CN106650102A - Method for confirming parameters of prediction model for endurance quality of ocean concrete based on grey correlation - Google Patents
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- 238000000034 method Methods 0.000 title claims abstract description 26
- 238000004458 analytical method Methods 0.000 claims abstract description 7
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- 238000003062 neural network model Methods 0.000 abstract description 3
- 238000012549 training Methods 0.000 abstract description 2
- 238000012163 sequencing technique Methods 0.000 abstract 1
- 230000008569 process Effects 0.000 description 7
- VEXZGXHMUGYJMC-UHFFFAOYSA-M Chloride anion Chemical compound [Cl-] VEXZGXHMUGYJMC-UHFFFAOYSA-M 0.000 description 6
- 238000013528 artificial neural network Methods 0.000 description 6
- 230000006870 function Effects 0.000 description 6
- 239000004568 cement Substances 0.000 description 5
- 239000000463 material Substances 0.000 description 5
- 238000011160 research Methods 0.000 description 5
- 238000012360 testing method Methods 0.000 description 5
- JLVVSXFLKOJNIY-UHFFFAOYSA-N Magnesium ion Chemical compound [Mg+2] JLVVSXFLKOJNIY-UHFFFAOYSA-N 0.000 description 4
- 229910001425 magnesium ion Inorganic materials 0.000 description 4
- QAOWNCQODCNURD-UHFFFAOYSA-L Sulfate Chemical compound [O-]S([O-])(=O)=O QAOWNCQODCNURD-UHFFFAOYSA-L 0.000 description 3
- 239000010881 fly ash Substances 0.000 description 3
- 239000002893 slag Substances 0.000 description 3
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Abstract
The invention discloses a method for confirming parameters of a prediction model for endurance quality of an ocean concrete based on grey correlation. The method comprises the following steps: taking a performance index of the ocean concrete as a target function and selecting an influence factor parameter of the target function from an existing document; excerpting the selected parameter data, settling into a factor sequence and settling the target function in the document into a target sequence; utilizing a grey relational analysis theory to calculate the correlation degree between the target sequence and the factor sequence, thereby acquiring a correlation coefficient value of the target sequence and the factor sequence, and then confirming an influence factor weight influencing the target function; sequencing the influence factor degree according to the magnitude of the correlation coefficient value, selecting the higher value of the influence degree as the selection for the prediction model index in the later period and confirming the weight of a modeling parameter according to the result of the correlation coefficient; multiplying the factor values by a grey correlation coefficient and then substituting into a neural network model for training and calculating, thereby realizing the prediction for the index.
Description
Technical field
The present invention relates to concrete durability research field, is endurance performance of concrete prediction under a kind of marine environment is on active service
Model parameter determination method.
Background technology
Used as the material of main part of great infrastructure construction, concrete material performance and its time variation directly affect great base
The military service performance of plinth engineering and service life.For the performance of ocean concrete has had substantial amounts of research, concrete itself because
Element and local environment factor all will produce impact to the performance of later concrete, the final strength of concrete be subject to various impacts because
The comprehensive function of element, influencing mechanism is complicated, and also impact intersected with each other between influence factor.It is related to difference in existing research
Factor affect, research emphasis is different, result of study be usually applicable only to some it is specific under the conditions of.
The Forecasting Methodology of endurance performance of concrete and service life is numerous under harsh and unforgiving environments, wherein artificial neural network, BP
The Forecasting Methodologies such as SVMs are widely used.Artificial neural network (Neural Networks, NN) is by a large amount of simple
The complex networks system that neuron is widely interconnected and formed, it possesses some essential characteristics of suitable human brain function, is
One complicated system based on black box computing, can be used for the computation model of the multifactor non-linear relation of computing.Nerve net
Network has large-scale parallel, distributed storage and process, self-organizing, self adaptation and self-learning ability, and being particularly suitable for processing needs together
When consider many factors and condition, inaccurate and fuzzy information-processing problem.It is modern intelligence based on the machine learning of data
Importance in energy technology, in the model based on black box child-operation, predicts that the selection of node is particularly important.Such as,
Using strength of cement, cement, sand, rubble unit dose, the ratio of mud and the maximum particle of crushed stone as input parameter, cured under same condition
The effective curing age intensity of test specimen is used as output parameter, it is possible to use neural network prediction model is predicted, neutral net
Solve the problems, such as concrete crushing strength this diversification, it is non-linear, involve a wide range of knowledge, comprehensive strong prediction.Consider
The influence factor of concrete itself and the environmental factor in the external world, make pre- by setting up deterioration of the neural network model to concrete
Survey, and the principal element of concrete performance power deterioration can be found by the means for counting.
