CN110990916B - Integration method for evaluating and predicting long-term operation safety of dam by considering hysteresis effect - Google Patents
Integration method for evaluating and predicting long-term operation safety of dam by considering hysteresis effect Download PDFInfo
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Abstract
The invention discloses a dam long-term operation safety evaluation and prediction integration method considering hysteresis effect, which comprises the following steps: step A, quantitatively calculating an index hysteresis effect; step B, establishing a complete safety evaluation index system; step C, calculating subjective weights of all indexes by an analytic hierarchy process, and providing a DIC (digital computer) method by combining a variation coefficient and a maximum mutual information coefficient to calculate objective weights of all indexes, wherein the objective weights are linearly weighted to obtain combined weights; step D, based on the multi-measuring-point long-term monitoring data of the concrete dam, the safety of the concrete dam is quantitatively calculated by using a TOPSIS method, and the long-term running state and trend of the concrete dam are obtained; and E, predicting the future long-term safety degree by adopting a neural network, and comparing the future long-term safety degree with a calculated value to provide early warning of dam abnormality. According to the invention, by establishing a concrete dam long-term operation safety evaluation and prediction system, the dam safety condition is quantitatively represented, future long-term safety prediction is carried out, the dam operation abnormal condition is early warned in advance, and the staff can take remedial measures in time.
Description
Technical Field
The invention relates to concrete dam operation safety evaluation, in particular to a concrete dam long-term operation safety evaluation and prediction integration method considering hysteresis effect.
Background
The dam makes great contribution to promoting economic development and guaranteeing the safety of people. However, with the increase of the operation years, the problems of dam body material performance reduction, foundation leakage and the like can occur in the dam, so that dam break accidents are extremely easy to occur, and huge losses are caused. Thus, dam safety issues are receiving increasing attention. In order to ensure the safety of the dam, timely abnormality discovery is very important for monitoring and evaluating the safety of the dam.
At present, many achievements are obtained by researching a concrete dam safety evaluation method at home and abroad, such as gravity dam stability analysis is carried out by applying fuzzy set theory and multi-objective genetic algorithm under the uncertainty condition, such as Ali Haghighi and the like; wu Zhongru and the like provide a general principle of dam operation evaluation, and based on an evidence theory and a fuzzy comprehensive analysis method, provide a fusion algorithm of interlayer evaluation, and establish a comprehensive evaluation model. Most of the researches are coupled with different theories and methods, and a comprehensive dam safety evaluation model is established. However, hysteresis effects of environmental influence factors are not considered, only the operation safety level of a short-term single measuring point of the dam is obtained, the long-term operation safety condition of the dam cannot be accurately known, and abnormal early warning and indexes needing important monitoring are provided for staff.
Disclosure of Invention
The invention aims to overcome the defects in the prior art and provides an integrated method for evaluating and predicting the long-term operation safety of a concrete dam by considering a hysteresis effect, so as to know the operation safety condition of the dam and early warn the abnormal condition.
The technical scheme adopted by the invention is as follows: a method for evaluating and predicting integration of long-term operation safety of a dam by considering hysteresis effect comprises the following steps:
step A, combining a correction moving average method and a cosine similarity method, and quantitatively calculating an index hysteresis effect;
step B, establishing a complete safety evaluation index system;
step C, calculating subjective weights of all indexes by an analytic hierarchy process, and providing a DIC (digital computer) method by combining a variation coefficient and a maximum mutual information coefficient to calculate objective weights of all indexes, wherein the subjective weights and the objective weights of all indexes are linearly weighted to obtain combined weights of all indexes;
step D, based on the multi-measuring-point long-term monitoring data of the concrete dam, the safety of the concrete dam is quantitatively calculated by using a TOPSIS method, and the long-term running state and trend of the concrete dam are obtained;
and E, predicting future long-term safety by adopting a neural network, and comparing the future long-term safety with the safety of the concrete dam calculated in the step D to provide early warning of dam abnormality.
