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BIM tabanlı dijital proje yönetim süreçlerinde proje verilerinin iş zekası destekli görselleştirilmesi ve karar destek süreçlerine etkisi

Business intelligence-supported visualization of project data in BIM-based digital project management processes and its impact on decision support processes

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  1. Tez No: 1024848
  2. Yazar: KÜBRA KİTAPCI
  3. Danışmanlar: PROF. DR. ÜMİT IŞIKDAĞ
  4. Tez Türü: Yüksek Lisans
  5. Konular: İnşaat Mühendisliği, Mimarlık, Yönetim Bilişim Sistemleri, Civil Engineering, Architecture, Management Information Systems
  6. Anahtar Kelimeler: Belirtilmemiş.
  7. Yıl: 2026
  8. Dil: Türkçe
  9. Üniversite: Mimar Sinan Güzel Sanatlar Üniversitesi
  10. Enstitü: Lisansüstü Eğitim Enstitüsü
  11. Ana Bilim Dalı: Enformatik Ana Bilim Dalı
  12. Bilim Dalı: Mimari ve Kentsel Enformatik Bilim Dalı
  13. Sayfa Sayısı: Belirtilmemiş.

Özet

İnşaat sektörü, küresel ekonominin en büyük bileşenlerinden biri olmasına karşın dijitalleşme düzeyi en düşük sektörler arasında yer almaktadır. Günümüzde, çok disiplinli inşaat projelerinde Yapı Bilgi Modellemesi (BIM) kullanımı giderek standartlaşsa da, yapı yaşam döngüsü boyunca üretilen karmaşık ve büyük hacimli verilerin paydaşlar tarafından anlaşılamaması, yönetilememesi ve stratejik bir karar destek mekanizmasına dönüştürülememesi sektördeki en büyük operasyonel zorluklardan biridir. Bu tez çalışmasında, karmaşık BIM verilerinin İş Zekası (BI) ortamlarına entegre edilerek etkileşimli 3B görselleştirme ve dinamik pano (dashboard) uygulamalarının proje yönetim süreçlerine ve paydaşlar arası veri şeffaflığına olan etkilerinin incelenmesi amaçlanmıştır. Araştırma kapsamında, BIM tabanlı veri yönetimi ve karar destek sisteminin değerlendirilmesi amacıyla nitel araştırma yöntemleri kullanılmıştır. İlk aşamada, ISO 19650 standartlarına uygun olarak Autodesk Construction Cloud (ACC) üzerinde kurgulanan Ortak Veri Ortamı (CDE) kullanılarak mimari, statik, mekanik, elektrik ve tesisat disiplinlerini içeren bir vaka modeli üzerinden veriler konsolide edilmiştir. Dynamo ile otomatize edilen ve Speckle gibi ara katman yazılımlarıyla da desteklenen veri setleri Power BI ortamına aktarılmış;“Konsept Tasarım, Disiplinler Arası Koordinasyon, İşletme Sürecine Hazırlık (FM) ve Proje Süreç Performansı”olmak üzere dört farklı operasyonel senaryo üzerinden etkileşimli görsel panolara dönüştürülmüştür. İkinci aşamada ise, geliştirilen bu görselleştirme sisteminin sektörel karşılığını ölçmek amacıyla, alanında uzman 8 profesyonel ile yarı yapılandırılmış mülakatlar gerçekleştirilmiş ve elde edilen bulgular tematik olarak analiz edilmiştir.. Çalışma sonucunda elde edilen uzman görüşleri doğrultusunda, verilerin etkileşimli bir şekilde görselleştirilmesinin projelerdeki iletişim kopukluklarını gidererek bilgi asimetrisini ortadan kaldırdığı değerlendirilmiştir. Katılımcıların deneyimlerine göre İş Zekası entegrasyonunun, geleneksel manuel takip mekanizmalarının yerini alarak proje yönetimini 'reaktif' bir yaklaşımdan 'proaktif ve veri temelli' bir karar destek sistemine dönüştürebileceği tespit edilmiştir. Ayrıca, karmaşık teknik verilerin basit Temel Performans Göstergelerine (KPI) ve etkileşimli görsellere indirgenmesinin, BIM yazılımlarını kullanamayan işveren ve yatırımcı gibi paydaşların sürece aktif katılımını destekleyerek şeffaf bir dijital yönetim kültürünün oluşmasına zemin hazırladığı ifade edilmiştir. Bununla birlikte, sektörde bu dönüşümün önündeki asıl engelin teknik altyapıdan ziyade; standart veri parametrelerinin eksikliği ve dijital süreçlere karşı gösterilen kültürel direnç olduğu sonucuna varılmıştır. Sonuç olarak, BIM ve İş Zekası entegrasyonu; verilerin görünürlüğünü ve analiz edilebilirliğini destekleyerek bilgi kayıplarının önüne geçilmesine katkı sağlayan, disiplinler arası etkileşimi artıran ve yapı yaşam döngüsünün bütününde rasyonel karar almayı mümkün kılan elde edilen istatistiksel verilerle gelecekteki projeler için stratejik bir kurumsal hafıza (benchmark) oluşturan vizyoner bir yaklaşım olarak değerlendirilmektedir.

