CN106650194B - Railway steel bridge static behavior appraisal procedure based on static strain prediction of extremum - Google Patents
Railway steel bridge static behavior appraisal procedure based on static strain prediction of extremum Download PDFInfo
- Publication number
- CN106650194B CN106650194B CN201610229916.3A CN201610229916A CN106650194B CN 106650194 B CN106650194 B CN 106650194B CN 201610229916 A CN201610229916 A CN 201610229916A CN 106650194 B CN106650194 B CN 106650194B
- Authority
- CN
- China
- Prior art keywords
- strain
- static
- value
- diurnal variation
- max
- 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.)
- Active
Links
Classifications
-
- G—PHYSICS
- G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
- G16Z—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS, NOT OTHERWISE PROVIDED FOR
- G16Z99/00—Subject matter not provided for in other main groups of this subclass
Landscapes
- Investigating Strength Of Materials By Application Of Mechanical Stress (AREA)
Abstract
The railway steel bridge static behavior appraisal procedure based on static strain prediction of extremum that the invention discloses a kind of, it is related to railway steel bridge static behavior evaluation areas, this method comprises the following steps, and step a. carries out strain data acquisition to the most unfavorable combination of railroad bridge key member；Step b. extracts the static strain ingredient in strain data using analysis method of wavelet packet；Step c. obtains the diurnal variation extreme value in static strain ingredient using first-order difference；Step d. utilizes the cumulative distribution character of generalized extreme value distribution Function Fitting diurnal variation extreme value；Step e. carries out static behavior assessment to the railroad bridge key member stress most unfavorable combination.It is that the case where straining extreme value, so that the strain extreme value data extracted are more accurate, and can guarantee the uniqueness of strain extreme value probabilistic statistical characteristics that the present invention, which can effectively avoid strain maximum value or minimum value not,.
Description
Technical field
The present invention relates to railway steel bridge static behavior evaluation areas, and in particular to a kind of iron based on static strain prediction of extremum
Road steel bridge static behavior appraisal procedure.
Background technique
The static behavior of railway steel bridge is directly related to the operation security of railroad train, if the railway under environmental load effect
The key member of steel bridge is more than maximum permissible value and fails timely discovery and regulation, can be extremely dangerous, or even the disconnected vehicle of beam occurs and ruins
Catastrophic event, therefore carry out railway steel bridge static behavior evaluation work be of great significance.
Currently, existing research worker carries out emphasis monitoring for the strain regime of railway steel bridge key member, and choose
The strain extreme value of monitoring data is compared with Theoretical Design maximum value, to judge the static behavior of railway steel bridge at this stage.However
For a kind of this method there are a disadvantage, i.e., the strain extreme value of limited monitoring data cannot represent railway steel bridge within the entire operation phase
Strain extreme value, therefore cannot achieve to railroad bridge in entire service phase static behavior assessment.Separately there is research worker
It attempts to carry out static behavior of the railroad bridge in entire service phase using the probabilistic statistical characteristics of monitoring data strain extreme value
Assessment, but the method there are still two problems have it is to be solved: (1) used by strain extreme value be one day internal strain curve maximum
Value and minimum value, but the maximum value and minimum value not necessarily extreme value of one day internal strain curve, that is, be likely to be strain curve
Endpoint value, therefore extract strain extreme value data it is not accurate enough；(2) probabilistic statistical characteristics for straining extreme value use probability density
Function describes, however probability density function is related with the strain column width of extreme value probability density histogram, causes the probability close
Degree function loses uniqueness.
Summary of the invention
In view of the deficiencies in the prior art, the purpose of the present invention is to provide a kind of titles.
