Raylı sistemlerde dijital ikiz teknolojisi ile bim, gis ve iot tabanlı gerçek zamanlı izleme altyapısının geliştirilmesi
Development of a real time monitoring infrastructure based on bim, gis and iot using digital twin technology in rail systems
- Tez No: 1020897
- Danışmanlar: PROF. DR. ZAİDE DURAN
- Tez Türü: Yüksek Lisans
- Konular: Jeodezi ve Fotogrametri, Geodesy and Photogrammetry
- Anahtar Kelimeler: Belirtilmemiş.
- Yıl: 2026
- Dil: Türkçe
- Üniversite: İstanbul Teknik Üniversitesi
- Enstitü: Lisansüstü Eğitim Enstitüsü
- Ana Bilim Dalı: Geomatik Mühendisliği Ana Bilim Dalı
- Bilim Dalı: Geomatik Mühendisliği Bilim Dalı
- Sayfa Sayısı: Belirtilmemiş.
Özet
Raylı sistemler; yüksek yolcu kapasitesi, kesintisiz işletme gerekliliği ve güvenlik odaklı yapısı nedeniyle sürekli izleme, bakım ve karar destek süreçlerinin etkin biçimde yönetilmesini gerektiren kritik ulaşım altyapılarıdır. Özellikle metro istasyonları; yürüyen merdivenler, asansörler, havalandırma sistemleri, teknik hacimler ve yolcu alanları gibi çok sayıda fiziksel bileşenin bir arada çalıştığı karmaşık yapılardır. Bu yapılarda çevresel koşulların, ekipman davranışlarının ve mekânsal verilerin bütünleşik biçimde izlenmesi, işletme sürekliliğinin sağlanması, bakım süreçlerinin iyileştirilmesi ve operasyonel görünürlüğün artırılması açısından önemli bir ihtiyaç oluşturmaktadır. Bu tez çalışmasında, raylı sistemlerde dijital ikiz teknolojisi bağlamında BIM, GIS ve IoT tabanlı gerçek zamanlı izleme altyapısının geliştirilmesi amaçlanmıştır. Çalışma kapsamında Yapı Bilgi Modellemesi, Coğrafi Bilgi Sistemleri ve Nesnelerin İnterneti sensörlerinden elde edilen veriler ortak bir dijital ortamda bütünleştirilmiş; metro istasyonu ölçeğinde gerçek zamanlı izleme, mekânsal yorumlama ve operasyonel görünürlük sağlayan izleme odaklı bir dijital ikiz yaklaşımı ortaya konulmuştur. Bu yaklaşım, farklı veri kaynaklarının tek bir platformda ilişkilendirilmesini ve fiziksel varlıkların dijital ortamda izlenebilir hale getirilmesini hedeflemektedir. Önerilen altyapıda, metro istasyonuna ait BIM modeli üç boyutlu mekânsal temsil için kullanılmış, GIS verileri istasyonun raylı sistem ağı içindeki konumunu ve coğrafi bağlamını sağlamış, IoT sensörleri ise çevresel ve ekipman temelli ölçümlerin sisteme aktarılmasına olanak tanımıştır. Çevresel izleme kapsamında sıcaklık, nem, karbondioksit, partikül madde, uçucu organik bileşenler ve ortam gürültüsü gibi parametreler; ekipman izleme kapsamında ise titreşim, yüzey sıcaklığı, manyetik akı ve akustik ölçüm verileri değerlendirilmiştir. Bu veriler, ilgili fiziksel varlıklar ve sensör konumları ile ilişkilendirilerek web tabanlı üç boyutlu bir izleme arayüzünde görselleştirilmiştir. Çalışma, Metro İstanbul tarafından işletilen M7 hattındaki Çırçır metro istasyonu üzerinde gerçekleştirilmiştir. Uygulama sürecinde sensör verilerinin toplanması, BIM modelinin web ortamına uygun hale getirilmesi, GIS verileri ile mekânsal ilişkinin kurulması ve tüm veri katmanlarının ortak bir dijital ikiz altyapısında birleştirilmesi ele alınmıştır. Böylece farklı kaynaklardan elde edilen verilerin tek bir izleme platformunda nasıl bütünleştirilebileceği ve bu bütünleşik yapının istasyon yönetimi açısından nasıl kullanılabileceği değerlendirilmiştir. Elde edilen bulgular, BIM, GIS ve IoT entegrasyonuna dayalı dijital ikiz altyapısının metro istasyonlarında çevresel koşulların ve ekipman durumlarının daha anlaşılır, izlenebilir ve mekânsal bağlam içinde değerlendirilebilir hale gelmesini sağladığını göstermektedir. Çevresel sensör verileri, istasyon içinde farklı bölgelerde ölçüm değerlerinin değişebildiğini ortaya koymuş; ekipman sensörleri ise yürüyen merdiven ve asansör gibi bileşenlerin çalışma davranışlarının sensör tabanlı ölçümlerle izlenebilir hale getirilebildiğini göstermiştir. Geliştirilen sistem, sensör verilerinin fiziksel konumlarıyla birlikte yorumlanmasına olanak tanıyarak operasyonel görünürlüğü artırmıştır. Sonuç olarak bu tez, raylı sistemlerde dijital ikiz teknolojisinin metro istasyonu ölçeğinde uygulanabilir olduğunu göstermektedir. Geliştirilen yapı, tam otonom veya yapay zekâ destekli bir dijital ikiz sistemi olmamakla birlikte, gerçek saha verilerinin BIM ve GIS tabanlı mekânsal verilerle ilişkilendirilmesine dayalı uygulanabilir bir izleme altyapısı sunmaktadır. Bu altyapı, ilerleyen aşamalarda kestirimci bakım, anomali tespiti, enerji verimliliği, sürdürülebilirlik değerlendirmesi ve karar destek sistemleri için kullanılabilecek temel bir veri entegrasyon zemini oluşturmaktadır.