During prediction more than, the node selection of forecast model is directly connected to model and whether reliable sets up, for
The prediction of the later stage performance of the complicated concrete in marine environment of influence factor at present, selects correct factor node both can improve
The precision and reliability of forecast model, it is also possible to improve the efficiency of model prediction, so as to set up efficiently accurately ocean concrete
Performance prediction model.At present, for the selection of factor fixed sum data really has some limitations, when model is set up
Data acquisition is obtained by the test of researcher, not representative, is typically only capable to be applied to single prediction
Model, the scope of application is narrow.Meanwhile, each factor of neural network node is different to the influence degree of index, by increasing weight system
Several amendments can realize the efficient and summary of neural network computing.In the selection needs and actual environment of the weight coefficient
Each factor is determined to the influence degree of index, and grey incidence coefficient can be used to weigh the influence degree.In neutral net
In modeling process, being modified as weight coefficient using grey incidence coefficient value can preferably realize coagulation in marine environment
The prediction of the later stage performance of soil.
The content of the invention
The purpose of the present invention is not comprehensive for selecting factors present in prior art, and data volume is insufficient, so as to lead
Final modeling is caused to exist inaccurate, generalization ability is low, not enough, applicability processes the problem for coordinating multifactor ability to precision, carries
Determine method for a kind of ocean concrete endurance quality prediction model parameterses based on grey correlation.
For achieving the above object, the technical solution used in the present invention is：
A kind of ocean concrete endurance quality prediction model parameterses based on grey correlation determine method, including following step
Suddenly：
Step one, using certain performance indications of ocean concrete as object function, from existing document this is filtered out
The influence factor parameter of object function；
Step 2, the data of the parameter that step one is filtered out are taken passages, and are organized into factor sequence, by the mesh in document
Offer of tender numerical value is organized into target sequence；
Step 3, is counted using grey relational grade analysis theory to the correlation degree between target sequence and factor sequence
Calculate, obtain both incidence coefficient values, it is determined that affecting the influence factor weight of object function；
Step 4, is ranked up according to the size of incidence coefficient value to influence factor degree, selects the larger of influence degree
Value, as the selection of later stage forecast model index, and the weight of modeling parameters is determined according to the result of incidence coefficient；
Step 5, the larger factor value of the influence degree that previous step is selected is multiplied by after its grey incidence coefficient brings nerve into
Network model is trained and computing, realizes the prediction of index.
In step one, parameter acquiring method is directly taken passages including data in literature, and document chart data reads and data are pushed away
Lead.
When document chart data reads, it is read out and takes passages using GetData softwares, and is taken passages using Excel softwares
Arrange.
In step 3, using formulaCalculate correlation coefficient η
(Xi), wherein X0It is target sequence, XiFor correlative factor sequence, ρ is resolution ratio.
In step 4, incidence coefficient is selected more than the influence factor of mean coefficient.
The dominant mechanism of the present invention is as follows：The principle of grey correlation theory is mainly by the Similar Broken Line between two sequences
Degree is differentiating degree of association size between the two, or perhaps the difference degree of the geometry of the curve of two sequences.When two
The geometry of curve closer to when, the degree of association between sequence is bigger, otherwise less.The method for dispersion degree compared with
Big irregular governed sample can also be used, convenience of calculation.The calculation procedure for mainly including has target sequence and factor sequence
It is determined that, dimensionless process is carried out to ordered series of numbers.Using formulaCalculate
Grey incidence coefficient η (Xi), wherein X0It is target sequence, XiFor correlative factor sequence, ρ is resolution ratio.By the grey correlation system
Numerical value is brought in computation model as weight coefficient and is modified, to improve the operation efficiency of model.