To achieve the above objectives, the technical solution adopted by the present invention is that: a kind of rail iron based on static strain prediction of extremum
Bridge static behavior appraisal procedure, this method comprises the following steps,
The strain data of step a. acquisition railroad bridge key member stress most unfavorable combination within L days sampling time；
Step b. extracts the static strain ingredient in the strain data using analysis method of wavelet packet；
Step c. divides the static strain ingredient as unit of day, to all static strain values in daily according to adopting
Collection sequencing does first-order difference processing, obtains static strain value in daily difference sequence, by the difference sequence in every day
Column are arbitrarily divided into more parts, and respectively sum to every portion difference sequence, then the summation to every portion difference sequence
As a result it takes absolute value and is added and acquire summation, within L days sampling time, the difference sequence segmentation number is identical in every day
In the case of compare the size of the summation acquired under different cut-points, determine the cut-point when summation maximum, compare
Size under cut-point of the difference sequence in the summation maximum, so that it is determined that the static strain ingredient is Sino-Japan in daily
Change maximum and diurnal variation minimum, and then the Sino-Japan change of static strain ingredient in the determining and more described L days sampling time
Change maximum DMAXWith diurnal variation minimum DMIN；
Step d. utilizes generalized extreme value distribution function G (DMAX) and F (DMIN) be fitted in the L days sampling time respectively it is described
Diurnal variation maximum D in static strain ingredientMAXWith diurnal variation minimum DMINCumulative distribution character:
Step e. carries out static behavior assessment to the railroad bridge key member stress most unfavorable combination: where
In formula, P is outcross probability, using Newton iteration method and in conjunction with the generalized extreme value distribution function G (DMAX) and F
(DMIN), equation (4) and equation (5) are solved, determines static strain standard value when outcross probability is PWith
WithIt indicatesWithIn maximum absolute value person, willWith design permissible valueDescribed in multilevel iudge
The static behavior of railroad bridge, ifThen at the static behavior of the railroad bridge key member stress most unfavorable combination
In safe condition；Conversely, then the static behavior of the railroad bridge key member stress most unfavorable combination is in non-secure states.
Based on the above technical solution, the strain is acquired using fiber Bragg grating strain sensor in the step a
Data, and using fiber grating temperature sensor as temperature-compensating.
Based on the above technical solution, sampling time L is greater than 200 days in the step a, and sample frequency is greater than 1/
600Hz is less than 1Hz.
Based on the above technical solution, the analysis method of wavelet packet is specifically included all described answers collected
Parameter constitutes strain sequence according to according to acquisition sequencing, after the strain sequence is subjected to the wavelet packet point on c-th of scale
Solution, obtains 2cA wavelet packet coefficient extracts first wavelet packet coefficient and is reconstructed, and obtains quiet in the strain sequence
Ingredient is strained, wherein c is measurement point number coefficient.
Based on the above technical solution, the measurement point number coefficient c is equal to 5.
Based on the above technical solution, the difference sequence of every day in the step c is arbitrarily divided into three parts.
Based on the above technical solution, diurnal variation maximum in the static strain ingredient in the L days sampling time
DMAXThe fitting of cumulative distribution character include calculating the diurnal variation maximum DMAXAccumulated probability value, and using described wide
Adopted Extremal distribution function G (DMAX) it is fitted, in which:
In formula, b, d and r respectively indicate G (DMAX) scale parameter, location parameter and form parameter, day for acquiring will be calculated
Change maximum DMAXWith diurnal variation maximum DMAXAccumulated probability value substituted into formula (2) respectively, utilize least square method determine
B, the best value of d and r；
Diurnal variation minimum D in the static strain ingredient in the L days sampling timeMINCumulative distribution character fitting
Including calculating the diurnal variation minimum DMINAccumulated probability value, and utilize the generalized extreme value distribution function F (DMIN) to it
Fitting, wherein
In formula, g, h and λ respectively indicate F (DMIN) scale parameter, location parameter and form parameter, day for acquiring will be calculated
Change very little value DMINWith diurnal variation minimum DMINAccumulated probability value substituted into formula (3) respectively, utilize least square method determine
G, the best value of h and λ；
Based on the above technical solution, the value of the outcross probability P is 0.01.
