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dc.contributor.advisorRaghu Prasad, B K
dc.contributor.authorNaddaf, Hamid Eskandari
dc.date.accessioned2010-07-16T05:12:21Z
dc.date.accessioned2018-07-31T05:42:21Z
dc.date.available2010-07-16T05:12:21Z
dc.date.available2018-07-31T05:42:21Z
dc.date.issued2010-07-16
dc.date.submitted2008
dc.identifier.urihttps://etd.iisc.ac.in/handle/2005/761
dc.description.abstractSelf-consolidating concrete (SCC) has wide use for placement in congested reinforced concrete structures in recent years. SCC represents one of the most outstanding advances in concrete technology during the last two decades. In the current work a great deal of cognizance pertaining to mechanical properties of SCC and comparison of fracture characteristics of notched and unnotched beams of plain concrete as well as using acoustic emission to understand the localization of crack patterns at different stages has been done. An artificial neural network (ANN) is proposed to predict the 28day compressive strength of a normal and high strength of SCC and HPC with high volume fly ash. The ANN is trained by the data available in literature on normal volume fly ash because data on SCC with high volume fly ash is not available in sufficient quantity. Fracture characteristics of notched and unnotched beams of plain self consolidating concrete using acoustic emission to understand the localization of crack patterns at different stages has been done. Considering this as a platform, further analysis has been done using moment tensor analysis as a new notion to evaluate fracture characteristics in terms of crack orientation, direction of crack propagation at nano and micro levels. Analysis of B-value (b-value based on energy) is also carried out, and this has introduced to a new idea of carrying out the analysis on the basis of energy which gives a clear picture of results when compared with the analysis carried out using amplitudes. Further a new concept is introduced to analyze crack smaller than micro (could be hepto cracks) in solid materials. Each crack formation corresponds to an AE event and is processed and analyzed for crack orientation, crack volume at hepto and micro levels using moment tensor analysis based on energy. Cracks which are tinier than microcracks (could be hepto), are formed in large numbers at very early stages of loading prior to peak load. The volume of hepto and micro cracks is difficult to measure physically, but could be characterized using AE data in moment tensor analysis based on energy. It is conjectured that the ratio of the volume of hepto to that of micro could reach a critical value which could be an indicator of onset of microcracks after the formation of hepto cracks.en_US
dc.language.isoen_USen_US
dc.relation.ispartofseriesG22894en_US
dc.subjectConcrete - Fracture Mechanicsen_US
dc.subjectSelf-Consolidating Concrete (SCC)en_US
dc.subjectPlain Concrete Beams - Fractureen_US
dc.subjectPlain Concrete Beams - Acoustic Emissionen_US
dc.subjectConcrete - Compressive Strength - Artificial Neural Network Modelsen_US
dc.subjectSelf-Consolidating Concrete - Propertiesen_US
dc.subjectArtificial Neural Network (ANN)en_US
dc.subjectFly Ashen_US
dc.subjectUn-notched Beamsen_US
dc.subjectNotched Beamsen_US
dc.subjectHepto Cracksen_US
dc.subjectMicrocracksen_US
dc.subject.classificationStructural Engineeringen_US
dc.titleFracture Characteristics Of Self Consolidating Concreteen_US
dc.typeThesisen_US
dc.degree.namePhDen_US
dc.degree.levelDoctoralen_US
dc.degree.disciplineFaculty of Engineeringen_US


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