BIM and Linked Data Integration for Automated Compliance Checking: A Literature Review

Öz

The construction industry is multidisciplinary. It requires strong collaboration and seamless data exchange between all engaged stakeholders across various project phases, including design, construction, and operation. Within these phases, quality and compliance control is essential. It ensures that the project meets requirements and helps identify errors or missing information. However, traditional model control processes, which rely on manual methods conducted by people, are often error-prone and time-consuming. To address these challenges, adopting automated workflows for compliance checking have become crucial to improve overall efficiency and minimize errors in construction projects. Aligned with this objective, Building Information Modeling (BIM) has emerged as an effective methodology for data exchange and collaboration. Despite its advantages, the information contained in BIM models can be challenging to manage and is often overlooked for updates, especially in large-scale projects. Integrating BIM with Semantic Web technologies significantly enhances its potential by enabling more efficient automated compliance checking processes. The primary objective of this research is to underline the potential of integrating Semantic Web technologies with BIM methodologies for automated compliance checking. It aims to provide an overview of the affordances, potentials, and limitations of these technologies within the Architecture, Engineering, Construction, Operations (AECO) domain, offering professionals a comprehensive technological foundation on how Semantic Web technologies can be applied in combination with BIM methodologies. Within this scope, the paper particularly examines the use of Linked Data approaches of the Semantic Web, for automated compliance checking within the construction industry, by conducting a systematic literature review following the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The findings highlight four key approaches, namely Web Ontology Language (OWL), Semantic Web Rule Language (SWRL), SPARQL Protocol and RDF Query Language (SPARQL), and Shapes Constraint Language (SHACL). Notably, SHACL emerges as the most extensively adopted and suitable approach for automated rule verification workflows within the broader Linked Data paradigm, as well as being the latest recommendation by the World Wide Web Committee (W3C). However, the existing literature also underscores persistent challenges, including the frequent development of custom ontologies, limited reuse of validation rules, and the absence of standardized constraint libraries. Overall, the research findings demonstrated that integrating Building Information Modeling with Linked Data technologies establishes a robust foundation for extendible and automated compliance verification, contributing to the rapid digital transformation within the AECO domain.

Anahtar Kelimeler

• AEC (Architecture, Engineering, Construction)
• Automated Compliance Checking
• BIM (Building Information Modeling)
• Linked Data
• Semantic Web

Otomatik Uygunluk Kontrolü İçin YBM ve Bağlantılı Veri Entegrasyonu: Bir Literatür Taraması

Öz

Çok disiplinli bir yapıya sahip olan inşaat sektörü, tüm paydaşlar arasında güçlü bir iş birliği ile kesintisiz veri alışverişini gerektirir. Bu aşamalar boyunca kalite ve uygunluk kontrolü temel bir gereklilik olup, projelerin belirlenen gereksinimleri karşılamasını sağlayarak hata ya da eksik bilgilerin tespit edilmesine olanak tanır. Ancak geleneksel, manuel model kontrol yaklaşımları zaman alıcı ve insan hatasına açık olduğundan, otomatik uygunluk kontrolüne yönelik yöntemlere duyulan ihtiyaç giderek artmaktadır. Bu hedef doğrultusunda, Yapı Bilgi Modellemesi (YBM), veri alışverişi ve iş birliği için etkili bir metodoloji olarak öne çıkmaktadır. Ancak, YBM modelleri içerisinde yer alan bilgilerin yönetimi, özellikle büyük ölçekli projelerde zorlayıcı olabilmektedir. YBM’nin Anlamsal Web teknolojileri ile entegrasyonu, daha verimli otomatik uygunluk kontrol süreçlerinin gerçekleştirilmesine olanak tanıyarak bu yaklaşımın potansiyelini önemli ölçüde artırmaktadır. Bu araştırmanın temel amacı, otomatik uygunluk kontrolü bağlamında Anlamsal Web teknolojilerinin BIM metodolojileri ile entegrasyonunun potansiyelini ortaya koymaktır. Çalışma, AECO (Mimarlık, Mühendislik, İnşaat, Operasyon) alanında bu teknolojilerin sunduğu olanaklar, potansiyeller ve sınırlar hakkında genel bir değerlendirme sunmayı amaçlamaktadır. Bu kapsamda makale, PRISMA (Sistematik Derlemeler ve Meta-Analizler için Tercih Edilen Raporlama Öğeleri) yöntemlerini izleyerek bir literatür taraması gerçekleştirmekte ve inşaat sektöründe otomatik uygunluk kontrolü için Bağlantılı Veri yaklaşımlarını incelemektedir. Elde edilen bulgular, dört temel yaklaşımı öne çıkarmaktadır. Bunların arasından özellikle Shapes Constraint Language (SHACL), otomatik kural doğrulama iş akışları için en yaygın kullanılan ve en uygun yaklaşım olarak öne çıkmaktadır. Bununla birlikte, mevcut literatür; özel ontolojilerin sıklıkla geliştirilmesi, doğrulama kurallarının sınırlı düzeyde yeniden kullanılabilmesi ve standartlaştırılmış kısıt kütüphanelerinin bulunmaması gibi kalıcı zorluklara da işaret etmektedir. Genel olarak çalışma, YBM’nin Bağlantılı Veri teknolojileri ile entegrasyonunun, genişletilebilir ve otomatik uygunluk doğrulaması için güçlü bir temel oluşturduğunu ve AECO alanındaki dijital dönüşüme önemli katkılar sağladığını göstermektedir.

