Concrete Mix Design Using Artificial Neural Network

dc.contributor.authorGarg, Rishi
dc.contributor.supervisorKwatra, Naveen
dc.date.accessioned2007-04-18T04:13:46Z
dc.date.available2007-04-18T04:13:46Z
dc.date.issued2007-04-18T04:13:46Z
dc.description.abstractConcrete is the most widely used construction material because of its flowability in most complicated form i.e. its ability to take any shape while wet, and its strength development characteristics when it hardens. Generally concrete is used to build protective structures, which are subjected to several extreme stress conditions. Concrete is the most widely used construction material manufactured at the site. This composite material is obtained by mixing cement, water and aggregates. Its production involves a number of operations according to prevailing site conditions. The ingredients of widely varying characteristics can be used to produce concrete of acceptable quality. The strength, durability and other characteristics of concrete depend upon the properties of its ingredients, the proportions of the mix, the method of compaction and other controls. The popularity of concrete as a construction material is due to the fact that it is made from commonly available ingredients and can be tailored to functional requirements in a particular situation. Among the various properties of concrete, its compressive strength is considered to be the most important. However, workability of concrete plays an important role in the mix design. Other factors such as W/C ratio, Fineness modulus of aggregate and specific gravity of cement have their own importance in mix design.en
dc.description.sponsorshipDepartment of Civil Engineering, Thapar University, Patiala.en
dc.format.extent234754 bytes
dc.format.mimetypeapplication/pdf
dc.identifier.urihttp://hdl.handle.net/123456789/202
dc.language.isoenen
dc.subjectArtificial Neural Networken
dc.subjectConcrete Mix Designen
dc.subjectBack Propagation Learningen
dc.subjectPattern Parameteren
dc.titleConcrete Mix Design Using Artificial Neural Networken
dc.typeThesisen

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