CN112183894B - Gravity dam earthquake risk probability analysis method - Google Patents
Gravity dam earthquake risk probability analysis method Download PDFInfo
- Publication number
- CN112183894B CN112183894B CN202011186100.XA CN202011186100A CN112183894B CN 112183894 B CN112183894 B CN 112183894B CN 202011186100 A CN202011186100 A CN 202011186100A CN 112183894 B CN112183894 B CN 112183894B
- Authority
- CN
- China
- Prior art keywords
- dam
- earthquake
- gravity dam
- gravity
- pga
- 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
Images
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/04—Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem"
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/044—Recurrent networks, e.g. Hopfield networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/04—Architecture, e.g. interconnection topology
- G06N3/045—Combinations of networks
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06N—COMPUTING ARRANGEMENTS BASED ON SPECIFIC COMPUTATIONAL MODELS
- G06N3/00—Computing arrangements based on biological models
- G06N3/02—Neural networks
- G06N3/08—Learning methods
- G06N3/084—Backpropagation, e.g. using gradient descent
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06Q—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES; SYSTEMS OR METHODS SPECIALLY ADAPTED FOR ADMINISTRATIVE, COMMERCIAL, FINANCIAL, MANAGERIAL OR SUPERVISORY PURPOSES, NOT OTHERWISE PROVIDED FOR
- G06Q10/00—Administration; Management
- G06Q10/06—Resources, workflows, human or project management; Enterprise or organisation planning; Enterprise or organisation modelling
- G06Q10/063—Operations research, analysis or management
- G06Q10/0635—Risk analysis of enterprise or organisation activities
Landscapes
- Engineering & Computer Science (AREA)
- Business, Economics & Management (AREA)
- Theoretical Computer Science (AREA)
- Physics & Mathematics (AREA)
- Human Resources & Organizations (AREA)
- General Physics & Mathematics (AREA)
- Strategic Management (AREA)
- Economics (AREA)
- Software Systems (AREA)
- Entrepreneurship & Innovation (AREA)
- General Health & Medical Sciences (AREA)
- Molecular Biology (AREA)
- Computing Systems (AREA)
- General Engineering & Computer Science (AREA)
- Data Mining & Analysis (AREA)
- Mathematical Physics (AREA)
- Computational Linguistics (AREA)
- Biophysics (AREA)
- Biomedical Technology (AREA)
- Evolutionary Computation (AREA)
- Artificial Intelligence (AREA)
- Health & Medical Sciences (AREA)
- Life Sciences & Earth Sciences (AREA)
- Game Theory and Decision Science (AREA)
- Development Economics (AREA)
- Marketing (AREA)
- Operations Research (AREA)
- Quality & Reliability (AREA)
- Tourism & Hospitality (AREA)
- General Business, Economics & Management (AREA)
- Educational Administration (AREA)
- Geophysics And Detection Of Objects (AREA)
Abstract
The invention discloses a gravity dam earthquake risk probability analysis method, which specifically comprises the following steps: s1: performing dynamic finite element analysis on the gravity dam under different vibration effects and judging the dam failure state; s2: screening earthquake dynamic intensity parameters for predicting failure states of the gravity dam; s3: establishing a BP neural network model for rapidly predicting the failure state of the gravity dam; s4: selecting earthquake motion records for predicting failure states of the gravity dam, and calculating earthquake motion intensity parameters; s5: estimating the failure probability of the gravity dam under different PGA levels; s6: drawing a gravity dam earthquake vulnerability curve; s7: calculating the earthquake risk probability of the gravity dam; s8: and evaluating the earthquake risk of the gravity dam according to the calculated earthquake risk probability.
Description
Technical Field
The invention relates to the technical field of gravity dam earthquake resistance analysis, in particular to a gravity dam earthquake risk probability analysis method.
Background
The reservoir dam has multiple functions of flood control, power generation, irrigation, water supply and the like, and is an important infrastructure of national economy. A concrete gravity dam (gravity dam for short) is one of dam types commonly used in hydraulic and hydroelectric engineering. Under the action of strong earthquake, the gravity dam can be damaged in different degrees, and engineering safety and normal operation are directly threatened, so that earthquake risk analysis and evaluation of the gravity dam in a high earthquake intensity area are always highly concerned by the industry and the society. The structural earthquake risk probability analysis can simultaneously consider the earthquake risk of a site and the earthquake vulnerability of a structure, can quantitatively analyze the influence of uncertain factors such as earthquake action effect and structural resistance, scientifically and reasonably evaluate the earthquake risk of the structure, provides scientific basis for structural earthquake disaster prediction and earthquake disaster loss evaluation, and is one of the frontier and hotspot problems in the field of gravity dam earthquake resistance research.
The prediction of the structural failure state and the drawing of the earthquake vulnerability curve are two main steps and key links of the earthquake risk probability analysis of the gravity dam. For the structural failure state prediction, a numerical simulation method (such as nonlinear finite element incremental dynamic analysis) is mainly adopted at present, the method has the advantages that the seismic damage characteristics of the gravity dam under different vibration effects can be accurately revealed, but the method has the defects that the calculation workload is huge, and the method is not suitable for the condition that a large amount of vibration input needs to be considered. For seismic vulnerability curve drawing, the current common method is to assume that the seismic demand capacity of the gravity dam conforms to the log normal distribution and construct a corresponding vulnerability curve equation. Because the actual probability distribution of the earthquake demand capacity of the gravity dam has deviation from the lognormal distribution and is limited by the calculation workload, parameters in a vulnerability curve equation can only be approximately fitted according to a small number of numerical simulation results, so that the vulnerability curve drawn according to the conventional method cannot accurately reflect the actual earthquake resistance performance of the gravity dam, and the accuracy and the rationality of the earthquake risk probability analysis result of the gravity dam are influenced.
Based on the method, the gravity dam earthquake risk probability analysis method is designed to solve the problems.
