CN110990916A - Evaluation and prediction integration method for long-term operation safety of dam considering hysteresis effect - Google Patents
Evaluation and prediction integration method for long-term operation safety of dam considering hysteresis effect Download PDFInfo
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Abstract
The invention discloses a long-term operation safety evaluation and prediction integration method of a dam 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 weight of each index through an analytic hierarchy process, combining the variation coefficient and the maximum mutual information coefficient to provide a DIC method to calculate objective weight of each index, and linearly weighting the objective weight of each index and the maximum mutual information coefficient to obtain combined weight; step D, quantitatively calculating the safety degree of the concrete dam by using a TOPSIS method based on multi-measuring-point long-term monitoring data of the concrete dam to obtain the long-term operation state and trend of the concrete dam; and E, predicting the future long-term safety degree by adopting a neural network, and comparing the future long-term safety degree with the calculated value to provide early warning of dam abnormity. According to the method, the long-term operation safety evaluation and prediction system of the concrete dam is established, the safety condition of the dam is quantitatively expressed, the long-term safety prediction in the future is carried out, the abnormal condition of the operation of the dam is early warned in advance, and workers can take remedial measures in time.
Description
Technical Field
The invention relates to the evaluation of the operation safety of a concrete dam, in particular to a long-term operation safety evaluation and prediction integration method of the concrete dam considering the 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 life, the dam body material performance is reduced, the foundation is leaked and other problems occur, so that the dam break accident is easily caused, and huge loss is caused. Thus, dam safety issues are receiving increasing attention. In order to ensure the safety of the dam and find abnormality in time, the monitoring and evaluation of the safety of the dam are very important.
At present, the research on the concrete dam safety evaluation method at home and abroad obtains a plurality of achievements, such as the application of fuzzy set theory and multi-target genetic algorithm to the analysis of the stability of the gravity dam under the uncertain condition by Ali Haghighi and the like; wu Zhong et al put forward the general principle of dam operation evaluation, and put forward a fusion algorithm of interlayer evaluation based on evidence theory and fuzzy comprehensive analysis method, and establish a comprehensive evaluation model. Most of the researches are coupled with different theories and methods to establish a comprehensive evaluation model of dam safety. However, the hysteresis effect of environmental influence factors is not considered, the operation safety level of a short-term single measuring point of the dam is only obtained, the long-term operation safety condition of the dam cannot be accurately known, and the indexes of abnormity early warning and important monitoring are provided for workers.
Disclosure of Invention
The invention aims to overcome the defects in the prior art, and provides a long-term operation safety evaluation and prediction integration method for a concrete dam considering a hysteresis effect, which is used for knowing the operation safety condition of the dam and giving an early warning for an abnormal condition.
The technical scheme adopted by the invention is as follows: a long-term operation safety evaluation and prediction integration method for a dam considering hysteresis effect comprises the following steps:
step A, combining a modified 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 the subjective weight of each index through an analytic hierarchy process, combining the variation coefficient and the maximum mutual information coefficient, proposing a DIC method to calculate the objective weight of each index, and linearly weighting the subjective weight and the objective weight of each index to obtain the combined weight of each index;
step D, quantitatively calculating the safety degree of the concrete dam by using a TOPSIS method based on multi-measuring-point long-term monitoring data of the concrete dam to obtain the long-term operation state and trend of the concrete dam;
and E, predicting the future long-term safety by adopting a neural network, and comparing the future long-term safety with the concrete dam safety calculated in the step D to provide early warning of dam abnormity.
Step a further comprises:
step A1, selecting the monitoring data of deformation, air temperature and reservoir water level for calculating the lag time of deformation relative to air temperature and reservoir water level;
a2, extracting a trend item of the monitoring data by applying a modified moving average method to obtain a residual period item and an irregular fluctuation item, and normalizing;
in the formula, FtIs an analog value of the next time, n1For the number of monitoring data contained in a single cycle of the moving average,
is front n1A measured value of the period;
step A3, applying a cosine similarity method to the normalized data, continuously sliding the data to find a position with the maximum cosine similarity, and solving the lag time of the air temperature and the reservoir water level;
in the formula, cos (theta)1As the remainder of the displacement vector and the air temperature vectorChord similarity, AiIs a displacement vector, BiIs the air temperature vector, cos (θ)2Is the cosine similarity of the displacement vector and the reservoir level vector, CiIs the reservoir level vector, n2The number of monitoring data selected for calculating the cosine similarity for the air temperature, the water level and the displacement.
