AU2021101948A4 - A concrete durability detection method based on cloud model and D-S evidence theory - Google Patents
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Abstract
The invention discloses a concrete durability detection method based on cloud model and
D-S evidence theory, which comprises the following steps: Si. Constructing a concrete
durability detection index system, wherein the concrete durability detection index system
comprises durability detection indexes, and durability evaluation standards and index weights
corresponding to each durability detection index; S2. Based on the concrete durability detection
index system, clouding the durability detection index with the cloud model, calculating the
membership degree of each durability detection index corresponding to different durability
grades, and normalizing the membership degree to generate evidence; S3. Integrating the
evidence corresponding to each durability detection index based on the improved evidence
theory DS to obtain the durability detection result of the concrete to be detected. The method
can accurately detect the durability of concrete.
1/4
FIGURES
SI- Constructing a concrete durability detection index system, wherein theconcrete
durability detection index system comprises durability detection indexes, and
durability evaluation standards andindexweights correspondingto each durability
detection index
S2. Based on the concrete durability detection index system, clouding the
durability detection index by the cloud model, calculating the membership degree
ofeach durability detection index corresponding to different durabilitygrades. and
normalizing the membership degree to generate evidence
S3. Based on the improved evidencetheory DS. integrating the evidence
corresponding to each durability testindex to obtain the durability testresult of the
concrete to be tested
Figure 1
Description
1/4
SI- Constructing a concrete durability detection index system, wherein theconcrete durability detection index system comprises durability detection indexes, and durability evaluation standards andindexweights correspondingto each durability detection index
S2. Based on the concrete durability detection index system, clouding the durability detection index by the cloud model, calculating the membership degree ofeach durability detection index corresponding to different durabilitygrades. and normalizing the membership degree to generate evidence
S3. Based on the improved evidencetheory DS. integrating the evidence corresponding to each durability testindex to obtain the durability testresult of the concrete to be tested
Figure 1
A concrete durability detection method based on cloud model and D-S evidence
theory
The invention relates to the technical field of concrete durability detection, in
particular to a concrete durability detection method based on cloud model and D-S
evidence theory.
Concrete is the most widely used and main building material in civil engineering
construction, and its durability has a direct impact on the safe use of engineering structures.
In practical engineering, the structural deterioration caused by the durability problem of
concrete has been common, and the economic loss caused by it is also very astonishing.
Therefore, how to improve the durability of concrete has become an urgent need of
engineering construction and concrete development.
At present, many scholars at home and abroad have carried out a series of related
research on the durability of concrete, qualitative analysis and quantitative design of
concrete durability. This paper summarizes the research results of concrete durability under
the coupling effect of environmental factors, studies the durability of high-performance
concrete based on orthogonal test method, and establishes the life prediction model.
According to the current research, most of them are mainly based on the test, theoretical
analysis and other methods. For the concrete durability detection, a set of practical,
reasonable and complete detection method has not been established. How to mine the
collected information effectively, so as to detect the durability of concrete and realize scientific risk identification, early warning and prevention and control has become an urgent problem to be solved.
The purpose of the invention is to provide a concrete durability detection method
based on cloud model and D-S evidence theory, so as to solve the technical problems
existing in the prior art, which can accurately detect the durability of concrete.
In order to achieve the above purpose, the invention provides the following scheme:
the invention provides a concrete durability detection method based on cloud model and
D-S evidence theory, which comprises the following steps:
Si. Constructing a concrete durability detection index system, wherein the concrete
durability detection index system comprises durability detection indexes, and durability
evaluation standards and index weights corresponding to each durability detection index;
S2. Based on the concrete durability detection index system, clouding the durability
detection index by the cloud model, calculating the membership degree of each durability
detection index corresponding to different durability grades, and normalizing the
membership degree to generate evidence;
S3. Based on the improved evidence theory DS, integrating the evidence
corresponding to each durability test index to obtain the durability test result of the concrete
to be tested.
Preferably, in SI, the concrete durability monitoring index system includes four
durability detection indexes: relative dynamic elastic modulus, chloride ion permeability
coefficient, mass loss rate and carbonation depth.
Preferably, in Si, the index weight of each durability test index is calculated by the variable weight theory.
Further, in step S2, the membership degree of each durability detection index
corresponding to different durability levels is calculated by the eigenvalue of the cloud
model.
Furthermore, the eigenvalues of the cloud model include expectation, entropy and
hyperentropy.