Özet (Çeviri)
Rail systems are critical transportation infrastructures that require continuous monitoring, effective maintenance management and reliable decision support due to their high passenger capacity, continuous operation requirements and safety-oriented structure. Metro stations, in particular, are complex built environments where many physical components and technical systems operate together. These include escalators, elevators, ventilation systems, technical rooms, passenger areas and various electromechanical assets. In such environments, the integrated monitoring of environmental conditions, equipment behavior and spatial data is an important requirement for operational continuity, maintenance planning and station management. In traditional station operation and maintenance processes, different types of data are often managed through separate systems. Spatial information may be stored in BIM or GIS-based platforms, while environmental and equipment-related measurements may be collected through independent sensors or maintenance records. When these data sources are not integrated, it becomes difficult to evaluate the current condition of a station as a whole. Therefore, there is a need for digital infrastructures that can combine physical assets, spatial context and real-time sensor data in a common environment. In this context, digital twin technology provides an important opportunity for rail systems by enabling physical assets to be represented, monitored and interpreted through digital models and sensor-based data. This thesis aims to develop a real-time monitoring infrastructure based on Building Information Modeling, Geographic Information Systems and Internet of Things technologies within the scope of digital twin technology in rail systems. The main objective of the study is to integrate BIM, GIS and IoT sensor data into a common digital environment and to propose a monitoring-oriented digital twin approach at the metro station scale. The proposed approach focuses on improving operational visibility by associating real field data with physical assets and their spatial locations. Rather than developing a fully autonomous or artificial intelligence-supported digital twin system, this thesis concentrates on establishing a practical data integration and visualization infrastructure that can provide a basis for future decision support, anomaly detection and predictive maintenance applications. Within the scope of the study, BIM is used as the main three-dimensional representation of the metro station. The BIM model provides spatial and asset-based information about the station environment and supports the interpretation of sensor data in relation to physical assets. In the proposed system, the BIM model is prepared for web-based visualization so that station assets and sensor locations can be viewed in a three-dimensional digital environment. This makes it possible to associate real-time data not only with numerical values, but also with the actual physical locations of the monitored components. GIS data are used to provide the geographical context of the station within the rail system network. While BIM represents the station at the building and asset scale, GIS contributes to the broader spatial context by showing the location of the station within the urban rail network. The integration of BIM and GIS allows the station to be evaluated both as an individual built environment and as a part of a larger transportation system. This relationship is important for infrastructure management because station-level data may need to be interpreted together with line-level, network-level or geographical information. IoT sensors constitute the real-time data layer of the proposed digital twin infrastructure. In this thesis, sensor-based monitoring is considered under two main categories: environmental monitoring and equipment monitoring. Environmental monitoring parameters include temperature, humidity, carbon dioxide concentration, particulate matter, volatile organic compounds and ambient noise. These parameters are important for understanding indoor environmental conditions, passenger comfort and station air quality. Equipment monitoring parameters include vibration, surface temperature, magnetic flux and acoustic measurements. These measurements are used to observe the operational behavior of station equipment such as escalators and elevators. By collecting and visualizing these data, the developed system enables station components to be monitored together with their spatial context. The implementation of the proposed infrastructure was carried out at Çırçır metro station on the M7 line operated by Metro Istanbul. Çırçır station was selected as the application area because it represents a real operational metro station environment where different types of station assets, passenger areas and technical systems coexist. Within the implementation process, sensor data were collected, the BIM model of the station was prepared for web-based visualization, GIS-based spatial relationships were established and all data layers were integrated into a common digital twin infrastructure. In this way, the study evaluates how heterogeneous data sources can be combined within a single monitoring platform and how this integrated structure can support station management processes. The system architecture developed in this thesis is based on the integration of spatial data, asset data and sensor data. The BIM model provides the geometric and asset-based representation of the station. GIS data provide the geographical and network-related context. IoT sensor data provide real-time or near real-time information about environmental conditions and