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, temperature data T and upstream reservoir water level WuAnd downstream reservoir level WdThe structural safety evaluation comprises dam body displacement DiDeformation of dam body DeStress S of the dam body and crack opening C, and the hysteresis evaluation comprises hysteresis rainfall RlLagging air temperature TlLagging upstream reservoir level WulAnd lags downstream reservoir level Wdl。
Step C further comprises:
step C1, calculating the subjective weight of each index by an analytic hierarchy process, comparing the 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 the maximum eigenvalue and eigenvector, and calculating the subjective weight;
A×W＝λmaxW (3)
in the formula, λmaxThe maximum eigenvalue is W, the eigenvector is W, and the subjective weight of each index is obtained by normalization
Step C3, performing a consistency check:
in the formula, CRFor consistency ratio, CIFor consistency check, RIIs an average random consistency index, n3Evaluation finger selected for safety evaluation systemMarking the number; if CR<0.1, the consistency is good, and the obtained subjective weight is reliable;
step C4, calculating coefficient of variation CV of single indexj：
In the formula, σjIs the standard deviation of the j-th index,
is the mean value of the jth index;
step C5, calculating the maximum mutual information coefficient mic (d) of every two indexes:
in the formula I*(D, x, y) represents the maximum mutual information value in all scales of the grid, B is the number of grids,
n4the number of samples is shown, x is the number of segments divided on an x axis of a scatter diagram, and y is the number of segments divided on a y axis of the scatter diagram;
step C6, calculating independent coefficient D of each indexj：
In the formula, bijRepresenting the maximum mutual information coefficient between the ith index and the jth index;
step C7, calculating objective weight omegaj：
Step C8, the subjective weight and the objective weight are linearly combined to obtain the combined weight of each index:
in the formula, wjIn order to combine the weights, the weights are combined,
is a subjective weight, ωjIs an objective weight, mu is a distribution coefficient of the subjective and objective weights, and w is more than or equal to 0j≤1,
Step D further comprises:
step D1, normalizing the monitoring data of each index, and establishing an initial evaluation matrix;
step D2, multiplying the initial evaluation matrix by the combined weight obtained in the step C to construct a final evaluation matrix Z;
d3, determining positive and negative ideal solutions of each index;
in the formula (I), the compound is shown in the specification,
for a positive ideal solution of each index,
is a negative ideal solution of each index, zijAn ith evaluation object representing a jth index, i being 1,2, …, m, m being the number of monitored data for determining positive and negative ideal solutions of each index;
step D4, calculating the distance between each index measured data of the concrete dam and the positive and negative ideal solutions by adopting the Euclidean distance, wherein the closer each index measured data of the concrete dam is to the positive ideal solution, the farther the index measured data of the concrete dam is from the negative ideal solution, the better the solution is;
in the formula (I), the compound is shown in the specification,
the distance from the actual safety state of the concrete dam to the ideal state of the concrete dam,
the distance from the actual safety state of the concrete dam to the negative ideal solution is obtained;
step D5, calculating the closeness C of the ideal pointiSafety degree of concrete dam:
Cithe larger the distance to the positive ideal solution is, the smaller the distance to the negative ideal solution is, and the higher the safety degree of the concrete dam is.
Step E further comprises:
step E1, three optimal parameters of the neural network are selected: the proportion of sample data distributed to the training set, the verification set and the test set, the number of hidden layer neurons and a training algorithm;
step E2, training a neural network model, predicting future long-term safety by using historical safety obtained by TOPSIS calculation, and calculating four indexes of mean square error MSE, mean absolute error MAE, mean absolute percentage error MAPE and maximum error ME:
in the formula, N is the number of samples,
the security level found for TOPSIS of the ith sample,
predicting a 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 abnormality of the concrete dam operation through the difference of the two values to early warn the working personnel in advance.
The invention has the beneficial effects that: the invention quantifies the hysteresis effect of environmental factors, calculates the structural safety degree, the environmental safety degree and the total safety degree according to the long-term multi-point monitoring data of the concrete dam, quantifies the long-term operation safety condition of the concrete dam from three numerical values, predicts the future safety degree according to the historical safety degree, provides early warning of the abnormal operation condition of the dam through the comparison of the calculated value and the predicted value, repairs in time and reduces the accident probability of the dam.
