GB2616952A - Multifractal quantitative characterization method for concrete multiple crack evolution - Google Patents
Multifractal quantitative characterization method for concrete multiple crack evolution Download PDFInfo
- Publication number
- GB2616952A GB2616952A GB2301204.0A GB202301204A GB2616952A GB 2616952 A GB2616952 A GB 2616952A GB 202301204 A GB202301204 A GB 202301204A GB 2616952 A GB2616952 A GB 2616952A
- Authority
- GB
- United Kingdom
- Prior art keywords
- concrete
- multifractal
- multiple crack
- crack distribution
- concrete multiple
- 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.)
- Granted
Links
- 239000004567 concrete Substances 0.000 title claims abstract description 171
- 238000012512 characterization method Methods 0.000 title claims abstract description 14
- 238000001228 spectrum Methods 0.000 claims abstract description 52
- 230000009471 action Effects 0.000 claims abstract description 16
- 230000007423 decrease Effects 0.000 claims description 3
- RCKMWOKWVGPNJF-UHFFFAOYSA-N diethylcarbamazine Chemical compound CCN(CC)C(=O)N1CCN(C)CC1 RCKMWOKWVGPNJF-UHFFFAOYSA-N 0.000 claims 1
- 238000005516 engineering process Methods 0.000 abstract description 4
- 238000011156 evaluation Methods 0.000 abstract description 3
- 238000012544 monitoring process Methods 0.000 abstract description 3
- 238000000034 method Methods 0.000 description 9
- 230000008569 process Effects 0.000 description 7
- 230000000750 progressive effect Effects 0.000 description 3
- 239000011150 reinforced concrete Substances 0.000 description 3
- 125000004122 cyclic group Chemical group 0.000 description 2
- 230000003068 static effect Effects 0.000 description 2
- 230000009286 beneficial effect Effects 0.000 description 1
- 238000003763 carbonization Methods 0.000 description 1
- 230000008859 change Effects 0.000 description 1
- 230000000739 chaotic effect Effects 0.000 description 1
- 230000001808 coupling effect Effects 0.000 description 1
- 230000003247 decreasing effect Effects 0.000 description 1
- 230000007547 defect Effects 0.000 description 1
- 238000001514 detection method Methods 0.000 description 1
- 238000000605 extraction Methods 0.000 description 1
- 230000036541 health Effects 0.000 description 1
- 239000000463 material Substances 0.000 description 1
- 238000007619 statistical method Methods 0.000 description 1
- 238000010792 warming Methods 0.000 description 1
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
- G06T7/0002—Inspection of images, e.g. flaw detection
- G06T7/0004—Industrial image inspection
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06F—ELECTRIC DIGITAL DATA PROCESSING
- G06F30/00—Computer-aided design [CAD]
- G06F30/10—Geometric CAD
- G06F30/13—Architectural design, e.g. computer-aided architectural design [CAAD] related to design of buildings, bridges, landscapes, production plants or roads
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10016—Video; Image sequence
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING OR COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30108—Industrial image inspection
- G06T2207/30132—Masonry; Concrete
Landscapes
- Engineering & Computer Science (AREA)
- Physics & Mathematics (AREA)
- General Physics & Mathematics (AREA)
- Theoretical Computer Science (AREA)
- Computer Vision & Pattern Recognition (AREA)
- Quality & Reliability (AREA)
- Geometry (AREA)
- Computer Hardware Design (AREA)
- Computational Mathematics (AREA)
- Mathematical Analysis (AREA)
- Mathematical Optimization (AREA)
- Pure & Applied Mathematics (AREA)
- Evolutionary Computation (AREA)
- General Engineering & Computer Science (AREA)
- Structural Engineering (AREA)
- Civil Engineering (AREA)
- Architecture (AREA)
- Investigating Materials By The Use Of Optical Means Adapted For Particular Applications (AREA)
Abstract
The present disclosure discloses a multifractal quantitative characterization method for concrete multiple crack evolution, including the steps of acquiring concrete multiple crack distribution images of a target region on a concrete structure and constructing a multifractal singular spectrum of each of the concrete multiple crack distribution images. A multifractal feature of crack distribution according to a curve feature of each multifractal singular spectrum is then judged and a plurality of multifractal singular spectra of the concrete multiple crack distribution images in the same coordinate system gathered according to a judgment result to form a multifractal singular spectrum cluster of the concrete multiple crack distribution images and a multi-parameter fractal feature factor set of the concrete multiple crack distribution images from the multifractal singular spectrum cluster is then extracted. Multi-parameter fractal feature factors provided by the present disclosure can characterize dynamic evolution of concrete multiple cracks under the load action, and a new technology for behaviour monitoring, performance evaluation and safety early warning of the concrete structure is formed.