Özet (Çeviri)

Although the construction sector is one of the largest components of the global economy, it remains among the sectors with the lowest levels of digitalization. The multidisciplinary nature of the sector causes the generation of massive amounts of heterogeneous and dynamic data during different phases of a project's lifecycle, such as design, planning, cost control, and site management. Today, even though the use of Building Information Modeling (BIM) is becoming increasingly standard in multidisciplinary construction projects, the inability of stakeholders to understand, manage, and transform the complex and large-volume data generated throughout the building lifecycle into a strategic decision support mechanism remains one of the most significant operational challenges in the sector. This thesis aims to investigate the effects of integrating complex BIM data into Business Intelligence (BI) environments, using interactive 3D visualization and dynamic dashboard applications, on project management processes and inter-stakeholder data transparency. Within the scope of the research, qualitative research methods were used to evaluate the BIM-based data management and decision support system. The research process was conducted in three main stages. In the first stage, a comprehensive literature review comprising 76 sources was conducted. The literature analysis covering the last 15 years revealed that while studies between 2011 and 2020 mostly served the static data storage and conceptual structure of BIM, the focus rapidly shifted towards dynamic decision support systems with the integration of Business Intelligence tools between 2021 and 2025 due to increasing data volumes. The literature review showed that while academic knowledge regarding data integration exists, there is a lack of a holistic methodological framework aimed at overcoming workforce habits and multi-stakeholder integration challenges, particularly in markets undergoing a digitalization process like Turkey. In the second stage, a“Medical Clinic”building project was selected as the case study. The primary factor in selecting this specific project is its rich geometric and semantic infrastructure, which perfectly supports the“multidisciplinary data integration”objective of the thesis. The existing project includes models belonging to the structural, architectural, electrical, mechanical, and plumbing disciplines, along with operation and maintenance data prepared in Excel format to be integrated into the equipment within these models. To manage the massive and complex data load generated throughout the project lifecycle in accordance with the standards, a comprehensive system architecture based on ISO 19650-2 requirements was established. In this architecture, Autodesk Construction Cloud (ACC) was positioned as a Common Data Environment (CDE) to create a“Single Source of Truth (SSOT)”for all stakeholders. The Speckle platform was utilized as a middleware to make the consolidated model data in the central data environment analyzable. The model data uploaded to ACC was retrieved and merged via Speckle, allowing the data coming from different disciplines and modeling tools to be managed within a federated model structure and seamlessly transferred to the Microsoft Power BI environment. This developed system architecture was transformed into interactive visual dashboards through four different operational scenarios. In the first scenario,“Concept Design Evaluation,”the revised areas and added wet volumes aligned with the spatial accessibility needs emerging in the project were analyzed. Moving beyond traditional 2D plan comparisons, the model data was supported by 3D visualizations, graphs, and relational tables, evaluating the spatial usage performance of design alternatives through specific Key Performance Indicators (KPIs) such as total area, total room count, and optimized space limits. In the second scenario,“Interdisciplinary Coordination Tracking,”clash detection analyses were performed on the ACC platform, and the identified clashes were defined as“Issues”within the system. Qualitative and quantitative data for each issue, such as current status (open, closed, in review), responsible person, and due dates, were recorded. This coordination data was transferred to Power BI, and pie charts showing the resolution distribution of issue data were used to identify which teams organized faster. The 3D visualization of the federated model data obtained via Speckle presented element-based distinctions by disciplines, allowing project managers to evaluate the overall coordination health and the accumulation of potential system risks on specific floors. In the third