Compared with the prior art, the advantages of the present invention are as follows:
(1) the railway steel bridge static behavior appraisal procedure based on static strain prediction of extremum in the present invention utilizes single order
Difference determine strain extreme value, thus effectively avoid strain maximum value or minimum value not be strain extreme value the case where so that extract
It is more accurate to strain extreme value data.
(2) the railway steel bridge static behavior appraisal procedure based on static strain prediction of extremum in the present invention utilizes cumulative
Probability value, which overcomes probabilistic statistical characteristics in conventional method, does not have the shortcomings that uniqueness, ensure that strain extreme value probabilistic statistical characteristics
Uniqueness, and this method more can accurately and effectively to railroad bridge in entire service phase static behavior assessment, can obtain
To being widely popularized and apply.
Detailed description of the invention
The flow chart of appraisal procedure in Fig. 1 present invention；
Fig. 2 is the elevation of Foundations of Dashengguan Changjiang River Bridge in the embodiment of the present invention；
Fig. 3 is the position of fiber Bragg grating strain sensor in the embodiment of the present invention；
Fig. 4 is the strain sequence that all strain values are constituted according to acquisition sequencing in the embodiment of the present invention；
Fig. 5 is the static strain ingredient strained in sequence in the embodiment of the present invention；
Fig. 6 is the change curve of diurnal variation maximum in the embodiment of the present invention；
Fig. 7 is the change curve of diurnal variation minimum in the embodiment of the present invention；
Fig. 8 is the fitting result of diurnal variation maximum generalized extreme value distribution in the embodiment of the present invention；
Fig. 9 is the fitting result of diurnal variation minimum generalized extreme value distribution in the embodiment of the present invention.
In figure: 1- top boom, 2- fiber grating temperature sensor.
Specific embodiment
Invention is further described in detail with reference to the accompanying drawings and embodiments.
The present invention provides a kind of railway steel bridge static behavior appraisal procedure based on static strain prediction of extremum, referring to Fig. 1 institute
The flow chart shown, method includes the following steps:
Step a. carries out strain data acquisition to the most unfavorable combination of railroad bridge key member:
Fiber Bragg grating strain sensor is coupled in data collection system by the present invention, and with fiber grating temperature sensor
As temperature-compensating, data acquisition is carried out to the most unfavorable combination of railroad bridge key member, stress most unfavorable combination refers to
At bridge force-bearing maximum, most concentration or bridge structure is easily destroyed place the most.Sample frequency is expressed as fHz, sampling time length
It is indicated with L, collected i-th of strain value is indicated using S (i), and i=1 ..., M, M are strain value total number.Wherein, it samples
Frequency is greater than 1/600Hz and is less than 1Hz, and sampling time L is greater than 200 days.
Step b. extracts the static strain ingredient in strain data using analysis method of wavelet packet:
Since strain data is obviously by the interference of train load, dependent variable is extracted using analysis method of wavelet packet
The static strain ingredient not interfered by train load in.Wherein analysis method of wavelet packet is a kind of commonly using for signal disposal and analysis
Method can be adaptive selected frequency band and match with signal spectrum, be a kind of essence according to characteristics of signals and analysis requirement
Thin signal decomposition method, concrete operations are as follows: answer collected all strain value S (i) according to acquisition sequencing composition
Become sequence, after by strain sequence carry out c-th of scale on WAVELET PACKET DECOMPOSITION, obtain 2cA wavelet packet coefficient, extracts first
A wavelet packet coefficient is simultaneously reconstructed, and obtains the static strain ingredient in strain sequence, and wherein c is measurement point number coefficient.
Step c. obtains the diurnal variation extreme value in static strain ingredient using first-order difference:
1. being divided as unit of day to static strain ingredient, j-th static strain value of the static strain ingredient in the m days is adopted
Use Sm(j) it indicates, wherein m=1,2 ..., L, j=1,2 ..., Nm, NmFor total number of the static strain value in the m days.