Disclosure of Invention
The invention aims to provide a gravity dam earthquake risk probability analysis method, aiming at the defects of low calculation efficiency and long consumed time of the existing gravity dam earthquake risk probability analysis method for predicting the structural failure state through numerical simulation, earthquake dynamic strength parameters are preferably selected through correlation analysis and used as input parameters of an artificial neural network, the neural network is used for replacing the consumed numerical simulation, the rapid prediction of the gravity dam failure state is realized, and the calculation workload is remarkably reduced. Aiming at the defects that when the vulnerability curve is drawn by the existing method, the earthquake demand capacity of the gravity dam needs to be assumed to accord with the logarithmic normal distribution, and the vulnerability curve parameters can only be approximately fitted by using a small number of numerical simulation results, the invention realizes the rapid prediction of the failure state of the gravity dam based on the neural network, so that the gravity dam failure probability estimation can be carried out by using a large amount of earthquake motion input, the vulnerability curve can be directly drawn according to the failure probability estimation result of the gravity dam, the earthquake demand capacity of the gravity dam does not need to be assumed to accord with the logarithmic normal distribution, the defects of the existing method are overcome, and the accuracy of the earthquake risk analysis of the gravity dam is improved.
The gravity dam seismic risk probability may be expressed as "site seismic risk x structural seismic vulnerability". The method comprises the steps of obtaining a relation curve of the overtaking probability of structural failure of the gravity dam and earthquake motion intensity (namely a gravity dam earthquake vulnerability curve) through earthquake vulnerability analysis, obtaining an annual overtaking probability curve (namely a field earthquake danger curve) of different earthquake motion intensities (generally represented by ground peak acceleration PGA) according to field earthquake danger analysis of the gravity dam, and finally calculating the earthquake risk probability of the gravity dam by utilizing the earthquake vulnerability curve and the earthquake danger curve.
In order to achieve the purpose, the invention provides the following technical scheme: a gravity dam earthquake risk probability analysis method specifically comprises the following steps:
s1: performing dynamic finite element analysis on the gravity dam under different vibration effects and judging the dam failure state;
s2: screening earthquake dynamic intensity parameters for predicting failure states of the gravity dam;
s3: establishing a BP neural network model for rapidly predicting the failure state of the gravity dam;
s4: selecting earthquake motion records for predicting failure states of the gravity dam, and calculating earthquake motion intensity parameters;
s5: estimating the failure probability of the gravity dam under different PGA levels;
s6: drawing a gravity dam earthquake vulnerability curve, and directly drawing PGA-F according to the gravity dam failure probability estimated values under different PGA levels obtained in S5fA curve, i.e. an earthquake vulnerability curve;
s7: calculating the earthquake risk probability of the gravity dam, dispersing the PGA into l levels, and calculating the earthquake risk probability of the gravity dam according to the earthquake vulnerability curve of the gravity dam obtained in S6 and the earthquake risk curve of the dam site provided by the earthquake bureau and the following formula:
in the formula, p (F)dam) The probability of gravity dam failure caused by earthquake possibly occurring around the dam address in a future period of time, namely the earthquake risk probability of the gravity dam; f (F)dam|PGA＝PGAi) Is PGA ═ PGAiThe failure probability of the gravity dam can be determined by the seismic vulnerability curve of the gravity dam obtained from S6; g (PGA ═ PGA)i) Is PGA ═ PGAiThe earthquake occurrence probability can be determined by an earthquake risk curve of a dam site;
s8: according to calculated p (F)dam) And evaluating the earthquake risk of the gravity dam by referring to the corresponding relation between the structure failure probability and the structure performance level.
Preferably, the specific step of S1 includes:
s1.1: selecting N actual measurement seismic motion records which accord with the field characteristics of the gravity dam to be analyzed from a seismic database, wherein each seismic motion record comprises 1 horizontal direction acceleration time-course curve and 1 vertical ground acceleration time-course curve;
s1.2: carrying out amplitude modulation processing on the selected acceleration time course curve, and carrying out amplitude modulation on the horizontal acceleration time course curve by taking 0.1g as a step length until the peak acceleration PGA is equal to 0.1g, 0.2g, … and 0.8g, wherein 8 PGAs are horizontal, and the vertical acceleration time course curve is subjected to amplitude modulation according to 2/3 of the horizontal PGA;
s1.3: inputting the amplitude-modulated 8 XN seismic motion records into a gravity dam finite element model, and performing incremental dynamic analysis;
s1.4: and judging the dam failure state according to the dam crack propagation condition and the overall damage index.
Preferably, the method for judging the dam failure state according to the dam crack propagation condition and the overall damage index comprises the following steps: if the dam body generates penetrating cracks and the overall damage index of the dam body is greater than or equal to 0.6, the dam is considered to be failed, and if the failure state is totally R times, the failure state is (8 multiplied by N-R) times.
1. Preferably, the dam body damage index is calculated according to a gravity dam seismic damage quantification method provided by Hariri-Ardebili, and for the kth crack appearing at the jth vulnerable part of the dam body, the damage index of the crack
Can be expressed as:
in the formula (I), the compound is shown in the specification,
and
respectively, the cracked path and the total path length, and the unit is m; beta is aΔIs the maximum displacement u from the dam crestmaxA dimensionless coefficient associated with dam height H;
calculating the damage index of each vulnerable area of the dam body according to the formula (2) according to the damage index of each crack
In the formula, n is the number of cracks contained in the jth vulnerable area;
the ratio of the energy dissipated by the kth crack to the total dissipated energy of the dam body is determined;
adding the damage indexes of all vulnerable areas to obtain the overall damage index of the gravity dam
In the formula, overall damage index
U is the number of dame vulnerable areas.
Preferably, the specific step of S2 includes:
s2.1: calculating sets of horizontal and vertical seismic intensity parameters (IM) for 8N seismic records in S1HAnd { IM }V}；
S2.2: according to the maximum value and the minimum value of the earthquake motion intensity parameter, carrying out normalization processing on the earthquake motion intensity parameter;
s2.3: computing Spearman rank correlation coefficients for normalized horizontal and vertical seismic intensity parameters and gravity dam global damage index
And
and screening seismic motion intensity parameters with strong correlation with gravity dam dynamic destruction according to the condition that the rank correlation coefficient is greater than or equal to 0.8, and taking the seismic motion intensity parameters as input parameters of the artificial neural network.
Preferably, the set of horizontal seismic intensity parameters { IMHExpressed by formulas (4) and (5):
in the formula, PGAH、SaavgHAnd PGVHThe parameters of earthquake motion intensity are respectively the peak acceleration, the mean value of the acceleration of the response spectrum and the peak speed of the earthquake motion time-course curve.