Step a further comprises:
a1, selecting monitoring data of deformation, air temperature and reservoir water level, and calculating lag time of the deformation relative to the air temperature and the reservoir water level;
a2, extracting trend items of the monitoring data by using a modified moving average method to obtain residual period items and irregular fluctuation items, and normalizing;
wherein F is t N is the analog value at the next time 1 The number of monitored data contained for a single period of moving average,for the first n 1 A phase actual measurement value;
a3, applying a cosine similarity method to the normalized data, continuously sliding the data to find the position with the maximum cosine similarity, and solving the lag time of the air temperature and the water level of the pool;
in cos (θ) 1 For displacement vector and Wen Xiangliang cosine similarity, A i Is a displacement vector, B i Cos (θ) as the air temperature vector 2 C is cosine similarity of displacement vector and library water level vector i For the reservoir water level vector, n 2 And the number of the monitoring data selected for the temperature, the water level and the displacement is used for calculating the cosine similarity.
In the step B, the established safety evaluation index system comprises 3 aspects of meteorological data acquisition, structural safety evaluation and hysteresis evaluation, wherein the meteorological data acquisition comprises rainfall data R, air temperature data T and upstream reservoir water level W u And downstream reservoir water level W d The structural safety evaluation comprises dam displacement D i Deformation D of dam body e Dam stress S and crack opening C, the hysteresis evaluation comprising hysteresis rainfall R l Temperature T at delayed time l Lag upstream reservoir Water level W ul And a lag downstream reservoir level W dl 。
Step C further comprises:
step C1, calculating subjective weights of all indexes by an analytic hierarchy process, comparing importance of every two indexes, and quantifying the relative importance of the two indexes according to a specified importance measurement standard to obtain a judgment matrix A;
step C2, calculating a maximum characteristic value and a characteristic vector, and solving subjective weight;
A×W＝λ max W (3)
wherein lambda is max For the maximum characteristic value, W is a characteristic vector, and the subjective weight of each index is obtained by normalization
Step C3, consistency test is carried out:
wherein C is R For the consistency ratio, C I R is a consistency check index I N is an average random uniformity index 3 The number of evaluation indexes selected for the safety evaluation system; if C R <0.1, the consistency is good, and the obtained subjective weight is reliable;
step C4, calculating the variation coefficient CV of the single index j ：
In sigma j Is the standard deviation of the j-th index,is the mean value of the j index;
and C5, calculating the maximum mutual information coefficient MIC (D) of every two indexes:
wherein I is * (D, x, y) represents the maximum mutual information value in the grids of all scales, B is the number of grids,n 4 for the number of samples, x is the number of segments divided on the x-axis of the scatter plot, and y is the number of segments divided on the y-axis of the scatter plot;
step C6, calculating the independent coefficient D of each index j ：
Wherein b is ij Representing the maximum mutual information coefficient between the ith index and the jth index;
step C7, calculating objective weight omega j ：
Step C8, the subjective weight and the objective weight are linearly combined to obtain the combined weight of each index:
wherein w is j For the combined weights to be used,is subjective weight omega j Mu is the distribution coefficient of subjective and objective weights and is 0.ltoreq.w j ≤1,/>
Step D further comprises:
step D1, normalizing the index monitoring data and establishing an initial evaluation matrix;
step D2, multiplying the initial evaluation matrix by the combination weight obtained in the step C to construct a final evaluation matrix Z;
step D3, determining positive and negative ideal solutions of all indexes;
in the method, in the process of the invention,for the positive ideal understanding of each index, +.>For negative ideal solution of each index, z ij The i-th evaluation object representing the j-th index, i=1, 2, …, m, m is the number of monitoring data under positive and negative ideal solutions of each index;
step D4, calculating the distance from the measured data of each index of the concrete dam to the positive ideal solution and the negative ideal solution by using Euclidean distance, wherein the closer the measured data of each index of the concrete dam is to the positive ideal solution, the farther the measured data of each index of the concrete dam is to the negative ideal solution, and the more optimal the solution is;
in the method, in the process of the invention,for the distance of the actual security state of the concrete dam to the ideal, +.>The distance from the actual safety state of the concrete dam to the negative ideal solution;
step D5, calculating the ideal point closeness C i As concrete dam safety:
C i the larger the distance from the positive ideal solution is, the smaller the distance from the negative ideal solution is, and the higher the safety of the concrete dam is.
