Using emerging technologies for monitoring surface water near railway tracks
Notice bibliographique
Résumé
The level of water near the railway track is a major factor affecting the safety of train passage. Prolonged periods of heavy rainfall, rapid snowmelt, flash flooding, river flooding, beaver dams or blockage of a culvert result in a rise of water levels. This situation has been the major cause of many derailments in Canada and resulted in fatalities and serious injuries, damage to environment, loss of property, and service disruption. Railway companies strive to identify the development of problematic water levels in the area surrounding the track. This includes visual inspection performed by qualified track inspectors to visually identify waterway blockage and levels issues and air reconnaissance patrols that take place once or twice each year. These inspections rely on the inspectors’ judgment and experience regarding the water level, have a limited range of coverage, and do not provide visibility on the water issue in the areas that are out of the vision range but still close enough to affect the track. The air reconnaissance patrol covers a larger area and provides a bird’s eye view of all the waterways and identifies blockage of waterway but they are not as frequent. Recent advances in satellite-based remote sensors and tremendous development in unmanned aerial vehicle (UAV) have promoted the field of sensing surface water to a new era. National Research Council Canada and Transport Canada undertook a collaborative research project to evaluate the feasibility of using satellite imagery (including synthetic aperture radar and optical images) and UAV-based RGB images to detect water near railway tracks using data from two test sites in Canada. In addition, Transportation Safety Board (TSB) Rail Occurrence database and TSB’s investigation reports were analyzed to identify the root causes of water-related derailment within Canada’s rail network in the last few decades. 4 Canadian railway operators were also interviewed to better understand their main water related issues. The results of this project suggested that even though these technologies cannot entirely replace the current methods of water inspection, they offer an additional and inexpensive method to provide trackside water information to track inspectors. It was also indicated that further investigations and testing of technologies over same section of track would be required for drawing a definitive conclusion.
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Comment cette classification a été obtenuedéplier
Prédiction distillée sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Apprise à partir de 10 348 étiquettes directes de Codex et de 10 348 étiquettes directes de Gemma. Le mode candidate est l'union des têtes enseignantes seuillées; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont ni des étiquettes humaines ni des étiquettes directes de modèles de pointe.
Scores Codex et Gemma par catégorie
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,000 | 0,000 |
| Méta-épidémiologie (sens strict) | 0,000 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,000 | 0,000 |
| Études des sciences et des technologies | 0,000 | 0,000 |
| Communication savante | 0,000 | 0,000 |
| Science ouverte | 0,000 | 0,000 |
| Intégrité de la recherche | 0,000 | 0,000 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,000 | 0,000 |
Scores machine (provisoires)
Les deux têtes enseignantes du modèle étudiant, lues sur ce travail. Un score ordonne la base pour la relecture; il n'affirme jamais une catégorie, et le statut de validation accompagne chaque rangée tel quel.
Scores de référence d'un modèle non mature (critères de maturité non atteints, 7 itérations). Un score ordonne; il n'affirme jamais une catégorie.
score_only:v0-immature-baseline · tel quel depuis la passe de notation : score_only signifie que le nombre peut ordonner les travaux, et qu'aucune étiquette de catégorie n'en découleClassification
machine, non validéePrédiction automatique; un appel candidat d’une seule tête enseignante, pas un consensus.
Le détail, modèle par modèle et score par score, se trouve en fin de page sous « Comment cette classification a été obtenue ».