Preferably, the normalized seismic intensity parameter is obtained according to equation (6)
Namely:
preferably, the specific step of S3 includes:
s3.1: 8 XN groups of horizontal and vertical seismic intensity parameters screened in the S2 and corresponding classification results of the failure states of the gravity dam are used for training, testing and verifying a BP neural network model, 80% of data are randomly extracted for training and testing the neural network model, and the rest 20% of data are used for verifying the prediction accuracy of the model;
s3.2: combining the horizontal earthquake dynamic intensity parameters and the vertical earthquake dynamic intensity parameters in pairs, namely
As input parameters for the BP neural network model.
S3.3: and optimizing the input parameters, the hidden layer number, the hidden layer node number, the excitation function and the training iteration number of the BP neural network model according to the model testing precision and the verification precision.
Preferably, the specific step of S4 includes:
s4.1: selecting M actual measurement seismic motion records which accord with the field characteristics of the gravity dam, and carrying out amplitude modulation on horizontal seismic motion acceleration time-course curves and vertical seismic motion acceleration time-course curves to PGA (0.1 g-0.8 g) by taking 0.01g as a step length to obtain 71 xM groups of horizontal seismic motion acceleration time-course curves and vertical seismic motion acceleration time-course curves;
s4.2: calculating corresponding earthquake motion intensity parameters according to the optimal input parameter combination determined in the step S3;
s4.3: and carrying out normalization processing on the corresponding earthquake motion intensity parameters.
Preferably, the specific step of S5 includes:
s5.1: inputting M groups of earthquake dynamic intensity parameters corresponding to a certain PGA obtained in S4 by utilizing a neural network model established in S3, and rapidly predicting the corresponding failure state of the gravity dam;
s5.2: if the gravity dam fails, record FdamIs 1, otherwise, is denoted FdamIs 0; statistics FdamNumber N of 1fThen the failure probability F of the gravity dam under the PGA levelfIs Nf/M；
S5.3: and according to the S5.2, the estimated values of the failure probability of the gravity dam under different PGA levels can be obtained.
Compared with the prior art, the invention has the beneficial effects that:
(1) the neural network model is used for replacing time-consuming dynamic finite element calculation, the failure states of the gravity dam corresponding to different PGA levels can be rapidly predicted, and the efficiency of earthquake risk probability analysis of the gravity dam is remarkably improved.
(2) Due to the limitation of large calculation workload of finite elements, the existing method cannot adopt a large amount of vibration input to analyze the seismic vulnerability of the gravity dam, and when a seismic vulnerability curve of the gravity dam is drawn, the seismic demand capacity of the gravity dam needs to be assumed to be in accordance with lognormal distribution, so that the actual seismic performance of the gravity dam cannot be accurately reflected by the vulnerability curve drawn based on the assumption, and the accuracy and the rationality of the seismic risk probability analysis result of the gravity dam are further influenced. The method utilizes the neural network model to quickly predict the failure state of the gravity dam, so that the gravity dam earthquake vulnerability analysis can be carried out by adopting a large amount of vibration input, the assumption that the earthquake demand capacity of the gravity dam conforms to the lognormal distribution is not needed, the defects of the existing method are overcome, and the accuracy of the earthquake risk probability analysis of the gravity dam is improved.
(3) Aiming at the problems that the existing common gravity dam earthquake risk probability analysis method is low in efficiency and needs to be further improved in precision, the method utilizes the neural network to replace time-consuming nonlinear dynamic finite element calculation, achieves rapid prediction of the failure state of the gravity dam, enables the gravity dam earthquake risk probability analysis to be possible by adopting a large amount of vibration input, overcomes the defects of the existing method, and remarkably improves the efficiency and the accuracy of performance-based earthquake-resistant reliability analysis and evaluation of the gravity dam.
Drawings
In order to more clearly illustrate the technical solutions of the embodiments of the present invention, the drawings used in the description of the embodiments will be 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 that other drawings can be obtained according to the drawings without creative efforts.
FIG. 1 is a flow chart of a gravity dam seismic risk probability analysis method of the present invention;
FIG. 2 is a schematic diagram illustrating the principle of gravity dam earthquake risk probability calculation according to the present invention;
FIG. 3 is a graph of seismic dynamic acceleration time-course of the invention after partial actual measurement and amplitude modulation;
FIG. 4 is a cross section of a 16# dam section of the YL gravity dam and a dam finite element grid diagram of the dam body;
FIG. 5 is a diagram of YL gravity dam fracture development under partial seismic motion in accordance with the present invention;
FIG. 6 is a diagram illustrating the distribution of the YL gravity dam damage index under the action of 520 seismic events according to the present invention;
FIG. 7 is a correlation coefficient diagram of the YL gravity dam overall damage index and the along-the-river seismic intensity parameter of the present invention;
FIG. 8 is a correlation coefficient diagram of the YL gravity dam overall damage index and vertical seismic oscillation intensity parameter of the present invention;
FIG. 9 is a graph of the YL gravity dam seismic vulnerability of the present invention;
fig. 10 is a graph of earthquake risk for the YL gravity dam site of the present invention.
Detailed Description
The technical solutions in the embodiments of the present invention will be clearly and completely described below with reference to the drawings in the embodiments of the present invention, and it is obvious that the described embodiments are only a part of the embodiments of the present invention, and not all of the embodiments. All other embodiments, which can be derived by a person skilled in the art from the embodiments given herein without making any creative effort, shall fall within the protection scope of the present invention.
A gravity dam earthquake risk probability analysis method is shown in a flow chart of fig. 1 and specifically comprises the following steps:
s1: performing dynamic finite element analysis on the gravity dam under different vibration effects and judging the dam failure state;
s1.1: selecting N actual measurement seismic motion records which accord with the field characteristics of the gravity dam to be analyzed from a seismic database, wherein each seismic motion record comprises 1 horizontal direction acceleration time-course curve and 1 vertical ground acceleration time-course curve;
s1.2: carrying out amplitude modulation processing on the selected acceleration time course curve, and carrying out amplitude modulation on the horizontal acceleration time course curve by taking 0.1g as a step length until the peak acceleration PGA is equal to 0.1g, 0.2g, … and 0.8g, wherein 8 PGAs are horizontal, and the vertical acceleration time course curve is subjected to amplitude modulation according to 2/3 of the horizontal PGA;
s1.3: inputting the amplitude-modulated 8 XN seismic motion records into a gravity dam finite element model, and performing incremental dynamic analysis;
s1.4: and judging the dam failure state according to the dam crack propagation condition and the overall damage index.