Drawings
FIG. 1: the invention relates to a flow chart of an evaluation and prediction integration method for long-term operation safety of a dam considering hysteresis effect;
FIG. 2: the cosine similarity curves of the air temperature and the reservoir water level of the engineering example are shown;
FIG. 3 a: the invention relates to a safety degree graph of an engineering example structure;
FIG. 3 b: the invention relates to an engineering example environment safety degree graph;
FIG. 3 c: the invention relates to a total safety degree graph of an engineering example;
FIG. 4 a: the invention relates to a safety degree trend chart of an engineering example structure;
FIG. 4 b: the invention relates to an engineering example environment safety degree trend graph;
FIG. 4 c: the invention relates to a general safety degree trend chart of an engineering example;
FIG. 5: the invention relates to a total safety degree prediction chart of an engineering example.
Detailed Description
In order to further understand the contents, features and effects of the present invention, the following embodiments are illustrated and described in detail with reference to the accompanying drawings:
as shown in the attached figure 1, the integrated evaluation and prediction method for the long-term operation safety of the dam considering the hysteresis effect is used for evaluating and predicting the safety condition of the long-term operation of the concrete dam and comprises the following steps:
and step A, combining a correction moving average method (MMA) and a cosine similarity method (CS) to quantitatively calculate the index hysteresis effect.
Step A1, selecting the monitoring data of deformation, air temperature and reservoir water level for calculating the lag time of deformation relative to air temperature and reservoir water level;
a2, extracting a trend item of the monitoring data by using MMA, obtaining a residual period item and an irregular fluctuation item, and normalizing;
in the formula, FtIs an analog value of the next time, n1For the number of monitoring data contained in a single cycle of the moving average,
is front n1A measured value of the period;
step A3, applying CS 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 reservoir water level;
in the formula, cos (theta)1Is the cosine similarity of the displacement vector and the air temperature vector, AiIs a displacement vector, BiIs the air temperature vector, cos (θ)2Is the cosine similarity of the displacement vector and the reservoir level vector, CiIs the reservoir level vector, n2The number of monitoring data selected for calculating the cosine similarity for the air temperature, the water level and the displacement.
And step B, establishing a complete safety evaluation index system. The established safety evaluation index system comprises 3 aspects of meteorological data acquisition, structural safety evaluation and hysteresis evaluation, and has 12 indexes. The meteorological data acquisition comprises rainfall data R, air temperature data T and upstream reservoir water level WuAnd downstream reservoir level WdThe structural safety evaluation comprises dam body displacement DiDeformation of dam body DeStress S of the dam body and crack opening C, and the hysteresis evaluation comprises hysteresis rainfall RlLagging air temperature TlLagging upstream reservoir level WulAnd lags downstream reservoir level Wdl。
And step C, calculating the subjective weight of each index by an Analytic Hierarchy Process (AHP), and providing a DIC (discovery and Independence coefficient) method by combining a Coefficient of Variation (CV) and a maximum Mutual Information Coefficient (MIC) to calculate the objective weight of each index, wherein the subjective weight and the objective weight of each index are linearly weighted to obtain the combined weight of each index.
Step C1, calculating the subjective weight of each index through AHP, comparing the importance of every two indexes, and quantizing the relative importance of the two indexes by adopting the measurement mode shown in the table 1 to obtain a judgment matrix A;
TABLE 1 importance Scale
Step C2, calculating the maximum eigenvalue and eigenvector, and calculating the subjective weight;
A×W＝λmaxW (3)
in the formula, λmaxNormalizing by maximum eigenvalue and W by eigenvectorObtaining the subjective weight of each index
Step C3, performing a consistency check:
in the formula, CRFor consistency ratio, CIFor consistency check, RIIs an average random consistency index, which can be found from Table 2, n3The number of evaluation indexes selected for a safety evaluation system; if CR<0.1, the consistency is good, and the obtained subjective weight is reliable;
TABLE 2 average random consistency index RIStandard value
Step C4, calculating coefficient of variation CV of single indexj：
In the formula, σjIs the standard deviation of the j-th index,
is the mean value of the jth index;
step C5, calculating the maximum mutual information coefficient mic (d) of every two indexes:
in the formula I*(D, x, y) represents the maximum mutual information value in all scales of the grid, B is the number of grids,
n4is the number of samplesThe quantity, x is the number of segments divided on the x axis of the scatter diagram, and y is the number of segments divided on the y axis of the scatter diagram;
step C6, calculating independent coefficient D of each indexj：
In the formula, bijRepresenting the maximum mutual information coefficient between the ith index and the jth index;
step C7, calculating objective weight omegaj：
Step C8, the subjective weight and the objective weight are linearly combined to obtain the combined weight of each index:
in the formula, wjIn order to combine the weights, the weights are combined,
is a subjective weight, ωjIs an objective weight, mu is a distribution coefficient of the subjective and objective weights, and w is more than or equal to 0j≤1,
And D, quantitatively calculating the safety of the concrete dam by using a TOPSIS (technique for order preference by Similarity to an Ideal solution) method based on multi-point long-term monitoring data of the concrete dam, and obtaining the long-term operation state and trend of the concrete dam.
