International Journal of Wireless and Microwave Technologies (IJWMT)
IJWMT Vol. 16, No. 3, 8 Jun. 2026
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A Physics-Informed Heterogeneous Graph Transformer Model for Managing Heterogeneous Big Data in Urban Construction Projects
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Author(s)
Index Terms
Physics-informed learning, heterogeneous graph transformer, graph neural network, predictive maintenance, building information modeling
Abstract
This research aims to enhance predictive maintenance and inspection planning in urban construction projects. Recent advances in graph neural networks and graph transformer architecture have demonstrated significant potential for modeling complex lifecycle processes of building systems. However, most existing approaches remain predominantly data-driven and lack integration of physics-informed modeling and real-time data, which limits their applicability in large-scale urban environments. This research addresses this gap by proposing an approach for managing heterogeneous big data in urban construction projects, enabling prediction of the technical condition and inspection needs of structural elements. The core contribution is the development of a physics-informed heterogeneous graph transformer model that integrates domain-specific physical knowledge into the learning process through physics-based features and regularization mechanisms. The results confirm that all validation criteria are simultaneously satisfied: the difference between training and validation accuracy remains within the threshold (
Cite This Paper
Olga Solovei, Tetiana Honcharenko, "A Physics-Informed Heterogeneous Graph Transformer Model for Managing Heterogeneous Big Data in Urban Construction Projects", International Journal of Wireless and Microwave Technologies(IJWMT), Vol.16, No.3, pp. 128-141, 2026. DOI:10.5815/ijwmt.2026.03.09
Reference
[1]D. Kumar, K. K. Maurya, S. K. Mandal, B. A. Mir, A. Nurdiawati, and S. G. Al-Ghamdi, “Life cycle assessment in the early design phase of buildings: Strategies, tools, and future directions,” Buildings, vol. 15, no. 10, p. 1612, 2025. doi:10.3390/buildings15101612.
[2]Y. Zhang et al., “MetaCity: Data-driven sustainable development of complex cities,” The Innovation, vol. 6, no. 2, 2025. doi:10.1016/j.xinn.2024.100775.
[3]W. Zhang, Z. Wang, Z. Yang, and Z. Cai, “BIM-based spatial semantic reasoning and lifecycle management driven by heterogeneous graph neural networks,” SSRN, 2024. Available: https://papers.ssrn.com/sol3/papers.cfm?abstract_id=5678364 (accessed Mar. 28, 2026).
[4]J. Han, X. Z. Lu, and J. R. Lin, “Pretrained graph neural network for embedding semantic, spatial, and topological data in building information models,” Computer-Aided Civil and Infrastructure Engineering, vol. 40, no. 26, pp. 4607–4631, 2025. doi:10.1111/mice.70073.
[5]G. Austern, T. Bloch, and Y. Abulafia, “Incorporating context into BIM-derived data–leveraging graph neural networks for building element classification,” Buildings, vol. 14, no. 2, p. 527, 2024. doi:10.3390/buildings14020527.
[6]N. Kayhani, B. McCabe, and B. Sankaran, “BIM-based construction quality assessment using graph neural networks,” in Proc. Int. Symp. Automation and Robotics in Construction (ISARC), vol. 40, pp. 9–16, 2023.
[7]H. Yu, T. Hou, and Y. Lin, “Enhancing graph transformer encoding with graph heterogeneous memory for improved recommendation performance,” Scientific Reports, vol. 15, no. 1, p. 44567, 2025. doi:10.1038/s41598-025-28266-1.
[8]C. Meng and S. Wang, “Transformer-based energy consumption prediction and optimization framework for BIM-enabled green buildings,” in Int. Conf. Computer Vision and Machine Learning (CVML 2025), vol. 14065, pp. 12–20, SPIE, Feb. 2026. doi:10.1117/12.3100406.
[9]O. Solovei, “Physical state models of structural elements for predicting building reliability using graph neural networks,” Herald of Khmelnytskyi National University. Technical Sciences, vol. 363, no. 2, pp. 563–575, 2026. doi:10.31891/2307-5732-2026-363-75.
[10]G. H. Nayak et al., “Transformer-based deep learning architecture for time series forecasting,” Software Impacts, vol. 22, p. 100716, 2024. doi:10.1016/j.simpa.2024.100716.
[11]A. Rogers, O. Kovaleva, and A. Rumshisky, “A primer on neural network architectures for natural language processing,” Journal of Artificial Intelligence Research, vol. 69, pp. 1–16, 2020.
[12] S. Khan et al., “Transformers in vision: A survey,” ACM Comput. Surv., vol. 54, no. 10s, pp. 1–41, 2022. doi:10.1145/3505244.
[13]C. Chen et al., “Uncertainty quantification on graph learning: A survey,” arXiv preprint arXiv:2404.14642, 2024.
[14]J. Von Oswald et al., “Transformers learn in-context by gradient descent,” in Int. Conf. Machine Learning, PMLR, pp. 35151–35174, July 2023.
[15]L. Prechelt, “Early stopping-but when?,” in Neural Networks: Tricks of the Trade, Berlin, Heidelberg: Springer, 2002, pp. 55–69.
[16]G. Nicora, M. Rios, A. Abu-Hanna, and R. Bellazzi, “Evaluating pointwise reliability of machine learning prediction,” Journal of Biomedical Informatics, vol. 127, p. 103996, 2022. doi:10.1016/j.jbi.2022.103996.
[17]S. Sathyanarayanan and B. R. Tantri, “Confusion matrix-based performance evaluation metrics,” African Journal of Biomedical Research, vol. 27, no. 4S, pp. 4023–4031, 2024. doi:10.53555/AJBR.v27i4S.4345.
[18]D. Chicco and G. Jurman, “The Matthews correlation coefficient (MCC) should replace the ROC AUC as the standard metric for assessing binary classification,” BioData Mining, vol. 16, no. 1, p. 4, 2023. doi:10.1186/s13040-023-00322-4.
[19]M. Raissi, P. Perdikaris, and G. E. Karniadakis, “Physics-informed neural networks: A deep learning framework for solving forward and inverse problems involving nonlinear partial differential equations,” Journal of Computational Physics, vol. 378, pp. 686–707, 2019.
[20]O. Solovei and T. Honcharenko, “Method for creating graphs based on BIM models for predictive monitoring of buildings,” Management of Development of Complex Systems, no. 64, pp. 247–258, 2025. doi:10.32347/2412-9933.2025.64.247-258.