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Title:

Predicting Unplanned Maintenance Needs Related to Rail Track Geometry

Accession Number:

01372838

Record Type:

Component

Abstract:

The present work puts forward a simple method to predict unplanned maintenance needs related to rail track geometric condition for future implementation in a Decision Support System for maintenance and renewal decisions. An exploratory analysis through logistic regression was conducted using the track geometry inspection records from the Portuguese Infrastructure Manager (REFER) databases, in order to predict spot maintenance needs depending on planned maintenance criteria and other explaining variables such as the presence of bridges and switches. Main findings showed that the standard deviation of horizontal alignment defects (filtered in the wavelength range 3-25m) is a statistically significant predictor of unplanned maintenance needs due to track geometry condition.

Supplemental Notes:

This paper was sponsored by TRB committee AR060 Railway Maintenance

Monograph Accession #:

01362476

Report/Paper Numbers:

12-2171

Language:

English

Corporate Authors:

Transportation Research Board

500 Fifth Street, NW
Washington, DC 20001 United States

Authors:

Andrade, António Ramos
Teixeira, Paulo Fonseca

Pagination:

15p

Publication Date:

2012

Conference:

Transportation Research Board 91st Annual Meeting

Location: Washington DC, United States
Date: 2012-1-22 to 2012-1-26
Sponsors: Transportation Research Board

Media Type:

Digital/other

Features:

References; Tables

Uncontrolled Terms:

Horizontal alignment

Subject Areas:

Maintenance and Preservation; Railroads; I60: Maintenance

Source Data:

Transportation Research Board Annual Meeting 2012 Paper #12-2171

Files:

TRIS, TRB

Created Date:

Feb 8 2012 5:08PM