2. pair all static strain values interior daily do first-order difference processing according to acquisition sequencing, first-order difference processing refers to
Be continuous adjacent two in static strain value differences, thus obtain static strain value daily in difference sequence.Wherein difference sequence
K-th of value in the m days is listed in using Dm(k) it indicates, k=1,2 ..., Nm-1。
3. the difference sequence of every day, which can according to need, is arbitrarily divided into more parts, the difference sequence in the present invention is divided
Three parts are segmented into, in order to conclude explanation, the present invention is by the difference sequence { D in the m daysm(1),Dm(2),...,Dm(Nm- 1) } divide
It is three parts, wherein first part of difference sequence is { Dm(1),Dm(2),...,Dm(p1,m), second part of difference sequence is { Dm(p1,m+
1),Dm(p1,m+2),...,Dm(p1,m+p2,m), third part difference sequence is { Dm(p2,m+1),Dm(p2,m+2),...,Dm(Nm-
1) } wherein p1,m、p2,mTwo cut-points of difference sequence in respectively the m days, and meet 1 < p1,m<p2,m<Nm-1。
4. all values in pair three parts of difference sequences are summed, Z is usedm,l(p1,m,p2,m) indicate that value is p in the m days1,m,p2,m
When the sum of l parts of difference sequences, wherein l=1,2,3, then acquire Z according to the following formulam,1(p1,m,p2,m)、Zm,2(p1,m,p2,m) and
Zm,3(p1,m,p2,mThe sum of) three:
Qm(p1,m,p2,m)=| Zm,1(p1,m,p2,m)|+|Zm,2(p1,m,p2,m)|+|Zm,3(p1,m,p2,m)| (1)
In formula, Qm(p1,m,p2,m) calculated result and p1,m, p2,mValue is related, to p1,m, p2,mBe chosen at constraint condition 1 <
p1,m<p2,m<NmAll possible value under -1 substitutes into formula (1) and calculates corresponding Qm(p1,m,p2,m) value, all Qm(p1,m,
p2,m) maximum value is certainly existed in value, it is assumed that this maximum value is by p1,m=a1,m、p2,m=a2,mIt obtains, then in the m days
Diurnal variation maximum in static strain ingredient is Dm(a1,m) and Dm(a2,m) the larger value in the two, diurnal variation minimum is Dm
(a1,m) and Dm(a2,m) smaller value in the two, determining diurnal variation maximum in the m days in static strain ingredient and minimum
After value, by comparing daily diurnal variation maximum and minimum, so that it is determined that diurnal variation maximum in all number of days L and
Minimum, wherein the diurnal variation maximum in all number of days L uses DMAXIt indicates, the diurnal variation minimum of all number of days L uses
DMINIt indicates.
The present invention determines strain extreme value using first-order difference, so that effectively avoiding strain maximum value or minimum value not is strain
The case where extreme value, so that the strain extreme value data extracted are more accurate.
Step d. utilizes the cumulative distribution character of generalized extreme value distribution Function Fitting diurnal variation extreme value:
1. calculating diurnal variation maximum DMAXAccumulated probability value, in stochastic finite element theory, accumulated probability value is fixed
Justice is the probability of happening that statistical variable is no more than a certain numerical value, and is fitted using generalized extreme value distribution function to it:
In formula, G (DMAX) indicate generalized extreme value distribution function, b, d and r respectively indicate G (DMAX) scale parameter, position
Parameter and form parameter will calculate the diurnal variation maximum acquired and diurnal variation maximum DMAXAccumulated probability value substitute into respectively
The best value of b, d and r are determined in formula (2) and using least square method；
2. calculating the accumulated probability value of diurnal variation minimum, and it is fitted using generalized extreme value distribution function:
In formula, F (DMIN) indicate generalized extreme value distribution function, g, h and λ respectively indicate F (DMIN) scale parameter, position
Parameter and form parameter will calculate the diurnal variation minimum acquired and diurnal variation minimum DMINAccumulated probability value substitute into respectively
The best value of g, h and λ are determined in formula (3) and using least square method.