Description
MULTIFRACTAL QUANTITATIVE CHARACTERIZATION METHOD FOR
CONCRETE MULTIPLE CRACK EVOLUTION
TECHNICAL FIELD
11011 The present disclosure relates to the technical field of health detection and safety early warming of a concrete structure, in particular to a multifractal quantitative characterization method for concrete multiple crack evolution.
BACKGROUND ART
[2] Concrete structures are likely to crack as being impacted by inherent defects of materials and external environments. Cracks lead to the increased carbonization and reduced strength of concrete structures, even causing catastrophic accidents. The generation and expansion of the cracks of the concrete structures are usually used for measuring the life cycle of the concrete structures. Thus, studying the crack evolution rule of the concrete structures is of great theoretical significance and engineering application value for behaviour monitoring, performance evaluation and safety early warning of the concrete structures. Evolution of the cracks of the concrete structures, especially evolution of multiple cracks of the concrete structures under the multi-factor coupling action, is an open, dissipative and chaotic nonlinear behaviour in nature, accompanied by the exchange with external matters and energy. Existing multifractal technologies can effectively describe the nonlinearity and complexity of instantaneous crack distribution in the evolution process of the concrete multiple cracks; however, the progressive expansion process of the cracks of the concrete structure is hardly characterized quantitatively.
SUMMARY
[3] The present disclosure aims at providing a multifractal quantitative characterization method for concrete multiple crack evolution, used for quantitatively characterizing the progressive expansion process of concrete multiple cracks.
[4] The present disclosure provides a multifractal quantitative characterization method for concrete multiple crack evolution, including: [5] acquiring concrete multiple crack distribution images of a target region on a concrete structure; [6] constructing a multifractal singular spectrum of each of the concrete multiple crack distribution images; [7] judging a multifractal feature of crack distribution according to a curve feature of each multifractal singular spectrum; [8] gathering a plurality of multitiactal singular spectra of the concrete multiple crack distribution images in the same coordinate system according to a judgment result to form a multifractal singular spectrum cluster of the concrete multiple crack distribution images; and [9] extracting a multi-parameter fractal feature factor set of the concrete multiple crack distribution images from the multifractal singular spectrum cluster.
[10] Preferably, the acquiring the concrete multiple crack distribution images of the target area on the concrete structure includes the following steps: [11] continuously applying a plurality of load steps to the concrete structure, and recording each concrete multiple crack distribution state image of the target region on the concrete structure under the action of each load step.
[12] Preferably, the constructing the multifractal singular spectrum of each of the concrete multiple crack distribution images includes the following steps: [13] performing binarization processing on each concrete multiple crack distribution image under the action of each load step to obtain a concrete multiple crack distribution binary image under the action of each load step; [14] respectively calculating a singularity exponent and a singularity dimension of each concrete multiple crack distribution binary image; and [15] drawing a multifractal singular spectrum of each binary image of concrete multiple crack distribution according to the singularity exponent and the singularity dimension of each binary image of concrete multiple crack distribution.
[16] Preferably, the calculating the singularity exponent and the singularity dimension of each concrete multiple crack distribution binary image includes the following steps: [17] calculating total pixels M of each binary image of concrete multiple crack distribution; [18] covering each binary image of concrete multiple crack distribution with boxes of different sizes, and respectively calculating local crack proportions pi (L) of cracks in each binary image of concrete multiple crack distribution in the boxes when the box size is L, specifically expressed as follows: Pi(L) -m1(L) (1) [19] where, tn; (L) represents pixels of a crack in the ith box when the box size is L; [20] normalizing the local crack proportion pi (L) to each box of different sizes, specifically calculated as follows: P(L) (2) pi(q,L) = N(i) Ei=i Pi (L) [21] where, q is a weight factor ranging from -10 to 10; [22] pi (q,L) represents a difference among local pixels of each binary image of concrete multiple crack distribution, ranging from 0 to 1, [23] calculating the singularity exponent of each binary image of concrete multiple crack distribution under different weight factors, specifically calculated as follows: a(q) = hm' 1,->0 logL ( p (q,/,)log [7), (1)] (3) [24] where, a(q) is the singularity exponent of each binary image of concrete multiple crack distribution under different weight factors; and [25] calculating the singularity dimension of each binary image of concrete multiple crack distribution under different weight factors, specifically calculated as follows: L-n logL * , * 7,,V(L) " (q alog ly 41 (4) [26] whered(g) is the singularity dimension of each binary image of concrete multiple crack distribution under different weight factors.
[27] Preferably, the multifractal singular spectrum of each binary image of concrete multiple crack distribution is drawn with the singularity exponent a(q) of the binary image of concrete multiple crack distribution under different weight factors as an abscissa and the singularity dimension f(q) of the binary image of concrete multiple crack distribution under different weight factors as an ordinate; and [28] when the weight factor q is greater than 0, the curve feature of the multifractal singular spectrum of the binary image of concrete multiple crack distribution shows that fig) increases with an increase of a(q), and when the weight factor q is less than 0, the curve feature of the multifractal singular spectrum of the binary image of concrete multiple crack distribution shows that fig) decreases with the increase of a(q), indicating multiple crack distribution on the concrete structure has a multifractal characteristic.
[29] Preferably, the multi-parameter fractal feature factor set of the concrete multiple crack distribution images includes: [30] a minimum singularity exponent amin, characterizing impact of a maximum local crack proportion of the target region on the concrete structure; [31] a maximum singularity exponent am, characterizing impact of a minimum local crack proportion of the target region on the concrete structure; [32] a multifractal singular spectrum width delta a, characterizing a non-uniformity degree of crack distribution of the target region on the concrete structure, expressed as follows: Aa = amax -amin (5) [33] a maximum singularity dimension fmai, characterizing an overall condition of crack distribution of the target region on the concrete structure; and [34] a singularity dimension difference delta f of the multifractal singular spectrum, characterizing distribution non-uniformity caused by a difference of varieties in different local crack proportions of the target region on the concrete structure, expressed as follows: Af = f (cini.x) -f (amin) (6) [35] where, f(a) is a singularity dimension value when the sinaularity exponent in the multifractal singular spectrum of each binary image of concrete multiple crack distribution is am.; and [36] i(ami") is a singularity dimension value when the singularity exponent in the multifractal singular spectrum of each binary image of concrete multiple crack distribution is ailii".
[37] Compared with the prior art, the present disclosure has the following beneficial effects: [38] the present disclosure is especially suitable for quantitatively characterizing the progressive expansion process of the concrete multiple cracks, breaks through the limitation that a traditional crack statistical method hardly characterize dynamic evolution of the concrete multiple cracks under the load action, and surpasses the technical level of existing multifractal technologies used for describing distribution of the concrete multiple cracks; and proposed multi-parameter fractal feature factors can quantitatively characterize dynamic evolution of the concrete multiple cracks under the load action, and a new technology for behaviour monitoring, performance evaluation and safety early warning of the concrete structure is formed.
BRIEF DESCRIPTION OF THE DRAWINGS
[39] The drawings are used for providing further understandings of the present disclosure, constitute one part of the specification and explain the present disclosure in conjunction with the embodiments of the present disclosure, and do not constitute any limitation to the present disclosure. In the drawings: [40] FIG. 1 is a schematic flowchart of a multifractal quantitative characterization method for concrete multiple crack evolution provided by the present disclosure; [41] FIG. 2 is a graph showing crack distribution of a reinforced concrete shear wall under the action of static cyclic loads provided by an embodiment of the present disclosure; [42] FIG. 3 is a binary image of concrete multiple crack distribution of a reinforced concrete shear wall provided by an embodiment of the present disclosure under the action of each load step; [43] FIG. 4 shows multi-parameter fractal feature factors of certain multiple cracks on a binary image of concrete multiple crack distribution provided by an embodiment of the present disclosure; and [44] FIG. 5 shows a multifractal singular spectrum cluster of crack distribution under five continuous load steps provided by an embodiment of the present disclosure.
DETAILED DESCRIPTION OF THE EMBODIMENTS
[45] The technical solutions in the embodiments of the present disclosure will be clearly and completely described below with reference to the drawings in the embodiments of the present disclosure, but it should be understood that the protection scope of the present disclosure is not limited by the above specific implementations.
Embodiment [46] As shown in FIGS. 1-5, a multifractal quantitative characterization method for concrete multiple crack evolution includes the steps: [47] step 1: concrete multiple crack distribution images of a target region on a concrete structure are acquired, including the following steps: a plurality of load steps are continuously applied to the concrete structure, and each concrete multiple crack distribution state image of the target region on the concrete structure under the action of each load step is recorded, to form the concrete multiple crack distribution images.
[48] In the embodiment, taking apparent. crack distribution of a reinforced concrete shear wall under the action of static cyclic loads as a study object, multifractal features in the crack evolution process under five continuous load steps are actually analysed. An original image of crack distribution is formed by marking on a wall body in the process of applying the loads as shown in FIG. 2 and then drawing by CAD in a certain proportion, and crack distribution images are exported in a format of jpg.
[49] Step 2: the multifractal singular spectrum of each of the concrete multiple crack distribution images is constructed, specifically including the following steps: [50] step 2.1: non-crack information is removed from the concrete multiple crack distribution images, and binarization processing is performed on each concrete multiple crack distribution image under the action of each load step to obtain a binary image of concrete multiple crack distribution under the action of each load step. As in the embodiment, the crack distribution images shown in the format of jpg do not contain the non-crack information and the crack distribution is clear, binarization processing is performed on the crack images only by Matlab, and the processed images are shown in FIG. 3.
[51] Step 2.2: a singularity exponent and a singularity dimension of each binary image of concrete multiple crack distribution are respectively calculated.
[52] The calculating the singularity exponent and the singularity dimension of each binary image of concrete multiple crack distribution specifically includes the following steps: [53] step 2.2.1: total pixels Ai of each binary image of concrete multiple crack distribution are calculated.
[54] Step 2.2.2: each binary image of concrete multiple crack distribution is respectively covered with boxes of different sizes, and local crack proportions pi (L) of cracks in each binary image of concrete multiple crack distribution in the boxes when the box size is L are respectively calculated, which is specifically expressed as follows: p(L) = mi(L) (1) [55] where, mi (L) represents pixels of a crack in the ith box when the box size is L. [56] Step 2.2.3: the local crack proportion pi (L) to each box of different sizes is normalized, which is specifically calculated as follows: 1(q, L) = p51(t (2) [57] where, q is a weight factor ranging from -10 to 10; [58] pi (q,L) represents a difference among local pixels of each binary image of concrete multiple crack distribution, ranging from 0 to 1; [59] a weight factor q is introduced for highlighting the difference among the local pixels: [60] when q is greater than 0, the larger local crack proportion is dominant; [61] when q is less than 0, the smaller local crack proportion is dominant; and [62] when q is equal to 0, the local crack proportion is not distinguished.
[63] Wherein, as for each weight factor q, a straight slope obtained by fitting a point set formed by logL as an abscissa and Ni (it) p1 (q, L)log[pi(L)] as an ordinate by a least square method is a(q); and a straight slope obtained by fitting a point set formed by (log L,E7(;) pi(q, 0 log[pi(L)]) as coordinates by the least square method isf(q).
[64] Step 2.2.4: the singularity exponent of each binary image of concrete multiple crack distribution under different weight factors is calculated, which is specifically calculated as follows: a(q) = lim (g,Olog [p(L)1 logL (3) [65] where, a(q) is the singularity exponent of each binary image of concrete multiple crack distribution under different weight factors.
[66] Step 2.2.5: the singularity dimension of each binary image of concrete multiple crack distribution under different weight factors is calculated, which is specifically calculated as follows: f (q) = Iim L-)0 (g,Olog [ft, 40] log! (4) [67] where,, fly) is the singularity dimension of each binary image of concrete multiple crack distribution under different weight factors.
[68] Step 2.3: a multifractal singular spectrum of each binary image of concrete multiple crack distribution is respectively drawn according to the singularity exponent and the singularity dimension of each binary image of concrete multiple crack distribution.
[69] The multifractal singular spectrum of each binary image of concrete multiple crack distribution is drawn with the singularity exponent a(q) of the binary image of concrete multiple crack distribution under different weight factors as an abscissa and the singularity dimension f(q) of the binary image of concrete multiple crack distribution under different weight factors as an ordinate.
[70] Step 3: a multifractal feature of crack distribution is judged according to a curve feature of each multifractal singular spectrum.
[71] The judging the multifractal feature of crack distribution on the concrete structure according to the curve feature of each multifractal singular spectrum of the binary image of concrete multiple crack distribution specifically includes the following steps: [72] when the weight factor q is greater than 0, the curve feature of the multifractal singular spectrum of the binary image of concrete multiple crack distribution shows that fig) increases with an increase of a(q), and when the weight factor q is less than 0, the curve feature of the multifractal singular spectrum of the binary image o I" concrete multiple crack distribution shows that It.q) decreases with the increase of a(q), indicating multiple crack distribution on the concrete structure has a multifractal characteristic. Each concrete multiple crack evolution state corresponds to one multifractal singular spectrum.
[73] For example, a curve of the multi-parameter fractal feature factors of certain multiple cracks in the embodiment is a convex curve with an opening with a certain width, which faces down, as shown in FIG. 4. Monotonicity of lig) arid a(q) meets the following condition that a part with q greater than 0 is monotonically increased, and a part with g less than 0 is monotonically decreased, which shows that crack distribution on the concrete structure has the multifractal characteristic.
[74] Step 4: a plurality of multifractal singular spectra of the concrete multiple crack distribution images are gathered in the same coordinate system according to a judgment result to form a multi fractal singular spectrum cluster of the concrete multiple crack distribution state sequence images.
[75] The multifractal singular spectra of binary images of concrete multiple crack distribution are gathered in the same coordinate system. In the embodiment, the multifractal singular spectra of crack distribution on the concrete structure under the five continuous load steps are gathered in the same coordinate system to form the multifractal singular spectrum cluster of the concrete multiple crack distribution images, as shown in FIG. 5.
[76] Step 5: a multi-parameter fractal feature factor set of the concrete multiple crack distribution images is extracted from the multi fractal singular spectrum cluster.
[77] When crack distribution on the concrete structure has the multifractal characteristic, the extraction of the multi-parameter fractal feature factor set may continue to be made. The multi-parameter fractal feature factor set of the concrete multiple crack distribution images is extracted, which characterizes the evolution process of the multiple cracks on the concrete structure under the load action.
[78] The naulti-paranaeter fractal feature factor set of the concrete multiple crack distribution images includes: [79] a minimum singularity exponent umi", characterizing impact of a maximum local crack proportion of the target region on the concrete structure; [80] a maximum singularity exponent a",."", characterizing impact of a minimum local crack proportion of the target region on the concrete structure; [81] a multifractal singularity spectrum width delta a, characterizing a non-uniformity degree of crack distribution of the target. region on the concrete structure, expressed as follows: Aa = amax -* (5) amm [82] a maximum singularity dimension in,' ax, characterizing an overall condition of crack distribution of the target region on the concrete structure; and [83] a singularity dimension difference delta f of the multifractal singular spectrum, characterizing distribution non-uniformity caused by a difference of varieties in different local crack proportions of the target region on the concrete structure, expressed as follows: Af = [(am.) -[(am * ) (6) [84] where, f(a",,,) is a singularity dimension value when the singularity exponent in the multifractal singular spectrum of each binary image of concrete multiple crack distribution is and [85] f(ami") is a singularity dimension value when the singularity exponent in the multifractal singular spectrum of each binary image of concrete multiple crack distribution is amin.
[86] Table 1 Multi-parameter fractal feature factor set of multiple crack distribution under five continuous load steps Load Main amax Au step 1 1.250 2.143 0.893 0.082 1.459 1.381 2.231 0.850 -0.119 1.573 3 1.432 2.286 0.854 -0.156 1.646 4 1.515 2.304 0.789 -0.122 1.722 1.522 2.268 0.746 0.144 1.753 [87] In the embodiment, the multi-parameter fractal feature factor set of the concrete multiple crack images under five continuous load steps is respectively extracted, as shown in Table 1.
[88] Finally, it should be noted that the embodiment disclosed above is merely a specific embodiment of the present disclosure, but the embodiments of the present disclosure are not limited thereto. Any change easily made by any person skilled in the art should fall within the protection scope of the present disclosure.
Claims (6)
- WHAT IS CLAIMED IS: 1. A multifractal quantitative characterization method for concrete multiple crack evolution, characterized by comprising: acquiring concrete multiple crack distribution images of a target region on a concrete structure, constructing a multifractal singular spectrum of each of the concrete multiple crack distribution images; judging a multifractal feature of crack distribution according to a curve feature of each multifractal singular spectrum; gathering a plurality of multifractal singular spectra of the concrete multiple crack distribution images in the same coordinate system according to a judgment result to form a multifractal singular spectrum cluster of the concrete multiple crack distribution images; and extracting a multi-parameter fractal feature factor set of the concrete multiple crack distribution images from the multifractal singular spectrum cluster.
- 2. The multifractal quantitative characterization method for concrete multiple crack evolution according to claim I, characterized in that the acquiring the concrete multiple crack distribution images of the target area on the concrete structure comprises the following steps: continuously applying a plurality of load steps to the concrete structure, and recording each concrete multiple crack distribution state image of the target region on the concrete structure under the action of each load step.
- 3. The multifractal quantitative characterization method for concrete multiple crack evolution according to claim 2, characterized in that the constructing the multifractal singular spectrum of each of the concrete multiple crack distribution images comprises the following steps: performing binarization processing on each concrete multiple crack distribution state image under the action of each load step to obtain a binary image of concrete multiple crack distribution under the action of each load step; respectively calculating a singularity exponent and a singularity dimension of each binary image of concrete multiple crack distribution; and drawing a multifractal singular spectrum of each binary image of concrete multiple crack distribution according to the singularity exponent and the singularity dimension of each binary image of concrete multiple crack distribution.
- 4. The multifractal quantitative characterization method for concrete multiple crack evolution according to claim 3, characterized in that the calculating the singularity exponent and the singularity dimension of each binary image of concrete multiple crack distribution comprises the following steps: calculating total pixels M of each binary image of concrete multiple crack distribution; covering each binary image of concrete multiple crack distribution with boxes of different sizes, and respectively calculating local crack proportions pi (L) of cracks in each binary image of concrete multiple crack distribution in the boxes when the box size is L, specifically expressed as follows: Pi(L) m'(L) ( I) where, m1 (L) represents pixels of a crack in the ith box when the box size is L; normalizing the local crack proportion pi (L) to each box of different sizes, specifically calculated as follows: Pi(CIP Li) N(L) q Et=1 (L) P( L) (2) where, q is a weight factor ranging from -10 to 10; y, (q,L) represents a difference among local pixels of each binary image of concrete multiple crack distribution, ranging from 0 to I; calculating the singularity exponent of each binary image of concrete multiple crack distribution under different weight factors, specifically calculated as follows: a(q) = lim LI=1 P,((1,010g [pi(L)] L-)0 logL viN (L) (3) where, a(q) is the singularity exponent of each binary image of concrete multiple crack distribution under different weight factors; and calculating the singularity dimension of each binary image of concrete multiple crack distribution under different weight factors, specifically calculated as follows: f (q) Iim = ' (q,L)logrg (a)] L-11 logl. (4) where, fig) is the singularity dimension of each binary image of concrete multiple crack distribution under different weight factors.
- 5. The multifractal quantitative characterization method for concrete multiple crack evolution according to claim 4, characterized in that the multifractal singular spectrum of each binary image of concrete multiple crack distribution is drawn with the singularity exponent a(q) of the binary image of concrete multiple crack distribution under different weight factors as an abscissa and the singularity dimension f(q) of the binary image of concrete multiple crack distribution under different weight factors as an ordinate; and when the weight factor q is greater than 0, the curve feature of the multifractal singular spectrum of the binary image of concrete multiple crack distribution shows that fig) increases with an increase of a(q), and when the weight factor q is less than 0, the curve feature of the multifractal singular spectrum of the binary image of concrete multiple crack distribution shows that fig) decreases with the increase of a(q), indicating multiple crack distribution on the concrete structure has a multifractal characteristic.
- 6. The multifractal quantitative characterization method for concrete multiple crack evolution according to claim 5, characterized in that the multi-parameter fractal feature factor set of the concrete multiple crack distribution state sequence images comprises: a minimum singularity exponent anna, characterizing impact of a maximum local crack proportion of the target region on the concrete structure; a maximum singularity exponent a., characterizing impact of a minimum local crack proportion of the target region on the concrete structure; a multifractal singular spectrum width delta a, characterizing a non-uniformity degree of crack distribution of the target region on the concrete structure, expressed as follows: = amax -amin (5) a maximum singularity dimension fmax, characterizing an overall condition of crack distribution of the target region on the concrete structure; and a singularity dimension difference deltafof the multifractal singular spectra characterizing distribution non-uniformity caused by a difference of varieties in different local crack proportions of the target region on the concrete structure, expressed as follows: At = f (amax) -f Camin) (6) where, f(a.) is a singularity dimension value when the singularity exponent in the multifractal singular spectrum of each binary image of concrete multiple crack distribution is a.; and f(amin) is a singularity dimension value when the singularity exponent in the multifractal singular spectrum of each binary image of concrete multiple crack distribution is ami".
Applications Claiming Priority (1)
| Application Number | Priority Date | Filing Date | Title |
|---|---|---|---|
| CN202210107576.2A CN114444186B (en) | 2022-01-28 | 2022-01-28 | Multi-fractal quantitative characterization method for crack evolution of concrete group |
Publications (3)
| Publication Number | Publication Date |
|---|---|
| GB202301204D0 GB202301204D0 (en) | 2023-03-15 |
| GB2616952A true GB2616952A (en) | 2023-09-27 |
| GB2616952B GB2616952B (en) | 2024-05-29 |
Family
ID=81370990
Family Applications (1)
| Application Number | Title | Priority Date | Filing Date |
|---|---|---|---|
| GB2301204.0A Active GB2616952B (en) | 2022-01-28 | 2023-01-27 | Multifractal quantitative characterization method for concrete multiple crack evolution |
Country Status (2)
| Country | Link |
|---|---|
| CN (1) | CN114444186B (en) |
| GB (1) | GB2616952B (en) |
Families Citing this family (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN117078233B (en) * | 2023-10-17 | 2024-03-26 | 深圳市城市交通规划设计研究中心股份有限公司 | Maintenance decision method based on road network maintenance comprehensive evaluation index |
| CN117538289B (en) * | 2024-01-10 | 2024-03-22 | 中铁十六局集团第一工程有限公司 | Nondestructive testing method for construction quality of steel reinforced concrete structure node |
Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111650088A (en) * | 2020-06-10 | 2020-09-11 | 河海大学 | Real-time detection method for rheological property of fluid concrete mixture |
| CN113222992A (en) * | 2021-06-21 | 2021-08-06 | 苏州大学 | Crack characteristic characterization method and system based on multi-fractal spectrum |