Step E further comprises:
step E1, selecting three optimal parameters of the neural network: the proportion of sample data distributed to a training set, a verification set and a test set, the number of neurons of an implicit layer and a training algorithm;
step E2, training a neural network model, predicting future long-term safety by using the historical safety calculated by TOPSIS, and calculating four indexes of mean square error MSE, mean absolute error MAE, mean absolute percentage error MAPE and maximum error ME:
wherein N is the number of samples,degree of safety determined for TOPSIS of the ith sample,/->Predicting the safety degree for the neural network of the ith sample;
and E3, comparing the safety degree predicted by the neural network with the safety degree calculated by the TOPSIS, and knowing the abnormal operation of the concrete dam through the difference between the two values to early warn the staff in advance.
The beneficial effects of the invention are as follows: according to the method, hysteresis effects of environmental factors are quantified, structural safety, environmental safety and total safety are calculated according to the long-term multi-measuring-point monitoring data of the concrete dam, the long-term operation safety condition of the concrete dam is quantified from three values, future safety is predicted according to the historical safety, early warning of the abnormal operation condition of the dam is provided through comparison of a calculated value and a predicted value, timely repair is carried out, and the probability of dam failure is reduced.
Drawings
Fig. 1: the invention relates to a dam long-term operation safety evaluation and prediction integration method flow chart considering hysteresis effect;
fig. 2: the invention relates to an engineering instance air temperature and reservoir water level cosine similarity curve;
fig. 3a: the invention discloses a safety degree diagram of an engineering example structure;
fig. 3b: the invention discloses an engineering instance environment safety degree diagram;
fig. 3c: the invention relates to a total safety degree diagram of engineering examples;
fig. 4a: the invention discloses a project example structure safety degree trend chart;
fig. 4b: the invention discloses an engineering instance environment safety degree trend chart;
fig. 4c: the invention relates to a total safety degree trend chart of engineering examples;
fig. 5: the invention relates to a project example total safety degree prediction diagram.
Detailed Description
For a further understanding of the invention, its features and advantages, reference is now made to the following examples, which are illustrated in the accompanying drawings in which:
as shown in figure 1, the integrated method for evaluating and predicting the long-term operation safety of the dam by considering the hysteresis effect is used for evaluating and predicting the long-term operation safety condition of the concrete dam, and comprises the following steps:
and step A, combining a modified moving average method (MMA) and a cosine similarity method (CS), and quantitatively calculating the index hysteresis effect.
A1, selecting monitoring data of deformation, air temperature and reservoir water level, and calculating lag time of the deformation relative to the air temperature and the reservoir water level;
a2, applying MMA, extracting trend items of the monitoring data to obtain residual period items and irregular fluctuation items, and normalizing;
wherein F is t N is the analog value at the next time 1 The number of monitored data contained for a single period of moving average,for the first n 1 A phase actual measurement value;
step A3, CS is applied to the normalized data, the position with the maximum cosine similarity is continuously searched by sliding the data, and the lag time of the air temperature and the water level of the pool is obtained;
in cos (θ) 1 For displacement vector and Wen Xiangliang cosine similarity, A i Is a displacement vector, B i Cos (θ) as the air temperature vector 2 C is cosine similarity of displacement vector and library water level vector i For the reservoir water level vector, n 2 And the number of the monitoring data selected for the temperature, the water level and the displacement is used for calculating the cosine similarity.