2. The method for judging the dam failure state according to the dam crack propagation condition and the overall damage index comprises the following steps: if the dam body generates penetrating cracks and the overall damage index of the dam body is greater than or equal to 0.6, the dam is considered to be failed, and if the failure state is totally R times, the failure state is (8 multiplied by N-R) times. The dam body damage index is calculated according to a gravity dam seismic damage quantification method provided by Hariri-Ardebili, and for the kth crack appearing at the jth vulnerable part of the dam body, the damage index of the crack
Can be expressed as:
in the formula (I), the compound is shown in the specification,
and
respectively, the cracked path and the total path length, and the unit is m; beta is aΔIs the maximum displacement u from the dam crestmaxA dimensionless coefficient associated with dam height H;
calculating the damage index of each vulnerable area of the dam body according to the formula (2) according to the damage index of each crack
In the formula, n is the number of cracks contained in the jth vulnerable area;
the ratio of the energy dissipated by the kth crack to the total dissipated energy of the dam body is determined;
adding the damage indexes of all vulnerable areas to obtain the overall damage index of the gravity dam
In the formula, overall damage index
U is the number of dame vulnerable areas. The vulnerable areas of the gravity dam are generally located near the dam head, the middle of the dam body and near the dam foundation, i.e., U is 3.
S2: screening earthquake dynamic intensity parameters for predicting failure states of the gravity dam;
s2.1: calculating sets of horizontal and vertical seismic intensity parameters (IM) for 8N seismic records in S1HAnd { IM }V}, using horizontal seismic intensity parameter set { IMHFor example, a set of horizontal seismic intensity parameters (IM)HExpressed by formulas (4) and (5):
in the formula, PGAH、SaavgHAnd PGVHThe parameters of earthquake motion intensity are respectively the peak acceleration, the mean value of the acceleration of the response spectrum and the peak speed of the earthquake motion time-course curve. The earthquake motion intensity parameters and the calculation formula thereof which are commonly used in engineering are shown in table 1.
S2.2: according to the maximum value and the minimum value of the earthquake motion intensity parameter, the earthquake motion intensity parameter is normalized, taking PGA as an example, the normalized earthquake motion intensity parameter is obtained according to the formula (6)
Namely:
s2.3: computing Spearman rank correlation coefficients for normalized horizontal and vertical seismic intensity parameters and gravity dam global damage index
And
and screening seismic motion intensity parameters with strong correlation with gravity dam dynamic destruction according to the condition that the rank correlation coefficient is greater than or equal to 0.8, and taking the seismic motion intensity parameters as input parameters of the artificial neural network.
S3: establishing a BP neural network model for rapidly predicting the failure state of the gravity dam;
s3.1: 8 XN groups of horizontal and vertical seismic intensity parameters screened in S2 and corresponding classification results of gravity dam failure states are used for training, testing and verifying a BP neural network model, 80% of data are randomly extracted for training and testing the neural network model (the data for training the neural network model accounts for 70%, the data for testing accounts for 30%), and the rest 20% of data are used for verifying the prediction accuracy of the model;
s3.2: combining the horizontal earthquake dynamic intensity parameters and the vertical earthquake dynamic intensity parameters in pairs, namely
As input parameters for the BP neural network model.
S3.3: and optimizing the input parameters, the hidden layer number, the hidden layer node number, the excitation function and the training iteration number of the BP neural network model according to the model testing precision and the verification precision.
S4: selecting earthquake motion records for predicting failure states of the gravity dam, and calculating earthquake motion intensity parameters;
s4.1: and (4) selecting M actual measurement seismic motion records which accord with the field characteristics of the gravity dam, (enough seismic motion records are required to be selected to ensure the accuracy of the estimation result of the failure probability of the gravity dam, for example, M is more than 100). Amplitude modulation is carried out on the horizontal seismic acceleration time-course curves and the vertical seismic acceleration time-course curves to PGA (0.1 g-0.8 g) by taking 0.01g as a step length, and 71 xM groups of horizontal seismic acceleration time-course curves and vertical seismic acceleration time-course curves are obtained;
s4.2: calculating corresponding earthquake motion intensity parameters by adopting a formula in the table 1 according to the optimal input parameter combination determined by the S3;
s4.3: and normalization processing was performed in a similar manner to the method of formula (6).
S5: the probability of gravity dam failure estimates at different PGA levels,
s5.1: inputting M groups of earthquake dynamic intensity parameters corresponding to a certain PGA obtained in S4 by utilizing a neural network model established in S3, rapidly predicting the corresponding gravity dam failure state,
s5.2: if the gravity dam fails, record FdamIs 1, otherwise, is denoted FdamIs 0; statistics FdamNumber N of 1fThen the failure probability F of the gravity dam under the PGA levelfIs Nf/M；
S5.3: and according to the S5.2, the estimated values of the failure probability of the gravity dam under different PGA levels can be obtained.
S6: drawing a gravity dam earthquake vulnerability curve, and directly drawing PGA-F according to the gravity dam failure probability estimated values under different PGA levels obtained in S5fA curve, i.e. an earthquake vulnerability curve;
s7: calculating the earthquake risk probability of the gravity dam, dispersing the PGA into l levels, calculating the earthquake risk probability of the gravity dam according to the earthquake vulnerability curve of the gravity dam obtained in S6 and the earthquake risk curve of the dam site provided by the earthquake bureau and the following formula, wherein the calculation principle is shown in figure 2.
In the formula, p (F)dam) The probability of gravity dam failure caused by earthquake possibly occurring around the dam address in a future period of time, namely the earthquake risk probability of the gravity dam; f (F)damPGA＝PGAi) Is PGA ═ PGAiGravity damThe failure probability can be determined by the gravity dam earthquake vulnerability curve obtained in S6; g (PGA ═ PGA)i) Is PGA ═ PGAiThe earthquake occurrence probability can be determined by an earthquake risk curve of a dam site;
s8: according to calculated p (F)dam) And evaluating the earthquake risk of the gravity dam by referring to the corresponding relation between the structure failure probability and the structure performance level.
