CN117150962B - Method and device for calculating surging probability of random sampling and storage device - Google Patents
Method and device for calculating surging probability of random sampling and storage device Download PDFInfo
- Publication number
- CN117150962B CN117150962B CN202311122865.0A CN202311122865A CN117150962B CN 117150962 B CN117150962 B CN 117150962B CN 202311122865 A CN202311122865 A CN 202311122865A CN 117150962 B CN117150962 B CN 117150962B
- Authority
- CN
- China
- Prior art keywords
- surge
- random
- landslide
- probability
- height
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Active
Links
- 238000005070 sampling Methods 0.000 title claims abstract description 22
- 238000000034 method Methods 0.000 title claims description 18
- 238000004364 calculation method Methods 0.000 claims abstract description 21
- 239000002245 particle Substances 0.000 claims description 20
- 239000012530 fluid Substances 0.000 claims description 9
- 238000012545 processing Methods 0.000 claims description 7
- 238000011835 investigation Methods 0.000 claims description 3
- 238000011084 recovery Methods 0.000 claims description 3
- 238000010586 diagram Methods 0.000 description 5
- 238000004458 analytical method Methods 0.000 description 4
- 239000007787 solid Substances 0.000 description 3
- XLYOFNOQVPJJNP-UHFFFAOYSA-N water Substances O XLYOFNOQVPJJNP-UHFFFAOYSA-N 0.000 description 3
- 238000004088 simulation Methods 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000010276 construction Methods 0.000 description 1
- 230000000694 effects Effects 0.000 description 1
- 238000002474 experimental method Methods 0.000 description 1
- 230000003993 interaction Effects 0.000 description 1
- 238000012986 modification Methods 0.000 description 1
- 230000004048 modification Effects 0.000 description 1
- 230000002265 prevention Effects 0.000 description 1
- 238000011160 research Methods 0.000 description 1
- 238000012502 risk assessment Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/20—Design optimisation, verification or simulation
- G06F30/28—Design optimisation, verification or simulation using fluid dynamics, e.g. using Navier-Stokes equations or computational fluid dynamics [CFD]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/08—Probabilistic or stochastic CAD
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2111/00—Details relating to CAD techniques
- G06F2111/10—Numerical modelling
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2113/00—Details relating to the application field
- G06F2113/08—Fluids
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F2119/00—Details relating to the type or aim of the analysis or the optimisation
- G06F2119/14—Force analysis or force optimisation, e.g. static or dynamic forces
Abstract
The application provides a calculation method of a surge override probability of random sampling, which comprises the following steps: according to a target area, a numerical model is built, a landslide parameter value range of the target area is analyzed, landslide input parameters required by building the numerical model are processed, and random variables are generated. And carrying out Latin hypercube sampling according to the random variable to generate a random sample, inputting the random sample into the numerical model, extracting the surge propagation height corresponding to the random variable, determining a surge propagation height threshold according to a disaster-stricken body of the target area, and calculating the overrun probability of the surge propagation height exceeding the surge propagation height threshold. The random samples are generated by Latin hypercube sampling of the selected random variables, and then are processed by a model, so that the overrun probability that the surge propagation distance exceeds the threshold height is calculated, the uncertainty of landslide input parameters is quantized, and the accuracy of landslide-surge disaster prediction is improved.
Description
Technical Field
The application relates to the field of geological disaster prevention and control, in particular to a method, equipment and storage equipment for calculating surge override probability of random sampling.
Background
A large number of hydropower stations are built in southwest regions of China. The construction and operation of the hydropower station are obviously changed to the surrounding original environment, wherein the most prominent expression is that a large number of landslides along the coast are induced or revived, and the traffic and transportation in a storage area are seriously affected by the surge disaster generated later. Landslide-surge prediction models are mainly conventional models and hydrodynamic (CFD) models. The traditional model generally fits a formula through a model experiment, and cannot consider the influence of actual complex terrain. The CFD model can overcome the limitations of the traditional model to a certain extent, and can carry out fine analysis on important information such as the propagation distance, the propagation height, the propagation speed and the like of the surge.
In the prior art, a deterministic analysis method is adopted for forward analysis of surge propagation, a determined value is input into a CFD model for simulation calculation, but in practice, the spatial variability of a landslide body and the difficulty in obtaining parameters are caused, so that the input parameters have larger uncertainty, and the error between a predicted result and an actual result is larger. Quantifying landslide parameter uncertainty into landslide-surge prediction is a problem that needs to be solved.
