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Digital Analysis of Geo-Referenced Concrete Scanning Electron Microscope (SEM) Images
The microstructural evaluation of complex cementitious materials has been made possible by the microscopic imaging tools such as Scanning Electron Microscope (SEM) and X-Ray Microanalysis. Particularly, the application of concrete SEM imaging and digital image analysis have become common in the analysis and mapping of concrete technology. In this study, six samples of two-dimensional (2D) SEM images were spatially resampled to produce Geo-referenced SEM sample images. Subsequently, they were analyzed and the intensity histogram plot was produced to facilitate visual interpretation. The consecutive digital image analysis performed was the enhancement and noise removal process using two filtering methods i.e. median and adaptive box filter. The filtered resampled images, then undergone the unsupervised K-Means classification process to collectively separate each individual pixel corresponds to the spectral data. By spatial segmentation of K-Means algorithms, the cluster groups generated were carefully reviewed before proceeding to the final analysis. From the resulting data, the mapping of the spatial distribution of k-cluster and the quantification of micro-cracks (voids) were performed. The results of the SEM images (1st - 4th sample) showed a higher percentage of k-cluster data indicating a good correlation with the major elemental composition of EDX analysis, namely Oxide (O), Silicon (Si) and Carbon (C). Meanwhile, the subjective visual assessment of the image (5th and 6th sample) has confirmed the micro-crack developments on the concrete SEM images upon which the crack density was 3.02 % and 1.30 %, respectively.
Digital Analysis of Geo-Referenced Concrete Scanning Electron Microscope (SEM) Images
The microstructural evaluation of complex cementitious materials has been made possible by the microscopic imaging tools such as Scanning Electron Microscope (SEM) and X-Ray Microanalysis. Particularly, the application of concrete SEM imaging and digital image analysis have become common in the analysis and mapping of concrete technology. In this study, six samples of two-dimensional (2D) SEM images were spatially resampled to produce Geo-referenced SEM sample images. Subsequently, they were analyzed and the intensity histogram plot was produced to facilitate visual interpretation. The consecutive digital image analysis performed was the enhancement and noise removal process using two filtering methods i.e. median and adaptive box filter. The filtered resampled images, then undergone the unsupervised K-Means classification process to collectively separate each individual pixel corresponds to the spectral data. By spatial segmentation of K-Means algorithms, the cluster groups generated were carefully reviewed before proceeding to the final analysis. From the resulting data, the mapping of the spatial distribution of k-cluster and the quantification of micro-cracks (voids) were performed. The results of the SEM images (1st - 4th sample) showed a higher percentage of k-cluster data indicating a good correlation with the major elemental composition of EDX analysis, namely Oxide (O), Silicon (Si) and Carbon (C). Meanwhile, the subjective visual assessment of the image (5th and 6th sample) has confirmed the micro-crack developments on the concrete SEM images upon which the crack density was 3.02 % and 1.30 %, respectively.
Digital Analysis of Geo-Referenced Concrete Scanning Electron Microscope (SEM) Images
Ahamad Mohd Sanusi S. (author) / Maizul Elly Nur Myaisara (author)
2020
Article (Journal)
Electronic Resource
Unknown
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