Examples
YL hydropower stations are located in the southwest region of China, are first-class (1) type projects, and mainly have the tasks of generating electricity, wherein the normal water storage level of a reservoir is 1332.00m, and the total storage capacity is 7.6 hundred million m3And 4 600MW hydroelectric generating sets are arranged in the power station. The YL hydropower station hub mainly comprises buildings such as a river dam, a power generation plant, a water diversion system and a tail water system. The river blocking dam is a roller compacted concrete gravity dam, the elevation of the dam crest is 1336.00m, the lowest elevation of the foundation surface is 1168.00m, the maximum dam height is 168m, the width of the dam crest is 20m, the upstream slope folding slope ratio is 1:0.3, and the downstream slope ratio is 1: 0.75. The method provided by the invention is adopted to carry out earthquake risk probability analysis on the gravity dam, and the specific implementation steps are as follows:
1) selecting 65 actually measured seismic motion records conforming to YL gravity dam site field characteristics from a seismic database of a Pacific ocean seismic engineering center (PEER), amplitude modulating a horizontal acceleration time course curve to PGA of 0.1g, 0.2g, … and 0.8g by taking 0.1g as a step length, wherein 8 PGAs are in total horizontal, amplitude modulating a vertical acceleration time course curve according to 2/3 of the horizontal PGA, and partial actually measured and amplitude modulated acceleration time course curves are shown in figure 3.
(2) And (3) establishing a two-dimensional finite element model (shown in figure 4) of the YL gravity dam highest water retaining dam section (16# dam section). Inputting the 520 earthquake motion records obtained in the step (1) into a finite element model, and performing incremental dynamic analysis. The dam crack development condition under the action of partial earthquake motion is shown in fig. 5, and the dam overall damage index obtained by the Hariri-Ardebili method is shown in fig. 6. The result shows that in 520 dynamic analyses, the failure frequency of the YL gravity dam is 301 (namely the dam body generates a penetrating crack, and the overall damage index of the dam body is greater than or equal to 0.6), and the non-failure frequency is 219.
(3) Calculating the horizontal and vertical intensity parameters of 520 earthquake motions in the step (1) according to the calculation formula of the common earthquake motion intensity parameters listed in the table 1, and carrying out normalization processing according to a method similar to the formula (6). The Spearman rank correlation coefficient of the normalized horizontal and vertical seismic intensity parameters with the overall damage index was calculated and the results are shown in fig. 7 and 8. And screening seismic oscillation strength parameters for predicting the failure state of the gravity dam according to the condition that the correlation coefficient is greater than or equal to 0.8, and obtaining results shown in tables 3 and 4.
(4) Using 520 groups of horizontal and vertical seismic intensity parameters screened out in the step (3) and corresponding classification results of failure states of the gravity dam for training, testing and verifying a BP neural network model, and randomly extracting 416 groups of data (accounting for 80%) for training and testing the neural network model, wherein 288 groups of data (accounting for 70%) and 128 groups of data (accounting for 30%) are used for training and testing the neural network model; the remaining 104 sets of data (20% by weight) were used to verify the prediction accuracy of the model. Combining the horizontal earthquake motion intensity parameters and the vertical earthquake motion intensity parameters screened in the step (3) pairwise, wherein 169 parameter combinations are counted and are respectively used as input parameters of the BP neural network model for model training, testing and verification.
The model testing precision and the verification precision corresponding to different earthquake motion intensity parameter combinations are shown in the tables 5 and 6. Comprehensively considering model test precision and verification precision, determining YL gravity dam failure prediction optimal input parameter combination as ASIH-ArmsVThe corresponding test precision is 95.3%, and the verification precision is 97.1%. Considering that the number of hidden layers of the neural network model is 1 or 2, the number of nodes of the hidden layer is 2-12, and the model test precision and the verification precision corresponding to different combinations of the number of hidden layers and the number of nodes of the hidden layer are shown in table 7. The results show that the model test accuracy and the verification accuracy are the highest when 1 hidden layer and 4 hidden layer nodes are adopted, and are respectively 94.5% and 98.1%. Compared with two common Sigmoid and Tanh excitation functions of a BP neural network, when the Sigmoid function is adopted, the test precision and the verification precision of the model are respectively 94.5% and 96.2%, and when the Tanh function is adopted, the test precision and the verification precision are respectively 94.5% and 98.1%, so that the Tanh function is selected as the excitation function. Maximum iteration number of model training is considered to be 20, 50,80. The method comprises the following steps of respectively carrying out neural network training under 8 conditions of 100, 200, 500, 800 and 1000 to obtain model test precision, verification precision and minimum mean square error (see table 8) corresponding to different iteration times, comprehensively considering the convergence condition of the mean square error, time consumption for calculation, test precision and verification precision, and determining the optimal iteration time of model training as 100.
(5) And (3) re-selecting 300 seismic motion records which accord with the field characteristics of the YL gravity dam site from the PEER seismic database, amplitude modulating the horizontal acceleration time-course curve to 0.1-0.8 g of PGA by taking 0.01g as a step length, and amplitude modulating the vertical acceleration time-course curve according to 2/3 of the horizontal PGA to obtain 21300 horizontal and vertical acceleration time-course curves. Calculating horizontal and vertical seismic intensity parameters ASIHAnd ArmsV21300 groups of ASI in totalHAnd ArmsVAnd carrying out normalization processing.
(6) Inputting 21300 groups of earthquake motion intensity parameters by using the BP neural network model established in the step (4)
And
predicting the corresponding gravity dam failure state, counting failure times, and calculating dam failure probability F under each PGA levelf. Drawing PGA-F according to the estimated value of the failure probability of the gravity dam under different PGA levelsfAnd (4) a curve, namely an earthquake vulnerability curve. The seismic vulnerability curve of the YL gravity dam drawn by the method of the invention and the seismic vulnerability curve drawn by the traditional method are shown in figure 9.
(7) And (3) calculating the YL gravity dam earthquake risk probability by adopting a formula (7) according to the earthquake risk curves (shown in figure 10) of 50 years and 100 years at the YL gravity dam site and the earthquake vulnerability curve obtained in the step (6) according to the calculation principle shown in figure 2. When an earthquake risk curve of 50 years is adopted, the YL gravity dam earthquake risk probability calculated according to the method is 0.58 per thousand, and the earthquake risk probability calculated according to the traditional method is 0.82 per thousand; when an earthquake risk curve of 100 years is adopted, the YL gravity dam earthquake risk probability calculated by the method is 0.99 per thousand, and the earthquake risk probability calculated by the traditional method is 1.37 per thousand.
