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A New High-Efficiency Procedure for Aggregate Gradation Determination of the Railway Ballast by Means Image Recognition Method
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| Zeitschriftentitel: | Archives of Civil Engineering |
|---|---|
| Personen und Körperschaften: | , |
| In: | Archives of Civil Engineering, 59, 2013, 4, S. 469-482 |
| Format: | E-Article |
| Sprache: | Unbestimmt |
| veröffentlicht: |
Polish Academy of Sciences Chancellery
|
| Schlagwörter: |
| author_facet |
Guerrieri, M. Parla, G. Guerrieri, M. Parla, G. |
|---|---|
| author |
Guerrieri, M. Parla, G. |
| spellingShingle |
Guerrieri, M. Parla, G. Archives of Civil Engineering A New High-Efficiency Procedure for Aggregate Gradation Determination of the Railway Ballast by Means Image Recognition Method Civil and Structural Engineering |
| author_sort |
guerrieri, m. |
| spelling |
Guerrieri, M. Parla, G. 1230-2945 Polish Academy of Sciences Chancellery Civil and Structural Engineering http://dx.doi.org/10.2478/ace-2013-0025 <jats:title>Abstract</jats:title><jats:p> The mechanical characteristics of the railway superstructure are related to the properties of the ballast, and especially to the particle size distribution of its grains. Under the constant stress-strain of carriages, the ballast can deteriorate over time, and consequently it should properly be monitored for safety reasons. The equipment which currently monitors the railway superstructure (like the Italian diagnostic train Archimede) do not make any “quantitative” evaluation of the ballast. The aim of this paper is therefore to propose a new methodology for extracting railway ballast particle size distribution by means of the image processing technique. The procedure has been tested on a regularly operating Italian railway line and the results have been compared with those obtained from laboratory experiments, thus assessing how effective is the methodology which could potentially be implemented also in diagnostic trains in the near future.</jats:p> A New High-Efficiency Procedure for Aggregate Gradation Determination of the Railway Ballast by Means Image Recognition Method Archives of Civil Engineering |
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| title |
A New High-Efficiency Procedure for Aggregate Gradation Determination of the Railway Ballast by Means Image Recognition Method |
| title_unstemmed |
A New High-Efficiency Procedure for Aggregate Gradation Determination of the Railway Ballast by Means Image Recognition Method |
| title_full |
A New High-Efficiency Procedure for Aggregate Gradation Determination of the Railway Ballast by Means Image Recognition Method |
| title_fullStr |
A New High-Efficiency Procedure for Aggregate Gradation Determination of the Railway Ballast by Means Image Recognition Method |
| title_full_unstemmed |
A New High-Efficiency Procedure for Aggregate Gradation Determination of the Railway Ballast by Means Image Recognition Method |
| title_short |
A New High-Efficiency Procedure for Aggregate Gradation Determination of the Railway Ballast by Means Image Recognition Method |
| title_sort |
a new high-efficiency procedure for aggregate gradation determination of the railway ballast by means image recognition method |
| topic |
Civil and Structural Engineering |
| url |
http://dx.doi.org/10.2478/ace-2013-0025 |
| publishDate |
2013 |
| physical |
469-482 |
| description |
<jats:title>Abstract</jats:title><jats:p> The mechanical characteristics of the railway superstructure are related to the properties of the ballast, and especially to the particle size distribution of its grains. Under the constant stress-strain of carriages, the ballast can deteriorate over time, and consequently it should properly be monitored for safety reasons. The equipment which currently monitors the railway superstructure (like the Italian diagnostic train Archimede) do not make any “quantitative” evaluation of the ballast. The aim of this paper is therefore to propose a new methodology for extracting railway ballast particle size distribution by means of the image processing technique. The procedure has been tested on a regularly operating Italian railway line and the results have been compared with those obtained from laboratory experiments, thus assessing how effective is the methodology which could potentially be implemented also in diagnostic trains in the near future.</jats:p> |
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| author | Guerrieri, M., Parla, G. |
| author_facet | Guerrieri, M., Parla, G., Guerrieri, M., Parla, G. |
| author_sort | guerrieri, m. |
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| description | <jats:title>Abstract</jats:title><jats:p> The mechanical characteristics of the railway superstructure are related to the properties of the ballast, and especially to the particle size distribution of its grains. Under the constant stress-strain of carriages, the ballast can deteriorate over time, and consequently it should properly be monitored for safety reasons. The equipment which currently monitors the railway superstructure (like the Italian diagnostic train Archimede) do not make any “quantitative” evaluation of the ballast. The aim of this paper is therefore to propose a new methodology for extracting railway ballast particle size distribution by means of the image processing technique. The procedure has been tested on a regularly operating Italian railway line and the results have been compared with those obtained from laboratory experiments, thus assessing how effective is the methodology which could potentially be implemented also in diagnostic trains in the near future.</jats:p> |
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| physical | 469-482 |
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| series | Archives of Civil Engineering |
| source_id | 49 |
| spelling | Guerrieri, M. Parla, G. 1230-2945 Polish Academy of Sciences Chancellery Civil and Structural Engineering http://dx.doi.org/10.2478/ace-2013-0025 <jats:title>Abstract</jats:title><jats:p> The mechanical characteristics of the railway superstructure are related to the properties of the ballast, and especially to the particle size distribution of its grains. Under the constant stress-strain of carriages, the ballast can deteriorate over time, and consequently it should properly be monitored for safety reasons. The equipment which currently monitors the railway superstructure (like the Italian diagnostic train Archimede) do not make any “quantitative” evaluation of the ballast. The aim of this paper is therefore to propose a new methodology for extracting railway ballast particle size distribution by means of the image processing technique. The procedure has been tested on a regularly operating Italian railway line and the results have been compared with those obtained from laboratory experiments, thus assessing how effective is the methodology which could potentially be implemented also in diagnostic trains in the near future.</jats:p> A New High-Efficiency Procedure for Aggregate Gradation Determination of the Railway Ballast by Means Image Recognition Method Archives of Civil Engineering |
| spellingShingle | Guerrieri, M., Parla, G., Archives of Civil Engineering, A New High-Efficiency Procedure for Aggregate Gradation Determination of the Railway Ballast by Means Image Recognition Method, Civil and Structural Engineering |
| title | A New High-Efficiency Procedure for Aggregate Gradation Determination of the Railway Ballast by Means Image Recognition Method |
| title_full | A New High-Efficiency Procedure for Aggregate Gradation Determination of the Railway Ballast by Means Image Recognition Method |
| title_fullStr | A New High-Efficiency Procedure for Aggregate Gradation Determination of the Railway Ballast by Means Image Recognition Method |
| title_full_unstemmed | A New High-Efficiency Procedure for Aggregate Gradation Determination of the Railway Ballast by Means Image Recognition Method |
| title_short | A New High-Efficiency Procedure for Aggregate Gradation Determination of the Railway Ballast by Means Image Recognition Method |
| title_sort | a new high-efficiency procedure for aggregate gradation determination of the railway ballast by means image recognition method |
| title_unstemmed | A New High-Efficiency Procedure for Aggregate Gradation Determination of the Railway Ballast by Means Image Recognition Method |
| topic | Civil and Structural Engineering |
| url | http://dx.doi.org/10.2478/ace-2013-0025 |