TY - JOUR
T1 - Evaluation of Railway Ballast Permeability Using Machine Vision–Based Degradation Analysis
AU - Huang, Haohang
AU - Moaveni, Maziar
AU - Schmidt, Scott
AU - Tutumluer, Erol
AU - Hart, John M.
N1 - Funding Information:
The Association of American Railroads (AAR), TRB Innovations Deserving Exploratory Analysis (IDEA) Rail Safety program funded by Federal Railroad Administration, BNSF Railway, and Union Pacific Railroad have provided financial support for the development of a machine vision–based ballast inspection system and the UI-CHAP permeameter equipment described in this paper. The authors greatly appreciate the contributions and technical support received from Rail Transportation and Engineering Center (RailTEC) and Illinois Center for Transportation (ICT) at the University of Illinois in Urbana-Champaign. Moreover, the authors would like to recognize the help and support that was provided by Dennis Morgart, William (Zach) Dombrow, and Mike Wnek from BNSF Railway, and David Davis, Mike McHenry, Dingqing Li, and Colin Basye from TTCI.
Funding Information:
The Association of American Railroads (AAR), TRB Innovations Deserving Exploratory Analysis (IDEA) Rail Safety program funded by Federal Railroad Administration, BNSF Railway, and Union Pacific Railroad have provided financial support for the development of a machine vision– based ballast inspection system and the UI-CHAP permeameter equipment described in this paper. The authors greatly appreciate the contributions and technical support received from Rail Transportation and Engineering Center (RailTEC) and Illinois Center for Transportation (ICT) at the University of Illinois in Urbana-Champaign. Moreover, the authors would like to recognize the help and support that was provided by Dennis Morgart, William (Zach) Dombrow, and Mike Wnek from BNSF Railway, and David Davis, Mike McHenry, Dingqing Li, and Colin Basye from TTCI.
Publisher Copyright:
© National Academy of Sciences: Transportation Research Board 2018.
PY - 2018/12/1
Y1 - 2018/12/1
N2 - Railway ballast degrades progressively as a result of accumulated traffic primarily through abrasion and particle breakage. Degraded ballast may cause reduced lateral and longitudinal stability, ineffective drainage, and excessive settlement of track structures, all of which would adversely affect the performance of ballasted track. Traditional methods of ballast degradation assessment involve time-consuming field sampling and laboratory sieve analysis; moreover, determining the level of track performance deterioration at which ballast maintenance is best considered still remains challenging. This paper investigates the permeability of railway ballast through laboratory testing and provides insight into its field drainage capacity under degraded condition using an innovative approach of field imaging. Constant head permeability tests were conducted on clean and degraded ballast samples which indicated nonlinear power-curve trends, especially for clean ballast, of unit flow amount with its hydraulic gradient. Imaging-based degradation analysis using machine vision technology was also performed on clean and degraded in-service ballast to correlate Fouling Index (FI) from laboratory sieving with Percent Degraded Segments (PDS) obtained from the recently developed image segmentation algorithm. Accordingly, a new Permeability Index (PI) is introduced in this paper to define ballast permeability in the form of a bilinear model developed from the machine vision–based ballast degradation analysis. Based on the findings of this study, a two-stage ballast cleaning process for determining the timeframe of ballasted track maintenance considering its drainage capacity is proposed.
AB - Railway ballast degrades progressively as a result of accumulated traffic primarily through abrasion and particle breakage. Degraded ballast may cause reduced lateral and longitudinal stability, ineffective drainage, and excessive settlement of track structures, all of which would adversely affect the performance of ballasted track. Traditional methods of ballast degradation assessment involve time-consuming field sampling and laboratory sieve analysis; moreover, determining the level of track performance deterioration at which ballast maintenance is best considered still remains challenging. This paper investigates the permeability of railway ballast through laboratory testing and provides insight into its field drainage capacity under degraded condition using an innovative approach of field imaging. Constant head permeability tests were conducted on clean and degraded ballast samples which indicated nonlinear power-curve trends, especially for clean ballast, of unit flow amount with its hydraulic gradient. Imaging-based degradation analysis using machine vision technology was also performed on clean and degraded in-service ballast to correlate Fouling Index (FI) from laboratory sieving with Percent Degraded Segments (PDS) obtained from the recently developed image segmentation algorithm. Accordingly, a new Permeability Index (PI) is introduced in this paper to define ballast permeability in the form of a bilinear model developed from the machine vision–based ballast degradation analysis. Based on the findings of this study, a two-stage ballast cleaning process for determining the timeframe of ballasted track maintenance considering its drainage capacity is proposed.
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U2 - 10.1177/0361198118790849
DO - 10.1177/0361198118790849
M3 - Article
AN - SCOPUS:85052286144
VL - 2672
SP - 62
EP - 73
JO - Transportation Research Record
JF - Transportation Research Record
SN - 0361-1981
IS - 10
ER -