The earthquake risk probability calculated according to the method of the present invention and table 2, the level of seismic reliability of YL gravity dam for the next 100 years is "above average". Whereas, according to conventional methods, the level of seismic reliability of YL gravity dams will be between "below average" and "above average" for the next 100 years. Because the traditional method can not adopt a large amount of vibration input to analyze the earthquake vulnerability of the gravity dam, and the earthquake demand capacity of the gravity dam needs to be supposed to accord with the lognormal distribution, the evaluation result can not accurately reflect the actual earthquake resistance performance of the gravity dam. The specific examples described herein show that the evaluation of the seismic performance of YL gravity dams using conventional methods would underestimate the seismic performance of the gravity dam.
TABLE 1 calculation formula of common earthquake dynamic intensity parameter
Table 2 correspondence of structure failure probability and structure performance level
| Probability of failure | 0.16 | 0.07 | 0.023 | 0.006 | 0.001 | 0.00003 | 0.0000003 |
| |
1 | 1.5 | 2 | 2.5 | 3 | 4 | 5 |
| Structural performance level | Danger of | Is very poor | Difference (D) | Sub-average | Higher than average | Good taste | Is very good |
TABLE 3 river-wise seismic intensity parameters with strong correlation to the overall damage index
| Seismic oscillation intensity parameter | Ic | Ia | Ea | Ars | EPA | SIR | ASI |
| Correlation coefficient | 0.903 | 0.899 | 0.899 | 0.899 | 0.887 | 0.884 | 0.884 |
| Seismic oscillation intensity parameter | Pa | Arms | RIa | PGA | PSA | CAV | |
| Correlation coefficient | 0.882 | 0.882 | 0.865 | 0.858 | 0.854 | 0.851 |
TABLE 4 vertical seismic intensity parameters with strong correlation to the overall damage index
| Seismic oscillation intensity parameter | Ia | Ea | Ars | Ic | RIa | PGA | SIR |
| Correlation coefficient | 0.880 | 0.880 | 0.880 | 0.879 | 0.865 | 0.858 | 0.856 |
| Seismic oscillation intensity parameter | Arms | Pa | PSA | EPA | CAV | ASI | |
| Correlation coefficient | 0.851 | 0.851 | 0.85 | 0.844 | 0.842 | 0.836 |
TABLE 5 neural network model test accuracy corresponding to different combinations of horizontal and vertical seismic intensity parameters
Note: h represents the horizontal direction (river direction) and V represents the vertical direction.
TABLE 6 neural network model verification accuracy corresponding to different combinations of horizontal and vertical seismic intensity parameters
Note: h represents the horizontal direction (river direction) and V represents the vertical direction.
TABLE 7 neural network model test accuracy and verification accuracy corresponding to different hidden layer number and hidden layer node number combinations
| L-m | 1-2 | 1-3 | 1-4 | 1-5 | 1-6 | 1-7 | 1-8 | 1-9 | 1-10 | 1-11 | 1-12 |
| Test accuracy | 0.953 | 0.953 | 0.945 | 0.953 | 0.953 | 0.953 | 0.953 | 0.953 | 0.953 | 0.953 | 0.953 |
| Verification accuracy | 0.971 | 0.952 | 0.981 | 0.971 | 0.971 | 0.971 | 0.971 | 0.971 | 0.952 | 0.981 | 0.971 |
| L-m | 2-2 | 2-3 | 2-4 | 2-5 | 2-6 | 2-7 | 2-8 | 2-9 | 2-10 | 2-11 | 2-12 |
| Test accuracy | 0.953 | 0.953 | 0.961 | 0.977 | 0.953 | 0.969 | 0.961 | 0.961 | 0.961 | 0.977 | 0.961 |
| Verification accuracy | 0.942 | 0.962 | 0.942 | 0.933 | 0.923 | 0.952 | 0.952 | 0.923 | 0.913 | 0.942 | 0.933 |
Note: l represents the number of hidden layers, and m represents the number of hidden layer nodes.
TABLE 8 neural network model test accuracy, validation accuracy and minimum MSE corresponding to different training iteration times
| Number of |
20 | 50 | 80 | 100 | 200 | 500 | 800 | 1000 |
| Test accuracy | 0.945 | 0.945 | 0.945 | 0.945 | 0.953 | 0.953 | 0.953 | 0.953 |
| Verification accuracy | 0.971 | 0.971 | 0.971 | 0.981 | 0.962 | 0.913 | 0.971 | 0.971 |
| Minimum mean square error | 0.074 | 0.073 | 0.071 | 0.069 | 0.071 | 0.065 | 0.067 | 0.056 |
In the description herein, references to the description of "one embodiment," "an example," "a specific example" or the like are intended to mean that a particular feature, structure, material, or characteristic described in connection with the embodiment or example is included in at least one embodiment or example of the invention. In this specification, the schematic representations of the terms used above do not necessarily refer to the same embodiment or example. Furthermore, the particular features, structures, materials, or characteristics described may be combined in any suitable manner in any one or more embodiments or examples.
The preferred embodiments of the invention disclosed above are intended to be illustrative only. The preferred embodiments are not intended to be exhaustive or to limit the invention to the precise embodiments disclosed. Obviously, many modifications and variations are possible in light of the above teaching. The embodiments were chosen and described in order to best explain the principles of the invention and the practical application, to thereby enable others skilled in the art to best utilize the invention. The invention is limited only by the claims and their full scope and equivalents.
Claims (10)
1. A gravity dam earthquake risk probability analysis method is characterized by comprising the following steps: the method specifically comprises the following steps:
s1: performing dynamic finite element analysis on the gravity dam under different vibration effects and judging the dam failure state;
s2: screening earthquake dynamic intensity parameters for predicting failure states of the gravity dam;
s3: establishing a BP neural network model for rapidly predicting the failure state of the gravity dam;
s4: selecting earthquake motion records for predicting failure states of the gravity dam, and calculating earthquake motion intensity parameters;
s5: estimating the failure probability of the gravity dam under different PGA levels;
s6: drawing a gravity dam earthquake vulnerability curve, and directly drawing PGA-F according to the gravity dam failure probability estimated values under different PGA levels obtained in S5fA curve, i.e. an earthquake vulnerability curve;
s7: calculating the earthquake risk probability of the gravity dam, dispersing the PGA into l levels, and calculating the earthquake risk probability of the gravity dam according to the earthquake vulnerability curve of the gravity dam obtained in S6 and the earthquake risk curve of the dam site provided by the earthquake bureau and the following formula:
in the formula, p (F)dam) The probability of gravity dam failure caused by earthquake possibly occurring around the dam address in a future period of time, namely the earthquake risk probability of the gravity dam; f (F)dam|PGA＝PGAi) Is PGA ═ PGAiThe failure probability of the gravity dam can be determined by the seismic vulnerability curve of the gravity dam obtained from S6; g (PGA ═ PGA)i) Is PGA ═ PGAiThe earthquake occurrence probability can be determined by an earthquake risk curve of a dam site;
s8: according to calculated p (F)dam) And evaluating the earthquake risk of the gravity dam by referring to the corresponding relation between the structure failure probability and the structure performance level.
