2026-09-20T04:43:19Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/619052026-06-05T20:27:36Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023col_1721.1_131024
Trent R. Gooding and Pat Hale.
Netemeyer, Kristopher David
Massachusetts Institute of Technology. Engineering Systems Division.
Massachusetts Institute of Technology. Department of Mechanical Engineering
Massachusetts Institute of Technology. Engineering Systems Division
2011-03-24T20:24:31Z
2011-03-24T20:24:31Z
2010
2010
http://hdl.handle.net/1721.1/61905
706827668
Thesis (Nav. E.)--Massachusetts Institute of Technology, Dept. of Mechanical Engineering; and, (S.M. in Engineering and Management)--Massachusetts Institute of Technology, Engineering Systems Division, 2010.
Paged in Arabic numerals except p. 1-8 and p. 89-90, which are in Roman numerals. Cataloged from PDF version of thesis.
Includes bibliographical references (p. lxxxix (89)-xc (90)).
Alternative methods for cost estimation are important in the early conceptual stages of a design when there is not enough detail to allow for a traditional quantity takeoff estimate to be performed. Much of the budgeting process takes place during the early stages of a design and it is important to be able to develop a budget quality estimate so a design is allocated the necessary resources to meet stakeholder requirements. Accurate project cost estimates early in the planning and design processes can also serve as a cost-control measure to assist in managing the design process. With an understanding of the most significant engineering decisions that affect project costs, project team members and stakeholders can proactively make cost-effective decisions during the design process rather than after construction begins and it is too late to prevent going over budget. This research examines the potential of Artificial Neural Networks (ANNs) as a tool to support the tasks of cost prediction, mapping costs to engineering decisions, and risk management during the early stages of a design's life-cycle. ANNs are a modeling tool based on the computational paradigm of the human brain and have proved to be a robust and reliable method for prediction, ranking, classification, and interpretation or processing of data.
Kristopher David Netemeyer.
S.M. in Engineering and Management
Nav.E.
177 p.
eng
Massachusetts Institute of Technology
M.I.T. theses are protected by
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http://dspace.mit.edu/handle/1721.1/7582
Mechanical Engineering.
Engineering Systems Division.
A quantitative methodology for mapping project costs to engineering decisions in naval ship design and procurement
Thesis
application/pdf
Publication
unknown
unknown
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<Abstract>Alternative methods for cost estimation are important in the early conceptual stages of a design when there is not enough detail to allow for a traditional quantity takeoff estimate to be performed. Much of the budgeting process takes place during the early stages of a design and it is important to be able to develop a budget quality estimate so a design is allocated the necessary resources to meet stakeholder requirements. Accurate project cost estimates early in the planning and design processes can also serve as a cost-control measure to assist in managing the design process. With an understanding of the most significant engineering decisions that affect project costs, project team members and stakeholders can proactively make cost-effective decisions during the design process rather than after construction begins and it is too late to prevent going over budget. This research examines the potential of Artificial Neural Networks (ANNs) as a tool to support the tasks of cost prediction, mapping costs to engineering decisions, and risk management during the early stages of a design's life-cycle. ANNs are a modeling tool based on the computational paradigm of the human brain and have proved to be a robust and reliable method for prediction, ranking, classification, and interpretation or processing of data.</Abstract>
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