Abstract
The construction industry in Uzbekistan, like many others worldwide, faces significant challenges in document management and compliance with the vast and complex regulatory environment, notably “Construction Norms and Regulations” (CNR) and “State Standard of the Republic of Uzbekistan” (SSU) standards. This research aims to develop and evaluate an artificial intelligence (AI)-powered system specifically designed to automate the review of construction documents within the Uzbek context. Utilizing a combination of natural language processing (NLP) and machine learning (ML) techniques, the proposed system aims to significantly reduce the manual effort and time required for document review processes while improving accuracy and compliance rates. Our methodology encompasses the collection and annotation of a substantial corpus of construction documents, the development of an AI model trained on this dataset, and a rigorous evaluation of the system's performance against manually reviewed benchmarks. Results indicate a substantial improvement in both efficiency and accuracy of document review processes, with the AI system achieving 95% accuracy in compliance detection compared to 81% for traditional manual methods, and reducing review time from over 30 hours to under 4 hours per document set. The system demonstrated precision of 0.89, recall of 0.95, and an F1-score of 0.95 across diverse case studies in Tashkent, Samarkand, and Bukhara. The contributions of this study are twofold: first, it provides a novel application of AI technologies for automating document review processes in the construction industry of Uzbekistan, addressing specific regulatory requirements; second, it contributes to the broader field of construction informatics by demonstrating the potential of AI and ML technologies in enhancing regulatory compliance and efficiency. This research lays the groundwork for further exploration into AI-powered document management systems and their potential to transform the construction industry's approach to regulatory compliance and project management.
References
1. Adamu, I. I., Okanlawon, T. T., Oyewobi, L. O., Shittu, A. A., & Jimoh, R. A. (2024). Revolutionising construction safety: benefits of harnessing artificial intelligence tools for dynamic monitoring of safety compliance on construction projects in Nigeria. International Journal of Building Pathology and Adaptation.
2. Agrawal, G. (2024). Accountability, trust, and transparency in AI systems from the perspective of public policy: Elevating ethical standards. In AI healthcare applications and security, ethical, and legal considerations (pp. 148-162). IGI Global.
3. Bafandegan Emroozi, V., Kazemi, M., Doostparast, M., & Pooya, A. (2024). Improving industrial maintenance efficiency: A holistic approach to integrated production and maintenance planning with human error optimization. Process Integration and Optimization for Sustainability, 8(2), 539-564.
4. Gomboin, Z., Sultangazin, A., Burabaev, K., & Makhkamov, T. (2025). Digital Public Services for Small and Medium-Sized Enterprises in Kazakhstan, Kyrgyzstan and Uzbekistan.
5. Golilarz, N. A., Hossain, E., Addeh, A., & Rahimi, K. A. (2024). AI Learning Algorithms: Deep Learning, Hybrid Models, and Large-Scale Model Integration. arXiv preprint arXiv:2410.09186.
6. Jakubik, J., Hemmer, P., Vössing, M., Blumenstiel, B., Bartos, A., & Mohr, K. (2022, June). Designing a human-in-the-loop system for object detection in floor plans. In Proceedings of the AAAI Conference on Artificial Intelligence (Vol. 36, No. 11, pp. 12524-12530).
7. Kamaev, A. (2025). Infrastructure Projects in Uzbekistan: Analyzing opportunities & risks in the context of the 2030 Strategy (Doctoral dissertation, Politecnico di Torino).
8. Komilova, M. S., & Tursunov, M. A. (2025). The Uniqueness of Construction Work in Uzbekistan. Spanish Journal of Innovation and Integrity, 40, 102-106.
9. Leite, F., Cho, Y., Behzadan, A. H., Lee, S., Choe, S., Fang, Y., ... & Hwang, S. (2016). Visualization, information modeling, and simulation: Grand challenges in the construction industry. Journal of Computing in Civil Engineering, 30(6), 04016035.
10. Mahadevkar, S. V., Patil, S., Kotecha, K., Soong, L. W., & Choudhury, T. (2024). Exploring AI-driven approaches for unstructured document analysis and future horizons. Journal of Big Data, 11(1), 92.
11. Memon, J., Sami, M., Khan, R. A., & Uddin, M. (2020). Handwritten optical character recognition (OCR): A comprehensive systematic literature review (SLR). IEEE Access, 8, 142642-142668.
12. Naik, V., Patel, P., & Kannan, R. (2023). Legal entity extraction: An experimental study of NER approach for legal documents. International Journal of Advanced Computer Science and Applications, 14(3).
13. Pan, Y., & Zhang, L. (2023). Integrating BIM and AI for smart construction management: Current status and future directions. Archives of Computational Methods in Engineering, 30(2), 1081-1110.
14. Panwar, V. (2024). AI-Powered Data Cleansing: Innovative Approaches for Ensuring Database Integrity and Accuracy. International Journal of Computer Trends and Technology, 72(4), 116-122.
15. Ravichandran, S., Kasturi Rangan, R., Manjesh, R., Karthik, S., & Hemanth, T. N. (2025, February). Page-Level Recognition and Reordering of Handwritten Documents: A Review. In International Congress on Information and Communication Technology (pp. 459-469). Singapore: Springer Nature Singapore.
16. Santos, J. C. D. (2025). Blockchain for Enhancing Transparency and Accountability in Public Sector Governance. In Enhancing Public Sector Accountability and Services Through Digital Innovation (pp. 73-128). IGI Global Scientific Publishing.
17. Ștefan, A. M., Rusu, N. R., Ovreiu, E., & Ciuc, M. (2024). Empowering healthcare: A comprehensive guide to implementing a robust medical information system—components, benefits, objectives, evaluation criteria, and seamless deployment strategies. Applied System Innovation, 7(3), 51.
18. Toochukwu, A. C. (2025). Sustainable construction practices: Balancing cost efficiency, environmental impact, and stakeholder collaboration. International Research Journal of Modernization in Engineering Technology and Science, 7(1), 4118-4140.
19. Witt, A., Huggins, A., Governatori, G., & Buckley, J. (2024). Encoding legislation: a methodology for enhancing technical validation, legal alignment and interdisciplinarity. Artificial Intelligence and Law, 32(2), 293-324.

This work is licensed under a Creative Commons Attribution-NonCommercial-NoDerivatives 4.0 International License.
Copyright (c) 2025 Dilmurod Rakhmatov (Author)

