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http://hdl.handle.net/10603/279764
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DC Field | Value | Language |
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dc.coverage.spatial | Optimization of time and cost estimation for prefabrication construction using artificial neural network | |
dc.date.accessioned | 2020-03-03T12:53:19Z | - |
dc.date.available | 2020-03-03T12:53:19Z | - |
dc.identifier.uri | http://hdl.handle.net/10603/279764 | - |
dc.description.abstract | The 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.extent | xix, 165p. | |
dc.language | English | |
dc.relation | p.158-164 | |
dc.rights | university | |
dc.title | Optimization of time and cost estimation for prefabrication construction using artificial neural network | |
dc.title.alternative | ||
dc.creator.researcher | Ashok manikandan S | |
dc.subject.keyword | Engineering and Technology,Engineering,Engineering Civil | |
dc.subject.keyword | Time and cost | |
dc.subject.keyword | Neural network | |
dc.description.note | ||
dc.contributor.guide | Pazhani K C | |
dc.publisher.place | Chennai | |
dc.publisher.university | Anna University | |
dc.publisher.institution | Faculty of Civil Engineering | |
dc.date.registered | n.d. | |
dc.date.completed | 2018 | |
dc.date.awarded | 30/06/2018 | |
dc.format.dimensions | 21cm | |
dc.format.accompanyingmaterial | None | |
dc.source.university | University | |
dc.type.degree | Ph.D. | |
Appears in Departments: | Faculty of Civil Engineering |
Files in This Item:
File | Description | Size | Format | |
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01_title.pdf | Attached File | 229.05 kB | Adobe PDF | View/Open |
02_certificates.pdf | 2.79 MB | Adobe PDF | View/Open | |
03_abstract.pdf | 103.2 kB | Adobe PDF | View/Open | |
04_acknowledgement.pdf | 101.22 kB | Adobe PDF | View/Open | |
05_contents.pdf | 222.65 kB | Adobe PDF | View/Open | |
06_chapter1.pdf | 497.26 kB | Adobe PDF | View/Open | |
07_chapter2.pdf | 265.54 kB | Adobe PDF | View/Open | |
08_chapter3.pdf | 6.6 MB | Adobe PDF | View/Open | |
09_chapter4.pdf | 4.4 MB | Adobe PDF | View/Open | |
10_conclusion.pdf | 156.43 kB | Adobe PDF | View/Open | |
11_appendices.pdf | 283.87 kB | Adobe PDF | View/Open | |
12_references.pdf | 235.93 kB | Adobe PDF | View/Open | |
13_publications.pdf | 148.2 kB | Adobe PDF | View/Open |
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