Image-based techniques for initial and long-term characterization of crack kinematics in reinforced concrete structures

被引:2
作者
Vincens, Baptiste [1 ]
Corres, Enrique [2 ]
Muttoni, Aurelio [2 ]
机构
[1] Sch Engn & Architecture Fribourg, Fribourg, Switzerland
[2] Ecole Polytech Fed Lausanne, Sch Architecture Civil & Environm Engn, Lausanne, Switzerland
关键词
Crack detection; Crack kinematics; Digital Image Correlation; Existing structures; Marker; Monitoring; Reinforced concrete; Long-term measurements; SHEAR-TRANSFER ACTIONS; BEHAVIOR; MEMBERS; IDENTIFICATION; PHOTOGRAMMETRY; DEFORMATION;
D O I
10.1016/j.engstruct.2024.118492
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
In the recent years, Digital Image Correlation (DIC) was applied with very promising results to monitor cracks in reinforced concrete structures. However, current DIC measurements present some limitations to characterize the existing crack (already present in the reference image) and for long-term monitoring due to the principles of the correlation algorithm. This paper presents two techniques to complement DIC in these two cases. The first one is based on direct detection using existing algorithms. The second one is based on the detection of markers fixed around the crack. Their relative position in different images is used to compute the crack displacement that occurred between the inspections. A conventional DIC set-up can be used for this technique. Simplified and refined methods are proposed to quantify the measurement uncertainty and to determine the number and position of markers. Both techniques are validated in laboratory conditions and in-situ in an existing concrete bridge. The combination of the two presented techniques with conventional DIC is promising and could be of interest for applications with complicated crack patterns where a detailed understanding of the crack kinematics is required.
引用
收藏
页数:17
相关论文
共 103 条
  • [1] Analysis of edge-detection techniques for crack identification in bridges
    Abdel-Qader, L
    Abudayyeh, O
    Kelly, ME
    [J]. JOURNAL OF COMPUTING IN CIVIL ENGINEERING, 2003, 17 (04) : 255 - 263
  • [2] Adam VF, 2021, PhD Thesis, P318
  • [3] Robust Pixel-Level Crack Detection Using Deep Fully Convolutional Neural Networks
    Alipour, Mohamad
    Harris, Devin K.
    Miller, Gregory R.
    [J]. JOURNAL OF COMPUTING IN CIVIL ENGINEERING, 2019, 33 (06)
  • [4] The influence of small amounts of shear reinforcement on the shear-transferring mechanisms in RC beams: An analysis based on refined experimental measurements
    Autrup, Frederik
    Jorgensen, Henrik Broner
    Hoang, Linh Cao
    [J]. STRUCTURAL CONCRETE, 2023, 24 (02) : 2844 - 2861
  • [5] Novel invisible markers for monitoring cracks on masonry structures
    Bal, Ihsan E.
    Dais, Dimitris
    Smyrou, Eleni
    Sarhosis, Vasilis
    [J]. CONSTRUCTION AND BUILDING MATERIALS, 2021, 300
  • [6] Crack measurement: Development, testing and applications of an automatic image-based algorithm
    Barazzetti, Luigi
    Scaioni, Marco
    [J]. ISPRS JOURNAL OF PHOTOGRAMMETRY AND REMOTE SENSING, 2009, 64 (03) : 285 - 296
  • [7] Barros F, 2022, Int J Struct Integr, V14
  • [8] Benning W., 2004, ISPRS C IST COMM
  • [9] Crack monitoring in reinforced concrete beams by distributed optical fiber sensors
    Berrocal, Carlos G.
    Fernandez, Ignasi
    Rempling, Rasmus
    [J]. STRUCTURE AND INFRASTRUCTURE ENGINEERING, 2021, 17 (01) : 124 - 139
  • [10] Brault A, 2015, Proc SPIE, V9435, P12