Please use this identifier to cite or link to this item: http://hdl.handle.net/10603/279764
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dc.coverage.spatialOptimization of time and cost estimation for prefabrication construction using artificial neural network
dc.date.accessioned2020-03-03T12:53:19Z-
dc.date.available2020-03-03T12:53:19Z-
dc.identifier.urihttp://hdl.handle.net/10603/279764-
dc.description.abstractThe success of any construction firm depends on the projects newlinecompleted within a stipulated time frame and at the agreed cost. The newlineconstruction industry is comprises of prefabrication manufacturing newlinecompanies, logistics parties, on-site construction fields, and so on. newlinePrefabrication process is the practice of assembling structural components at a newlinemanufacturing site and transporting them as completed or semi-assembled newlinecomponents to the construction site. Optimization, which includes newlinemaximizing or minimizing a real function, is achieved by choosing the input newlinevalues systematically from within an allowed set and computing the value of newlinethe function. The generalization of optimization theory and techniques to newlineother formulations consists of a large area of applied mathematics. In general, newlineoptimization finds the best available values of some objective functions newlinewithin a given set of defined domains or a set of constraints. Optimization newlinealso includes various types of objective functions and different types of newlinedomains. The present study focuses on the optimization techniques to newlineminimize time and cost in prefabricated constructions.Artificial Neural Networks (ANNs) are used for optimization, due to their ability to resolve qualitative and quantitative problems encountered in newlinethe construction industry. In an ANN, the input layer, hidden layer, and output newlinelayer are performed based on the weight of the hidden layer. The layers are newlineoptimized by using various optimization techniques. In the construction newlinemanagement, ANN covers an extensive part of the problems such as cost newlineestimation, decision making, predicting the percentage of markup, and newlineproduction rate in the construction industry. The fundamental benefit of newlineprefabricated methodology is the quick completion of the process. The other newlinegenuine advantage of the prefabrication process is its inbuilt flexible nature newline newline
dc.format.extentxix, 165p.
dc.languageEnglish
dc.relationp.158-164
dc.rightsuniversity
dc.titleOptimization of time and cost estimation for prefabrication construction using artificial neural network
dc.title.alternative
dc.creator.researcherAshok manikandan S
dc.subject.keywordEngineering and Technology,Engineering,Engineering Civil
dc.subject.keywordTime and cost
dc.subject.keywordNeural network
dc.description.note
dc.contributor.guidePazhani K C
dc.publisher.placeChennai
dc.publisher.universityAnna University
dc.publisher.institutionFaculty of Civil Engineering
dc.date.registeredn.d.
dc.date.completed2018
dc.date.awarded30/06/2018
dc.format.dimensions21cm
dc.format.accompanyingmaterialNone
dc.source.universityUniversity
dc.type.degreePh.D.
Appears in Departments:Faculty of Civil Engineering

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01_title.pdfAttached File229.05 kBAdobe PDFView/Open
02_certificates.pdf2.79 MBAdobe PDFView/Open
03_abstract.pdf103.2 kBAdobe PDFView/Open
04_acknowledgement.pdf101.22 kBAdobe PDFView/Open
05_contents.pdf222.65 kBAdobe PDFView/Open
06_chapter1.pdf497.26 kBAdobe PDFView/Open
07_chapter2.pdf265.54 kBAdobe PDFView/Open
08_chapter3.pdf6.6 MBAdobe PDFView/Open
09_chapter4.pdf4.4 MBAdobe PDFView/Open
10_conclusion.pdf156.43 kBAdobe PDFView/Open
11_appendices.pdf283.87 kBAdobe PDFView/Open
12_references.pdf235.93 kBAdobe PDFView/Open
13_publications.pdf148.2 kBAdobe PDFView/Open


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