Quantitative evaluation and traceability analysis of concrete apparent shade and apparent uniformity
Source abstract
Abstract
Appearance quality serves as a comprehensive indicator of material homogeneity and construction quality. However, research on appearance quality traceability remains limited, especially regarding the influence of controllable factors during production. To address this gap, concrete was prepared using different mix proportions, formwork types, and release agents, with appearance images collected at specified curing ages. The average grayscale ( Ave ) and the coefficient of variation of grayscale ( Cv ) were employed as quantitative indicators for apparent shade and uniformity, respectively, using digital image processing techniques. Then, statistical analysis revealed that formwork type, release agent and curing age together explained the variation in apparent shade. Among these factors, only formwork was used to explain the change in apparent uniformity. Finally, through validity test, internal test, external test and residual analysis, the stability and prediction range of the models were confirmed. This method can be used for rapid batch screening, like initial inspection on construction sites.
Source: openalex_inverted_index · Quality: baseline
Research context
OpenAlex topics: Infrastructure Maintenance and Monitoring · Concrete Corrosion and Durability · Non-Destructive Testing Techniques
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
References (55)
Show source-provided references
1. Data-driven vision-based inspection for reinforced concrete beams and slabs: Quantitative damage and load estimation10.1016/j.autcon.2018.09.024 ↗
2. JGJ 169-2009 · 2009
3. ASTM Standard C457/C457M-11 · 2003
4. Hamed, Gehad Mohammed. An expert system for concrete diagnosis. MS thesis. King Fahd University of Petroleum and Minerals (1983).
5. Inspection of nuclear power plant containment structures10.1016/S0029-5493(99)00125-9 ↗
6. Fluid flow in cracks as related to low-permeability gas sands10.2118/11623-PA ↗
7. Preserving microstructure of concrete under load using the Wood's metal technique10.1016/S1365-1609(99)00099-4 ↗
8. Residual compressive and bond strengths of limestone aggregate concrete subjected to elevated temperatures10.1680/macr.1992.44.159.117 ↗
9. The evaluation of paint performance for exterior applications in Singapore's tropical environment10.1016/0360-1323(96)00009-1 ↗
10. A deep learning approach for fast detection and classification of concrete damage10.1016/j.autcon.2021.103785 ↗
11. Collection and evaluation method of fair-faced concrete appearance quality and its engineering application
12. Evaluating concrete surfaces using an image analysis process10.1016/j.conbuildmat.2005.01.025 ↗
13. Scherer. An image analysis procedure to quantify the air void system of mortar and concrete10.1617/s11527-014-0381-9 ↗
14. Classification and quantification of cracks in concrete structures using deep learning image-based techniques10.1016/j.cemconcomp.2020.103781 ↗
15. Concrete crack pixel classification using an encoder decoder based deep learning architecture. advances in visual computing. ISVC 201910.1007/978-3-030-33720-9_46 ↗
16. Restoration intervention of exposed white concrete buildings: The case of ‘Pavilhão do Conhecimento’
17. Image analysis for detection of bugholes on concrete surface10.1016/j.conbuildmat.2017.01.098 ↗
18. Expert system applied for classifying self-compacting concrete surface finish10.1016/j.advengsoft.2013.04.005 ↗
19. End-to-end concrete appearance analysis based on pixel-wise semantic segmentation and CIE Lab10.1016/j.cemconres.2022.106926 ↗
20. NB-CNN. Deep learning-based crack detection using convolutional neural network and naïve bayes data fusion10.1109/TIE.2017.2764844 ↗
21. Scaled subprofile modeling and convolutional neural networks for the identification of Parkinson’s disease in 3D nuclear imaging data10.1142/S0129065719500102 ↗
22. Four decades of computing in civil engineering, CIGOS 2019
23. Concrete crack detection with handwriting script interferences using faster region-based convolutional neural network10.1111/mice.12497 ↗
24. Autonomous concrete crack detection using deep fully convolutional neural network10.1016/j.autcon.2018.11.028 ↗
25. Automatic crack detection for tunnel inspection using deep learning and heuristic image post-processing10.1007/s10489-018-01396-y ↗
26. Automatic identification and quantification of dense microcracks in high-performance fiber-reinforced cementitious composites through deep learning-based computer vision10.1016/j.cemconres.2021.106532 ↗
27. A method for concrete crack detection using U-Net based image inpainting technique
28. A deep learning-based method for pixel-level crack detection on concrete bridges10.1049/ipr2.12512 ↗
29. Concrete cracks detection based on deep learning image Classification, 18th10.3390/ICEM18-05387 ↗
30. Vision-guided crack identification and size quantification framework for dam underwater concrete structures
31. Underwater crack pixel-wise identification and quantification for dams via lightweight semantic segmentation and transfer learning10.1016/j.autcon.2022.104600 ↗
32. GB 175-2023 · 2023
33. GB/T 50080-2016 · 2016
34. GB/T 50081-2019 · 2019
35. Boughen · 2004
36. Use and analysis of color models in image processing10.7439/ijasr.v1i8.2460 ↗
37. Use of scanners for colorimetric analysis of microfluidic paper-based analytical devices (µPADs): a practical guide10.1016/j.microc.2023.108879 ↗
38. Metal oxide (ZnO, TiO2) layer affects sensitivity of multisample colorimetric detection in microfluidic paper-based biosensor
39. Standard RGB color spaces, in: proc ISTSID 7th10.2352/CIC.1999.7.1.art00024 ↗
40. Human Perception of Color
41. Measuring S., M., and L cone sensitivities in the living human eye using phase-sensitive AO-OCT | IOVS | ARVO Journals, (2024).
42. Regression analysis for continuous independent variables in medical research: statistical standard and guideline of
43. Regression Analysis
44. DIY bootstrap: getting the nonparametric bootstrap confidence interval in SPSS for any statistics or function of statistics (when this bootstrap is appropriate)
45. Influence of various amounts of limestone powder on performance of Portland limestone cement concretes10.1016/j.cemconcomp.2009.08.003 ↗
46. The rheological properties of ternary binders containing Portland cement, limestone, and metakaolin or fly ash10.1016/j.cemconres.2013.07.007 ↗
47. Pore solution composition of alkali-activated slag/fly ash pastes10.1016/j.cemconres.2018.10.010 ↗
48. Performance evaluation of pebble concrete for pavement: a study on the sucre highway project10.3390/ma17204994 ↗
49. Activation effects and micro quantitative characterization of high-volume ground granulated blast furnace slag in cement-based composites10.1016/j.cemconcomp.2020.103556 ↗
50. Ability of the R3 test to evaluate differences in early age reactivity of 16 industrial ground granulated blast furnace slags (GGBS)10.1016/j.cemconres.2020.105998 ↗
51. Effect of limestone on rheological, shrinkage and mechanical properties of alkali – Activated slag/fly ash grouting materials10.1016/j.conbuildmat.2018.09.209 ↗
52. Correlation between adhesion energy of release agents on the formwork and demoulding performances10.1016/j.conbuildmat.2014.11.061 ↗
53. Effect of polymer and conventional molds on the aesthetical surface quality of concretes10.1016/j.conbuildmat.2021.124375 ↗
54. The effect of controlled permeable formwork (CPF) liner on the surface quality of concretes10.1016/j.cemconcomp.2016.11.012 ↗
55. Automated crack segmentation in close-range building façade inspection images using deep learning techniques