2. The gravity dam earthquake risk probability analysis method according to claim 1, characterized in that: the specific step of S1 includes:
s1.1: selecting N actual measurement seismic motion records which accord with the field characteristics of the gravity dam to be analyzed from a seismic database, wherein each seismic motion record comprises 1 horizontal direction acceleration time-course curve and 1 vertical ground acceleration time-course curve;
s1.2: carrying out amplitude modulation processing on the selected acceleration time course curve, and carrying out amplitude modulation on the horizontal acceleration time course curve by taking 0.1g as a step length until the peak acceleration PGA is equal to 0.1g, 0.2g, … and 0.8g, wherein 8 PGAs are horizontal, and the vertical acceleration time course curve is subjected to amplitude modulation according to 2/3 of the horizontal PGA;
s1.3: inputting the amplitude-modulated 8 XN seismic motion records into a gravity dam finite element model, and performing incremental dynamic analysis;
s1.4: and judging the dam failure state according to the dam crack propagation condition and the overall damage index.
3. The gravity dam earthquake risk probability analysis method according to claim 2, wherein: the method for judging the dam failure state according to the dam crack propagation condition and the overall damage index comprises the following steps: if the dam body generates penetrating cracks and the overall damage index of the dam body is greater than or equal to 0.6, the dam is considered to be failed, and if the failure state is totally R times, the failure state is (8 multiplied by N-R) times.
4. The gravity dam earthquake risk probability analysis method according to claim 2, wherein: the dam body damage index is calculated according to a gravity dam seismic damage quantification method provided by Hariri-Ardebili, and for the kth crack appearing at the jth vulnerable part of the dam body, the damage index of the crack
Can be expressed as:
in the formula (I), the compound is shown in the specification,
and
respectively, the cracked path and the total path length, and the unit is m; beta is aΔIs the maximum displacement u from the dam crestmaxA dimensionless coefficient associated with dam height H;
calculating the damage index of each vulnerable area of the dam body according to the formula (2) according to the damage index of each crack
In the formula, n is the number of cracks contained in the jth vulnerable area;
the ratio of the energy dissipated by the kth crack to the total dissipated energy of the dam body is determined;
adding the damage indexes of all vulnerable areas to obtain the overall damage index of the gravity dam
In the formula, overall damage index
U is the number of dame vulnerable areas.
5. The gravity dam earthquake risk probability analysis method according to any one of claims 1 to 4, wherein: the specific step of S2 includes:
s2.1: calculating the horizontal and vertical earthquake motion intensity of 8 XN earthquake motion records in S1Degree parameter set (IM)HAnd { IM }V}；
S2.2: according to the maximum value and the minimum value of the earthquake motion intensity parameter, carrying out normalization processing on the earthquake motion intensity parameter;
s2.3: computing Spearman rank correlation coefficients for normalized horizontal and vertical seismic intensity parameters and gravity dam global damage index
And
and screening seismic motion intensity parameters with strong correlation with gravity dam dynamic destruction according to the condition that the rank correlation coefficient is greater than or equal to 0.8, and taking the seismic motion intensity parameters as input parameters of the artificial neural network.
6. The gravity dam earthquake risk probability analysis method according to claim 5, wherein: the horizontal seismic intensity parameter set { IMHExpressed by formulas (4) and (5):
in the formula, PGAH、SaavgHAnd PGVHThe seismic motion intensity parameters are seismic motion time history curvesPeak acceleration of the line, mean of the response spectrum acceleration and peak velocity.
7. The gravity dam earthquake risk probability analysis method according to claim 6, wherein: obtaining the normalized earthquake motion intensity parameter according to the formula (6)
Namely:
8. the gravity dam earthquake risk probability analysis method according to claim 5, wherein: the specific step of S3 includes:
s3.1: 8 XN groups of horizontal and vertical seismic intensity parameters screened in the S2 and corresponding classification results of the failure states of the gravity dam are used for training, testing and verifying a BP neural network model, 80% of data are randomly extracted for training and testing the neural network model, and the rest 20% of data are used for verifying the prediction accuracy of the model;
s3.2: combining the horizontal earthquake dynamic intensity parameters and the vertical earthquake dynamic intensity parameters in pairs, namely
As input parameters of the BP neural network model;
s3.3: and optimizing the input parameters, the hidden layer number, the hidden layer node number, the excitation function and the training iteration number of the BP neural network model according to the model testing precision and the verification precision.
9. The gravity dam earthquake risk probability analysis method according to claim 6, wherein: the specific step of S4 includes:
s4.1: selecting M actual measurement seismic motion records which accord with the field characteristics of the gravity dam, and carrying out amplitude modulation on horizontal seismic motion acceleration time-course curves and vertical seismic motion acceleration time-course curves to PGA (0.1 g-0.8 g) by taking 0.01g as a step length to obtain 71 xM groups of horizontal seismic motion acceleration time-course curves and vertical seismic motion acceleration time-course curves;
s4.2: calculating corresponding earthquake motion intensity parameters according to the optimal input parameter combination determined in the step S3;
s4.3: and carrying out normalization processing on the corresponding earthquake motion intensity parameters.
