4, and the BP-GA model outputs real-time rock-soil mechanical parameters.
The trained BP-GA model is a mature neural network model which can be predicted, and after the number of each neuron of an input layer is input, corresponding rock-soil mechanical parameters can be obtained by using the trained neural network. The monitoring data is substituted for displacement value and rainfall, and only needs to be input together with other parameter values, and the algorithm has a data normalization function.
In conclusion, the embodiment of the invention overcomes the problems of size effect, disturbance and the like of obtaining geotechnical parameters by using an indoor test, overcomes the problems of high cost, low efficiency and discrete result of an outdoor test, can avoid the influence of subjective factors of an engineering similarity method, and can obtain more accurate geotechnical mechanical parameters even for people without strong geological background.
In the past, the mechanical parameters of the rock and soil are mainly static, and the dynamic rock and soil parameters can be obtained through a BP-GA model based on real-time monitoring data.
Those of ordinary skill in the art will understand that: the figures are merely schematic representations of one embodiment, and the blocks or flow diagrams in the figures are not necessarily required to practice the present invention.
From the above description of the embodiments, it is clear to those skilled in the art that the present invention can be implemented by software plus necessary general hardware platform. Based on such understanding, the technical solutions of the present invention may be embodied in the form of a software product, which may be stored in a storage medium, such as ROM/RAM, magnetic disk, optical disk, etc., and includes instructions for causing a computer device (which may be a personal computer, a server, or a network device, etc.) to execute the method according to the embodiments or some parts of the embodiments.
The embodiments in the present specification are described in a progressive manner, and the same and similar parts among the embodiments are referred to each other, and each embodiment focuses on the differences from the other embodiments. In particular, for apparatus or system embodiments, since they are substantially similar to method embodiments, they are described in relative terms, as long as they are described in partial descriptions of method embodiments. The above-described embodiments of the apparatus and system are merely illustrative, and the units described as separate parts may or may not be physically separate, and the parts displayed as units may or may not be physical units, may be located in one place, or may be distributed on a plurality of network units. Some or all of the modules may be selected according to actual needs to achieve the purpose of the solution of the present embodiment. One of ordinary skill in the art can understand and implement it without inventive effort.
The above description is only for the preferred embodiment of the present invention, but the scope of the present invention is not limited thereto, and any changes or substitutions that can be easily conceived by those skilled in the art within the technical scope of the present invention are included in the scope of the present invention. Therefore, the protection scope of the present invention shall be subject to the protection scope of the claims.
Claims (4)
1. A real-time detection method for geotechnical parameters of an engineering field side slope to be detected is characterized by comprising the following steps:
performing on-site reconnaissance on an engineering field to be detected to obtain actual geotechnical parameters of a side slope of the engineering field to be detected;
the method comprises the steps that monitoring data of a slope of an engineering field to be detected are obtained by detection equipment and transmitted to a processor, and the processor utilizes a numerical analysis model to continuously trial calculate inversion geotechnical parameters until the deviation between the inversion geotechnical parameters and the actual geotechnical parameters meets the precision requirement;
inputting the inverted rock-soil mechanical parameters serving as training samples into a BP-GA model for training until the accuracy requirement is met, and obtaining a trained BP-GA model;
the processor inputs the monitoring data of the engineering field side slope to be detected, which is received in real time, into the trained BP-GA model, and the BP-GA model outputs real-time geotechnical parameters of the engineering field side slope to be detected.
2. The method of claim 1, wherein the performing the site survey on the project site to be tested to obtain the actual geomechanical parameters of the slope of the project site to be tested comprises:
the method comprises the steps of carrying out site survey on an engineering field to be detected to obtain a rock core and a soil sample, and carrying out a triaxial compression test on the rock core and the soil sample indoors to obtain corresponding actual geotechnical mechanical parameters, wherein the actual geotechnical mechanical parameters comprise cohesive force, an internal friction angle, an elastic modulus and compressive strength.