Disclosure of Invention
The method and the device aim to solve the technical problem that the conventional surge propagation analysis method is difficult to quantify the uncertainty of landslide parameters into the surge prediction, and provide a randomly sampled surge exceeding probability calculation method, equipment and storage equipment.
The above object of the present application is achieved by the following technical solutions:
s1: constructing a numerical model according to a target area, and analyzing a landslide parameter value range of the target area;
s2: processing landslide input parameters required by constructing the numerical model to generate random variables;
s3: according to the random variable, latin hypercube sampling is carried out to generate a random sample;
s4: inputting the random sample into the numerical model, and extracting the surge propagation height corresponding to the random variable;
s5: determining a surge propagation height threshold according to the disaster-affected body of the target area;
s6: and calculating the overrun probability that the surge propagation height exceeds the surge propagation height threshold value.
Optionally, step S1 includes:
s11: importing the topographic map of the target area into software, deleting redundant lines, and reserving contour lines with elevations;
s12: introducing the contour line into a rho model, generating a surface by curtain-laying operation, stretching the surface into the numerical model, and deriving the numerical model as a stl file;
s13: and analyzing the landslide parameter value range of the target area according to the on-site investigation data and the stl file.
Optionally, step S2 includes:
s21: calculating the landslide input parameters according to an effective dynamic viscosity formula, wherein the landslide input parameters comprise: a landslide mass density parameter, a landslide mass particle size parameter, a mean value, a standard deviation and a distribution type parameter; the effective dynamic viscosity formula is as follows:
wherein ρ is g And ρ w Particle and fluid densities, respectively; τ eff And τ w The effective dynamic viscosity and the fluid viscosity respectively; e is the elastic recovery coefficient after the impact of the particles; lambda is the maximum volume ratio of particle diameter to particle gap; du/dy is the shear rate;
s22: and processing the landslide input parameters to generate the random variable.
Optionally, step S3 includes:
according to the landslide parameter value range, latin hypercube sampling of the random variable is realized through Matlab, N random samples are generated, and the random number Y in the ith subinterval is as follows:
where Y is the sample number, i=1, 2, …, N.
Optionally, step S6 includes:
s61: the override probability is the probability that the swell propagation height exceeds the swell propagation height threshold, expressed as a performance function as follows:
F(Y)＝H thr -H m
wherein H is m Calculated surge height for numerical model, H thr The method is characterized in that the method is a set surge height value;
s62: judging that the surge propagation height exceeds the surge propagation height threshold;
when F (Y) < 0, the surge height is greater than the surge propagation height threshold;
when F (Y) =0, the surge height is equal to the surge propagation height threshold;
when F (Y) is greater than 0, the surge height is less than the surge propagation height threshold;
s63: counting the numerical calculation value of random samples with F (Y) less than or equal to 0 so as to calculate the surmounting probability P of the surge in the target water area e The following are provided:
wherein,θ is a vector expression form of a plurality of variables, representing the total variable; f (θ) represents an expression of a performance function in the case of various variables.
A storage device stores instructions and data for implementing a method for calculating surge override probability of random sampling.
A randomly sampled surge override probability calculation device, comprising: a processor and a storage device; the processor loads and executes instructions and data in the storage device to realize a method for calculating the surge override probability of random sampling.
The beneficial effects that this application provided technical scheme brought are:
constructing a numerical model of the target area and analyzing a landslide parameter value range of the target area; determining a random variable constructing a numerical model, carrying out Latin hypercube sampling on the random variable to generate a random sample, processing the random sample through the numerical model, and determining the final override probability, thereby calculating the probability that the propagation height of the surge exceeds the threshold height, quantifying the uncertainty of the input parameters of the landslide, and improving the accuracy of landslide-surge disaster prediction.
Drawings
The application will be further described with reference to the accompanying drawings and examples, in which:
FIG. 1 is a step diagram of a randomly sampled surge override probability calculation method in an embodiment of the present application;
FIG. 2 is a Latin hypercube sample plot of a randomly sampled surge override probability calculation method in an embodiment of the application;
FIG. 3 is an overrun probability graph of a randomly sampled surge overrun probability calculation method in an embodiment of the present application;
fig. 4 is a schematic diagram of the operation of the hardware device in the embodiment of the present application.
Detailed Description
For a clearer understanding of technical features, objects, and effects of the present application, a detailed description of specific embodiments of the present application will be made with reference to the accompanying drawings.
The embodiment of the application provides a method, equipment and storage equipment for calculating the surging probability of random sampling.
