Reliability-based optimization of rail inspection

Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2004.

Bibliographic Details
Main Author: Kashima, Takashi, 1971-
Other Authors: Jerome J. Connor.
Format: Thesis
Language:eng
Published: Massachusetts Institute of Technology 2005
Subjects:
Online Access:http://hdl.handle.net/1721.1/29388
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author Kashima, Takashi, 1971-
author2 Jerome J. Connor.
author_facet Jerome J. Connor.
Kashima, Takashi, 1971-
author_sort Kashima, Takashi, 1971-
collection MIT
description Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2004.
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spelling mit-1721.1/293882019-04-12T21:13:20Z Reliability-based optimization of rail inspection Kashima, Takashi, 1971- Jerome J. Connor. Massachusetts Institute of Technology. Dept. of Civil and Environmental Engineering. Massachusetts Institute of Technology. Dept. of Civil and Environmental Engineering. Civil and Environmental Engineering. Thesis (S.M.)--Massachusetts Institute of Technology, Dept. of Civil and Environmental Engineering, 2004. Includes bibliographical references (p. 123-126). This thesis proposes a quantitative method to optimize inspection/repair intervention in rail defect management. Rail defect management is important for track maintenance, since rails are the most significant and basic component of the track systems. Rail inspection is a fundamental intervention to prevent rail failure. Railroads have evolved the rail inspection interval based on their empirical judgement and on field data. A crack size is predicted by linear elastic fracture mechanics (LEFM). The proposed method identifies the time-varying rail reliability due to deterioration, using data obtained from LEFM and from first-order reliability methods (FORM), which consider the uncertainty regarding the model. Since FORM is an approximation method, Monte Carlo simulation confirms the results. To represent practical situations regarding rail defect management, an event tree (ET) analysis is performed. The ET is modeled to all events and actions with respect to inspection/repair intervention. The ET analysis evaluates the expected reliability of a rail after inspections and possible remedial actions. Based on these results, a life-cycle cost (LCC) model is formulated, taking into consideration the time value of money. To this end, applications of the model to optimization of inspection intervals and to investigation of the effect of nondestructive testing and remedial actions on the LCC and the interval are analyzed. (cont.) As a result, it is possible to extend the present inspection interval. Additionally, the effect of detectability of nondestructive testing on the LCC is more significant than that of accuracy of nondestructive testing, and a proactive maintenance policy may reduce both the expected total cost and the number of inspections. by Takashi Kashima. S.M. 2005-10-14T20:16:21Z 2005-10-14T20:16:21Z 2004 2004 Thesis http://hdl.handle.net/1721.1/29388 56125220 eng M.I.T. theses are protected by copyright. They may be viewed from this source for any purpose, but reproduction or distribution in any format is prohibited without written permission. See provided URL for inquiries about permission. http://dspace.mit.edu/handle/1721.1/7582 126 p. 3393398 bytes 3393204 bytes application/pdf application/pdf application/pdf Massachusetts Institute of Technology
spellingShingle Civil and Environmental Engineering.
Kashima, Takashi, 1971-
Reliability-based optimization of rail inspection
title Reliability-based optimization of rail inspection
title_full Reliability-based optimization of rail inspection
title_fullStr Reliability-based optimization of rail inspection
title_full_unstemmed Reliability-based optimization of rail inspection
title_short Reliability-based optimization of rail inspection
title_sort reliability based optimization of rail inspection
topic Civil and Environmental Engineering.
url http://hdl.handle.net/1721.1/29388
work_keys_str_mv AT kashimatakashi1971 reliabilitybasedoptimizationofrailinspection