Step e. carries out static behavior assessment to the railroad bridge key member stress most unfavorable combination:
Equation (4) and equation (5) are solved using Newton iteration method, determines static strain standard value when outcross probability is PWithOutcross probability refers to over a period to come, may meet with the probability for being greater than or equal to given parameters value.
WithIt indicatesWithIn maximum absolute value person, willWith design permissible valueDescribed in multilevel iudge
The static behavior of railroad bridge, ifThen at the static behavior of the railroad bridge key member stress most unfavorable combination
In safe condition；Conversely, then the static behavior of the railroad bridge key member stress most unfavorable combination is in non-secure states.
Newton iteration method is a kind of important method for solving equattion root, great advantage be equation or it is single flat nearby
Side's convergence；Consider according to 100 years design service phases of railroad bridge, the value of outcross probability P is 0.01.
Below by taking the chord member axial strain of Foundations of Dashengguan Changjiang River Bridge as an example, illustrate specific implementation process of the invention.
Fiber Bragg grating strain sensor cloth set shown in the elevation and Fig. 3 of Foundations of Dashengguan Changjiang River Bridge shown in Figure 2
Schematic diagram is set, fiber Bragg grating strain sensor 2 is mounted on to the axial position of Foundations of Dashengguan Changjiang River Bridge span centre side purlin top boom 1,
And be coupled in data collection system, using the temperature sensor of fiber grating as temperature-compensating, to 1 axial strain of top boom into
The acquisition of row data.Sample frequency f is 1Hz, and sampling time length L is 232 days, and collected i-th of strain value uses S (i) table
Show, i=1 ..., 33408.
It is shown in Figure 4, it is answered for all strain value S (i) of Foundations of Dashengguan Changjiang River Bridge according to what acquisition sequencing was constituted
Become sequence.
It is shown in Figure 5, strain sequence is subjected to the WAVELET PACKET DECOMPOSITION on the 5th scale, obtains 32 wavelet packet coefficients,
It extracts first wavelet packet coefficient and is reconstructed, obtain the static strain ingredient in strain sequence.
Static strain ingredient is divided as unit of day, j-th static strain value of the static strain ingredient in the m days uses
Sm(j) it indicates, wherein m=1,2 ..., 232, j=1,2 ..., 144.It is first according to acquisition to all static strain values in daily
First-order difference processing is sequentially done afterwards, obtains difference sequence of the static strain value in daily, k-th in the m days of difference sequence
Value uses Dm(k) it indicates, k=1,2 ..., 143.
The diurnal variation maximum D in 232 days is determined according to the 3rd and step 4 in above-mentioned steps cMAXWith diurnal variation minimum
DMIN, respectively referring to shown in Fig. 6 and Fig. 7.According to relevant calculation step in step d, using least square method determine b, d and r and
G, the optimal parameter value of h and λ, wherein b=41.3228, d=33.0127, r=-0.4071；G=54.4519, h=-
24.6663, λ=- 0.612298, substitute into each numerical value be calculated diurnal variation maximum generalized extreme value distribution fitting result and
The fitting result of diurnal variation minimum generalized extreme value distribution, respectively referring to shown in Fig. 8 and Fig. 9.
Using Newton iteration method in above-mentioned steps e equation (4) and equation (5) solve, determine have outcross probability be P
Static strain standard valueWithWherein: outcross probability P is 0.01.
Calculated result are as follows:Then it choosesWithIn maximum absolutely
To value, i.e.,It willWith design permissible valueThe static load of multilevel iudge railroad bridge
Performance: known win completely closes railroad bridge steel selection Q420, thenKnown toThen the static behavior of key member stress most unfavorable combination is in a safe condition.
