INNOVATIVE APPROACHES AND PRACTICES OF INFORMATION TECHNOLOGY FOR THE TECHNICAL CONDITION ASSESSMENT OF BUILDING STRUCTURES
DOI:
https://doi.org/10.36773/1818-1112-2025-138-3-59-62Keywords:
building information modeling (BIM), digital twin, structural health monitoring (SHM), internet of things (IoT), artificial intelligence (AI), systematic reviewAbstract
The accelerated digitalization of the construction industry emphasizes the need for developing accurate and efficient methods for assessing the technical condition of buildings and structures. Despite the growing adoption of Building Information Modeling (BIM) and Digital Twin (DT) technologies, their potential for diagnostic purposes and forecasting the residual service life of structures remains underutilized. This paper presents the results of a systematic literature review aimed at identifying and analyzing specific approaches at the intersection of BIM, DT, the Internet of Things (IoT), and Artificial Intelligence (AI) methods, specifically focused on condition assessment. The SLR procedure, covering 100 relevant publications, enabled structuring the research field along three aspects: types of monitoring data and sensors used, methods for integrating diagnostic data with digital models, and analysis algorithms for damage detection and degradation forecasting. The results indicate a transition from passive digital models to active diagnostic systems operating in near real-time. However, a key barrier remains the fragmentation of solutions: data on physical condition often exist in isolation from the semantic context of the BIM model, while analysis algorithms are not adapted to handle spatially distributed and multi-parametric information streams. Based on the conducted analysis, the article proposes a conceptual framework for building comprehensive diagnostic DT systems. This framework includes the semantic enrichment of models with condition attributes, unified protocols for streaming sensor data, and hybrid analytical algorithms combining physical degradation models with machine learning. This research contributes to the systematization of knowledge in the field of IT-enabled structural health monitoring and outlines directions for further applied development.
References
Digital technologies for construction sustainability: Status quo, challenges, and future prospects / W. Lu, J. Lou, B. K. Ababio [et al.] // npj Mater. Sustain. – 2024. – Vol. 2. – P. 10. – DOI: 10.1038/s44296-024-00010-2.
Begić, H. A systematic review of Construction 4.0 in the context of the BIM 4.0 premise / H. Begić, M. Galić // Buildings. – 2021. – Vol. 11 (8). – P. 337. – DOI: 10.3390/buildings11080337.
Construction 4.0 : A literature review / E. Forcael, I. Ferrari, A. Opazo-Vega, R. García-Alvarado // Sustainability. – 2020. – Vol. 12 (22). – DOI: 10.3390/su12229755.
Enabling Building Information Model-driven human-robot collaborative construction workflows with closed-loop digital twins / X. Wang, H. Yu, W. McGee [et al.] // Computers in Industry. – 2024. – May. – DOI: 10.1016/j.compind.2024.104112.
The potential of demand response as a tool for decarbonization in the energy transition / G. Chantzis, E. Giama, S. Nižetić, A. M. Papadopoulos // Energy and Buildings. – 2023. – Vol. 296. – DOI: 10.1016/j.enbuild.2023.113255.
Investigation of heavy gas dispersion characteristics in a static environment : Spatial distribution and volume flux prediction / Y. Ma, A. Li, J. Che [et al.] // Building and Environment. – 2023. – Vol. 242 (8). – DOI: 10.1016/j.buildenv.2023.110501.
Naqvi, S. A. R. Autonomous pre-conditioning and improved personalization in shared workspaces through data-driven predictive control / S. A. R. Naqvi, K. Kar, S. Mishra // Energy and Buildings. – 2023. – Vol. 285 (4). – DOI: 10.1016/j.enbuild.2023.112897.
Towards an Occupancy-Oriented Digital Twin for Facility Management: Test Campaign and Sensors Assessment / E. Seghezzi, M. Locatelli, L. Pellegrini [et al.] // Appl. Sci. – 2021. – Vol. 11. – P. 3108. – DOI: 10.3390/app11073108.