Step D1, normalizing the monitoring data of each index, and establishing an initial evaluation matrix;
step D2, multiplying the initial evaluation matrix by the combined weight obtained in the step C8 to construct a final evaluation matrix Z;
d3, determining positive and negative ideal solutions of each index;
in the formula (I), the compound is shown in the specification,
for a positive ideal solution of each index,
is a negative ideal solution of each index, zijAn ith evaluation object representing a jth index, i being 1,2, …, m, m being the number of monitored data for determining positive and negative ideal solutions of each index;
step D4, calculating the distance between each index measured data of the concrete dam and the positive and negative ideal solutions by adopting the Euclidean distance, wherein the closer each index measured data of the concrete dam is to the positive ideal solution, the farther the index measured data of the concrete dam is from the negative ideal solution, the better the solution is;
in the formula (I), the compound is shown in the specification,
the distance from the actual safety state of the concrete dam to the ideal state of the concrete dam,
the distance from the actual safety state of the concrete dam to the negative ideal solution is obtained;
step D5, calculating the closeness C of the ideal pointiSafety degree of concrete dam:
Cithe larger the distance to the positive ideal solutionAnd the smaller the distance to the negative ideal solution is, the higher the safety of the concrete dam is.
And E, predicting the future long-term safety by adopting a neural network, and comparing the future long-term safety with the concrete dam safety calculated in the step D to provide early warning of dam abnormity.
Step E1, three optimal parameters of the neural network are selected: the proportion of sample data distributed to the training set, the verification set and the test set, the number of hidden layer neurons and a training algorithm;
step E2, training a neural network model, predicting future long-term safety by using historical safety obtained by TOPSIS calculation, and calculating four indexes of mean square error MSE, mean absolute error MAE, mean absolute percentage error MAPE and maximum error ME:
in the formula, N is the number of samples,
the security level found for TOPSIS of the ith sample,
predicting a 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 abnormality of the concrete dam operation through the difference of the two values to early warn the working personnel in advance.
According to the method, the long-term operation safety evaluation and prediction system of the concrete dam is established, the safety condition of the dam is quantitatively expressed, the long-term safety prediction in the future is carried out, the abnormal condition of the operation of the dam is early warned in advance, and workers can take remedial measures in time.
Examples
And selecting a monitoring index of a certain actual project, and respectively establishing a concrete dam structure safety evaluation index system, an environment evaluation index system and a total evaluation index system. The structural safety evaluation index is obtained by selecting the strain (S) of the dam foundation of the sand-washed gate dam section and the stress (S) of a typical measuring point at the top of a sand-washed gate holet) Stress of typical measuring point on side of gate hole (S)s) Dam foundation deformation measured by a joint meter (D)e) And dam face displacement (D)i)5 indexes. Selecting air temperature (T), reservoir water level (W) and lagging air temperature (T) according to environment evaluation indexesl) Lagged reservoir water level (W)l)4 indexes. The total evaluation indexes are 9 indexes including comprehensive structural safety indexes and environmental indexes.
And B, calculating the lag time of the air temperature and the reservoir water level respectively to be 55 days and 30 days (as shown in figure 2) according to the step A, and taking the air temperature and the reservoir water level with the lag time taken into consideration as environment evaluation indexes.
