CN107220218A - The probability forecasting method of reinforcement in concrete corrosion rate - Google Patents
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Abstract
The probability forecasting method of reinforcement in concrete corrosion rate, comprises the following steps：(1) the random sample point of probabilistic model parameter is produced；(2) the random sample point of reinforcement corrosion speed is calculated；(3) average and standard deviation of reinforcement corrosion speed are calculated.This method can be according to the ratio of mud and chloride ion content of concrete, and the temperature and relative humidity of environment where concrete structure, determine the average and standard deviation of reinforcement in concrete corrosion rate, the probability density characteristicses of reinforcement in concrete corrosion rate can not only be described rationally, but also the precision of prediction of traditional certainty empirical predictive models can be calibrated, so as to overcome traditional certainty empirical predictive models can not describe the defect present in the probability density characteristicses of reinforcement in concrete corrosion rate.
Description
Technical field
The present invention relates to the Forecasting Methodology of reinforcement in concrete corrosion rate, specifically a kind of reinforcement in concrete corrosion
The probability forecasting method of speed.
Background technology
Influenceed by concrete carbonization and Chloride Attack effect, it is rotten that existing R.C. structures tend to occur reinforcing bar
Erosion, causes deterioration of its durability.Because reinforcement corrosion speed is to determine that the crucial of reinforced concrete structure durability degeneration speed is joined
Number, so analyzing for reinforcement corrosion speed adds with predicting the service life assessment for existing R.C. structures and safeguarding
Gu decision-making is significant.
Chinese patent application 201010253580.7 discloses a kind of online Corrosion monitoring instrument of armored concrete and method,
Utilize the reinforcement corrosion in the detector device to test concrete being made up of disk electrode, polarization loop and restriction of current loop
Speed.
Chinese patent application 201220065238.9 discloses a kind of measurement reinforcement in concrete macro cell current corrosion speed
The device of rate.The device is by integrated transporting discharging, multiple order low pass filter, AD converter, computer connecting wires, LCDs, high speed
Single-chip microcomputer, integrated transporting discharging, reference electrode, temperature inductor and resistance and wire composition, can measure the grand electric current of reinforcement corrosion
And the corrosion potential after anode and negative electrode coupling.
Above two on-the-spot test method is required to configure special detection instrument apparatus, and due to reinforcement corrosion speed
On-the-spot test precision by point position, reinforcing bar spatial distribution, environment temperature and relative humidity and Chloride Ion in Concrete
The influence of content, causes test result discreteness larger.In consideration of it, experiment number of the domestic and foreign scholars based on reinforcement corrosion speed
According to establishing the certainty empirical predictive models of reinforcement in concrete corrosion rate.
Jiang De in 2004 is steady, Li Guo, Yuan Yingshu are delivered one is entitled《Concrete reinforcing steel corrosion rate is multifactor
The experimental study of influence》Paper in, based on the reinforcing bar accelerated corrosion test data under artificial climate environment, consider water
The influence of gray scale, environment temperature and envionmental humidity, establishes the certainty empirical predictive models of reinforcement corrosion speed.
Deliver one of the fruit of Lee in 2004 is entitled《The environmental behaviour of armored concrete durability and basic degradation model
Research》Academic dissertation in, it is considered to the influence of envionmental humidity, the ratio of mud and chloride ion content, establish under bar in chlorine salt solution
The certainty empirical predictive models of reinforcement corrosion speed.
Pour-Ghaz M., Isgor O.B. in 2009 and Ghods P. are published in《Corrosion Science》One
A piece is entitled《The effect of temperature on the corrosion of steel in concrete.Part
1: Simulated polarization resistance tests and model development》Paper in, it is comprehensive
The influence for considering temperature, limiting current density and concrete resistivity is closed, the certainty experience of reinforcement corrosion speed is established
Forecast model.
Do not engrave within 2013 one delivered entitled《Corrosion mechanism and corrosion rate the practicality prediction of reinforcement in concrete
Scale-model investigation》Academic dissertation in, using the ratio of mud, chloride ion content and relative air humidity of concrete be used as control ginseng
Number, establishes the certainty empirical predictive models of reinforcement corrosion speed, but environment temperature can not be considered to reinforcement corrosion speed
Influence.
The certainty empirical predictive models of above-mentioned reinforcement in concrete corrosion rate, are to return to divide based on least square method
What analysis was determined, the global optimum predicted the outcome can only be ensured from average meaning, it is impossible to describe reinforcement in concrete corrosion rate
Probability density characteristicses.However, due to concrete raw material, defect in concrete and distribution of pores and the space of reinforcing bar
Be present randomness in distribution, cause under identical material parameter and environmental condition, the reinforcement corrosion speed in concrete often has
There is significant randomness.
The content of the invention
It is an object of the invention to provide a kind of probability forecasting method of reinforcement in concrete corrosion rate, biography can be overcome
The defect united present in certainty empirical predictive models.
The technical scheme is that：A kind of probability forecasting method of reinforcement in concrete corrosion rate, including following step
Suddenly：
(1) the random sample point of probabilistic model parameter is produced：Determine probabilistic model parameter θi(i=1,2's ..., 6) is general
Rate distribution pattern, average, standard deviation and coefficient correlation, and carry out random sampling, each probabilistic model ginseng using Monte Carlo method
Number θi(i=1,2 ..., 6) produce n random sample point θij(i=1 ... 2, j=, 6；n)；
(2) the random sample point of reinforcement corrosion speed is calculated：According to the ratio of mud R of concreteW/CAnd chloride ion content
CCl-, and concrete structure where environment temperature T and relative humidity rRH, with reference to probabilistic model parameter θ in step (1)i(i
=1,2 ..., 6) random sample point θij(i=1,2 ..., 6；J=1,2 ..., n), utilize reinforcement corrosion speed icorrWith
Machine sample point computation model, calculates reinforcement corrosion speed icorrN random sample point icorr,j(j=1,2 ..., n), it is described
Reinforcement corrosion speed icorrRandom sample point icorr,j(j=1,2 ..., computation model n) is：
In formula, icorrj, (j=1,2 ..., n) be reinforcement corrosion speed j-th of random sample point, unit be μ A/
cm2；θij(i=1,2 ..., 6；J=1,2 ..., n) it is probabilistic model parameter θiJ-th of random sample of (i=1,2 ..., 6)
Point；RW/CFor the ratio of mud；The percentage of concrete quality is accounted for for Chloride Ion in Concrete content；T is concrete structure institute
In the temperature of environment, unit for DEG C；rRHThe relative humidity of environment where concrete structure, unit is %；ξ is standard normal
Distribution variables；
(3) average and standard deviation of reinforcement corrosion speed are calculated：According to reinforcement corrosion speed i in step (2)corrN
Random sample point icorr,j(j=1,2 ..., n), utilize reinforcement corrosion speed icorrAverage and standard deviation computation model, meter
Calculate reinforcement corrosion speed icorrAverageAnd standard deviationDescribed reinforcement corrosion speed icorrAverage and standard deviation
Computation model is respectively：
In formula, n is the number of random sample point.
Described probabilistic model parameter θiThe probability distribution of (i=1,2 ..., 6), averageStandard deviationAnd phase
Relation numberFor：
The number n of described random sample point scope is 5000 to 1000000.
The beneficial effects of the present invention are：
The temperature and phase of environment where considering the ratio of mud and chloride ion content of concrete, and concrete structure
Influence to humidity, proposes a kind of probability forecasting method of reinforcement in concrete corrosion rate, can not only rationally describe first
The probability density characteristicses of reinforcement in concrete corrosion rate, but also the pre- of traditional certainty empirical predictive models can be calibrated
Precision is surveyed, so as to overcome traditional certainty empirical predictive models can not describe the probability distribution of reinforcement in concrete corrosion rate
Defect present in characteristic.
Brief description of the drawings
Fig. 1 probabilistic model parameter θs110000 random sample points.
Fig. 2 probabilistic model parameter θs210000 random sample points.
Fig. 3 probabilistic model parameter θs310000 random sample points.
Fig. 4 probabilistic model parameter θs410000 random sample points.
Fig. 5 probabilistic model parameter θs510000 random sample points.
Fig. 6 probabilistic model parameter θs610000 random sample points.
Fig. 7 probabilistic model parameter θs1And θ2Between scatterplot distribution and coefficient correlation
Fig. 8 probabilistic model parameter θs1And θ4Between scatterplot distribution and coefficient correlation
Fig. 9 reinforcement corrosion speed icorr10000 random sample points.
The predicted value of Figure 10 present invention and certainty empirical predictive models A1, A2 and A3 predicted value and measured value
Comparative analysis.
Figure 11 reinforcement corrosion speed icorr10000 random sample points.
The predicted value of Figure 12 present invention and certainty empirical predictive models A1, A2 and A3 predicted value and reinforcement corrosion
The comparative analysis of the test value of speed.
Embodiment
Technical scheme and validity and superiority are described further below by specific embodiment.
Embodiment 1
This example to utilize present invention determine that the probabilistic forecasting value of reinforcement in concrete corrosion rate, and with tradition determination
The instantiation that the calculated value of sex experience forecast model and the measured value of reinforcement corrosion speed are analyzed, including with
Lower step：
(1) the random sample point of probabilistic model parameter is produced：
Determine probabilistic model parameter θiProbability distribution, average, standard deviation and the coefficient correlation of (i=1,2 ..., 6),
Such as table 1：
The θ of table 1iThe probability distribution of (i=1,2 ..., 6), averageStandard deviationAnd coefficient correlation
According to probabilistic model parameter θ in table 1iProbability distribution, average, standard deviation and the correlation of (i=1,2 ..., 6)
Coefficient, random sampling, each probabilistic model parameter θ are carried out using Monte Carlo methodi(i=1,2 ..., 6) n are produced with press proof
This θij(i=1,2 ..., 6；J=1,2 ..., n).The number of samples of this example is chosen for n=10000.Probabilistic model parameter
θ1、θ2、θ3、θ4、θ5、θ6Respective 10000 random samples point difference is as shown in Figures 1 to 6；Utilize probabilistic model parameter θ1、
θ2、θ3、θ4、θ5、θ6Respective 10000 random samples point, can further analyze the phase relation between probabilistic model parameter
Number, with probabilistic model parameter θ1And θ2And θ1And θ4Exemplified by, the distribution of its scatterplot and coefficient correlation difference are as shown in Figure 7 and Figure 8.
(2) the random sample point of reinforcement corrosion speed is calculated：
It can be seen from the mix-design data and field test data of concrete structure, the ratio of mud R of concreteW/C=
0.43rd, chloride ion content CCl-=0.14, T=39.41 DEG C of the temperature of environment, relative humidity r where concrete structureRH=
75%.By the random sample point θ of probabilistic model parameter in step (1)ij(i=1,2 ..., 6；J=1,2 ..., 10000), band
Enter reinforcement corrosion speed icorrRandom sample point computation model
Calculating obtains reinforcement corrosion speed icorr10000 random sample points, as shown in Figure 9.
(3) average and standard deviation of reinforcement corrosion speed are calculated：
The reinforcement corrosion speed i obtained in step (2)corr10000 random sample point icorr,j(j=1,
2 ..., 10000), utilize reinforcement corrosion speed icorrAverage and standard deviation computation model calculate reinforcement corrosion speed icorr's
AverageAnd standard deviation
In order to verify effectiveness of the invention and applicability, existing reinforcement in concrete corrosion rate both at home and abroad is chosen
Three kinds of certainty empirical predictive models be analyzed, be respectively：Model A1 is (referring to paper (1) Jiang Dewen, Li Guo, Yuan
Meet experimental study [J] concrete of daybreak concrete reinforcing steel corrosion rate multifactor impacts, 2004, (7):3-4+11), mould
Type A2 (studies [D] Xuzhou referring to the environmental behaviour and basic degradation model of paper (2) Lee fruit armored concrete durability：In
Mining industry university building engineering college of state, 2004.) and model A3 (referring to paper (3) Pour-Ghaz M., Isgor O.B.,
Ghods P.The effect of temperature on the corrosion of steel in concrete.Part
1:Simulated polarization resistance tests and model development[J].Corrosion
Science,2009,51(2):415-425.).According to the ratio of mud R of concreteW/C=0.43, chloride ion contentAnd T=39.41 DEG C of the temperature and relative humidity r of environment where concrete structureRH=75%, utilize model
A1, model A2 and model A3 calculate the reinforcement corrosion speed in concrete respectively, are designated as respectivelyWith
The reinforcement corrosion speed i according to determined by the present inventioncorrAverageAnd standard deviationExamined by K-S, reinforcement in concrete corrosion rate does not refuse obedience to normal distribution, so can
With the reinforcement corrosion speed i according to determined by the present inventioncorrAverage and standard deviation, steel is described using probability density distribution
Muscle corrosion rate icorrProbability density characteristicses, as shown in Figure 10.As shown in Figure 10, average determined by the present disclosureThe measured value of reinforcement corrosion speedIllustrate average and the actual measurement of the present invention
Value is coincide preferably, and precision of prediction is higher；It is respectively by model A1, model A2, model A3 the reinforcement corrosion speed calculatedWithAs shown in Figure 10, three certainty warps
The difference that predicts the outcome for testing forecast model is larger, and predicting the outcome for wherein model A1 is substantially bigger than normal, and model A2 predicts the outcome
It is substantially less than normal.
Embodiment 2
This example comprises the following steps to determine the instantiation of the probabilistic forecasting value of reinforcement in concrete corrosion rate：
(1) the random sample point of probabilistic model parameter is produced：
Determine probabilistic model parameter θiProbability distribution, average, standard deviation and the coefficient correlation of (i=1,2 ..., 6),
Such as table 1.According to probabilistic model parameter θ in table 1iProbability distribution, average, standard deviation and the phase relation of (i=1,2 ..., 6)
Number, random sampling, each probabilistic model parameter θ are carried out using Monte Carlo methodi(i=1,2 ...) 6 n random samples of generation
Point θij(i=1,2 ..., 6；J=1,2 ..., n).The number of samples of this example is chosen for n=10000.Probabilistic model parameter
θ1、θ2、θ3、θ4、θ5、θ6Respective 10000 random samples point difference is as shown in Figures 1 to 6；Utilize probabilistic model parameter θ1、
θ2、θ3、θ4、θ5、θ6Respective 10000 random samples point, can further analyze the phase relation between probabilistic model parameter
Number, with probabilistic model parameter θ1And θ2And θ1And θ4Exemplified by, the distribution of its scatterplot and coefficient correlation difference are as shown in Figure 7 and Figure 8.
(2) the random sample point of reinforcement corrosion speed is calculated：
It can be seen from the mix-design data and field test data of concrete structure, the ratio of mud R of concreteW/C=
0.43rd, chloride ion contentAnd T=33 DEG C of the temperature of environment, relative humidity r where concrete structureRH=
75%.By the random sample point θ of probabilistic model parameter in step (1)ij(i=1,2 ..., 6；J=1,2 ..., 10000), band
Enter reinforcement corrosion speed icorrRandom sample point computation model
Calculating obtains reinforcement corrosion speed icorr10000 random sample points, as shown in figure 11.
(3) average and standard deviation of reinforcement corrosion speed are calculated：
The reinforcement corrosion speed i obtained in step (2)corr10000 random sample point icorr,j(j=1,
2 ..., 10000), utilize reinforcement corrosion speed icorrAverage and standard deviation computation model calculate reinforcement corrosion speed icorr's
AverageAnd standard deviation
Two adjacent test point test reinforcing bar corrosion rates are chosen on concrete structure, measured value is respectivelyWithIllustrate that the measured value of the reinforcement corrosion speed of the two adjacent measuring points is present
Difference, it is main reason is that concrete raw material, defect in concrete and distribution of pores and the spatial distribution of reinforcing bar
There is randomness, so that cause under identical material parameter and environmental condition, the reinforcement corrosion speed of two adjacent measuring points
Measured value has differences.
The reinforcement corrosion speed i according to determined by the present inventioncorrAverageAnd standard deviationExamined by K-S, reinforcement in concrete corrosion rate does not refuse obedience to normal distribution, so can
With the reinforcement corrosion speed i according to determined by the present inventioncorrAverage and standard deviation, steel is described using probability density distribution
Muscle corrosion rate icorrProbability density characteristicses, as shown in figure 12.Meanwhile, it can also calibrate tradition using probability density distribution
The precision of prediction of certainty empirical predictive models, as shown in figure 12.From Figure 12, model A1 predicted valueMuch larger than measured value and the average of the present invention, illustrate to predict the outcome obvious bigger than normal；On the contrary, model A2
Predicted valueMuch smaller than measured value and the average of the present invention, illustrate to predict the outcome obvious less than normal；With model
A1 is compared with model A2, model A3 predicted valueIt is more nearly with the average of measured value and the present invention,
Illustrate that model A3 precision of prediction is relatively higher.
From two above example can be seen that the present invention can according to the ratio of mud and chloride ion content of concrete, and
The temperature and relative humidity of environment, determine the average and standard deviation of reinforcement in concrete corrosion rate, enter where concrete structure
And probability density distribution is utilized, the probability density characteristicses of reinforcement in concrete corrosion rate can not only be described rationally, but also
The precision of prediction of traditional certainty empirical predictive models can be calibrated, so as to overcome traditional certainty empirical predictive models
Defect present in the probability density characteristicses of reinforcement in concrete corrosion rate can not be described.
Claims (3)
1. the probability forecasting method of reinforcement in concrete corrosion rate, it is characterised in that comprise the following steps：
(1) the random sample point of probabilistic model parameter is produced：Determine probabilistic model parameter θiThe probability distribution of (i=1,2 ..., 6)
Type, average, standard deviation and coefficient correlation, and carry out random sampling, each probabilistic model parameter θ using Monte Carlo methodi(i
=1,2 ..., 6) produce n random sample point
(2) the random sample point of reinforcement corrosion speed is calculated：According to the ratio of mud R of concreteW/CAnd chloride ion contentAnd
The temperature T and relative humidity r of environment where concrete structureRH, with reference to probabilistic model parameter θ in step (1)i(i=1,2 ...,
6) random sample point θij(i=1,2 ..., 6；J=1,2 ..., n), utilize reinforcement corrosion speed icorrRandom sample point meter
Model is calculated, reinforcement corrosion speed i is calculatedcorrN random sample point icorr,j(j=1,2 ..., n), described reinforcement corrosion
Speed icorrRandom sample point computation model be：
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In formula, icorr,j(j=1,2 ..., n) be reinforcement corrosion speed j-th of random sample point, unit be μ A/cm2；θij(i
=1,2 ..., 6；J=1,2 ..., n) it is probabilistic model parameter θiJ-th of random sample point of (i=1,2 ..., 6)；RW/CFor
The ratio of mud；The percentage of concrete quality is accounted for for Chloride Ion in Concrete content；T is the temperature of environment where concrete structure
Degree, unit for DEG C；rRHThe relative humidity of environment where concrete structure, unit is %；ξ is that standardized normal distribution becomes at random
Amount；
(3) average and standard deviation of reinforcement corrosion speed are calculated：According to reinforcement corrosion speed i in step (2)corrN with press proof
This icorr,j(j=1,2 ..., n), utilize reinforcement corrosion speed icorrAverage and standard deviation computation model, calculate reinforcing bar rotten
Lose speed icorrAverageAnd standard deviationDescribed reinforcement corrosion speed icorrAverage and standard deviation computation model
Respectively：
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2. the probability forecasting method of reinforcement in concrete corrosion rate according to claim 1, it is characterised in that described
Probabilistic model parameter θiThe probability distribution of (i=1,2 ..., 6), averageStandard deviationAnd coefficient correlationFor：
3. the probability forecasting method of reinforcement in concrete corrosion rate according to claim 1, it is characterised in that described
The number n of random sample point scope is 5000 to 1000000.
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Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
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| CN107941803A (en) * | 2017-11-15 | 2018-04-20 | 广西大学 | A kind of measurement device and analysis method of reinforcing bar full angle corrosion character parameter |
| CN110230258A (en) * | 2018-03-05 | 2019-09-13 | 河北高达电子科技有限公司 | A kind of squeezing trolley slurry-outlet quantity metering method and squeezing trolley |
| CN110826199A (en) * | 2019-10-21 | 2020-02-21 | 清华大学 | Method for updating concrete structure durability prediction model based on incomplete information |
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