And B, establishing a complete safety evaluation index system. Established by the methodThe safety evaluation index system comprises 3 aspects of meteorological data acquisition, structural safety evaluation and hysteresis evaluation, and the total number of indexes is 12. The meteorological data acquisition comprises rainfall data R, air temperature data T and upstream reservoir water level W u And downstream reservoir water level W d The structural safety evaluation comprises dam displacement D i Deformation D of dam body e Dam stress S and crack opening C, the hysteresis evaluation comprising hysteresis rainfall R l Temperature T at delayed time l Lag upstream reservoir Water level W ul And a lag downstream reservoir level W dl 。
And step C, calculating subjective weights of all indexes by an Analytic Hierarchy Process (AHP), and providing DIC (Discreteness and Independence Coefficient) method by combining a variation Coefficient (CV) and a maximum Mutual Information Coefficient (MIC), and calculating subjective weights of all indexes, wherein the subjective weights and the objective weights of all indexes are weighted linearly to obtain the combination weights of all indexes.
Step C1, calculating subjective weights of all indexes through AHP, comparing importance of every two indexes, and quantifying the relative importance of the two indexes by adopting a measurement mode shown in table 1 to obtain a judgment matrix A;
TABLE 1 importance metrics table
Step C2, calculating a maximum characteristic value and a characteristic vector, and solving subjective weight;
A×W＝λ max W (3)
wherein lambda is max For the maximum characteristic value, W is a characteristic vector, and the subjective weight of each index is obtained by normalization
Step C3, consistency test is carried out:
wherein C is R For the consistency ratio, C I R is a consistency check index I As an average random uniformity index, n can be found from Table 2 3 The number of evaluation indexes selected for the safety evaluation system; if C R <0.1, the consistency is good, and the obtained subjective weight is reliable;
TABLE 2 average random uniformity index R I Standard value
Step C4, calculating the variation coefficient CV of the single index j ：
In sigma j Is the standard deviation of the j-th index,is the mean value of the j index;
and C5, calculating the maximum mutual information coefficient MIC (D) of every two indexes:
wherein I is * (D, x, y) represents the maximum mutual information value in the grids of all scales, B is the number of grids,n 4 for the number of samples, x is the number of segments divided on the x-axis of the scatter plot, and y is the number of segments divided on the y-axis of the scatter plot;
step C6, calculating the independent coefficient D of each index j ：
Wherein b is ij Representing the maximum mutual information coefficient between the ith index and the jth index;
step C7, calculating objective weight omega j ：
Step C8, the subjective weight and the objective weight are linearly combined to obtain the combined weight of each index:
wherein w is j For the combined weights to be used,is subjective weight omega j Mu is the distribution coefficient of subjective and objective weights and is 0.ltoreq.w j ≤1,/>
And D, based on the multi-measuring-point long-term monitoring data of the concrete dam, the safety of the concrete dam is quantitatively calculated by using a TOPSIS (Technique for Order Preference by Similarity to an Ideal Solution) method, and the long-term running state and trend of the concrete dam are obtained.
Step D1, normalizing the index monitoring data and establishing an initial evaluation matrix;
step D2, multiplying the initial evaluation matrix by the combination weight obtained in the step C8 to construct a final evaluation matrix Z;
step D3, determining positive and negative ideal solutions of all indexes;
in the method, in the process of the invention,for the positive ideal understanding of each index, +.>For negative ideal solution of each index, z ij The i-th evaluation object representing the j-th index, i=1, 2, …, m, m is the number of monitoring data under positive and negative ideal solutions of each index;
step D4, calculating the distance from the measured data of each index of the concrete dam to the positive ideal solution and the negative ideal solution by using Euclidean distance, wherein the closer the measured data of each index of the concrete dam is to the positive ideal solution, the farther the measured data of each index of the concrete dam is to the negative ideal solution, and the more optimal the solution is;
in the method, in the process of the invention,for the distance of the actual security state of the concrete dam to the ideal, +.>The distance from the actual safety state of the concrete dam to the negative ideal solution;
step D5, calculating the ideal point closeness C i As concrete dam safety:
C i the larger the distance from the positive ideal solution is, the smaller the distance from the negative ideal solution is, and the higher the safety of the concrete dam is.