scenario,“Preparation for Facility Management (FM) Data Tracking,”operational data in Excel format, such as equipment information, maintenance periods, warranty durations, and manufacturer details, were autonomously processed into the models using Dynamo visual programming algorithms to prevent manual data entry errors. The operational readiness status (data completeness) of the assets was visualized on the 3D model with a specific color-coding system: elements that do not require FM parameters and are not on the asset list were colored gray; elements present on the asset list but with unprocessed data were red; elements with partially processed parameter data were yellow; and elements with fully processed data were green. An intermediate table numerically listed the progress rates of bidding, site installation, and FM readiness on a discipline and category basis, calculating the exact amounts of missing data. In the fourth scenario,“Monitoring and Analysis of Project Process Performance,”the performance of administrative workflows such as coordination problems (issues), documentation approval processes (submittals), and requests for information (RFIs) was monitored. By consolidating fragmented process data with dynamic charts presenting process distributions according to open, under review, and closed statuses, an early warning system was created. This hierarchical dashboard structure allowed users to measure the general speed of the process through KPIs, analyze where the process is blocked through charts, and dive directly into the details of delayed processes to determine which problem caused the bottleneck. In the third stage of the research, to measure the sectoral equivalence of this developed visualization system, semi-structured interviews were conducted with 8 professionals who are experts in their fields, and the qualitative data was evaluated using thematic analysis. According to the interview findings, it was determined that the most fundamental problem encountered in multidisciplinary projects is“digital adaptation and process resistance”alongside“information flow disconnects,”rather than a lack of technical infrastructure. According to the thematic findings, it was stated that the greatest benefit of normalizing data coming from different formats is the creation of a“Common Data Language and Fast Data Consumption.”Participants emphasized that the unification of different disciplines on a single dashboard dismantles the“my file, your file”mentality, prevents communication confusion, and provides an effective transition between data. Based on the experts' experiences, it was established that Business Intelligence integration replaces traditional manual tracking mechanisms, transforming the reactive (intervening after a problem occurs) management approach into a proactive, data-driven decision support system. The dashboards functioned as an early warning mechanism, ensuring that risks and delays were identified before they reflected on the construction site, thus minimizing the dependency on human initiative. Furthermore, it was stated that reducing complex technical data to simple KPIs and interactive visuals facilitates the transparent participation of stakeholders who cannot use BIM software, such as investors and employers, in the decision-making processes, thereby paving the way for a trust-based digital management culture. However, experts highlighted a critical risk regarding“Dark Data (unused data).”They warned that dashboards must be designed with a specific operational purpose; otherwise, they risk turning into mere“show-case”tools that only appeal to the eye. It was concluded that the main barrier to this digital transformation is the lack of standard data parameters (shared parameters) across the industry. Finally, the concept of“Corporate Memory”was heavily emphasized. The historical performance data (e.g., average issue resolution times, discipline-based delays) collected through these dashboards does not expire at the end of the project; instead, it creates a strategic benchmark and a“lessons learned”repository to be utilized in the planning and bidding phases of future projects. In conclusion, purpose-driven BIM and Business Intelligence integration is evaluated as a strategic corporate necessity rather than merely a technological update. By replacing fragmented structures with an interactive data ecosystem, the proposed framework prevents information loss by supporting data visibility, increases interdisciplinary interaction, eliminates information asymmetry, and enables rational, data-driven decision-making throughout the entire building lifecycle.

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