Wang, T. Integration of building information modeling and project management in construction project life cycle / T. Wang, H.-M. Chen // Automation in Construction. – 2023. – June. – DOI: 10.1016/j.autcon.2023.104832.
Retrospective carbon reduction potential of residential buildings in China based on equity and efficiency coupling / L. Chen, Sh. Liu, W. Cai [et al.] // Building and Environment. – 2023. – Vol. 242. – DOI: 10.1016/j.buildenv.2023.110588.
Calibration method for an open source model to simulate building energy at territorial scale / M. Rit, R. Girard, J. Villot [et al.] // Energy and Buildings. – 2023. – Vol. 293. – P. 113205. – DOI: 10.1016/j.enbuild.2023.113205.
3D laser scanning for predicting the alignment of large-span segmental precast assembled concrete cable-stayed bridges / X.-X. Li, E. Deng, Y.-W. Wang, Y.-Q. Ni // Automation in Construction. – 2023. – August. – DOI: 10.1016/j.autcon.2023.105056.
Structural displacement estimation by fusing vision camera and accelerometer using hybrid computer vision algorithm and adaptive multi-rate Kalman filter / Z. Ma, Ch. Jaemook, P. Liu, H. Sohn // Automation in Construction. – 2022. – May. – DOI: 10.1016/j.autcon.2022.104338.
Field experiment and numerical simulation for airflow evaluation in a data center with row-based cooling / Y. Wang, X. Bai, Y. Fu [et al.] // Energy and Buildings. – 2023. – June. – DOI: 10.1016/j.enbuild.2023.113231.
Developing a novel control strategy for frosting suppression based on condensing-frosting performance maps for variable speed air source heat pumps / Sh. Liu, X. Bai, Sh. Deng [et al.] // Energy and Buildings. – 2023. – Vol. 289. – DOI: 10.1016/j.enbuild.2023.113049.
Iqbal, F. Digital Twin-Enabled Building Information Modeling–Internet of Things (BIM-IoT) Framework for Optimizing Indoor Thermal Comfort Using Machine Learning / F. Iqbal, S. Mirzabeigi // Buildings. – 2025. – Vol. 15. – P. 1584. – DOI: 10.3390/buildings15101584.
Coupling convolutional neural networks with gated recurrent units to model illuminance distribution from light pipe systems / J. Ngarambe, P. N. Duhirwe, T. V. Quang [et al.] // Building and Environment. – 2023. – Vol. 23. – DOI: 10.1016/j.buildenv.2023.110276.
Lin, M. J. Research on intelligent management of engineering construction safety oriented to Internet of Things + BIM / M. J. Lin, H. Zhang, H. Wu // 6GN for Future Wireless Networks. – 2022. – May. – P. 590–596. – DOI: 10.1007/978-3-031-04245-4-51.
Building Information Modeling (BIM) for transportation infrastructure – Literature review, applications, challenges, and recommendations / A. Costin, A. Adibfar, H. Hu, S. S. Chen // Automation in Construction. – 2018. – Vol. 94. – P. 257–281. – DOI: 10.1016/j.autcon.2018.07.001.
Development and Application of Digital Twin–BIM Technology for Bridge Management / E. E. Tita, G. Watanabe, P. Shao, K. Arii // Appl. Sci. – 2023. – Vol. 13. – P. 7435. – DOI: 10.3390/app13137435.
Downloads
Published
How to Cite
Issue
Section
License
This work is licensed under a Creative Commons Attribution-NonCommercial 4.0 International License.
The work is provided under the terms of Creative Commons public license Attribution-NonCommercial 4.0 International (CC BY-NC 4.0). This license allows an unlimited number of persons to reproduce and share the Licensed Material in all media and formats. Any use of the Licensed Material shall contain an identification of its Creator(s) and must be for non-commercial purposes only. Users may not prevent other individuals from taking any actions allowed by the license.