The present invention is not limited to the above-described embodiments, for those skilled in the art, is not departing from
Under the premise of the principle of the invention, several improvements and modifications can also be made, these improvements and modifications are also considered as protection of the invention
Within the scope of.The content being not described in detail in this specification belongs to the prior art well known to professional and technical personnel in the field.
Claims (8)
1. a kind of railway steel bridge static behavior appraisal procedure based on static strain prediction of extremum, it is characterised in that: this method includes
Following steps,
The strain data of step a. acquisition railroad bridge key member stress most unfavorable combination within L days sampling time；
Step b. extracts the static strain ingredient in the strain data using analysis method of wavelet packet；
Step c. divides the static strain ingredient as unit of day, first according to acquisition to all static strain values in daily
First-order difference processing is sequentially done afterwards, obtains static strain value in daily difference sequence, the difference sequence in every day is appointed
Meaning is divided into more parts, and respectively sums to every portion difference sequence, then to the summed result of every portion difference sequence
It takes absolute value and is added and acquire summation, within L days sampling time, the identical situation of the difference sequence segmentation number in every day
The size for the summation that lower comparison acquires under different cut-points, determines the cut-point when summation maximum, relatively described in
Size under cut-point of the difference sequence in the summation maximum, so that it is determined that diurnal variation in the static strain ingredient in daily
Maximum and diurnal variation minimum, and then determine and diurnal variation pole in the static strain ingredient in L days sampling time
Big value DMAXWith diurnal variation minimum DMIN；
Step d. utilizes generalized extreme value distribution function G (DMAX) and F (DMIN) it is fitted described in the L days sampling time quiet answer respectively
Become a point middle diurnal variation maximum DMAXWith diurnal variation minimum DMINCumulative distribution character:
Step e. carries out static behavior assessment to the railroad bridge key member stress most unfavorable combination: where
In formula, P is outcross probability, using Newton iteration method and in conjunction with the generalized extreme value distribution function G (DMAX) and F (DMIN),
Equation (4) and equation (5) are solved, determines static strain standard value when outcross probability is PWith
WithIt indicatesWithIn maximum absolute value person, willWith design permissible valueRailway bridge described in multilevel iudge
The static behavior of beam, ifThen the static behavior of the railroad bridge key member stress most unfavorable combination is in safety
State；Conversely, then the static behavior of the railroad bridge key member stress most unfavorable combination is in non-secure states.
2. the railway steel bridge static behavior appraisal procedure based on static strain prediction of extremum, feature exist as described in claim 1
In: the strain data is acquired using fiber Bragg grating strain sensor in the step a, and is made with fiber grating temperature sensor
For temperature-compensating.
3. the railway steel bridge static behavior appraisal procedure based on static strain prediction of extremum, feature exist as described in claim 1
In: sampling time L is greater than 200 days in the step a, and sample frequency is greater than 1/600Hz and is less than 1Hz.
4. the railway steel bridge static behavior appraisal procedure based on static strain prediction of extremum, feature exist as described in claim 1
In: the analysis method of wavelet packet, which is specifically included, answers collected all strain datas according to acquisition sequencing composition
Become sequence, after by it is described strain sequence carry out c-th of scale on WAVELET PACKET DECOMPOSITION, obtain 2cA wavelet packet coefficient, extracts
First wavelet packet coefficient is simultaneously reconstructed, and obtains the static strain ingredient in the strain sequence, wherein c is measurement point number system
Number.
5. the railway steel bridge static behavior appraisal procedure based on static strain prediction of extremum, feature exist as claimed in claim 4
In: the measurement point number coefficient c is equal to 5.
6. the railway steel bridge static behavior appraisal procedure based on static strain prediction of extremum, feature exist as described in claim 1
In: the difference sequence of every day in the step c is arbitrarily divided into three parts.