Additionally, the membership degree is normalized as follows:
In the formula, V is the membership degree after normalization, and(,
P (11), (III), (I ) and Aretare the membership degrees of the ith durability test
index corresponding to the jth durability grade when the durability grades are I,II, III, IV
and V, respectively.
In S3, integrating the evidence corresponding to each durability test index includes
the following contents:
To begin with, based on the improved evidence theory DS, each durability test index
is integrated separately.
Then, the integration results of relative dynamic elastic modulus, chloride ion
permeability coefficient, mass loss rate and carbonization depth are integrated again by
weighting treatment.
Beneficial effects
1. The concrete durability detection method based on cloud model and D-S evidence
theory provided by the invention establishes a relatively complete and scientific concrete
durability detection index system and durability evaluation standard, and provides an applicable basis for durability detection of similar projects.
2. The concrete durability detection method based on cloud model and D-S evidence
theory provided in the present invention considers the fuzziness and randomness of risk
factor data in the system, and makes full use of multi-source uncertainty information in
concrete durability detection and construction risk pre-assessment by utilizing the
advantages of cloud model expressing uncertainty and qualitative and quantitative
conversion, as well as the strong integration ability of evidence theory.
3. According to the concrete durability detection method based on the cloud model
and D-S evidence theory provided in the present invention, the proposed improved
evidence theory can deal with conflicts in the evidence source stage, purify information
sources, improve the quality and relevance of evidence combination, realize the effective
integration of multi-source conflict evidence, and obtain accurate durability detection
results.
In order to explain the embodiments of the present invention or the technical scheme
in the prior art more clearly, the drawings needed in the embodiments will be briefly
introduced below. Obviously, the drawings in the following description are only some
embodiments of the present invention, and for ordinary technicians in the field, other
drawings can be obtained according to these drawings without paying creative labor.
Fig. 1 is the flow chart of the concrete durability detection method based on cloud
model and D-S evidence theory;
Fig. 2 shows the division interval given by experts in the first round of relative
dynamic elastic modulus detection index in the embodiment of the present invention;
Fig. 3 shows the division interval given by the second round of experts of the
relative dynamic elastic modulus detection index in the embodiment of the present
invention;
Fig. 4 is the division interval given by the third round of experts of the relative
dynamic elastic modulus detection index in the embodiment of the present invention;
Fig. 5 is the characteristic diagram of a cloud model of concrete relative dynamic
elastic modulus index in an embodiment of the present invention.
The technical scheme in the embodiments of the present invention will be described
clearly and completely with reference to the drawings in the embodiments of the present
invention. Obviously, the described embodiments are only part of the embodiments of the
present invention, not all of them. Based on the embodiments of the present invention, all
other embodiments obtained by ordinary technicians in the field without creative labor
belong to the scope of protection of the present invention.
In order to make the above objects, features and advantages of the present invention
more obvious and easy to understand, the present invention will be further explained in
detail with reference to the drawings and specific embodiments.
As shown in Fig. 1, this embodiment provides a concrete durability detection method
based on cloud model and D-S evidence theory, which specifically includes the following
steps:
Si. Constructing a concrete durability detection index system. The concrete durability
detection index system includes durability detection index, durability evaluation standard
and index weight corresponding to each durability detection index;
In this embodiment, the concrete durability monitoring index system includes the
following components: relative dynamic elastic modulus, chloride ion permeability
coefficient, mass loss rate and carbonation depth.
The acquisition methods of durability evaluation standards corresponding to each
durability test index include the following content:
The historical experimental data law is adopted as the prior knowledge to analyze the
influence of durability test index on concrete durability, and the grade interval of durability
test index attribute is preliminarily judged. On the basis of prior knowledge, a number of
experts are used to give the grade interval division results of each durability test index
attribute. In order to eliminate the cognitive differences among experts, the cloud model is
used to realize the communication and integration of knowledge decision-making of expert
group. Suppose n experts participate in the decision-making, and the i-th expert thinks that
[i (L),c (R)]i = 1,2.., n is the interval of relative dynamic elastic modulus belonging
to a certain level. The judgment of n experts is comprehensively estimated by using the
formula of reverse cloud algorithm, and three cloud parameters of a specific level interval
are obtained to generate the query cloud map. The three cloud parameters are expectation,
entropy and hyper entropy. In order to promote the convergence of the decision-making
results, n experts are arranged to conduct multiple rounds of inquiry. Each expert needs to
give the next round of feedback on the basis of the previous round of query cloud chart, so
as to gradually eliminate the differences of experts' opinions, and obtain a more reasonable
level interval division result.
ci = (c(L) + cg(R))/2 Ex = Zi/n
En = IrT Eci-ExI 2 n
He = -Zn(ci - Ex) 2 - En 2
In the formula, ExEn, andHe are the expectation, entropy and hyper entropy of
cloud model respectively.
In this embodiment, taking the interval boundary division of the relative dynamic
elastic modulus index of concrete in the III state as an example, ten experts are selected to
conduct three rounds of expert inquiry, and the interval division results given by the experts
in the first round are integrated as shown in Table 1, and the obtained inquiry cloud map is
shown in Figure 2. It can be seen from Figure 2 that the contour of the first round of expert
inquiry is relatively divergent, and the aggregation effect is poor. At this time, the
consensus among experts is poor. In the second round of inquiry, experts adjust the division
interval according to the mainstream opinions, and the obtained cloud image gradually
converges and becomes smaller obviously. The obtained query cloud image is shown in
Figure 3. After the third round of inquiry, the query cloud image obtained is as follows: as
shown in Figure 4, the concept expressed by the cloud is further clarified, and the cloud
droplet cohesion is higher. According to the final results of three rounds of inquiry, the
upper and lower limits of the interval are determined by the "3EN" principle of normal
cloud model, that is, [Ex - 3En, Ex + 3En] [75,80].
Table 1
Expert Expert Expert Expert Expert Expert Expert Expert Expert Expert 1 2 3 4 5 6 7 8 9 10 Min 63 65 71 72 64 68 71 73 72 71 Max 82 83 82 85 84 81 80 85 83 82
Considering the discreteness of concrete performance, the fluctuation of durability
test indicators will be caused by the change of mix proportion and raw materials, and the
importance of concrete in different parts is different. In order to reduce the subjectivity in
determining the weight of durability test indicators and reflect the active participation of
test objects in comprehensive evaluation, the weight of indicators is determined by
adopting variable weight theory, and the variable weight W ' of each durability test
indicator is calculated as follows.
W = w 0 x S / LwS, (i=1,2,3,4) Based on the above-mentioned mixed prior knowledge and expert group decision
making method, the attribute interval division of relative dynamic modulus of elasticity is
divided, and the results of grade interval division and index weight are obtained as shown
in Table 2.
Table 2
Concrete durability test Index Gradation index weight I II III IV V Relative dynamic modulus 0.35 [85,100] [80,85] [75,80] [70,75] [60,70] of elasticity/(%) Permeability coefficient of 0.35 [0,1.5] [1.5,3] [3,4.5] [4.5,6] [6,7] chloride ion/(10-"m 2/s) Quality loss rate/(%) 0.2 [0,2] [2,3.5] [3.5,5] [5,6.5] [6.5,8] Carbonation depth/(mm) 0.1 [0,6] [6,12] [12,18] [18,24] [24,30]
S2. Based on the concrete durability detection index system, the cloud model is used
to cloud the durability test indexes, and the membership degree of each durability detection
index corresponding to different durability levels is calculated, and the membership degree
is normalized to generate evidence.
The normal cloud is used to transform the five grade intervals of each durability test index into a cloud model, and the information transmitted by the cloud is expressed by the digital characteristics of the cloud model. For the interval with upper and lower bounds
such as [Cmin, Cmx], the eigenvalue ( Ex, En, He) of cloud model is calculated by the
following formula:
Ex = (Cmin + Cmax)/2 En = (Cmax - Cmin)/6 He = S Cmin and Cmax are the minimum and maximum values of the risk level interval
corresponding to the cloud model, and S is a constant, which is adjusted according to the fuzzy threshold of the variable itself and the actual situation. Cloud models are respectively established based on each concrete durability grade state Tj(j=l,2,3,4,5) corresponding to the concrete durability test index. According to the
characteristic value of the ith durability test index, the membership degree Mg(k)of the
characteristic value corresponding to the jth durability grade is obtained by the following formula:
-+i-Exgi(k))2_
pY (k)=- e 2(E' g(k))2 ,k = IIIIIIIVV E'i(k)=En(k)+He(k)xrand() Intheformula, xi represents the eigenvalue of the ith durability test index, and
E,(k),E,,(k),He,(k) represents the expectation, entropy and hyper entropy of the cloud
model corresponding to the jth durability grade of the ith durability test index. E'g(k) is
the expected value of Eg.(k), He(k) is a normal random number generated by standard
deviation, and rand( ) is a random value between 0 and 1. H,(k) represents the
uncertainty measure of the standard characteristic value of the ith durability test for the jth durability grade. Since E'(k) is randomly generated, the value of py(k) will change with the change of E, 1 (k). Multiple simulations will generate K cloud droplets for the interval cloud model. The average of K membership degrees obtained according to the following formula is used as the membership degree of Bi relative to Tj to eliminate the greater uncertainty. K pq(k)=> pU(k)/K t=1,12,...,IK t=1 Considering the measurement error, external interference and other reasons, the uncertainty of concrete durability test results will be caused. Therefore, in this embodiment, the membership degree is normalized to obtain the normalized membership degree V, that is, the evidence, as shown in the following formula:
% =1- max(pU(I),pU(II),pg(I),pI (IV),pg(V))
S3. Based on the improved evidence theory DS, the evidence corresponding to each
durability test index is integrated, and the durability test result of the concrete to be tested
is obtained.
The improved evidence theory DS includes the following content:
(1) Conflict test:
The similarity coefficient d12 of the two evidences El and E2 under the recognition 0 framework is calculated by the following formula:
d12 = EmAnjm1(A)m 2 (Bj) (m (B ))
In this formula, and are basic trust distribution functions corresponding to
and , and a and bare focal elements corresponding to El and E2, respectively.
The similarity coefficient between evidences is adopted to calculate the support
degree of evidence and further obtain the credibility of evidence, as shown in the following
formula:
Sup(m) = 2:nId, i = 1,2..., Mj =1,2,..., N
Crd(mg) = , i = 1,2,-. M
In this formula, Sup(m ) represents the support degree of the ith evidence,
Crd(m1 represents the credibility of the ith evidence, M represents the number of )
durability test indexes, and N represents the number of durability grades.
The conflict evidence detection factor is calculated by the evidence credibility, as
shown in the following formula:
pl-crd(m) As shown in the formula:
a,=minCrd(mi).P=maxCrd(mi) 1sisM ' 1sisM
It can be seen that the greater is, the greater the deviation between the credibility
of evidence and other evidence, which means the greater the conflict in decision
making.
The evidence classification can be realized by setting the evidence conflict detection
factor and threshold T, as shown in the following formula. When Ai< T, , it is
considered as credible evidence, and the evidence remains unchanged; When , it
is considered as acceptable conflict evidence, and this part of evidence is partially preprocessed, corrected and reused, as shown in the following formula:
m*(A ) = ri(AI)A; T , m*(A 1 ) M(j In the formula, mi is credible evidence, m is amended evidence, and the credibility is used to amend some conflict evidence to obtain amended evidence
, as shown in the following formula:
M Crd(mj) X mL(A0)Ai * 0 fCrd(m,) x m(0) + 1 - Crd (mL)A, = 0
(2) Evidence integration and decision-making:
After preprocessing the conflict evidence, the following formula is used to integrate multiple pieces of evidence.
m(A)~ ~2(). ~l ~ ~ MM-EA A n1G n= nA ... m1A? (A) - M2 (A2)-- m.. (AN) m(A) = m1@m - 2 P,--- @mM= 1 - K
K= m(A1) -m 2 (A 2 ) -.. mM(AN) AifnAl n---nAN-=
The final reliability distribution results, namely durability test results, are obtained by reasoning with the integrated evidence. According to the maximum membership criterion, the maximum value of the risk level reliability distribution is determined as the decision making basis of the final monitoring results, and K represents the number of membership degrees. After the concrete specimens are integrated based on a certain durability test index with evidence theory, it is necessary to integrate the integration results of relative dynamic elastic modulus, chloride ion permeability coefficient, mass loss rate and carbonation depth again to obtain the durability grade status of the concrete specimens. At this time, the four evidences need to be weighted as follows:
Wm(A)= W(A).m(A) m(O)+ W(A)-m(A) AcO m(A) is the initial basic probability assignment of evidence A, W(A) is the weight coefficient assignment of evidence a, m(O) is the initial basic probability assignment of complete set 0, and Wm(A) is the weighted basic probability assignment of evidence A.
In order to further verify the effectiveness of the concrete durability detection method based on cloud model and D-S evidence theory, this embodiment takes 25 groups of concrete specimens as examples to verify the method of the invention.
(1) Data collection and evidence expression
According to the concrete durability test index system, 25 groups of concrete specimens with the size of 100 mm x 100 mm x 400 mm prism are randomly selected. The durability test indexes are measured in the same cycle period, as shown in Table 3. According to the value range given in Table 2, the three numerical characteristics of each risk state of durability test index are calculated, as shown in Table 4. Fig. 5 shows the cloud model distribution curve of the relative dynamic elastic modulus index of concrete. Combined with the data in Table 3 and table 4, the membership degree of all indexes to the five durability grades is calculated, and the basic probability assignment of 25 groups of concrete test specimens for the four durability detection indexes is constructed. The basic reliability distribution (BPAS) of the obtained relative dynamic elastic modulus index is given in Table 5.
Table 3
Relatve dyamic Permeability Dete on index Relativedynami coefficient f Quality loss Carbonation Specimen ber modulus chloride ion/(10- rate/(%) depth/(mm) elasticity/(%o) 12 2 mn /S) 1 92.3 1.3 1.6 7.2 2 87.5 1.5 2.2 5.3 3 81.3 2.5 3.7 9.4
24 71.2 5.3 6.7 23.4 25 66.7 5.9 7.7 28.4 Table 4
Durability Charact Durability level test index e I II III IV V Relative Ex 92.5 82.5 77.5 72.5 65 dynamic En 2.500 0.833 0.833 0.833 1.667 modulusof He 0.002 0.002 0.002 0.002 0.002 elasticity _____ _______ _______ ________ ____ ____
Permeability Ex 0.75 2.25 3.75 5.25 6.5 coefficient En 0.25 0.25 0.25 0.25 0.167 of chloride He 0.002 0.002 0.002 0.002 0.002
Ex 1 2.75 4.25 5.75 7.25 Qualityloss En 0.333 0.25 0.25 0.25 0.25 rate He 0.002 0.002 0.002 0.002 0.002 . Ex 3 9 15 21 27 Carbonation En 1 1 1 1 1 depth He 0.002 0.002 0.002 0.002 0.002 Table 5 Number I II III IV V 0 1 0.9968 0.0000 0.0000 0.0000 0.0000 0.0032 2 0.1352 0.0000 0.0000 0.0000 0.0000 0.8648 3 0.0000 0.3534 0.0000 0.0000 0.0000 0.6466
24 0.0000 0.0000 0.0000 0.2956 0.0000 0.7044 25 0.0000 0.0000 0.0000 0.0000 0.5943 0.4057 (2) Information integration and decision-making
Carrying out conflict detection on 25 groups of index evidences of durability detection index, and calculating the evidence support degree and obtain the credibility according to the formula, as shown in Table 6.
Table 6
Evidence Support Weight Evidence Support Weight 1 8.726 0.064 14 6.539 0.048 2 3.234 0.024 15 7.259 0.053 3 4.623 0.034 16 3.816 0.028 4 4.129 0.030 17 2.368 0.017 5 2.962 0.022 18 4.596 0.034 6 8.512 0.062 19 6.025 0.044 7 4.563 0.033 20 5.374 0.039 8 2.842 0.021 21 7.523 0.055 9 7.839 0.057 22 4.583 0.033 10 6.753 0.049 23 5.549 0.040 11 6.239 0.046 24 4.256 0.031 12 8.146 0.059 25 6.016 0.044 13 4.528 0.033 The conflict evidence is modified, and the revised evidence is integrated according to the combination rules. According to the chloride ion permeability coefficient, mass loss rate and carbonation depth of concrete durability test indicators, 25 groups of concrete specimens corresponding evidence were obtained according to the above index expression and evidence generation method, and the integration is conducted according to the combination rules, and the integration results of each durability test index of concrete were obtained, as shown in Table 7. Table 7 Concrete Durability rating durability test I II III IV V E index Relative dynamic modulus of 0.7746 0.1856 0.0395 0.0000 0.0000 0.0003 elasticity Permeability coefficient of 0.6837 0.2981 0.0180 0.0000 0.0000 0.0002 chloride ion Quality loss rate 0.7254 0.2719 0.0026 0.0000 0.0000 0.0001 Carbonation 0.8255 0.1528 0.0215 0.0000 0.0000 0.0002 depth It can be seen from Table 7 that the concrete specimens perform well in the evaluation of various durability test indexes, among which the reliability distribution of the integration results of relative dynamic elastic modulus index in level I is the largest, which is 0.7746; the reliability distribution of chloride ion permeability coefficient index integration results in level I is the largest, which is 0.6837; the reliability distribution of quality loss rate index integration results in level I is the largest, which is 0.7254; and the reliability distribution of integration results of quality loss rate index in level I is the largest, which is 0.7254; the reliability distribution of integration results of carbonation depth index in level I is the largest, which is 0.8255.
The relative dynamic elastic modulus, chloride ion permeability coefficient, mass loss rate and carbonation depth are fused again to obtain the durability grade status of the concrete specimens. The integration results are shown in Table 8.
Table 8 Concrete durability Durability rating test index I II III IV V E Relative dynamic modulus of 0.7739 0.1854 0.0395 0.0000 0.0000 0.0012 elasticity Permeability coefficient of 0.6833 0.2979 0.0180 0.0000 0.0000 0.0008 chloride ion Quality loss rate 0.7250 0.2718 0.0026 0.0000 0.0000 0.0006 Carbonation depth 0.8238 0.1525 0.0215 0.0000 0.0000 0.0022 e ablionrts 0.9927 0.0073 0.0000 0.0000 0.0000 0.00
The final test results of concrete durability are determined by the durability grade with the highest reliability distribution under the principle of maximum membership degree. According to table 8, the reliability distribution of the final integration results of concrete specimens under freeze-thaw cycle test at grade I is the largest, which is 0.9927. Therefore, it is considered that the durability grade of concrete in the service year of the project is grade I, and the results are consistent with the actual test results, The accuracy of the selected model of the invention to the concrete durability test results is explained.
The above embodiments only describe the preferred mode of the invention, but do not limit the scope of the invention. On the premise of not departing from the design spirit of the invention, various modifications and improvements made by ordinary technicians in the field to the technical scheme of the invention shall fall within the protection scope determined by the claims of the invention.
Claims (7)
1. The concrete durability detection method based on cloud model and D-S evidence
theory is characterized by comprising the following steps:
Si. Constructing a concrete durability detection index system, wherein the concrete
durability detection index system comprises durability detection indexes, and durability
evaluation standards and index weights corresponding to each durability detection index;
S2. Based on the concrete durability detection index system, clouding the durability
detection index by the cloud model, calculating the membership degree of each durability
detection index corresponding to different durability grades, and normalizing the
membership degree to generate evidence;
S3. Based on the improved evidence theory DS, integrating the evidence
corresponding to each durability test index to obtain the durability test result of the concrete
to be tested.
2. The concrete durability detection method based on cloud model and D-S evidence
theory according to claim 1 is characterized in the following content: in S1, the concrete
durability monitoring index system includes four durability detection indexes: relative
dynamic elastic modulus, chloride ion permeability coefficient, mass loss rate and
carbonation depth.
3. The concrete durability detection method based on cloud model and D-S evidence
theory according to claim 2 is characterized in that in S1, the index weight of each
durability test index is calculated by the variable weight theory.
4. The concrete durability detection method based on cloud model and D-S evidence
theory according to claim 1 is characterized in that in step S2, the membership degree of each durability detection index corresponding to different durability levels is calculated by the eigenvalue of the cloud model.
5. The concrete durability detection method based on cloud model and D-S evidence
theory according to claim 4 is characterized in that the eigenvalues of the cloud model
include expectation, entropy and hyperentropy.
6. The concrete durability detection method based on cloud model and D-S evidence
theory according to claim 1 is characterized in that in S2, the membership degree is
normalized as follows:
-- max(p (Ip (II),pU (I),pu (IV),p (V)
In the formula, % is the membership degree after normalization, and (I,
pY (II) pt (III)pf (IV) and N (V) are the membership degrees of the ith durability test
index corresponding to the jth durability grade when the durability grades are I,II, III, IV
and V, respectively.
7. The concrete durability detection method based on cloud model and D-S evidence
theory according to claim 2 is characterized in that in S3, integrating the evidence
corresponding to each durability test index includes the following contents:
To begin with, based on the improved evidence theory DS, each durability test index
is integrated separately.
Then, the integration results of relative dynamic elastic modulus, chloride ion
permeability coefficient, mass loss rate and carbonization depth are integrated again by
weighting treatment.
FIGURES 1/4
Figure 1
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| CN113298006B (en) * | 2021-06-04 | 2024-01-19 | 西北工业大学 | Novel abnormal target detection method based on brain-computer fusion cognition and decision |
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