equipment behavior. These data layers are brought together in a web-based three-dimensional monitoring interface. Through this interface, users can view the station model, observe sensor locations, monitor measurement values and interpret the data in relation to the spatial context of the station. One of the main contributions of the study is the association of sensor measurements with physical assets and their spatial positions. In many monitoring systems, sensor data are evaluated only as numerical values listed in tables, charts or dashboards. However, in complex built environments such as metro stations, the physical location of a sensor is critical for correct interpretation. For example, temperature, humidity or carbon dioxide values may differ between passenger areas, technical rooms and enclosed spaces. Similarly, vibration or surface temperature measurements from an escalator or elevator become more meaningful when they are directly associated with the monitored equipment. The proposed infrastructure addresses this issue by linking sensor data with BIM-based assets and GIS-supported spatial context. The findings of the study indicate that a digital twin infrastructure based on BIM, GIS and IoT integration can improve the monitoring and interpretation of metro station conditions. Environmental sensor data showed that measurement values may vary between different zones of the station. This demonstrates the importance of spatially aware monitoring, especially in large and complex station environments. Equipment sensor data showed that the operational behavior of components such as escalators and elevators can be observed through sensor-based measurements. The developed system improves operational visibility by enabling sensor data to be interpreted together with their physical locations. The developed monitoring infrastructure also supports a more understandable evaluation of operational data. By bringing different data layers together, the system enables users to understand the relationship between physical assets, sensor measurements and spatial context. This can support more informed maintenance planning, faster interpretation of operational conditions and improved awareness of station-level performance. When an abnormal measurement is observed, the user can evaluate where the related sensor is located, which physical asset it is associated with and how this information relates to the overall station environment. This type of spatially integrated monitoring is valuable for transportation facilities where many systems operate simultaneously. Another important aspect of the study is its practical applicability. The proposed structure was developed with real field data and tested in an actual metro station environment. Therefore, the study does not remain at a purely conceptual level. Instead, it demonstrates how BIM, GIS and IoT technologies can be combined in a real operational context. This is significant because the implementation of digital twin technologies in rail systems requires not only theoretical models, but also practical workflows for data preparation, integration, visualization and interpretation. The thesis shows that a monitoring-oriented digital twin infrastructure can be established by using available spatial models and sensor data. The study also clarifies the current limitations of the developed system. The proposed infrastructure should not be considered a fully autonomous digital twin or a complete artificial intelligence-based maintenance platform. It does not automatically predict failures, generate maintenance decisions or optimize station operations without human interpretation. Instead, it provides a foundational monitoring and integration layer. This layer can be further developed in future studies by adding advanced analytics, machine learning algorithms, anomaly detection methods and predictive maintenance models. In terms of future potential, the infrastructure developed in this thesis can support several application areas. By collecting equipment-related sensor data over time, it may become possible to identify changing patterns in vibration, temperature, magnetic flux or acoustic measurements. These patterns can later be used to support predictive maintenance and anomaly detection studies. Energy efficiency and sustainability assessments may also benefit from such an integrated infrastructure, especially when environmental parameters and equipment behavior are evaluated together with spatial and operational data. In addition, the integration of BIM, GIS and IoT can contribute to decision support systems by helping station managers and maintenance teams evaluate station conditions in a more structured and location-based manner. As a result, this thesis demonstrates that digital twin technology is applicable at the metro station scale in rail systems. The developed infrastructure integrates BIM-based spatial representation, GIS-based geographical context and IoT-based sensor data within a common digital environment. The study shows that environmental conditions and equipment status in metro stations can be monitored and interpreted more effectively when they are associated with physical assets and spatial locations. Although the proposed structure is not a fully autonomous or artificial intelligence-supported digital twin system, it provides an applicable and expandable monitoring infrastructure based on real field data. This infrastructure creates a fundamental data integration basis for future applications such as predictive maintenance, anomaly detection, energy efficiency, sustainability assessment and decision support systems in rail systems.
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