Anahtar Kelimeler

• AEC (Mimarlık, Mühendislik, İnşaat)
• Anlamsal Web
• Bağlantılı Veri
• Otomatik Uygunluk Kontrolü
• YBM (Yapı Bilgi Modellemesi)

Kaynakça

1. Anim, J. K., Robaldo, L., & Wyner, A. (2024). A SHACL-based approach for enhancing automated compliance checking with RDF data. Information, 15(12), Article 759. https://doi.org/10.3390/info15120759
2. Boje, C., & Li, H. (2018). Crowd simulation-based knowledge mining supporting building evacuation design. Advanced Engineering Informatics, 37, 103–118. https://doi.org/10.1016/j.aei.2018.05.002
3. Ding, L. Y., Zhong, B. T., Wu, S., & Luo, H. B. (2016). Construction risk knowledge management in BIM using ontology and semantic web technology. Safety Science, 87, 202–213. https://doi.org/10.1016/j.ssci.2016.04.008
4. Eastman, C., Lee, J.-M., Jeong, Y.-S., & Lee, J.-K. (2009). Automatic rule-based checking of building designs. Automation in Construction, 18(8), 1011–1033. https://doi.org/10.1016/j.autcon.2009.07.002
5. Elshani, D., Lombardi, A., Fisher, A., Staab, S., Hernández, D., & Wortmann, T. (2022). Knowledge graphs for multidisciplinary co-design: Introducing RDF to BHoM. CEUR Workshop Proceedings, 3213, 32–42. https://ceur-ws.org/Vol-3213/paper03.pdf
6. Hagedorn, P., Fauth, J., Zentgraf, S., Seiß, S., König, M., & Brilakis, I. (2025). OntoBPR: An ontology-based framework for performing building permit reviews using standardized information containers. Advanced Engineering Informatics, 66, Article 103369. https://doi.org/10.1016/j.aei.2025.103369
7. Hagedorn, P., & König, M. (2021). BPMN-related ontology for modeling the construction information delivery of linked building data. CEUR Workshop Proceedings, 3081, 91–102. https://ceur-ws.org/Vol3081/08paper.pdf
8. Hagedorn, P., Pauwels, P., & König, M. (2023). Semantic rule checking of cross-domain building data in information containers for linked document delivery using the shapes constraint language. Automation in Construction, 156, Article 105106. https://doi.org/10.1016/j.autcon.2023.105106

