Accuracy and Project Cost Comparison Between Photogrammetry and LiDAR-based Methods in Sewer Manhole Inspection Data Capture and Condition Assessment
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Résumé
Accuracy and Project Cost Comparison Between Photogrammetry and LiDAR-based Methods in Sewer Manhole Inspection Data Capture and Condition AssessmentAbstractSewer infrastructure management is on the verge of significant transformation driven by advancements in cloud computing, photogrammetry, and availability of high-resolution 360 'Action Cameras'. This presentation takes a critical look at this innovative approach, which uses data captured with consumer-available 360 'Action Cameras' to convert manhole inspection videos into intricate, textured 3D models of sewer systems. Notably, this session will feature a comprehensive objective comparison of field data accuracy, production/efficiency, limitations, and lessons learned. At the core of this approach lies photogrammetry, a sophisticated mathematical method that translates digital pixels in inspection videos into precise 3D points. Within the rendered 3D models, users can swiftly assess sewer components, perform custom measurements with centimeter-level accuracy, and can export this data into popular CAD software, for additional uses. The presentation explores how this new approach addresses longstanding challenges utilities have faced in sewer manhole assessment. Central to this discussion is a deep dive into the objective comparison of field data accuracy and field production and efficiency when contrasted with legacy manhole scanning systems. We will explore real-world scenarios and outcomes on projects in Houston, TX; Los Angeles, CA; Alexandria, VA; Toronto, ON; and other North American locations, shedding light on the practical implications of this technology. In addition, new cloud-based workflows for data capture, data transfer, stakeholder alert/review, and final submittal will also be described and compared to traditional methods of on-premises and physical data transfer. Furthermore, this presentation will scrutinize the limitations of this approach and draw a comprehensive comparison between photogrammetry and LiDAR, two prevalent methods in the industry. As we peer into the future of sewer infrastructure management, this session promises to provide valuable insights, backed by empirical data, and aims to foster an informed discussion about newly available options for sewer utilities, services contractors, and civil engineers. The presentation will conclude with summaries of known use-cases, as well as future use-cases and capabilities enabled by current technologies, such as Digital Twins and 3D modeling of horizontal sewer structures. Join us in this pivotal exploration of advancements that are shaping the future of sewer infrastructure management.This paper was presented at the WEF Collection Systems and Stormwater Conference, April 9-12, 2024.SpeakerMcGarry, TimPresentation time10:45:0011:15:00Session time08:30:0011:45:00SessionCollection System InspectionSession number28Session locationConnecticut Convention Center, Hartford, ConnecticutTopicCoastal Systems, Collection Systems, Condition Assessment, Consent Orders, Construction, Design considerations, Flow control, Force Mains, Infiltration/Inflow, Innovative Technology, LiDAR surveying, Pipe, Pipe Failures, Real Time Decision Support System, Real-Time Control, Rehabilitation, Slip line, Utility Management, Wastewater ManagementTopicCoastal Systems, Collection Systems, Condition Assessment, Consent Orders, Construction, Design considerations, Flow control, Force Mains, Infiltration/Inflow, Innovative Technology, LiDAR surveying, Pipe, Pipe Failures, Real Time Decision Support System, Real-Time Control, Rehabilitation, Slip line, Utility Management, Wastewater ManagementAuthor(s)Sullivan, EricAuthor(s)E. Sullivan1, T. McGarry1Author affiliation(s)SewerAI 1SourceProceedings of the Water Environment FederationDocument typeConference PaperPublisherWater Environment FederationPrint publication date Apr 2024DOI10.2175/193864718825159365Volume / Issue Content sourceCollection Systems and Stormwater ConferenceCopyright2024Word count20
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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 ».