Evaluation of Cracking Patterns in Cement Composites—From Basics to Advances: A Review
Abstract
:1. Introduction
2. Causes of Cracks and Cracking Patterns in Cement Composites
- —the Young’s modulus of elasticity [N/m2],
- —the surface energy [N/m],
- —half the length of the crack [m].
3. Techniques for the Detection of Individual Cracks and the Whole Cracking Pattern
3.1. Global Thresholding
3.2. Locally Adaptive Thresholding
3.3. Otsu Thresholding
3.4. Genetic Algorithms
3.5. Fuzzy Logic Based Techniques
3.6. Artificial Neural Networks
3.7. Dijkstra Algorithm
3.8. The Bayesian Classifier
3.9. The AdaBoost Classifier
3.10. Summary of Techniques for Identifying Cracks and Their Accuracy in the Literature Review Works
4. Parameters to Describe the Morphology of the Cracking Patterns
4.1. Opening Width of the Cracks
4.2. Length and Orientation of the Cracks
4.3. The Crack Density
- —the crack density [mm−1],
- —the average crack length [mm],
- —the area of the test section [mm2].
4.4. The Crack Area
4.5. The Fractal Dimension
- formlessness—an unambiguous shape is impossible to determine,
- description by a recursive relationship, not a mathematical formula,
- self-similarity—an isolated fragment resembles its larger whole.
4.6. A Summary of the Use of Individual Parameters to Describe the Morphology of the Cracking Patterns
5. Influence of Morphology of the Cracking Patterns on the Properties of Cement Composites
5.1. Performance at Elevated Temperatures
5.2. Shrinkage and Drying
5.3. Water Permeability
5.4. Mechanical Strength
- —the damage index; ,
- —the fractal dimension of the current status of the cracking pattern,
- —the fractal dimension of the cracking pattern computed during the first inspection.
5.5. Chemical Corrosion
6. Summary
- the process of cracks formation in brittle cement composites and their evolution into an extensive and complex system of cracks,
- methods and techniques of digital extraction of the cracking patterns for their further evaluation,
- quantitative parameters used to describe the complexity of the cracking patterns,
- evaluation of the influence of morphology of the cracking patterns on selected properties of cement composites.
Funding
Acknowledgments
Conflicts of Interest
References
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Type of Cracks | Reasons | Occurence Time | Typical Location |
---|---|---|---|
plastic settlement | excessive secretion of cement milk—bleeding; rapid initial drying | 10 min–3 h | sections of considerable depth; top of column, trough and coffered panels |
plastic shrinkage | rapid initial drying; reinforcement at the surface; low speed of drained water discharge | 30 min–6 h | surfaces; slabs |
early thermal contraction | excessive heat generation; excessive temperature gradients; rapid cooling | 1 day–(2–3 weeks) | thick walls; thick plates |
prolonged shrinkage during drying | ineffective connections; excessive contraction; inadequate care | several weeks or months | thin panels; walls |
map-cracking | excessive rubbing; rich mix; inadequate care | 1–7 days | walls; plates |
corrosion of the reinforcement | insufficient cover; poor concrete quality | over 2 years | columns; beams |
alkaline reactions in the aggregate | reactive aggregate; cement with high alkali content | over 5 years | moistened elements |
ankle cracks | aggregates damaged by frost | over 10 years | free ends of plates |
Digital Image Processing Technique | References | Accuracy Level (Only Where the Authors Have Given It) |
---|---|---|
Global thresholding | [54,55,56,84] | ≤90% [54] |
Locally adaptive thresholding | [55,56,57,58,59,60] | 90–95% [58] |
Otsu thresholding | [61,62,63,64,73] | ≥95% [63] |
Edge detection | [54,63,71] | ≤90% [54]; 90–95% [71]; ≥95% [63] |
Probabilistic approach | [55] | - |
Median filtering | [55] | - |
Sobel’s filtering | [62] | - |
Morphological approach | [57,58,59,77,81] | 90–95% [58]; 90–95% [77] |
Genetic algorithms | [65,67,68,69] | ≤90% [67] |
Fuzzy logic based techniques | [70,71,72,73] | 90–95% [71] |
Artificial neural networks | [71,75,76,77,78,79,80,81] | ≤90% [76,78]; 90–95% [71,77]; ≥95% [75,79,80] |
Wavelet approach | [76] | ≤90% [76] |
Dijkstra algorithm | [83,84,85,86] | - |
The Bayesian classifier | [88,89,90,91,92] | 90–95% [90] |
The AdaBoost classifier | [94,95] | ≤90% [95] |
Statistical methods | [89] | - |
Parameter | References |
---|---|
Opening width of the cracks | [9,12,13,14,15,16,96,97,100,101,102,105,106,107,108,109,110,111,112,113,114,115,116] |
Length and orientation of the cracks | [8,12,13,14,15,16,98,99,107,109,112,113] |
The crack density | [8,9,13,96,99,100,110,111,112,114] |
The crack area | [12,13,14,15,16,98,101,102,106,107,109] |
The fractal dimension | [105,115,116,117,118,119] |
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Szeląg, M. Evaluation of Cracking Patterns in Cement Composites—From Basics to Advances: A Review. Materials 2020, 13, 2490. https://doi.org/10.3390/ma13112490
Szeląg M. Evaluation of Cracking Patterns in Cement Composites—From Basics to Advances: A Review. Materials. 2020; 13(11):2490. https://doi.org/10.3390/ma13112490
Chicago/Turabian StyleSzeląg, Maciej. 2020. "Evaluation of Cracking Patterns in Cement Composites—From Basics to Advances: A Review" Materials 13, no. 11: 2490. https://doi.org/10.3390/ma13112490
APA StyleSzeląg, M. (2020). Evaluation of Cracking Patterns in Cement Composites—From Basics to Advances: A Review. Materials, 13(11), 2490. https://doi.org/10.3390/ma13112490