2026-09-21T01:53:38Zhttps://dspace.mit.edu/server/oai/requestoai:dspace.mit.edu:1721.1/1179592022-01-28T15:16:28Zcom_1721.1_7582com_1721.1_7581col_1721.1_131023
Georgia Perakis and Konstantin Turitsyn.
Tuttman, Max (Max B.)
Leaders for Global Operations Program.
Leaders for Global Operations Program at MIT
Massachusetts Institute of Technology. Department of Mechanical Engineering
Sloan School of Management
2018-09-17T15:51:35Z
2018-09-17T15:51:35Z
2018
2018
http://hdl.handle.net/1721.1/117959
1051237535
Thesis: M.B.A., Massachusetts Institute of Technology, Sloan School of Management, in conjunction with the Leaders for Global Operations Program at MIT, 2018.
Thesis: S.M., Massachusetts Institute of Technology, Department of Mechanical Engineering, in conjunction with the Leaders for Global Operations Program at MIT, 2018.
Cataloged from PDF version of thesis.
Includes bibliographical references (pages 67-68).
This thesis proposes methods to both estimate optimal aggregate investment levels for a system of transmission towers by means of an integrated corrosion and failure simulation as well as a method to identify specific assets in need of investment through a statistical model of structural health. Limited tower replacements over the past decade have resulted in an overall aging of PG&E's transmission system, leading to managerial concerns about potential increased maintenance and replacement costs going forward. The utility is seeking to be able to forecast its future needs despite a minimal history of asset failure. This work establishes long-term investment scenarios by simulating asset aging due to atmospheric corrosion and integrating those simulations with maintenance, replacement, and failure cost estimates. In addition, the aggregate investment forecasts are supplemented with an asset health ranking methodology that enables more targeted resource deployment. Implementation of the simulation based forecasting provides long-term spend estimates - on the order of many decades - and enables the production of sensitivity analyses based on underlying parameters grounded in physical system properties. This advances current industry spend forecasting which relies on qualitative risk assessments and past cost trends. Asset health indices generated from structural properties and environmental data are also shown to correctly rank a structure with a historic reported structural issue as at higher risk than a structure without a reported issue at a rate of 70%.
by Max Tuttman.
M.B.A.
S.M.
68 pages
eng
Massachusetts Institute of Technology
MIT theses are protected by copyright. They may be viewed, downloaded, or printed from this source but further reproduction or distribution in any format is prohibited without written permission.
http://dspace.mit.edu/handle/1721.1/7582
Sloan School of Management.
Mechanical Engineering.
Leaders for Global Operations Program.
Development of a sustainable transmission structure replacement and maintenance strategy
Thesis
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