9. Hamdan, A.-H., & Scherer, R. J. (2020). Integration of BIM-related bridge information in an ontological knowledgebase. CEUR Workshop Proceedings, 2636, 77–90. https://ceur-ws.org/Vol2636/06paper.pdf
10. Kaltenegger, J., Frandsen, K. M., & Petrova, E. (2025). An ontology-driven framework for digital transformation and performance assessment of building materials. Building and Environment, 271, Article 112565. https://doi.org/10.1016/j.buildenv.2025.112565
11. Keuchel, D., & Spicher, N. (2021). Automatic detection of metadata errors in a registry of clinical studies using Shapes Constraint Language (SHACL) graphs. Studies in Health Technology and Informatics, 281, 372-376. https://doi.org/10.3233/shti210183
12. Kruiper, R., Kumar, B., Watson, R., Sadeghineko, F., Gray, A., & Konstas, I. (2024). A platform-based natural language processing-driven strategy for digitalising regulatory compliance processes for the built environment. Advanced Engineering Informatics, 62(Part B), Article 102653. https://doi.org/10.1016/j.aei.2024.102653
13. Liu, H., Gao, G., Zhang, H., Liu, Y.-S., Song, Y., & Gu, M. (2023). MVDLite: A fast validation algorithm for Model View Definition rules. Advanced Engineering Informatics, 58, Article 102132. https://doi.org/10.1016/j.aei.2023.102132
14. Nuyts, E., Bonduel, M., & Verstraeten, R. (2024). Comparative analysis of approaches for automated compliance checking of construction data. Advanced Engineering Informatics, 60, Article 102443. https://doi.org/10.1016/j.aei.2024.102443
15. Pauwels, P., Zhang, S., & Lee, Y.-C. (2017). Semantic web technologies in AEC industry: A literature overview. Automation in Construction, 73, 145–165. https://doi.org/10.1016/j.autcon.2016.10.003
16. Pauwels, P., van den Bersselaar, E., & Verhelst, L. (2024). Validation of technical requirements for a BIM model using semantic web technologies. Advanced Engineering Informatics, 60, Article 102426. https://doi.org/10.1016/j.aei.2024.102426
17. Publio, G., & Gayo, J. E. L. (2025). xpSHACL: Explainable SHACL validation using retrieval-augmented generation and Large Language Models. ArXiv, abs/2507.08432. https://doi.org/10.48550/arxiv.2507.08432
18. Rasmussen, M. H., Lefrançois, M., Pauwels, P., Hviid, C. A., & Karlshøj, J. (2019). Managing interrelated project information in AEC knowledge graphs. Automation in Construction, 108, Article 102956. https://doi.org/10.1016/j.autcon.2019.102956
19. Seiß, S., Lünig, J. N., & Melzner, J. (2023). Supporting appraisal cost estimation by linked data. In Proceedings of the European Conference on Computing in Construction (EC3) / 40th International CIB W78 Conference. https://doi.org/10.35490/EC3.2023.287
20. Senthilvel, M., & Beetz, J. (2020). A visual programming approach for validating linked building data. In L. C. Ungureanu & T. Hartmann (Eds.), Proceedings of the EG-ICE 2020 Workshop on Intelligent Computing in Engineering (pp. 403–411). Universitätsverlag der TU Berlin. https://doi.org/10.14279/DEPOSITONCE-9977
21. Soman, R. K., Molina-Solana, M., & Whyte, J. K. (2020). Linked-data based constraint-checking (LDCC) to support look-ahead planning in construction. Automation in Construction, 120, Article 103369. https://doi.org/10.1016/j.autcon.2020.103369
22. Stepien, M., Jodehl, A., Vonthron, A., König, M., & Thewes, M. (2022). An approach for cross-data querying and spatial reasoning of tunnel alignments. Advanced Engineering Informatics, 54, Article 101728. https://doi.org/10.1016/j.aei.2022.101728
23. Teclaw, W., Luczkowski, M., Labonnote, N., & Hjelseth, E. (2025). Building information model and schema cross-validation using semantics: Conceptual framework. International Journal of Architectural Computing, 0(0), 1–24. https://doi.org/10.1177/14780771251352954
24. Umbrich, J., Hogan, A., Polleres, A., & Decker, S. (2015). Link traversal querying for a diverse Web of Data. Semantic Web, 6(6), 585-624. https://doi.org/10.3233/sw-140164
25. Utkucu, D., & Sacks, R. (2025). Ontology for holistic building performance modeling and analysis. Automation in Construction, 175, Article 106197. https://doi.org/10.1016/j.autcon.2025.106197
26. Yin, M., Tang, L., Webster, C., Xu, S., Li, X., & Ying, H. (2023). An ontology-aided, natural language-based approach for multi-constraint BIM model querying. Journal of Building Engineering, 76, Article 107066. https://doi.org/10.1016/j.jobe.2023.107066
27. Yousfi, A., Poirier, É. A., & Forgues, D. (2025). A core ontology for whole life costing in construction projects. Buildings, 15(14), Article 2381. https://doi.org/10.3390/buildings15142381
28. Zangeneh, P., & McCabe, B. (2020). Ontology-based knowledge representation for industrial megaprojects analytics using linked data and the semantic web. Advanced Engineering Informatics, 46, Article 101164. https://doi.org/10.1016/j.aei.2020.101164