And E, predicting future long-term safety by adopting a neural network, and comparing the future long-term safety with the safety of the concrete dam calculated in the step D to provide early warning of dam abnormality.
Step E1, selecting three optimal parameters of the neural network: the proportion of sample data distributed to a training set, a verification set and a test set, the number of neurons of an implicit layer and a training algorithm;
step E2, training a neural network model, predicting future long-term safety by using the historical safety calculated by TOPSIS, and calculating four indexes of mean square error MSE, mean absolute error MAE, mean absolute percentage error MAPE and maximum error ME:
wherein N is the number of samples,degree of safety determined for TOPSIS of the ith sample,/->Predicting the safety degree for the neural network of the ith sample;
and E3, comparing the safety degree predicted by the neural network with the safety degree calculated by the TOPSIS, and knowing the abnormal operation of the concrete dam through the difference between the two values to early warn the staff in advance.
According to the invention, by establishing a concrete dam long-term operation safety evaluation and prediction system, the dam safety condition is quantitatively represented, future long-term safety prediction is carried out, the dam operation abnormal condition is early warned in advance, and the staff can take remedial measures in time.
Examples
And selecting a monitoring index of a certain actual engineering, and respectively establishing a concrete dam structure safety evaluation index system, an environment evaluation index system and a total evaluation index system. Structural safety evaluation indexes are selected, wherein dam foundation strain (S) of a dam section of the sand washing gate and stress (S) of typical measuring points at the top of a gate hole of the sand washing gate are selected t ) Stress of typical measuring point of gate hole side (S s ) Dam foundation deformation (D) e ) And dam face displacement (D) i ) 5 indexes. The environmental evaluation index selects air temperature (T), reservoir water level (W) and lag air temperature (T) l ) Hysteresis reservoir level (W) l ) 4 indexes. The total evaluation index integrates the structural safety index and the environmental index, and the total number of the indexes is 9.
And C, calculating the lag time of the air temperature and the reservoir water level according to the step A to be 55 days and 30 days (as shown in figure 2), and taking the air temperature and the reservoir water level taking the lag time into consideration as environment evaluation indexes.
According to the steps C1 to C3, subjective weights of all indexes are calculated through AHP, and consistency test is good. And C4, calculating CV and MIC of each index according to the steps C4 to C7, and obtaining the objective weight of the index. And C8, linearly weighting the subjective weight and the objective weight, and taking 0.5 for the distribution coefficient to obtain the combination weight of each index. The weights of the structural safety index, the environmental index and the total index are shown in tables 3, 4 and 5.
TABLE 3 structural safety assessment index weight
TABLE 4 environmental assessment index weight
Table 5 total evaluation index weight
And D, respectively calculating the structural safety degree, the environmental safety degree and the total safety degree according to the step D, and extracting the safety degree trend to obtain the long-term operation safety condition of the concrete dam, as shown in figures 3a to 4 c.
The concrete dam structure in fig. 3a shows significant periodic variations in safety, which occurs at a minimum in the 8, 9 months of the year. Because the structural safety indexes mainly include stress, strain, deformation and dam body displacement in the dam foundation, the air temperature is high in 8 months and 9 months, the temperature stress is increased due to the fact that concrete is heated and expanded, the condition of low safety degree can occur, and engineering practice is met. The environmental safety of fig. 3b also shows a periodic variation as a whole, with the safety minimum occurring at 1,2 months per year. As the environmental indexes are mainly air temperature and reservoir water level, frost heaving effect can be generated at low temperature in winter, which is unfavorable for dam safety and accords with engineering practice. The environment safety is lower in 7 and 8 months, the temperature stress of the concrete is increased due to high temperature in summer, the place where the dam site is located is rainy in summer, the water level of the reservoir rises, and the evaluation result accords with the actual condition. Fig. 3c shows regular periodic variation of the total safety, and the variation trend is substantially the same as the structural safety, which indicates that the safety condition of the dam is mainly determined by the structural safety index.
