Metrolinx’s Toronto Electrification Project: Phase 1 — The Engineering Survey
Notice bibliographique
Résumé
Metrolinx, Toronto’s rail authority currently has 200 engineering projects underway with a value of $16 billion. One of the largest projects is a $4 billion Electrification Project for the Toronto commuter rail lines. In support of the engineering design of the project, in November of 2015 Tulloch Engineering was contracted to provide a complete engineering survey of six Metrolinx railway commuter corridors originating from Union Station in Toronto, Canada. Tulloch used a unique combination of mobile LiDAR, static LiDAR, and conventional infill ground survey to complete the project. LiDAR, which stands for Light Detection and Ranging, is a surveying method that measures distance to a target by illuminating that target with a laser light. Using LiDAR technology provided significant advantages to the Electrification Project over using convention ground survey techniques. Metrolinx is a Canadian crown corporation responsible for the Greater Toronto and Hamilton Area’s GO Transit rail and bus commuter system. GO Transit trains currently carry 190,000 commuters per day. Electrification of Metrolinx GO Transit rail commuter rail corridors requires the upgrading of infrastructure and providing a means of getting the electricity to the trains which includes new electrical substations, overhead power lines and new equipment. The electrification is part of the GO Regional Express Rail program, which will expand the capacity of the GO rail network to provide customers with faster, more frequent and more convenient service to and from dozens of stations in core sections of the GO rail network throughout the day, evenings and weekends. Electrification is planned for most of Metrolinx commuter rail corridors by 2022–2024. The engineering technical and program management consultant for the Electrification Project is Gannett Fleming. An initial requirement for Metrolinx Electrification project is an up to date engineering survey to enable the preliminary engineering design. Our survey project involves surveying approximately 170 miles of railway corridor for 6 GO Transit tracks originating from Union Station in downtown Toronto. Our mobile LiDAR survey system was mounted on a GO Transit hi-rail truck; with most of the surveying occurring at night due to the heavy train traffic and since LiDAR is an active sensor. Tulloch provided a unique hybrid surveying approach, using mobile LiDAR surveying to collect all the visible features in the corridor, followed by conventional ground surveys to fill in missing features obscured from the LiDAR system’s field of view and static LiDAR surveys for some of the bridges inaccessible with mobile LiDAR. This is the first time Metrolinx has contracted an engineering survey using these multiple survey technologies. This survey approach reduces delivery timelines, limits track disruptions, and greatly improves safety. A major advantage of mobile LiDAR surveying for the GO-Transit rail corridors is that collection can occur at night when train activity is low and in a fraction of the time it takes to survey using conventional ground crews. This enabled project schedules to be advanced, as base mapping was completed in about 60% of the normal time required for the engineering survey. Using mobile scanning on the tracks reduced safety risks associated with on track field surveys. In addition, the resultant LiDAR point cloud can be revisited in the office, and additional features and critical information picked up without having to send field crews back to do so. The homogeneous nature of the point cloud, combined with the conventional in-fill survey provides a rich, full feature data set that can be used at various stages in the engineering design process.
Récupéré en direct depuis OpenAlex et désinversé. Les résumés ne sont pas conservés dans cette base de données : les index inversés représentent 8,6 Go des 9,3 Go de texte de la base, et le serveur dispose de 13 Go libres.
Comment cette classification a été obtenuedéplier
Prédiction machine sur la base complète
Imitation des enseignantsNi prévalence calibrée, ni vérité terrain. Validation humaine à venir. Le volet Gemma est une étiquette directe du modèle pour chaque travail de la base, lue sur la notice réduite au titre. Le volet Codex est un classifieur appris des 10 348 étiquettes directes de Codex et calibré sur les taux pondérés de l'échantillon; les champs sans appui suffisant ne portent aucun appel Codex. Le mode candidate est l'union des deux volets; le consensus est leur intersection. Ces sorties portent le statut machine_predicted_unvalidated et ne sont pas des étiquettes humaines.
Scores du classifieur distillé par catégorie (deux têtes)
| Catégorie | Codex | Gemma |
|---|---|---|
| Métarecherche | 0,001 | 0,001 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,000 |
| Méta-épidémiologie (sens large) | 0,000 | 0,000 |
| Bibliométrie | 0,002 | 0,002 |
| Études des sciences et des technologies | 0,002 | 0,001 |
| Communication savante | 0,002 | 0,000 |
| Science ouverte | 0,001 | 0,001 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,015 | 0,004 |
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 source (Gemma direct ou Codex distillé), 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 ».