Referring to fig. 1, fig. 1 is a step diagram of a method for calculating a surge override probability of random sampling in an embodiment of the present application, which specifically includes the following steps:
s1: constructing a numerical model according to a target area, and analyzing a landslide parameter value range of the target area;
s2: processing landslide input parameters required by constructing the numerical model to generate random variables;
s3: according to the random variable, latin hypercube sampling is carried out to generate a random sample;
s4: inputting the random sample into the numerical model, and extracting the surge propagation height corresponding to the random variable;
s5: determining a surge propagation height threshold according to the disaster-affected body of the target area;
s6: and calculating the overrun probability that the surge propagation height exceeds the surge propagation height threshold value.
In the application, the surge propagation height threshold value can be determined according to different positions of the disaster-stricken body of the target area and the property parameters of the disaster-stricken body.
Specifically, according to the results of the overrun probabilities of different positions of the disaster-stricken body, an overrun probability map of the surge of the target area exceeding the threshold height of the disaster-stricken body is drawn, as shown in fig. 3.
The step S1 comprises the following steps:
s11: importing the topographic map of the target area into software, deleting redundant lines, and reserving contour lines with elevations;
s12: introducing the contour line into a rho model, generating a surface by curtain-laying operation, stretching the surface into the numerical model, and deriving the numerical model as a stl file;
s13: and analyzing the landslide parameter value range of the target area according to the on-site investigation data and the stl file.
In this application, software includes, but is not limited to, autoCAD, and drape operations are operations that are self-contained in the Rhino software.
The step S2 comprises the following steps:
s21: calculating the landslide input parameters according to an effective dynamic viscosity formula, wherein the landslide input parameters comprise: a landslide mass density parameter, a landslide mass particle size parameter, a mean value, a standard deviation and a distribution type parameter; the effective dynamic viscosity formula is as follows:
wherein ρ is g And ρ w Particle and fluid densities, respectively; τ eff And τ w The effective dynamic viscosity and the fluid viscosity respectively; e is the elastic recovery coefficient after the impact of the particles; lambda is the maximum volume ratio of particle diameter to particle gap; du/dy is the shear rate;
s22: and processing the landslide input parameters to generate the random variable.
Specifically, a particle flow model is used for landslide-surge simulation, wherein the particle flow model is regarded as incompressible unidirectional continuous fluid, interaction force between solids and between the solids and the fluid is mainly simulated, smooth particles are used for describing combined movement of the solids and the fluid, and effective dynamic viscosity plays a vital role in determining the volume and impact speed of a sliding body entering water; the effective dynamic viscosity formula is mainly related to the density of the landslide body and the size of landslide body particles; according to the actual condition of the target area, the landslide body density, the value range of landslide body particles, the average value, the standard deviation, the distribution type and other landslide body input parameters are determined by combining an effective dynamic viscosity formula.
Referring to fig. 2, fig. 2 is a latin hypercube sample diagram of a method for calculating a surge override probability of random sampling in an embodiment of the application.
The step S3 comprises the following steps:
according to the landslide parameter value range, latin hypercube sampling of the random variable is realized through Matlab, N random samples are generated, and the random number Y in the ith subinterval is as follows:
where Y is the sample number, i=1, 2, …, N.
Referring to fig. 3, fig. 3 is an override probability chart of a randomly sampled surge override probability calculation method in an embodiment of the present application.
The step S6 comprises the following steps:
s61: the override probability is the probability that the swell propagation height exceeds the swell propagation height threshold, expressed as a performance function as follows:
F(Y)＝H thr -H m
wherein H is m Calculated surge height for numerical model, H thr The method is characterized in that the method is a set surge height value;
s62: judging that the surge propagation height exceeds the surge propagation height threshold;
when F (Y) < 0, the surge height is greater than the surge propagation height threshold;
when F (Y) =0, the surge height is equal to the surge propagation height threshold;
when F (Y) is greater than 0, the surge height is less than the surge propagation height threshold;
s63: counting the numerical calculation value of random samples with F (Y) less than or equal to 0 so as to calculate the surmounting probability P of the surge in the target water area e The following are provided:
wherein,θ is a vector expression form of a plurality of variables, representing the total variable; f (θ) represents an expression of a performance function in the case of various variables.
In particular, in risk assessment of research areas, engineering practitioners are mainly concerned about the influence of wave disasters on ships and wharfs. The impact strength of wave action on ships and wharfs is generally expressed by impact force, the impact force is related to wave height, and the impact of the tonnage of a disaster-affected carrier is small. Wave heights exceeding 2m and 5m are considered to cause impact damage to ships and wharfs, and in this application, the set surge height values may be set to 2m and 5m.