In general, the monitored amount of each index changes with the increase of time, and is an irreversible process. As can be seen from fig. 4a to fig. 4c, the structural safety fluctuates slightly, the overall change is not more than 0.2, the dam safety condition is stable, and the dam safety condition can be judged to be good; the environmental safety has an ascending trend, the later period tends to be stable, the period fluctuates slightly, and the safety condition of the dam is good. The total safety trend is periodically fluctuated and basically consistent with the structural safety trend, the safety value exceeds 0.67, and the safety condition of the dam is good.
And E1, selecting different parameter combinations to respectively train and predict, and selecting three parameters with highest prediction precision. 70% of the samples are used as training sets, 20% of the samples are used as verification sets and 10% of the samples are used as test sets, 1 hidden layers containing 10 neurons are arranged, and Bayesian Regularization algorithm training models are selected.
According to step E2, the future long-term total safety is predicted by the historical safety, the prediction precision index is shown in table 6, and the comparison between the predicted value and the calculated value is shown in fig. 5. The neural network has good fitting effect in the training set, and the predicted value and the calculated value have the same trend in the test set, but the calculated value is lower, so that the dam safety condition is abnormal in the period of time, and the dam safety condition needs to be detected and maintained in time.
TABLE 6 prediction precision index of total safety
Although the preferred embodiments of the present invention have been described above with reference to the accompanying drawings, the present invention is not limited to the above-described embodiments, which are merely illustrative and not restrictive, and many forms may be made by those having ordinary skill in the art without departing from the spirit of the present invention and the scope of the appended claims, which are within the scope of the present invention.
Claims (2)
1. The method for evaluating and predicting the long-term operation safety of the dam by considering the hysteresis effect is characterized by comprising the following steps of:
step A, combining a correction moving average method and a cosine similarity method, and quantitatively calculating an index hysteresis effect;
step B, establishing a complete safety evaluation index system, wherein the safety evaluation index system comprises 3 aspects of meteorological data acquisition, structural safety evaluation and hysteresis evaluation, and the meteorological data acquisition comprises rainfall data R, air temperature data T and upstream reservoir water level W u And downstream reservoir water level W d The structural safety evaluation comprises dam displacement D i Deformation D of dam body e Dam stress S and crack opening C, the hysteresis evaluation comprising hysteresis rainfall R l Temperature T at delayed time l Late upstream librariesWater level W ul And a lag downstream reservoir level W dl ；
Step C, calculating subjective weights of all indexes by an analytic hierarchy process, combining a variation coefficient and a maximum mutual information coefficient, providing a DIC method to calculate objective weights of all indexes, and linearly weighting the subjective weights and the objective weights of all indexes to obtain combined weights of all indexes, wherein the step C comprises the following steps:
step C1, calculating subjective weights of all indexes by an analytic hierarchy process, comparing importance of every two indexes, and quantifying the relative importance of the two indexes according to a specified importance measurement standard to obtain a judgment matrix A;
step C2, calculating a maximum characteristic value and a characteristic vector, and solving subjective weight;
A×W＝λ max W (3)
wherein lambda is max For the maximum characteristic value, W is a characteristic vector, and the subjective weight of each index is obtained by normalization
Step C3, consistency test is carried out:
wherein C is R For the consistency ratio, C I R is a consistency check index I N is an average random uniformity index 3 The number of evaluation indexes selected for the safety evaluation system; if C R <0.1, the consistency is good, and the obtained subjective weight is reliable;
step C4, calculating the variation coefficient CV of the single index j ：
In sigma j Is the standard deviation of the j-th index,is the mean value of the j index;
and C5, calculating the maximum mutual information coefficient MIC (D) of every two indexes:
wherein I is * (D, x, y) represents the maximum mutual information value in the grids of all scales, B is the number of grids,n 4 for the number of samples, x is the number of segments divided on the x-axis of the scatter plot, and y is the number of segments divided on the y-axis of the scatter plot;