Family Cites Families (5)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN104036493B (en) * | 2014-05-21 | 2017-02-01 | 浙江大学 | No-reference image quality evaluation method based on multifractal spectrum |
| US9836631B1 (en) * | 2015-04-26 | 2017-12-05 | Aic Innovations Group, Inc. | Method and apparatus for fractal identification |
| CN105910902A (en) * | 2016-05-11 | 2016-08-31 | 青岛理工大学 | Fractalanalysis method for crack propagation path of concretemember |
| CN110926328A (en) * | 2018-09-20 | 2020-03-27 | 中国石油化工股份有限公司 | Method and device for measuring characteristics of crack surface of rock crack |
| CN111681272B (en) * | 2020-06-09 | 2023-05-30 | 上海交通大学 | SAR image processing method based on singular power spectrum |
-
2022
- 2022-01-28 CN CN202210107576.2A patent/CN114444186B/en active Active
-
2023
- 2023-01-27 GB GB2301204.0A patent/GB2616952B/en active Active
Patent Citations (2)
| Publication number | Priority date | Publication date | Assignee | Title |
|---|---|---|---|---|
| CN111650088A (en) * | 2020-06-10 | 2020-09-11 | 河海大学 | Real-time detection method for rheological property of fluid concrete mixture |
| CN113222992A (en) * | 2021-06-21 | 2021-08-06 | 苏州大学 | Crack characteristic characterization method and system based on multi-fractal spectrum |
Also Published As
| Publication number | Publication date |
|---|---|
| GB2616952B (en) | 2024-05-29 |
| GB202301204D0 (en) | 2023-03-15 |
| CN114444186A (en) | 2022-05-06 |
| CN114444186B (en) | 2023-09-15 |
Similar Documents
| Publication | Publication Date | Title |
|---|---|---|
| GB2616952A (en) | Multifractal quantitative characterization method for concrete multiple crack evolution | |
| CN114911209B (en) | Garlic processing wastewater treatment management system based on data analysis | |
| CN116304766B (en) | Multi-sensor-based quick assessment method for state of switch cabinet | |
| CN114966467A (en) | Power transmission line state evaluation method based on digital twinning | |
| US11344987B2 (en) | Method for monitoring chatter in machining process | |
| CN116992391B (en) | Hard carbon process environment-friendly monitoring data acquisition and processing method | |
| CN110930057A (en) | Quantitative evaluation method for reliability of distribution transformer test result based on LOF algorithm | |
| CN116184950B (en) | Multisource data extraction and analysis system for automobile production line | |
| CN112611353B (en) | Dam monitoring alarm system and method | |
| CN116204390B (en) | Seal monitoring management method and system based on data analysis | |
| CN117829593A (en) | BIM-based fabricated building management method and system | |
| CN117439268A (en) | Electric energy quality detection system of smart power grid | |
| CN116844315A (en) | Artificial intelligent early warning method, system and storage medium | |
| CN117031312A (en) | New energy automobile thermal management integrated module detection method | |
| CN110849968A (en) | Crane main beam damage acoustic emission nondestructive detection method based on self-adaptive optimization VMD | |
| CN114967628A (en) | Health food production management and control system that disinfects based on 5G intelligence mill | |
| CN115248292A (en) | Transformer fault analysis and diagnosis method and system | |
| CN109507541B (en) | Power transmission line state judgment method based on historical data analysis | |
| CN112883085B (en) | Bridge dynamic load safety online real-time monitoring, analyzing, early warning and management platform based on big data and cloud computing | |
| CN105759748A (en) | Semiconductor production machine hardware performance dynamic monitoring system and monitoring method | |
| CN116579748B (en) | Safety production management system based on wireless communication technology | |
| CN113487126A (en) | Dynamic detection method for unqualified product | |
| CN117849556B (en) | Intelligent early warning system is put in office of driving motor position sensor | |
| CN116659591B (en) | Operation early warning system suitable for chemical distillation plant | |
| CN118111740A (en) | Running performance monitoring system suitable for industrial high-pressure cleaning machine |