Referring to fig. 4, fig. 4 is a schematic working diagram of a hardware device according to an embodiment of the present application, where the hardware device specifically includes: a randomly sampled surge override probability calculation device 401, a processor 402, and a storage device 403.
Randomly sampled surge override probability calculation device 401: the randomly sampled surge override probability calculation device 401 implements a randomly sampled surge override probability calculation method.
Processor 402: the processor 402 loads and executes instructions and data in the memory device 403 for implementing a randomly sampled surge override probability calculation method.
Storage device 403: storage device 403 stores instructions and data; the storage device 403 is used to implement a randomly sampled surge override probability calculation method.
The foregoing description of the preferred embodiments of the present application is not intended to be limiting, but rather is intended to cover any and all modifications, equivalents, alternatives, and improvements within the spirit and principles of the present application.
Claims (5)
1. A calculation method of the surging probability of random sampling is characterized by comprising the following steps:
s1: according to the target area, a numerical model is built and the landslide parameter value range of the target area is analyzed, specifically as follows:
s11: importing the topographic map of the target area into software, deleting redundant lines, and reserving contour lines with elevations;
s12: introducing the contour line into a rho model, generating a surface by curtain-laying operation, stretching the surface into the numerical model, and deriving the numerical model as a stl file;
s13: analyzing the landslide parameter value range of the target area according to the on-site investigation data and the stl file;
s2: and processing landslide input parameters required for constructing the numerical model to generate random variables, wherein the method comprises the following steps of:
s21: calculating the landslide input parameters according to an effective dynamic viscosity formula, wherein the landslide input parameters comprise: a landslide mass density parameter, a landslide mass particle size parameter, a mean value, a standard deviation and a distribution type parameter; the effective dynamic viscosity formula is as follows:
wherein ρ is g And ρ w Particle and fluid densities, respectively;and->The effective dynamic viscosity and the fluid viscosity respectively; e is the elastic recovery coefficient after the impact of the particles; lambda is the maximum volume ratio of particle diameter to particle gap; du/dy is the shear rate;
s22: processing the landslide input parameters to generate the random variable;
s3: according to the random variable, latin hypercube sampling is carried out to generate a random sample;
s4: inputting the random sample into the numerical model, and extracting the surge propagation height corresponding to the random variable;
s5: determining a surge propagation height threshold according to the disaster-affected body of the target area;
s6: and calculating the overrun probability that the surge propagation height exceeds the surge propagation height threshold value.
2. The method for calculating the surge override probability of random sampling as defined in claim 1, wherein step S3 comprises:
according to the landslide parameter value range, latin hypercube sampling of the random variable is realized through Matlab, N random samples are generated, and the random number Y in the ith subinterval is as follows:
where Y is the sample number, i=1, 2, …, N.
3. The method for calculating the surge override probability of random sampling as defined in claim 1, wherein step S6 comprises:
s61: the override probability is the probability that the swell propagation height exceeds the swell propagation height threshold, expressed as a performance function as follows:
F(Y)＝H thr -H m
wherein H is m Calculated surge height for numerical model, H thr The method is characterized in that the method is a set surge height value;
s62: judging that the surge propagation height exceeds the surge propagation height threshold;
when F (Y) < 0, the surge height is greater than the surge propagation height threshold;
when F (Y) =0, the surge height is equal to the surge propagation height threshold;
when F (Y) is greater than 0, the surge height is less than the surge propagation height threshold;
s63: counting the numerical calculation value of the random sample with F (Y) less than or equal to 0 so as to calculate the surmounting probability P of the surge in the target area e The following are provided:
wherein,θ is a vector expression form of a plurality of variables, representing the total variable; f (θ) represents an expression of a performance function in the case of various variables.
4. A memory device, characterized by: the storage device stores instructions and data for implementing the surge override probability calculation method of any one of the random sampling of claims 1 to 3.