step C6, calculating the independent coefficient D of each index j ：
Wherein b is ij Representing the maximum mutual information coefficient between the ith index and the jth index;
step C7, calculating objective weight omega j ：
Step C8, the subjective weight and the objective weight are linearly combined to obtain the combined weight of each index:
wherein w is j For the combined weights to be used,is subjective weight omega j Mu is the distribution coefficient of subjective and objective weights and is 0.ltoreq.w j ≤1,/>
And D, based on the multi-measuring-point long-term monitoring data of the concrete dam, calculating the safety of the concrete dam by using a TOPSIS method to obtain the long-term running state and trend of the concrete dam, wherein the method comprises the following steps:
step D1, normalizing the index monitoring data and establishing an initial evaluation matrix;
step D2, multiplying the initial evaluation matrix by the combination weight obtained in the step C to construct a final evaluation matrix Z;
step D3, determining positive and negative ideal solutions of all indexes;
in the method, in the process of the invention,for the positive ideal understanding of each index, +.>For negative ideal solution of each index, z ij The i-th evaluation object representing the j-th index, i=1, 2, …, m, m is the number of monitoring data under positive and negative ideal solutions of each index;
step D4, calculating the distance from the measured data of each index of the concrete dam to the positive ideal solution and the negative ideal solution by using Euclidean distance, wherein the closer the measured data of each index of the concrete dam is to the positive ideal solution, the farther the measured data of each index of the concrete dam is to the negative ideal solution, and the more optimal the solution is;
in the method, in the process of the invention,for the distance of the actual security state of the concrete dam to the ideal, +.>The distance from the actual safety state of the concrete dam to the negative ideal solution;
step D5, calculating the ideal point closeness C i As concrete dam safety:
C i the larger the distance from the positive ideal solution is, the smaller the distance from the positive ideal solution is, and the larger the distance from the negative ideal solution is, the higher the safety of the concrete dam is;
and E, predicting future long-term safety by adopting a neural network, comparing the future long-term safety with the safety of the concrete dam calculated in the step D, and providing early warning of dam abnormality, wherein the method comprises the following steps:
step E1, selecting three optimal parameters of the neural network: the proportion of sample data distributed to a training set, a verification set and a test set, the number of neurons of an implicit layer and a training algorithm;
step E2, training a neural network model, predicting future long-term safety by using the historical safety calculated by TOPSIS, and calculating four indexes of mean square error MSE, mean absolute error MAE, mean absolute percentage error MAPE and maximum error ME:
wherein N is the number of samples,degree of safety determined for TOPSIS of the ith sample,/->Predicting the safety degree for the neural network of the ith sample;
and E3, comparing the safety degree predicted by the neural network with the safety degree calculated by the TOPSIS, and knowing the abnormal operation of the concrete dam through the difference between the two values to early warn the staff in advance.
2. The integrated method for evaluating and predicting long-term operational safety of a dam with consideration of hysteresis effect as set forth in claim 1, wherein the step a further comprises:
a1, selecting monitoring data of deformation, air temperature and reservoir water level, and calculating lag time of the deformation relative to the air temperature and the reservoir water level;
a2, extracting trend items of the monitoring data by using a modified moving average method to obtain residual period items and irregular fluctuation items, and normalizing;
wherein F is t N is the analog value at the next time 1 The number of monitored data contained for a single period of moving average,for the first n 1 A phase actual measurement value;
a3, applying a cosine similarity method to the normalized data, continuously sliding the data to find the position with the maximum cosine similarity, and solving the lag time of the air temperature and the water level of the pool;
in cos (θ) 1 For displacement vector and Wen Xiangliang cosine similarity, A i Is a displacement vector, B i Cos (θ) as the air temperature vector 2 C is cosine similarity of displacement vector and library water level vector i For the reservoir water level vector, n 2 And the number of the monitoring data selected for the temperature, the water level and the displacement is used for calculating the cosine similarity.
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