Improving facilities lifecycle management using RFID localization and BIM-based visual analytics
Notice bibliographique
Résumé
Indoor localization has gained importance as it has the potential to improve various processes related to the lifecycle management of facilities, such as the manual search to find assets.In the operation and maintenance phase, the lack of standards for interoperability and the difficulties related to the processing of large amount of accumulated data from different sources cause several process inefficiencies.For example, identifying failure cause-effect patterns in order to prepare maintenance plans is difficult due to the complex interactions and interdependencies between different building components and the existence of the related data in multiple, fragmented sources.Building Information Modelling (BIM) is emerging as a method for creating, sharing, exchanging and managing the information throughout the lifecycle of buildings.Radio Frequency Identification (RFID), on the other hand, has emerged as an automatic data collection technology, and has been used in different applications for the lifecycle management of facilities.The previous research of the author proposed permanently attaching RFID tags to assets where the memory of the tags is populated with their accumulated lifecycle information taken from a standard BIM database to enhance various lifecycle processes.This thesis builds on this framework and investigates several methods for supporting lifecycle management processes of assets by using BIM, RFID iv and visual analytics.It investigates the usage of location-related data that can be retrieved from a BIM and are stored on RFID tags.It also investigates the usage of RFID technology for indoor localization of RFID-equipped assets using handheld readers.The research proposes using the location data saved on the tags attached to fixed assets to locate them on the floor plan.These tags also act as reference tags to locate moveable assets using received signal pattern matching and clustering algorithms.Additionally, the research investigates extending BIM to incorporate RFID information.It provides the opportunity to interrelate BIM and RFID data using predefined relationships.For this purpose, a requirements' gathering is performed to add new entities, data types, relationships, and property sets to the BIM.Moreover, the research investigates the potential of BIM visualization to help facilities managers make better decisions in the operation and maintenance phase of the lifecycle.It proposes a knowledge-assisted BIMbased visual analytics approach for failure root-cause detection in facilities management where various sources of lifecycle data are integrated with a BIM and used for interactive visualization exploiting the heuristic problem solving ability of field experts.v ACNOWLEDGEMENT My greatest appreciation goes to my supervisor, Dr. Amin Hammad for his intellectual and personal support, encouragement and patience.His guidance, advice and criticism was my most valuable asset during my studies.Overall, I feel very fortunate having the opportunity to know him and work with him.I would like to thank my research colleagues for their kind support in developing the simulation environment, preparing 3D models and performing field tests.I would like to acknowledge the contributions of Mr. Mohammad Soltani for developing the simulation environment, software programming of the RFID data logger application, and his help performing RFID localization field tests.His enthusiasm in conducting research was a great asset in our collaboration.I appreciate his recommendations related to RFID localization and BIM extension modules of my research.Mr. Shayan Setayeshgar has contributed to this research by developing several 3D models for the BIM extension project.His technical knowledge together with his teamwork skills made our collaboration very successful.Mr. Yoosef Asen developed the BIM model for the Genomics Research Center and assisted in developing the case study for FM visual analytics project.Mr. Kehinde Adetiloye helped in developing the mobile application for fixed asset localization.The
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 | 0,002 | 0,004 |
| Méta-épidémiologie (sens strict) | 0,001 | 0,001 |
| Méta-épidémiologie (sens large) | 0,001 | 0,001 |
| Bibliométrie | 0,005 | 0,003 |
| Études des sciences et des technologies | 0,001 | 0,001 |
| Communication savante | 0,004 | 0,005 |
| Science ouverte | 0,002 | 0,003 |
| Intégrité de la recherche | 0,001 | 0,001 |
| Charge utile insuffisante (le modèle a refusé de juger) | 0,003 | 0,001 |
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 ».