Thin crack observation in a reinforced concrete bridge pier test using image processing and analysis

Yang, Yuan–Sen; Yang, Chung-Ming; Huang, Chang‐Wei

doi:10.1016/j.advengsoft.2015.02.005

Cited by 59 publications

(35 citation statements)

References 16 publications

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“…However, this is a local analysis of cracks and parameters of individual cracks are measured, e.g., the opening width, crack length, etc. [ 4 , 5 , 6 , 7 , 8 , 9 , 10 , 11 , 12 ]. There is a lack of research work, in which the cluster crack structure is analyzed in a global way, and the results obtained are related to material properties.…”

Section: Introductionmentioning

confidence: 99%

The Influence of Metakaolinite on the Development of Thermal Cracks in a Cement Matrix

Szeląg

2018

Materials

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In the paper the cluster cracks of cement paste that has been modified with metakaolinite was analyzed. The samples were loaded with an elevated temperature based on a thermal shock. To describe the crack structure, three stereological parameters were proposed to measure: (i) the cluster average area (A¯); (ii) the cluster average perimeter (L¯); and (iii) the crack average width (I¯). The computer image analysis was implemented in the study, and 4 series of samples were subjected to the examination. In two series, metakaolinite was used as a substitute for 10% of a cement’s mass. An assessment of the basic physico-mechanical characteristics of the cement matrix was also carried out. The structure of the cement paste was considered as a highly concentrated dispersion system, in which the interactions between the cement’s grains at the initial stage of the structure self-assembly affect the crack characteristics. The study has been supplemented with microstructural investigations using a scanning electron microscope and an X-ray microanalyzer. The conducted research indicated the direction of changes in the geometrical characteristics of thermal cracks if the technological variables of the material are subjected to modification. It was also confirmed that the cluster structures have fractal character and can be analyzed and observed on many levels of a structural heterogeneity.

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Section: Introductionmentioning

confidence: 99%

The Influence of Metakaolinite on the Development of Thermal Cracks in a Cement Matrix

Szeląg

2018

Materials

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“…Alternatively, the displacement field-based method identifies cracks according to the displacement field of the measurement region, where the displacement field is analyzed by image analysis techniques [24,34]. Due to the high precision of image-based displacement field measurements, displacement field-based methods are capable of detecting thin cracks with widths of much less than one pixel.…”

Section: Image Measurement Of Cracks On Concrete Surfacesmentioning

confidence: 99%

Damage Indexing Method for Shear Critical Tubular Reinforced Concrete Structures Based on Crack Image Analysis

Yang

Chang

2019

Sensors

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Image analysis techniques have been applied to measure the displacements, strain field, and crack distribution of structures in the laboratory environment, and present strong potential for use in structural health monitoring applications. Compared with accelerometers, image analysis is good at monitoring area-based responses, such as crack patterns at critical regions of reinforced concrete (RC) structures. While the quantitative relationship between cracks and structural damage depends on many factors, cracks need to be detected and quantified in an automatic manner for further investigation into structural health monitoring. This work proposes a damage-indexing method by integrating an image-based crack measurement method and a crack quantification method. The image-based crack measurement method identifies cracks locations, opening widths, and orientations. Fractal dimension analysis gives the flexural cracks and shear cracks an overall damage index ranging between 0 and 1. According to the orientations of the cracks analyzed by image analysis, the cracks can be classified as either shear or flexural, and the overall damage index can be separated into shear and flexural damage indices. These damage indices not only quantify the damage of an RC structure, but also the contents of shear and flexural failures. While the engineering significance of the damage indices is structure dependent, when the damage indexing method is used for structural health monitoring, the damage indices safety thresholds can further be defined based on the structure type under consideration. Finally, this paper demonstrates this method by using the results of two experiments on RC tubular containment vessel structures.

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“…Cracking in reinforced concrete structures can lead to catastrophic failure of the entire structure, due to the fact that cracks provide access to corrosive chemicals (Krauss & Rogalla, ). The current empirical methods, such as manual visual inspection for bridge condition assessment, generally involve sending trained inspectors onsite to highlight cracks with physical markings and to capture images of defects for photographic record (Dias‐da‐Costa, Valença, Júlio, & Araújo, ; G. Li, He, Ju, & Du, ; Y. Yang, Yang, & Huang, ). However, the use of manual bridge inspection to perform crack detection is subjective, time consuming, qualitative, and incapable of evaluating areas that are difficult to access or inspect (Yeum & Dyke, ).…”

Section: Introductionmentioning

confidence: 99%

“…The current empirical methods, such as manual visual inspection for bridge condition assessment, generally involve sending trained inspectors onsite to highlight cracks with physical markings and to capture images of defects for photographic record (Dias-da-Costa, Valença, Júlio, & Araújo, 2017;G. Li, He, Ju, & Du, 2014;Y. Yang, Yang, & Huang, 2015).…”

Section: Introductionmentioning

confidence: 99%

Concrete crack detection with handwriting script interferences using faster region‐based convolutional neural network

Deng

Lee

2019

Computer aided Civil Eng

157

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The current bridge maintenance practice generally involves manual visual inspection, which is highly subjective and unreliable. A technique that can automatically detect defects, for example, surface cracks, is essential so that early warnings can be triggered to prevent disaster due to structural failure. In this study, to permit automatic identification of concrete cracks, an ad‐hoc faster region‐based convolutional neural network (faster R‐CNN) was applied to contaminated real‐world images taken from concrete bridges with complex backgrounds, including handwriting. A dataset of 5,009 cropped images was generated and labeled for two different objects, cracks and handwriting. The proposed network was then trained and tested using the generated image dataset. Four full‐scale images that contained complex disturbance information were used to assess the performance of the trained network. The results of this study demonstrate that faster R‐CNN can automatically locate crack from raw images, even with the presence of handwriting scripts. For comparative study, the proposed network is also compared with You Only Look Once v2 detection technique.

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Thin crack observation in a reinforced concrete bridge pier test using image processing and analysis

Cited by 59 publications

References 16 publications

The Influence of Metakaolinite on the Development of Thermal Cracks in a Cement Matrix

The Influence of Metakaolinite on the Development of Thermal Cracks in a Cement Matrix

Damage Indexing Method for Shear Critical Tubular Reinforced Concrete Structures Based on Crack Image Analysis

Concrete crack detection with handwriting script interferences using faster region‐based convolutional neural network

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