Beneficial effect：The present invention with grey correlation theory as analysis method, to the correlation between object function and sub- factor
Coefficient is calculated, and data source takes passages the reading with document chart in substantial amounts of data in literature.Database is in extensive range, is suitable for
Property is strong；Data acquisition derives from a large amount of domestic and foreign literatures, and document is representative and reliability.Especially for material in the present invention
The analysis of material factor can well realize the guidance to Concrete Design, and the node that forecast model can be realized in addition is selected.
It is of the invention with it is existing test adaptation determine parameter select compared with, advantage is：First, can largely economize on resources, subtract
Few test, realizes the forecast analysis of concrete performance；Second, compare more single research, the method is based under the conditions of various
Test data, with more preferable universality and reliability.
Description of the drawings
The step of Fig. 1 is embodiment flow chart；
Fig. 2 is the precision of model convergence in embodiment；
Fig. 3 is the simulation result of the performance for verifying network.
Specific embodiment
Embodiments of the invention are elaborated below：The implementation case is the base premised on technical solution of the present invention
Implemented under plinth, provided detailed implementation process, but protection scope of the present invention is not limited to following case study on implementation：
Embodiment
Weight meter based on area's Chloride Diffusion Coefficient in Concrete prediction model parameterses under the ocean water of grey incidence coefficient
Calculate.Step such as Fig. 1, reads domestic and foreign literature, is related to domestic and foreign literature about 40, selects to affect ocean concrete chlorine ion binding capacity
The influence factor of coefficient includes material factor：Raw material water-cement ratio, curing time, intensity before exposure, flyash and fine slag contents,
Environmental factor：Chlorine ion concentration, sulfate ion concentration, Exposure Temperature and exposure age.192 groups of extracted valid data, such as table
1.Grey correlation theory analysis is carried out to it.By the grey incidence coefficient value for being calculated each factor and target factor.Specifically
Numerical value is as follows：Water-cement ratio：0.686, curing time：0.671, intensity：0.706, doping quantity of fly ash：0.580, contents of ground slag：
0.656, chlorine ion concentration 0.771, sulfate ion concentration 0.663, magnesium ion concentration：0.657, loaded value：0.583, temperature：
0.667, open-assembly time：0.691.As a result obtain：Chlorine ion concentration>Intensity>Open-assembly time>Water-cement ratio>Curing time>Temperature>
Sulfate ion concentration>Magnesium ion concentration>Contents of ground slag>Payload values>Doping quantity of fly ash.Intensity is considered here, and chlorion is dense
Degree, open-assembly time, sulfate radical, magnesium ion is major influence factors, predicts ionic diffusion coefficient.Weight coefficient value is closed using each
Contact number determines that then each weight coefficient is with the ratio of total correlation coefficient.Intensity：0.202, chlorine ion concentration：0.221, exposure
Time：0.198, sulfate radical：0.191, magnesium ion：0.188.68 groups of data, such as table 2 are chosen, it is original according to weight coefficient process
Sample is obtained the training sample in later stage.And set up neural network model.
Table is taken passages in 1 192 groups of data preparations of table
68 groups of samples after the process of the weight coefficient of table 2
Fig. 2 is the precision of model convergence, and when step-up error is 0.00001, network is restrained in 43 computings, that is,
Say that network can use；Fig. 3 verifies the performance of network, using sim functions, simulation result, then result and target is fitted.
The above is only the preferred embodiment of the present invention, it should be pointed out that：For the ordinary skill people of the art
For member, under the premise without departing from the principles of the invention, some improvements and modifications can also be made, these improvements and modifications also should
It is considered as protection scope of the present invention.
Claims (5)
1. a kind of ocean concrete endurance quality prediction model parameterses based on grey correlation determine method, it is characterised in that：Bag
Include following steps：
Step one, using certain performance indications of ocean concrete as object function, filters out the target from existing document
The influence factor parameter of function；
Step 2, the data of the parameter that step one is filtered out are taken passages, and are organized into factor sequence, by the target letter in document
Numerical value is organized into target sequence；
Step 3, is calculated the correlation degree between target sequence and factor sequence using grey relational grade analysis theory,
Both incidence coefficient values are obtained, it is determined that affecting the influence factor weight of object function；
Step 4, is ranked up according to the size of incidence coefficient value to influence factor degree, selects the larger value of influence degree,
As the selection of later stage forecast model index, and the weight of modeling parameters is determined according to the result of incidence coefficient；
Step 5, the larger factor value of the influence degree that previous step is selected is multiplied by after its grey incidence coefficient brings neutral net into
Model is trained and computing, realizes the prediction of index.
