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dc.contributor.authorWaris, M.I.
dc.contributor.authorMir, J.
dc.contributor.authorPlevris, V.
dc.contributor.authorAhmad, A.
dc.date.accessioned2021-06-10T12:06:57Z
dc.date.available2021-06-10T12:06:57Z
dc.date.created2020-10-28T18:40:18Z
dc.date.issued2020
dc.identifier.citationIOP Conference Series: Materials Science and Engineering. 2020, 899, (1-7).en_US
dc.identifier.issn1757-8981
dc.identifier.urihttps://hdl.handle.net/11250/2758850
dc.description.abstractQuality of concrete is majorly ascertained through its compressive strength which has a significant role in the stability of concrete structures. In this study, artificial neural network (ANN) and image processing (IP) techniques were used to predict the concrete compressive strength (fc) with cement replacement material (CRM), i.e., Fly Ash (FA) and Silica Fumes (SF). 18 concrete cylinders were cast with different mix ratios and with different % of CRM. Half of them were tested for compression strength in the laboratory and remaining cylinders were cut into three slices each, for prediction of compressive strength through the proposed technique. Images were obtained using a DSLR camera under defined conditions to extract the features. Based on the extracted features, ANN modelling was performed for predicting fc. A comparison of experimental results and ANN results (R = 0.9865) proved ANN models can be used as a prediction tool for compressive strength of concrete.en_US
dc.language.isoengen_US
dc.publisherIOP Publishingen_US
dc.relation.ispartofseriesIOP Conference Series: Materials Science and Engineering;volume 899
dc.rightsNavngivelse 4.0 Internasjonal*
dc.rights.urihttp://creativecommons.org/licenses/by/4.0/deed.no*
dc.subjectArtificial neural networksen_US
dc.subjectImage processingen_US
dc.subjectConcretesen_US
dc.subjectCompressive strengthsen_US
dc.titlePredicting compressive strength of CRM samples using Image processing and ANNen_US
dc.typePeer revieweden_US
dc.typeJournal articleen_US
dc.description.versionpublishedVersionen_US
dc.source.articlenumber012014en_US
cristin.ispublishedtrue
cristin.fulltextpostprint
cristin.qualitycode1
dc.identifier.doihttps://doi.org/10.1088/1757-899X/899/1/012014
dc.identifier.cristin1843082
dc.source.journalIOP Conference Series: Materials Science and Engineeringen_US
dc.source.volume899en_US
dc.source.pagenumber7en_US


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