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Use of machine learning based technique to X-ray microtomographic images of concrete for phase segmentation at meso-scale

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dc.contributor.author Barai, Sudhirkumar
dc.contributor.author Pradhan, Subhasis
dc.date.accessioned 2022-12-23T10:53:43Z
dc.date.available 2022-12-23T10:53:43Z
dc.date.issued 2020-07
dc.identifier.uri https://www.sciencedirect.com/science/article/pii/S0950061820307492
dc.identifier.uri http://dspace.bits-pilani.ac.in:8080/xmlui/handle/123456789/8081
dc.description.abstract The paper discusses the technical limitation of the gray value thresholding technique to detect the voids, aggregate and mortar phases. A two-stage image processing methodology is proposed for the segmentation of the three phases of concrete using the X-ray microtomographic images. In the first stage, the gray value thresholding technique is used to detect the voids. A machine learning based technique is proposed in the second stage for the segmentation of aggregate and mortar. The training data is used to model a planar decision boundary using the logistic regression method. For this, the radial distance from the centre of the image, gray value, and gray value of the filtered embossed image features are considered. The accuracy of the model to quantify the voids is validated with the commercial software. The machine learning model based on logistic regression method exhibits very good accuracy () in detecting the aggregate. en_US
dc.language.iso en en_US
dc.publisher Elsevier en_US
dc.subject Civil Engineering en_US
dc.subject Concrete en_US
dc.subject X-ray microtomography en_US
dc.subject Gray value en_US
dc.subject Threshold gray value en_US
dc.title Use of machine learning based technique to X-ray microtomographic images of concrete for phase segmentation at meso-scale en_US
dc.type Article en_US


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