5. A randomly sampled surge override probability calculation device, characterized by: comprising the following steps: a processor and a storage device; the processor loads and executes instructions and data in the storage device for implementing the randomly sampled surge override probability calculation method of any one of claims 1-3.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311122865.0A CN117150962B (en) | 2023-08-30 | 2023-08-30 | Method and device for calculating surging probability of random sampling and storage device |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202311122865.0A CN117150962B (en) | 2023-08-30 | 2023-08-30 | Method and device for calculating surging probability of random sampling and storage device |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN117150962A CN117150962A (en) | 2023-12-01 |
| CN117150962B true CN117150962B (en) | 2024-04-02 |
Family
ID=88886418
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202311122865.0A Active CN117150962B (en) | 2023-08-30 | 2023-08-30 | Method and device for calculating surging probability of random sampling and storage device |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN117150962B (en) |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111445120A (en) * | 2020-03-24 | 2020-07-24 | 成都理工大学 | Landslide movement distance transcendental probability calculation method |
| KR102496082B1 (en) * | 2022-05-25 | 2023-02-06 | 경북대학교 산학협력단 | Design optimizition method for centrifugal pump and, computing apparatus for performing the method |
| CN115906256A (en) * | 2022-12-05 | 2023-04-04 | 中国电建集团成都勘测设计研究院有限公司 | Reservoir landslide surge numerical simulation method and system |
-
2023
- 2023-08-30 CN CN202311122865.0A patent/CN117150962B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111445120A (en) * | 2020-03-24 | 2020-07-24 | 成都理工大学 | Landslide movement distance transcendental probability calculation method |
| KR102496082B1 (en) * | 2022-05-25 | 2023-02-06 | 경북대학교 산학협력단 | Design optimizition method for centrifugal pump and, computing apparatus for performing the method |
| CN115906256A (en) * | 2022-12-05 | 2023-04-04 | 中国电建集团成都勘测设计研究院有限公司 | Reservoir landslide surge numerical simulation method and system |
Non-Patent Citations (1)
| Title |
|---|
| 库区单体滑坡灾害风险定量评估研究;李红英;谭跃虎;李二兵;;地下空间与工程学报;20131115(S2);全文 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN117150962A (en) | 2023-12-01 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Mergili et al. | A strategy for GIS-based 3-D slope stability modelling over large areas | |
| CN111563621B (en) | Method, system, device and storage medium for evaluating danger of regional landslide | |
| Samui et al. | Multivariate adaptive regression spline and least square support vector machine for prediction of undrained shear strength of clay | |
| CN112288169B (en) | Two-dimensional landslide dynamics parameter probability inverse analysis and slip overrun probability evaluation method | |
| Yue et al. | An adaptive method of high accuracy surface modeling and its application to simulating elevation surfaces | |
| CN113033108B (en) | Side slope reliability judging method based on AdaBoost algorithm | |
| Qiu et al. | Seepage monitoring models study of earth-rock dams influenced by rainstorms | |
| Mamat et al. | Slope stability prediction of road embankment on soft ground treated with prefabricated vertical drains using artificial neural network | |
| Smith et al. | Use of weights of evidence statistics to define inference rules to disaggregate soil survey maps | |
| Gatto et al. | X-SLIP: A SLIP-based multi-approach algorithm to predict the spatial–temporal triggering of rainfall-induced shallow landslides over large areas | |
| Pham | Application of feedforward neural network and SPT results in the estimation of seismic soil liquefaction triggering | |
| Xie et al. | Prediction model of the slope angle of rocky slope stability based on random forest algorithm | |
| CN117150962B (en) | Method and device for calculating surging probability of random sampling and storage device | |
| Li et al. | Soil seismic response modeling of KiK-net downhole array sites with CNN and LSTM networks | |
| Du et al. | The method of solving structural reliability with multiparameter correlation problem | |
| Yin et al. | The prediction of pile foundation buried depth based on BP neural network optimized by quantum particle swarm optimization | |
| Popescu et al. | Probabilistic risk assessment of landslide related geohazards | |
| Fu et al. | Slope stability analysis based on big data and convolutional neural network | |
| Al-Juboori et al. | Reliability-based optimum design of hydraulic water retaining structure constructed on heterogeneous porous media: Utilizing stochastic ensemble surrogate model-based linked simulation optimization model | |
| Otkhozoria et al. | Labview In The Research Of Fractal Properties Of The Topology Of Networks And Stochastic Processes | |
| Li et al. | The study on forecasting the surface rupture width under strong earthquake based on information diffusion methodology | |
| Wang et al. | Development of a Hybrid Method to Predict the Slope Surface Deformation Utilizing the Time Series and GA-Elman Models | |
| Chai et al. | Recognition Method of Landslide Remote Sensing Image based on EfficientNet | |
| CN117494264B (en) | Earthwork allocation method and system based on fusion of BIM and GIS | |
| Jangir et al. | Evaluating Adaptive Neuro-Fuzzy Inference System (ANFIS) To Assess Liquefaction Potential And Settlements Using CPT Test Data |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant |