CN110517460A - A kind of induced joint concrete temperature state interval prediction and warning method - Google Patents
A kind of induced joint concrete temperature state interval prediction and warning method Download PDFInfo
- Publication number
- CN110517460A CN110517460A CN201910808672.8A CN201910808672A CN110517460A CN 110517460 A CN110517460 A CN 110517460A CN 201910808672 A CN201910808672 A CN 201910808672A CN 110517460 A CN110517460 A CN 110517460A
- Authority
- CN
- China
- Prior art keywords
- temperature
- concrete
- value
- early warning
- warning
- Prior art date
- Legal status (The legal status is an assumption and is not a legal conclusion. Google has not performed a legal analysis and makes no representation as to the accuracy of the status listed.)
- Pending
Links
Classifications
-
- G—PHYSICS
- G08—SIGNALLING
- G08B—SIGNALLING OR CALLING SYSTEMS; ORDER TELEGRAPHS; ALARM SYSTEMS
- G08B21/00—Alarms responsive to a single specified undesired or abnormal condition and not otherwise provided for
- G08B21/18—Status alarms
- G08B21/182—Level alarms, e.g. alarms responsive to variables exceeding a threshold
Abstract
A kind of induced joint concrete temperature state interval prediction and warning method, comprising the following steps: step 1: temperature dynamic prediction model is established, and storehouse concrete temperature is poured to following several days by temperature dynamic prediction model and carries out rolling forecast；Step 2: according to storehouse concrete temperature changing rule has been poured, drafting temperature control stage one phase of concrete typical age dual control warning index upper lower limit value, obtain state of temperature early warning section；Step 3: classification early warning mechanism is established according to the constrained feature of different monoliths, Dam Site temperature change feature and concrete temperature state change feature；Step 4: according to prediction result and early warning section, the alert grade of state of temperature being judged by early warning mechanism of classifying, and take corresponding responsive measures, realize the mobilism correction of temperature control measures.Technology of the invention solves the problems, such as: overcoming the problems, such as that existing concrete temperature pre-warning method objectivity and timeliness are insufficient, a kind of induced joint concrete temperature state interval method for early warning is provided, the phase temperature control stage the most violent to temperature change realizes process early warning.
Description
Technical field
The invention belongs to hydraulic and hydroelectric engineering Temperature Controlling of Mass Concrete crack prevention technique fields, more particularly to a kind of high arch
Dam concrete state of temperature interval prediction method for early warning.
Background technique
Temperature load is one of primary load of arch dam, and for induced joint due to needing to bear biggish horizontal thrust, figure is huge
Greatly, structure is complicated, and strength grade of concrete used is high, and hydrated cementitious thermal discharge is huge, therefore high arch dam construction phase temperature controlled anticracking
Difficulty is big.Maximum temperature is the critical index of temperature controlled anticracking, and induced joint concrete maximum temperature is excessive to will form biggish base
The plinth temperature difference, internal-external temperature difference, the upper and lower level temperature difference and cooling extent, it is easier to biggish tensile stress is generated, when tensile stress is more than to allow
Crack will be generated when tensile stress, the presence in crack can seriously threaten the durability and stability of structure.Concrete early stage power
The growth for learning performance is heavily dependent on own temperature course and environment temperature, after the completion of concreting, as hydrated cementitious is put
Heat, concrete temperature increases, and the change of concrete internal temperature will affect hydrated reaction of cement rate, and concrete temperature is higher
Hydrated cementitious degree is higher, and early strength increases faster；If when environment temperature is too low that the maximum temperature control of concrete is too low,
The mechanical characteristic that largely can also cut down concrete develops rate, unfavorable to concrete early stage cracking resistance；Engineering construction scene
In, if concrete strength is waited to reach demoulding again after requirement, and affect dam construction progress.In addition, maximum temperature is too low, dam
Body entirety temperature drop amplitude is less than normal, and causing transverse joint to open, opportunity is delayed, opening width reduces, and influences joint grouting opportunity and grouting indirectly
Effect.Therefore, maximum temperature can neither be excessively high, can not be too low.And most of engineering units only focus on it is exceeded in maximum temperature
The problem of, too low phenomenon is controlled to maximum temperature and is ignored often, it is necessary to by maximum temperature control in suitable section.
Real-time monitoring is carried out to concrete temperature development process using digital supervision technology, the temperature that notes abnormalities is alarmed immediately
It is common modes of warning, but there are significant limitations for this mode: when the temperature of a certain moment concrete is in normal range (NR)
It is interior, but concrete temperature has biggish heating or cooling trend, if subsequent time temperature will not occur in time violent this moment
The state of variation issues early warning, will lose optimal control opportunity.Rate of temperature change is re-introduced on the basis of temperature index to refer to
The variation tendency of mark energy concentrated expression temperature, i.e., control concrete temperature by temperature and rate of temperature change warning index.Separately
Outside, main at present to draft warning index by probabilistic method, the method is excessively subjective, when index sample distribution rule and classical distribution
When mismatch, the warning index drafted out can not reflected sample actual distribution rule.
Such as the patent document that application publication number is CN103122634A discloses a kind of concrete pouring bin temperature double-control
The dynamic early-warning method of index, it specifically includes the following steps:
(1) it is based on probability statistical analysis, obtains the appearance of the concrete pouring bin typical age of different subregions and different periods
Perhaps temperature and allowable temperature change rate；
(2) dynamic monitoring and calculate current concrete pouring bin typical age temperature and rate of temperature change and with allow temperature
Degree and allowable temperature change rate compare；
(3) according to comparative situation, reference temperature(TR) early warning given mode, a is normal, executes by existing temperature control measures, b temperature
Tracking and monitoring, c pay close attention to and take more effective temperature control measures, d to pay close attention to and take more effective temperature control measures level Four early warning
Prompt；
(4) above step is repeated, the temperature pre-warning prompt of different concrete pouring bins is obtained.
Defect existing for the method for early warning are as follows:
(1) the temperature statistics model established is empirical model, the predictive difference of extension, for specifying pouring under external condition
Storehouse concrete temperature can not make prediction；
(2) to draft the temperature pre-warning index under typical age using little probability method more subjective, can not reflect and pour sample
The temperature distribution rule in this storehouse；
(3) maximum temperature is too high or too low can all have an adverse effect to induced joint concrete structure, above-mentioned method for early warning
Single the early warning upper limit or lower limit value have only been drafted, has not been considered from section early warning angle；
(4) early warning value of the 2nd day typical age, the 2.5th day, the 3rd day are only drafted, pre-warning time section is too short, works as concrete
When the age other than the 2nd~3 day drastic change occurring, this method can not identify temperature, early warning.
(5) above method modes of warning timeliness is insufficient, when having missed the best adjustment when finding state of temperature exception
Between, early warning should be carried out on the basis of prediction.
The purpose of the application is to solve above-mentioned technological deficiency.
Summary of the invention
Technology of the invention solves the problems, such as: overcoming existing concrete temperature pre-warning method objectivity and timeliness insufficient
Problem provides a kind of induced joint concrete temperature state interval method for early warning, a phase temperature control rank the most violent to temperature change
Duan Shixian process early warning.
In order to achieve the above-mentioned object of the invention, the present invention adopts the following technical scheme that:
A kind of induced joint concrete temperature state interval prediction and warning method, comprising the following steps:
Step 1: establishing temperature dynamic prediction model, and storehouse is poured to following several days by temperature dynamic prediction model and is mixed
Solidifying soil temperature degree carries out rolling forecast；
Step 2: according to storehouse concrete temperature changing rule has been poured, drafting temperature control stage one phase of concrete typical age dual control
Warning index upper lower limit value obtains state of temperature early warning section；
Step 3: special according to the constrained feature of different monoliths, Dam Site temperature change feature and concrete temperature state change
Sign establishes classification early warning mechanism；
Step 4: according to prediction result and early warning section, the alert grade of state of temperature being judged by early warning mechanism of classifying, and take
Corresponding responsive measures realize the mobilism correction of temperature control measures.
In step 1, it establishes temperature dynamic prediction model: concrete current observed temperature dynamic in storehouse is poured according to induced joint
Update the concrete initial temperature T having in heat source water pipe cooling Calculation Modelsi, it is based on Real-time Monitoring Data, by nonlinear optimization side
Method updates the adiabatic temperature rise parameter θ in prediction model0And Coefficient m1, to pour within following n days storehouse concrete temperature roll it is pre-
It surveys.
In step 2, temperature control stage one phase of concrete typical age dual control warning index is drafted with maximum entropy method (MEM), obtained
State of temperature early warning section.
In step 2, the warning index drafted out by maximum entropy method (MEM) more meets true distribution；Early warning is drafted respectively
The upper limit and early warning lower limit, a phase temperature control phase temperature status early warning section, compensate for the deficiency of unilateral early warning.
All monoliths are divided into riverbed and abutment sections, the early warning period is divided into high temperature season and low temperature season, according to mixed
Warning grade is divided alert, the middle police that attaches most importance to, light police, without alert four alert grades by the influence for coagulating soil different temperature condition.
In step 1, according to the current observed temperature of storehouse concrete is poured, dynamically update has heat source water pipe cooling Calculation Models
In Ti, the temperature for then pouring storehouse concrete to following n days predicts that concrete temperature dynamic prediction model is as follows:
φ (t)=e-pt (2)
P=dka/D2 (3)
+ 0.320 ξ of k=2.09-1.35 ξ2 (6)
ξ=λ L/cwρwqw (7)
T (t) is concrete temperature；T is cooling time；φ (t) is water cooling function, and p is constant, leads warm system with concrete
Number, thermal coefficient, cooling water pipe length, water flowing flow and water pipe equivalent diameter are related；A is temperature diffusivity, c is outside cooling water pipe
Radius, r0For cooling water pipe inside radius, b is cooling cylinder equivalent redius；cw、ρw、qwRespectively cooling water specific heat, cooling watertight
Degree, water flowing flow；D is used to consider the influence of b/c and cooling water pipe material；s1、s2Respectively the horizontal space of water pipe and it is vertical between
Away from；λ is concrete thermal coefficient, λ1For cooling water pipe thermal coefficient；L is cooling water pipe length；D is equivalent column diameter；ψ
It (t) is function related with adiabatic temperature rise；Adiabatic temperature rise of concrete θ0Initial value is obtained according to observed temperature inverting, s, m1、m2For to
Determine coefficient, s=0.60, m2=0.1m1；For tiMoment corresponds to cooling water pipe water flowing temperature, TiFor tiConcrete at the end of period
Temperature, φiFor tiPeriod corresponding water cooling function, tiAt the time of to change concrete initial temperature, water flowing water temperature or water flowing flow,
When water temperature or changes in flow rate time t need to be since 0.
Important parameter in prediction model is optimized, by θ0With m1As Optimal Parameters, predicted value and measured value are calculated
Residual sum of squares (RSS) E, by nonlinear optimization method solving optimization parameter, calculating formula is
In formula: T (t) is the concrete temperature predicted, and n is that one phase of concrete temperature control terminates age, b1、b2Respectively θ0
Adiabatic temperature rise optimizing section minimum value and maximum value are determined according to storehouse concrete test value is poured with inverting value.b3、b4Respectively
Parameter m1The minimum value and maximum value in optimizing section, initial value generally take 0.6.
Using predicted value and the residual sum of squares (RSS) minimum value of measured value as objective function, calculates and solve θ0And m1Optimal value.
In step 2, dual control warning index is drafted by maximum entropy method (MEM), first most by PSO Algorithm sample
Big entropy probability density function coefficient, steps are as follows
If stochastic variable ξ is defined on the I of section, the probability density function of ξ is f (x), and x meets following constraint condition:
In formula: I is integral space, and general approximation takes For expectation, σ is standard deviation；μiFor the i-th rank
Moment of the orign,xjFor j-th of sample value, n is sample number；N is the order of square used, and temperature monitoring sequence is turned
It is melted into the sample of x- σ/μ form, comes to be acquired using method of Lagrange multipliers so that entropy H (x) reaches maximum value by adjusting f (x)
It is as follows to establish Lagrangian for maximum value:
It enables
Above formula is the analytical form of maximum entropy probability density function；
Formula (13) substitution formula (11) is had
After arrangement
Convolution (11), (13), (14) calculate
Next solve Lagrange multiplier coefficient, by formula (16) be converted into residual error level off to 0 objective function i.e.
In formula, riFor residual error, target function type residual sum of squares (RSS) minimum value is solved by optimization algorithm, i.e.,As r < ε or all | ri| when < ε, it is believed that formula convergence, to solve (λ0,λ1,λ2,λ3,λ4)。
In step 2, using particle swarm algorithm come the Lagrange multiplier in Optimization Solution maximum entropy probability density function
The more new formula of coefficient, standard particle speed and position is
V (k+1)=ω V (k)+c1·rand1·[Pbest(k)-x(k)]+c2·rand2[Gbest(k)-x(k)] (18)
X (k+1)=X (k)+V (k+1) (19)
In formula: k is current iteration number；V is the speed of particle；ω is inertia weight；c1、c2For nonnegative constant accelerate because
Son usually takes c1=c2=2；rand1And rand2The random number in [0,1] section is distributed in for two；PbestFor individual extreme value；
GbestFor group's extreme value of population.
When calculating pours the typical age temperature of storehouse concrete and the corresponding maximum entropy probability density function of rate of temperature change,
During solving probability density function coefficient, dynamic change value is set by inertia weight, enables ω=0.9-0.5k/Mmax,
Middle greatest iteration walks MmaxTaking 1000, k is current iteration number；Population N takes 30.Since temperature and rate of temperature change become at random
The characteristic of amount can be described substantially with preceding 4 rank square, so only calculating λ1、λ2、λ3、λ4, i.e. search space D is 4 dimensions.In order to mention
Formula (13) can be reduced to by high algorithm the convergence speed and precision when calculating maximum entropy probability density functionWherein μ and σ be respectively typical age temperature and rate of temperature change sample mean value and
Standard deviation.
After acquiring the maximum entropy density function f (x) for finding out monitoring effect quantity X according to formula (13), further according in formula (15)
The property of inverse cumulative distribution function, passes through temperature or the abnormal probability P of rate of temperature changeα, it is pre- that state of temperature can be obtained
Alert index.
Enable Xmin、XmaxFor temperature under typical age or the section extreme value of rate of temperature change, then it is double to pour storehouse concrete temperature
Control Indexes Abnormality a possibility that be
By adopting the above technical scheme, following technical effect can be brought:
(1) judge that drafting the development of temperature control measures Under Concrete state of temperature goes through in advance by temperature dynamic prediction model
Journey considers temperature index historical rethinking rule using maximum entropy method (MEM) intended temperature and rate of temperature change dual control warning index, intends
The warning index made is more objective.Storehouse concrete early warning is poured in the presence of abnormal to state of temperature, is in time engineering site temperature
It controls work and guidance is provided, realize the mobilism correction of temperature control measures；
(2) the method for the present invention is utilized, the early warning to a phase control temperature change procedure is able to achieve, temperature control measures are drafted in judgement
State of temperature state of development early warning is made to concrete temperature in time by level Four early warning mechanism extremely, it is mixed to improve induced joint
The ability of solidifying soil temperature controlled anticracking.
Detailed description of the invention
Fig. 1 is induced joint concrete temperature status early warning schematic diagram of the invention；
Fig. 2 is early warning schematic diagram in classification section of the invention；
Fig. 3 is flow chart of the invention.
Specific embodiment
A kind of induced joint concrete temperature state interval prediction and warning method, comprising the following steps:
Step 1: establishing temperature dynamic prediction model, and storehouse is poured to following several days by temperature dynamic prediction model and is mixed
Solidifying soil temperature degree carries out rolling forecast；
Step 2: according to storehouse concrete temperature changing rule has been poured, drafting temperature control stage one phase of concrete typical age dual control
Warning index upper lower limit value obtains state of temperature early warning section；
Step 3: special according to the constrained feature of different monoliths, Dam Site temperature change feature and concrete temperature state change
Sign establishes classification early warning mechanism；
Step 4: according to prediction result and early warning section, the alert grade of state of temperature being judged by early warning mechanism of classifying, and take
Corresponding responsive measures realize the mobilism correction of temperature control measures.
In step 1, the dynamic prediction model has heat according to pouring the current observed temperature dynamic of storehouse concrete and updating
T in main water supply tube cooling Calculation Modelsi, storehouse concrete temperature then, which was poured, to following n days predicts.Concrete temperature dynamic
Prediction model is as follows:
φ (t)=e-pt (2)
P=dka/D2 (3)
+ 0.320 ξ of k=2.09-1.35 ξ2 (6)
ξ=λ L/cwρwqw (7)
T (t) is concrete temperature；T is cooling time；φ (t) is water cooling function, and p is constant, leads warm system with concrete
Number, thermal coefficient, cooling water pipe length, water flowing flow and water pipe equivalent diameter are related；A is temperature diffusivity, c is outside cooling water pipe
Radius, r0For cooling water pipe inside radius, b is cooling cylinder equivalent redius；cw、ρw、qwRespectively cooling water specific heat, cooling watertight
Degree, water flowing flow；D is used to consider the influence of b/c and cooling water pipe material；s1、s2Respectively the horizontal space of water pipe and it is vertical between
Away from；λ is concrete thermal coefficient, λ1For cooling water pipe thermal coefficient；L is cooling water pipe length；D is equivalent column diameter；ψ
It (t) is function related with adiabatic temperature rise；Adiabatic temperature rise of concrete θ0Initial value is obtained according to observed temperature inverting, s, m1、m2For to
Determine coefficient, s=0.60, m2=0.1m1；For tiMoment corresponds to cooling water pipe water flowing temperature, TiFor tiConcrete at the end of period
Temperature, φiFor tiPeriod corresponding water cooling function, tiAt the time of to change concrete initial temperature, water flowing water temperature or water flowing flow,
When water temperature or changes in flow rate time t need to be since 0.
The adiabatic temperature rise θ known to dynamic prediction model0It is the important component of prediction type, θ0Value size is by direct shadow
Ring Adiabatic temperature rise of concrete item size.Since each actual concrete thermal characteristic in storehouse that pours is variant, to improve prediction
Model accuracy optimizes the important parameter in prediction model.
By θ0With m1As Optimal Parameters, the residual sum of squares (RSS) E of predicted value and measured value is calculated, nonlinear optimization method is passed through
Solving optimization parameter, calculating formula are
In formula: T (t) is the concrete temperature predicted, and n is that one phase of concrete temperature control terminates age, b1、b2Respectively θ0
Adiabatic temperature rise optimizing section minimum value and maximum value are determined according to storehouse concrete test value is poured with inverting value.b3、b4Respectively
Parameter m1The minimum value and maximum value in optimizing section, initial value generally take 0.6.
Using predicted value and the residual sum of squares (RSS) minimum value of measured value as objective function, is calculated by Matlab and solve θ0And m1
Optimal value.
The maximum entropy method (MEM) drafts dual control warning index in step 2, passes through PSO Algorithm sample first
Maximum entropy probability density function coefficient
If stochastic variable ξ is defined on the I of section, the probability density function of ξ is f (x), and x meets following constraint condition:
In formula: I is integral space, and general approximation takes For expectation, σ is standard deviation；μiFor the i-th rank
Moment of the orign,xjFor j-th of sample value, n is sample number；N is the order of square used.Temperature monitoring sequence is turned
It is melted into the sample of x- σ/μ form, comes to be acquired using method of Lagrange multipliers so that entropy H (x) reaches maximum value by adjusting f (x)
Maximum value.It is as follows to establish Lagrangian:
It enables
Above formula is the analytical form of maximum entropy probability density function.
Formula (13) substitution formula (11) is had
After arrangement
Convolution (11), (13), (14) calculate
Next solve Lagrange multiplier coefficient, by formula (13) be converted into residual error level off to 0 objective function i.e.
In formula, riFor residual error, target function type residual sum of squares (RSS) minimum value is solved by optimization algorithm, i.e.,As r < ε or all | ri| when < ε, it is believed that formula convergence, to solve (λ0,λ1,λ2,λ3,λ4)。
In order to obtain as much as possible with actual sample be distributed close to maximum entropy probability density function, using particle swarm algorithm
Carry out the Lagrange multiplier coefficient in Optimization Solution maximum entropy probability density function.The more other algorithms of particle swarm algorithm have simple
It easily realizes, the advantages such as parameter is few, stronger global convergence ability and robustness.The more new formula of standard particle speed and position is
V (k+1)=ω V (k)+c1·rand1·[Pbest(k)-x(k)]+c2·rand2[Gbest(k)-x(k)] (18)
X (k+1)=X (k)+V (k+1) (19)
In formula: k is current iteration number；V is the speed of particle；ω is inertia weight；c1、c2For nonnegative constant accelerate because
Son usually takes c1=c2=2；rand1And rand2The random number in [0,1] section is distributed in for two；PbestFor individual extreme value；
GbestFor group's extreme value of population.
When calculating pours the typical age temperature of storehouse concrete and the corresponding maximum entropy probability density function of rate of temperature change,
During solving probability density function coefficient, particle swarm algorithm falls into locally optimal solution when calculating in order to prevent, by inertia
Weight is set as dynamic change value, enables ω=0.9-0.5k/Mmax, wherein greatest iteration walks MmaxTaking 1000, k is current iteration time
Number；Population N takes 30.Since the characteristic of temperature and rate of temperature change stochastic variable can be described substantially with preceding 4 rank square, so
Only calculate λ1、λ2、λ3、λ4, i.e. search space D is 4 dimensions.In order to improve algorithm the convergence speed and precision, maximum entropy probability is being calculated
When density function, formula (10) can be reduced toWherein μ and σ is respectively typical age temperature
The mean value and standard deviation of degree and rate of temperature change sample.
After acquiring the maximum entropy density function f (x) for finding out monitoring effect quantity X according to formula (13), further according in formula (15)
The property of inverse cumulative distribution function, passes through temperature or the abnormal probability P of rate of temperature changeα, it is pre- that state of temperature can be obtained
Alert index.
Enable Xmin、XmaxFor temperature under typical age or the section extreme value of rate of temperature change, then it is double to pour storehouse concrete temperature
Control Indexes Abnormality a possibility that be
The classification early warning mechanism in step 3, it is contemplated that concrete different strength concrete thermal property
Otherness is arranged not same district by dam concrete strength grade, considers the different controlled features of monolith, all monoliths are divided into
Riverbed and abutment sections, it is contemplated that the early warning period is divided into high temperature season and low temperature season by the difference of different periods temperature control measures,
According to the influence of concrete different temperature condition, by warning grade division attach most importance to alert, middle police, it is light it is alert, without alert four alert grades, according to
The difference of the concrete temperature and rate of temperature change and early warning upper lower limit value that predict, is in turn divided into nothing for state of temperature early warning
Alert, light alert, middle police warns four ranks again, I grades (warning again), identifies warning level using red, regulating measures temperature has disengaged from pre-
Alert line still has the tendency that continuing to be detached from normal interval, if being 1. more than the early warning upper limit, increases water flowing flow or reduces water flowing water
Temperature；2. reducing water flowing flow if being lower than early warning lower limit or improving water flowing water temperature.II grades (middle police), using orange mark early warning
Rank, regulating measures are paid close attention to, appropriate to increase water flowing flow or reduce water flowing water if 1. having more than the trend of the early warning upper limit
Temperature；2. appropriate to reduce water flowing flow or improve water flowing water temperature if having the tendency that lower than early warning lower limit.III level (light alert), using Huang
Colour code knows warning level, and regulating measures temperature has disengaged from early warning line, but has the tendency that returning normal interval, and execution is drafted temperature control and arranged
It applies, tracking and monitoring, water flowing strategy is constantly adjusted according to practical temperature control situation.IV grades (no police), warning level is identified using blue,
Regulating measures are normal, execute by the temperature control measures drafted.
As shown in Figure 1, IV grades of curves indicate the concrete temperature and rate of temperature change that predict by dynamic prediction model
It is in early warning line, executing the temperature control measures drafted can guarantee that concrete temperature develops in normal interval, therefore, engineering list
Position executes measure of drafting and keeps normal monitoring；It is pre- that III level curve indicates that concrete temperature and rate of temperature change all have passed past
The alert upper limit is lower than early warning lower limit, the trend that the oriented normal interval of temperature returns, although illustrating that temperature has had been detached from normally
In section, but the temperature control measures drafted can play the effect of correction temperature control, and engineering unit, which should execute, drafts temperature control measures, and emphasis
Tracking and monitoring constantly adjusts temperature control measures according to practical temperature control effect；II grades of curves indicate that the concrete temperature predicted is in
In normal interval, but rate of temperature change has more than early warning upper limit value or the trend lower than early warning lower limit value, therefore, engineering unit pair
The temperature control measures needs drafted are optimized and revised, and are repeated to predict, can just be held until prediction result reaches in dual control forewarning index section
The temperature control measures that row is drafted, and tracking and monitoring；I grades of curves indicate that the concrete temperature predicted and rate of temperature change all have disengaged from
Normal interval, and have the tendency that continuing illustrating the temperature control measures drafted far from normal interval there are wretched insufficiency, needing to adjust immediately
Whole temperature control measures carry out concrete change procedure according to temperature control measures after adjustment to repeat prediction, until prediction result reaches double
Corresponding temperature control scheme just can be performed in control forewarning index bound.
1 grading forewarning system interval table of table
Wherein, TnWithIndicate temperature and rate of temperature change of the age of concrete at n days；[Tn] andIndicate temperature and rate of temperature change warning index of the concrete at age n days.Wherein
△ T is the temperature difference of adjacent monitoring moment point, and not preferably greater than 0.125 day, i.e. concrete temperature monitoring frequency should not exceed 3 hours one
Group.
When carrying out state of temperature early warning, first pass through dynamic prediction model predict draft temperature control measures under the conditions of after n days
Then the temperature changing process of concrete drafts typical age warning index by maximum entropy method (MEM), by predicted value and warning index
Value compares, and then carries out early warning by above-mentioned early warning mechanism.
Early warning comparison in step 4 is moved according to storehouse concrete primary condition and boundary condition is poured by temperature
State prediction model predicts a phase temperature control stage concrete temperature changing process line, and the temperature and temperature obtained under typical age becomes
Rate.Cell and Time Distribution section divides monolith statistics to pour temperature and rate of temperature change sample under storehouse concrete typical age, passes through
Maximum entropy method (MEM) drafts temperature and rate of temperature change warning index under typical age.By the predicted value under concrete typical age and in advance
Alert index value comparison drafts concrete temperature state under temperature control measures by early warning mechanism judgement and warns grade, and takes responsive measures.
Above scheme of the embodiment of the present invention is based on Real-time Monitoring Data, by the dynamic prediction model of foundation, drafts most
Big entropy dual control early warning value, classification early warning mechanism, realize induced joint concrete temperature state interval early warning, this method makes full use of
Poured storehouse concrete observed temperature state change rule, achieved the purpose that real-time early warning, dynamic regulation.
Claims (10)
1. a kind of induced joint concrete temperature state interval prediction and warning method, which comprises the following steps:
Step 1: establishing temperature dynamic prediction model, and storehouse concrete is poured to following several days by temperature dynamic prediction model
Temperature carries out rolling forecast；
Step 2: according to storehouse concrete temperature changing rule has been poured, drafting the typical age dual control early warning of temperature control stage one phase of concrete
Index upper lower limit value obtains state of temperature early warning section；
Step 3: being built according to the constrained feature of different monoliths, Dam Site temperature change feature and concrete temperature state change feature
Vertical classification early warning mechanism；
Step 4: according to temperature prediction result and early warning section, concrete under temperature control measures being drafted by classification early warning mechanism judgement
State of temperature warns grade, and takes corresponding responsive measures, realizes the mobilism correction of temperature control measures.
2. induced joint concrete temperature state interval prediction and warning method according to claim 1, it is characterised in that: in step
In rapid 1, temperature dynamic prediction model is established, pouring the current observed temperature dynamic of storehouse concrete according to induced joint and update has heat source water
Concrete initial temperature T in pipe cooling Calculation Modelsi, it is based on Real-time Monitoring Data, prediction mould is updated by nonlinear optimization method
Adiabatic temperature rise parameter θ in type0And Coefficient m1, storehouse concrete temperature, which was poured, to following n days carries out rolling forecast.
3. induced joint concrete temperature state interval prediction and warning method according to claim 2, it is characterised in that: in step
In rapid 1, the T having in heat source water pipe cooling Calculation Models is updated according to the current observed temperature dynamic of storehouse concrete is pouredi, then right
The temperature for pouring storehouse concrete is predicted within following n days, and concrete temperature dynamic prediction model is as follows:
φ (t)=e-pt (2)
P=dka/D2 (3)
+ 0.320 ξ of k=2.09-1.35 ξ2 (6)
ξ=λ L/cwρwqw (7)
T (t) is concrete temperature；T is cooling time；φ (t) be water cooling function, p is constant, with concrete temperature diffusivity, lead
Hot coefficient, cooling water pipe length, water flowing flow and water pipe equivalent diameter are related；A is temperature diffusivity, c be cooling water pipe outer radius,
r0For cooling water pipe inside radius, b is cooling cylinder equivalent redius；cw、ρw、qwRespectively cooling water specific heat, cooling water density, logical
Water flow；D is used to consider the influence of b/c and cooling water pipe material；s1、s2The respectively horizontal space and vertical interval of water pipe；λ
For concrete thermal coefficient, λ1For cooling water pipe thermal coefficient；L is cooling water pipe length；D is equivalent column diameter；ψ (t) is
Function related with adiabatic temperature rise；Adiabatic temperature rise of concrete θ0Initial value is obtained according to observed temperature inverting, s, m1、m2For system undetermined
Number, s=0.60, m2=0.1m1；For tiMoment corresponds to cooling water pipe water flowing temperature, TiFor tiThe temperature of concrete at the end of period
Degree, φiFor tiPeriod corresponding water cooling function, tiAt the time of to change concrete initial temperature, water flowing water temperature or water flowing flow, work as water
Temperature or changes in flow rate time t need to be since 0.
4. induced joint concrete temperature state interval prediction and warning method according to claim 3, it is characterised in that: to pre-
Survey the important parameter θ in model0With m1It optimizes, using predicted value and the residual sum of squares (RSS) minimum value of measured value as target letter
Number is calculated by nonlinear optimization method and solves θ0And m1Optimal value, calculating formula is
In formula: E (t) is the residual sum of squares (RSS) of concrete temperature predicted value and measured value, and T (t) is the concrete temperature predicted,
N is that one phase of concrete temperature control terminates age, b1、b2Respectively adiabatic temperature rise θ0Optimizing section minimum value and maximum value, according to pouring
Storehouse concrete test value and inverting value are determining, b3、b4Respectively parameter m1The minimum value and maximum value in optimizing section, initial value one
As take 0.6.
5. induced joint concrete temperature state interval prediction and warning method according to claim 1, it is characterised in that: in step
In rapid 2, temperature control stage one phase of concrete typical age dual control warning index is drafted with maximum entropy method (MEM), it is pre- to obtain state of temperature
Between police region.
6. induced joint concrete temperature state interval prediction and warning method according to claim 1 or 5, it is characterised in that:
In step 2, the warning index drafted out by maximum entropy method (MEM) more meets concrete temperature state actual distribution rule.
7. induced joint concrete temperature state interval prediction and warning method according to claim 6, it is characterised in that: in step
In rapid 2, one phase of concrete temperature control phase temperature status early warning upper limit value and early warning lower limit value have been drafted respectively with maximum entropy method (MEM),
Early warning section has been obtained, the deficiency of unilateral early warning is compensated for.
8. induced joint concrete temperature state interval prediction and warning method, feature described according to claim 1 or 5 or 7 exist
In: according to the influence of concrete different temperature condition, warning grade is divided to alert, the middle police that attaches most importance to, light police, without alert four alert grades.
9. induced joint concrete temperature state interval prediction and warning method, feature described according to claim 1 or 5 or 7 exist
In: in step 2, dual control warning index is drafted by maximum entropy method (MEM), steps are as follows: first by PSO Algorithm sample
Maximum entropy probability density function coefficient
If stochastic variable ξ is defined on the I of section, the probability density function of ξ is f (x), and x meets following constraint condition:
In formula: I is integral space, and general approximation takes For expectation, σ is standard deviation；μiFor the i-th rank origin
Square,xjFor j-th of sample value, n is sample number；N be square used order, by temperature monitoring it is Sequence Transformed at
The sample of x- σ/μ form comes to acquire maximum using method of Lagrange multipliers so that entropy H (x) reaches maximum value by adjusting f (x)
Value, it is as follows to establish Lagrangian:
It enables
Above formula is the analytical form of maximum entropy probability density function；
Formula (13) substitution formula (11) is had
After arrangement
Convolution (11), (13), (14) calculate
Next solve Lagrange multiplier coefficient lambda, by formula (13) be converted into residual error level off to 0 objective function i.e.
In formula, riFor residual error, target function type residual sum of squares (RSS) minimum value is solved by optimization algorithm, i.e.,
As r < ε or all | ri| when < ε, it is believed that formula convergence, to solve (λ0,λ1,λ2,λ3,λ4)。
10. induced joint concrete temperature state interval prediction and warning method according to claim 9, it is characterised in that: In
In step 2, using particle swarm algorithm come the Lagrange multiplier coefficient in Optimization Solution maximum entropy probability density function, standard grain
The more new formula of sub- speed and position is
V (k+1)=ω V (k)+c1·rand1·[Pbest(k)-x(k)]+c2·rand2[Gbest(k)-x(k)] (18)
X (k+1)=X (k)+V (k+1) (19)
In formula: k is current iteration number；V is the speed of particle；ω is inertia weight；c1、c2For nonnegative constant accelerated factor, lead to
Often take c1=c2=2；rand1And rand2The random number in [0,1] section is distributed in for two；PbestFor individual extreme value；GbestFor kind
Group's extreme value of group.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910808672.8A CN110517460A (en) | 2019-08-29 | 2019-08-29 | A kind of induced joint concrete temperature state interval prediction and warning method |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201910808672.8A CN110517460A (en) | 2019-08-29 | 2019-08-29 | A kind of induced joint concrete temperature state interval prediction and warning method |
Publications (1)
| Publication Number | Publication Date |
|---|---|
| CN110517460A true CN110517460A (en) | 2019-11-29 |
Family
ID=68628983
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201910808672.8A Pending CN110517460A (en) | 2019-08-29 | 2019-08-29 | A kind of induced joint concrete temperature state interval prediction and warning method |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN110517460A (en) |
Cited By (3)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113177338A (en) * | 2021-04-25 | 2021-07-27 | 三峡大学 | Construction method of arch dam orifice simulation calculation model considering solar radiation heat |
| CN115392082A (en) * | 2022-08-23 | 2022-11-25 | 湖南科技大学 | On-site large-volume concrete hydration heat temperature prediction system and method |
| CN115482650A (en) * | 2022-10-27 | 2022-12-16 | 安徽龙振建设有限公司 | Concrete pouring auxiliary system based on BIM |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2467898A1 (en) * | 2004-05-21 | 2005-11-21 | Pure Technologies Ltd. | Fiber optic sensor method and apparatus |
| CN103122634A (en) * | 2012-11-29 | 2013-05-29 | 中国长江三峡集团公司 | Dynamic early warning method of concrete poured storehouse temperature double-control index |
-
2019
- 2019-08-29 CN CN201910808672.8A patent/CN110517460A/en active Pending
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CA2467898A1 (en) * | 2004-05-21 | 2005-11-21 | Pure Technologies Ltd. | Fiber optic sensor method and apparatus |
| CN103122634A (en) * | 2012-11-29 | 2013-05-29 | 中国长江三峡集团公司 | Dynamic early warning method of concrete poured storehouse temperature double-control index |
Non-Patent Citations (3)
| Title |
|---|
| 周建兵 等: "向家坝导流底孔回填混凝土温度动态预测", 《长江科学院院报》 * |
| 朱伯芳: "大体积混凝土非金属水管冷却的降温计算", 《水利发电》 * |
| 黄耀英 等: "混凝土浇筑仓温度双控指标拟定的最大熵法", 《长江科学院院报》 * |
Cited By (4)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN113177338A (en) * | 2021-04-25 | 2021-07-27 | 三峡大学 | Construction method of arch dam orifice simulation calculation model considering solar radiation heat |
| CN115392082A (en) * | 2022-08-23 | 2022-11-25 | 湖南科技大学 | On-site large-volume concrete hydration heat temperature prediction system and method |
| CN115392082B (en) * | 2022-08-23 | 2023-04-18 | 湖南科技大学 | On-site large-volume concrete hydration heat temperature prediction system and method |
| CN115482650A (en) * | 2022-10-27 | 2022-12-16 | 安徽龙振建设有限公司 | Concrete pouring auxiliary system based on BIM |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| CN110517460A (en) | A kind of induced joint concrete temperature state interval prediction and warning method | |
| WO2021057349A1 (en) | Intelligent control system and method for heavy medium separation process | |
| CN100507027C (en) | Method for dynamic setting and control of hot-roll heating furnace temperature | |
| CN110479776B (en) | Closed-loop cooling control system and control method for rolled hot-rolled rod and wire | |
| CN106773682B (en) | The glass furnace bottom of pond temperature intelligent forecast Control Algorithm being dynamically determined based on time lag | |
| CN103631140B (en) | Based on the coke oven heating-combustion process fire path temperature Automatic adjustment method of Performance Evaluation | |
| CN105005632B (en) | The blast furnace crucible corrosion Forecasting Methodology of multiple layer refractory tile stove wall construction | |
| CN104019662B (en) | Rotary kiln control system | |
| CN109653205B (en) | Mass concrete curing method and automatic curing equipment thereof | |
| CN104101105B (en) | The composite control method of a kind of heating crude oil furnace temperature and device thereof | |
| CN111046457B (en) | Temperature control curve model for construction full-period concrete arch dam | |
| CN104110008B (en) | A kind of method quickly regulated and controled for concrete dam middle and late stage water flowing | |
| CN103611734A (en) | Laminar cooling temperature control method and system | |
| CN103173584A (en) | Blast furnace burden-distribution control system with self-learning control function | |
| WO2023130666A1 (en) | Strip steel plate convexity prediction method based on data-driving and mechanism model fusion | |
| CN109948824A (en) | A method of thermal substation thermic load is predicted using mode identification technology | |
| CN109631150A (en) | A kind of accurate heat supply method of temperature based on secondary network, pressure and user's room temperature | |
| CN109519957B (en) | A kind of ultra-supercritical boiler closed loop optimized control method of combustion | |
| CN114737003B (en) | Automatic control method and system for combustion of blast furnace hot blast stove based on heat storage model | |
| CN103122634A (en) | Dynamic early warning method of concrete poured storehouse temperature double-control index | |
| CN1603424A (en) | Bessemerizing control method based on intelligent compound dynamic model with sublance converter | |
| CN103605284A (en) | Dynamic matrix control optimization-based waste plastic cracking furnace pressure controlling method | |
| CN112947626A (en) | Variable topology grid for dam temperature control and control method thereof | |
| WO2024021150A1 (en) | Method for establishing coagulation intelligent monitoring linkage system | |
| CN116736907A (en) | Intelligent regulation and control method for production temperature of low borosilicate glass |
Legal Events
| Date | Code | Title | Description |
|---|---|---|---|
| PB01 | Publication | ||
| PB01 | Publication | ||
| SE01 | Entry into force of request for substantive examination | ||
| SE01 | Entry into force of request for substantive examination | ||
| RJ01 | Rejection of invention patent application after publication | ||
| RJ01 | Rejection of invention patent application after publication |
Application publication date: 20191129 |