7. the railway steel bridge static behavior appraisal procedure based on static strain prediction of extremum, feature exist as described in claim 1
In: diurnal variation maximum D in the static strain ingredient in the L days sampling timeMAXThe fitting of cumulative distribution character include,
Calculate the diurnal variation maximum DMAXAccumulated probability value, and utilize the generalized extreme value distribution function G (DMAX) it is fitted,
Wherein:
In formula, b, d and r respectively indicate G (DMAX) scale parameter, location parameter and form parameter, the diurnal variation that acquires will be calculated
Maximum DMAXWith diurnal variation maximum DMAXAccumulated probability value substituted into formula (2) respectively, using least square method determine b, d and
The best value of r；
Diurnal variation minimum D in the static strain ingredient in the L days sampling timeMINThe fitting of cumulative distribution character include,
Calculate the diurnal variation minimum DMINAccumulated probability value, and utilize the generalized extreme value distribution function F (DMIN) it is fitted,
Wherein
In formula, g, h and λ respectively indicate F (DMIN) scale parameter, location parameter and form parameter, the diurnal variation that acquires will be calculated
Minimum DMINWith diurnal variation minimum DMINAccumulated probability value substituted into formula (3) respectively, using least square method determine g, h and
The best value of λ.
8. the railway steel bridge static behavior appraisal procedure based on static strain prediction of extremum, feature exist as described in claim 1
In: the value of the outcross probability P is 0.01.
Priority Applications (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610229916.3A CN106650194B (en) | 2016-04-14 | 2016-04-14 | Railway steel bridge static behavior appraisal procedure based on static strain prediction of extremum |
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN201610229916.3A CN106650194B (en) | 2016-04-14 | 2016-04-14 | Railway steel bridge static behavior appraisal procedure based on static strain prediction of extremum |
Publications (2)
| Publication Number | Publication Date |
|---|---|
| CN106650194A CN106650194A (en) | 2017-05-10 |
| CN106650194B true CN106650194B (en) | 2019-03-15 |
Family
ID=58848716
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| CN201610229916.3A Active CN106650194B (en) | 2016-04-14 | 2016-04-14 | Railway steel bridge static behavior appraisal procedure based on static strain prediction of extremum |
Country Status (1)
| Country | Link |
|---|---|
| CN (1) | CN106650194B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN107066772B (en) * | 2017-06-08 | 2021-04-09 | 贾宏宇 | Probability evaluation method for collision gap width of bridge system under non-stationary earthquake action |
| CN113554183B (en) * | 2021-08-03 | 2022-05-13 | 同济大学 | Extreme value prediction method based on unsupervised machine learning algorithm |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2081132A2 (en) * | 2008-01-18 | 2009-07-22 | Rolls-Royce plc | Novelty detection |
| CN104537139A (en) * | 2014-11-10 | 2015-04-22 | 浙江大学 | Method for determining load effect of wind-wave coupling design of long-span bridge structure |
Family Cites Families (1)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| US6799124B2 (en) * | 2000-12-15 | 2004-09-28 | Westinghouse Electric Company Llc | Method of optimizing risk informed inspections of heat exchangers |
-
2016
- 2016-04-14 CN CN201610229916.3A patent/CN106650194B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| EP2081132A2 (en) * | 2008-01-18 | 2009-07-22 | Rolls-Royce plc | Novelty detection |
| CN104537139A (en) * | 2014-11-10 | 2015-04-22 | 浙江大学 | Method for determining load effect of wind-wave coupling design of long-span bridge structure |
Non-Patent Citations (2)
| Title |
|---|
| 《Parameter estimation of the generalized extreme value distribution for structural health monitoring》;Hyun Woo Park;《Probabilistic Engineering Mechanics》;20060104;全文 |