According to the steps C1 to C3, subjective weight of each index is calculated through AHP, and the consistency test is good. And calculating CV and MIC of each index according to the steps C4 to C7 to obtain the objective weight of the index. According to step C8, the subjective weight and the objective weight are linearly weighted, and the distribution coefficient is 0.5, thereby obtaining the combined 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 evaluation index weights
TABLE 4 environmental evaluation index weights
TABLE 5 Total evaluation index weights
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 safety of the concrete dam structure in fig. 3a shows a significant periodic variation, with the safety occurring at the lowest values at 8 and 9 months per year. As the structural safety indexes mainly include stress, strain, deformation and dam body displacement in the dam foundation, the temperature is high in 8 and 9 months, the temperature stress is increased due to the fact that concrete expands due to heating, the condition of low safety degree can occur, and the dam foundation conforms to engineering practice. The environmental security level of fig. 3b also varies periodically as a whole, with the lowest security level occurring in months 1 and 2 of each year. Because the environmental indexes are mainly air temperature and reservoir water level, frost heaving effect can be generated at low temperature in winter, the dam body safety is not facilitated, and the dam body safety is in accordance with engineering practice. The environmental safety degree is lower in 7 and 8 months, the temperature and the stress of the concrete temperature are increased due to high temperature in summer, the water level of the reservoir is increased due to excessive rain in summer at the location of the dam site, and the evaluation result is consistent with the actual condition. Fig. 3c shows that the total security degree shows regular periodic variation, and the variation trend is basically the same as the structural security degree, which indicates that the safety condition of the dam is mainly determined by the structural security index.
In general, each index monitoring amount changes along with the increase of time, and is an irreversible process. As can be seen from fig. 4a to 4c, the structural safety fluctuates slightly, the overall change does not exceed 0.2, and the dam safety condition is stable and can be judged to be good; the environmental safety degree tends to rise, the later period tends to be stable, the periodic small fluctuation is realized, and the safety condition of the dam is good. The total safety degree trend is in periodic fluctuation and basically consistent with the structural safety degree trend, the safety values all exceed 0.67, and the safety condition of the dam is good.
According to step E1, different parameter combinations are selected for training and prediction respectively, and the three parameters with the highest prediction accuracy are selected. 70% of samples are used as a training set, 20% are used as a verification set and 10% are used as a test set, 1 hidden layer containing 10 neurons is set, and a Bayesian Regularization algorithm training model is selected.
According to step E2, the future long-term total safety degree is predicted from the historical safety degree, the prediction accuracy 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, the trend of the predicted value and the calculated value in the testing set is the same, but the calculated value is lower, which indicates that the dam safety condition is abnormal in this period and needs to be detected and maintained in time.
TABLE 6 Total safety prediction accuracy index
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 those skilled in the art can make many modifications without departing from the spirit and scope of the present invention as defined in the appended claims.
Claims (6)
1. A long-term operation safety evaluation and prediction integration method for a dam considering hysteresis effect is characterized by comprising the following steps:
step A, combining a modified 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 the subjective weight of each index through an analytic hierarchy process, combining the variation coefficient and the maximum mutual information coefficient, proposing a DIC method to calculate the objective weight of each index, and linearly weighting the subjective weight and the objective weight of each index to obtain the combined weight of each index;
step D, quantitatively calculating the safety degree of the concrete dam by using a TOPSIS method based on multi-measuring-point long-term monitoring data of the concrete dam to obtain the long-term operation state and trend of the concrete dam;
and E, predicting the future long-term safety by adopting a neural network, and comparing the future long-term safety with the concrete dam safety calculated in the step D to provide early warning of dam abnormity.
2. The integrated method for evaluating and predicting the safety degree of the long-term operation of the dam considering the hysteresis effect as claimed in claim 1, wherein the step A further comprises the following steps:
step A1, selecting the monitoring data of deformation, air temperature and reservoir water level for calculating the lag time of deformation relative to air temperature and reservoir water level;
a2, extracting a trend item of the monitoring data by applying a modified moving average method to obtain a residual period item and an irregular fluctuation item, and normalizing;
in the formula, FtIs an analog value of the next time, n1For the number of monitoring data contained in a single cycle of the moving average,
is front n1A measured value of the period;
step A3, applying a cosine similarity method to the normalized data, continuously sliding the data to find a position with the maximum cosine similarity, and solving the lag time of the air temperature and the reservoir water level;
in the formula, cos (theta)1Is the cosine similarity of the displacement vector and the air temperature vector, AiIs a displacement vector, BiIs the air temperature vector, cos (θ)2As displacement vector and reservoir levelVector cosine similarity, CiIs the reservoir level vector, n2The number of monitoring data selected for calculating the cosine similarity for the air temperature, the water level and the displacement.