10. The gravity dam earthquake risk probability analysis method according to claim 1, characterized in that: the specific step of S5 includes:
s5.1: inputting M groups of earthquake dynamic intensity parameters corresponding to a certain PGA obtained in S4 by utilizing a neural network model established in S3, and rapidly predicting the corresponding failure state of the gravity dam;
s5.2: if the gravity dam fails, recording Fdam as 1, otherwise recording Fdam as 0; counting the number Nf of Fdam being 1, and then the estimated value of the failure probability Ff of the gravity dam under the PGA level is Nf/M;
s5.3: and according to the S5.2, the estimated values of the failure probability of the gravity dam under different PGA levels can be obtained.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011186100.XA CN112183894B (en) | 2020-10-29 | 2020-10-29 | Gravity dam earthquake risk probability analysis method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202011186100.XA CN112183894B (en) | 2020-10-29 | 2020-10-29 | Gravity dam earthquake risk probability analysis method |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN112183894A CN112183894A (en) | 2021-01-05 |
| CN112183894B true CN112183894B (en) | 2021-07-06 |
Family
ID=73917398
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN202011186100.XA Active CN112183894B (en) | 2020-10-29 | 2020-10-29 | Gravity dam earthquake risk probability analysis method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN112183894B (en) |
Families Citing this family (6)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113158302B (en) * | 2021-04-01 | 2023-08-01 | 安徽建工集团股份有限公司 | Method for evaluating earthquake vulnerability of base shock isolation system based on component reliability index |
| CN113239435B (en) * | 2021-05-11 | 2022-09-20 | 青岛理工大学 | Method for determining optimal water discharge speed of reservoir |
| CN113221426B (en) * | 2021-05-28 | 2023-11-03 | 沈阳建筑大学 | Nuclear power plant regional anti-seismic safety assessment method under seismic sequence effect |
| CN113610340A (en) * | 2021-06-25 | 2021-11-05 | 上海交通大学 | Method and device for analyzing earthquake vulnerability of electric facility of transformer substation |
| CN114048670B (en) * | 2021-10-19 | 2022-11-01 | 东南大学 | Intelligent algorithm for rapidly evaluating damage of regional concrete bridge after earthquake |
| CN113987833B (en) * | 2021-11-12 | 2024-06-07 | 中国人民解放军国防科技大学 | Vulnerability analysis method for concrete gravity dam target |
Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106958232A (en) * | 2017-04-24 | 2017-07-18 | 中国水利水电科学研究院 | For the method for Gravity Dams Against Sliding Stability, device and gravity dam |
| CN107292545A (en) * | 2017-08-23 | 2017-10-24 | 中铁二院贵阳勘察设计研究院有限责任公司 | A kind of Summary of seismic analysis for bridges based on seismic risk assessment |
| CN111523806A (en) * | 2020-04-23 | 2020-08-11 | 北京鑫大禹水利建筑工程有限公司 | Gravity dam risk assessment calculation method based on hierarchical analysis and fuzzy comprehensive evaluation |
-
2020
- 2020-10-29 CN CN202011186100.XA patent/CN112183894B/en active Active
Patent Citations (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN106958232A (en) * | 2017-04-24 | 2017-07-18 | 中国水利水电科学研究院 | For the method for Gravity Dams Against Sliding Stability, device and gravity dam |
| CN107292545A (en) * | 2017-08-23 | 2017-10-24 | 中铁二院贵阳勘察设计研究院有限责任公司 | A kind of Summary of seismic analysis for bridges based on seismic risk assessment |
| CN111523806A (en) * | 2020-04-23 | 2020-08-11 | 北京鑫大禹水利建筑工程有限公司 | Gravity dam risk assessment calculation method based on hierarchical analysis and fuzzy comprehensive evaluation |
Non-Patent Citations (2)
| Title |
|---|
| Effect of the spatial variability of strength parameters on the dynamic damage characteristics of gravity dams;Xiang Lu 等;《Engineering Structures》;20190111;第183卷;第281-289页 * |
| 基于LSSVM的重力坝地震稳定易损性分析;李浩瑾 等;《振动与冲击》;20131231;第32卷(第4期);第146-152页 * |
Also Published As
| Publication number | Publication date |
|---|---|
| CN112183894A (en) | 2021-01-05 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN112183894B (en) | Gravity dam earthquake risk probability analysis method | |
| US11922335B2 (en) | Method and system for evaluating macro resilience of offshore oil well control equipment | |
| Yılmaz et al. | A statistical approach to estimate the wind speed distribution: the case of Gelibolu region | |
| CN106101252A (en) | Information Security Risk guard system based on big data and trust computing | |
| CN112001626A (en) | Method for evaluating toughness of power distribution network in typhoon weather, storage medium and equipment | |
| CN102867110A (en) | Rainstorm disaster risk evaluation method for foundation slope of transmission line tower | |
| CN107273273A (en) | A kind of distributed type assemblies hardware fault method for early warning and system | |
| CN111507509A (en) | Risk assessment method for extreme events of power system | |
| Gunduz et al. | An early cost estimation model for hydroelectric power plant projects using neural networks and multiple regression analysis | |
| Nazarnia et al. | Characterizing infrastructure resilience in disasters using dynamic network analysis of consumers’ service disruption patterns | |
| CN105279384A (en) | Wind turbine cabin wind speed-based method and device for calculating wind speed of incoming flow | |
| CN108769018B (en) | Multidimensional and multi-granularity network space security measurement method | |
| CN111967777A (en) | Comprehensive evaluation method for energy storage and promotion of clean energy utilization value | |
| Xiang et al. | A set pair analysis model for suitability evaluation of human settlement environment | |
| CN113221426B (en) | Nuclear power plant regional anti-seismic safety assessment method under seismic sequence effect | |
| Stojković et al. | Assessment of water resources system resilience under hazardous events using system dynamic approach and artificial neural networks | |
| Noori et al. | Effective prediction of scour downstream of ski-jump buckets using artificial neural networks | |
| CN108988347A (en) | A kind of adjusting method and system that power grid Transient Voltage Stability sample set classification is unbalance | |
| Anilan et al. | Application of Artificial Neural Networks and regression analysis to L-moments based regional frequency analysis in the Eastern Black Sea Basin, Turkey | |
| CN108268025A (en) | Random perturbation lower network networked control systems elasticity assessment method | |
| CN106603294A (en) | Comprehensive vulnerability assessment method based on power communication network structure and state | |
| CN111507606B (en) | Toughness evaluation method for complex energy interconnection system | |
| Chen et al. | Review on the research status of power system risk identification under typhoon disaster | |
| CN114091329A (en) | Regional photovoltaic power generation amount calculation method based on inverse distance interpolation and extreme learning machine | |
| Malliouri et al. | Reliability analysis of rubble mound breakwaters-An easy-to-use methodology |
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 |