| 《大跨度桥梁健康监测与状态评估研究展望》;吴来义 等;《山西建筑》;20150630;第41卷(第17期);全文 |
Also Published As
| Publication number | Publication date |
|---|---|
| CN106650194A (en) | 2017-05-10 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| Penalba et al. | Precipitation response to El Niño/La Niña events in Southern South America–emphasis in regional drought occurrences | |
| Rutgersson et al. | Observed changes and variability of atmospheric parameters in the Baltic Sea region during the last 200 years | |
| CN107292545B (en) | Bridge anti-seismic analysis method based on seismic risk assessment | |
| Briffa et al. | Tree-ring variables as proxy-climate indicators: problems with low-frequency signals | |
| Chen et al. | Abrupt spatiotemporal land and water changes and their potential drivers in Poyang Lake, 2000–2012 | |
| Ramos et al. | A ranking of high‐resolution daily precipitation extreme events for the Iberian Peninsula | |
| De Luca et al. | Coastal dune systems and disturbance factors: monitoring and analysis in central Italy | |
| Gentile et al. | Continuous monitoring of the Milan Cathedral: dynamic characteristics and vibration-based SHM | |
| CN106650194B (en) | Railway steel bridge static behavior appraisal procedure based on static strain prediction of extremum | |
| Slimani et al. | Ecological response of Cedrus atlantica to climate variability in the Massif of Guetiane (Algeria) | |
| CN104915534A (en) | Deformation analysis and decision-making method of electric power tower based on sequence learning | |
| Kopabayeva et al. | Tree-ring chronologies of Pinus sylvestris from Burabai Region (Kazakhstan) and their response to climate change | |
| Esper et al. | Interpretation of tree-ring chronologies (interpretation von jahrringchronologien) | |
| Waterfall et al. | Targetless precision monitoring of road and rail bridges using video cameras | |
| Lana et al. | Time trends, irregularity and multifractal structure on the monthly rainfall regime at Barcelona, NE Spain, years 1786–2019 | |
| Seeboonruang | An empirical decomposition of deep groundwater time series and possible link to climate variability | |
| Suchrow et al. | Patterns of plant species richness along environmental gradients in German North Sea salt marshes | |
| Wang et al. | Decadal-scale precipitation variations in arid and semiarid zones of northern China during the last 500 years | |
| Annella et al. | On the role of local and large-scale atmospheric variability in snow cover duration: a case study of Montevergine Observatory (Southern Italy) | |
| Woodhouse et al. | An 1800-yr record of decadal-scale hydroclimatic variability in the upper Arkansas River basin from bristlecone pine | |
| Larsen et al. | Climatic effects on the synchrony and stability of temperate headwater invertebrates over four decades | |
| Alessio et al. | Temperature and precipitation in Northeast China during the last 150 years: relationship to large-scale climatic variability | |
| Buckley | Environmental sensitivity mapping—What, why and how | |
| Moyo et al. | Application of Box-Jenkins models for assessing the effect of unusual events recorded by structural health monitoring systems | |
| CN109959493A (en) | A kind of cable-stayed bridge cable damage real-time quantitative appraisal procedure based on natural bow modeling |
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 | ||
| GR01 | Patent grant | ||
| GR01 | Patent grant | ||
| CP03 | Change of name, title or address | ||
| CP03 | Change of name, title or address |
Address after: No.8, boxue Road, Wuhan Economic and Technological Development Zone, Hubei Province, 430056 Patentee after: CHINA RAILWAY MAJOR BRIDGE RECONNAISSANCE & DESIGN INSTITUTE Co.,Ltd. Patentee after: China Railway Bridge and Tunnel Technology Co.,Ltd. Address before: 430056 No.8, boxue Road, Wuhan Economic and Technological Development Zone (Zhuankou), Wuhan City, Hubei Province Patentee before: CHINA RAILWAY MAJOR BRIDGE RECONNAISSANCE & DESIGN INSTITUTE Co.,Ltd. Patentee before: CHINA RAILWAY MAJOR BRIDGE (NANJING) BRIDGE AND RUNNEL INSPECT & RETROFIT Co.,Ltd. |