3. The method as claimed in claim 1, wherein the safety evaluation index system includes 3 aspects of meteorological data acquisition, structural safety evaluation and hysteresis evaluation, the meteorological data acquisition includes rainfall data R, air temperature data T, upstream reservoir water level W, and the like, and the integrated method for evaluating and predicting long-term operation safety of the dam considering hysteresis effect is characterized in that in the step B, the established safety evaluation index system includes 3 aspects of meteorological data acquisition, structural safety evaluation and hysteresis evaluationuAnd downstream reservoir level WdThe structural safety evaluation comprises dam body displacement DiDeformation of dam body DeStress S of the dam body and crack opening C, and the hysteresis evaluation comprises hysteresis rainfall RlLagging air temperature TlLagging upstream reservoir level WulAnd lags downstream reservoir level Wdl。
4. The method as claimed in claim 1, wherein the step C further comprises:
step C1, calculating the subjective weight of each index by an analytic hierarchy process, comparing the 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 the maximum eigenvalue and eigenvector, and calculating the subjective weight;
A×W＝λmaxW (3)
in the formula, λmaxThe maximum eigenvalue is W, the eigenvector is W, and the subjective weight of each index is obtained by normalization
Step C3, performing a consistency check:
in the formula, CRFor consistency ratio, CIFor consistency check, RIIs an average random consistency index, n3The number of evaluation indexes selected for a safety evaluation system; if CR<0.1, the consistency is good, and the obtained subjective weight is reliable;
step C4, calculating coefficient of variation CV of single indexj：
In the formula, σjIs the standard deviation of the j-th index,
is the mean value of the jth index;
step C5, calculating the maximum mutual information coefficient mic (d) of every two indexes:
in the formula I*(D, x, y) represents the maximum mutual information value in all scales of the grid, B is the number of grids,
n4the number of samples is shown, x is the number of segments divided on an x axis of a scatter diagram, and y is the number of segments divided on a y axis of the scatter diagram;
step C6, calculating independent coefficient D of each indexj：
In the formula, bijRepresenting the maximum mutual information coefficient between the ith index and the jth index;
step C7, calculating objective weight omegaj：
Step C8, the subjective weight and the objective weight are linearly combined to obtain the combined weight of each index:
in the formula, wjIn order to combine the weights, the weights are combined,
is a subjective weight, ωjIs an objective weight, mu is a distribution coefficient of the subjective and objective weights, and w is more than or equal to 0j≤1,
5. The method as claimed in claim 1, wherein the step D further comprises:
step D1, normalizing the monitoring data of each index, and establishing an initial evaluation matrix;
step D2, multiplying the initial evaluation matrix by the combined weight obtained in the step C to construct a final evaluation matrix Z;
d3, determining positive and negative ideal solutions of each index;
in the formula (I), the compound is shown in the specification,
for a positive ideal solution of each index,
for each indexNegative ideal solution of, zijAn ith evaluation object representing a jth index, i being 1,2, …, m, m being the number of monitored data for determining positive and negative ideal solutions of each index;
step D4, calculating the distance between each index measured data of the concrete dam and the positive and negative ideal solutions by adopting the Euclidean distance, wherein the closer each index measured data of the concrete dam is to the positive ideal solution, the farther the index measured data of the concrete dam is from the negative ideal solution, the better the solution is;
in the formula (I), the compound is shown in the specification,
the distance from the actual safety state of the concrete dam to the ideal state of the concrete dam,
the distance from the actual safety state of the concrete dam to the negative ideal solution is obtained;
step D5, calculating the closeness C of the ideal pointiSafety degree of concrete dam:
Cithe larger the distance to the positive ideal solution is, the smaller the distance to the negative ideal solution is, and the higher the safety degree of the concrete dam is.
6. The method as claimed in claim 1, wherein the step E further comprises:
step E1, three optimal parameters of the neural network are selected: the proportion of sample data distributed to the training set, the verification set and the test set, the number of hidden layer neurons and a training algorithm;
step E2, training a neural network model, predicting future long-term safety by using historical safety obtained by TOPSIS calculation, and calculating four indexes of mean square error MSE, mean absolute error MAE, mean absolute percentage error MAPE and maximum error ME:
in the formula, N is the number of samples,
the security level found for TOPSIS of the ith sample,
predicting a 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 abnormality of the concrete dam operation through the difference of the two values to early warn the working personnel in advance.
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| CN114329680B (en) * | 2020-10-09 | 2024-04-16 | 神华神东煤炭集团有限责任公司 | Mining area underground reservoir pillar dam body stability evaluation method and application thereof |
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