2. the ocean concrete endurance quality prediction model parameterses determination side based on grey correlation according to claim 1
Method, it is characterised in that：In step one, parameter acquiring method is directly taken passages including data in literature, document chart data read and
Data are derived.
3. the ocean concrete endurance quality prediction model parameterses determination side based on grey correlation according to claim 2
Method, it is characterised in that：When document chart data reads, it is read out and takes passages using GetData softwares, and it is soft using Excel
Part is taken passages and is arranged.
4. the ocean concrete endurance quality prediction model parameterses determination side based on grey correlation according to claim 1
Method, it is characterised in that：In step 3, using formulaCalculate and close
Contact number η (Xi), wherein X0It is target sequence, XiFor correlative factor sequence, ρ is resolution ratio.
5. the ocean concrete endurance quality prediction model parameterses determination side based on grey correlation according to claim 1
Method, it is characterised in that：In step 4, incidence coefficient is selected more than the influence factor of mean coefficient.
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| CN108615094A (en) * | 2018-05-04 | 2018-10-02 | 上海海洋大学 | A kind of prediction technique and system of Penaeus Vannmei remaining shelf life |
| CN110706757A (en) * | 2019-09-30 | 2020-01-17 | 武钢资源集团有限公司 | Method for predicting concentration of residual flocculant in mineral separation backwater |
| CN110991067A (en) * | 2019-12-11 | 2020-04-10 | 河海大学 | Underground reinforced concrete structure carbonization life intelligent modeling method based on big data |
| CN111855975A (en) * | 2020-08-05 | 2020-10-30 | 四川大学 | Key parameter determination method for realizing performance prediction of confined concrete |
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| CN113554222A (en) * | 2021-07-19 | 2021-10-26 | 中国水利水电科学研究院 | Dynamic optimization and intelligent regulation and control configuration method for bonding dam generalized bonding material |
| CN113571138A (en) * | 2021-08-10 | 2021-10-29 | 郑州大学 | Alkali-activated mortar bonding performance analysis method based on gray correlation and weight contribution |
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| CN110991067A (en) * | 2019-12-11 | 2020-04-10 | 河海大学 | Underground reinforced concrete structure carbonization life intelligent modeling method based on big data |
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| CN111855975A (en) * | 2020-08-05 | 2020-10-30 | 四川大学 | Key parameter determination method for realizing performance prediction of confined concrete |
| CN111855975B (en) * | 2020-08-05 | 2022-06-07 | 四川大学 | Key parameter determination method for realizing performance prediction of confined concrete |
| CN111931426B (en) * | 2020-09-25 | 2021-01-26 | 大唐环境产业集团股份有限公司 | Method and equipment for determining influence factors of concentration of nitrogen oxides at inlet of SCR (Selective catalytic reduction) reactor |
| CN111931426A (en) * | 2020-09-25 | 2020-11-13 | 大唐环境产业集团股份有限公司 | Method and equipment for determining influence factors of concentration of nitrogen oxides at inlet of SCR (Selective catalytic reduction) reactor |
| CN112992293A (en) * | 2021-03-10 | 2021-06-18 | 西北工业大学 | Concrete strength evolution prediction method in marine environment based on big data analysis |
| CN113554222A (en) * | 2021-07-19 | 2021-10-26 | 中国水利水电科学研究院 | Dynamic optimization and intelligent regulation and control configuration method for bonding dam generalized bonding material |
| CN113554222B (en) * | 2021-07-19 | 2023-11-28 | 中国水利水电科学研究院 | Dynamic optimization and intelligent regulation configuration method for wide-source cementing material of cementing dam |
| CN113571138A (en) * | 2021-08-10 | 2021-10-29 | 郑州大学 | Alkali-activated mortar bonding performance analysis method based on gray correlation and weight contribution |
| CN113571138B (en) * | 2021-08-10 | 2024-03-29 | 郑州大学 | Grey correlation and weight contribution-based alkali-activated mortar bonding performance analysis method |
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Application publication date: 20170510 |