3. The method according to claim 1, wherein the detection device acquires monitoring data of the slope of the engineering field to be detected, the monitoring data is transmitted to the processor, and the processor continuously calculates the inverse geomechanical parameters by trial calculation through the numerical analysis model until the deviation between the inverse geomechanical parameters and the actual geomechanical parameters meets the precision requirement, and the method comprises the following steps:
arranging detection equipment on a slope of the engineering field to be detected, acquiring monitoring data of the slope of the engineering field to be detected, wherein the monitoring data comprises earth surface displacement and rainfall, and the detection equipment transmits the monitoring data to a processor in real time through a network;
the processor utilizes finite element analysis software Flac3d to establish a numerical analysis model, and utilizes the numerical analysis model to continuously perform trial calculation to invert rock-soil mechanical parameters based on monitoring data and test data determined by indoor tests and outdoor tests by taking displacement as a known quantity;
the processor calculates the deviation according to the following formula:
deviation (inverse rock-soil mechanics parameter-actual rock-soil mechanics parameter)/actual rock-soil mechanics parameter
And judging whether the deviation is smaller than a set judgment threshold value, if so, determining that the inverse geotechnical parameters meet the precision requirement, otherwise, returning to recalculation until the deviation is smaller than the set judgment threshold value, and obtaining trial-computation inverse geotechnical parameters meeting the precision requirement.
4. The method according to any one of claims 1 to 3, wherein the training is performed by inputting the inverse geomechanical parameters into a BP-GA model as training samples until the accuracy requirement is met, so as to obtain a trained BP-GA model, and the method comprises the following steps:
the processor takes monitoring data meeting the precision requirement and corresponding rock-soil mechanical parameters as training samples, takes a displacement value, rainfall, rock-soil body types, a compression modulus, a Poisson ratio and an elastic modulus as input layer neurons, takes cohesive force and an internal friction angle as output layer neurons, determines the number of the neurons in the middle hidden layer by (the number of the input layers plus the number of the output layers)/2 to (2 times of the number of the neurons in the input layers plus 1), and trains the BP-GA model until the precision requirement is met;
in the training process, the output data of the BP-GA model is the cohesive force and the internal friction angle of each layer of rock-soil body corresponding to the data of the input layer, the training of the BP-GA model takes the root mean square difference smaller than 0.01 as the judgment standard, and if the root mean square difference meets the judgment standard, the model training is finished; if the number of the hidden neurons in the middle layer does not meet the precision requirement, the number of the hidden neurons in the middle layer is adjusted again until the precision requirement is met, and the trained BP-GA model is obtained.
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| CN116910889A (en) * | 2023-09-13 | 2023-10-20 | 中国电建集团西北勘测设计研究院有限公司 | Combined inversion method and system for slope mechanical parameters and unloading loose zone |
| CN117648874A (en) * | 2024-01-30 | 2024-03-05 | 中国电建集团西北勘测设计研究院有限公司 | Slope excavation full-period mechanical parameter dynamic inversion method based on monitoring displacement |
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| CN107220467A (en) * | 2017-07-07 | 2017-09-29 | 中国水利水电科学研究院 | The Forecasting Methodology of retaining phase storehouse bank rock side slope deformation |
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| CN107220467A (en) * | 2017-07-07 | 2017-09-29 | 中国水利水电科学研究院 | The Forecasting Methodology of retaining phase storehouse bank rock side slope deformation |
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Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN116910889A (en) * | 2023-09-13 | 2023-10-20 | 中国电建集团西北勘测设计研究院有限公司 | Combined inversion method and system for slope mechanical parameters and unloading loose zone |
| CN116910889B (en) * | 2023-09-13 | 2024-01-05 | 中国电建集团西北勘测设计研究院有限公司 | Combined inversion method and system for slope mechanical parameters and unloading loose zone |
| CN117648874A (en) * | 2024-01-30 | 2024-03-05 | 中国电建集团西北勘测设计研究院有限公司 | Slope excavation full-period mechanical parameter dynamic inversion method based on monitoring displacement |
| CN117648874B (en) * | 2024-01-30 | 2024-05-03 | 中国电建集团西北勘测设计研究院有限公司 | Slope excavation full-period mechanical parameter dynamic inversion method based on monitoring displacement |
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